Essays on supplier responsiveness and buyer firm value - Nyenrode ...

ISBN 978-90-8980-055-8
© M. Abrahim Zaka, 2013
All rights reserved. Save exceptions stated by the law, no part of this publication may
be reproduced, stored in a retrieval system of any nature, or transmitted in any form
or by any means, electronic, mechanical, photocopying, recording or otherwise, included a
complete or partial transcription, without the prior written permission of the author, application
for which should be addressed to author.

ISBN 978-90-8980-055-8
© M. Abrahim Zaka, 2013
All rights reserved. Save exceptions stated by the law, no part of this publication may
be reproduced, stored in a retrieval system of any nature, or transmitted in any form
or by any means, electronic, mechanical, photocopying, recording or otherwise, included a
complete or partial transcription, without the prior written permission of the author, application
for which should be addressed to author.

Examination Committee
Supervisor: Prof. dr. Henry Robben
Co-Supervisor: Prof dr. Kees Van Montfort
Other members: Prof dr. ing. Jacques Reijniers MBA
Prof dr. Ron Tuninga
Dr. W.G. (Wim) Biemans

Table of Contents
CHAPTER 1 14
1.1 INTRODUCTION 14
1.2 OUTLINE OF THE DISSERTATION 14
1.3 RESEARCH QUESTIONS 15
1.4 SCOPE PHD THESIS 18
1.5 RESEARCH METHODOLOGY BASED ON THE RESEARCH QUESTIONS 18
1.5.1 Sample and data collection 19
1.6 DATA ANALYSIS 19
1.6.1 Data analysis, Chapter 2 19
1.6.2 Data analysis, Chapter 3 19
1.6.3 Data analysis, Chapter 4 20
1.7 LIMITATIONS 20
CHAPTER 2 21
2.1 INTRODUCTION 22
2.2 MARKET ORIENTATION 25
2.2.1 Cultural Perspective of Market Orientation 26
2.2.2 Can a Culture be Developed? Is it Within Management’s Control 27
2.2.3 The Cultural Perspective of Market Orientation and the Ability to Take Risks 27
2.2.4 A Cultural Perspective of Market Orientation and Innovation 28
2.2.5 What Do We Learn from the Cultural Perspective of Market Orientation? 28
2.2.6 The Process-Based View of Market Orientation 29
2.2.7 Business-to-Business Market Orientation 30
2.2.8 Moderators of the Market Orientation-Performance Link 30
2.2.9 What We Learn from the Prior Studies of Market Orientation 32
2.2.10 Market Orientation and Marketing Accountability 32
2.2.11 Measuring the Marketing Orientation – Finance Link 33
2.2.12 Contributions 33

2.3 DEVELOPMENT OF THE CONCEPTUAL MODEL 35
2.4 METHODOLOGY 40
2.4.1 Questionnaire Design and Scale Development 40
2.4.2 Sample 55
2.4.3 Multicollinearity 56
2.5 MEASUREMENT MODEL 59
2.6 THE STRUCTURAL MODEL 64
2.7 DISCUSSIONS AND RECOMMENDATIONS 66
2.7.1 H 1 : Supplier Responsiveness and IdRR 67
2.7.2 H 2 : Supplier Responsiveness and Buyer Satisfaction, and H 3 : Buyer Satisfaction and
Buyer Firm Value 68
2.7.3 H 4 : IdRR and Supplier Brand Value 68
2.7.4 H 5 : Supplier Brand Value and Buyer Firm Value 69
2.8 MANAGERIAL IMPLICATIONS 70
2.9 LIMITATIONS AND FUTURE DIRECTIONS 72
2.10 CONCLUSIONS 72
CHAPTER 3 74
3.1 INTRODUCTION 75
3.1.1 Different Results of Studies of Market Orientation in The Netherlands and the United
States 76
3.1.2 Cultural Context 78
3.1.3 Contribution to Chapter 2 79
3.1.4 Hypotheses Generation 79
3.2 METHODOLOGY 83
3.2.1 Sample Description 83
3.2.3 Normality of Data 86
3.2.4.1 Model 1 87
3.2.4.2 Model 2 87
3.2.4.3 Model 3 88

3.3 DISCUSSION 90
3.4 RECOMMENDATIONS 92
3.5 CONCLUSIONS 93
CHAPTER 4 95
4.1 INTRODUCTION 96
4.2 INTENDED THEORETICAL CONTRIBUTION 98
4.3 SPECIFIC CONTRIBUTION TO CHAPTER 2 98
4.4 HYPOTHESIS DEVELOPMENT 99
4.5 RESEARCH DESIGN 101
4.5.1 Sample 101
4.5.2 Descriptive Statistics 101
4.5.3 Normality of the Data 102
4.5.4 Data Analysis 102
4.5.5 Measurement Model 102
4.6 SEM 104
4.7 DISCUSSION 107
4.7.1 Measurement Model Hypothesis 107
4.7.2 SEM Model Hypotheses 108
4.7.3 Some Insights from Informal Discussions 110
4.7.4 Managerial Implications 111
4.8 LIMITATIONS 112
4.9 FUTURE DIRECTIONS 112
4.10 CONCLUSIONS 113
CHAPTER 5 114

5.1 RESEARCH FINDINGS 114
5.2 OVERALL GLOBAL RESEARCH QUESTION ANSWER 114
5.3 OVERALL CONCLUSIONS 116
5.4 THEORETICAL IMPLICATIONS 117
5.5 PRACTICAL IMPLICATIONS 119
5.6 CLOSING WORDS 120
SUMMARY IN ENGLISH 121
SAMENVATTING 128
REFERENCES 136
APPENDIX A 146
APPENDIX B 161

List of Figures
Figure 2.1 Conceptual Model 35
Figure 3.1 The Netherland and the U.S. Compared on the Hofstede Dimensions 79

List of Tables
Table 2.1 Representative Studies of Market Orientation and Their Focus 25
Table 2.2 Modified Items of the MARKOR Scale 43-46
Table 2.3 Factor Analysis Supplier Responsiveness 47
Table 2.4 Items Measuring IdRR 49
Table 2.5 Rotated IdRR Component Matrix 50
Table 2.6 List of Modified Satisfaction Items 52
Table 2.7 Component Matrix Buyer Satisfaction 53
Table 2.8 Supplier Brand Value 53
Table 2.9 Matrix of Supplier Brand Value Components 54
Table 2.10 Buyer Firm Value 54
Table 2.11 Matrix of Buyer Firm Value Components 54
Table 2.12 Descriptive Statistics 56
Table 2.13 Multicollinearity Statistics 57
Table 2.14 Multicollinearity Statistics 57
Table 2.15 Multicollinearity Statistics 57
Table 2.16 Multicollinearity Statistics 57
Table 2.17 Inter-item Correlations 58
Table 2.18 Questionnaire Items and Codes 60
Table 2.19 Average Variance Extracted 61
Table 2.20 Construct Reliability 62
Table 2.21 Measurement Model Indices 63
Table 2.22 Standardized Residual Covariances 63
Table 2.23 Structural Model Indices 65
Table 3.1 The Netherlands Investment in the United States 75
Table 3.2 Descriptive Statistics USA and Netherlands 84
Table 3.3 Kurtosis and Skewness 86
Table 3.4 Summary of Goodness of Fit Statistics, SE Hypotheses Results 89
Table 4.1 Sample Description 101
Table 4.2 Standardized Regression Weights for CFA 103
Table 4.3 Average Variance Extracted 103
Table 4.4 Construct Reliability 103
Table 4.5 SEM Results 105
Table 4.6 Summary of Goodness of Fit Statistics, SE Hypotheses Results 106
Table 4.7 Measurement Model Indices 108

To the loving memory of my aunt
Shahima Karim-Ilyas

PREFACE
Oscar Wilde once said, “I am not young enough to know everything.” As my life progresses I
see more truth in this statement. My life illustrates how a formerly self-professed know-it-all
eventually realized that there is always much to learn. Beginning with my school days, when I
believed I knew everything, and my belief was not without merit, after all, my sources were
indisputable: Disney and DC comics. This belief in my knowledge was still with me at the start
of my PhD. I believed back then that I would revolutionize marketing, but alas, then I met the
arch nemesis of every PhD candidate, the critics. Critics who knew the field better than I ever
imagined they would. Indeed, every step of the PhD process has been humbling as I realized
there was so little I knew. So the question some of you might ask is what do I know now?
I know that without the help of my supervisor Henry Robben and co-supervisor Kees Van
Montford my thesis would never have been completed. Their support has been unwavering and
to them I am greatly indebted. Henry and Kees, I couldn’t have asked for better supervisors.
Henry you truly are a marketing guru! And Kees you are a statistician par excellence! I will miss
the time spent in long discussions with the both of you about my PhD. I know that without the
help extended to me by Kitty Koleijmier I would not have completed this PhD. To her I am
extremely thankful. In addition, I owe a great deal to Ed Peelen as he never refused to help me,
despite the fact I wasn’t his PhD student! I am also obliged to Henry’s dear friend Peter Gouw.
Peter, thank you for all the help with the data in the PhD process!
I know that it was not only faculty members who helped me complete my dissertation,
but also many others who supported me throughout the process. If I was to rank them, I think my
brother Fasi Zaka would be on the top of the list. He believed in me when I didn’t believe in
myself and he was instrumental in prodding and cajoling me on a flight back to The Netherlands
from Pakistan when I was thinking of quitting my PhD. I am also grateful for all the financial
support he has provided me throughout my life. Though, I must add, that being his brother I hope
he never asks me to repay him. I am beholden to my mother who has always supported me. I
know that I have bothered her unduly at times when the stress of the PhD overwhelmed me, and
for that I seek her forgiveness. I would never have loved education so much if it were not for her
proselytization that I must always strive to ameliorate myself. I would also like to thank my
father for his moral support. In addition, I am also grateful to my maternal uncle Amin Jan
(Meno Mama) whose constant support to me and my family has been unswerving. Though there
are many other relatives I could thank, I will briefly name the most important ones, namely:
Abdul Karim Jan Khan, Gul Rang Bibi, Nazli Ayub, Saleem Jan Khan, Asif Ayub, Ihsanullah
Kaka, Ali Ayub, Anwar Ayub, beloved Junaid (the last true Khan of Uthmanzai), and Kunat Jan.
I am also grateful to all my friends and colleagues in The Netherlands, Pakistan, and the
U.S. Especially, Dr.Cyrus M.D. and Dr.Asad M.D., the two people who inspired me to get a
‘doctor’ title. Even though Cyrus may never agree that I’m a real doctor, however, I think we
should agree to disagree. I am also grateful to Nies and Javed Khan for all their encouragement
and help.
Most importantly, I would like to acknowledge he who needs no mention, Almighty God.
I know that He has blessed me with more than I could ever ask for in this life. I pray that the

Almighty continues to shower me with His mercy and allows me to serve my fellow human
beings.

Chapter 1
Introduction to the Thesis
1.1 Introduction
This dissertation focuses on the relationship between strategic supplier market orientation and
buyer firm value. The mediators of the relationship that are specifically considered include:
idiosyncratic relational risk, buyer satisfaction, and supplier brand value. Moreover, when it
comes to market orientation, most previous studies have assumed the seller firm’s perspective. In
this dissertation, we have added to prior studies of market orientation by: (1) adopting the
customer’s perspective, (2) introducing a new market orientation and performance mediator
called IdRR, (3) and questioning the positive relationship between market orientation and risk.
The implications of this study for management are: (1) that the market orientation of a supplier
can be used as a criterion for minimizing risks where it is not possible to create a portfolio of
strategic suppliers for risk minimization and (2) that insulation to idiosyncratic relational risk can
be used as an indicator of the value created for the customer, in addition to buyer satisfaction.
The aforementioned concepts do not have standardized definitions, as various researchers
have often given their own definitions. Market orientation has many and varied definitions.
Indeed, Kohli and Jaworski (1990) observed,
Given its (market orientation) widely acknowledged importance, one might expect the
concept to have a clear meaning, a rich tradition of theory development, and a related
body of empirical findings. On the contrary, a close examination of the literature reveals
a lack of clear definition, little careful attention to measurement issues, and virtually no
empirically based theory.
Since then, more research on the subject of market orientation has been conducted, but there has
been no convergence toward the adoption of a standardized definition. However, for the purpose
of this thesis, market orientation is defined as the implementation of a marketing concept (Kohli
& Jaworski, 1990). The second concept in this dissertation is idiosyncratic relational risk (IdRR),
a term borrowed primarily from the finance literature, where idiosyncratic risk in the stock
market is the risk that can be diversified away by creating a portfolio of stocks (Angelidis &
Tessaromatis, 2008; Goyal & Santa-Clara, 2003). In this dissertation, IdRR is defined as the risk
to which buyers are exposed to by entering into a relationship with a strategic supplier
(Srivastava, Shervani, & Fahey, 1998). The third concept we deal with is satisfaction, defined as
the cumulative buyer evaluation of the supplier’s performance against the buyer’s expectations
(Oliver, 1993). The fourth concept that this dissertation deals with is the supplier’s brand value.
The supplier’s brand value is defined as the buyer’s loyalty to the supplier’s brand. The fifth
concept in this dissertation is the buyer firm’s value. The buyer’s firm value refers to the buyer’s
future cash flows (Srivastava et al., 1998).
The rest of this chapter is organized as follows. Below, we present an outline of this
dissertation. Next, we introduce our research questions and the goals of this dissertation. We also
elaborate on the theories that we use and a description of the methodology employed in this
dissertation.
1.2 Outline of the Dissertation
This dissertation has five chapters. In this section, we present an outline of the chapters. In
Chapter 1, we introduce the variables in this dissertation: supplier market orientation
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(responsiveness), relational idiosyncratic risk, buyer satisfaction, supplier brand value, and
buyer’s firm value. We discuss the motivating factors behind the dissertation, present our
research questions, explain the research methodology, and, finally, provide a review of the
dissertation.
In Chapter 2, we present our first study, an empirical investigation into whether the
strategy of responsive strategic suppliers pays off for the buyer and the seller. This study also
compares two mediating variables: the first is the relational indicator of buyer satisfaction, and
the second is the objective indicator of buyer idiosyncratic relational risk. The theoretical
underpinnings of this study are derived from the market orientation literature, financial theories,
and the market-based assets theory. These theories are used to provide the rationale for the five
hypotheses. The findings of this study are that strategic supplier responsiveness mitigates risk
and leads to positive outcomes for both the supplier and the buyer. The data used in this study
were collected from 164 purchasing managers in publically listed and unlisted firms from the
U.S. The firms included in the study operated in consumer markets, business markets, or a
combination of both.
In Chapter 3, we examine the model generated in Chapter 2 across national borders. This
study compares how strategic supplier responsiveness importance varies across cultural and
national borders. The Netherlands, a more feminine culture, holds both relational indicators and
objective indicators in equal importance, whereas, in the United States, only objective indicators
matter. The implications of this finding are important for firms looking to form transatlantic
alliances. We include, in addition to the data from the U.S., data from The Netherlands. The
sample from The Netherlands included both publically listed and unlisted firms. The total sample
size from The Netherlands was 92. The firms included in the study, like those in the prior study,
operated in consumer markets, business markets, or a combination of both.
In Chapter 4, we compare objective and subjective performance measures of strategic
supplier responsiveness. For objective measures, we use the net income for firms from Google
financials that uses input data from the Reuters database. For subjective measures, we use
managerial indicators from our surveys. We find that, although the models fit in both cases,
when accounting are data used our hypotheses linking supplier responsiveness with buyer firm
value and supplier brand value are insignificant. This holds important implications for managers
as to whether they should trust mental heuristics when judging their organizational performance.
Finally, in Chapter 5, we conclude the dissertation and discuss our findings.
1.3 Research Questions
Over the course of the last two decades, a global trend has involved collaborating with and
growing closer to suppliers. One example of the importance of key supplier relationships is the
Malcolm Balridge award competition, in which participants are assessed on their key supplier
relationships. Moreover, as illustrated in Figure 1.1, in the future, stable and reliable supplier
relationships will become more valuable because 68 percent of the respondents in a McKinsey
survey expected the supply risk to increase over the next five years (McKinsey, 2010).
Furthermore, global supply markets, in general, are increasingly becoming more competitive and
riskier. As a consequence, to reduce uncertainty about the firm’s future market position, firms
need adaptable suppliers, as exemplified by Toyota’s supply pyramid (Reeves & Deimler, 2011).
15

Figure 1.1
Mckinsey Survey Results
The concept of greater interaction with suppliers has been adopted from Japan. The
Japanese Keiretsu concept is a network of distributors that closely collaborate with their
suppliers (Reeves & Deimler, 2011). Numerous manufacturers in the United States have
attempted to emulate this concept, but for many it did not work out and relations with
distributors are now worse than before (Reeves & Deimler, 2011). For example, Ford has
introduced reverse auctions to make the suppliers minimize their prices (Reeves & Deimler,
2011).
At the same time, the information technology revolution has made it easier for firms such
as Wal-Mart to collect information about their customers and share it with their suppliers. These
new technologies include ECR, VMI, QR, EDI, RFID, etc. These new technologies give a
greater real-time understanding of what is happening to market demand. However, in tandem,
Wal-Mart imposes harsh supply terms in its contracts with suppliers (Schmitt, 2009). One
example of Wal-Mart’s harsh terms is the supplier’s paying shelf rent for products that do not
sell as quickly as required. Although there is greater buyer or distributor willingness and
preference to share information with suppliers, there seems to be comparatively less willingness
to share risk.
The reason for the mixed supplier-buyer love-hate relationship in the strategic context is
the complex reality of today. Take Apple and Samsung as an example: Apple is reliant on
Samsung’s superior chip manufacturing facilities but still pursues litigation to raise potential
sales barriers in various markets to protect its market share. Strategic suppliers, to some degree,
16

always remain potential threats and competitors. The logic of Porter’s (1985) five forces
framework still applies in the strategic context because the five forces still compete for the same
profit pie. For example, Samsung a strategic supplier of component processors to Apple moved
into Apple’s market of mobile devices. Despite the mixed relationship between some strategic
suppliers and their buyers, other buyers use ingenuous ways to leverage their suppliers’ marketbased
strengths. For instance, Dell, Toshiba, Acer, and HP are some firms that employ the
ingredient branding strategy, and by doing so, they leverage their chip supplier’s strength to
increase their own products’ attractiveness. Hence, a powerful supplier is not always viewed as a
threat, and the relationship between a strategic supplier and buyer can be mutually beneficial.
However, the underpinning of success in the marketplace is an understanding of the
customer’s needs (market orientation) (Cane, 2010). The detractors of the market orientation
philosophy have claimed that it leads to a narrow focus and ignores potential future customer
needs. In addition, Collins (2009) argues that one of the reasons that many previously identified
“good to great” companies have failed is expansion to too many customer industries rather than
focusing on the customer’s needs. Given the muddled nature of the strategic supplier-buyer
relationship, what is unclear is whether focusing on the customer’s needs pays off for the
supplier and the buyer. Therefore, this dissertation focuses on the following research global
question:
“What is the impact of supplier market orientation through idiosyncratic relational risk,
buyer satisfaction, and supplier brand value on buyer firm value?”
To further answer this global research question we have three sub-questions. They are as
follows:
To answer our global research question, we followed a stepwise process, organizing our
research question into three sets of problems. Chapter 2 addresses the following question:
“Does it make relational and financial sense to be a responsive strategic supplier?”
Chapter 3 addresses whether, in this age of globalization, whether the model proposed in
Chapter 2 would hold true in The Netherlands because, as stated earlier, many firms have
attempted to adopt truly close buyer and supplier relationships, and these differences are nationspecific.
Given the importance of The Netherlands and the United States as two major trading
partners, we therefore, investigate whether the model developed in Chapter 2 would hold in The
Netherlands. Therefore, our second research question is:
“Does our model of strategic supplier responsiveness hold across national/cultural
borders?”
The final issue is how we measure performance. Chapter 4 deals with the methodological
issue of whether perceptual or objective performance measures should be used. In the past, many
studies on market orientation have used perceptual performance measures. However, they have
often not correlated well with subjective performance measures. We investigate whether there is
empirical evidence for the correlation between subjective and objective measures, in the context
of purchasing managers when dealing with strategic suppliers, based on the model developed in
Chapter 2. Therefore, our third research question is:
“What are the differences between firm-level perceptual measures of performance and
accounting measures of performance?”
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1.4 Scope PhD thesis
This thesis focuses on answering the research question about how a marketing focus influences
relational idiosyncratic risk and consequently buyer firm value. The primary motivation behind
the research questions is the gap in marketing literature because of a scholarly focus on the intraorganizational
perspective of market orientation and risk relationship. As a consequence of a
focus on intra-organizational perspective of market orientation and risk scholars have ignored the
inter-organizational perspective, specifically in the strategic buyer-supplier relationship context.
Therefore, this thesis focuses on market orientation and idiosyncratic relational risk in the interorganizational
context.
Moreover, strategic buyer and supplier relationships have been extensively researched in
many domains in addition to marketing, such as, sales and purchasing. Indeed, sales and
purchasing have made important contributions to the topic of strategic buyer and supplier
relationships, but as the main contribution of this thesis is an academic one, therefore, it is
important to make a clear academic contribution to scientific literature. Hence, as the research
area of interest is in the marketing domain, namely market orientation, the theoretical lens
chosen for this dissertation is a marketing one. However, this does lead to a limitation of
focusing on marketing, and as a consequence sales and purchasing literature have not
substantially cross-pollinated the current study.
In addition, as mentioned earlier this thesis focuses on the strategic buyer and supplier
relationship context. The primary reason for choosing this context is because organizational
market orientation is an expensive and time consuming exercise. Therefore, for a supplier to be
market oriented the buyer needs to be committed to continuing the relationship for the long-term
period in order to regain the initial investment in the relationship. This this thesis deals with
those relationships that are strategic, meaning the product or service supplied is high in terms of
financial risk and supply risk. But focusing on strategic relationships means that this thesis does
not cover non-strategic relationships.
This thesis focuses primarily on strategic supplier market orientation and its impact on
their buyer in the context of two countries, namely the United States and The Netherlands.
Although, there are many other countries that could have been included such as China or Japan,
the primary research interest of the researchers involved in this thesis was in The Netherlands
and the United States. Moreover, because of time and budget constraints a including a larger
number of countries was not possible. In addition, as stated in Chapter 3 The Netherlands and the
United States are similar on all dimensions of the Hofstede index except masculinity and
femininity. Therefore, it was of interest to determine whether supplier responsiveness would
achieve the same effect in both countries and whether this effect would be channeled through
similar mediators.
This thesis was primarily conducted for an academic objective. Therefore, we have not
explicitly focused on practical outcomes. However, the theoretical insights from this dissertation
can be used by managers to confirm or disconfirm beliefs they may have. Moreover, the
introduction of IdRR as a metric could be considered a practical contribution of this thesis.
1.5 Research Methodology Based on the Research Questions
This section reviews the sample used in this dissertation, the data collection procedure, and the
statistical method used for the analysis of the data. Throughout this dissertation, the methodology
18

is common to all papers because the surveys used for the United States and The Netherlands are
identical and the data were collected in the same time frame.
1.5.1 Sample and data collection
The responses we have used in this dissertation were collected from a professional panel of
purchasing managers. The respondents worked for both publically and privately listed companies
from the U.S. and The Netherlands. We use key informant methodology in selecting our
respondents (Philips, 1981). On average, the respondents were purchasing managers with more
than seven years of experience with strategic purchases. The data collection employed an
Internet survey because of speed, cost, and efficiency considerations.
We initially consulted academics about our survey. We then conducted a test survey with
students on campus to check the robustness of the instrument. This survey took place during the
spring of 2009. We collected data during the summer of 2009. In Chapter 2 we collected
publically listed and unlisted U.S. firms’ data. In Chapter 3, we used both U.S. and Dutch data.
In Chapter 4, we used a sub-sample of the sample used in Chapter 2. We selected only those
respondents who worked for publically listed companies. We then used the difference in net
income over a three-year period (2009-2011) as a source of objective firm earnings. We acquired
the net income data from Google financials, powered by the Reuters database.
1.6 Data Analysis
We used quantitative and qualitative data analysis. In our quantitative analysis, we used factor
analysis, confirmatory factor analysis, and structural equation modeling. For the qualitative data
analysis, we conducted a textual analysis of the interviews we conducted. In the following three
paragraphs, we explain the data analysis used in Chapters 2, 3, and 4.
1.6.1 Data analysis, Chapter 2
In Chapter 2, we conducted an exploratory factor analysis based on data collected from the
United States. We applied a factor analysis to five of our scales: supplier responsiveness, buyer
satisfaction, IdRR, supplier brand value, and buyer firm value. Based on the results of the factor
analysis, we chose the most appropriate items for each scale. In addition, we checked for
multicollinearity by running a series of linear regression equations in SPSS with our independent
variables and mediators. The VIF for all four equations was less than ten, meaning that
multicollinearity was not an issue with our data (Hair, Anderson, Tatham, & Black, 2006).
We then conducted a confirmatory factor analysis with AMOS 17. We used 39 items
from our original questionnaire. After a number of iterations, we achieved model fit with 10
items. The standardized residual covariances were all less than plus two or greater than minus
two and all the inter-item correlations were above plus 0.3 or less than minus 0.3. We also
achieved construct reliability and acceptable levels of average variance extracted.
We finally analyzed our data analysis using a structural equation model using our 10
items and 5 constructs. We used the maximum likelihood estimation method in AMOS 17. The
results indicated that four of our hypotheses were supported and one hypothesis was not
supported. The goodness of fit indices were within acceptable range.
1.6.2 Data analysis, Chapter 3
We conducted a factor analysis based on data from The Netherlands of publically listed and
privately owned companies with the 10 items used in Chapter 2 and 5 constructs (n=92). We
analyzed the model of Chapter 2 using structural equation modeling and data from The
19

Netherlands. We used the maximum likelihood estimation method. Because of sample size
considerations (described in detail in Chapter 3) we decided to split the model tested in Chapter 2
into two sub-models, Model 2 and Model 3. Model 2 includes the supplier responsiveness, buyer
satisfaction, and buyer firm value variables. Model 3 includes the supplier responsiveness, IdRR
supplier brand value, and buyer firm value variables. We found that the fit statistics were
acceptable for both models and that the data supported all our hypotheses. Furthermore, we
compared and contrasted the hypotheses’ significance with the hypotheses in Chapter 2.
1.6.3 Data analysis, Chapter 4
We analyzed the two models in Chapter 2 based on 87 respondents who worked for publically
listed companies in the United States. We initially conducted a confirmatory factor analysis
(CFA). The fit statistics were all within the acceptable range for the model. Next, we analyzed
the data using structural equation models and applied the maximum likelihood estimation
procedure. In our subjective data model, four of our hypotheses were supported by the data,
whereas in our objective data model, only three of our hypotheses gained support.
1.7 Limitations
Many additional future research directions can be suggested on the basis of the limitations of the
current research. The following are some of the limitations of this dissertation:
Longitudinal research design. Our cross-sectional study allowed us to draw associations
at best but not conclusions about causality. To further improve this design, data collected for a
time series analysis could allow the deduction of evidence regarding causality. However, there
has been some recent evidence that inferences from cross-sectional studies about causality may
not be much different from inferences about longitudinal studies (Rindfleisch et al., 2008).
Limited sample of industries for depth. We traded off depth for width in our industry
sectors. However, specific sectors may vary more with supplier responsiveness than others. For
instance, in the commodities industry, price may be more of a determining factor, whereas, in the
technical goods industrial sector, the supplier response may be a key determining factor as to
whether relationships continue. Future studies could concentrate on a few sectors and check the
robustness of our findings across various moderators, such as market turbulence and increased
technological risk.
Narrow cultural comparison. Although the US and The Netherlands are different in some
respects, they share similarities as well. Both are industrialized nations, both are vibrant
democracies, both have capitalist economies, both have a degree of similar occidental culture,
and both nations are internationally competitive economies (Deshpande & Webster, 1989). Some
cultural idiosyncrasies do occur, such as acceptance of boasting on job interviews in the United
States and the preference for modesty in job interviews in The Netherlands, but nevertheless,
they both can be considered western cultures. On the other hand comparison with an Asian
culture in Chapter 3 would have provided a more diverse cultural comparison and insights into
whether the model proposed in Chapter 2 holds across more diverse cultural settings.
Existing relationships. In the current study we only examined existing relationships. It is
possible that the current study could be improved upon by examining different stages in a
relationship and examine whether the importance of market orientation and idiosyncratic
relational risk varies across nations and in different stages of the relationship.
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Chapter 2
In Pursuit of the Buyer: Does it Make Relational and Financial Sense to be a Responsive
Strategic Supplier?
Abstract
Do responsive strategic suppliers create substantial value in the buyer-supplier
relationship? The evidence from the marketing literature is mixed. Closer collaboration with a
strategic supplier is known to influence risk and relational outcomes, but this influence does not
always generate substantial benefits for the buyer as well as for the supplier. For business-tobusiness
(B2B) strategic relationships, substantial benefits are important; if channel partners do
not receive reasonable benefits from a relationship, then the partnership could suffer. In this
study, we examine how strategic supplier responsiveness creates meaningful value for both the
buyer and the supplier through the mediator of idiosyncratic relational risk.
We used survey data (n=164) from listed and unlisted companies in the United States and
employed structural equation modeling for our analysis. The primary finding of interest is that a
supplier’s responsiveness (i.e., market orientation) is negatively associated with a buyer’s
idiosyncratic relational risk. This challenges the widely held belief that risk and market
orientation are positively related. In tandem, the results show that being responsive reduces the
supplier’s risk in terms of the repurchase likelihood by their strategic buyer. At the end of this
paper, we discuss the managerial and academic implications.
Keywords: supplier responsiveness, idiosyncratic relational risk, buyer satisfaction,
supplier brand value and buyer firm value.
21

2.1 Introduction
A central tenant of marketing is market orientation. The most important facet of market orientation in
a business-to-business (B2B) context is the supplier’s response to the buyer’s needs (Kohli &
Jaworski, 1990). This responsiveness is described from the seller or supplier’s perspective as a
business philosophy by McNamara (1972). Other scholars have conceptualized it as a business
strategy with performance implications (Narver & Slater, 1990), whereas some argue that it is an
approach with which to reduce business relational idiosyncratic risk. From a customer’s perspective,
market orientation is the perceived supplier’s market responsiveness to their needs, both latent and
explicit (Slater & Narver, 1998). 1 Both the supplier and customer recognize market orientation as an
important framework for remaining competitive.
The importance of market orientation in the academic marketing domain can be gauged by the
3,835 instances in which, according to Google Scholar, Kohli and Jaworski’s (1990) article has been
cited. From a managerial perspective, it can be judged by the performance of highly market-oriented
companies such as Apple (iPod/iPhone), Sony (Walkman), Philips (Senseo coffee machines) and
Google (search engines). These companies illustrate the positive link between a firm’s market
orientation and market performance.
Despite the importance that market orientation has in both academic and business contexts, the
influence of strategic supplier responsiveness on customer marketplace performance is an academic
area that is in its nascent stages. In this regard, recent evidence from the business world suggests that a
lack of strategic supplier responsiveness or market orientation could be disastrous for the customer.
Toyota is one such example; the automobile manufacturer’s strategy of leaner and more efficient
suppliers was a global managerial mantra. However, its supplier’s recent breaking system fiasco in the
United States caused Toyota to lose a fifth of its market value and prompted an expensive automobile
recall (Kim & Krolicki, 2010). The current study investigates whether supplier responsiveness reduces
idiosyncratic relational risk (IdRR) and if it generates value for both the supplier and the buyer. We
use two different sets of metrics to assess the value that is generated for the supplier and buyer. For the
supplier, we assess whether the value that is generated ameliorates intangible market-based intellectual
assets. This is because in times of trouble, intangible non-financial benefits usually bind buyers to their
sellers (Narayandas, 2005). Therefore, such non-financial tangible assets provide the firm with
valuable resources. For the buyer, we assess the expected cash flow, which is a widely accepted and
frequently used measure of firm value. In the next section we define the five key constructs of this
paper, namely: IdRR, supplier responsiveness, buyer satisfaction, supplier brand value, and buyer firm
value.
The first construct is IdRR. We borrow the term idiosyncratic risk from the finance literature
and modify it to IdRR. Two kinds of risk are explored within the finance literatures. One form is
diversifiable, which means that adopting a portfolio strategy in which companies may invest can
eliminate idiosyncratic risk. The mechanism that eliminates the investor’s risk is that some of the
security prices of their portfolio may fall, whereas other companies’ prices will rise. Overall, this leads
to a reduction in the investor’s risk, as he has avoided placing all eggs in a single investment basket.
Hence, the idea of diversifiability is essentially the act of diversifying investments so that risk can be
1 For the purpose of this dissertation, supplier responsiveness refers to the customer’s perspective of the supplier’s
market orientation.
22

educed. Another kind of risk is one that cannot be diversified by a portfolio strategy, and that
phenomenon affects all firms within the market. An example of such undiversifiable risk would be the
threat of sudden fuel price increases. An undiversifiable risk that affects the whole market is called a
systematic risk, and the other form a diversifiable risk that affects individual firms but not the whole
market, is called idiosyncratic risk (Sharpe, 1964). Idiosyncratic risk is the more important of the two
kinds of risk because it typically comprises eighty percent of the risk that firms face on the stock
market (Srinivasan & Hanssens, 2009). We refer to buyer IdRR as the risk that exists because of
entering into a specific relationship with another company. On the continuum of customer and supplier
relationships that range from transactional to strategic alliances, those that are strategic involve the
most customer risk (Kraljic, 1983). The reason why the risk in strategic relationships is mostly
idiosyncratic is because it is reduced by an understanding of the partner’s idiosyncratic business needs
and willingness to take steps to meet those needs or in other words, it can be diversified. This
perspective is different from systematic risk, where the firm has no ability to control such risk. In a
way, our conceptualization is parallel to the concept of idiosyncratic risk on the stock market.
The second construct is supplier responsiveness or market orientation. Broadly speaking,
market orientation is the implementation of the marketing concept (Kohli & Jaworski, 1990). There
are two widely accepted conceptualizations of market orientation in the extant literature; the first
considers market orientation to be a process that is composed of three core components: intelligence
generation, dissemination, and supplier response (Kohli & Jaworski, 1990). The supplier’s response or
responsiveness is considered the most important of the three because it is the sum of all of the market
orientation efforts by the supplier to provide the buyer with a solution (Kohli & Jaworski, 1990). The
second approach considers market orientation as a firm culture which gives rise to market-oriented
behaviors. For instance, Narver and Slater’s (1990) cultural perspective of market orientation
considers three sets of market-oriented behavior, namely customer orientation, competitor orientation,
and interfunctional coordination. These behaviors are linked to the firm’s profitability. In spite of the
conceptual differences of both approaches, they are geared towards the same goal of creating superior
customer value through a response that is based on market intelligence.
Moreover, supplier responsiveness is the only component by which a customer can judge the
level of the supplier’s market orientation. The other two components of intelligence generation and
intelligence dissemination are not always visible to the customer; hence, the customer cannot directly
assess the quality of intelligence generation and dissemination. But the customer can judge it indirectly
through the supplier’s responsiveness. Therefore, for the purpose of our research, we consider supplier
responsiveness as the accumulative supplier response that is created by internal supplier firm
intelligence generation and dissemination processes to meet their buyer’s needs.
Our third construct is buyer satisfaction, which is an important relational outcome of the
supplier’s responsiveness (Kohli & Jaworski, 1990). An analysis of the strategic context of the buyersupplier
relationship is incomplete without considering the customer’s evaluation of the supplier’s
response. Customer satisfaction represents the customer’s evaluation and some scholars consider
satisfaction as a measurement of the cumulative buyer experience. Thus, buyer satisfaction has been
defined as “an overall evaluation based on the total purchase and consumption experience with a good
or service over time" (Anderson, Fornell, & Lehmann, 1994). This study uses cumulative buyer
satisfaction instead of measuring transactional satisfaction because we are interested in the overall
buyer assessment of the strategic supplier. The drawback of a transactional definition is that it would
23

only provide an assessment of the satisfaction from the most recent transaction and not from the whole
relationship (Oliver, 1993). Furthermore, within the channel management literature, satisfaction as a
construct is classified in two subtypes. The first form is economic satisfaction, which is the overall
positive affective response to the economic benefits that flow from the buyer towards the seller from
the relationship with its channel partner (Geyskens, Steenkamp, & Kumar, 1999). The second type is
non-economic satisfaction, which is defined as the overall affective response to the non-economic
aspects of a channel relationship (Geyskens et al., 1999). Although some scholars treat both economic
and non-economic satisfaction as distinct constructs, the relationship of both constructs with firm
performance is positive (Geyskens et al., 1999). Therefore, this study uses the term buyer satisfaction
to imply the buyer’s cumulative economic and non-economic satisfaction with its strategic supplier
firm (Dwyer & Gassenheimer, 1992).
Our fourth construct is supplier brand value. According to Keller (1993), customer-based brand
knowledge is comprised of two components: brand image and brand awareness. Brand image
encompasses the collective neural associations about the memory of a brand. From the perspective of a
strategic buying center team, we define this as the shared collective memories about the supplier’s
brand, which provides it with leverage because of customer preference that it would otherwise not
have without the brand.
Our fifth construct is buyer firm value. Prior studies have assessed firm value by discounting
expected cash flows to evaluate the impact of marketing strategy on the firm’s value. This perspective
has been widely adopted in the finance, and marketing literatures (Srinivasan & Hanssens, 2009). The
more stable and greater the firm’s current cash flows, the greater the expectation of the firm’s value.
Furthermore, firm growth drives cash flows and, hence, value (Srivastava, Shervani, & Fahey, 1999).
Therefore, we take growth and stability in cash flows (i.e., both current and expected perceptions) as a
measure of the buyer’s firm value (Koller, Goedhart, & Wessels, 2005).
This concludes the brief description of the constructs that are used in this chapter. In summary,
prior research has suggested the relevance of investigating the consequences of market orientation or
supplier responsiveness by using three of the previously discussed constructs, namely buyer
satisfaction, supplier brand value, and buyer firm value. A new addition to the literature is the
construct of IdRR, which we have propounded by synthesizing previous research. In the current
research, we integrate the new proposed construct of IdRR and the other four constructs into our
conceptual model. In the next section, we discuss prior studies of market orientation and examine the
major themes that they have covered so far. Afterwards, we build upon these previous studies through
our conceptual model.
The contexts in which market orientation has been studied are wide and varied. Some
frameworks include different market types, such as B2B and business to consumer (B2C) (Kirca,
Jayachandran, & Bearden, 2005; Siguaw, Simpson, & Baker, 1998). 2 Different methodological
approaches have also been used, including qualitative methodologies such as theory in use, and
grounded theory, and quantitative survey data (Gebhardt, Carpenter, & Sherry, 2006; Narver & Slater,
1990; Siguaw et al., 1998).
2 For a detailed examination of the various kinds of market orientation–performance studies, please refer to the meta
analysis by Kirca et al. (2005).
24

2.2 Market Orientation
In this section, we will describe purposefully selected prior studies on market orientation. The
criteria on which we based our choice of articles are whether the studies represented the
important theoretical streams of the market orientation literature. We introduce two important
conceptualizations of market orientation: the process-based perspective and the culture-based
perspective. We then discuss whether a market-oriented culture is a resource and the linkage
between the cultural perspective of market orientation and risk. Next, we discuss the link
between the cultural perspective of market orientation and innovation, and eventually we discuss
what we have learned from the prior literature synthesis on the cultural perspective of market
orientation. We then commence our discussion about the process-based view of market
orientation. Afterwards, we discuss market orientation in B2B contexts and different moderators
of the market orientation and performance link. Furthermore, we discuss the link between market
orientation and marketing accountability and finance. Finally, we discuss the contribution that
our current study makes to the literature.
As previously described in this chapter, there is a broad bifurcation in the market orientation
literature along two lines, one that conceptualizes market orientation as a process and another that
conceptualizes it as a culture (Langerak, Hultink, & Robben, 2004a). Both streams have linked it to
firm performance (see Table 2.1).
Table 2.1
Representative Studies of Market Orientation and Their Focus
Emphasis of Paper
Study Number
Responsiveness as the Most Important Component of Market
Orientation 4,3,13
Market orientation as a culture 2,3,4,5,6,7,8,9,10,11,12
Market orientation as a process 1,13
B2B market orientation 5
Market orientation from a systems approach 3
NPD as an antecedent to market orientation 12
NPD as a mediator of the market orientation performance
relationship.
Market orientation-performance moderators:
1) Technology 4
2) Market turbulence 4
3) Competition 4
Narver and Slater (1990) 1
Gebhardt, Carpenter, and Sherry Jr. (2006) 2
Homburg, Grozdanovic, and Klarmann (2007) 3
Kohli and Jaworski (1990) 4
Siguaw, Simpson, and Baker (1998) 5
Grewal and Tansuhaj (2001) 6
Slater and Narver (1995) 7
Voss and Voss (2000) 8
Atuahene-Gima (1996) 9
Marinova (2004) 10
Zhou, Yim, and Tse (2005) 11
Verhoef and Leeflang (2009) 12
Jaworski and Kohli (1993) 13
25

Table 2.1 highlights several prominent studies that focus on market orientation. For example,
the work of Kohli and Jaworski (1990) is one of the most highly cited market orientation studies; it is
the conceptual basis for much market orientation research. Kohli and Jaworski (1990) consider
responsiveness as the most important component of market orientation, and their position has been
widely accepted in research. Furthermore, Kohli and Jaworski (1990) theorize about the impact of
three components of market orientation on business performance. Gebhardt et al. (2006) use grounded
theory methodology to provide four stages of how to develop a market-oriented culture in an
organization. Homburg, Grozdanovic, and Klarmann (2007) examine the drivers of responsiveness
towards the customer and responsiveness towards the competitor. Narver and Slater (1990) investigate
the influence of market orientation on business performance in both commodity and non-commodity
businesses. Sigauw et al. (1998) investigate the mechanism by which manufacturing firms influence
their distributors to become more market oriented. Grewal and Tansuhaj (2001) examined the impact
of market orientation and strategic flexibility on firm performance after an economic crisis. Slater and
Narver (1995) propose that market orientation combined with an entrepreneurial drive and the right
organizational climate produces the learning organization. Voss and Voss (2000) examine the role of
market orientation and product orientation on firm performance. Marinova (2004) evaluates the impact
of market knowledge diffusion on innovations within organizations at a micro level. Zhou, Yim, and
Tse (2005) examine the impact of market orientation on firm performance via the mediators of
technological and market innovations. Verhoef and Leeflang (2009) examine whether marketing
accountability increases the motivation for firms to be market oriented. Jaworski and Kohli (1993)
empirically examine the impact of a market orientation on firm performance. In the following sections
(i.e., 2.2.1 through 2.2.11), we explain the studies in Table 1 in detail.
2.2.1 Cultural Perspective of Market Orientation
The cultural perspective of market orientation put forth by Narver and Slater (1990) is widely
accepted. They argued that firms are motivated to acquire sustainable competitive advantages and, as a
result, develop a culture that would help them to meet that end. They argue that this culture gives rise
to three sets of market-oriented behavior, namely customer orientation, competitor orientation, and
interfunctional coordination. Customer and competitor orientation are concerned with acquiring
information about both the former and latter, whereas interfunctional coordination involves
organization-wide resources to be used for the creation of superior value for their customers (Narver &
Slater, 1990). They also examine two decision criteria, namely long-term orientation and profitability.
They consider profits to be a part of the process of market orientation, and profitability to be a
consequence of that process. They find that market orientation and firm size have an inverse
relationship, meaning that the greater the firm’s size, the less market orientation matters. This is
because of the efficiencies they enjoy in the marketplace as a result of economies of scale. Similarly,
they find that firms with few market orientated competitors face low barriers of entry into their target
markets. However, their study was limited to a commodities market. They were unsuccessful in
developing valid measures for profit and long-term focus, but they do find a link between market
orientation and the profitability of a business. On the contrary, their study does not focus on specific
attitudes that cause behavior, but rather on the end product that is behavior. This is a disconnect,
because if the attitude that causes the behavior has not been measured, then how could the researchers
26

conclude that the culture or attitude geared towards acquiring sustainable competitive advantages leads
to these specific behaviors?
2.2.2 Can a Culture be Developed? Is it Within Management’s Control
Qualitative evidence suggests that an organization can become more market oriented if it desires and
can purposefully develop the required culture to maintain such an orientation (Gebhardt et al., 2006).
Gebhardt et al. (2006) outline the steps that are involved in developing a market orientation, namely
initiation, reconstitution, institutionalization, and maintenance. The second step of the process of
creating a market orientation involves value and norm development, the removal of dissenters within
the organization, and hiring of believers. On the one hand, according to Gebhardt et al. (2006), the
development of a market-oriented culture falls within the locus of management control, therefore
making it valid to collect information from respondents about the firm’s culture. On the other hand,
Slater and Narver (1995) suggest that generative organizational learning that is a part of the learning
process of market orientation is beyond the locus of organizational control; hence, developing a
market orientation is not fully in the locus of control of a firm.
The inherent difference in the literature can be resolved by incorporating the concept of IdRR
into the market orientation literature. IdRR is the consequence of risk caused by value-creating
business processes. By reducing IdRR, the supplier firm is adding certainty and standardizing some of
the critical or core business processes that are involved in creating customer value. Hence, the more
standardized a process is, the more control the supplier has over it. In turn, control is thought to enable
the supplier to easily launch remedial measures when necessary.
2.2.3 The Cultural Perspective of Market Orientation and the Ability to Take Risks
Slater and Narver suggest that market orientation is “inherently entrepreneurial” when it focuses
on latent customer needs, but the entrepreneurial values must be clear in the seller’s organization
(1995, p. 68). In essence, they suggest that if the seller focuses on the customer’s latent needs,
then the seller requires a tolerance for taking risks. Furthermore, Slater and Narver (1995)
reinforce the perspective of the process-based view that market orientation involves innovation
and the willingness to take risk beyond the normal level that is involved with regular business
activities. Moreover, they argue in favor of continuous investment in business innovations, as
any new innovation will eventually be imitated. Salter and Narver’s perspective of innovations
are similar to the arguments regarding creative destruction by Schumpter (1942). Meaning a firm
must be willing to cannibalize its own innovations and continuously reinvest in the production of
new developments in order to remain competitive (p.81). In our opinion, creative destruction is
risky because the new innovation may not always pay off as much as did the old cannibalized
product. However, Slater and Narver (1995) provide examples about innovations that are low
risk and high return and, in their opinion, a market orientation encourages firms to find such
opportunities. For example, they believe that user-generated innovation reduces risk and
increases firm education about markets and new technologies. Furthermore, they argue that firms
that are meeting the customers’ latent needs attempt to use low-cost market experiments,
implying that the risk involved with monetary losses when supplier firms’ responses do not work
out is not too high. Whether the seller firms’ risk-taking during innovation through market
orientation is more than when firms do not innovate or are not market oriented are elements that
they do not discuss. In summation, on one hand Slater and Narver (1995) agree with the process
perspective that market orientation needs organizational risk tolerance and risk taking. On the
27

other hand, they later argue that market orientation encourages firms to innovate by using lowrisk
and high-return strategies. The contradiction in their explanation is about whether market
orientation involves increased or diminished risk taking for the buyer and the supplier. In
addition, they also do not investigate how market orientation influences idiosyncratic relational
risk, or the risk attributed to the buyer-and-seller relationship. Such a perspective is one
shortcoming that we investigate in the current chapter.
2.2.4 A Cultural Perspective of Market Orientation and Innovation
Atuahene-Gima (1996) found that market orientation positively influences the exploitation of
innovation competencies and the ability to replace existing competencies with those that are new.
However, the latter choice is also positively moderated by perceived market opportunities or, in
other words, the greater the market opportunity to launch a new corporate competency, the more
likely the firm is to launch it. New firm competencies are also more likely to lead to radical
innovations, whereas the use of old firm competencies will negatively influence firm radical
innovation. Vice versa, existing competencies are likely to lead to incremental innovations,
whereas new competencies will negatively influence incremental capabilities. Furthermore,
interfunctional coordination positively moderates the overall influence of competence
exploitation and exploration on radical innovations. Overall, what this study shows is that a
market orientation is a necessary culture which gives rise to the behaviors leading to incremental
and radical innovations in firms.
Similarly, Marinova (2004) finds that market knowledge diffusion (i.e., information
generation and dissemination about customers and competitors), positively influences the
innovation effort that is exerted by a firm and its performance. In the absence of knowledge
diffusion, the innovation effort does not influence the firm’s performance in terms of market
share or earnings. Hence, market orientation seems to be a necessary antecedent to successful
market innovation endeavor within firms.
2.2.5 What Do We Learn from the Cultural Perspective of Market Orientation?
There is a broad consensus within the extant literature that market orientation can be conceptualized as
a cultural phenomenon. Despite this recognition and the relative difficulty and controversy in how to
measure a culture, many researchers have assessed behaviors that are caused by a culture rather than
attempting to measure the culture itself. This seems to be an effective way to counter the difficulty in
measuring cultures directly, since behaviors serve as a useful proxy of the attitudes that comprise a
culture (Deshpande & Webster, 1989).
There are other fundamental questions on which our prior synthesis provides conflicting
evidence. There are four main issues, namely:
• Can a market orientation be developed?
• Is market orientation a cultural marketing resource?
• Is it possible to identify latent needs?
• Is market orientation a continuous process or firm-level culture?
Below we will discuss there four issues:
i) Can it be developed? The perspectives on this outlook vary, with some scholars arguing that
it can be developed whereas other scholars argue that it cannot (Deshpande & Webster, 1989).
However, in our opinion, the reality seems to be somewhere in between. Some elements of a marketoriented
culture can be developed, but some elements, which require esoteric knowledge of the
28

customer, can only be developed over time and as a part of a general learning curve. This lack of
esoteric customer knowledge is because the firm may not know that such knowledge exists, and what
overall benefit it would be to the firm culture. ii) Is a cultural market orientation a resource? The
learning upon which a customer’s understanding is based is not always something that the firm can
intentionally develop. Hence, it qualifies on one of the important tests of a resource, namely that it is
inimitable (Srivastava, Shervani, & Fahey, 1998). iii) Taking this argument a step further, then the
latent needs identification, which is an outcome of the cultural process of market orientation, cannot
always be purposefully developed, since the required insights may be just a function of pure
coincidence. iv) Market orientation is viewed as a continuous process or a culture of innovation (Kohli
& Jaworski, 1990; Langerak et al., 2004a; Langerak et al., 2004b). Firms and managers want to avoid
sunk costs or the cannibalization of existing product markets. Therefore, in our opinion, a culture must
exist within the firm to allow a focus on innovations and that fosters a managerial set of measurement
tools that will allow them to differentiate themselves in the marketplace. Overall, although the
evidence on the cultural perspective of market orientation varies, there is some support for the notion
that it does have a link to firm performance.
2.2.6 The Process-Based View of Market Orientation
The process-based view of marketing orientation considers it to be the implementation of the
marketing concept (Kohli & Jaworski, 1990). As mentioned earlier, there are three components
of market orientation. Each component of market orientation sequentially paves the way for the
next step in the process of marketing orientation. Hence, intelligence is generated, disseminated,
and, finally, a response is created. But managers discriminate between trustworthy and
untrustworthy sources of intelligence before issuing a response that is based on the intelligence
(Kohli & Jaworski, 1990). Therefore, all intelligence available will never be paid attention to and
only trustworthy sources of intelligence will generate a response. In order to be continuously
trusted, the responsibility lies with the intelligence provider to be accurate because faulty
intelligence could erode the trust that the intelligence provider enjoys. In a strategic supplier-andbuyer
relationship, a customer’s trust in the supplier relationship is important because it has
positive consequences, such as customer committment (Siguaw et al., 1998).
Besides intelligence sharing, there are other ways of building customer goodwill, such as
through innovations. Being market-oriented, by default, leads to innovations; according to Kohli
and Jaworski (1990), “a market orientation involves something new or different in response to
market conditions, it can be viewed as a form of innovative behavior” (p. 11). The process-based
view holds that market orientation corresponds with the process of innovating (Kohli &
Jaworski, 1990). Therefore, market orientation involves trying new or innovative solutions on
target markets, and such is an inherently risky endeavor. Hence, according to the process-based
view of market orientation, managers must be tolerant of risk taking if they are to be market
oriented (Kohli & Jaworski, 1990).
Is there really a difference between the process-based versus cultural-based view of
market orientation? Although Kohli and Jaworski (1990) do recognize that market orientation
can be a plan or philosophy, they do not explicitly recognize market orientation as a part of an
organizational culture. The authors eventually recognized that both the cultural perspective and
process-based perspective are of significance. Likewise, the cultural perspective of Slater and
Narver (1990) operationalizes the culture as specific behaviors or a series of activities.
29

Therefore, there is a convergence in both streams that a market orientation is implemented by
behaviors.
2.2.7 Business-to-Business Market Orientation
The one study that examined the influence of suppliers’ market orientation on B2B distributors’
market orientation suffers from a conceptual limitation in that, first, it does not articulate the
mechanism which would motivate a supplier to choose a market-oriented buyer and, second, the
ways in which the supplier influences the buyer firm’s culture (Siguaw et al., 1998). Regarding
the latter limitation, Siguaw et al. (1998) use reference group theory to explain how supplier
market orientation influences distributor market orientation. They also consider only two
situations: In the first, suppliers are more powerful and more market-oriented than distributors.
As a result, distributors become increasingly market oriented with the hope of pleasing their
suppliers and thus, continuing the existing relationship. The second situation is where the
supplier and distributor are of equal power. Then, a distributor or supplier would benchmark
against the channel partners’ superior performance and optimize his or her own performance in
order to meet the channel partners’ superior performance. The explanations for both situations, in
our opinion, are inadequate. First, having a market-oriented supplier does not necessarily mean
that they would want to have market-oriented distributors unless the distributor market
orientation influences the supplier’s own profits and has the potential to increase their market
share (the link to market share is disputed by some authors, such as Kohli and Jaworski’s 1993
work). These are links which the authors do not explore; such is an important oversight because
they ignore that possibility that if the flow of supplier benefits was contingent upon a channel
partner’s market orientation, then this, in turn, would motivate a supplier to look for market
orientation in their channel partner. Second, they could have measured the ‘supplier’s desire for a
market oriented buyer,’ which would have been an attitudinal indicator measuring their desire to
have distributors who are market oriented. This approach would have been similar to attitudinal
measures used in other domains of marketing, such as innovations (Tellis, Prabhu, & Chandy,
2009), which have shed light on internal firm motivators towards the introduction of radical
market innovations. In conclusion, the literature on B2B inter-organizational market orientation
and its effects are limited. The theoretical explanation provided by Siguaw et al. (1998) leaves
key questions unanswered (i.e., as discussed above). Furthermore, in our opinion for a model to
be strong, there needs to be some direct measure of value created for the buyer or distributor as
well as the supplier. These concerns are addressed later in this chapter when our conceptual
model is developed where we discuss the concept of IdRR insulation and supplier brand value.
2.2.8 Moderators of the Market Orientation-Performance Link
Most studies in the market orientation framework highlight three important moderators regarding
the market orientation-performance relationship: technological turbulence, competitive intensity,
and environmental turbulence (Jaworski & Kohli, 1993; Grewal & Tansuhaj, 2001; Pulendran,
Speed, & Widing, 2000). These external environmental moderators influence the level of a
firm’s market orientation and the market orientations’ linkage with performance (Jaworski &
Kohli, 1993). In the following section, we examine the studies that have examined the influence
of the environmental factors on the firms’ market orientation and performance linkage.
The first environmental moderator of the market orientation-performance linkage is
technological turbulence. Technological turbulence has little evidence to support its role as a
30

moderator of the market orientation-performance relationship. Most studies have found that
technological turbulence is of insignificant influence on the market orientation-performance
relationship, whereas some have found that it positively moderates the relationship and fewer
have found that it negatively moderates the relationship (Kirca et al., 2005). The study that does
find evidence of moderation finds it in technologically turbulent export markets (Kirca et al.,
2005). Most studies have found an insignificant relationship, perhaps due to the rarity of
technological shocks within their samples. A study that has found a positive influence is Grewal
and Tansuhaj (2001). However, their sample is taken from an Asian economy, specifically
Thailand, and the relationship may not hold for other settings in European and North American
countries. Furthermore, they propose that market orientation, in times of market turbulence,
negatively influences performance because a firm’s market understanding is built on knowledge
that no longer applies to the currently changing market scenario. Another study which finds that
partial support for low technological turbulence influencing the market orientation-performance
relationship is that conducted by Slater and Narver (1994). They have similar findings to Grewal
and Tansuhaj (2001), in that market orientation is more important in times of low technological
turbulence than in times of higher technological turbulence. Most studies, however, find that
technological turbulence has an insignificant influence on the market orientation-performance
relationship (Kirca et al., 2005). This result is not surprising since, unless technological
turbulence can influence the knowledge capabilities of the firm, it would not be expected to
moderate the relationship between market orientation and performance. Our stance on this issue
is that, in technology driven markets, market orientation matters less as the new technology
could cause disruptions in consumer-buying patterns. As a result, this makes it difficult for the
market-sensing mechanism (e.g., intelligence generation and dissemination) to determine and
predict what the future needs of the customers will be. Therefore, market orientation has a
limited impact on firm performance.
The second moderator that is commonly cited in the market orientation-performance
relationship is market turbulence. The evidence for it as a positive versus insignificant moderator
is almost evenly split. There is less evidence for it as a negative moderator, which could be
because firms which have ‘strategic flexibility’ are able to change their internal processes to
meet adverse situations (Grewal & Tansuhaj, 2001). In situations where it is insignificant, it
could be that market orientation is an immutable part of the firm’s culture but that it does not
possess strategic flexibility or the ability to change in time. Our stance on this issue is that
market orientation gives insights according to customer understanding and takes time to develop;
in rapidly changing environments, it is not possible for supplier firms to understand how their
customers’ needs are changing. Therefore, we concur with the finding of previous scholars that
market orientation has limited applicability in the context of high market turbulence.
The third moderator is competitive intensity. Extant literature suggests that customer
insights provided by market orientation become more important as competitive rivalry increases
(Kirca et al., 2005). The evidence on whether competitive intensity moderates the market
orientation performance relationship is mixed in prior literature (Kirca et al., 2005). We agree
with the view that as competitive rivalry increases, the firm must be more market oriented.
Because greater competition means that customers have more choices and in order to maintain
31

usiness performance, firms need to respond to customers’ needs with offerings that are based on
unique insights that the competition does not know.
2.2.9 What We Learn from the Prior Studies of Market Orientation
The prior section on market orientation focused on a few core issues. First, it addressed market
orientation as a culture and a set of activities or processes within the firm. Second, it explored the
relationship between market orientation and its role in generating sustainable and valuable
resources for the firm. Finally, it examined the role of innovation and market orientation. Then a
market orientation would lead to an organizational culture that encourages the development of
sustainable competitive advantages for the firm.
2.2.10 Market Orientation and Marketing Accountability
There has been a tremendous call for marketing accountability with the need for marketing
managers to speak the language of finance (Bolton, Lemon, & Verhoef, 2004). Verhoef and
Leeflang (2009) investigated which variables lead to the creation of marketing’s influence within
the firm. They found that a connection with the customer and creativity do not have a significant
amount of influence on marketing’s importance within the firm. However, accountability and
innovativeness do. Then, they find that the greater the marketing department’s influence within
the firm, the more likely it will be that the firm is market oriented. Although they do accept the
process-based perspective of marketing orientation (Kohli & Jaworski, 1990), their
conceptualization suffers from a fundamental flaw. Kohli and Jaworski (1990) state that market
orientation is a process of continuous innovations, whereas Verhoef and Leeflang (2009) define
marketing innovativeness as the degree to which the marketing department contributes to the
development of new products within the firm. Verhoef and Leeflang (2009) then conceptualize a
sub-component of market orientation, innovativeness, as an antecedent to market orientation,
which is in serious theoretical conflict with existing conceptualizations. Their definition of
marketing department innovativeness is as the degree to which it contributes to the developed
new products within the firm. For example, new product development has been documented as a
mediator between the market orientation and performance linkage (Langerak et al., 2004a).
Verhoef and Leeflang (2009) also include customer connection or, in other words, the ability to
translate customer needs into solutions and communicate them to cross-functional departments
as an indirect antecedent to market orientation. This concept is very similar to the intelligence
generation dissemination (Kohli & Jaworski, 1990) and cross-functional alignment concepts
(Narver & Slater, 1990). Given that this is another redundant component of their model, its effect
is understandably insignificant. But the value of Verhoef and Leeflang’s (2009) model is that
they were able to show that accountability, meaning being accountable within the organization,
and having the right metrics to measure that performance leads to a motivation to be market
oriented. This result develops because of the belief that performance improves through a market
orientation. Therefore, studies that examine the role of market orientation in different contexts
should also include measures that lead to corporate accountability so that managers can then
correctly measure their performance. This mechanism, in turn, could act as an objective
motivator for organizations to be market oriented.
In essence, what we learn from Verhoef and Leelang (2009) is that accountability of the
marketing department increases its influence within the firm. In turn, this leads to the firm being
more market oriented and that positively influences firm performance. Hence, marketing firms
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need to measure the value and risk that market orientation creates for a firm which, in turn, gives
the marketing department greater influence in the firm.
2.2.11 Measuring the Marketing Orientation – Finance Link
The market orientation-performance link has typically been measured by seller’s self-reported
perceptual performance metrics, such as profitability, profits, and market share (Kohli &
Jaworski, 1990; Narver & Slater, 1990). However, these metrics have largely ignored how
market orientation affects customer risk perceptions and performance metrics that lead us to the
conclusion that this is an area within the literature than needs further inquiry. Prior studies have
examined management aversion to risk as an antecedent to organizational market orientation, but
the consequences of market orientation for relational risk, to our knowledge, have not been
examined (Kohli & Jaworski, 1990). This lack of focus on risk within the market orientation
literature may be because many managers assume risk assessments as incomprehensible and
these include complex financial models which are better left off to the financial services industry
and investment professionals (Buehler, Freeman, & Hulme, 2008). To address the lack of focus
on risk in the extant market orientation literature, we examine the role of the supplier’s market
orientation in generating risk insulation for their customers. This is consistent with prior research
in marketing that examines the role of market-based assets in creating insulation from stock
market risk. However, we examine whether a supplier’s market orientation generates risk
insulation for customers at the firm level.
2.2.12 Contributions
To our knowledge, ours is the first study in the marketing literature to examine the process of
market orientation with the focus on the mediating role of idiosyncratic relational risk. In the
current study, we aim to make four contributions. First, we examine the role of supplier
responsiveness in creating value in an ongoing channel relationship. According to some scholars,
market orientation, with its strong conceptual and empirical support, has become an important
construct that affects a firm’s strategies (Zhou et al., 2005). Other research has shown that when
dynamics of disequilibrium exist in the relationship between a buyer and seller, they could lead
to a deterioration of the relationship, provided that positive relational sentiments do not prevent
such a deterioration (Kumar, Scheer, & Steenkamp, 1995). In situations where the dynamics of
relational disequilibrium exist along any relationship dimension (i.e., power, trust, etc.), buyers
need cues to indicate to them how likely a supplier is to meet their needs and create value. We
propose that a supplier’s market orientation can provide buyers with such a cue. The effect of
responsiveness on the buyer’s profitability is a measure of the value that is created by the
strategic supplier’s market orientation. Our second contribution is that we focus on IdRR. Prior
research on channel relationships has focused on intangible relational benefits but not IdRR. We
assess whether market orientation can insulate strategic relationships from IdRR. We believe that
ours is the first study that focuses on the market orientation-idiosyncratic risk link at the firm
level. Our third contribution is that we challenge the assumption that market orientation and firm
risk are positively related; we propose that they are negatively related in the strategic relationship
context. This is a unique and counterintuitive insight because the current dominant logic is that
market orientation is synonymous with innovating (i.e., excluding radical innovations).
Innovating means taking risks. However, we find that innovating based on an idiosyncratic
understanding of the other party’s business processes mitigates idiosyncratic risk. What this
33

means is that the more better a strategic supplier knows their customer, the more they know how
and what to innovate. The effect of this is risk reduction, rather than enhanced risk taking. Our
fourth contribution is that prior studies of market orientation have focused on intra-firm benefits
from market orientation. We focus on inter-firm benefits of market orientation. The classic
market orientation literature assesses the performance of the firm implementing the market
orientation philosophy or strategy (Jaworski & Kohli, 1993; Kohli & Jaworski, 1990; Narver &
Slater, 1990). The perspective in most studies that we have reviewed is that of the seller, but
some scholars have advocated using the customer’s perspective (Steinman, Deshpande, &
Farley, 2000). The aim of having a market orientation is to generate superior customer value
(Narver & Slater, 1990). Moreover, the customer is the best to judge whether the value that is
created by a seller market orientation (Steinman et al., 2000). Furthermore, the use of a customer
perspective helps suppliers to avoid a perceptual gap that might exist between their perception
and the customer’s perception of how market-oriented the supplier is (Steinman et al., 2000). In
addition, using the customer’s perspective reveals the degree to which the customer benefits, in
terms of value, from the supplier’s market orientation efforts (Steinman et al., 2000). In tandem,
the customer perspective also reveals how much customers would be willing to pay for the
supplier’s product or relationship; in other words, it allows us to measure the value that the
supplier generates for the relationship. Hence, using the customer’s perspective allows us to
measure value or benefits for the supplier and buyer, rather than the value or benefits acquired by
one entity alone in the supplier and buyer relationship.
In the current study, we incorporate the customer’s perspective of market orientation and
highlight the benefits that the buyer and the supplier can acquire from a supplier’s market
orientation. This customer perspective in our opinion is more meaningful because it is a marketbased
perspective rather than an experiential reflection of a sales manager or executive.
The rest of this chapter is organized as follows: First, we develop our hypotheses, then
we describe our research design and present our findings. Finally, we discuss the academic and
managerial implications, general discussions, limitations, future research directions, and
conclusions.
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2.3 Development of the Conceptual Model
Figure 2.1
Conceptual Model
Figure 2.1 depicts the conceptual model for this study. It shows the hypotheses for the present
research.
In the following paragraphs, we have set out to synthesize the literature and develop our
rationale for the first hypothesis. IdRR is a construct we have developed and upon which we will
expound in this thesis as a mediator of the market orientation and performance relationship. We will
first consider the literature on general marketing risk and the market orientation relationship. Next we
will explicitly focus on IdRR and state our first hypothesis.
35

Supplier responsiveness or market orientation’s fundamental purpose is the creation and
maintenance of superior customer value (Narver & Slater, 1990). Moreover, market orientation
encourages a focus on the customer’s needs. As a consequence of customer focus, the supplier firm
develops an idiosyncratic understanding of the current needs of the buyer. Furthermore, customer
understanding helps the supplier avoid risk taking and provides the buyer with reliable solutions for
their current needs.
Supplier responsiveness involves generating market responses in the form of products,
services, and strategic information. Supplier responsiveness also positively influences the relational
factors of a B2B supplier-and-buyer relationship (Narver & Slater, 1990; Siguaw et al., 1998). In
addition, channel partners are willing to share strategic information with their supplier when
relationships are healthy and of a strategic nature (Frazier, Maltz, Antia, & Rindfleisch, 2009). This
information-sharing process is important, as such information allows the supplier to come up with low
risk and high use solutions. For example, a supplier’s market orientation affords them the ability to
identify expert or lead users of a product. These lead users, in turn, provide valuable intelligence on
breakthrough innovations which are used in the supplier’s products. Some studies have suggested that
the lead users have invented up to 80% of breakthrough innovations (Von Hippel, 1976). Hence, the
supplier needs to invest very little in the development of new products, besides investing in a good
market orientation process. A consequence of this investment is that the risk associated with user
adoption and product failure is very low. Furthermore, an employee of a consulting company observed
that “most of our profits come from value co-creation activities. The customer’s inputs are essential to
creating solutions which are low in risk and which offer high returns.”
Not all components of a market orientation are negatively associated with managerial risk
averseness, specifically intelligence generation and intelligence dissemination (Jaworski & Kohli,
1993). This is observable when seller firms have the necessary technology in place to launch a
successful innovation, but do not do so. For instance, consider Xerox, which had most of the knowhow
for the first personal computers but did not launch them (USC, 2008). Thus, although the
knowledge existed, the response element was lacking. Hence, we infer from the results presented by
Jaworski and Kohli (1993) that managers who are risk averse are indifferent to developing new
knowledge, but they do have concerns when it comes to launching or responding to market needs
based on new knowledge.
Some scholars suggest that one way of perceived customer risk reduction is by enhancing
buyer tolerance for supplier risk taking (Roselius, 1971). Supplier market orientation or supplier
responsiveness increases buyer tolerance for risk taking by strengthening the buyer and supplier
relationship (Siguaw et al., 1998). Supplier market orientation causes the supplier to collaborate with
the buyer to a greater degree and share important information with the buyer. As a consequence, the
buyer is more likely to accept the supplier’s motives as being a genuine expression of the supplier’s
interest in their welfare (Siguaw et al., 1998). Overall, the result here is an increase in the buyer’s level
of trust in the supplier and that, in turn, allows the supplier to take greater risks without a negative
reaction from the customer (Siguaw et al., 1998).
Furthermore, risk reduction is an important aim of both the buyer and the supplier (Roselius,
1971). A risk reduction tactic used by the buyer and the supplier is called ‘risk relievers’ and refers to
the implementation of any marketing action or concept which causes the probability of a purchase loss
to decrease (Roselius, 1971). Stated another way, the implementation of the marketing concept or
36

market orientation reduces risk for the buyer and the supplier. The risk reduction occurs when action,
based on the knowledge about the customer and marketplace in general, is taken. Inherently, market
orientation includes a learning organization that involves producing solutions or responses based on
environmental learning (Slater & Narver, 1995). Hence, the supplier’s response must be based on the
implementation of the marketing concept (i.e., supplier responsiveness), in order to reduce the specific
risk for both the supplier and the buyer in their relationship or in their transactions (i.e., idiosyncratic
relational risk). Hence, our first hypothesis is:
H1: Supplier responsiveness negatively influences IdRR.
Market Orientation and Buyer Satisfaction. The aim of a market orientation is to satisfy
the customer’s needs with a long-term focus on the relationship with the customer (Steinman,
Deshpande, & Farley, 2000; Kalwani & Naryandas, 1995; Kotler & Armstrong, 1991; Day,
1998). A market orientation is also a measure of the supplier satisfying customer needs through
market-sensing capabilities (Day, 1994).
Buyer satisfaction with their supplier is the result of positive expectation disconfirmation,
and buyer dissatisfaction with their supplier is when negative expectation disconfirmation occurs
(Oliver, 1980). Buyer expectations are influenced by three factors: (i) the product or service
itself, past experiences with the supplier that set the norm for future exchanges, and symbolic
expectations the brand (Oliver, 1980); (ii) the communications from sales personnel or
organizational spanners (Oliver, 1980); sales personnel’s influence on the customer’s
expectations by their promise of delivering a certain level of a supplier’s offering. If the sales
personnel’s organization is not market-oriented enough, then there could be a lack of
communication from the salesperson to the firms operations about the customer’s expectation; in
turn, that will lead to customer dissatisfaction when the customer’s needs are not met. Another
scenario is that the salesperson could not be fully informed about his organization’s delivery
capability, specifically in the case of services; as a result, he or she could overextend promises to
the customer. Then, the customer will be dissatisfied when expectations are not met. (iii) The
third aspect to consider is comprised of individual factors, including persuadability and
intellectual distortion (Oliver, 1980).
However, in a market orientation philosophy, it is not enough to meet customer
expectations; instead, the ability to exceed customer expectations is required in order to create
superior value (Narver & Slater, 1990; Anderson, Fornell, & Lehmann, 1994). Such superior
value is delivered by: i) operational excellence, which involves providing better channel and
business process management while maintaining close customer links; ii) customer intimacy,
which involves customizing responses to customer needs and implementing early warning
systems for detecting changes in the market place; and iii) product leadership, which involves
continuous product and service innovation through market-sensing capabilities to achieve a
position of leadership in the market. In addition, market sensing capabilities allow firms to
identify potential product leadership opportunities early on (Treacy & Wiersema, 1993).
Some authors criticize market orientation for encouraging too narrow a focus and the
perception that it leads suppliers to ignore upcoming markets and potential new entrants (Hamel &
Prahald, 1994). As a consequence, this narrow focus may lead to customer dissatisfaction. However,
focusing on markets allows suppliers to acquire idiosyncratic knowledge about how their specific
clientele operates and, therefore, they are able to drive down costs for customers (Kalwani &
37

Naryandas, 1995). Further, idiosyncratic understanding or knowledge allows the supplier to surpass
the customers’ expectation and results in greater customer satisfaction. It also leads to greater
profitability for the strategic supplier and that profitability serves as a motivating factor for suppliers to
focus on their customers’ needs (Kalwani & Naryandas, 1995). This reasoning leads to our second
hypothesis:
H2: Strategic supplier responsiveness positively influences buyer satisfaction.
Buyer Satisfaction and Buyer Firm Value. In the channel management context, buyer
satisfaction is an affective evaluation of accumulative economic and non-economic benefits from a
business relationship (Gyskens et al., 1999). Extant literature has focused on the relationship between
buyer satisfaction and the resulting supplier’s profits (Gyskens et al., 1999). This focus is
understandable, as marketing managers need to provide finance managers with return on investment
measures (Bolton et al., 2004). Yet this focus on the return of the supplier’s investment causes
researchers to neglect whether a buyer’s satisfaction increases the buyer’s own profits. A buyer’s
satisfaction would matter more to a supplier if it has a verifiable link to the buyer’s own profits, as
B2B buyers increase business with suppliers who have a direct impact on their sales. For example,
premium buyers, such as Audi, use OEM manufacturers of components that complement their
premium position and increase their sales, one example of such a component supplier to Audi is Bose
(Ghosh & John, 2009). Therefore, for buyer satisfaction to be meaningful, it must bring economic
value to the supplier. Therefore, we formulate the following hypothesis:
H3: Buyer satisfaction is positively associated with buyer firm value.
Buyer Idiosyncratic Risk and Supplier Brand Value. Risk has been approached within extant
marketing literature at different levels, namely: i) a financial market level, ii) a customer perception
level, and iii) a managerial perceptual risk at the firm level. All three levels of risk include supply risk
perceptions. Generally, supply risk is thought to be highest when a buyer purchases strategic items
from a supplier or, in other words, strategic suppliers provide those items which involve the greatest
supply risk (Kraljic, 1983). For clarification, we refer to this supplier-associated risk in strategic
contexts as IdRR.
Buyers are usually willing to make idiosyncratic investments and, hence, acquire exposure to
idiosyncratic relational risk if any one of three conditions are fulfilled: a) the product is rare, b) the
manufacturing is complex, or c) they are able to buffer against technological uncertainty (Bensaou &
Anderson, 1999). For example, from the perspective of a financial institution such as a bank, the
efficiency of the strategic relationship determines the level of idiosyncratic relational risk. The more
calibrated the processes are, the less the idiosyncratic relational risk and costs of borrowing from a
bank become. Reductions in the liability of buyer and supplier organizations imply that the financial
leverage ratio for them improves and that they can borrow more money at lower rates. This reduction
occurs because risk determines the cost of capital for an organization (Srivastava et al., 1998 ). A
firm’s reputation is also dependent upon their alliances and strategic partners. This dependence
becomes visible by market reactions to firm mergers and acquisitions and alliance announcements. For
instance, the Porsche and Volkswagen merger was motivated by Porsche’s attempt to secure its
supplier from a takeover by rivals (Boston, 2009). This move elicited a positive reaction from the
market, which viewed this maneuver as a merger with a positively reputed supplier. Therefore,
relationships with suppliers who have a good market reputation lead to an increase in firm value. In
turn, this increase causes a decrease in the cost of capital as the firm exhibits a greater ability to pay
38

ack loans and the forced sale value of the firm increases in the eyes of the lenders. From another
customer’s perspective, this also holds true. When customers do borrow from financial institutions, the
better their own reputation and the lower the cost of capital is for them. If the debt is secured by
issuing corporate bonds then less risky firms pay a lower rate of interest to their debtors since they are
considered a more secure investment (Rego, Billet, & Morgan 2009). Hence, a financial benefit of
having a lower idiosyncratic relational risk drives down the cost of capital.
Askari Commercial Bank Limited’s (ACBL) lending practices exemplify the benefits of having
a positive market reputation. Prior to giving loans at the corporate level, banking officers are required
to check both hard and soft indicators. Soft indicators include an evaluation of the firm’s strategic
suppliers. If key suppliers have positive relational assets and a good working relationship with the
customer, a customer’s costs of borrowing go down.
In an effort to generate lower buyer idiosyncratic relational risk, strategic suppliers must take
on greater risk. In other words, increases in strategic supplier responsiveness are accompanied by
increases in business risk for the supplier (Kohli & Jaworski, 1990). Why should a strategic supplier
make an effort to reduce buyer’s IdRR? This reduction is justified because lowering a buyer’s risk
gives rise to positive buyer evaluations of the supplier’s brand. This increase in evaluation is important
for the supplier because intangible non-financial and financial benefits keep the buyer loyal to the
supplier (Naryandas, 2005). Brand value is measured by how much the customer values the supplier’s
brand or, in other words, customer-based brand equity. It follows that supplier brands which offer
greater tangible and intangible value will be more highly valued than those who do not. One aspect of
acquiring positive customer brand evaluation is by idiosyncratic relational risk reduction. Such a
reduction can be achieved through providing customers with reliable, unique, rare, and inimitable
skills and networks (Srivastava et al., 1998) or, in other words, through unique resources. In addition,
brands reduce a customer’s risk by the quality security that they provide when purchasing or using a
branded product (McQuiston, 2004). Moreover, the impact of the brand is not limited to the customers
alone, but can also be transmitted to the financial marketplace to generate value for shareholders via
quality information signals (Aaker & Jacobson, 1994). Furthermore, buyers in the general marketplace
search for suppliers according to their reputation (Saxton, 1997). For marketing managers, purchasing
managers and company executives are under pressure to justify their marketing expenditures. A
reduction in IdRR provides them a metric to justify such expenditures or investments. Strategic
suppliers who are responsive will have a positive marketplace reputation and will positively influence
the financial market’s evaluation of the buyer’s marketing expenditure on their strategic relationships.
In turn, this positive financial market evaluation will lead to customers further positively assessing and
valuing their strategic suppliers. The greater the reliance of the buyer, the more important the supplier
becomes and the greater their brand value. Therefore:
H4: The lower the IdRR, the higher the supplier’s brand value.
Strategic Supplier Brand Value and Buyer Firm Value. Different individuals will decipher a
meaning differently depending on the associations that they have in their minds. The two kinds of
brand associations are pertinent to our current study: symbolic and functional associations. Functional
associations are based on the intrinsic benefits of the product (Keller, 1993). Functional benefits are
involved in meeting a customer’s problem avoidance concerns (Keller, 1993). Symbolic benefits refer
to ‘badge’ associations that provide the organization with some extrinsic benefit (Keller, 1993). These
benefits are typically used to signal some information to the outside world, such as financial markets.
39

They might also include the ease with which a marketing department could justify marketing
expenditures on a strategic supplier because of their strong marketplace reputation, prior performance
in areas such as customer profit improvement, or help them to avoid risk through the supplier’s
superior knowledge networks (Hague & Jackson, 1994). Hence, being associated with a good supplier
brand has both tangible and intangible benefits for the buyer. Furthermore, in B2B markets, buyers are
likely to choose well-known brands if either one of the following conditions exists: (a) when product
failure has serious repercussions for the enterprise, (b) the product requires substantial service support,
or (c) the product is complex (Hutton, 1997). The first of the three conditions always exists in the case
of strategic suppliers. Furthermore, in B2B markets, psychological factors such as the supplier’s brand
reputation may matter more than the physical product attributes (Shaw, Giglierano, & Kallis, 1991).
Hence, well-known brands would be a logical choice for B2B purchase managers. From the above
evidence, we can conclude that a supplier’s brand strength through the mechanism of functional and
symbolic brand associations positively influences the buyer’s firm value. Therefore:
H5: The higher the strategic supplier’s brand value (perceptions) the greater will be the buyer’s
firm value.
2.4 Methodology
2.4.1 Questionnaire Design and Scale Development
Churchill (1979) suggests that items for a survey should be generated by using literature surveys,
experience surveys, critical incidents, focus groups, and insight-stimulating examples. We used a
literature survey and an experience survey because literature was readily available via the EBSCO
database and key academics who have taught purchasing managers and were easily accessible.
Furthermore, we needed unique insights that would give us a perspective on the idiosyncratic working
of organizations. Such insights provide researchers with a deeper understanding of the phenomenon
they are investigating. Therefore, focus groups were ruled out because they generate breadth but not
depth of insights (Barabba & Zaltman, 1991), and insight-stimulating examples were used, albeit
indirectly, when interviewees narrated some examples. For the literature review, we analyzed the
relevant papers on market orientation, market-based assets theory, and satisfaction literature.
Supplier Responsiveness or Market Orientation. The first step of developing measures for
market orientation is domain specification. This has been addressed in the definitions on page 22,
where responsiveness is identified as the most important component of market orientation. The main
criterion for proposing a new definition in marketing is if existing definitions do not suffice for an
investigation (Churchill, 1979). Following that dictum, we do not add any unnecessary new
definitions. In this chapter, we do, however, adapt the market orientation scale (MARKOR)
constructed by Kohli, Jaworski, and Kumar (1993). In the remainder of this section, we first discuss
whether market orientation should be measured by one general factor or three sub-component factors.
Second, we discuss the reasons why we left out some questions of the MARKOR scale and the
motives of why we adapted the remaining items of the scale.
As our conceptualization of market orientation is based upon the Kohli and Jaworski (1990)
paper, we selected the MARKOR scale that Kohli et al. (1993) developed to measure the concept of
market orientation. Kohli and Jaworski (1990) conceptualized market orientation as a process that
included three distinct components. These component factors of market orientation lack discriminant
validity when measured by MARKOR, and that means that the constructs are not truly distinct from
one another (Kohli et al., 1993). Furthermore, the lack of discriminant validity increases when
40

espondents from functions besides marketing are asked. As the components of the MARKOR scales
lack discriminant validity, it is an issue of concern as market orientation involves responding to the
customer’s needs by generating, disseminating, and responding to information throughout the
organization, and not specifically only within the marketing department. Hence, when offering their
assessments of market orientation regardless of their functional duties, respondents should be able to
distinguish between the components of market orientation. If they cannot distinguish between the
components of market orientation, a single general market orientation factor should be used. The
results presented by Kohli et al. (1993) improved when the items of the MARKOR scales and latent
constructs were reduced. The best results they presented were with one general latent factor of market
orientation and two latent component factors of market orientation. However, even the best results
presented by Kohli et al. (1993) were not within the acceptable goodness of fit statistics range.
Therefore, further item reduction would be necessary to improve the goodness of fit statistics when we
use their scale.
The first issue that we had to address was whether to adopt a component-wise approach or a
general factor approach of market orientation. We informally interviewed fellow academics and MBA
students with work experience about what they viewed as the most important component of market
orientation from the customer’s perspective, and it was agreed that the response component was the
most important. In addition, some of the academics and students were of the opinion that the
responsiveness factor was representative of the general market orientation factor from the customer’s
perspective, as the customer is not involved in the supplier’s internal operations and every marketoriented
action is a response from the customer’s perspective. In addition to previously mentioned
reasons for focusing on the customer, informal conversations with academics motivated us, in part, to
focus on the customer, and to some extent we were motivated by the call of Kohli et al. (1993) to
“assess the soundness of the measures” of the MARKOR scale (pg. 473).
In the next stage, we modified the MARKOR scale items. Some of the 32 items of scale were
modified to reflect the customer’s perspective. We did this by first selecting those questions about
which we suspected the customer would have knowledge. This resulted in 20 items being selected.
Next, we reworded the questions in order to adapt them to the customer’s perspective of the supplier’s
responsiveness or market orientation. This was done in order to meet Churchill’s (1979) requirements
that items are able to “convey different shades of meaning” (p. 68). Furthermore, slightly changing the
original scale allows us to create better measures, as Churchill (1979) states, “By incorporating
slightly different nuances of meaning in statements in the item pool, the researcher provides a better
foundation for the eventual measure.” (p. 68).
Table 2.2 provides the original scale of items and our modifications to them. We eliminated the
items on which we concluded that customers could not provide an accurate assessment. Therefore, we
did not include the following MARKOR questions in our questionnaire: 4, 8, 10, 12, 13, 16, 17, 18,
20, 24, 27, and 30. Question 4 states that, “We are slow to detect changes in our customers’ product
preferences.” It was excluded because we came to the conclusion that no customer could decide the
accurate level of research that a supplier conducted in house. Many internal supplier processes are kept
secret and, therefore, the customer would not have the required knowledge to answer question 4.
Similarly, question 8 on the MARKOR scale is, “In our business unit, intelligence on our competitors
is generated independently by several departments;” customers would not know the inner working of
several departments. In addition, Questions 8 and 10 of the MARKOR scale were excluded because
41

they ask about intimate supplier information of which a customer would not be aware. Although some
colleagues argued that, in a close supplier and buyer relationship, the buyer would know about the
quality of the suppliers’ processes, but our colleagues agreed that the exact inner workings of the
supplier’s organizations may not be that transparent. Questions 12 and 13 on the MARKOR scale are
about interdepartmental meetings. These questions are meant for the supplier firm rather than the
customer so, therefore, we excluded them. Questions 16, 17, and 18 on the MARKOR scale relate to
the dissemination of information within the supplier’s firm. We concluded that the customer is not in a
position to be able to reliably respond to information about supplier information dissemination as the
supplier internal information dissemination process does not involve any interaction with the
customer. Question 20 on the MARKOR scale was that the principles of market segmentation guided
the new product development, but it is a question meant for the supplier firm rather than the customer
firm because only a supplier employee would be able to reasonably answer this question. Question 24
on the MARKOR scale is about the frequency of supplier cross-functional department meetings, a
process of which the customer would not be aware. Likewise, question 27 again addresses internal
supplier coordination and we do not believe that the customer will have knowledge of such a process.
Finally, question 30 was considered to be similar to question 26 on the MARKOR scale, so we omitted
it in order to avoid redundancy.
Table 2.2 provides the questions that were revised on the original MARKOR scales. The
revised questions include 1, 2, 3, 5, 6, 7, 9, 11, 14, 15, 19, 21, 22, 23, 25, 26, 28, 29, 31, and 32. The
questions were reworded to measure the customer’s assessment of the supplier’s responsiveness. In
order to reword these questions, we placed the focus on the supplier, whereas the original MARKOR
scale was a self-assessment measure for the customer. For example, question 3 of the MARKOR scale
states that “In this business unit we do a lot of in house market research.” We modified question 3 to
focus on the customers’ assessment as follows, “The supplier seems to have good internal market
intelligence capabilities.” Similarly, question 15 stated that, ”When something important happens to a
major market customer, the whole business unit knows about it within a short period.” We modified
question 15 to focus on the customer’s assessment as follows, ”We just need to communicate our
needs to one of the various functional departments (e.g., marketing, manufacturing, operations,
finance) and everyone concerned (cross functionally) within the supplier’s firm will immediately
updated on our new situation.
42

Table 2.2
Modified Items of the MARKOR Scale
Original Item Need and Reason for change of item Revised Item
1.In this business unit, we
meet with customers at least once
a year to find out what
products or services they will
need in the future.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The supplier meets with us
at least once a year to find
out what products and
services we will need in the future.
2. Individuals from our
manufacturing department
interact directly with customers to
learn how to better serve them.
3. In this business unit, we do a
lot of in house market research.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
Individuals from the suppliers’
manufacturing department interact
directly with us to inquire as to how
they can improve their services or
products.
The supplier seems to have good
internal market intelligence generation
capabilities.
4. We are slow to detect changes in our customers’ product preferences.
5. We poll end users at least The question needed to be
once a year to assess the
reworded to include the
quality of our products and customer’s perspective
services.
about the supplier.
The supplier regularly contacts us to
assess the quality of their products and
services.
6. We often talk with or survey
those who can influence
our end users’ purchases
(e.g., retailers and distributors).
7. We collect industry information
through an informal means.
8. In our business unit, intelligence
on our competitors is
independently generated by several
departments.
9. We are slow to detect
changes in our industry.(R)
10. We periodically review
changes in business environment.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
This supplier has high quality
information regarding their channel
partners (e.g., retailers, distributors and
us).
The supplier often interacts with us in
informal meetings (e.g., lunch with our
managers) to collect information about
our business.
The supplier is slow to detect
fundamental shifts in our industry (e.g.,
competition, technology, regulation).
43

Table 2.2
Modified Items of the MARKOR Scale (Continued)
Original Item
Need and Reason for
change of item
Revised Item
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
11. A lot of informal “hall talk”
in this business concerns our
competitors’ tactics or strategy.
12. We have interdepartmental
meetings at least once a quarter to
discuss market trends and
developments.
13. Marketing personnel in
our business unit spend more
time discussing customers’ future
needs with other functional
departments.
A lot of the informal conversations
with our suppliers concern the
market conditions and our
competitors’ strategies.
14. Our business unit periodically
circulates documents
(e.g. reports and newsletters) that
provide information on our customers.
15. When something important
happens to a major market
customer, the whole business
unit knows about it within a
short period of time.
16. Data on customer satisfaction
are disseminated at all levels in
this business unit on a regular
basis.
17. There is a minimum
communication between
marketing and manufacturing
departments concerning market
developments. (R)
18. When one department finds out
something important about
Competitors, it is slow to alert other
departments. (R)
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The supplier provides us with
periodic market intelligence
(e.g. newsletters and reports) with
content of interest to many
cross-functional departments
within our firm.
We simply need to communicate our
needs to one of the various functional
departments (e.g., marketing,
manufacturing, operations, finance)
and everyone concerned (cross
functionally) within the supplier’s
firm will be immediately updated on
our new situation.
44

Table 2.2
Modified Items of the MARKOR Scale (Continued)
Original Item
Need and Reason for
change of item
Revised Item
19. It takes us forever to decide how to
respond to our competitor's price
changes. (R)
20.Principles of market segmentation
drive new product development efforts
in this business unit.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The supplier takes a very
long time to respond to
changes in their competitors’
pricing strategies.
21. For one reason or another, we tend
to ignore changes in our customer's
product or service needs. (R)
22. We periodically review our product
development efforts to ensure that they
are in line with what customers want.
23. Our business plans are driven more
by technological advances than by
market research. (R)
24. Several departments get
together periodically to
plan a response to
changes occurring in our
business environment.
25. The product lines we sell depend
more on internal politics than real
market needs. (R)
26. If a major competitor were to launch
an intensive campaign targeted at our
customers, we would immediately
implement a response.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
For one reason or another, the supplier
tends to ignore changes in your
product or service needs.
The supplier seems to be driven
by the customers (your) perspective in
new product development efforts.
The supplier's business plans seem to
be driven more by technological
advances than by market research.
The supplier's response to us is often
not aligned with our needs because of
internal supplier firm politics.
If the supplier's major competitor
were to launch an intensive campaign
targeted at you, then the supplier
would normally implement a counter
campaign directed towards you.
45

Table 2.2
Modified Items of the MARKOR Scale (Continued)
Original Item
Need and Reason for
Revised Item
change of item
27. The activities of the different
departments in this business unit are
well coordinated.*
28. Customer complaints fall on
deaf ears in this business unit. (R)*
29. Even if we came up with a
great marketing plan, we probably
would not be able to implement
it in a timely fashion. (R)*
30. We are quick to respond to
significant changes in our
competitors' pricing structures. *
31. When we find out that
customers are unhappy with the
quality of our service, we
immediately take corrective
action.*
32. When we find that customers
would like us to modify a product
or service, the departments
involved make concerted efforts
to do so.*
(R) Reverse worded scales.
The question needed to be
reworded to include the
customer’s perspective
about the supplier.
The question needed to be
reworded to include the
customers’ perspective
about the supplier.
The question needed to be
reworded to include the
customers perspective
about the supplier.
The question needed to be
reworded to include the
customers’ perspective
about the supplier.
The suppliers do not heed your
complaints.
The supplier sometimes has excellent
marketing plans, but they seem to be
implemented too slowly or too late.
If the supplier were to find out that
you are unhappy with the quality of
their service, they would immediately
take corrective action.
If the suppliers find out that you would
like them to modify a product or
service, they would make concerted
efforts to do so.
We conducted an exploratory factor analysis of the modified market orientation scales
that we use in this study and the results are shown in Table 2.3. In SPSS 17, we selected an
factor analysis with VARIMAX rotation. We exclude the factor analysis coefficient values that
were less than 0.5. The resulting solution gave us two factors for the supplier responsiveness or
market orientation scale. All of the items that were reverse worded and coded on the original
MARKOR scale were one factor, and we called this factor the non-responsiveness component
since the questions were measuring whether there was a lack of responsiveness. The other factor
measured the degree of responsiveness and we called this the supplier responsiveness factor. In
the current research, we only use the supplier responsiveness factor scale since it corresponds
with our current research needs. Moreover, the KMO test value is 0.858 that means that the data
is suitable for a factor analysis and more than the minimum level of 0.5. In addition, the supplier
responsiveness scale also qualifies on the Bartlett’s test of sphericity with a significant result at p
46

IdRR: We will briefly address the IdRR definition again to make the domain specification
unequivocal. IdRR is the specific risk that arises as a consequence of the supplier/buyer business
relationship. A business relationship involves the value created by the marketing process as well as all
of the core business processes which create customer value (Srivastava, Shervani, & Fahey, 1999). To
be specific, there are three core processes: new product development process (NPD), supply chain
management process (SCM), and customer relationship management (CRM), which is also referred to
as marketing process. Hence, IdRR incorporates the idiosyncratic risk that is generated by all three
aforementioned core business processes, while trying to generate value in a supplier and buyer
relationship.
There is a convergence of opinion in the marketing literature about the three core business
processes as primary creators of value for a business enterprise. For instance, Porter (1985) terms them
primary activities in the value chain. Three of the primary activities of Porter’s value chain, namely
inbound logistics, operations, and outbound logistics, correspond with the SCM and NPD business
processes, whereas sales, marketing and service correspond with CRM. Similarly, Lehmann and Jocz
(1997) agree that these three core business activities, in addition to market orientation, are the focus of
marketing or value creation for the customer. In summation, based on the above literature synthesis,
the three core business activities involved in customer value creation are NPD, SCM, and CRM.
Therefore, we also accept them as the core business activities in this dissertation.
The next step is to determine whether IdRR is composed of three sub-components or one
general factor that measures idiosyncratic risk because of NPD, SCM, and CRM. Srivastava et al.
(1999) suggest that the three core business processes are “not independent” of each other while
creating value for the customer (p. 169). Moreover, all of the core business processes must be
interdependent upon each other in order to create customer value (Srivastava et al., 1999). Hence, the
three core business processes create value by a shared mutual process. Furthermore, the mutual
process of value creation involves a mutually shared idiosyncratic risk that threatens value creation by
the supplier and buyer relationship. Therefore, we measure IdRR by one common latent factor for the
risk that results from all three core business processes. In addition, informal conversations with some
customers revealed a lack of face validity for three separate sub-component IdRR processes, but face
validity was present for a single general IdRR factor. Hence, in this research, we use a single latent
factor to measure IdRR.
Subsequently, we generate scale items that comprise the domain of IdRR. Based on a literature
review, we selected components of the three core business processes to measure IdRR. From the
literature review, we selected two studies with which to create a measure of IdRR, namely Day (1994)
and Srivastava et al. (1999). We decided to use the Srivastava et al. (1999) study for item generation
because it explicitly focused on the linkage of value creation by business processes and the resulting
risk. Our next step was to create the items that measured IdRR and were caused by the CRM, SCM,
and NPD value creation processes. The SCM processes that measure IdRR are controlling
manufacturing costs, inventory management, resolution of problems that arise in the relationship,
simplifying processes and reducing uncertainty by investments. Similarly, the NPD processes that
measure IdRR supplier investments in the buyers’ NPD capabilities, and the sharing of market
intelligence about markets where the buyers operate. The CRM processes that involve IdRR are the
service quality, resolving relational problems caused by the supplier, investment in technology for
efficiency, channel and distribution decisions, cooperation in the reduction of sales costs, sharing of
48

market intelligence, offering flexible payment schedules, power in the relationships, and relationship
stability. Table 2.4 presents the aforementioned items that are used in measuring IdRR.
Table 2.4
Items Measuring IdRR
IdRR Items
Business process
1.To what extent does your strategic supplier help you reduce your factory or SCM
manufacturing costs?
2.To what extent does your strategic supplier help you increase your
CRM
product or service quality?
3.In the past, has your strategic supplier been willing to invest in technology that SCM and CRM
would improve your supply chain cost efficiency?
4.In the past, has your strategic supplier been willing to invest in technology that NPD
would improve your research and development and engineering cost efficiency?
5.To what extent does you strategic supplier influence your channel or distribution CRM
costs?
6.Does the strategic supplier help you to reduce your sales cost?
CRM
7.Does your strategic supplier also provide you with intelligence on the markets in
which you operate?
8. To what extent does your strategic supplier influence your inventory management? SCM
9.Does your strategic supplier offer you flexible payment schedules?
CRM
10.To what extent does your supplier help you manage your inventory?
11.To what degree does your strategic supplier influence your cash conversion cycle?
12.A definitional clarification: The cash conversion cycle is the time duration in
which a firm is able to convert its resources into cash. It is actually the total time
period required to first convert resources into inventories, then inventories into
finished goods, then goods into sales, and then sales into cash.
NPD and
CRM
General Question
13.To what extent have your strategic suppliers tried to simplify your
SCM
supply chain processes?
14.To what extent do those strategic investments give you power in negotiations over y SCM and CRM
strategic suppliers?
15.To what degree do those specific investments make it difficult for the strategic SCM and CRM
supplier to end their relationship with you?
Following the same procedure as was applied to the factor analysis of supplier
responsiveness, we factor-analyzed the IdRR scale. Table 2.5 presents the factor analysis results
with a Varimax rotation since factor 2 consists of less than three items, which is less than the
minimum requirement for a reliable scale (Hair et al., 2006). Therefore, we only use factor 1 to
measure IdRR in our analysis and drop out item 14 in Table 2.5.
49

Table 2.5
Rotated IdRR Component Matrix
IdRR Items 1 2
1.To what extent does your strategic supplier help you reduce your factory .731
or manufacturing costs?
2.In the past has your strategic supplier been willing to invest in technology that
would improve your supply chain cost efficiency?
.645
3.In the past has your strategic supplier been willing to invest in technology that
would improve your research and development and engineering cost efficiency?
.646
4.To what extent does you strategic supplier influence your channel or
distribution costs?
.627
5.Does the strategic supplier help you in reducing your sales cost? .563
6.Does your strategic supplier also provide you with intelligence on the markets
in which you operate?
.736
7.To what extent does your strategic supplier influence your inventory
management?
.777
8.Does your strategic supplier offer you flexible payment schedules? .607
9.To what extent does your supplier help you manage your inventory? .723
10.To what degree does your strategic supplier influence your cash conversion
cycle? A definitional clarification: The cash conversion cycle is the time
duration in which a firm is able to convert its resources into cash. It is actually
.684
the total time period.
11.To what extent does using your strategic supplier influence your ability to
minimize the time needed for your firm to provide solutions for your customers?
.519
12.To what extent have your strategic suppliers tried to simplify your supply
chain processes?
.732
13.To what extent do your strategic suppliers correctly invoice your company? .686
14.To what degree do those specific investments make it difficult for the
strategic supplier to end their relationship with you?
.836
15.To what extent does the strategic supplier help you increase your products or
service quality?
.695
Extraction Method: Principal Component Analysis. KMO=0.910, Chi-Suaqre=1056, p

We considered several scales before arriving at a conclusion about which scale was best and
whether we should use one unified or two separate constructs to measure economic and non-economic
satisfaction. We first considered the scale developed by Rukert and Churchill (1984), which they used
to measure satisfaction on the following dimensions: product, financial, assistance, and social
interaction. The product dimension assesses the demand, quality, and awareness of a supplier’s
offering (Rukert & Churchill, 1984). The financial dimension evaluates the financial attractiveness of
the supplier’s offering (Rukert & Churchill, 1984). The assistance dimension evaluates how much
assistance the channel member or customer receives from the supplier in promoting their products
(Rukert & Churchill, 1984), while the social interaction dimension evaluates the interactions between
the supplier’s sales representatives and the buyer (Rukert & Churchill, 1984). Moreover, Rukert and
Churchill (1984) measured satisfaction through what they termed direct and indirect measurement,
specifically they reworded the questions in the direct measurement scale to directly inquire about
satisfaction from the customer and in the indirect scale to indirectly acquire satisfaction information
from the customer. Only seven items on the indirect scale passed the 0.7 Chronbach’s alpha threshold
value (Hair et al., 2006). In effect, only the social interaction dimension of the satisfaction construct
had reliable item measurements. Similarly, when Rukert and Churchill (1984) measured satisfaction
directly, they only had two items that were reliable. Although Rukert and Churchill (1984) do present
some scale results that have sufficient Chronbach’s alpha values, they doubt their own results when
they state that “one should not put much faith in an alpha computed on only two or three items” (p.
229). Hence, drawing from this analysis, we can conclude that only a few items reliably measure
satisfaction and that they are not loading in enough quantity to warrant a separate dimension
measurement or a secondary factor analysis.
Some scholars, such as Ganesan (1994), have used a single reliable construct for the
measurement of channel member satisfaction (Chronbach’s alpha of 0.94). But Ganesan (1994)
focuses only on the emotional or non-economic satisfaction, whereas we have specified the domain to
include both economic and non-economic evaluations by the customer. Therefore, this scale was ruled
out for use in our research.
The scale created by Dwyer and Gassenheimer (1992) measures economic and non-economic
distributors or buyer satisfaction with their supplier on one latent variable. Moreover, the scale that
they constructed is reliable and covers the four dimensions given by Rukert and Churchill (1984).
Therefore, we decided to use the scale specified by Dwyer and Gassenheimer (1992). We slightly
modified the sentences to suit our study, as detailed in Table 2.6 and included an additional question
generated by ourselves, “what is your overall satisfaction with this supplier?”
51

Table 2.6
List of Modified Satisfaction Items
Original Item
Dwyer and Gassenheimer (1992)
1.Profits generated from manufacturer's
(A, B, C) product lines.
Modified Items
How satisfied are you with the profits
generated from carrying the supplier’s
products lines, if any?
2.Overall manner in which you were treated by the
manufacturer's regional office or headquarters.
3.Overall “sales support”/relationship with
the manufacturer's local sales representative.
4.New product market opportunities that the
manufacturer provides you.
How satisfied are you with the overall
manner with which the strategic
supplier’s representatives (regional
or head office) treat you?
How satisfied are you with the sales
support provided by the manufacturer's
local sales representative?
How satisfied are you with the new
product market opportunities the
supplier provides you?
5.Sales growth potential from carrying manufacturer's produHow satisfied are you with the sales growth potential from
lines.
carrying the supplier’s products lines, if any?
6.Fairness and honesty of the
manufacturer.
7. Interest and concern manufacturer has
displayed in helping you to accomplish your
goals and objectives.
How satisfied are you with this
supplier’s overall fairness and honesty
while dealing with you?
How satisfied are you with the
interest and concern that this supplier has in
helping you meet your objectives?
We analyzed the satisfaction scale by means of a principal component factor analysis.
The factor analysis gave us one factor, and therefore, SPSS 17 gave us the message that a
Varimax rotation was not possible with one component. The scale is very good with a KMO of
0.909 and qualifies on the Bartlett’s test of sphericity. The results are presented in Table 2.7.
52

Table 2.7
Component Matrix Buyer Satisfaction
Buyer Satisfaction
Component
1. What is your overall satisfaction with this supplier? 0.730
2.How satisfied are you with the new product market
0.774
opportunities that the supplier provides to you?
3.How satisfied are you with this supplier’s overall
0.708
fairness and honesty while dealing with you?
4.How satisfied are you with the interest and concern
0.765
that the supplier exhibited in helping you to meet your
objectives?
5.How satisfied are you with the profits generated from
0.714
carrying the supplier's product lines, if any?
6.How satisfied are you with the sales growth potential
0.706
from carrying the supplier's product lines, if any?
7.How satisfied are you with the overall manner with
0.763
which the strategic supplier's representatives (regional
or head office) treat you?
Extraction Method: Principal component analysis. KMO =0.903, Chi Square = 556, df= 28 p

Table 2.9
Matrix of Supplier Brand Value Components
Supplier Brand Value
1.To what extent does being associated with your strategic supplier's brand give you leverage with
your customers?
Component
2.How likely are you to repurchase from these suppliers? .744
3.How likely are you to tolerate a price increase by your strategic supplier? .583
Extraction Method: Principal Component Analysis. KMO = 0.574, Chi-Square= 22.576, p

een monitored to make sure they gave the most accurate reply possible. However, since it was an
Internet survey, we were able to look at whether informants rapidly clicked through a survey by
examining the time it took them to complete the survey and whether they just choose one option as an
answer throughout the survey. Hence, we removed those responses that had responses that had one
response filled throughout the questionnaire. For example, if respondents just choose the option 1 for
all the answers. This, as a result, reduces the potential CMV bias in our sample response.
Two common survey procedures that were particularly prone to CMV are single-point format
(i.e., using a five- or seven-point scale throughout the survey) and common scale anchors (for
example, 1= strongly agree and 5= strongly disagree). For the first concern, we had to make a trade off
and accept a single point format almost throughout the survey. There were two main reasons for our
decision, such as time concerns (i.e., since we needed a number of questions answered in a limited
amount of time and the use of a different point format would increase the amount of time required per
survey filling) and the fact that our use of panel data would significantly increase the per response
cost. For the common scale anchors, we decided to use different terminology throughout the survey,
hence reducing the error caused by these anchors (Rindfleisch et al., 2009).
Marketing has lower CMV error than other social science disciplines (Rindfleisch et al., 2009).
CMV is a cause for concern because it reduces the researchers’ ability to correctly critically infer
information from the data (Rindfleisch et al., 2009). Specifically, it increases the probability of type I
and type II errors.
One important way in which common method variance increases is as the level of construct
abstraction increases (Rindfleisch et al., 2009). Some scholars have suggested a three-level taxonomy
for categorizing the level of construct abstraction (Crampton & Wagner, 1994; Rindfleisch et al.,
2009). The first and least abstract levels of constructs are those that can be externally verified, for
example, advertising expenditure. The second and middle level of abstraction are the outer expression
of internal states. For example, firm experience in high technology markets. The third and most
abstract level is an internal state, such as customer satisfaction. Based on this taxonomy, we have one
variable that is at the most abstract level (i.e., buyer satisfaction); we have two variables that are
moderately abstract (i.e., market orientation and IdRR), and two variables that are at the least abstract
level (i.e., supplier brand value and buyer firm value). Therefore, we tried to find existing scales
wherever possible, as they have been tried and tested, and help minimize any fears of CMV error in
our study.
2.4.2 Sample
Table 2.12 provides a description of the sample. Our sample was collected by using the GMI online
panel and we received a response rate of 6% in the United States. Firms were chosen on the basis of
whether they were situated in the United States. Whenever possible we tried to collect firms that were
publically listed. Although the firms in our sample have a variety of products and have had
relationships that range from transactional to strategic in nature, we only investigated their strategic
relationships. The sample frame was comprised of 164 purchasing managers of strategic products. We
sent out the questionnaire via email to the GMI panel in three waves according to Donney and Canon’s
(1997) recommendations. Specifically, the entire list (N=2800) was mailed a questionnaire and a letter
requesting their participation and one week later, a reminder was sent via email to those participants
who had not responded. A total of 194 responses were received; of that total, 164 were finally used in
our analysis. Thirty were discarded because they were incomplete or incorrectly filled. For instance,
55

some respondents would not provide their company name; instead, they typed in random letters or
scant information regarding their line of business. Therefore, such responses were discarded. We used
the list wise deletion method which involves selecting those cases that were incompletely filled.
The buyer’s primary business was most frequently mentioned as both industrial and consumer
markets 49%, then consumer markets 32.3%, and industrial markets only 18.7%. Most buyers in our
sample stated an average of 9.72 years of buying experience, out of which 7.36 years were spent as
strategic buyers for their current employers. Of our sample, 30.9% worked for upper management
meaning senior managers and above, whereas 53.1% worked for mid-management. Approximately
one-third (i.e., 35.4%) of the strategic buyers provided service-based market offerings, 23.1%
provided goods as market offerings, and the remaining 41.5% offered a mixture of both goods and
services as their market offering.
Table 2.12
Descriptive Statistics
Sample Description
Percentage
n=164
Type of Market
Percentage
Industrial and Consumer Markets 49.0
Consumer Markets 32.3
Industrial Markets 18.7
Number of Years of Buying Experience
Years
Average Buying Experience 9.72
Average Strategic Buying Experience 7.36
Level of Management
Percentage
Upper Management 30.9
Middle Management 53.1
Type of Market Offering
Percentage
Service Based Market offering 35.4
Goods Based Market offering 23.1
Goods and Service based market offering 41.5
Response rate 6.0
In order to assess non-response bias, early and late responses were compared (Donney &
Cannon, 1997). About 80% of our initial responses came during the first panel request to fill out our
questionnaire. The aspects along which early and late responses were compared were by the following
descriptive variables: the number of years of purchasing experience, role and level in the organization,
and familiarity with the strategic supplier. There were no statistically significant differences between
early and late responses. Therefore, non-response bias is not a serious concern for this study.
For issues regarding normality of the current data set please refer to appendix B. We had some
issues with skewness, but that was rectified by a square root transformation of the data.
2.4.3 Multicollinearity
We check for multicollinearity by using the VIF statistic and ran four linear regressions with a new
dependent variable each time. The dependent variables in order were supplier responsiveness, IdRR,
56

satisfaction and strategic supplier brand value. As becomes evident from Tables 2.13-2.16, none
crossed over the value of VIF=10. Second, none of the tolerance levels is less than .10, which means
that multicollinearity is not a serious concern for the current study (Hair et al., 2006, p. 193).
Table 2.13
Multicollinearity Statistics
Model
Collinearity Statistics
Tolerance VIF
1 Supplier Responsiveness .463 2.696
Strategic Supplier Brand Value .491 2.037
Buyer Satisfaction .371 2.161
a. Dependent Variable: IdRR
Table 2.14
Multicollinearity Statistics
Model
Collinearity Statistics
Tolerance
VIF
2 Supplier Responsiveness .475 2.107
Strategic Supplier Brand Value .542 1.864
IdRR .439 2.278
a.Dependent Variable: Buyer Satisfaction
Table 2.15
Multicollinearity Statistics
Model
Collinearity Statistics
Tolerance
VIF
3 Supplier Responsiveness .429 2.329
IdRR .378 2.648
Buyer Satisfaction .352 2.837
a.Dependent Variable: Strategic Supplier Brand Value
Table 2.16
Multicollinearity Statistics
Model
Collinearity
Tolerance
Statistics
VIF
4 IdRR .400 2.499
Buyer Satisfaction .347 2.884
Strategic Supplier Brand Value .482 2.073
a. Dependent Variable: Supplier Responsiveness
57

Table 2.17
Inter-item Correlations
SIQ CustNPD CF CCC IM AssBrand RP. Sales. Fair. BFV
SIQ 1.00
CustNPD 0.50 1.00
CF 0.52 0.35 1.00
CCC -0.39 -0.35 -0.38 1.00
IM -0.40 -0.37 -0.33 0.54 1.00
AssBrand 0.34 0.39 0.26 -0.43 -0.51 1.00
RP 0.49 0.31 0.26 -0.25 -0.32 0.33 1.00
Sales 0.44 0.43 0.40 -0.40 -0.49 0.50 0.45 1.00
Fair 0.31 0.34 0.40 -0.28 -0.43 0.44 0.36 0.58 1.00
BFV 0.38 0.38 0.38 -0.44 -0.37 0.45 0.26 0.36 0.34 1.00
*Please look at Table 2.18 for interpretation of the abbreviation.
58

2.5 Measurement Model
For creating our measurement model, we followed the steps used by Hair et al.
(2006). We (1) specified, (2) identified, (3) estimated, (4) evaluated, and (5)
respecified the model. We used AMOS version 17 for our analysis. The four variables
in the measurement model are supplier responsiveness, idiosyncratic relational risk,
satisfaction, and supplier brand value. As buyer firm value is represented by a single
observed variable it is not included in the measurement model as AMOS 17 cannot
handle a latent construct measured by a single observed item. Although there is a
standard procedure of fixing the regression weight to 1 of the latent variable for single
item factor, but as it is an artificial procedure recognized by many scholars as being
based on an unrealistic assumption (Hair et al., 2006, p. 599). Therefore, in order not
to compromise our results we did not include it in the measurement model.
We had a total of 48 items and we used 38 items of our survey to create our
measurement model; specifically, the survey consisted of 12 items that measured
supplier responsiveness, 14 items that addressed IdRR, 8 items that focused on buyer
satisfaction, 3 items to assess supplier brand value, and 1 item to confirm buyer firm
value. We employed AMOS 17 to draw covariations between all of the latent
constructs and observed items. We assessed our model by using several criteria,
namely the magnitude of each items’ error variance estimate, item cross-loadings
indicated by large modification indices, and significant residual covariation (Kohli et
al., 1993). Our initial model, which was entitled MOD1, was unidentifiable. Using
modification indices, we deleted items to improve the model’s fit and achieve
identification. Finally, we achieved an excellent model fit with 10 items following the
instructions of AMOS modification indices to improve the fit statistics. The questions
representing the 10 items are provided in Table 2.18.
59

Table 2.18
Questionnaire Items and Codes
Item Response Scale Code
1.This supplier has high quality information regarding
their channel partners (e.g., retailers, distributors, and
us).
2.The supplier seems to be driven by the customer’s
(your) perspective in new product development efforts.
Extremely agree =1, Agree=2, Neutral=3,
Disagree=4, Extremely disagree=5
Extremely agree =1, Agree=2, Neutral=3,
Disagree=4, Extremely disagree=5
SIQ
CustNPD
3.The supplier’s strategy in the marketplace optimally
utilizes all of its various functional departments.
4.How satisfied are you with this supplier’s overall
fairness and honesty while dealing with you?
5.How satisfied are you with the sales support provided
by the manufacturer's local sales representative?
6.To what extent does being associated with the strategic
supplier’s brand give you leverage with your customers?
7.How likely are you to repurchase from these suppliers?
8.To what extent does your supplier help you manage
your inventory?
Extremely agree =1, Agree=2, Neutral=3,
Disagree=4, Extremely disagree=5
Extremely satisfied=1, Satisfied=2,
Neutral=3, Dissatisfied=4, Extremely
dissatisfied=5
Extremely satisfied=1, Satisfied=2,
Neutral=3, Dissatisfied=4, Extremely
dissatisfied=5
Highly positive=1, Positive=2,
Neutral=3, Negative=4, Highly
negative=5
Extremely likely=1, Likely=2, Neutral=3,
Unlikely=4, Extremely unlikely=5
Highly helps=5, Helps=4, Neutral=3,
Problematic=2, Highly problematic=1
CF
Fair
Sales
AssBrand
RP
IM
9.To what degree does your strategic supplier influence
your cash conversion cycle? Definitional clarification:
The cash conversion cycle is the time duration in which a
firm is able to convert its resources into cash. It is
actually the total time period required to first convert
resources into inventories, then inventories into finished
goods, then goods into sales, and then sales into cash.
Highly positively=5, Positively=4,
Neutral=3, Negatively=2, Highly
negatively=1
CCC
10.To what extent will your strategic supplier influence
your future cash flow growth and stability?
Extremely positive influence=1 Positive
influence=2, Neutral=3, Negative
influence=4, Extremely negatively=5.
BFV
In the second step, we investigated the model’s identification. The initial full,
38-item model was unidentifiable. However, after a repeated process of reducing the
number of observed variables, we finally achieved a good model fit and identification
with 10 items. Model identification is measured by the difference between the number
of distinct sample moments (45) and the distinct parameters to be estimated (23). The
resulting degrees of freedom are 22, indicating that the model has been identified. The
reduction was attained by looking at the modification indices and low standardized
regression weights. Table 2.21 presents the recommended fit indices and our
measurement model’s actual fit indices. Our model fit indices are excellent.
60

In the third step, we used maximum likelihood estimation in AMOS to
estimate the model. The recommended sample size is between 100-150, but valid
results have also been found with smaller sample sizes of up to 50 (Hair et al.,
2006).Therefore, since our sample size of 164 is over the 100 threshold and well
above the minimum size of 50, we decided to use the maximum likelihood option.
Another technique that we could have used was asymptomatically distribution free,
which offers an advantage over all other techniques since it does not require normally
distributed data. However, as the data we are using is within the acceptable range of
the normal limit, this issue is not a consideration for us. In addition, asymptomatically
distribution free requires larger sample sizes than our current sample size of 164.
Therefore, we decided not to use it. We also used the options of fitting the saturated
and independence models, minimization history, standardized estimates, and squared
multiple correlations. The latter two components would eventually help us to estimate
the average variance that was extracted and the reliability of the findings. The
standardized residual covariances are less than two, which means the measurement
model is acceptable.
Another criterion is that the standardized regression weights should be above
0.5 (i.e., preferably 0.7). All of our measures, as presented in Table 2.19, fulfill this
criteria. A close examination of the standardized regression weights within the CFA
model indicates that none is extremely close to one. This result provides evidence that
multicollinearity is not a problem with our data. The highest standardized regression
weight we have is 0.838.
Table 2.19 represents the average variance extracted (AVE) for each the
constructs in the model tested. AVE is the shared amount of variance indicators have
with a construct. The minimal acceptable level as a rule of thumb is 50%. Two of our
constructs buyer satisfaction and IdRR pass the 50% threshold. Supplier
responsiveness is close to the 50% threshold and supplier brand value does has bad
construct validity. However, in Chapter 4 when we repeat this analysis using a sub
sample of publically listed companies from the data used in Chapter 2 and we find
that the construct supplier brand value satisfies the criteria of surpassing the 0.5
threshold for AVE.
Table 2.19 Average Variance Extracted
Construct Observed Variable (SRW) Squared Multiple Correlations
Supplier Responsiveness SIQ (.773) .598
Supplier Responsiveness CustNPD (.643) .414
Supplier Responsiveness CF (.631) .398
Average Variance Extracted 47 %
IdRR CCC (.682) .465
IdRR IM (.796) .633
Average Variance Extracted 54 %
Supplier Brand Value AssBrand (.645) .415
Supplier Brand Value RP (.579) .335
Average Variance Extracted 37.5%
Buyer Satisfaction Sales (.838) .703
Buyer Satisfaction Fairness (695) .483
Average Variance Extracted 59.2 %
SRW=Standardized Regression Weights.
61

Table 2.20 represented the reliability for the constructs in our model. The
values are above 0.7, and for exploratory scales lower values of construct reliability
are acceptable. In light of the exploratory nature of the supplier brand value scale as it
created by us we have decided to keep it in the model. Therefore, as per Table 2.20
our constructs are reliable (Hair et al., 2006,p. 612).
Table 2.20
Construct Reliability
Construct Name
Construct Reliability
1.Supplier Responsiveness 0.72
2.Idiosyncratic Relational Risk 0.71
3.Strategic Supplier Brand Value 0.54
4.Buyer Satisfaction 0.74
In the fourth step, we assessed our model; the results of this process are
presented in Table 2.21. One of the first tests to check for model fit in this stage is the
χ 2 test. The χ 2 tests whether the estimated covariance index is equal to the observed
covariance index. Ideally our estimated model should be exactly the same as our
predicted model, showing no difference between the χ 2 mean value for the null and
estimated models. The null hypothesis is that there is no difference between the
observed and estimated matrices. The χ 2 shows that the matrices are different in our
model as the null hypothesis is supported at the 0.001 level (Hair et al., 2006). This
means that our predicted model does fit the observed model. Although the χ 2 statistic
is a powerful indicator, it alone is not enough to indicate the model fit (Joreskog &
Sorbom, 1984). This is because of χ 2 sensitivity to sample size. For example, as the
sample size increases above 200, the χ 2 test tends to show significance because an
increased sample size inflated the χ 2 values. As it approaches or moves away from
100, it tends to indicate insignificance (Hair et al., 2006). Since our sample size is
164, we rely on the χ 2 indicator as one test of whether our model shows a relationship
between the variables in the model. Our findings are consistent with the
recommendations of Hair et al. (2006), who specified that the models should be
insignificant. However, Hair et al. (2006) also state that SEM has no single fit statistic
that best describes the overall model fit or strength of its predictions. Therefore,
below we use a number of absolute and relative fit measures to assess our
confirmatory factor model.
GFI is a widely accepted absolute fit measure. A model is considered
acceptable when it has a GFI value of ≥.9. Our model meets this limit with a value of
0.951 as shown in Table 2.21.
RMSEA is sometimes called a badness-of-fit measure and is an absolute fit
measure. RMSEA measures how good a model fits the whole population rather than
just the sample. It is typically a good indicator for larger samples. The RMSEA is
used to avoid a Type II error, which occurs when a model that actually fits the data is
rejected. Acceptable values for RMSEA are between .05 and .08; less than .05
indicates a good fit. By this standard, our current model is acceptable with a RMSEA
of 0.068. The second category of fit indices are incremental fit measures. These
indices compare our proposed model with the null model (Hair et al., 2006). The
62

incremental fit index (IFI) is one from the range of incremental fit measures. Our
model has an incremental fit index (IFI) value of .966 indicating a good fit. The next
incremental fit index that we use is the comparative fit index (CFI) and it is
specifically meant for samples with small sizes (Hair et al., 2006). We surpass the
minimum level of 0.9 with our model score of 0.965, which is considered a good fit.
Moreover, the Tucker Lewis index (TLI) has a value of 0.942 and that is above the
minimal level of 0.90. Since our model meets the criteria of the goodness of fit
measures, we accept the measurement model.
Table 2.21
Measurement Model Indices
Goodness of fit index Recommended Values Values
χ 2 /degrees of freedom ≤ 3.00 1.76
Tucker Lewis Index (TLI) ≥ 0.90 0.94
Goodness-of-Fit Index
≥ 0.90 0.95
(GFI)
Incremental Fit Index (IFI) ≥ 0.90 0.96
Comparative Fit Index
≤ 0.90 0.96
(CFI)
Root Mean Square Error
of Approximation
(RMSEA)
≤ 0.08 0.06
In Table 2.22, we examine the standardized residual covariance. Table 2.22
presents the results of the difference between the sample and implied covariances.
Each residual covariance has been divided by an estimate of its standard error
(Jöreskog & Sörbom, 1984). The remaining 9 items in Table 2.22 standardized
residual covariances have a standard normal distribution, as none of the items has a
value that exceeds two in absolute value.
Table 2.22
Standardized Residual Covariances
1 2 3 4 5 6 7 8 9
IM 0
Sales -0.08 0
Fair -0.39 0.00 0
RP 0.78 0.12 -0.09 0.75
AssBrand -0.76 -0.15 0.19 -0.39 0
CCC 0.00 0.12 0.71 1.00 -0.52 0
CF 0.29 0.18 1.07 -0.39 -0.95 -0.85 0
CustNPD -0.13 0.52 0.20 0.10 0.55 -0.43 -0.63 0
SIQ 0.44 -0.29 -0.89 1.61 -0.93 -0.21 0.38 -0.03 0
63

2.6 The Structural Model
The second part in our model estimation is assessing the structural model. The SEM
technique was chosen because it is an important tool for academic research as well as
for analyzing survey data and covariance structures in data (Hair et al., 2006). We
follow the seven stages of structural equation modeling that were developed by Hair
et al. (2006).
The first stage is developing a modeling strategy. We had two alternative
strategies from which to choose while developing our modeling strategy. The first
alternative is developing a single model development strategy (i.e., also called a
confirmatory model strategy) and the second alternative is developing a competing
model strategy. The benefit of the first alternative is that it leads a researcher to focus
on one model and, in turn, this leads to a rigorous application of the SEM technique.
In tandem, a single model development strategy suffers from the lack of identification
of alternative models (Hair et al., 2006). The benefit of the second alternative is that it
allows a researcher to assess alternative theories, but we had already evaluated
alternative theories during our literature review and synthesis. Moreover, we have
already arrived at a specific model that we wanted to test. Hence, a single model
strategy best suited our current needs.
We also dealt with whether we had enough evidence to determine causality.
The most important criterion is a sound theoretical reasoning that we have developed
in the earlier section on our hypotheses development and literature synthesis. Supplier
responsiveness, IdRR, buyer satisfaction, supplier brand value, and buyer firm value
have statistical associations and cause and effect antecedence based on prior research
that was covered in the literature synthesis. We chose the most important variables
after our literature synthesis, thus significantly reducing the possibility of a lack of
alternative causal variables. A limitation of this approach is that we are not able to
investigate all other possible models that our research might include. While
specifying the initial model we had to adhere to the rule of five observations for each
measured variable in the model (Hair et al., 2006, p. 166). Our initial model with 39
items and four latent constructs and one observed construct did not meet this criterion.
With the help of modification indices, this was later reduced to 10 measured variables
and five constructs. This gave us a ratio of about 16 respondents for every variable
(164/10), which exceeds the minimum recommended limit of five respondents for
every item (Hair et al., 2006, p. 166). Our four latent concepts and one observed
construct are fewer than the maximum recommended of 20 for using the SEM
technique.
In the second and third stages, we constructed a path diagram of causal
relationships. We have one exogenous construct, which is supplier responsiveness.
We have four endogenous constructs, namely IdRR, satisfaction, supplier brand
value, and buyer firm value. We also tested for unidimensionality. Our results
indicated that our constructs load on more than one factor and, therefore,
unidimensionality is not a concern in the current study.
In the fourth stage of our research, we determined the input matrix type and
estimated the proposed model. We choose the covariance matrix because it is a true
test of theory (Hair et al., 2006, p. 603). The correlation matrix is not suited for theory
testing as it only represents a pattern of relationships. We used the maximum
likelihood estimation method and our sample size of 164 was above the recommended
64

ange of 100-150 (Hair et al., 2006, p. 605). In AMOS we also selected covariance
supplied and to be analyzed as the maximum likelihood option and we selected the
option random permutations.
In the fifth stage, we assessed our structural model. Model identification is
measured in AMOS by the difference between the number of distinct sample
moments (55) and the distinct parameters to be estimated (25). The resulting degrees
of freedom 30 indicate that the model has been identified. We achieved a good model
fit and identification with 10 items, as shown in Table 2.23:
Table 2.23
Structural Model Indices
Goodness of fit index Recommended Values Values
χ 2 /degrees of freedom ≤ 3.00 1.83
Tucker Lewis Index (TLI) ≥ 0.90 0.93
Goodness-of-Fit Index (GFI) ≥ 0.90 0.94
Incremental Fit Index (IFI) ≥ 0.90 0.95
Comparative Fit Index (CFI) ≤ 0.90 0.95
Root Mean Square Error of
Approximation
(RMSEA)
≤ 0.08 0.072
In stage six, we interpret our model. The fit statistics for our model are good.
The data supported our first hypothesis and show that supplier responsiveness is an
antecedent to IdRR. The constructs are negatively related: β=-0.988, p

influences buyer firm value. Our fifth hypothesis is supported by the data with a
standardized regression weight of β=0.976, p < .01.
2.7 Discussions and Recommendations
Although market orientation is a central tenant of marketing, the inter-organizational risk
implications of supplier responsiveness to the customer’s needs has never been examined in
a systematic manner. We introduced the concept of IdRR, which indicates to what extent
supplier responsiveness reduces relational risk and results in positive relational outcomes
for both the strategic supplier and buyer. The results provide considerable support for our
model. In the following sections, we first discuss our contributions to theory and practice
and then we generally discuss the results of the tests of the five hypotheses by comparing
them with extant literature.
We make several contributions to marketing theory and practice. First, we shed new
light on the market orientation and risk relationship and build a comprehensive model that
enunciates the relationship between supplier responsiveness, IdRR, and buyer firm value.
Our findings challenge the status quo belief that market orientation is an inherently risky
endeavor by providing some evidence that supplier responsiveness negatively influences
IdRR. Moreover, we contribute to the marketing domain by extending the scope of the
current market orientation models, which to date have mostly focused on the supplier’s
market orientation perspective and the supplier’s marketplace performance. Furthermore,
extant literature has not addressed the issue of incorporating the customer’s perspective
and the customer’s firm value in market orientation and performance models.
Second, we introduce IdRR as a new construct in the literature and an important
mediator of the supplier responsiveness and buyer firm value. Using IdRR, academics and
businesses will be able to estimate whether market orientation increases or reduces the
relational risk between a supplier and buyer. Furthermore, at a time when there is an
increased need for accountability within firms, IdRR as a measure allows managers to
communicate some of the value that the relationship has accrued due to market orientation.
Hence, by using IdRR to explain the benefits of market orientation, it adds clarity to the
returns on investment from market orientation.
Third, we provide evidence that market orientation is a single unified general
construct and not three separate general factors. The market orientation construct has
lacked discriminant validity in some important studies, but scholars still continue to model
it as sub-components rather than a general factor (Kohli et al., 1993; Narver & Slater,
1990). In the current study, we revisited it as a general factor and we have found that
market orientation or supplier responsiveness is a single factor rather than three distinct
components.
Fourth, we find that buyer satisfaction is not related to a buyer’s firm value. In the
extant literature, scholars so far have focused on the buyer satisfaction and supplier
business performance relationship (Gyskens et al., 1999). Despite this focus, scholars have
found limited evidence of a buyer satisfaction and supplier performance relationship
(Gyskens et al., 1999). Although the buyer satisfaction and supplier performance
relationship is important, even more important is the issue as to whether buyer satisfaction
is a meaningful indicator. As satisfaction in channel contexts has an economic aspect, it
should be related to a buyer’s firm value in order to be meaningful as an indicator. If the
buyer is satisfied with the economic value provided by the strategic supplier, it in turn
should increase the buyer’s cash flow, and therefore, buyer firm value.
Fifth, our model provides preliminary evidence that IdRR reduction is a reliable
66

indicator that has a link to customer firm value and supplier brand value. For managers, this
finding provides a framework for thought. However, it may not be possible or desirable to
solely focus on those activities that reduce IdRR because some market orientation activities
may influence both IdRR and buyer satisfaction, but a firm should rely more on IdRR
measures than customer satisfaction measures. In the course of this research we found that
some firms, such as Fabory in The Netherlands, regularly survey their suppliers for risk and
their satisfaction with the supplier. Fabory does not discriminate between risk and
satisfaction measures while making decisions. Our research dictates that firms should focus
on IdRR measures.
2.7.1 H 1 : Supplier Responsiveness and IdRR
Since we have developed the construct of IdRR ourselves, we have not been able to find
other comparable studies that have used this construct. Moreover, we look for the closest
comparable constructs, and that is managerial risk aversion in the market orientation
literature. There is one additional difference between managerial risk aversion and IdRR:
Managerial risk aversion is measured from an intra-organizational perspective, whereas
IdRR is measured from an inter-organizational perspective.
Our research challenges the traditional assumption in marketing that risk and market
orientation are positively related (Jaworski & Kohli, 1993). We find that market orientation
and IdRR are negatively related in an inter-organizational context. This finding is relevant
because it sheds new light upon the dynamics of inter-organizational IdRR in relational
exchanges, in comparison with the intra-organizational context of market orientation and
risk. Linamar (LNR) and Caterpillar exemplify this hypothesis in the B2B context.
Caterpillar chose LNR because of its expertise in making diesel engines and since then
Caterpillar’s marketplace performance has improved as its risk has decreased (Marketwire,
2007). Eventually, this outcome caused LNR to be awarded the status of a strategic supplier
for Caterpillar (Marketwire, 2007).
Our findings are different from studies that examine the relationship between
market orientation and top management risk aversion in an intra-organizational context.
Jaworski and Kohli (1993) found that risk aversion and responsiveness are negatively
related in an intra-organizational context, but they find insignificant relationships for the
other dimensions of market orientation, namely, intelligence generation and dissemination.
In other words, they find that risk perceptions and responsiveness are positively related and
an insignificant relationship between top management risk aversion and the overall
construct of market orientation. Alternatively, we find that supplier responsiveness and
IdRR, which are both measured by perceptual metrics, are negatively related in the interorganizational
context. Similarly, the strength of the relationship between supplier
responsiveness and IdRR is stronger in our study with a standardized regression weight of
β=-0.98, p

top management risk aversion on the responsiveness component of market orientation
could be weak. In addition, another study has also found an insignificant relationship
between top management risk aversion and market orientation (Pulendran et al., 2000). The
Pulendran et al. (2000) study reinforces our belief that there could be an alternative
relationship between market orientation and risk, whether risk is measured from a
managerial or an IdRR perspective. Our findings provide such an alternative relationship
and we have found that market orientation and IdRR are negatively related.
However, some additional issues warrant a discussion in the context of the market
orientation and risk relationship. Our findings offer an additional mechanism to supplier
diversification for organizational IdRR reduction. The portfolio approach that marketing
has adopted from finance for dealing with idiosyncratic risk through supplier, product, and
service diversification is not the only effective mechanism for reducing risk. An additional
effective mechanism of relational risk reduction is through market-oriented strategic
suppliers. Usually, in strategic items, it is not possible to have numerous suppliers because
of the unique nature of the product that they supply. Therefore, this limits the use of the
aforementioned portfolio approach. Alternatively, a strategic supplier may provide tailormade,
specific market-based solutions for the buyer then, as a consequence, the buyer’s
IdRR is reduced as well. The business world is rife with examples of companies that use
market-oriented strategic suppliers. For instance, Hoeschst, the German petrochemical
giant, has a strategic relationship with Kuwait petroleum (Kraljic, 1983). Dow Chemicals in
the United States and BASF in Europe follow similar strategies (Kraljic, 1983). Overall, the
importance of our findings is that we have been able to show, to some extent, that in an
inter-organizational context market orientation is a relational risk-reducing rather than
enhancing, mechanism.
2.7.2 H 2 : Supplier Responsiveness and Buyer Satisfaction, and H 3 : Buyer
Satisfaction and Buyer Firm Value
We consider hypotheses 2 and 3 together because they form a chain of antecedent mediator
and consequence. We find that between supplier responsiveness and satisfaction a positive
relationship exists. This finding reinforces previous intra-firm research that shows that
supplier responsiveness or market orientation creates buyer satisfaction (Kohli & Jaworski,
1990; Narver & Slater, 1990). Managers do judge supplier performance against their own
predispositions of what is expected. We also find that buyer satisfaction is not linked to the
buyer’s firm value. This finding relates to other similar observations by Narayandas (2005).
Other researchers have found that satisfaction is a distinct process from customer value
creation in B2B (Eggert & Ulaga, 2002). Hence, an increase in buyer satisfaction is not an
indicator that buyer value has been created in B2B. This is inline with our finding that
buyer satisfaction does not influence buyer firm value.
2.7.3 H 4 : IdRR and Supplier Brand Value
We find that IdRR is inversely related to supplier brand value. This finding is consistent
with a theory posited by Kotler and Pfoertsch (2006) that states that for B2B brand value,
one core function is to reduce risk. We take that analysis a step further by showing that a
reduction of customer risk also leads to an increase in supplier brand value in a B2B
context. This is also similar to other marketing studies that postulate that market-based
assets are inversely related to financial risk (McAlister et al., 2007). One study examines
the relationship between negative customer experience (i.e., which is measured by a
negative experience with a corporate brand) and the positive association with an
idiosyncratic firm level (Luo, 2007). Another study compares three portfolios of different
68

and strengths and their stock market returns (Singh, Faircloth, & Nejadmalayeri, 2005).
Portfolios with the strongest brands performed the best in terms of returns and risk
insulation. What their finding seems to suggest is that the customer ranking of brands has
direct consequences for the seller’s or supplier’s financial market performance. Hence, our
finding that reducing IdRR produces higher supplier brand value reinforces such
perspectives and is significant for the supplier if they wish to increase their brand value.
However, our findings also differ from previous studies. On one hand, previous
research has focused on customer experience and seller idiosyncratic risk (Luo, 2007). On
the other hand, we have focused on IdRR and supplier firm intangible assets. We examine
the effect directly from customer perceptions, whereas prior studies have used the financial
markets to measure seller risk exposure (Luo, 2007; McAlister et al., 2007). Our approach
is superior to the prior studies as the latter are based on the efficient market hypothesis and
assume that financial markets perfectly transmit real-world happenings to investors.
However, in reality, markets are at best moderately efficient, but never perfectly efficient
(Cassidy, 2009). So we can never be very certain about what caused an increase in
idiosyncratic risk on the stock market while basing inferences on the efficient market
hypothesis. However, as a respondent, the customer’s answers reflect what the customer is
thinking and such a perspective offers a greater deal of certainty than the efficient market
hypothesis. Therefore, using the customer perspective provides stronger reasoning for the
effect of supplier or seller marketing activities on customer risk.
A company that exemplifies the causal effect of hypothesis 4 is the firm Liveops.
The firm reduces its customer’s idiosyncratic risk by collecting intelligence on its agent’s
performance while serving customer queries (Girotra & Netessine, 2011). As a result, only
those agents who best meet the needs of the clients remain employed by the company. This
ensures that the relationship continues and implies that, as the supplier, Liveops’s cash flow
has less variability or risk.
2.7.4 H 5 : Supplier Brand Value and Buyer Firm Value
The data provide evidence for our fifth hypothesis that supplier brand value positively
influences buyer firm value. Our finding is similar to those by Ghosh and John (2009).
They found that in a B2B context, supplier brands with strong equity provide leverage to a
customer’s brand via differentiation. Such differentiation helps the customer to maintain
their competitive position in the marketplace and, hence, affects the customer’s earnings.
Our results confirm their findings that indeed a supplier’s brand does provide leverage to
the buyer’s firm earnings and value.
The mechanism for this identification in front of the end customer (i.e., the buyer’s
customers) is brand association. This is similar to other studies in which some authors find
that branding is the essence of differentiation in B2B markets and serves as a means of
reducing uncertainty or risk (Mudambi, Doyle, & Wong, 1997). Risk in marketing implies
the probability of incurring a loss. So, insulation or a reduction in risk means an increase in
cash flow stability and, hence, an increase in buyer firm value. The support for our
hypothesis reinforces this perspective.
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2.8 Managerial Implications
The following points include practical examples to illustrate the managerial implications of
our research.
2.8.1 Findings Apply to Both Heterogeneous and Homogeneous Competitive
Markets
In today’s marketplace, increasing complexity and competition make it imperative for
businesses to differentiate their offerings in terms of competitive advantages (Day, 1994).
One important source of such strategic supply competitive edge is market-oriented
suppliers. For instance, consider Porsche Consulting’s services. It operates in the
heterogeneous consulting services market (J. D. Power, 2010). Porsche, the automobile
manufacturer, has consistently ranked at the top of J. D. Powers and Associate’s annual
initial quality study (J. D. Power, 2010). Porsche has perfected its understanding of the
automotive and supply chain management processes. It has internalized the philosophy of
market orientation up to an extreme degree to identify latent needs. As a result, customers
of its consultancy services enjoy increased earnings and avoid crisis or risk (Kotler &
Pfoertsch, 2006, p. 87; Scholz, 2009). Examples of customers who have enjoyed such
benefits include Lufthansa AG, Meyer Werft AG, and Strabag AG. Lufthansa reduced the
time that was required to inspect aircraft by 30%, Strabag significantly improved its
construction processes, and Meyer Werft introduced a new learning academy to ensure that
they fully internalized the suggestions from Porsche. Such experiences are reflected by
Lufthansa’s air services chief who said, “Porsche is an interesting partner for us” (Cremer,
2010, para. 3).
The degree of customer confidence in Porsche’s abilities is reflected by the
statement of Porsche’s consulting services chief officer, Eberhard Weilben: “Many of our
clients come and say please turn us into the Porsches of our industries” (Cremer, 2010,
para. 14).
Mr. Weilben’s quote reflects the importance that such strategic customers
place on the supplier’s brand value. Hence, our example illustrates that responsive
suppliers do create value in such heterogeneous competitive marketplaces by reducing
risk and improving performance.
We have sampled data from diversified companies such as Google, Apple,
Microsoft, YUM brands, Chippolette, Caterpillar, and Red Roof Inns. Our sample
includes 86 publically listed firms. The remaining 78 are privately owned companies.
These companies operate in a variety of competitive heterogeneous to homogeneous
marketplaces. Hence, we advise managers in both forms of marketplace to implement
a supplier responsiveness strategy to reduce IdRR. On the other hand, we realize that
many firms are concerned by their economies of scale. Thus, in our opinion, a
responsiveness strategy should only be applied in strategic relationships. The payoffs
will only be great enough for the idiosyncratic response costs that are incurred when
the volume of transactions is substantial. This is normally the case in strategic
relationships.
2.8.2 Innovation Does Not Increase Relational Risk and Increases Supplier
Brand Value
We consider that any kind of response to an idiosyncratic need is a market innovation.
Since it is based on specific knowledge for a specific firm, it is in a sense, therefore, new to
the world. However, responses to idiosyncratic needs do not necessarily involve an extreme
degree of innovation; as a result, all responses need not be resource intensive. But risk
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accompanies any innovation, according to the extant marketing literature (Kohli &
Jaworski, 1990). Our findings are that supplier responsiveness does reduce IdRR. This is an
important insight for managers because of the widespread management assumption that
innovation or market orientation is positively associated with risk (Slater & Narver, 1995).
This insight can be used by marketing managers to communicate to senior management the
dual benefits of being market oriented. First, for the buyers and suppliers it is a means of
reducing their own IdRR; second, for suppliers it is a means of increasing brand value, and
third, for buyers it is a means of increasing their profits. Further, both suppliers and buyers
can convince management to give marketing managers greater budgets for market
orientation.
From our review of the literature, we have found that innovation is generally
considered to be a process that is involved with risk. Evidence from the business world in
an inter-organizational context suggests otherwise. Consider the list of brands on both the
Interbrand B2B brand rankings and BusinessWeek’s innovation index (BusinessWeek,
2012). Brands such as Intel that follow an ingredient branding strategy through accelerated
innovations not only secure a competitive position in the marketplace, but also add
customer value and insulation from risk. But not all brands follow an ingredient branding
strategy. In that case, we suggest that buyer firms choose those ingredient suppliers who
reduce their IdRR. A strategic supplier status is normally conferred after a thorough
examination of the strategic supplier’s capabilities, we believe that IdRR is an important
indicator to check for potential value creation from the relationship.
2.8.3 Understand the Customer Business Process Specifically Related to IdRR
For business managers, an oft-repeated mantra is understanding their customers. At a
general level, this is an expensive and time-consuming exercise. Therefore, as a rule of
thumb, for suppliers it should represent a sustainable business volume from which they can
acquire their returns. The next step would be to focus on IdRR. Managers need to map the
business processes which cause the most potential risk to their relationship. Trade-offs need
to be made in terms of time and cost effectiveness. For instance, consider the Caterpillar
and Linamar strategic relationship. Linamar is an expert in “collaborative design,
development and manufacture of precision metallic components, modules and systems for
global vehicle markets” (Marketwire, 2007). Through their market-sensing mechanism
Linamar has been able to considerably reduce Caterpillar’s IdRR and help them to maintain
the quality they offer to the market. Likewise, Caterpillar has a “partners in quality”
program by which it ensures regular interaction with its key customers to facilitate the
smooth flow of operations. To summarize, customer understanding is a key component to
reducing IdRR and ensuring relational benefits.
2.8.4 Responsiveness as a Strategy for Sustainable Brand Equity
Strategic supplier responsiveness is an investment in creating idiosyncratic market-based
assets. As illustrated by our model, strategic supplier responsiveness reduces IdRR. In turn,
IdRR positively effects supplier brand value. Hence, supplier brand value is increased by
idiosyncratic relational risk reduction. From the buyer’s perspective, it is difficult to find an
immediate replacement for the supplier because IdRR is reduced by the idiosyncratic
responses of the supplier. These are developed over time by the supplier and are unique.
According to the resource-based view of the firm, assets that are inimitable, rare, valuable,
lack perfect substitutes can be sources of competitive advantages for the firm. Following
that line of thought, the brand value that is created for the supplier is a competitive
resource. It is sustainable because of the difficulty that competitors would have in creating
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a similar offering. Hence, this locks the customer to the supplier and makes such brand
value sustainable.
2.9 Limitations and Future Directions
Many additional directions for future research can be suggested on the basis of the of the
current research. The first and arguably the most fundamental limitation deals with the
research design. For instance, longitudinal managerial perception may not be consistent
across the time period of measurement, which could imply that the relationships that we
measured are not linear. The research design suggestions that future studies could utilize in
order to improve upon our study are:
Longitudinal research design. Our cross-sectional study allowed us to draw
associations at best but not conclusions about causality. To further improve this design,
data collected for a time series analysis could allow the deduction of evidence regarding
causality. However, there has been some recent evidence that inferences from crosssectional
studies about causality may not be much different from inferences about
longitudinal studies (Rindfleisch et al., 2008).
Limited sample of industries for depth. We traded off depth for width within our
industry sectors. However, specific sectors may vary more with supplier responsiveness
than others. For instance, in the commodities industry, price may be more of a determining
factor, whereas in the technical goods industrial sector the supplier response may be a key
determining factor as to whether relationships continue. Future studies could concentrate on
a few sectors and check the robustness of our findings across various moderators, such as
market turbulence and increased technological risk.
Quasi-experimental design using financial and perceptual data in tandem. Prior
studies of market orientation have found conflicts between objective and subjective
measures of performance (Narver & Slater, 1990). To determine whether our findings
actually reflect real financial market sentiments and accounting data, future studies can use
financial data in tandem with perceptual data.
Other additions which might be added to the prior suggestions include:
Wider comparison of measurement metrics. In the current study, we only use
idiosyncratic risk, which within itself is a important indicator of the potential returns
involved with any relationship. However, risk is not the sole metric in the literature. Future
studies might include a wider array of metrics to examine which are better indicators than
others and why. For instance, examining idiosyncratic risk in comparison to inventory
turnover metrics or debt leverage metrics could be an interesting avenue.
Comparison of more strategies with supplier responsiveness for reducing
idiosyncratic risk. We only examine one strategy, which is market orientation as a means of
reducing idiosyncratic risk. However, there are other strategies and orientations, such as a
technology orientation or a market leadership strategy. The ways in which these strategies
and orientations influence strategic supplier-buyer relationships and their idiosyncratic risk
outcomes could be of potential interest.
2.10 Conclusions
In summation, we have empirically investigated a model that proposes a link between
supplier responsiveness and buyer cash flows in an inter-organizational context. We
have found that, to a certain extent, supplier responsiveness indirectly affects buyer
cash flows through the moderating of IdRR and supplier brand value. Specifically, we
found that market orientation has a negative relationship with IdRR. This is an
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important finding because it provides counterintuitive evidence to the extant literature
that market orientation and perceived risk are positively linked. Furthermore, being
market oriented or responsive pays off for the supplier organization as their brand
value increases. In addition, we also found that supplier responsiveness influences
buyer satisfaction, but buyer satisfaction does not have a relationship with their cash
flows. This perspective reinforces prior studies’ findings that satisfaction and
performance do not have the same relationship as is commonly reported in the B2C
literature.
Future researchers could focus on supplier responsiveness and buyer
performance in other contexts, such as cross-national contexts. Does the mediating
role of IdRR hold in such contexts? Or would it differ? Answering such questions
would provide a better understanding and the full spectrum under which a market
orientation or supplier responsiveness strategy would yield best results. This has
added significance in a time of recession when markets are under increased pressure
to provide their managers with tangible marketing expenditure ROI (Bolton, 2004).
Moreover, it would also follow the concerns of some researchers, who state that
models based on U.S. data need not stand for other countries (Hofstede, 1980). An
additional pertinent question worth investigating would be whether our model would
hold if subjective indicators are used. Another important research avenue is the
moderating role of contract type in the supplier responsiveness and performance
relationship. Since contracts are a staple means of governing relationships in B2B
markets, it would be interesting to understand how different contract types influence
buyers’ perceptions of the usefulness of supplier responsiveness. For example, do
contracts that have stiff and steep penalty clauses reduce the influence of the
supplier’s responsiveness on buyer performance, as the buyer expects nothing but the
best? Or does it reduce the supplier’s willingness to be extremely responsive because
they do not want to risk being penalized?
The two major conclusions from our current study are: First, supplier
responsiveness is a mechanism that involves relational risk reduction. Second, buyer
satisfaction in strategic contexts will not result in increased buyer profits. Hence,
suppliers should not rely upon it as an indicator of relational health.
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Chapter 3
To Be or Not to Be? Does it Make Relational and Financial Sense
to be a Responsive Strategic Supplier? An International Comparison
Abstract
This study investigates the role strategic supplier responsiveness plays in reducing
relational idiosyncratic risk and increasing customer satisfaction, in two countries.
The role of buyer satisfaction in strategic relationships as a metric varies across
national cultures. However, the role of idiosyncratic relational risk as a metric
remains constant. The findings will be of use to marketing managers and sales teams,
who can use them for determining the degree of market orientation they should
implement to meet their customers’ needs. It will also help to identify those processes
that make them more responsive in areas of key customer concern, thus increasing a
buyer’s firm value and enhancing buyer perceptions about their brand. In the end, we
discuss the managerial and academic implications of the research.
Keywords: supplier responsiveness, idiosyncratic relational risk, cross national
comparisons
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3.1 Introduction
The Netherlands, despite its limited natural resources, is on the forefront of world
commerce, with companies such as Philips and Unilever to its credit. Furthermore,
The Netherlands is one of the major trading partners of the United States of America
("U.S. Trade Balance," 2012). It also happens to be one of the largest foreign
investors in the US ("Foreign Direct Investment," 2010). A large part of the Dutch
economy is based on B2B trade. For example, they are the world’s third largest
agricultural exporter, and they have the world’s seventh largest port and terminal
facilities and Europe’s largest port (Nightingale, 2009; "Rotterdam’s Economy,"
2011).
On the one hand, there are important differences between the US and The
Netherlands. U.S. attitudes, be they at the consumer level or the national level, differ
towards different issues. For instance, the US is more welcoming of foreign
investments and less ecologically concerned than The Netherlands. Even the laws
regulating corporate governance are different (ING, 2010). The consequence of the
above mentioned differences are that different markets require different marketing
strategies and tactics. Therefore, in this chapter we will investigate whether the model
proposed in Chapter 2 will yield similar results as it did in the last chapter.
Table 3.1
The Netherlands Investment in the United States
On the other hand, in this era of convergence of demand via globalization,
many similarities now exist between customers or business partners in different
countries, and many businesses create products that cater to the needs of global
customers and cut across national boundaries (Holt, Quelch, & Taylor, 2004). For
example, Caterpillar’s construction equipment and after-sales service and Intel’s
ingredient branding strategy are product responses that cater to the needs and wants of
segments of customers across national boundaries. Then, given the importance of The
Netherlands and the United States as two major trading partners and the individual
firm level B2B relationships that make this trade possible, what is the role of
idiosyncratic relational risk in firm relationships? On an abstract economic level, at
the start of the recent credit crisis, The Netherlands was less affected when compared
75

to other EU members because The Netherlands is a competitive economy as a result
of its flexible labor market and limited dependency on foreign capital (Masselink &
Noord, 2009). Other scholars argue the Dutch economy is more prudent because of
their culture (B.Dyson, personal communication, October, 2010). The Hofstede
cultural index is a useful measure for marketing purposes to examine how to measure
different national cultures (Tellis, Prabhu, & Chandy, 2009). Scores on the Hofstede
index for the US and The Netherlands are very similar on many cultural dimensions.
However, prior research has provided some evidence to indicate that firms act
independently of some national cultural traits (Tellis et al., 2009). This is important
because findings in marketing academia from one country may not be applicable to
other counties with similar cultures. Hence, based on the importance of our findings
in Chapter 2, a pertinent question to ask is whether the market orientation and buyer
performance relationship will hold across national cultures and borders.
The contribution of this study is that we test the robustness of our previously
developed conceptual model across national borders in Chapter 2. We investigate
whether the supplier responsiveness and buyer performance relationship mediated by
Idiosyncratic Relational Risk (IdRR) holds true across the different cultures we
employ our model. Moreover, this study adds to the limited number of B2B market
orientation studies in The Netherlands and sheds new light on the responsiveness
mechanics in the Dutch context.
3.1.1 Different Results of Studies of Market Orientation in The Netherlands and
the United States
The impact of market orientation on organizational performance varies across the
world (Ellis, 2006). Ellis (2006) used a meta-analysis to analyze 58 market orientation
studies. He finds that the managerial value of market orientation diminished the
stronger a national culture varied from U.S. culture. Furthermore, Ellis (2006) found
that the United States had the strongest effect size for market orientation (r=0.36),
followed by Western Europe (r= 0.25). The effect of market orientation on
performance is sizably higher in the United States than in Western Europe. In light of
these variations in results in the impact of market orientation across national
boundaries and cultures, it is relevant for this study to examine the comparable
existing studies of market orientation in The Netherlands and the United States. This
will allow us to understand along which dimensions prior studies have differed and
possibly give us insights into how our sample from The Netherlands and the United
States may differ. The following five studies represent some of the important
comparable research that exists.
The first study by Langerak (2003) finds in a sample of firms from The
Netherlands that market orientation influences organizational performance and is
mediated by a differentiation strategy but not a low cost strategy. A differentiation
strategy and a low cost strategy are two important generic positioning strategies
(Langerak, 2003). To the contrary, a study in the United States by Narver and Slater
(1990) provides evidence that both a differentiation and a low cost strategy are
positively influenced by market orientation. Furthermore, Narver and Slater (1990)
find that market orientation positively influences organizational performance, and the
effect is channeled through a differentiation strategy and a low cost strategy. One of
the limitations of Langerak’s (2003) study was the small size of the firms in the
sample, with 76% of the firms having fewer than 200 employees. This leaves open the
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possibility that firm size may influence the outcomes of market orientation. Secondly,
as his sample is mainly from the medical and electronics industry, it is also possible
that the results may vary across industries. To address these shortcomings in the
current study, we use a sample with a greater variation of industries and a larger
number of employees.
The second to fourth studies in our literature synthesis are by Langerak,
Hultink, and Robben. In their first study, Langerak, Hultink, and Robben (2004b) find
in a sample from The Netherlands that market orientation has an insignificant,
positive, direct effect on organizational performance. However, they do find that
market orientation has an indirect, positive effect on organizational performance,
channeled in the following order: (1) market orientation positively influences strategic
planning and idea generation; (2) strategic planning and idea generation positively
influence new product development, and; (3) new product development positively
influences market orientation. In a second study, Langerak, Hultink, and Robben
(2007) investigate the mediating role of new product development between market
orientation and organizational performance. They test three structural equation
models with varying results. In their first model they achieve a poor structural fit;
however, in contrast to their 2004 study (Langerak et al., 2004b) they find that market
orientation has a direct positive influence on organizational performance. A poor
structural fit indicates that the model is misspecified (Hair, Anderson, Tatham, &
Black, 2006). This implies that the model may not be theoretically justified, as the
observed data does not fit the hypothetical model. The final model that Langerak et al.
(2007) test show a good fit, but it only includes an indirect effect of market
orientation on organizational performance channeled through new product
development mediators. In essence, their findings suggest that a direct effect of
market orientation on organizational performance is not supported. In their third
study, Langerak, Hultink, and Robben (2004a) find in a sample of Dutch firms that
market orientation does not directly positively influence organizational performance,
but rather it does so indirectly through NPD activities. The findings of these three
studies are inconsistent with prior studies that used U.S. samples (Slater & Narver,
1994; Pelham & Wilson, 1996). These studies provide some evidence that the process
of market orientation in The Netherlands indirectly influences firm performance via
new product development mediators, whereas results from other studies suggest that
in the United States both direct and indirect effects exist (Han, Kim, & Srivastava,
1998; Slater & Narver, 1994).
In the fifth study of our literature synthesis, Verhees and Meulenberg (2004)
split the construct of market orientation into two components, behavioral market
orientation and attitudinal market orientation. They find that in The Netherlands,
behavioral market orientation does directly and positively influence firm
performance, whereas attitudinal market orientation does not. Consistent with
previous studies mentioned above, they do find that the overall construct of market
orientation does influence organizational performance through the mediator of
product innovation. Their findings may not be generalizable to all organizations
within The Netherlands, as they only study small enterprises within the flower
market.
The above studies provide some evidence that the pathway from market
orientation to organizational performance differs in the United States and The
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Netherlands. Moreover, the above studies are all from the intra-organizational
perspective and none are from the inter-organizational perspective. In light of the
important findings of Chapter 2 from the buyer’s perspective, regarding the role of
IdRR, buyer satisfaction and supplier brand value as moderators of the supplier
responsiveness and buyer organizational performance relationship, it is pertinent to
examine whether those findings will hold across nations.
3.1.2 Cultural Context
There are a number of theories that describe national culture within the marketing and
management literatures, and the Hofstede index is one of the most widely used
(Hofstede, 1980; Hofstede, 1984; House, Javidan, Hanges, & Dorfman, 2002;
Schwartz, 1994; Trompenaars, 1994). The Hofstede index is based on IBM data
collected from 82 countries. This makes the Hofstede index the largest index in terms
of the number of countries from which data is collected and the most widely used one.
There are other indexes, such as Schwartz, which uses data from 31 countries; the
globe framework, which uses data from 58 countries; and Trompenaars’s framework,
which uses data from 54 countries. Furthermore, the Hofstede index is based on IBM
data, and IBM acquires a substantial percentage of its revenues from B2B activities.
Because of the Hofstede index’s widespread use as an index in the marketing
literature, we use it as an explanatory framework in this paper, but we do not
specifically test any of its dimensions.
To compare the United States and The Netherlands, we first need to
understand the national cultures of both countries and how they affect decisionmaking.
The Hofstede index was initially composed of four dimensions, namely,
power distance, uncertainty avoidance, masculinity-femininity, and individualismcollectivism
(Hofstede, 1984). A fifth dimension, long-term orientation, was later
added when it was observed that in some Asian cultures uncertainty avoidance was
not a suitable measure (Hofstede & Bond, 1988). As is reflected in Figure 3.1 below,
The Netherlands and the United States are similar on all dimensions of national
culture except masculinity-femininity. The United States is a masculine culture,
whereas The Netherlands is a feminine culture. This implies that managers within the
United States should be competitive, be ambitious when it comes to career
aspirations, and that there is general difference when it comes to gender roles, with
fewer women present in upper management. On the other hand, in The Netherlands,
managers are expected to be competitive, yet they can use their intuition and gut
feeling while reaching decisions. Managers are expected to have modest career
ambitions, and there is more gender egalitarianism within organizations (Hofstede,
2001, p.318).
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Figure 3.1
The Netherlands and United States Compared on the Hofstede Dimensions
3.1.3 Contribution to Chapter 2
The model described in Chapter 2 makes a contribution to the literature by providing
evidence that market orientation or supplier responsiveness is negatively related to
IdRR. However, since the model has only been based on US data, it remains to be
tested whether that model is applicable to cross-cultural contexts. For any theory to be
generalizable, it has to be applicable in a wide variety of situations. Our current study
builds on the research presented in Chapter 2 by testing it across two nations. We find
that the main hypothesis and theoretical contribution regarding the market orientation-
Idiosyncratic relational risk-performance relationship holds true across nations.
However, we find that there is a difference in soft relational indicators of buyer
satisfaction. We explain these differences in light of the Hofstede index, suggesting
that national culture may moderate the soft indicator-performance relationship. In
sum, the current paper validates the main findings of the Chapter 2 model by
providing some evidence that IdRR matters across nations.
3.1.4 Hypotheses Generation
Supplier responsiveness and IdRR. The influence of a firm’s market orientation on
firm performance transcends borders, and evidence of it exists in countries other than
the United States, such as The Netherlands (Langerak, Hultink, & Robben, 2004a;
Langerak, Hultink & Robben, 2004b; Langerak, Hultink, & Robben, 2007). These
prior studies have also hypothesized that the market orientation-performance
relationship is mediated by NPD activities (Han et al., 1998; Langerak et al., 2004a;
Langerak et al., 2004b; Langerak et al., 2007). However, in the light of some of these
studies, market orientation seems to have similar outcomes in the United States and
The Netherlands. Furthermore, as discussed earlier, the national risk averseness of
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The Netherlands is similar to the United States. Therefore, we expect that market
orientation will have a similar impact on IdRR in The Netherlands and the United
States.
H1: Supplier responsiveness negatively influences idiosyncratic relational
risk, both in the USA and The Netherlands.
Supplier responsiveness and buyer satisfaction. Satisfaction is a process of
buyer evaluation in hindsight. After a comprehensive literature review of satisfaction,
we did not come across any studies that specifically suggest that the process of
cerebral satisfaction evaluation varied because of culture. However, some studies did
suggest the level of satisfaction varied across nations; for instance, job satisfaction
between employees in The Netherlands and Spain (Carbonell & Praag, 2006).
The Netherlands and the United States when compared on the project global
leadership and organizational behavior effectiveness cultural index (GLOBE), differ
significantly on two important dimensions (House et al., 2002). These are
assertiveness and performance orientation. US managers are more assertive and
performance oriented than Dutch managers (House et al., 2002). By that logic then,
hard performance indicators should matter more in the US than soft indicators such as
satisfaction. Furthermore, this does not imply that there is a difference in the cerebral
process by which managers or customers in different countries make evaluative
judgments, such as satisfaction, because human beings from diverse ethnic
backgrounds have the same biological cerebral capabilities and thought processes
(Zaltman, 2003). Human beings, however, differ along the lines of their cultural
interpretation of information (Hofstede, 1980). This is because cultures are the
collective mental programming of the environment. As the environment changes from
one nation to another so do the elements of collective mental programming change
(Hofstede, 1980). These changes result in different national cultures.
In terms of buyer satisfaction, there is no evidence within the extant literature
that the cerebral process of purchase evaluation differs across nations, but there is
evidence to suggest that the criteria against which customers evaluate purchases
differ. These can be attributed to changes because of national culture. For instance, in
The Netherlands and Germany, greater formality in business communications would
be expected in contrast to business transactions within the United States. A lack of
such formality could lead to customer dissatisfaction and cause problems in strategic
supplier relationships. Further, satisfaction in strategic relationships can vary because
of the supplier’s response (Chapter 2), and the influence of the supplier’s response on
the intensity of satisfaction varies because of cultural factors. For example, retail
brand Tanoli from The Netherlands values European-based strategic suppliers more
because their responses meet the current fashion trends or supply market offerings
that anticipate latent fashion trends. On a different note, American suppliers are
responsive in certain dimensions, such as delivery time and payment dates but not in
areas such as customization of designs. This difference is because fashion trends vary
in the United States as compared to Europe. United States suppliers provide responses
based on their own cultural environment, whereas the trends in Europe are diverse
and more differentiated. This results in significant buyer dissatisfaction for retailers in
The Netherlands (Tanoli, personal communication, 2010). In both situations suppliers
are responsive, but not all actions of responsiveness contribute to buyer satisfaction.
When a buyer forms a strategic partnership with a supplier, they have reached a
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comprehensive agreement on each other’s requirements. Hence, strategic supplier
responses should generate buyer satisfaction across The Netherlands and United
States. Therefore:
H2: Supplier responsiveness positively influences buyer satisfaction, both in
USA and The Netherlands.
In the extant marketing literature, the hypothesized relationship between
satisfaction and firm performance has focused on firm performance. Various
measures such as loyalty, increased product usage levels, secure future revenues, and
increased customer perceptions of quality are positively related to customer
satisfaction. However, this is evidence from a B2C setting and the role of customer
satisfaction in B2B relationships is disputed. In B2C contexts, an individual’s
satisfaction reflects their own experience with the product and will likely influence
their future purchase intentions. In a B2B setting, the purchase center does not
directly have any experience with the product and may have a number of indicators to
consider besides satisfaction reports while making the purchase decision (Kotler,
Armstrong, Saunders, & Wong 2008, p.292). Another contrast with the B2C
satisfaction measurement is that in large buying centers, often the purchasers even
lack direct consumption experience (Kotler et al., 2008, p.292).
Because of differences like these, Narayandas (2005) is of the opinion that
satisfaction does not have the same influence on firm profitability or customer loyalty
in B2B markets as it does in B2C markets. However, from a cultural perspective,
satisfaction may have a different importance in different societies. Consider the
example of The Netherlands and the United States. The position of The Netherlands
on the Hofstede index is similar to that of the United States, except on their cultural
dimension of masculinity. The US represents a masculine society, where there is less
emphasis upon caring for others as compared with more feminine countries such as
The Netherlands. It then could be argued that intangible indicators such as satisfaction
would have more influence in feminine societies than in masculine ones, because in
such cultures soft or feminine indicators and behaviors matter more (Hofstede, 1998,
pp.4-27). At the macro level this difference between masculine and feminine societies
can also be observed by the amount of money they give to charity. Feminine societies
give more than masculine societies, and this reflects their compassion for humanity
(Hofstede, 1998, pp.4-27). Furthermore, masculine countries are more monetary
oriented than feminine ones. In masculine countries, employees generally would opt
for higher salaries compared to shorter working hours, whereas in feminine countries
the opposite holds true (Hofstede, 1998, pp.4-27). Hence, we would expect that within
organizations in feminine countries like The Netherlands relational indicators such as
satisfaction would matter more than in masculine countries like the United States.
Another reason could be the homogeneity in society. Since the US is a heterogeneous
society, the emphasis of a soft metric such as satisfaction on the overall purchase
decision could be less direct, resulting in diverse expectations. Hence, in the United
States, focusing on performance-based measures could possibly provide clarity to
channel partners about customer expectations when compared with relational
performance factors. The Netherlands, more cultural homogeneity could imply that
soft cultural factors such as satisfaction would have on the firm level performance.
Hence, we expect the following hypotheses across countries:
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H3: Buyer satisfaction positively influences buyer firm value more strongly
in The Netherlands than in the United States.
Idiosyncratic relational risk and supplier brand value. Some aspects of an
individual’s risk-taking behavior are influenced by their national culture. One
example of this is the popular cushion hypothesis (Weber & Hsee, 1999). Individual
investors take more risk if they feel that their collectivist society will take care of
them if things were to go wrong (Weber & Hsee, 1999). On the other hand, in
societies that are individualistic, investors will take less risk because no one is there to
bail them out in case things go wrong (Weber & Hsee, 1999). Indeed, national culture
does matter when individuals are making decisions with regards to risk (Weber &
Hsee, 1999). National culture also influences their risk preference. The Dutch shoe
manufacturer Van Bommel is a case in point. They offer a fifteen-year guarantee with
every high quality pair of shoes, yet they chose the slightly costlier option of
producing in the UK rather than in China or Portugal, because of quality concerns and
risk avoidance. Many other high quality manufacturers do produce in Portugal (Zara)
and China (Apple). But Van Bommel’s decision is a case of traditional
manufacturers’ concerns leading them to what they perceive to be a safe decision
(Dyson, personal communication, 2010).
As risk preferences vary across nations, it would follow in turn that customer’s
risk preference would influence their preference of channel partners. Some managers
may prefer riskier business partners because of the potential higher fluctuation in
profits. Others may want more stability in their earnings (Weber & Hsee, 1999).
Although much research exists on consumer branding and risk preference
(Erdem, Zhao, & Valenzuela, 2004; Mitchell, 1999), there is limited research in the
B2B branding domain on the relationship between B2B brands and risk. Our literature
search revealed limited evidence in the marketing literature available on risk-taking
behavior, buying centers, and brand preferences. However, we did find one B2B
branding text that dealt with the issue by Kotler and Pfoertsch (2006, p.9). They
indicate that B2B brand functionality is negatively related to customer risk. Therefore,
we expect:
H4: Idiosyncratic relational risk negatively influences supplier brand value in
the USA and The Netherlands.
Supplier brand value and buyer firm value. A supplier’s brand value is likely
to have a spillover effect on a buyer’s firm future value if they add unique value to the
buyer firm. For instance, Ghosh and John (2009) found that the supplier’s brand was
more likely to be preferred in the future when the supplier’s components added
unique value to the buyer’s brand. Such unique value has a financial dimension for
the buyer through increased marketplace performance. Intel is a stellar example of
such a strategy, and it has a spillover on any product it is a component of (Whitwell,
2005). Consider NutraSweet, whose patent ran out but is still able to charge a
premium for its brand because of the public confidence in NutraSweet. Consumers are
willing to pay more for products with NutraSweet logos or as an ingredient (Norris,
1992). Other examples of supplier brand which add value to the customers brand
include Dupont with nylon for clothes, Raython’s Lycra and Good Year tires on
Adidas shoe soles. Harvard Business School professor Jack Quelch describes it best in
a rhetorical question:
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Why do we pay more for an orange with a Sunkist sticker? Because inspecting
the outside of the orange doesn’t guarantee the quality of what’s inside. We
need the assurance of the Sunkist brand. A variant on this theme is ingredient
branding: putting the brand of an ingredient on the outside of a product to
increase its appeal (Quelch, 2007, para 1).
One thing that is common to all these brands is that they are global brands
with their origins in the United States. They all sell well within The Netherlands.
After a comprehensive literature search, we could find no evidence to suggest that a
spillover effect of being associated with a suppliers’ brand value does not occur
within The Netherlands. Therefore, we expect the following hypothesis:
H5: Supplier brand value positively influences a buyer firm’s value in the
USA and The Netherlands.
3.2 Methodology
3.2.1 Sample Description
The data for this study were collected in The Netherlands and the United States in the
year 2009. As is reflected by Table 3.2, both samples’ descriptive statistics are
similar. The type of market from which data was collected in the USA had 49% of the
firms catering to both industrial and consumer markets, whereas in The Netherlands
the figure was 41.7%. Firms operating exclusively in industrial markets in the USA
were 18.7%, whereas in The Netherlands the figure was 35.5%. Firms operating
solely in consumer markets in the USA were 32.3%, and the figure for The
Netherlands was 33.3%. We collected sample responses from industrial buyers in
both countries. The average number of years of buying experience in the USA was
9.72 years, and strategic buying experience was 7.36 years, whereas in The
Netherlands, the average buying experience was 7.56 years and strategic buying
experience was 5.96 years. The percentage of respondents from upper management in
the USA was 30.9%, and middle management was 53.1%, and 16% were firm
employees, whereas in The Netherlands the percentage of respondents from upper
management was 20.8%, and 55.2% were from middle management, and 24% were
firm employees. The type of firm market offerings in the USA was 35.4% for servicebased,
23.1% for goods-based, and 41.5% for both goods and service-based market
offering. In The Netherlands 46.9% was service-based market offering, 21.9% was
goods-based and 31.2% for both goods and service-based market offering.
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Table 3.2
Descriptive Statistics
USA
Sample Description
n=164 Response Rate = 6%
Type of Market
Percentage
Industrial and Consumer Markets 49
Consumer Markets 32.3
Industrial Markets 18.7
Number of Years of Buying Experience
Years
Average Buying Experience 9.72
Average Strategic Buying Experience 7.36
Level of Management
Percentage
Upper Management 30.9
Middle Management 53.1
Type of Market Offering
Percentage
Service-Based Market offering 35.4
Goods-Based Market offering 23.1
Goods and Service-Based Market offering 41.5
Sample Description
n=92 Response Rate=3%
Netherlands
Type of Market
Percentage
Industrial and Consumer Markets 41.7
Consumer Markets 33.3
Industrial Markets 36.5
Number of Years of Buying Experience
Years
Average Buying Experience 7.56
Average Strategic Buying Experience 5.96
Level of Management
Percentage
Upper Management 20.8
Middle Management 55.2
Type of Market Offering
Percentage
Service-Based Market offering 46.9
Goods-Based Market offering 21.9
Goods and Service-Based Market offering 31.2
3.2.2 Variables
We checked for regional variations in the definitions of the original model.
We first comprehensively searched the literature for different definitions of the five
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constructs, for cross-cultural factors. We could find none specific to The Netherlands.
We then proceeded to informally discuss with academics and practitioners/industry
specialists about their interpretations of each of the five variables’ definitions. The
results are discussed below.
Supplier responsiveness. The definition provided in Chapter 2 was
“accumulative market response generated by internal supplier firm processes to meet
their buyer needs.” This definition is based on a synthesis of the definitions of Kohli
and Jaworski (1990) and Narver and Slater (1990) of the concept of market
orientation. However, from our discussions with fellow academics three major themes
occurred. First, a supplier’s responsiveness includes a market offering component
(goods and services). The second component was the speed of the strategic supplier’s
response or timeliness. The third component was market intelligence sharing
exemplified by activities such as potential market lead sharing. These concepts
converge with our definition; therefore, we used the original survey scale for supplier
responsiveness employed in Chapter 2. Chapter 2 used the MARKOR scale
developed by Kohli et al. (1993) for our analysis. Since the purpose of this chapter is
to test whether the model in Chapter 2 applies in different markets.
Idiosyncratic relational risk. The definition provided in Chapter 2 was “the
risk which exists because of entering into a specific relationship with another
company.” This definition was postulated because of the confluence of two
perspectives, namely: financial market understanding of the concept of idiosyncratic
risk and management perspective of supplier risk (Sharpe, 1964; Krajlic, 1983).
Central to this concept is the tenet that idiosyncratic relational risk is avoidable by
preempting the risk via marketing measures the supplier enacts. Our informal
discussions with fellow academics and peers reinforced the face validity of the scales
used in Chapter 2 for relational idiosyncratic risk measurement in The Netherlands.
Therefore, we used the scales used in Chapter 2.
Buyer satisfaction. Chapter 2 defined buyer satisfaction as “an overall
evaluation based on the total purchase and consumption experience with a good or
service over time” (Anderson, Fornell, & Lehmann, 1994). They include both
economic and non-economic satisfaction in their conceptualization. Our informal
discussions reinforced this understanding, and therefore, we used the same scale for
this paper. We used the four items the original scale used in Chapter 2. These were
adapted from the original satisfaction scale used by Ruekert and Churchill (1984).
Supplier brand value. The supplier’s brand value is defined in Chapter 2 from
the perspective of the strategic buying center team as the shared collective memories
about the supplier’s brand, which provide it leverage that it would otherwise not have
without the brand. Therefore, we used the two original items developed in Chapter 2.
There was no difference in our understanding of supplier brand value from the
concept in the literature.
Buyer firm future value. Buyer firm future value is computed by a single
measure that is composed of volatility and variability in the future cash flows because
of the strategic supplier. The informal discussions and literature review lead us to
believe that volatility and variability in buyer cash flows are important measures for
measuring buyer firm value in both the United States and The Netherlands. The items
used in this chapter for measuring the constructs are the same as those in Chapter 2.
Please refer to Table 2.18 for the items used and the codes.
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3.2.3 Normality of Data
Table 3.3 presents the results for the normality of the distribution of the variables in
our model based on the Dutch data. As recommended by Baumgartner and Homburg
(1996), we first conducted tests of normality on our data. We found that two variables
in our data SIQ and RP did exhibit skewness. The criteria we used to assess for
skewness and kurtosis was whether the skewness or kurtosis ratio to its respective
standard error was less than 2 (Bachman, 2004, p. 74). We transformed SIQ and RP
variables by taking their square root computed by SPSS 17, under the compute new
variable tab. The result was that the new computed variables SIQ and RP were
normally distributed.
Table 3.3
Kurtosis and Skewness
N Mean Std. Dev Skewness Kurtosis
Stat Stat Stat Stat Std.
Error
Ratio
1**
Stat
Std.
Error
Ratio
2**
SIQ 92 2.27 0.77 0.51 0.25 2.04 0.92 0.50 1.86
CustNPD 92 2.35 0.84 0.38 0.25 1.52 0.20 0.50 0.41
CF 92 2.49 0.76 -0.04 0.25 -0.15 -0.31 0.50 -0.62
RP 92 2.11 0.91 0.77 0.25 3.08 0.85 0.50 1.71
Fairness 92 2.30 0.77 0.46 0.25 1.84 0.87 0.50 1.75
SALES 92 2.30 0.82 0.35 0.25 1.39 0.35 0.50 0.69
IM 92 3.53 0.88 -0.20 0.25 -0.79 0.28 0.50 0.56
CCC 92 3.51 0.83 -0.15 0.25 -0.61 0.67 0.50 1.35
AssBrand 92 2.27 0.80 0.26 0.25 1.02 0.40 0.50 0.80
BV 92 2.33 0.70 0.01 0.25 0.05 -0.22 0.50 -0.44
SIQ (***) 92 1.48 0.26 -0.08 0.25 -0.33 0.26 0.50 0.51
RP (***) 92 1.41 0.39 0.22 0.25 0.88 -0.39 0.48 -0.62
***SIQ and RP. SIQ and RP are transformed by taking the square root of SIQ and RP in order to
correct for the skewness of both variables.
**The Ratio 1 column represents the skewness statistic to its standard error ratio. The ratio 2 column
represents the kurtosis statistic to its standard error ratio. Each ratio value should be less than 2 in
absolute value.
3.2.4 SEM
We estimated two SEM models in our analysis for comparison with the model in
chapter 2. The reason we estimated two models is because of the rule that a 10
respondents are required per measured item in a SEM model (Hair et al., 2006, p.
604). Our sample size of 92 respondents and 10 observed variables did not let us meet
that ratio. Our second model in this chapter labeled Model 2 consists of the variables
supplier responsiveness, IdRR, supplier brand value, and buyer firm value. Our third
model in this chapter labeled Model 3 consists of the variables supplier
responsiveness, buyer satisfaction, and buyer firm value. For a large model with 10
observed variables we normally should have about 150-200 respondents and
recommended of 10 respondents per item (Hair et al, 2006, p. 604). The two models
(2 and 3) fulfill the criteria of 10 respondents per item. In the rest of this section we
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will discuss the SEM analysis of these two models. We present the models’ goodness
of fit statistics and hypotheses results in Table 3.4.
3.2.4.1 Model 1
Model 1 is a replica of the model in Chapter 2. However, as it does not fulfill the
criteria of 10 respondents per variable we have tested it in two smaller models called
models 2 and 3. Therefore we do no discuss Model 1 in further detail in this chapter.
For details of the fit statistics and hypotheses tests please refer to Table 3.4 of this
chapter.
3.2.4.2 Model 2
We have one exogenous construct, which is supplier responsiveness. We have three
endogenous constructs, IdRR, supplier brand value, and buyer firm value. We then
tested for unidimensionality. Thus the current model has a total of 8 items. Moreover,
our results indicated that our factors load on more than one factor, and therefore,
unidimensionality is not a concern in the current study.
In the next stage of our research we determined the input matrix type and
estimated the proposed model. We chose the covariance matrix because it is a true test
of theory (Hair et al., 2006 p.603). The correlation matrix is not suited for theory
testing as it only represents the pattern of relationships. We used the maximum
likelihood estimation method and our sample size of 92 was slightly below the
recommended 100-150 (Hair et al., 2006, p 605). In AMOS, we also selected
covariance supplied and to be analyzed as the maximum likelihood option, and we
selected the option random permutations.
Further, we estimated our structural model in AMOS. Model identification is
measured by the difference between the number of distinct sample moments (36) and
the distinct parameters to be estimated (16). The resulting degrees of freedom are 20,
indicating that the model has been identified. We achieved a good model fit and
identification with 9 items. Our model exceeds the minimum GFI limit of > 0.9 with a
value of 0.945, and this is better than the Chapter 2 model with 0.939. The RMSEA
values are acceptable between .05 and .08, with an upper threshold limit of 0.1(Hair et
al., 2006, p.634). By this standard, our current model is good with a RMSEA of
0.040, and it is better than Chapter 2 structural model’s RMSEA of 0.072 3 . The
second category of fit indices are incremental fit measures. Our model has a Tucker-
Lewis index (TLI) value of 0.981 and is more than the minimum acceptable level of
0.9, whereas the model of Chapter 2 had a value of 0.930. The last incremental fit
index we use is the comparative fit index (CFI) and it is specifically meant for
samples with small sizes (Hair et al., 2006). We surpass the minimum level of 0.9
with our model score of 0.987, slightly better than the Chapter 2 value of 0.953.
Moreover, the model has a χ 2 /degrees of freedom ratio of 1.14 and that is considered
good. Since model 2 meets the criteria of the goodness of fit measures we accept the
measurement model. The fit statistics for model 2 overall were very good.
Hypothesis 1 is significant. It indicates also that supplier responsiveness is
indeed an antecedent to IdRR and that both the constructs are negatively related. The
β= -0.993, and this is similar to the β= -0.980 in the model in Chapter 2. This
indicates a strong negative effect of supplier responsiveness on IdRR. This finding
supports our hypothesis that supplier responsiveness negatively influences IdRR,
3 Please look at Table 3.4 for Chapter 2 structural model fit indices.
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since it is significant at p

BFV
Hypothesis 4
IdRR->SBV
Hypothesis 5
SBV->BFV
Not Supported -3.837* Not Tested 0.500***
-0.992*** -0.993*** -0.993*** Not Tested
0.976*** 3.942*** 0.590*** Not Tested
Note. ***significant at the .001 level, *significant at the .01 level, SE=Standardized Estimates,
Insig=Insignificant, S.R.= Supplier responsiveness, IdRR=Idiosyncratic relational risk, BS=Buyer
satisfaction, SBV=Supplier brand value, BFV= Buyer firm value.
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3.3 Discussion
The main objective of this study was to understand whether the model developed in
Chapter 2 would be robust and hold across nations. To that end we were interested in
finding whether IdRR is an important mediator as stated in Chapter 2 and whether
buyer satisfaction (relational indicator) is linked to buyer profits.
We first tested a full replica of the model in Chapter 2. Model 1 did not was
too large for our small sample size; therefore, we decided to split the model into two
smaller models (the satisfaction mediator route and the IdRR and Supplier brand
value mediator route) and rerun the analysis.
As reflected by Table 3.4, Model 2 has better fit statistics on four indices
namely, GFI (0.945), CFI (0.987), TLI (0.981) and RMSEA (0.040), than the model
in Chapter 2. Whereas, model 3 is only better on GFI (0.95) than the model in Chapter
2. This is probably because there are fewer items and more data points to estimate
those items, which results in a better fit. The effects for hypotheses 1 and 4 are
stronger in model 2 than in the model in Chapter 2. For hypothesis 5 the effects are
stronger in the Chapter 2 model. For hypothesis 1 we conjecture that as The
Netherlands is culturally homogeneous, it is easier for suppliers to reduce their IdRR,
as they better understand each other’s needs. As Hofstede (2003) states,
heterogeneous societies can have great variations along their cultural dimensions. On
the other hand, the US managers tend to score higher in uncertainty avoidance than
Dutch managers do on the Hofstede index (Figure 3.1). But as The Netherlands is
relatively ethnically homogeneous, we understand that managers believe that their
strategic suppliers are more attuned to their needs because of cultural understanding.
This results in the effect of supplier responsiveness of IdRR being slightly stronger in
The Netherlands. An alternative explanation could be that, in essence, reducing IdRR
involves creating an idiosyncratic response and that does not have much application
to other customers of the strategic supplier. As the United States is highly masculine
and oriented towards achieving maximum profits, it is possible that suppliers are
willing to create an idiosyncratic response to a degree, and when diminishing returns
start setting in they stop further investments in such responses. On the other hand, in
The Netherlands, with its greater focus on relationships because of its feminine
culture, no attention is paid to this point of diminishing returns. For hypotheses 4 we
find that the effect of IdRR on supplier brand value is marginally stronger in The
Netherlands than the model Chapter 2.
There is not much in the extant literature on this hypothesis for comparison in
B2B contexts. However, risk reduction has been identified as an important function of
B2B brands (Sanzo et al., 2003). What makes our finding unique is that we have
proposed risk reduction as an antecedent to purchasing B2B brands in The
Netherlands and the United States. This is similar to findings from consumer research
where risk reduction is a motivating factor for consumer to purchase a brand (Kotler,
& Pfoertsch, 2006). This direct experience ensures that customer IdRR will be
reduced, and hence, our findings are pertinent as they provide evidence that B2B
buyers are also motivated by risk reduction factors. Hypothesis 5 provides evidence
that being associated with a strategic supplier’s brand increases the buyer firm’s
value. This could be because of one of two mechanisms. One could be because of the
benefits of ingredient branding. And the other reason could be because of a networked
effect that signals to external customers that the buyer’s firm produces high quality
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products. Overall, the effects of supplier brand value is stronger in the United States
than in The Netherlands.
In hypothesis 3 we find that buyer satisfaction has a positive influence on
buyer firm value in model 2, but in the model in Chapter 2 it does not. In the United
States at a more granular level it might be that relational metrics alone (satisfaction)
do not influence the buyer’s firm value, or indeed the customer’s intention to
repurchase from the supplier. This reinforces the existing perspective that customer
satisfaction as a metric is of little or no significance in B2B strategic contexts in the
United States. Furthermore, this also adds a new dimension to the intra-organizational
perspective by Ghosh and John (2009) and Drucker (1984) that firm commitment to
customer satisfaction would lead to superior firm performance. Firms in the strategic
context seem to be committed to measurable value creation rather than generating
satisfaction alone. But Bowman and Narayandas (2004) find that in the United States,
high volume customers have a lower baseline satisfaction, and that large customers
are less likely to change their vendors because of specific investments they have made
with their vendors and a long-term profit focus. Our findings are partially in line with
what they found as in the United States customer satisfaction does not matter in
strategic contexts. On the other hand, in The Netherlands because of its more
feminine culture, we find that satisfaction does matter. A major contribution of this
research is that we have highlighted that the role of satisfaction varies across nations
in B2B contexts. Furthermore, we have also added to prior research in The
Netherlands that showed customer satisfaction positively influences suppliers’
financial performance by adding a new perspective that buyer satisfaction positively
influences the buyer firms own value.
The role risk played in the strategic supplier market orientation and customer
performance is different in different national cultural contexts. In general, we were
able to provide some evidence that a strategic supplier’s responsiveness influences the
customer’s idiosyncratic risk and transcends national borders in industrialized
countries. Furthermore, this influences the customer’s future firm value and loyalty
towards the supplier’s brand. This is an important finding because in traditional
marketing thought (Kohli & Jaworski, 1993), the firm’s responsiveness is positively
associated with the firm’s risk. However, our study demonstrates that in the context of
strategic relationships the supplier’s responsiveness is associated with a lower risk
profile, since the customer’s variation and volatility in their cash flows and profits
decreases.
Bowman and Naryandas (2004) find that high volume customers have a lower
baseline satisfaction. They argue that large customers are less likely to change their
vendors because of specific investments they have made with their vendors and a
long-term profit focus. Our findings are partially in line with what they found. We
find that indeed customer satisfaction does not matter in strategic contexts; however,
it is not only because of a long-term focus, but rather because of their idiosyncratic
risk. In other words, relation specific investments or costs are just one component of
the overall idiosyncratic risk. Likewise the long-term focus linkage to a market
orientation is a widely recognized, yet empirically disputed link (Slater & Narver,
1990; Grewal & Tansuhaj, 2001). But even in studies where evidence for market
orientation influencing the firm’s profitability was found, evidence for a linkage to
long-term orientation has been scant. However, since we examine strategic
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elationship, which in their nature are long term, it would be safe to conclude that it
does play a role within the choice of market orientation as a strategic choice.
Soft measures that gauge the experience of a brand are more meaningful in
buying center contexts where such soft measures have commonly shared meaning
amongst its members. It would be possible to have such commonly shared perceptions
in a culture where there is more cultural homogeneity as compared with
heterogeneity. One of the benefits of having a solid metric to measure against such as
insulation from idiosyncratic risk is that it helps avoid the communications pitfall
which can occur with other soft measures whose value may differ in the perspective
of different team members.
Furthermore, contrary to existing thought our findings offer evidence that
resonance does not exist between non-financial relational outcomes and risk-based
relational outcomes in the United States. However, within The Netherlands we do
find evidence for such a relationship. Therefore, from our evidence the managers
within the United States should look for two independent sets of metrics, whereas in
The Netherlands it may be possible to use idiosyncratic relational risk as a proxy
measure for satisfaction with the relationship. However, even in that case it would be
better to have independent metrics because the magnitude of satisfaction and
idiosyncratic risk may not be the same. The final decision of which metric to use is
for every organization to gauge for itself, depending on which responsive maneuvers
will reduce their customers’ risks and, in turn, increase the value of their brand. Since
every customer is unique and represents a significant volume of the seller’s sales
volume, then our perspective is that the supplier should develop unique responses to
each strategic customer or segment to reduce their risk.
3.4 Recommendations
Our current study is not free from limitations. For example, our research design could
be improved upon. Some researchers have indicated that ethnographies should be
employed to research issues related to culture (Langerak, 2001). The advantage in
their opinion is that organizational culture can be better documented by long-term
observation techniques, such as ethnographies. They consider one of the shortcomings
of questionnaires to be that they are culturally biased. However, to overcome this
shortcoming, we formed a team of two researchers from diverse national cultural
backgrounds while framing the questions. This step enabled us to cross-check
questions for any cultural biases. Some cultural biases may be present in more than
one culture but the process of cross-checking involved checking for biases common to
both cultures, documenting them while framing our questions in the questionnaire,
and taking care to avoid the biases. We do recognize that we may have not eliminated
all cultural biases, but we were able to do so to the best of our ability.
Another limitation is the narrow cultural comparison we have at the moment.
Although the US and The Netherlands are different in some respects, they share
similarities as well. Both are industrialized nations, both are vibrant democracies,
both have capitalist economies, both have a degree of similar occidental culture, and
both nations are internationally competitive economies (Deshpande & Webster,
1989). Some cultural idiosyncrasies do occur, such as acceptance of boasting on job
interviews in the United States and the preference for modesty in job interviews in
The Netherlands, but nevertheless, they both can be considered western cultures.
On the other hand comparison with an Asian culture would have provided a
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more diverse comparison. China is a growing world trade power, it is a communist
nation, and it has a collectivist culture and a Confucian value system (Hofstede,
2003). These cultural differences result in different risk preferences in individual
members of society. For instance, the Chinese have a greater risk preference than
Americans, because their collectivist culture cushions them from losses. Future
researchers could investigate how firms from societies with extremely different risk
preferences react to supplier responsiveness. This would also add a new dimension to
the problem we studied as to whether cultural risk preference is a moderating factor in
the supplier responsiveness and buyer performance relationship.
In some Eurasian and Asian economies, relational practices such as Blat and
Gaunxi are an integral part of the business culture. However, in western cultures such
practices may be viewed as unethical and detrimental to the corporate interests and
reputation. Specifically, when forming strategic supplier relationships with
corporations that find such cultural practices acceptable, buyers may fear a negative
spillover on their brand reputation. Such cultural effects have been overlooked in our
current study. Further examples include questions such as: Does IdRR vary because
of national culture? Or should culture specific practices such as Blat or Guanxi be
considered a part of IdRR?
Another limitation of our current study is that we only considered perceptions.
Indeed, do perceptions reinforce objective measures across cultures? Are some
cultures more prone to be overly jubilant when it comes to redirecting earnings from
investments? Are some cultures overly pessimistic when predicting losses which may
across from strategic relationships? Do cultures influence the relative importance of
firm perception of marketing strategies such as market orientation?
Another way to improve upon our current research could be to use a
dashboard of risk metrics to neutralize cultural biases. Experts have advocated
dashboards as a means of ending managerial biases in information processing and
decision making in businesses (Hofstede, 2003). Furthermore, some managers, when
faced with an overload of data, tend to take risky shortcuts that can lead to managerial
biases (Pauwels et al., 2008). In our current study we do not cover a dashboard of
metrics and whether a dashboard of metrics is a tool for insulation for making
culturally biased evaluations in cross-national studies of supplier responsiveness and
buyer performance. Moreover, a dashboard of metrics that includes idiosyncratic
relational risk would also be worthwhile investigating, as it would lead to insights in
whether the dashboard of metrics is truly capturing the impact of market orientation
expenditure and would further expound the usefulness of IdRR as a metric.
Finally, we examined only existing relationships. It is possible that the current
study could be improved upon by examining different stages in a relationship and
examine whether the importance of market orientation and idiosyncratic risk varies
across nations and in different stages of the relationship.
3.5 Conclusions
To conclude, we explored the role of idiosyncratic relational risk and satisfaction as
mediators of supplier market orientation and customer performance relationship in
two different cultural settings. In this course, we confirmed that idiosyncratic risk
insulation was a pivotal part of a supplier buyer relationship across cultural
boundaries. Our findings suggest that suppliers pay attention to calibrating their
response mechanisms so as to reduce their strategic customer’s risk, regardless of
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their country of operation. However, we do recognize that we studied it in North
American and European contexts, and our findings may not hold true in different
cultural settings, such as Asia or Africa.
Our results also show that certain earned relational outcomes, such as
satisfaction, may be of little significance to the firm across national cultures. The
practical implication for businesses is that they have to adroitly choose which
outcomes they wish to create or accumulate as market-based “assets.” Another
implication is that changes in the cultural setting change the relative importance of
metrics. As most metrics have been developed in the United States, it might be that
those metrics are irrelevant in other cultural settings (Wierenga & Van Bruggen,
1997). To summarize, there are two major conclusions from our current study. Firstly,
market orientation is a strategy that is valued across a variety of national and firm
cultural spectrums. Secondly, businesses need culturally adapted, relational, process,
and performance indicators to accurately gauge the performance of their marketing
initiatives and strategic relationships.
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Chapter 4
The Quest for Performance Substitutes:
The Case of Objective and Subjective Performance Measures
Abstract
A recent study about strategic supplier market orientation has made two challenging
assertions. The first is the negative relationship between idiosyncratic relational risk
and buyer firm’s value. The second is that relational indicators are not a predictor of a
buyer’s firm value, but insulation to idiosyncratic relational risk is. However,
managerial perceptions are a limitation of the data set used in Chapter 2. Using
objective data from the Reuters database and subjective data from Chapter 2, we find
that results from the objective model do not confirm the results of the subjective
model. This has important implications for businesses, primarily because there is a
trend for managers to rely on their own heuristics and in strategic contexts, heuristics
may be misleading. The consequences include potentially damaged strategic
relationships.
Keywords: supplier responsiveness, idiosyncratic relational risk, subjective
and objective performance
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“It is one of the commonest of mistakes to consider that the limit of our power
of perception is also the limit of all there is to perceive.”
—C. W. Leadbeater (n.d.)
"On the face of it, shareholder value is the dumbest idea in the world."
—Jack Welch
Former CEO, General Electric (Guerrera,
2009).
4.1 Introduction
The financial crisis of 2009 is an example of the incongruence between subjective and
objective measurement. Prior to the financial crisis, many firms had benefitted because of
investors’ irrational exuberance about financial markets (Akerlof & Shiller, 2010). This
benefit occurred even though many firm’s objective financial indicators did not reflect such
exuberance. The outcome of the dissonance between subjective and objective measurement
can be grave, as is illustrated by the large number of bankruptcies in 2009. Similarly, in the
market orientation literature, numerous studies have included objective and subjective or
perceptual measures and have contradictory or different results (Jaworski & Kohli, 1993;
Narver & Slater, 1990). We define subjective metrics as those based on managerial
heuristics and objective metrics as those based on some externally verifiable accounting or
financial procedure.
All subjective or perceptual data are based on human perceptions. Human
perceptions are notoriously fickle and not necessarily a representative of market realities
(Cassidy, 2009, pp. 10-11). This is not because people or managers are irrational, but rather
because their limited mental capabilities do not at times allow them to process all the
complicated factors or indicators (Cassidy, 2009, pp. 10-11). Furthermore, managers 4
usually only remember more recent events (Foer, 2011). This is a weakness when
measuring supplier performance because the relationship is based on a continuum of
interactions, not just the most recent interactions. Tversky and Kahneman (1974)
investigated heuristics or mental short cuts. The first mental short cut is representativeness,
meaning that managers generalize with insufficient evidence (Cassidy, 2009, p. 195;
Tversky & Kahneman, 1974). Even when experimental subjects were provided with
statistical data to guide their decisions, they still jumped to conclusions for which they did
not have much evidence (Cassidy, 2009, pp. 195-196). This jumping to conclusions also
leads to the phenomenon of overconfidence, which has led to many financial fiascos
(Cassidy, 2009, pp. 195-196). Managers who are overconfident take short-term relational
trends with strategic suppliers as indicators of the long-term trends. This overconfidence
could have serious repercussions if firms get stuck with adverse long-term consequences
they did not anticipate. The second mental short cut is the availability of scenarios (Tversky
& Kahneman, 1974). Managers' experiences dictate how likely they are to perceive that an
event is to occur (Cassidy, 2009, pp. 196-197). For instance, many reinsurance 5 companies
will not insure for a calamity after it has occurred (Greeley, 2011). Even though the
likelihood of another natural disaster is extremely low in an area in which one has already
occurred, managerial perceptions sway reinsurance managers from providing coverage for
such disasters in the future because this is an extreme situation that they fear. This means
that they exclude a lucrative potential market from which they could generate future
4 The terms "people" and "managers" are used interchangeably in this Chapter.
5 Reinsurance companies insure insurance companies.
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insurance premiums. Hence, perceptual judgments (that are the basis for perceptual data) do
not always accurately represent reality. The third mental short cut is an adjustment from an
anchor (Cassidy, 2009, p. 197; Tversky & Kahneman, 1974), which means that managers
use reference points for decisions. If, for instance, current market conditions are the
reference point, then managers would assume that market conditions will not change a lot
in the future. This mental adjustment because of an anchor leads to confirmation biases or
interpreting evidence to confirm their predispositions based on their reference points
(Cassidy, 2009, pp. 196-197). Alternatively, it can be counter argued that B2B managers,
humans will think laterally (Zaltman, 2003), use mental short cuts, and rely on perceptual
metrics. However, most firms at the end of the day have to justify their marketing
expenditures to the finance department, and unless performance shows up on financial or
objective metrics, it is unlikely that a relationship will continue (Bolton, 2004; Srivastava,
Shervani, & Fahey, 1998). Hence, using perceptually based measures or evaluations has
many weaknesses.
Objective metrics have some advantages over subjective metrics. Objective metrics
are suitable for a long-term focus, whereas subjective metrics can at best be used for the
short term (Rajan & Reichelstein, 2006). Moreover, objective metrics are externally
verifiable, whereas subjective metrics are not (Rajan & Reichelstein, 2006). External
verification makes objective metrics more transparent and reliable than subjective metrics.
For strategic buyers, this objectivity would mean that an income statement would reflect a
manager’s position that a certain supplier is reducing its costs and therefore increasing its
profits. Objective metrics are standardized by procedures such as generally accepted
accounting principles (GAAP). This standardization should make it easier for channel
partners and strategic customers to communicate the value generated to external
stakeholders who understand how these standardized performance measures were
calculated. Furthermore, it makes it easier to benchmark suppliers against each other and
see who is generating more value for the firm. Objective measures are comparatively
readily accepted by financial institutions compared to subjective metrics. Therefore, it is
easier to acquire access to capital when a company can prove performance via objective
metrics. Hence, objective indicators are important to business, and any marketing
investment (e.g., in relationships or philosophies such as market orientation) by the firm
needs to be translated into financial language. This ensures that marketing keeps receiving a
fair allocation of the firm’s overall budget (Bolton, 2004).
The study in Chapter 2 has made a significant contribution to the understanding of
supplier marker orientation and idiosyncratic relational risk (IdRR). The study posited that
supplier responsiveness and IdRR are negatively and not positively associated. The findings
are based on subjective or perceptual data. Against the background of the study in Chapter
2, we question whether the results are robust and would stand if both subjective and
objective performance measures are used. Thus, we use a structural model based on
accounting data and compare it with the original perceptual data model in Chapter 2.
We define subjective performance measures as heuristic individual evaluations of a
firm’s performance. Such evaluations are not as static for the firm at the corporate level as
accounting or financial results are; they vary from respondent to respondent. Subjective
evaluations or measures reflect the general understanding of the manager. We assume that
the reliability of such measures increases when managers acquire experience and an
understanding of the markets in which they operate. Objective performance measures are
calculated using standardized accounting and financial procedures. Objective measures also
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have their own limitations. Some of these limitations have to do with the way in which data
are collected and employee motivation while compiling them. For instance, San Miguel
(1997) has argued that they may not be very reliable since many times, the data have been
complied by people who do not have sufficient training or familiarity with accounting such
data. Furthermore, he argues that some managers may lack interest when such data are
being compiled, which may adversely affect their reliability.
We also use the definitions delineated in Chapter 2 to define the five variables in the
model. 6 This is because the model in Chapter 2 is the basis for comparison of objective and
subjective measures in the current study. We only modify the way we measure the variable
buyer firm value. For the current model, we use the net income difference between the
years 2009 and 2011 in the SEM model instead of the subjective measures used in Chapter
2.
4.2 Intended Theoretical Contribution
To our knowledge, this is the first study to examine the issue of objective versus
subjective performance measures in the context of supplier responsiveness or market
orientation and buyer IdRR. In the current study, we aim to make four contributions.
First, we provide evidence that the type of data used for supplier responsivenessidiosyncratic
risk-performance analysis can change the strength and direction of the
relationships in the Chapter 2 model. We were able to show that to some extent, there
is a disconnect between key indicators when subjective and objective data are used.
We believe this is because of a lack of metric substitutability rather than a problem
with the model itself. Second, we explain why managers’ evaluations may not be in
line with the objective data indicators. For example, because objective data are
calculated at a macro-organizational level, they may not reflect the performance of the
various functions at different levels within the organization. On the other hand, a
manager’s heuristic ability can shed light on latent areas of performance that
traditional objective performance measures cannot. Third, we explain that financial
managers often fail to see the value of marketing initiatives because they do not
understand the intangible value such initiatives create. Fourth, from a managerial
perspective, our study suggests specific steps that businesses can implement to avoid
the dissonance between subjective and objective measures.
4.3 Specific Contribution to Chapter 2
The model in Chapter 2 contributes to the literature by providing evidence that market
orientation or supplier responsiveness is negatively related to IdRR. However, since
the model is only based on perceptual data (for both market orientation and
performance), it remains to be seen whether the model in Chapter 2 is applicable if
objective performance data are used in tandem with subjective market orientation
data. For any model to be generalizable, its findings should be replicated with both
subjective and objective data sources. One of the strengths of a theory is fulfilling this
criterion. Our current study elaborates the model in Chapter 2 by testing it using
perceptual and objective performance data. We believe that the main hypothesis and
6 One issue is that our perceptual measures for firm value and objective measure of accounting profits
are not the same. However, since accounting profits are indicators of the firm’s value, we have used
them for this analysis. Moreover, cash flows and accounting profits are positively correlated. Current
accounting indicators signal to the market how well the company is doing and would be an incentive
for investors to purchase stocks if profits are good. This would in turn drive up the value of the stock
because the market would anticipate increased future earnings.
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theoretical contribution regarding the market orientation-IdRR-performance
relationship is supported by objective accounting data. We found differences in the
significance of the hypothesis tests in Chapter 2, which we explain these differences
by observing that numerous previous studies with arrived at different results between
subjective and objective indicators. This may have more to do with the inherently
different nature of objective metrics rather than a rationale flaw in the model. In
summation, the current study has tested whether the model developed in Chapter 2 is
more likely to hold true when subjective performance data are used compared to
objective performance data.
The rest of this study is organized as follows. We first present the different
focuses of previous studies that have examined the market orientation and
performance relationship. Next, we present the three categories of previous studies.
Then we generate hypotheses from a literature synthesis. We describe our data
collection process and analyze the data. We compare our hypothesized model with the
model developed in chapter 2. Finally, we discuss the results and the academic
contributions and managerial implications of our research. In the end, we describe
limitations of our current research and present our conclusions.
4.4 Hypothesis Development
In the marketing literature, three kinds of market orientation performance studies
exist. The first only use subjective measures, the second only use objective measures,
and the third use both objective and subjective measures.
The first category often finds that market orientation is positively associated
with subjective performance measures (Anderson et al., 1994). Subjective indicators
are used most often to validate the market orientation and performance relationship
(Gonzalez-Benito & Gonzalez-Benito, 2005). It is expected that if the same
respondent is used for perceptual as well as performance measures, these measures
should be aligned with one another. Since the model in chapter 2 uses subjective
process and performance measures, it falls within this first category of studies.
However, this also is a weakness of the model because whether it would hold true if
tested with objective data has not been examined.
The second category of studies finds that market orientation is positively
associated with objective measures (Jaworski & Kohli, 1993). We conducted held
informal conversations with B2B managers to find out why objective metrics
mattered when measuring the market orientation and performance linkage. Managers
believed that objective metrics mattered because they were developed by a rigorous
scientific process that was communicable to employees. Furthermore, in their opinion,
objective metrics were less susceptible to misinterpretation because of their
widespread use. Third, they helped communicate a company’s performance to
financial markets, but their relevance within the firm was not of the same magnitude
as it was for the financial markets. Fourth, since many objective measures are
standardized, they can be used for benchmarking against competitors. Fifth, they
believe that objective metrics have greater sway with external stakeholders such as
financial institutions. Despite numerous advantages of objective metrics, managers
did not believe that objective metrics could truly capture all the intangible nonfinancial
value created by marketing investments and maneuvers. The implications of
these opinions are that subjective and objective measures will not always reinforce
each other. This trend is also reflected within the literature. For instance, Esslemont
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and Lewis (1991) used the objective measures of ROI growth and profit margins.
They found no relationship between market orientation and performance. On the other
hand, Hooley et al. (1990) used the same subjective measure of ROI and found a
positive relationship for market orientation and performance. Why is there a
difference between studies about the market orientation and performance relationship
when they are using similar performance measures? In this specific case, it could that
Hooley et al. (1990) used the chief marketing officer as a respondent, and his vast
knowledge gave him strategic perspective of what was going on in the organization.
Therefore, their subjective assessment was aligned with the objective of ROI. In
Esslemont and Lewis' (1991) study, respondents replied differently because of their
lack of understanding of the objective measures. However, with the model in Chapter
2, we expect that since the managers on average have over seven years of experience
in their current positions and are relatively senior in their respective organizations,
their responses would reinforce objective indicators.
The third category of studies use identical measures of performance, but they
find different results between market orientation and performance linkage. On the one
hand, Tse (1995) found no relationship between market orientation and the objective
performance measure of hotel occupancy rates. On the other hand, Naidu and
Narayana (1991) found that in hospitals, market orientation and occupancy rates have
a positive relationship. In the case of Tse (1995), it could be said that the customers of
the lodges are looking for convenient accommodation rather than a market-oriented
lodge. In the case of hospitals, because of their high interest in personal well-being,
customers assess hospitals on their market orientation. Alternatively, motor lodges are
assessed on convenience of location rather than as a place to spend a pleasure
vacation, so market orientation does not matter. In the model in Chapter 2, the
relational setting are strategic; hence, we expect market orientation would matter to
the customer because a more market-oriented supplier would be more in tune with
their latent and explicit needs.
In the case of companies listed or unlisted on the stock exchange, an
alternative explanation might contribute to the variance in subjective and objective
measurement. Listed and unlisted companies vary on a number of dimensions such as
their approach toward risk, financial leverage, and access to capital. Moreover, the
corporate earnings requirements differ for listed and unlisted companies. This
difference could be why the correlation between earnings estimates for listed and
unlisted firms differ. Since the model in Chapter 2 used a mixed sample of both listed
and unlisted companies, a comparison with a group of listed companies might lead to
potential dissonance in results.
Two streams of research can be used to explain why at times subjective and
objective measurement may not reinforce each other. The first stream emphasizes
optimal choices by management because of heightened information utilization. The
second emphasizes that managers are not adept at making optimal choices with all the
information they have (Porter & Millar, 1985; Cyret & March, 1963). In the context
of objective and subjective measurement of the model in Chapter 2, the decisions
were strategic. Strategic decisions imply that they were high involvement decisions.
Hence, evaluating any specific investment in firm processes by suppliers will be
detailed and comprehensive. Under such circumstances, any subjective evaluation
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should reinforce objective measures because of the relative importance it has for the
firm and the concrete efforts that go into evaluating such investments.
Therefore, we expect that we would replicate the findings of the model in
Chapter 2 if we use objective data. Therefore:
H1(a): The fit statistics are for the objective and subjective models are similar
(b) the strength and direction of the effects of the perceptual model are similar
to the strength and direction of objective and subjective measurement models;
4.5 Research Design
4.5.1 Sample
We used a subsample of the data used in Chapter 2 that was composed of 87
publically listed companies. Our response rate was 6% for the United States
(N=2800). For the objective data, we used the difference in net income earnings
between 2011 and 2009. We collected the data from Google financials, which is
powered by the Reuters database.
4.5.2 Descriptive Statistics
The buyers’ primary business was most frequently mentioned as both industrial and
consumer markets (47.6%), consumer markets (27%), and industrial markets (25.4%).
Buyers in our sample were experienced, with an average of 9.6 years of experience,
out of which 7.9 years were spent as strategic buyers for their current employers. 31.3
% of our sample worked for upper management, whereas 54.6 % worked for midmanagement.
25.6 % of the strategic buyers provided service-based market offerings,
24.4 % provided goods as market offerings, and the remaining 40% offered a mixture
of both goods and services as their market offering. The average market value of all
the firms used in this analysis was over $1 billion U.S. Table 4.1 presents the sample
description.
Table 4.1
Sample Description
Type of Market n=87
Percentage
Industrial and Consumer Markets 47.6
Consumer Markets 27
Industrial Markets 25.4
Number of Years of Buying Experience
Years
Average Buying Experience 9.6
Average Strategic Buying Experience 7.9
Level of Management
Percentage
Upper Management 31.3
Middle Management 54.6
Type of Market Offering
Percentage
Service-based Market Offering 25.6
Goods-based Market Offering 24.4
Goods- and Service-Based Market
40
Offering
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4.5.3 Normality of the Data
We checked for skewness and kurtosis to determine whether the data were normally
distributed in Chapter 2. For details on items skewness and kurtosis please refer to
Appendix B.
4.5.4 Data Analysis
We followed a two-step model for comparison with the model in Chapter 2. Therefore, we
tried to emulate the methodology of the Chapter 2 model as far as possible. Below, we
discuss the measurement model that was our first step and then discuss the structural
model.
4.5.5 Measurement Model
For the measurement model, we used the seven steps specified by Hair et al. (2006).
First, we specified the model, then we identified the model, next we estimated the
model, later we evaluated it, and finally, we respecified the model.
Our specified model was a replica of the model in Chapter 2 using its scale.
We used AMOS version 17 for our analysis. The four latent variables in the
measurement model are supplier responsiveness, IdRR, buyer satisfaction, supplier
brand value. We used all the items of the original model in Chapter 2. The items were
later on reduced to 9 items following the instructions of the AMOS modification
indices to improve the fit statistics.
We achieved a good model fit and identification with 9 items, 45 distinct
sample moments, 25 distinct sample parameters, and hence 20 degrees of freedom.
Our sample size of 87 is above of the bare minimum of 50 needed to estimate
a maximum likelihood model (MLE) (Hair et al., 2006). However, because of
persistent Heywood cases that is a common problem when small sample sizes are
used with MLE, we switched to generalized least squares for the confirmatory models
estimation (Lisrel, 2012). We also used the options of fitting the saturated and
independence models, minimization history, standardized estimates, and squared
multiple correlations.
Another criterion is that the squared average variance or standardized
regression weights should be above 0.5 and preferably 0.7. All our measures as
reflected in Table 4.2 fulfill this criteria. Examining the standardized regression
weights of the CFA model closely, we find that none is very close to one. This
implies that multicollinearity is not a problem with our data. The highest standardized
regression weight is 0.849. From Table 4.3, we can observe that all the average
variance extracted is above the 50% mark, which means that it is acceptable.
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Table 4.2
Standardized Regression Weights for CFA
Latent Variable Items Estimate
Strategic Supplier
Responsiveness
CF 0.736
SIQ 0.846
CustNPD 0.866
IdRR IM 0.849
CCC 0.739
Supplier Brand Value AssBrand 0.812
RP 0.758
Buyer Satisfaction Sales 0.804
Fair 0.772
Table 4.3
Average Variance Extracted
Item
Squared multiple correlations
CF 0.542
SIQ 0.715
CustNPD 0.750
Average Variance Extracted 66.90 %
IM 0.721
CCC 0.546
Average Variance Extracted 63.35 %
AssBrand 0.659
RP 0.574
Average Variance Extracted 61.65 %
Fair
Sales
0.596
0.646
Average Variance Extracted 62.12 %
In Table 4.4, the construct reliabilities are presented. All are above the 0.7
Alpha Chronbach value recommended by Hair et al. (2006).
Table 4.4
Construct Reliability
Construct
Construct Reliability
Supplier Responsiveness .857
IdRR .774
Supplier Brand Value .762
Buyer Satisfaction .766
Our chi square statistic is 2 = 24.34, p>.228 with 20 degrees of freedom. The
2 to degrees of freedom ration is 1.2. Our chi square results are within the acceptable
range as delineated by Hair et al. (2006). The model has a GFI value of 0.937, which
is considered good. The RMSEA value is good at 0.050. The CFI value is acceptable
at 0.906, and so is the IFI value at 0.930.
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4.6 SEM
The SEM model was identified with a good fit with 10 measured variables. This gave
us a ratio of about eight respondents for every observed variable (87/10), which is
below the recommended limit of ten respondents for every item (Hair et al., 2006, p.
166). We have five constructs, which is less than the maximum recommended of 20
for using the SEM technique. The results of the SEM are presented in Table 4.5.
We have one exogenous construct, supplier responsiveness. We have four
endogenous constructs: IdRR, satisfaction, supplier brand value, and buyer firm
value. We also tested for unidimensionality. Our results indicated that our factors load
on more than one factor, so unidimensionality is not a concern. We used the
maximum likelihood option for estimating our model. The model has 55 distinct
sample moments, 35 distinct parameters to estimate, and 30 degrees of freedom. A
good model fit is achieved with 10 items. Our model has a good GFI value of 0.909.
Our current model is acceptable with a RMSEA of 0.087. The second category of fit
indices is incremental fit measures. Our model has a TLI value of.923 an incremental
fit index, which is considered good. The comparative fit index (CFI) is an indices
specifically meant for samples with small sizes (Hair et al., 2006). The objective
model scores well with 0.949 and is a very good fit.
The hypothesis supplier responsiveness negatively influences IdRR is
supported. It indicates also that supplier responsiveness is indeed an antecedent to
IdRR with a β=-0.957, p

Table 4.5
SEM Results
Construct Direction Construct Standardized
Estimate
P Values Hypothesis Conclusion
IdRR Supplier Res -.957 *** 1 Supported
Satisfaction Supplier Res .863 *** 2 Supported
Supplier Brand
Value
Buyer Firm
Profit
Buyer Firm
Profit
IdRR -.995 *** 4 Supported
Buyer
Satisfaction
Supplier Brand
Value
.339 .259 3 Not Sup.
-.361 .243 5 Not Sup.
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Table 4.6
Summary of Goodness of Fit Statistics and SE Hypotheses Results
U.S. Data Model Chapter 2 Objective Model Chapter 4
GFI 0.939 0.909
CFI 0.953 0.949
TLI 0.930 0.923
RMSEA 0.072 0.087
Hypothesis 1 (S.R->IdRR) -0.980*** -.957***
Hypothesis 2 (S.R->B.S) 0.873*** .863***
Hypothesis 3 (B.S.->BFV) Insignificant Insignificant
Hypothesis 4 (IdRR->SBV) -0.992*** -.995***
Hypothesis 5 (SBV->BFV or BFP) 0.976*** Insignificant
***significant at the.0001 level. *significant at the.01 level. SE=Standard Estimates
N.B. Buyer firm value is measured based on buyer perceptions; therefore, the model in chapter 2 is called in the subjective model whereas the model in this chapter measures
buyer firm value using accounting data and therefore is called the objective model. S.R.= Supplier responsiveness, IdRR=Idiosyncratic relational risk, BS=Buyer satisfaction,
SBV=Supplier brand value, BFV= Buyer firm value/Buyer firm profits (objective).
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4.7 Discussion
We start this discussion section with an important clarification. The purpose of this
current paper was not to take an absolute position on whether objective or subjective
measurement was superior, nor was the intention to enter into a philosophical debate
about whether any measures could ever be considered objective. Rather, it was to
determine whether objective and subjective performance measures reinforce each other.
We have not found evidence to indicate that this occurs in our current data set.
For this paper, we adopt the theoretical lens of market orientation. We argue that
both objective and subjective data can be used in tandem for the model in Chapter 2.
Hence, we expected to find that regardless of the data type used in the model, supplier
responsiveness reduces IdRR and results in improved buyer firm performance. Our
current research draws on prior research in market orientation and B2B literature, and our
results contribute to both literature streams. In the rest of this section, we discuss the
measurement model hypothesis results and the structural model hypothesis results.
4.7.1 Measurement Model Hypothesis
In our hypothesis 1(a), we predicted that our measurement model would be similar to the
model in Chapter 2. At least in terms of fit statistics, our prediction was correct. Overall,
our fit statistics were acceptable. The objective model has a slightly worse RMSEA (0.5)
than the model in chapter 2 RMSEA (0.67). The model in this Chapter has a GFI and CFI
of .937 and .906, whereas the Chapter 2 has a .95 for GFI and .96 for CFI. The
differences are minor differences between both CFAs in terms of goodness of fit indices.
This is because the data used in the CFA in Chapter 4 is a subsample of the data used in
Chapter 2. Furthermore, our construct reliability and construct validity are acceptable for
the CFA of Chapter 4. The construct reliability and construct validity are also acceptable
for the CFA of Chapter 2 except for the variable supplier brand value. This difference in
construct validity can be explained as managers publically listed firms understand
supplier brand value as a variable of importance whereas this importance is not of such a
great concern to managers of unlisted companies. For supplier responsiveness in Chapter
4 the average variance extracted (AVE) was 66.90% and our construct reliability (CR)
was 0.857 compared with the 47% AVE of the model in Chapter 2 and a CR of 0.72. For
IdRR, we got an AVE of 63.35% and CR of 0.774 compared with the AVE of 54% of the
model in Chapter 2 and CR of 0.71. For buyer satisfaction the AVE is 62.12% in Chapter
4 and a CR of 0.76, whereas the model in chapter 2 had an AVE of 59.2% and a CR of
0.74. For supplier brand value, we had an AVE of 61.65% and a CR of 0.76 compared
with the model in Chapter 2 that had an AVE of 37.5% and a CR of 0.54.
Finding confirmatory factor analysis studies for comparison has been challenging
because of the limited number of similar studies. At a general level, numerous intraorganization
market orientation studies have been undertaken, but inter-organization
studies are very scarce, and studies examining objective and subjective data in marketing
with confirmatory factor analysis are even more limited (Kohli & Jaworski, 1990;
Kohli et al., 1993). In addition to the confirmatory factor analysis, we also conducted a
bivariate correlation analysis between subjective and objective measures for comparison
purposes. Like Jaworski and Kohli (1993), we found an insignificant correlation between
107

oth measures. Although Jaworski and Kohli (1993) attributed this to a lagged effect that
market orientation has on performance, we took care to use objective performance, which
measured the difference in corporate earnings over a three-year period. Hence, our
findings suggest that it is not because of a lagged effect that subjective and objective
measures do not reinforce each other. Our findings also contradict the findings of Dawes
(1999), Jayachandran and Varadarajan (2006), and Naman and Slevin (1993), who found
that objective and subjective measures are strongly and positively linked. Rather, our
findings reinforce to some extent the findings of Harris (2001) that objective measures
may work for a narrower range of items than subjective measures do.
Table 4.7
Measurement Model Indices
Goodness of fit index Chapter 2 Model Chapter 4 Model
χ 2 /degrees of freedom 1.76 1.217
Tucker Lewis Index (TLI) 0.94 0.83
Goodness-of-Fit Index
0.95 0.93
(GFI)
Incremental Fit Index (IFI) 0.96 0.93
Comparative Fit Index
0.96 0.90
(CFI)
Root Mean Square Error
0.06 0.05
of Approximation
(RMSEA)
4.7.2 SEM Model Hypotheses
Our hypothesis 1 (b) was related to the structural model and predicted a fit between the
objective data (Chapter 4) and the subjective data models (Chapter 2). For this purpose,
we will discuss each of the five hypotheses of the model in Chapter 2 that we tested in the
current chapter using the objective data dependent variable buyer firm value (measured
by the difference in net income between the years 2009 and 2011).
Supplier responsiveness to IdRR.
To be marketing oriented is not to be safe because you’re running a risk. You
have to invest in your ideas. To not be market oriented is to be safe. [It means
doing nothing] the same old [thing]. You’re not investing in your business, not
[taking] risks.
—president of an industrial services
company (adapted from Kohli &
Jaworski, 1990)
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Jaowrski and Kohli (1993) provided an intra-organizational perspective on
supplier or seller responsiveness and managerial risk perceptions. Numerous other studies
have reflected their findings. However, to our knowledge, few studies examine supplier
responsiveness and inter-organizational risk (Baker, Simpson, & Siguaw, 1999). For
instance, Baker et al. (1999) focused on positive relational indicators, but did not focus
specifically on IdRR. To the best of our knowledge, aside from the Chapter 2 and Chapter
3 studies, only our current study solely examines the influence of supplier responsiveness
on IdRR in the context of publically listed firms. The Chapter 2 sample was a mixture of
publically and privately held companies. Normally, publically and privately listed firms
differ on a number of issues, including corporate accountability, risk averseness, and
innovativeness. Therefore, the contribution of our current study is that it provides
evidence that publically listed firms have the mediator of IdRR in supplier responsiveness
and performance linkage. This means that being a publically listed firm does not reduce
the importance of supplier responsiveness in insulating it from IdRR. However, this
effect is stronger in the model in Chapter 2. Hence, we believe that privately listed firms
are more susceptible to the actions of their strategic suppliers because of their smaller
size and limited resources.
IdRR to supplier brand value. The hypothesis examines the influence of IdRR on
supplier brand value. Prior research has indicated that B2B brands help customers
evaluate whether a brand choice reduces their perceived organizational risk (Baker et al.,
1999). In a way, our results are similar to Mudambi, Doyle, & Wong, (1997) contention
that risk reduction leads to greater perceived value for the suppliers' brand. But our study
offers an additional insight because risk reduction is an outcome of the process of market
orientation rather than direct branding efforts by the firm. Furthermore, in line with
Mudambi et al. (1997), we assume that this value is created because of intangible value or
benefits from the relationship. This means that idiosyncratic processes that are not visible
to the competitors serve as unique generators of value that would be difficult to replicate.
This is important because firms that are competent at creating unique brand value have
higher returns (Mudambi et al., 1997). For B2B managers, this could be a strong selling
point when they are arguing for a greater emphasis on branding in front of top
management. Compared to the study in Chapter 2, we find that the standardized
regression weight for this hypothesis is weaker in the objective model. In line with our
prior suggestion that smaller firms are more susceptible to supplier actions than larger
firms are, the effects are stronger in Chapter 2, which is based on a sample of both
publically and privately traded firms.
Supplier responsiveness to buyer satisfaction, buyer satisfaction to buyer firm
profit, supplier brand value to buyer firm profit. Our next set of hypotheses deal with the
influence of supplier responsiveness on buyer satisfaction and buyer satisfactions
influence on buyer firm profits. Supplier responsiveness results in buyer satisfaction. In
turn, we found that satisfaction did not positively influence objective firm earning data or
profits. There could be one of two explanations for these results. The first is that objective
and subjective performance data are imperfect substitutes, so the results differ. However,
we overrule this explanation because the subjective model also did not confirm this
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hypothesis. Hence, we offer an alternative explanation that arises from a rhetorical
question. Does customer satisfaction hold meaning for a B2B supplier? The literature is
muddled about the role of satisfaction (Naryandas, 2005). Our research adds to this
because we find no relationship between buyers' satisfaction and their own profits. This
implies that satisfaction is not a valid indicator of firm profits in business markets. Even
though we define satisfaction as an evaluation of the economic and noneconomic aspects
of a relationship, our confirmatory factor analysis greatly reduced the number of
measures we had and limited them to items measuring only the relational and
noneconomic aspects of satisfaction. Therefore, it is possible that our current model has
only captured the noneconomic portion of customer satisfaction that is not linked to
customer profits.
Our fifth hypothesis is insignificant, which contrasts with the model in Chapter 2
that found a significant positive effect. We believe this is because of the incompatibility
of subjective and objective measures. It seems that subjective measures do not fully
capture the entirety of what objective measures do. Hence, we find an insignificant
relationship.
4.7.3 Some Insights from Informal Discussions
We conducted informal discussions with academics and practitioners to further explain
the results of our model. We focused on determining the reason for the disconnect
between managers' reported self-measures and accounting measures? The first
explanation could be that managers tend to be overly optimistic about the future and
accounting measures of the past do not reinforce their understanding. The second
explanation could be that managers do not understand their business models fully. This is
because relevant information may not be diffused within the organization. (Indeed, this
could lead future researchers to question the relevance of responsive suppliers when firms
themselves are not market-oriented). The third possibility could be that managers lack the
overall accounting background to come to judgments about profitability that reflects
accounting measures. The fourth explanation could be that the complexity of the
relationship makes it difficult for an individual to heuristically decide which direction it is
going in. The fifth explanation could be that current accounting measures are inadequate
for measuring the value created by such relationships.
But the discrepancy between employee perceptions and financial measures is not
something unique. For instance, our sample included many big name firms such as
Microsoft. A recent venture by Microsoft to be the strategic supplier to Nokia of
Windows 7 has been received negatively by investors, plummeting their stock prices
downward. Although representatives of both firms are enthusiastic and optimistic about
the current alliance, the financial markets do not share their enthusiasm.
We also did not assume that purchasing managers had never been exposed to
accounting measures. From discussions we engaged in we learned that they had been
exposure to financial measures. Such measures most probably are taken into account
when purchasing managers make financial judgments or predictions. But in all likelihood,
purchasing managers' evaluations differ from objective measures because of one of the
above five possibilities.
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Using the findings of our current studies, firms could be motivated to implements
programs that bring managers' perceptions more in line with accounting performance
data. One positive outcome of such a maneuver is that it could improve the buyersupplier
relationships since it would make their expectations of each other more realistic.
4.7.4 Managerial Implications
The findings of this study that the model in Chapter 2 is not validated when objective
data are used for buyer-firm value causes us some concern. We decided to collect some
managerial opinions about subjective and objective assessments. We conducted informal
discussions with four current or former managers in B2B and B2C firms. The
respondents were from mid-management with experience in channel and relationship
management. On average, the managers had more than one decade of experience in a
managerial capacity. We now synthesize the responses to the two key questions asked
during the interviews: (1) From your experience, do managers' subjective assessments
(e.g., estimating risk or profit by a guess or experience) differ substantially from actual
accounting or performance data? If so, why? (2) Does the impact of a managerial model
diminish if it only supports perceptual data and not objective data?
Our interviews revealed that managers do find variations in subjective and accounting
data. A main reason for this was the lack of substitutability of both measures. Another
important issue was that many targets do not have objective measures. Furthermore, most
assessments are time-bound. It normally is quicker to conduct a subjective assessment
compared to an objective assessment. Managers believed that objective indicators are
most effective when used in tandem with subjective indicators. Likewise, they argued
that many objective indicators were too broad and not firm-specific, so they considered
them difficult to comprehend and not suitable at times. But perceptions of the usefulness
of subjective and objective indicators also varied by functional department. The
proponents of both subjective and objective indicators were managers from marketing,
whereas, the primary opponents were from accounting. The consensus of the managers
was that objective and subjective measures were not used solely based on their reliability,
but rather depending upon the top managements' inclination toward certain categories of
metrics. This was especially the case with marketing within the creative industries.
Another issue was that perceptual metrics allowed firms to measure their own
idiosyncratic processes that gave them a competitive edge in the marketplace. As long as
a model focused on firm-level idiosyncrasies, a perceptual model would be fine.
However, if they were to measure more widespread and general performance aspects,
then a model that works with objective indicators might be required.
In summation, the implications of the answers of our two questions for the model
in Chapter 2 are: i) subjective assessment and objective assessment do matter; ii) one
metric is only better than the other depending upon the situation in which the metric is
used; iii) subjective metrics are more versatile than objective metrics; iv) firms often do
not expect subjective and objective metrics to reinforce each other; and v) models that
measure idiosyncratic process can use perceptual metrics because objective metrics
normally are not specific enough. Therefore, despite its lack of verification by objective
indicators, the perceptual performance-based model by Chapter 2 would matter to
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managers because of the insights it offers.
4.8 Limitations
The first limitation of this paper is that we do not consider the moderator of market
turbulence. Although the data were collected during the financial recession, we did not
wait for a post-recession window or sample to check whether market turbulence
influenced the customers' evaluations of responsive or market-oriented strategic
suppliers. Prior research (Grewal & Tansuhaj, 2001) has provided some evidence to
indicate that market orientation during recessions may actually exacerbate a decline in the
relationship since the suppliers' or sellers' response is based on knowledge structure built
on a market prior to the changes brought about by the recession.
The second limitation is the tradeoff we had to make between cross-sectional and
longitudinal data. Therefore, we could not shed light on the causality of the relationships.
This shortcoming could be overcome by constructing an index of respondents over a
number of years or periods (Diamantopoulos & Winklhofer, 2001). It is possible that in
future studies, further additions could be made to the model we have proposed.
The third limitation is that we do not compare IdRR on both the perceptual and
objective data levels. This shortcoming is hard to overcome because there are no readily
available objective measures for this construct. However, a composite measure could be
created by future researchers based on accounting and financial data.
The fourth limitation is that we could have added more value measurement
variables rather than only IdRR insulation. This would have provided us the opportunity
to benchmark on a dashboard and examine how IdRR insulation performs against similar
measures of value creation. This is important because a measure needs to be
benchmarked against similar measures to determine whether it is a valid indicator.
The fifth limitation is that we only had objective data on only one variable. We do
not receive any variation of the prior three hypotheses because the data are identical. In
future studies, all variables could be objective, which would give a further indication of
how well the data fit.
The sixth limitation of this paper is its research design. We use a survey, which
limits the amount of control we have over the variables. An experimental design would
have allowed us to test for a wider variety of metrics and control variables. This could
reveal whether some objective and subjective metrics do reinforce each other.
4.9 Future Directions
Although there are many possible future directions, we had to limit ourselves to
those which we felt were most important. These are listed below.
i) One course for future researchers could be to examine the role of firm
culture in accepting subjective or objective measures as valid indicators of
performance. This is important because it could also explain why
resources are misdirected because of inaccurate perceptions and their
reliability as valid measurement tools.
ii) In the current study, we used corporate-level objective indicators. Using
department-level specific metrics instead could resonate better with
managers' understanding of how much profit their departments produce.
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iii)
iv)
Indeed, there may not be much difference between objective and
subjective measurement indicators at the same level throughout the
organization.
We based our analysis on the assumption that the purchase manager had
the most influence. However, as Gordon, Calatone, and Di Bennetto
(1993) predicted, the most influential is not necessarily the most senior
person in a buying center. Future researchers could examine whether the
most influential person has greater accuracy in predicting objective
measures.
Another interesting avenue for future studies could be investigating why
some employees are better than others at providing perceptions that
reinforce objective measures. Could this possibly be because of a better
understanding of firm processes, or is it because of a relevant educational
background?
4.10 Conclusions
The litmus test for marketing metrics is the insights they generate. Do the insights
matter and can the findings be replicated by similar metrics?
——Fasi Zaka, Marketing Director at the British
Council (personal communication, 2011)
In many B2B markets, strategic suppliers can differentiate their offerings by
customer-centric strategies such as market orientation. But market orientation involves
substantial investment and effort by the supplier and the buyer. Therefore, measuring the
buyers' returns on such investments is of interest for the suppliers to gauge how
successful they have been. We examined two broad categories of measurement metrics,
namely objective and subjective performance metrics. Consequently, we identified an
important flaw in current management assumptions: Despite managements' assumptions
that their heuristic or subjective judgments should reinforce objective performance
measurements of key supplier relationships, they do not. Qualitative data showed that this
was not because of a problem with the logic of the Chapter 2 model. Rather, it was
because of the limitations of managers and current metrics.
We proposed the following steps to overcome this problem: first, instituting
programs for marketing managers to equip them with the skills needed for conducting
heuristic evaluations that are more in line with accounting evaluations; second, educating
finance managers about the intangible value that marketing creates; third, creating new
hybrid measures that represent both the marketing and accounting perspectives; and
fourth, simplifying existing objective measurement procedures and methods so managers
can comprehend what the core elements are in calculating market returns. Once such
measures have been instituted, we believe that the measurement gap between objective
and subjective performance measures for key supplier relationships will diminish.
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Chapter 5
Conclusions
This final chapter presents the conclusions and managerial and academic implications of
the research. Section 5.1 presents a short synopsis of the three studies in the dissertation.
The subsequent section 5.2 presents the answers of the research questions. The following
section, 5.3, contains a discussion of the general conclusions derived from this research.
The next section, section 5.4, provides the theoretical implications of the studies, and the
chapter ends with concluding remarks.
5.1 Research Findings
The first essay of this dissertation examines the influence of supplier responsiveness on
buyer firm performance based on a sample of 164 business–to-business (B2B) firms in
the United States. In this study, it is emphasized that supplier responsiveness creates
dyadic value by reducing idiosyncratic relational risk (IdRR). IdRR is risk that exists
because of entering into a specific relationship with another company. IdRR reduction is
the mechanism by which market-based assets are created for the supplier. On the other
hand, soft indicators such as satisfaction have shown no link to dyadic value or buyer
firm profits alone. This study challenges the long-held assumption that market orientation
and risk are positively related and, at least in strategic B2B contexts, we find evidence to
the contrary.
The second essay looks at differences between the application of the model
presented in the Chapter 2 in different cultural contexts. The research sample included 92
firms from the Netherlands and the original 164 from the U.S. The core emphasis of this
study was determining whether the mediators of the strategic supplier responsiveness and
buyer firm performance were the same across national cultural boundaries. Using the
Hofstede index as the driving logic behind the comparison of the two samples, our model
showed significant differences. In the United States, soft indicators did not present a link
to buyer firm performance, whereas they did in The Netherlands. The implication for
management is that, when developing marketing strategy, they have to adopt periscopic
perspectives beyond their own cultural spheres. This might include adopting a regional
branding strategy where stark national differences cannot be served by a single brand
message.
The third essay is motivated by the need to examine whether different
performance measures would reinforce each other in the model outlined in Chapter 2.
Managers habitually trust their heuristic judgments. The extant literature on the accuracy
of such judgments is mixed. Such judgments are often formed as a result of cumulative
background information, both objective and subjective. We anticipated that, since
strategic relationships involve a great degree of cooperation, a manager’s assessment
would be aligned with objective performance measures. However, we found that
subjective and objective performance measures did not provide similar results.
5.2 Overall Global Research Question Answer
To our global level research question that “What is the impact of supplier market
orientation through idiosyncratic relational risk, buyer satisfaction, and supplier brand
value on buyer firm value?” we find, indeed supplier market orientation does influence
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uyer firm performance through a series of mediators. However, not all mediators have a
significant influence on buyer firm value. Particularly, in the context of the U.S. and The
Netherlands. We find that buyer satisfaction does not matter in the U.S., but it does
matter in The Netherlands. We argue that this is a result of the different national cultures,
specifically a Dutch feminine culture focuses more on soft factors, such as informal
interaction between the buyer and supplier. However, the U.S. is a more masculine
society where hard factors matter more such as factors related to the bottom line such as
IdRR. Given, this importance of hard or objective factors in the United States, led us to
question whether perceptual metrics would reinforce objective factors because marketers
use many heuristic measures in decision making. Moreover, we were motivated in part
on recent research findings that the marketing department’s influence in the firm
increases when they are more accountable for the money that they have spent (Verhoef &
Leeflang, 2009). Hence, for the supplier’s firm to learn about their buyer’s needs, and
later formulate a response to those buyer’s needs the supplier’s marketing department
requires a sizeable marketing budget. And in order to be allocated adequate budgets the
supplier’s marketing department needs to be able to communicate the value created by
them to their own financial department and their buyer’s financial departments.
Therefore, we investigated whether perceptual metrics based on managerial perceptions
would be confirmed by metrics preferred by financial managers namely, objective
accounting metrics. However, we find that they are not. The primary reason we suggest is
that perceptual metrics are not perfectly substitutable by objective accounting metrics.
Hence, we recommend that firms to develop substitutable perceptual metrics.
In summation, we have found that strategic supplier responsiveness does
influence buyer firm value, although this effect does not always flow through the same
channel. On the one hand, in the case of the United States a supplier’s responsiveness
must reduce IdRR and then consequently creates value for the supplier’s brand through
brand preference exhibited by the buyer, whereas for the buyer it creates value through
stable and accelerated cash flows. On the other hand, in countries with a feminine culture
like The Netherlands we find an additional way of increasing buyer firm and that is
through increasing buyer satisfaction. Hence, an implication for managers is that they
should rely on satisfaction only if satisfaction is a reliable indicator in the national
cultural context. In addition, IdRR is a preferable metric as it is applicable in more
contexts than buyer satisfaction. This is because IdRR measures idiosyncratic risk, and
risk has its origins in the field of finance which makes it a more easy to relate to financial
concepts, and therefore, is more acceptable to managers. Moreover, we recommend that
managers develop perceptual measures for marketing and firm performance that are
substitutable with financial measures because based on our data we found that currently
managers use performance measures that are not substitutable. Hence, though marketing
may generate value for buyers through strategic supplier responsiveness, it does not show
up on balance sheet measures, and hence, the marketing effort is difficult to justify to the
customers.
In conclusion, strategic supplier responsiveness is an important mechanism of
creating supplier and buyer firm value.
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5.3 Overall Conclusions
This dissertation focuses on strategic supplier responsiveness, also known as B2B market
orientation, idiosyncratic relational risk, cross-cultural comparisons, and subjective and
objective performance measurement. The extant literature on business-to-business market
orientation includes limited research focusing primarily on interesting questions such as
whether market orientation cultures can be diffused across organizational boundaries
(Siguaw et al., 1998). However, the B2B market orientation as a research domain has
many more interesting questions that need to be answered. In line with market orientation
from the intra-firm perspective, the mediators between the market orientation or
responsiveness strategy and the buyer performance implications are one such avenue.
Particularly, among the wide array of questions one could chose from, risk was a
component that was enticing because of the macroeconomic factors that have made risk a
key topic of interest and because market orientation is thought to exacerbate intraorganizational
risk. Then, would it logically also apply in the inter-organizational
context? Therefore, our primary research question investigated in Chapter 2 was:
“Does it Make Relational and Financial Sense to be a Responsive Strategic Supplier?”
Our primary research question examined whether the much-touted philosophy of market
orientation mattered in B2B contexts. Our study led us to conclude that it does matter.
Unlike the intra-firm contexts where market orientation is positively related to top
management risk-taking, in strategic B2B contexts, we found that it is negatively related
to IdRR. To buyers, any supplier maneuver matters that reduces their risk; as one
manager remarked, “No buyer penalizes a supplier for making him safe unless he is
completely irrational” (Tanoli, personal communication, 2012). Our results provide
evidence that a strategic supplier market orientation reduced a specific type of relational
risk, IdRR. The importance of the identification of this linkage is that, in the marketing
literature, a lot of emphasis is placed on reducing overall relational risk but not IdRR, the
risk component that matters the most because of its positive influence on intangible
market-based supplier assets and buyer firm performance. Overall, the current chapter
has, to an extent, provided evidence to indicate that supplier responsiveness has strong
performance implications for the buyer and, in addition, implications for the supplier’s
market-based assets.
In Chapter 3, we deal with the question of whether supplier responsiveness
mattered across national cultural barriers. We found that, even with relatively similar
cultures, there are relatively significant changes. For example, in the United States, soft
indicators such as customer satisfaction do not seem to matter much, whereas they do in
the Netherlands. However, the core finding that remains consistent is that IdRR matters
across borders. The conclusions of the second paper are far-reaching. Primarily, it is
avoiding the familiarity trap. Even though markets may seem the same, peculiar cultural
idiosyncrasies do exist. For a marketing manager sitting in Washington or Amsterdam,
this could mean the difference between keeping a client account or losing it. On the other
hand, this finding may seem simplistic to some scholars, but it matters because many
mistakes that do have far reaching results are simplistic. Overall, Chapter 3 has
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specifically highlighted the importance of national cultural barriers while employing a
market orientation strategy.
In Chapter 4, we investigate both subjective and objective indicators for
evaluating the buyer’s performance as a result of the strategic supplier’s responsiveness.
As most managers use perceptual judgments or evaluations when they face complex tasks
(Cassidy, 2009), it is significant to understand whether such judgments reinforce each
other. We find that they do not reinforce each other. This is important because there is a
disconnect between what managers perceive to be important and what existing metrics
measure. Firms need to streamline their performance measurement and ensure that
existing metrics reinforce what managers with experience believe is important for the
firm. On the other hand, firms can institute training programs to ensure that managers
evaluate along those performance dimensions that matter to the firm. This could ensure
that subjective and objective performance measurements reinforce each other. This is
important because managers have to make day-to-day decisions, and if they make
decisions against criteria that are not shared by the firm, then it will not show up during
objective performance measurement.
5.4 Theoretical Implications
Regarding market orientation theory, the emphasis is on the intra-organizational
perspective and an IdRR reduction mechanism. These are new insights in the B2B
literature. The market orientation literature has so far had two major limitations, in our
opinion. The first is that the market orientation literature has focused solely on the intraorganizational
perspective. The customer’s input is absent in the extant literature model
testing (Kohli & Jaworski, 1990; Kohli et al., 1993; Narver & Slater, 1990; Slater &
Narver, 1994). Second, the relationship between market orientation and relational risk has
been overlooked by many studies (Langerak, 2001; Langerak et al., 2004; Langerak et al.,
2007). Starting with the latter limitation, we have highlighted the concept of IdRR.
Rather than focus on management predispositions and predictive perceptions, we cut to
the chase and asked buying center managers to reflect upon their experiences with their
strategic suppliers. Our results and contribution to this literature stream is that we have
brought forth a hitherto latent relationship between market orientation and idiosyncratic
relational risk. We have brought a counter-intuitive relationship to light. It is just a start,
but we have identified a negative relationship that has been assumed to be positive so far.
As a result, risk-taking companies were only thought to be more likely to be marketoriented.
This has had far-reaching consequences, one of which has been to delay
launching new products in times of enhanced risk, such as during recessions. However,
truly market-oriented companies that understand the latent needs component of the
market have been launched during recessions. Apple is at the top of the list of these
companies. As one analyst remarked, “Apple is an industry, not a company” (Farzad,
2011, para10).
Given the recent aversion to risk-taking because of the Wall Street scandals,
philosophies such as market orientation have been described in some business circles as a
bad philosophy. Our current contribution is a step in removing this stain from market
orientation, and we believe that we have provided some supporting evidence.
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Regarding the resource-based view and market-based assets theory, market-based
assets are those assets that fulfill the requirements of the resource-based view (Srivastava,
Shervani, & Fahey, 1998). Primarily, these are whether the assets are inimitable, hold
value, are rare, and lack perfect substitutes. The link is discussed in chapter two in terms
of building up market-based assets through internal processes. A key antecedent of
building market-based assets that was not previously identified is IdRR. IdRR qualifies
on all four of the resource-based view tests. Furthermore, a reduction in IdRR can be
detected by the external marketplace by increased buyer firm performance. This leads to
a position of prestige for the supplier and adds value to the supplier’s brand that is
difficult to replicate because this increase in brand value is based on knowledge that the
supplier has, and it can only be gained by working in a specific sector and with a specific
customer. Linimar is a case in point. It has recently outperformed market expectations
because of its responsiveness as a supplier and, as a result, it has acquired a position of
prestige in the market (Marketwire, 2007).
Similar to the contribution to the market orientation theory of this dissertation,
market-based assets theory has focused on the inter-firm perspective rather than the intrafirm
perspective. By focusing on the benefits that both the supplier and the buyer acquire
via IdRR reduction, this study adds a new dimension to the theory.
Regarding brand theory, in business-to-business branding, some scholars have
argued that successful B2B brands require risk reduction, increased information
efficiency, and image benefit creation (Kotler, & Pfoertsch, 2006). Though these facets
have been highlighted, how the different processes work and the sequence of brand value
creation through these various mechanisms has not been explored. In chapter two of this
dissertation, we explore the sequence of activities that creates brand value. This led to the
supplier responsiveness/IdRR/supplier brand value/customer value sequence. This
dissertation is a clarification of how the activities of brand building interact with each
other to create supplier brand value and customer performance value.
Regarding performance measurement theory, in the fourth chapter of this
dissertation, we deal with the thorny issue of performance measures. In the taxonomy of
marketing performance measures, a broad categorization that is applied is the distinction
between subjective and objective performance measures. Though some scholars have
recognized that subjective and objective performance measures are imperfect substitutes
for one another, other scholars expect that such measures should reinforce each other
because they measure similar phenomena. One of our findings is straightforward:
subjective and objective performance measures do not reinforce each other in the context
of the model outlined in Chapter 2. However, our qualitative interviews revealed
auxiliary insights, one of which was that managers do not expect such measures to
reinforce each other. This could be because subjective and objective measurement differ.
The contribution of our research is that we recognize that, although marketing initiatives
have a positive influence on performance, revealing the performance of all types of
measurement is not suitable.
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5.5 Practical Implications
From a managerial perspective, we provide insights into how investments in market
orientation affect IdRR. If we consider the risk impact, our results suggest that an
increase in market orientation by one standard deviation reduces IdRR by 0.98 standard
deviations. In other words, an increase in marketing focus idiosyncratic reduces relational
risk by 0.98 standard deviations. Suppliers can use this reduction in idiosyncratic
relational risk as justification towards buyers for charging higher prices. In these difficult
times of recession, when sales are difficult to come across cost saving is a preferred
alternative to finding new clients. Moreover, it would also serve the purpose of internal
justification to the financial department as how marketing investment produces returns
and higher margins.
Market orientation is a process of continuous innovation (Kohli & Jaworski,
1990). More risk is typically involved in the case of commercializing innovations. In
terms of risk of introducing innovations risk increases as the degree of newness of the
innovation increases (Chandy & Tellis, 2000). Launching extremely new to the world
innovations are a high risk and high return endeavor. However, involving the strategic
supplier can reduce the cost by up to 18% of launching the innovation (Siemens, 2013).
Our research indicates that as a unique supplier’s response is based on the understanding
of a specific client that is highly involved with the customer be it producing an innovation
or otherwise, leads to a reduction idiosyncratic relational risk resulting in accelerated and
stable cash flows for the buyer. Hence, from a strategy perspective we propose that
marketing managers focus on spending marketing budgets on those strategic activities
where they can possibly reduce the IdRR. However, we recognize that this may not
always be a possibility, but supplier’s should try their utmost to reduce IdRR as much as
is possible.
Another practical contribution is the difference in the role of satisfaction strategic
buyer supplier relationships in The Netherlands and the United States. Our data indicates
that buyer satisfaction does have a relationship with the buyer’s own profits in The
Netherlands, but not in the United States. Our findings were confirmed by an informal
confirmation with a senior manager at the HMG group (a strategic supplier of bean bags
to Leenbakker and Hema). Our findings are of relevance because many managers in The
Netherlands hold satisfaction as an important metric when evaluating their relationships.
But when they want to expand their business to the US or vice versa they have to develop
more hard core metrics, one of which we have expounded in this thesis, namely IdRR.
In addition, our research investigates perceptual and objective or accounting based
performance measure. We find that all perceptual measures cannot be confirmed by
accounting measures. The practical implication of this research finding is that businesses
should focus on bridging the gap between perceptual and accounting measures. Firms
could adopt measures that are likely translatable into financial measures or adopt a
dashboard strategy that show financial managers the value marketing is creating.
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5.6 Closing Words
This dissertation has examined the role of strategic supplier responsiveness in interorganizational
risk reduction, its influence on satisfaction, buyer firm value, the
mediating role of satisfaction, cross-cultural comparisons, and the difference in
evaluations based on subjective and performance indicators. The true value of this
research lies in its fundamental challenging of the link between supplier responsiveness
and organizational risk. To a degree, we have provided evidence that this link is negative
and not positive as assumed by many prior studies. However, further research will
confirm whether or not our findings can be applied in a wide variety of contexts.
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Summary in English
This dissertation focuses on market orientation in the strategic supplier and buyer
relationship context and further focuses on the concepts of idiosyncratic relation risk
(IdRR), buyer satisfaction and supplier brand value as mediators of that relationship.
Moreover, this dissertation adopts an inter-organizational perspective as opposed to intraorganizational
perspectives in previous studies (Kohli & Jaworski, 1990; Narver &
Slater, 1990; Slater & Narver, 1995). In the next sections we will cover the key
definitions, the literature gaps, the research questions, a synopsis of each chapter,
theoretical implications, practical implications, future research directions and a closing
word.
The key concepts in this study are supplier responsiveness, buyer satisfaction,
IdRR, supplier brand value, buyer firm value, and subjective and objective performance
measures. The term supplier responsiveness is defined as the accumulative supplier
response that is created by internal supplier firm intelligence generation and
dissemination processes to meet their buyer’s needs. The concept of buyer satisfaction is
defined as the buyer’s cumulative economic and non-economic satisfaction with its
strategic supplier firm. The term of IdRR is defined as the risk that exists because of
entering into a specific relationship with another company. The concept of supplier brand
value is defined from the perspective of a strategic buying center team, and is defined as
the shared collective memories about the supplier’s brand, which provides it with
leverage because of customer preference that it would otherwise not have without the
brand. The term buyer firm value is defined as growth and stability in buyer cash flows.
The concept of subjective performance measures is defined as heuristic individual
evaluations of a firm’s performance and the concept of objective performance measures
are defined as those measures which are calculated using standardized accounting and
financial procedures.
So far in the extant literature a general approach has been to focus on the intraorganizational
market orientation-mediators-performance relationship (Kohli & Jaworski,
1990; Narver & Slater, 1990; Slater & Narver, 1995). For example, Kohli and Jaworski
(1990) theorize about the impact of three components of market orientation on business
performance. Whereas, Jaworski and Kohli (1993) empirically examine the impact of a
market orientation on firm performance. Furthermore, Narver and Slater (1990)
investigate the influence of market orientation on business performance in both
commodity and non-commodity businesses. In addition, Grewal and Tansuhaj (2001)
examined the impact of market orientation and strategic flexibility on firm performance
after an economic crisis. Moreover, Slater and Narver (1995) propose that market
orientation combined with an entrepreneurial drive and the right organizational climate
produces the learning organization. Further, Voss and Voss (2000) examine the role of
market orientation and product orientation on firm performance. Alternatively,
Zhou et al. (2005) examine the impact of market orientation on firm performance via the
mediators of technological and market innovations. Whereas, Verhoef and Leeflang
(2009) examine whether marketing accountability increases the motivation for firms to be
market oriented. In summation, the above studies on market orientation examine the
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intra-organizational impact of market orientation on firm performance. Few studies such
as Kohli and Jaworski (1990) and Jaworski and Kohli (1993) examine the impact of risk
aversion on market orientation. But what is not covered is the inter-organizational impact
of supplier market orientation on relational risk and buyer firm performance. This is an
important oversight as one of the motives for selecting a supplier’s brand is their ability
to reduce the buyer’s risk in business to business contexts Kotler and Pfoertsch (2006).
Neither have the previous studies explicitly modeled buyer satisfaction as a mediator in
the inter-organizational supplier market orientation and buyer firm value setting. This is
important as many previous studies and marketing texts mention buyer satisfaction as a
desired outcome of market orientation. Moreover, to the best of our knowledge buyer
satisfaction has not been empirically tested in most studies from an inter-organizational
perspective. Nor have previous studies covered the mediating role of a supplier’s brand
plays in the supplier market orientation and buyer firm value. This is an important
oversight as a marketing philosophy or market orientation is pointless unless it creates
some market based assets such as brand value to store the value created for the supplier.
In addition, the supplier’s market orientation must result in benefits for the buyer such as
improved cash flows in order for the relationship to continue. Therefore, at a global level
our core research question for this dissertation is:
“What is the impact of supplier market orientation through idiosyncratic relational
risk, buyer satisfaction, and supplier brand value on buyer firm performance?”
Chapter 1 presents an introduction to the research topics of supplier market
orientation and also presents the motivation for this dissertation. Further, this chapter
presents research questions, research methodology, an overview of the remaining
chapters and the research’s limitations.
Chapter 2 of this thesis examines the influence of supplier responsiveness on
buyer firm performance based on a sample of 164 business–to-business (B2B) firms in
the United States. In this study, it is emphasized that supplier responsiveness creates
dyadic value by reducing idiosyncratic relational risk (IdRR). IdRR is risk that exists
because of entering into a specific relationship with another company. IdRR reduction is
the mechanism by which market-based assets are created for the supplier. On the other
hand, soft indicators such as satisfaction have shown no link to dyadic value or buyer
firm profits alone. This study challenges the long-held assumption that market orientation
and risk are positively related and, at least in strategic B2B contexts, we find evidence to
the contrary.
Chapter 3 looks at differences between the application of the model presented in
the Chapter 2 in different cultural contexts. The research sample included 92 firms from
the Netherlands and the original 164 from the U.S. The core emphasis of this study was
determining whether the mediators of the strategic supplier responsiveness and buyer
firm performance were the same across national cultural boundaries. Using the Hofstede
index as the driving logic behind the comparison of the two samples, our model showed
significant differences. In the United States, soft indicators did not present a link to buyer
firm performance, whereas they did in The Netherlands. The implication for management
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is that, when developing marketing strategy, they have to adopt periscopic perspectives
beyond their own cultural spheres.
Chapter 4 is motivated by the need to examine whether different performance
measures would reinforce each other in the model outlined in Chapter 2. Managers
habitually trust their heuristic judgments. The extant literature on the accuracy of such
judgments is mixed. Such judgments are often formed as a result of cumulative
background information, both objective and subjective. We anticipated that, since
strategic relationships involve a great degree of cooperation, a manager’s assessment
would be aligned with objective performance measures. However, we found that
subjective and objective performance measures did not provide similar results.
Chapter 5 is our final chapter of this dissertation and commences by presenting a
short synopsis of the three studies in the dissertation. In the following section there is a
discussion of the general conclusions derived from this research. And next the theoretical
implications of the studies are discussed, and the chapter ends with concluding remarks.
Theoretical Contributions
In chapter 2 we shed new light on the market orientation and risk relationship and build a
comprehensive model that enunciates the relationship between supplier responsiveness,
IdRR, and buyer firm value. Our findings challenge the status quo belief that market
orientation is an inherently risky endeavor by providing evidence that supplier
responsiveness negatively influences IdRR. Moreover, we contribute to the
marketing domain by extending the scope of the current market orientation models,
which to date have mostly focused on the intra-organizational perspective. Furthermore,
the extant literature has not addressed the issue of incorporating the
customer’s perspective and the customer’s firm value in market orientation
and performance models and we contribute to the literature by incorporating these
perspectives.
Second, we introduce IdRR as a new construct in the literature and an important
mediator of the supplier responsiveness and buyer firm value. Using IdRR, academics and
businesses will be able to estimate whether market orientation increases or reduces the
relational risk between a supplier and buyer. Furthermore, at a time when there is an increased
need for accountability within firms, IdRR as a measure allows managers to communicate
some of the value that the relationship has accrued due to market orientation. Hence, by using
IdRR to explain the benefits of market orientation, it adds clarity to the returns on investment
from market orientation.
Third, we provide evidence that market orientation is a single unified general construct
and not three separate general factors. The market orientation construct has lacked discriminant
validity in some important studies, but scholars still continue to model it as sub-components
rather than a general factor (Kohli et al., 1993; Narver & Slater, 1990). In the current study, we
revisited it as a general factor and we have found that market orientation or supplier
responsiveness is a single factor rather than three distinct components.
Fourth, we find that buyer satisfaction is not related to a buyer’s firm value in the U.S.
In the extant literature, scholars so far have focused on the buyer satisfaction and supplier
business performance relationship (Gyskens et al., 1999). Despite this focus, scholars have
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found limited evidence of a buyer satisfaction and supplier performance relationship (Gyskens
et al., 1999). Although the buyer satisfaction and supplier performance relationship is
important, even more important is the issue as to whether buyer satisfaction is a meaningful
indicator. As satisfaction in channel contexts has an economic aspect, it should be related to a
buyer’s firm value in order to be meaningful as an indicator. If the buyer is satisfied with the
economic value provided by the strategic supplier, it in turn should increase the buyer’s cash
flow, and therefore, buyer firm value.
Fifth, our model provides preliminary evidence that IdRR reduction is a reliable
indicator that has a link to customer firm value and supplier brand value. For managers, this
finding provides a framework for thought. However, it may not be possible or desirable to
solely focus on those activities that reduce IdRR because some market orientation activities
may influence both IdRR and buyer satisfaction, but a firm should rely more on IdRR measures
than customer satisfaction measures.
Chapter 3 builds on the research presented in Chapter 2 by testing it across two
nations. We find that the main hypothesis and theoretical contribution regarding the
supplier responsiveness-idiosyncratic risk-performance relationship holds true across
nations. However, we find that there is a difference in soft relational indicators of buyer
satisfaction. We explain these differences in light of the Hofstede index, suggesting that
national culture may moderate the soft indicator-performance relationship. In sum, the
current paper validates the main findings of the Chapter 2 model by providing evidence
that IdRR matters across nation.
In chapter 4 to the best of our knowledge is the first study to examine the issue of
objective versus subjective performance measures in the context of strategic supplier
responsiveness or market orientation and buyer IdRR. In Chapter 4 we make three
theoretical contributions.
First, we provide evidence that the type of data used for supplier responsivenessidiosyncratic
risk-performance analysis can change the strength and direction of the
relationships in the Chapter 2 model. We were able to show that there is a disconnect
between key indicators when subjective and objective data are used. We believe this is
because of a lack of metric substitutability rather than a problem with the model itself.
Second, we explain why managers’ evaluations may not be in line with the
objective data indicators. For example, because objective data are calculated at a macroorganizational
level, they may not reflect the performance of the various functions at
different levels within the organization. On the other hand, a manager’s heuristic ability
can shed light on latent areas of performance that traditional objective performance
measures cannot.
Third, we explain that financial managers often fail to see the value of marketing
initiatives because they do not understand the intangible value such initiatives create.
Practical Implications
From a managerial perspective, we provide insights into how investments in market
orientation affect IdRR. If we consider the financial impact, our results suggest that an
increase in market orientation by one standard deviation reduces IdRR by 0.98 standard
deviations. In other words, an increase in marketing focus reduces idiosyncratic relational
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isk by 0.98 standard deviations. Suppliers can use this reduction in idiosyncratic
relational risk as justification towards buyers for charging higher prices. In these difficult
times of recession, when sales are difficult to come across cost saving is a preferred
alternative to finding new clients. Moreover, it would also serve the purpose of internal
justification to the financial department as how marketing investment produces returns
and higher margins.
Market orientation is a process of continuous innovation (Kohli & Jaworski,
1990). More risk is typically involved in the case of commercializing innovations. In
terms of risk of introducing innovations risk increases as the degree of newness of the
innovation increases (Chandy & Tellis, 2000). Launching extremely new to the world
innovations are a high risk and high return endeavor. However, involving the strategic
supplier can reduce the cost by up to 18% of launching the innovation (Siemens, 2013).
Our research indicates that as a unique supplier’s response is based on the understanding
of a specific client that is highly involved with the customer be it producing an innovation
or otherwise, leads to a reduction idiosyncratic relational risk resulting in accelerated and
stable cash flows for the buyer. Hence, from a strategy perspective we propose that
marketing managers focus on spending marketing budgets on those strategic activities
where they can possibly reduce the IdRR. However, we recognize that this may not
always be a possibility, but supplier’s should try their utmost to reduce IdRR as much as
is possible.
Another practical contribution is the difference in the role of satisfaction strategic
buyer supplier relationships in The Netherlands and the U.S. Our data indicates that buyer
satisfaction does have a relationship with the buyer’s own profits. However, in The
Netherlands it does. Our findings were confirmed by an informal confirmation with a
senior manager at the HMG group (a strategic supplier of bean bags to Leenbakker and
Hema). Our findings are of relevance because many managers in The Netherlands hold
satisfaction as an important metric when evaluating their relationships. But when they
want to expand their business to the US or vice versa they have to develop more hard
core metrics, one of which we have expounded in this thesis, namely IdRR.
In addition, our research investigates perceptual and objective or accounting based
performance measure. We find that all perceptual measures cannot be confirmed by
accounting measures. The practical implication of this research finding is that businesses
should focus on bridging the gap between perceptual and accounting measures. Firms
could adopt measures that are likely translatable into financial measures or adopt a
dashboard strategy that show financial managers the value marketing is creating.
Future Research Directions
Although we have tried in this thesis to address the research questions as
comprehensively as possible, but there are still many possible future research directions
open to future researchers. Below, we have summarized and organized some interesting
research avenues for future researchers:
i) Longitudinal research design. Our cross-sectional study allowed us to draw associations at
best but not conclusions about causality. To further improve this design, data collected for a
time series analysis could allow the deduction of evidence regarding causality. However, there
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has been some recent evidence that inferences from cross-sectional studies about causality may
not be much different from inferences about longitudinal studies (Rindfleisch et al., 2008).
ii) Limited sample of industries for depth. We traded off depth for width within our industry
sectors. However, specific sectors may vary more with supplier responsiveness than others. For
instance, in the commodities industry, price may be more of a determining factor, whereas in
the technical goods industrial sector the supplier response may be a key determining factor as
to whether relationships continue. Future studies could concentrate on a few sectors and check
the robustness of our findings across various moderators, such as market turbulence and
increased technological risk.
iii) Quasi-experimental design using financial and perceptual data in tandem. Prior studies of
market orientation have found conflicts between objective and subjective measures of
performance (Narver & Slater, 1990). To determine whether our findings actually reflect real
financial market sentiments and accounting data, future studies can use financial data in
tandem with perceptual data.
iv) Wider comparison of measurement metrics. In the current study, we only use idiosyncratic
risk, which within itself is a important indicator of the potential returns involved with any
relationship. However, risk is not the sole metric in the literature. Future studies might include
a wider array of metrics to examine which are better indicators than others and why. For
instance, examining idiosyncratic risk in comparison to inventory turnover metrics or debt
leverage metrics could be an interesting avenue.
v) Comparison of more strategies with supplier responsiveness for reducing idiosyncratic risk.
We only examine one strategy, which is market orientation as a means of reducing
idiosyncratic risk. However, there are other strategies and orientations, such as a technology
orientation or a market leadership strategy. The ways in which these strategies and orientations
influence strategic supplier-buyer relationships and their idiosyncratic risk outcomes could be
of potential interest.
vi) Use of various research methods other than surveys. The research design could be
improved upon specifically with reference to Chapter 3. Some researchers have indicated that
ethnographies should be employed to research issues related to culture (Langerak, 2001). The
advantage in their opinion is that organizational culture can be better documented by long-term
observation techniques, such as ethnographies. Therefore, future researchers could employ an
ethnography for a deeper understanding of the supplier responsiveness-buyer firm performance
relationship.
vii) Comparison of more diverse countries. In chapter 3 although the US and The
Netherlands are different in some respects, they share similarities as well. Both are
industrialized nations, both are vibrant democracies, both have capitalist economies, both
have a degree of similar occidental culture, and both nations are internationally
competitive economies (Deshpande & Webster, 1989). Some cultural idiosyncrasies do
occur, such as acceptance of boasting on job interviews in the United States and the
preference for modesty in job interviews in The Netherlands, but nevertheless, they both
can be considered western cultures.
However a comparison with an Asian culture would have provided a more diverse
comparison. China is a growing world trade power, it is a communist nation, and it has a
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collectivist culture and a Confucian value system (Hofstede, 2003). These cultural
differences result in different risk preferences in individual members of society. For
instance, the Chinese have a greater risk preference than Americans, because their
collectivist culture cushions them from losses. Future researchers could investigate how
firms from societies with extremely different risk preferences react to supplier
responsiveness. This would also add a new dimension to the problem we studied as to
whether cultural risk preference is a moderating factor in the supplier responsiveness and
buyer performance relationship.
In some Eurasian and Asian economies, relational practices such as Blat and
Gaunxi are an integral part of the business culture. However, in western cultures such
practices may be viewed as unethical and detrimental to the corporate interests and
reputation. Specifically, when forming strategic supplier relationships with corporations
that find such cultural practices acceptable, buyers may fear a negative spillover on their
brand reputation. Such cultural effects have been overlooked in our current study. Further
examples include questions such as: Does IdRR vary because of national culture? Or
should culture specific practices such as Blat or Guanxi be considered a part of IdRR?
ix) Use of both subjective and objective metrics for international comparisons. Another
limitation of our current study is that we only considered perceptions. Indeed, do
perceptions reinforce objective measures across cultures? Are some cultures more prone
to be overly jubilant when it comes to redirecting earnings from investments? Are some
cultures overly pessimistic when predicting losses which may across from strategic
relationships? Do cultures influence the relative importance of firm perception of
marketing strategies such as market orientation?
x) Role of firm culture: One course for future researchers could be to examine the
role of firm culture in accepting subjective or objective measures as valid indicators of
performance. This is important because it could also explain why resources are
misdirected because of inaccurate perceptions and their reliability as valid measurement
tools.
xi) Using department-level specific metrics: In the current study, we used
corporate-level objective indicators. Using department-level specific metrics instead
could resonate better with managers' understanding of how much profit their departments
produce. Indeed, there may not be much difference between objective and subjective
measurement indicators at the same level throughout the organization.
Closing Word
This dissertation has examined the role of strategic supplier responsiveness in interorganizational
risk reduction, its influence on satisfaction, supplier brand value, buyer
firm value, cross-cultural comparisons, and the difference in evaluations based on
subjective and performance indicators. The true value of this research lies in its
fundamental challenging of the link between supplier responsiveness and organizational
risk. To a degree, we have provided evidence that this link is negative and not positive as
assumed by many prior studies. However, further research will confirm whether or not
our findings can be applied in a wide variety of contexts.
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Samenvatting
Dit proefschrift richt zich op marktgerichtheid in de strategische leverancier en koper
relatie context en verder vestigt het de aandacht op de concepten van idiosyncratisch
relatie risico (IdRR), klanttevredenheid en leverancier merkwaarde als mediatoren van
die relatie. Bovendien gaat dit proefschrift uit van een inter-organisatorisch perspectief, in
tegenstelling tot de intra-organisatorische perspectieven in eerdere studies (Kohli &
Jaworski, 1990; Narver & Slater, 1990; Slater & Narver, 1995). In de volgende
paragrafen zullen we de belangrijkste definities , de hiaten in de literatuur, de
onderzoeksvragen, een overzicht van elk hoofdstuk, theoretische implicaties, praktische
implicaties, richtingen voor toekomstig onderzoek en een afsluitende woord bespreken.
De belangrijkste concepten in dit onderzoek zijn leverancier responsiviteit,
klanttevredenheid, IdRR, leverancier merkwaarde, koper vaste waarde, en subjectieve en
objectieve prestatie-indicatoren. De term leverancier responsiviteit wordt gedefinieerd als
de cumulatieve leverancier respons die wordt gecreëerd door interne leverancier stevige
intelligentie ontwikkeling en verspreidingsprocessen om de behoeften van de koper
tevreden te stellen. Het concept van klanttevredenheid wordt gedefinieerd als de
cumulatieve economische en niet-economische tevredenheid van de koper met zijn
strategische leverancier. De term IdRR wordt gedefinieerd als het risico dat bestaat als
gevolg van het aangaan van een specifieke relatie met een ander bedrijf. Het concept van
de leverancier merkwaarde wordt gedefinieerd vanuit het perspectief van een strategische
aankoop centrumteam, en wordt gedefinieerd als de gedeelde collectieve herinneringen
aan het merk van de leverancier, die het kracht geven als gevolg van voorkeur van de
klant dat het anders niet zouden hebben zonder het merk. De term koper bedrijfswaarde
wordt gedefinieerd als groei en stabiliteit in koper kasstromen. Het concept van
subjectieve prestatiemaatstaven wordt gedefinieerd als heuristisch individuele evaluaties
van de prestaties van een bedrijf en het concept van objectieve prestatie-indicatoren
wordt gedefinieerd als die maatregel die wordt berekend met behulp van
gestandaardiseerde boekhoudkundige en financiële procedures.
Tot nu toe is in de bestaande literatuur een algemene oriëntatie steeds gericht geweest op
de intra-organisatorische marktoriëntatie-bemiddelaars-prestatierelatie (Kohli &
Jaworski, 1990; Narver & Slater, 1990; Slater & Narver, 1995). Bijvoorbeeld, Kohli en
Jaworski (1990) theoretiseren over de impact van drie componenten van marktgerichtheid
op de bedrijfsprestaties. Overwegende dat, Jaworski en Kohli (1993) empirisch de
invloed van een markt oriëntatie op bedrijfsprestaties onderzoeken. Bovendien
onderzoeken Narver en Slater (1990) de invloed van marktoriëntatie op de
bedrijfsprestaties in zowel grondstoffen-en non-commodity bedrijven. Daarenboven
onderzoeken Grewal en Tansuhaj (2001) de invloed van marktoriëntatie en strategische
flexibiliteit op bedrijfsprestatie na een economische crisis. Slater en Narver (1995) stellen
voor dat de marktgerichtheid in combinatie met een ondernemende drijfveer en het juiste
organisatieklimaat de lerende organisatie produceert. Verder, onderzoeken Voss en Voss
(2000) de rol van de marktgerichtheid en productoriëntatie op bedrijfsprestaties. Als
alternatief onderzoeken Zhou, Yim, en Tse (2005) de invloed van marktoriëntatie op
bedrijfsprestaties via de bemiddelaars van technologische en marktinnovaties.
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Overwegende dat Verhoef en Leeflang (2009) onderzoeken of marketing verantwoording
de motivatie voor bedrijven om marktgericht te denken verhoogt. Ter samenvatting, de
bovenstaande studies over marktgerichtheid onderzoeken de intra-organisatorische
impact van marktgerichtheid op bedrijfsprestaties. Weinig studies zoals Kohli en
Jaworski (1990) en Jaworski en Kohli (1993) onderzoeken de impact van de risicoaversie
op marktgerichtheid. Maar wat niet besproken wordt, is de interorganisatorische
impact van de leverancier marktoriëntatie op relationele risico's en koper
bedrijfsprestaties. Dit is een belangrijke fout aangezien één van de motieven voor het
selecteren van een leveranciermerk het vermogen is om het risico van de koper in een
business to business context te verminderen Kotler en Pfoertsch (2006).Noch hebben de
eerdere studies klanttevredenheid als bemiddelaar in de inter-organisatorische
leveranciermarktgerichtheid en koper bedrijfswaarde setting expliciet gemodelleerd. Dit
is belangrijk omdat veel eerdere studies en marketingteksten klanttevredenheid
vermelden als een gewenste uitkomst van marktgerichtheid. Bovendien werd naar onze
kennis klanttevredenheid in de meeste studies nog niet empirisch getoetst vanuit een
inter-organisatorisch perspectief. Noch hebben eerdere studies de bemiddelende rol dat
het merk van een leverancier speelt in de leverancier marktoriëntatie en koper
bedrijfswaarde besproken. Dit is een belangrijke fout aangezien een marketing filosofie
of marktoriëntatie zinloos is, tenzij het een aantal op de markt gebaseerde activa creëert ,
zoals merkwaarde om de waarde die voor de leverancier gecreëerd werd te bewaren.
Bovendien moet de leverancier marktgerichtheid voordelen opleveren voor de koper
zoals verbeterde kasstromen zodat de relatie blijft voortduren. Daarom op mondiaal
niveau is de kern van onze vraagstelling voor dit proefschrift:
"Wat is de impact van de leverancier marktoriëntatie via idiosyncratisch
relationeel risico, klanttevredenheid, en leverancier merkwaarde op koper
bedrijfsprestaties?"
Hoofdstuk 1 geeft een inleiding tot de onderzoeksthema's van van leverancier
marktoriëntatie en presenteert ook de motivatie voor dit proefschrift. Verder worden in
dit hoofdstuk de onderzoeksvragen gecreëerd, onderzoeksmethoden, een overzicht van de
resterende hoofdstukken en het onderzoek van de beperkingen besproken.
Hoofdstuk 2 van dit proefschrift onderzoekt de invloed van de leverancier
responsiviteit op de bedrijfsprestaties op basis van een steekproef van 164 business-tobusiness
(B2B) bedrijven in de Verenigde Staten. In deze studie wordt benadrukt dat
leverancier responsiviteit een dyadische waarde creëert door het verminderen van het
idiosyncratische relationeel risico (IdRR). IdRR is een risico dat bestaat als gevolg van
het aangaan van een specifieke relatie met een ander bedrijf. IdRR reductie is het
mechanisme waarmee het markt gebaseerde vermogen wordt gemaakt voor de
leverancier. Aan de andere kant, hebben zachte indicatoren, zoals de tevredenheid
aangetoond dat er geen link is met dyadische waarde of koper bedrijfswinst alleen. Deze
studie daagt de lang gekoesterde veronderstelling dat marktgerichtheid en risico positief
gerelateerd zijn uit en, althans in strategische B2B context, vinden we bewijzen van het
tegendeel.
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Hoofdstuk 3 gaat in op de verschillen tussen de toepassing van het model gepresenteerd
in de Hoofdstuk 2 in verschillende culturele contexten. De steekproef omvat 92 bedrijven
uit Nederland en de oorspronkelijke 164 uit de VS. De kern nadruk van deze studie was
het bepalen van de vraag of de bemiddelaars van de strategische leverancier responsiviteit
en koper bedrijfsprestaties hetzelfde waren over de nationale culturele grenzen. Met de
Hofstede index als drijvende logica achter de vergelijking van de twee monsters, toonde
ons model significante verschillen. In de Verenigde Staten, hebben zachte indicatoren
geen link naar koper bedrijfsprestaties naar voren gebracht, terwijl ze dat wel deden in
Nederland. De gevolgen voor het management zijn dat, bij het ontwikkelen van een
marketingstrategie, ze periscopische perspectieven moeten aannemen die verder reiken
dan hun eigen cultureel gebied.
Hoofdstuk 4 is geïnspireerd door de noodzaak om te onderzoeken of de verschillende
prestatie-indicatoren elkaar zouden versterken in het model beschreven in hoofdstuk
2.Managers vertrouwen gewoonlijk op hun heuristische oordelen. De bestaande literatuur
over de nauwkeurigheid van dergelijke uitspraken is gemengd. Dergelijke oordelen zijn
vaak gevormd als gevolg van cumulatieve achtergrondinformatie, zowel objectieve als
subjectieve. We verwachten dat, aangezien strategische relaties leiden tot een grote mate
van samenwerking, een manager beoordeling zou worden afgestemd op objectieve
prestatie-indicatoren. We vonden echter dat subjectieve en objectieve
prestatiemaatregelen leveren geen vergelijkbare resultaten.
Hoofdstuk 5 is het laatste hoofdstuk van dit proefschrift en begint met het presenteren
van een korte samenvatting van de drie studies in het proefschrift. De volgende paragraaf
is een bespreking van de algemene conclusies uit dit onderzoek. En vervolgens worden
de theoretische implicaties van de studies besproken, en het hoofdstuk eindigt met een
slotbeschouwing.
Theoretische bijdragen
In hoofdstuk 2 werpen we een nieuw licht op de marktgerichtheid. de risicorelatie en het
opbouwen van een omvattend model dat de relatie tussen leverancier responsiviteit, IdRR
formuleert, en de koper bedrijfswaarde.Onze bevindingen betwisten het status quo
geloof dat marktgerichtheid een inherente riskante onderneming is door het verstrekken
van het bewijs dat leverancier responsiviteit IdRR negatief beïnvloedt. Bovendien dragen
we bij aan het marketing domein door de uitbreiding van de werkingssfeer van de huidige
marktoriëntatie modellen, die zich tot nu toe vooral hebben gericht op een intraorganisatorisch
perspectief. Bovendien heeft de bestaande literatuur het vraagstuk van de
integratie van het klantperspectief en van de klant bedrijfswaarde in marktoriëntatie en
de prestatiemodellen niet aangesproken en dragen we daarbij bij aan de literatuur door het
opnemen van deze perspectieven.
Ten tweede, we introduceren IdRR als een nieuwe constructie in de literatuur en
een belangrijke mediator van de leverancier responsiviteit en koper bedrijfswaarde. Met
behulp van IdRR, zullen academici en het bedrijfsleven in staat zijn om in te schatten of
de marktgerichtheid het relationele risico tussen een leverancier en afnemer verhoogt of
verlaagt. Bovendien, op een moment dat er een verhoogde behoefte aan verantwoording
nodig is binnen bedrijven, stelt IdRR als een maatregel beheerders in staat om een deel
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van de waarde te communiceren dat de relatie heeft opgebouwd als gevolg van
marktgerichtheid. Vandaar dat, door gebruik te maken van IdRR om de voordelen van
marktoriëntatie uit te leggen, het rendement op de investering van marktgerichtheid
verhelderd wordt.
Ten derde, leveren we het bewijs dat marktgerichtheid een eengemaakt algemeen
construct is en geen drie afzonderlijke algemene factoren. Het marktgerichtheid construct
ontbrak validiteit in belangrijke studies, maar academici modelleren het nog steeds
veeleer als subcomponenten dan een algemene factor (Kohli et al., 1993;. Narver en
Slater, 1990). In de huidige studie hebben we het herzien als een algemene factor en
hebben we geconstateerd dat marktgerichtheid of leverancierresponsiviteit een enkele
factor in plaats van drie aparte componenten is.
Ten vierde zien we dat klanttevredenheid niet gerelateerd is aan een koper bedrijfswaarde
in de VS. In de bestaande literatuur, hebben academici tot nu toe zich gericht op de
klanttevredenheid en leverancier bedrijfsprestatierelatie (Gyskens et al.., 1999). Ondanks
deze focus, hebben onderzoekers onvoldoende bewijs gevonden van een koper
tevredenheid en prestaties en leverancierprestatierelatie (Gyskens et al.., 1999). Hoewel
de klanttevredenheid en de leverancierprestatierelatie belangrijk is, is de vraag of
klanttevredenheid een belangrijke indicator is nog belangrijker. Zoals de tevredenheid in
kanaal contexten een economisch aspect heeft, dient het te worden gerelateerd aan een
koper bedrijfswaarde om een zinvolle indicator te zijn. Indien de koper tevreden is met de
economische waarde die gegeven wordt door de strategische leverancier, zou het op zijn
beurt de koper cash flow, en daarom, de koper bedrijfswaarde moeten verhogen.
Ten vijfde, ons model levert voorlopig bewijs dat IdRR reductie een betrouwbare
indicator is die een link tussen klant bedrijfswaarde en leverancier merkwaarde legt. Voor
managers, biedt deze bevinding een manier van denken. Het kan echter niet mogelijk of
wenselijk zijn om zich uitsluitend te richten op die activiteiten die IdRR verminderen
omdat sommige marktgerichtheid activiteiten zowel IdRR en klanttevredenheid kunnen
beïnvloeden, maar een bedrijf moet meer vertrouwen op IdRR maatregelen dan
klanttevredenheid maatregelen.
Hoofdstuk 3 bouwt voort op het onderzoek dat in hoofdstuk 2 wordt
gepresenteerd doorheen de twee landen. We hebben bevonden dat de belangrijkste
hypothese en theoretische bijdrage van de leverancier responsiviteit-idiosyncratische riskperformance
relatie geldt voor alle landen. We zien echter dat er een verschil is in zachte
relationele indicatoren van klanttevredenheid. We leggen deze verschillen uit in het licht
van de Hofstede-index, wat suggereert dat de nationale cultuur de zachte indicatoren
prestatierelatie kan beïnvloeden. Kortom, de huidige paper valideert de belangrijkste
bevindingen van het hoofdstuk 2 model door aan te tonen dat IdRR belangrijk is over alle
landen.
Hoofdstuk 4 is naar onze wetenschap de eerste studie om de kwestie van de objectieve
versus subjectieve prestatiemaatstaven te onderzoeken in het kader van strategische
leverancier responsiviteit of marktoriëntatie en de koper IdRR. In hoofdstuk 4 maken we
drie theoretische bijdragen.
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Ten eerste, leveren we het bewijs dat de aard van de gegevens die worden gebruikt voor
de leverancier responsiviteit-idiosyncratische risico-performance-analyse de sterkte en de
richting van de relaties in het Hoofdstuk 2 model veranderen. We waren in staat om aan
te tonen dat er sprake is van een discrepantie tussen de belangrijkste indicatoren als
subjectieve en objectieve gegevens worden gebruikt. Wij geloven dat dit komt door een
gebrek aan metrische substitueerbaarheid in plaats van een probleem met het model zelf.
Ten tweede verklaren we waarom managerevaluaties niet in lijn kunnen zijn met de
objectieve gegevens indicatoren. Bijvoorbeeld omdat objectieve gegevens zijn berekend
op een macro-organisatorisch niveau, kunnen zij niet de prestaties van de verschillende
functies op verschillende niveaus binnen de organisatie reflecteren. Aan de andere kant
kunnen de heuristische mogelijkheden van een manager licht werpen op latente gebieden
van de prestaties die de traditionele objectieve prestatie-indicatoren niet kunnen
aangeven.
Ten derde, leggen we uit dat financiële managers vaak de waarde van marketing
initiatieven niet inzien, omdat ze de immateriële waarde die dergelijke initiatieven
creëren niet begrijpen.
Praktische implicaties
Vanuit een bestuurlijk perspectief, proberen we inzicht te bieden in de wijze waarop de
investeringen in marktoriëntatie de IdRR beïnvloeden. Als we kijken naar de financiële
gevolgen, suggereren onze resultaten dat een toename van de marktgerichtheid met een
standaarddeviatie de IdRR vermindert met 0,98 standaarddeviaties. Met andere woorden
betekent dit dat een toename in marketingfocus het idiosyncratische relationele risico
vermindert met 0,98 standaardafwijkingen. De vermindering van het idiosyncratische
relationele risico kan worden gebruikt door leveranciers als rechtvaardiging voor het
vragen van hogere prijzen. Door aan de klant aan de voordelen door kostenbesparing aan
te tonen. In deze moeilijke tijden van recessie, wanneer verkoop moeilijk is, is
kostenbesparing een voorkeursalternatief op het vinden van nieuwe klanten. Bovendien
zou het ook het doel van interne verantwoording aan de financiële afdeling kunnen
dienen om te laten zien hoe marketing investering rendement levert en hogere marges.
Marktgerichtheid is een proces van continue innovatie (Kohli & Jaworski,
1990).Meer risico is meestal betrokken in het geval van commercialiseringsinnovaties. In
termen van risico in het introduceren van innovaties, neem het risico toe naarmate de
mate van nieuwheid van de innovatie toeneemt (Chandy & Tellis, 2000).De lancering van
zeer nieuwe wereldlijke innovaties zijn een hoog risico en streven naar een hoog
rendement. Echter, de kosten met betrekking tot de strategische leverancier kan de kost
tot en met 18% van de lancering van de innovatie verminderen (Siemens, 2013). Ons
onderzoek geeft aan dat als de reactie van een unieke leverancier gebaseerd is op het
begrip van een specifieke klant die zeer betrokken is bij de klant zij het in de productie
van een innovatie of op enigzins andere wijze, leidt dit tot een vermindering van het
idiosyncratische relationele risico wat resulteert in een versnelde en stabiele kasstroom
voor de koper . Vandaar dat, vanuit een strategie perspectief wij voorstellen dat
marketing managers zich richten op de besteding van marketingbudgetten op die
strategische activiteiten waar ze eventueel het IdRR kunnen verminderen Echter, we
132

erkennen dat dit niet altijd een mogelijkheid is, maar de leverancier moet zijn uiterste best
proberen om de IdRR zo klein mogelijk te houden.
Een andere praktische bijdrage is het verschil in de rol van tevredenheid
strategische koper leveranciersrelaties in Nederland en de VS Uit onze gegevens blijkt
dat klanttevredenheid wel een relatie heeft met de eigen winst van de koper. Echter, in
Nederland is dit wel het geval. Onze bevindingen werden bevestigd door een informele
bevestiging met een senior manager bij de HMG-groep (een strategische leverancier van
zitzakken aan Leenbakker en Hema).Onze bevindingen zijn van belang, omdat veel
managers in Nederland tevredenheid als een belangrijke maatstaf bij de beoordeling van
hun relaties beschouwen. Maar wanneer ze hun activiteiten willen uitbreiden naar de
Verenigde Staten of omgekeerd moeten ze meer harde kern metrics ontwikkelen,
waarvan wij er één in dit proefschrift hebben uiteengezet, namelijk IdRR .
Daarnaast onderzoekt dit proefschrift perceptuele en objectieve of op
boekhoudkundige gebaseerde prestatie-maatregels. We hebben bevonden dat alle
perceptuele maatregelen niet kunnen worden bevestigd door boekhoudkundige
maatregelen. De praktische implicatie van de bevinding van dit onderzoek is dat
bedrijven zich moeten richten op het overbruggen van de kloof tussen perceptuele en
boekhoudkundige maatregelen. Bedrijven kunnen maatregelen nemen die waarschijnlijk
vertaalbaar zijn in financiële maatregelen of nemen een dashboard strategie aan die
financiële managers de waarde laten zien die marketing kan creëren.
Toekomstige onderzoeksrichtingen
Hoewel we hebben geprobeerd in dit proefschrift de onderzoeksvragen zo grondig
mogelijk aan te pakken, liggen er nog steeds veel mogelijke toekomstige
onderzoeksrichtingen open voor toekomstige onderzoekers. Hieronder hebben we enkele
interessante onderzoekmogelijkheden samengevat en gestructureerd voor toekomstige
onderzoekers:
i) Longitudinaal onderzoeksontwerp. Onze cross-sectionele studie liet ons toe om
associaties te maken, maar niet om enige conclusies over causaliteit te maken. Ter
verbetering van dit ontwerp, kan verzamelde gegevens voor een tijdreeks analyse
mogelijk te maken van de aftrek van bewijs met betrekking tot causaliteit. Toch is er
sprake van recent bewijs dat gevolgtrekkingen uit cross-sectionele studies over causaliteit
niet veel verschillen van conclusies over longitudinale studies (Rindfleisch et al., 2008).
ii) Beperkte steekproef van bedrijven voor diepte. We hebben diepte voor breedte
afgewogen binnen onze industrie sectoren. Echter specifieke sectoren variëren meer met
leverancier responsiviteit dan anderen. Bijvoorbeeld, in de grondstoffen-industrie, kan de
prijs een bepalende factor zijn, terwijl in de technische goederen industriële sector het
antwoord van de leverancier een belangrijke bepalende factor kan zijn in de vraag of
relaties voort duren. Toekomstige studies zouden zich kunnen concentreren op een klein
aantal sectoren en de sterkte van onze bevindingen nagaan over verschillende moderators,
zoals marktturbulentie en verhoogd technologisch risico.
iii) Quasi-experimenteel ontwerp met behulp van financiële en perceptuele gegevens in
tandem. Eerdere studies van marktgerichtheid hebben conflicten tussen objectieve en
subjectieve maatstaven voor de prestaties gevonden (Narver & Slater, 1990).Om te
133

epalen of onze bevindingen daadwerkelijk echte financiële marktsentimenten en
boekhoudkundige gegevens weergeven, kunnen toekomstige studies gebruik maken van
financiële gegevens in combinatie met perceptuele gegevens.
iv) Bredere vergelijking van de meting metrics. In de huidige studie hebben we alleen
gebruik gemaakt van het idiosyncratisch risico, wat op zich een belangrijke indicator van
de potentiële rendementen die betrokken bij elke relatie kan zijn. Echter, het risico is niet
de enige metriek in de literatuur. Toekomstige studies kunnen onder meer een breder
scala aan statistieken onderzoeken die betere indicatoren zijn dan anderen en waarom. Zo
kan het onderzoek van het idiosyncratische risico in vergelijking met inventaris omzet
statistieken of schuld leverage metrics een interessante invalshoek zijn.
v) Vergelijking van meer strategieën met leverancier responsiviteit voor het verminderen
van idiosyncratische risico. We onderzoeken slechts één strategie namelijk die van
marktgerichtheid als middel om idiosyncratisch risico te verminderen. Echter, er zijn
andere strategieën en oriëntaties, zoals een technologische oriëntatie of een
marktleiderschap strategie. De wijze waarop deze strategieën en oriëntaties strategische
leverancier-afnemer relaties beïnvloeden en hun idiosyncratisch risico uitkomsten zouden
van potentieel belang kunnen zijn.
vi) Het gebruik van onderzoeksmethoden anders dan enquêtes. De onderzoeksopzet kan
worden verbeterd met name met verwijzing naar hoofdstuk 3. Sommige onderzoekers
hebben aangegeven dat etnografieën moeten gebruikt worden bij onderzoekskwesties die
verband houden met cultuur (Langerak, 2001).Het voordeel in hun opinie is dat
organisatiecultuur beter kan worden gedocumenteerd door langdurige
observatietechnieken, zoals etnografieën. Daarom zouden toekomstige onderzoekers
gebruik kunnen maken van een etnografie voor een beter begrip van de leverancier
responsiviteit-koper bedrijfsprestaties relatie.
vii) Vergelijking van meer diverse landen. In hoofdstuk 3 hoewel de VS en Nederland
verschillend zijn in sommige opzichten,delen ze ook overeenkomsten. Beide zijn
geïndustrialiseerde landen, ze hebben levendige democratieën, kapitalistische
economieën, een zekere mate van soortgelijke westerse cultuur, en beide landen zijn
internationaal concurrerende economieën ( Deshpande & Webster, 1989 ).Sommige
culturele eigenaardigheden doen zich voor, zoals de aanvaarding van opscheppen over
sollicitatiegesprekken in de Verenigde Staten en de voorkeur voor bescheidenheid in
sollicitatiegesprekken in Nederland, maar toch, kunnen ze beide worden beschouwd als
westerse culturen. Echter, een vergelijking met een Aziatische cultuur is een meer diverse
vergelijking. China is een groeiende wereldmacht, het is een communistische natie, en het
heeft een collectivistische cultuur en een confucianistisch waardesysteem (Hofstede,
2003).Deze culturele verschillen leiden tot verschillende risico voorkeuren bij individuele
leden van de samenleving. Bijvoorbeeld, de Chinezen hebben een groter risico voorkeur
dan de Amerikanen, omdat hun collectivistische cultuur hen beschermt tegen
verlies. Toekomstige onderzoekers kunnen onderzoeken hoe bedrijven uit samenlevingen
met extreem verschillende risico voorkeuren reageren op leverancier responsiviteit. Dit
zou ook een nieuwe dimensie kunnen toevoegen aan het probleem dat we bestudeerden
134

ij de vraag of cultureel risico voorkeur een matigende factor in de leverancier
responsiviteit en koper prestatie-verhouding is.
In sommige Euraziatische en Aziatische economieën, vormen relationele praktijken zoals
Blat en Gaunxi een integraal onderdeel van de bedrijfscultuur. Echter, in westerse
culturen kunnen dergelijke praktijken worden beschouwd als onethisch en schadelijk
voor de zakelijke belangen en reputatie. In het bijzonder bij het vormen van strategische
leveranciersrelaties met bedrijven die vinden dat dergelijke culturele praktijken
aanvaardbaar zijn, kunnen kopers een negatieve spill-over op hun merkreputatie
vrezen. Dergelijke culturele effecten zijn over het hoofd gezien in onze huidige
studie. Andere voorbeelden zijn vragen als: Variëert IdRR door de nationale cultuur? Of
moet cultuur specifieke praktijken , zoals die van Blat of Guanxi als een deel van IdRR
worden beschouwd?
ix) gebruik van zowel subjectieve als objectieve maatstaven voor internationale
vergelijkingen. Een andere beperking van onze huidige studie is dat we alleen percepties
overwegen. Versterken percepties objectieve metingen tussen culturen? Zijn sommige
culturen meer geneigd om overdreven blij te zijn als het gaat om omgeleide inkomsten uit
beleggingen? Zijn sommige culturen te pessimistisch bij het voorspellen van verliezen die
uit strategische relaties kunnen komen? Beïnvloeden culturen het relatieve belang van
bedrijfsperceptie van marketing strategieën, zoals marktoriëntatie?
x) De rol van bedrijfscultuur: Een cursus voor toekomstige onderzoekers zou kunnen zijn
om de rol van de bedrijfscultuur te onderzoeken in het aanvaarden van subjectieve of
objectieve metingen als geldige prestatie-indicatoren. Dit is belangrijk omdat het kan ook
verklaren waarom middelen fout worden besteed als gevolg van onjuiste percepties en
hun betrouwbaarheid als geldige meetinstrumenten.
xi) Het gebruik van afdeling-niveau specifieke statistieken: In de huidige studie,
gebruikten we corporate-niveau objectieve indicatoren. Het gebruik van afdeling-niveau
specifieke statistieken zou kunnen beter resoneren met managers inzicht in hoeveel winst
hun afdelingen kunnen produceren. Het kan inderdaad zo zijn dat er niet veel verschilis
tussen de objectieve en subjectieve meting indicatoren op hetzelfde niveau in de
organisatie.
Slotwoord
Dit proefschrift heeft de rol van strategische leverancier responsiviteit in interorganisatorische
risicobeperking onderzocht, de invloed ervan op de tevredenheid, koper
bedrijfswaarde, de bemiddelende rol van tevredenheid, cross-culturele vergelijkingen, en
het verschil in evaluaties op basis van subjectieve prestatie-indicatoren. De werkelijke
waarde van dit onderzoek ligt in de fundamentele uitdaging van het verband tussen
leverancier responsiviteit en organisatorische risico's. Tot op zekere hoogte hebben we
aangetoond dat deze link negatief en niet positief aangenomen wordt door vele eerdere
studies. Echter, verder onderzoek zal bevestigen dat onze bevindingen kunnen worden
toegepast in een grote verscheidenheid van contexten.
135

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145

Appendix A
Appendix A provides an overview of all questions that were used in the final analyses in
this thesis. The questions relate to the five variables in this thesis. The questions are
provided beneath the name of the variable they are measuring. The scale for each
question has been provided. The original questionnaire collected data on three strategic
suppliers, but in this dissertation only data on the most important strategic supplier has
been used (strategic supplier A). For an unabridged version of the questionnaire, please
email the author on abrahimx@yahoo.com.
General Directions
Directions
Our main aim is to uncover how the value of your firm can be influenced by the actions
of your strategic suppliers.
The following information is important when answering this questionnaire:
• Please answer all questions. You cannot proceed to the next page if you do not answer
all the questions.
• For the sake of the scientific validity of our findings, we ask you to give an honest and
realistic evaluation of your situation.
• All data are treated confidentially. The strictest anonymity is constantly guaranteed.
• A management summary of the research report will be sent to you once it has been
compiled.
Professor Dr. Henry Robben
Nyenrode Business Universiteit
h.robben@nyenrode.nl
Mr. Abrahim Zaka
Nyenrode Business Universiteit
a.zakakhan@nyenrode.nl
146

1. The name of the company where you currently work:
Parent company
Strategic business
unit
2. What percentage of total profit of your company does your strategic business unit
represent inside the group of the parent company? Please state the percentage as a whole
number between1 and 100.
3. We sell the products or services of this line-of-business to (please check one box):
Industrial markets (other companies or organizations)
Consumer markets (individual end customers and households)
Both
4. This product line is oriented towards (please check one box):
Services
Goods
A combination of both
5. What type of responsibility center is your company?
Revenue center. Company unit is assessed on sales performance.
Expense center. Company unit is assessed on the basis of its costs.
Profit center. Company unit is assessed on the basis of profits.
Investment center. Company unit is assessed on return on investment.
147

6. Please state the names of the strategic suppliers of your firm. Please remember the
symbol (A, B and C) assigned to each separate supplier. A should be your strongest
strategic supplier, B should be your average strategic supplier, and C should be your
weakest strategic supplier. IMPORTANT! PLEASE STATE THE NAME OF SPECIFIC
COMPANIES. For example write Delta airlines and do not write all North American
airlines.
A) Strongest
strategic supplier.
B) Average
strategic supplier.
C) Weakest
strategic supplier
Supplier Responsiveness
7. The supplier meets with us at least once a year to find out what products and
services we will need in the future.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
8. Individuals from the suppliers’ manufacturing department interact directly with us to
inquire as to how they can improve their services or products.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
148

9.The supplier seems to have good internal market intelligence generation capabilities.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
10. The supplier regularly contacts us to assess the quality of their products and services.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
11. This supplier has high quality information regarding their channel partners (e.g.,
retailers, distributors and us).
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
12. The supplier often interacts with us in informal meetings (e.g., lunch with our
managers) to collect information about our business.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
149

13. A lot of the informal conversations with our suppliers concern the market conditions
and our competitors’ strategies.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
14. The supplier is slow to detect fundamental shifts in our industry (e.g., competition,
technology, regulation).
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
15. The supplier provides us with periodic market intelligence (e.g. newsletters and
reports) with content of interest to many cross-functional departments within our firm.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
16. We simply need to communicate our needs to one of the various functional
departments (e.g., marketing, manufacturing, operations, finance) and everyone
concerned (cross functionally) within the supplier’s firm will be immediately updated on
our new situation.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
150

17. The supplier takes a very long time to respond to changes in their competitors’
pricing strategies.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
18. For one reason or another, the supplier tends to ignore changes in your product or
service needs.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
19. The supplier seems to be driven by the customers (your) perspective in new product
development efforts.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
20. The supplier's business plans seem to be driven more by technological advances than
by market research.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
151

21. The supplier's response to us is often not aligned with our needs because of internal
supplier firm politics.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
22. If the supplier's major competitor were to launch an intensive campaign targeted at
you, then the supplier would normally implement a counter campaign directed towards
you.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
23.The suppliers do not heed your complaints.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
24. The supplier sometimes has excellent marketing plans, but they seem to be
implemented too slowly or too late.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
152

25. If the supplier were to find out that you are unhappy with the quality of their service,
they would immediately take corrective action.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
26. If the suppliers find out that you would like them to modify a product or service, they
would make concerted efforts to do so.
Extremely
agree
Agree Neutral Disagree Extremely
disagree
Scale: Extremely agree =1, Agree=2, Neutral=3, Disagree=4, Extremely
disagree=5
IdRR
27.To what extent does your strategic supplier help you reduce your factory or
manufacturing costs?
Highly
reduces
Reduces Neutral Increases Highly
Increases
Scale: Highly reduces =5, Reduces=4, Neutral=3, Increases=2, Highly Increases=1
28.To what extent does your strategic supplier help you increase your
product or service quality?
Highly
increases
quality
Increases
quality
Neutral
Reduces
quality
Highly
reduces
quality
Scale: Highly reduces =5, Reduces=4, Neutral=3, Increases=2, Highly Increases=1
153

29.In the past, has your strategic supplier been willing to invest in technology that would
improve your supply chain cost efficiency?
Highly
willing
Willing Neutral Unwilling Highly
unwilling
Scale: Highly willing =5, Willing=4, Neutral=3, Unwilling=2, Highly unwilling=1
30.In the past, has your strategic supplier been willing to invest in technology that would
improve your research and development and engineering cost efficiency?
Highly
willing
Willing Neutral Unwilling Highly
unwilling
Scale: Highly willing =5, Willing=4, Neutral=3, Unwilling=2, Highly unwilling=1
31.To what extent does you strategic supplier influence your channel or distribution
costs?
Highly
reduces
Reduces Neutral Increases Highly
increases
Scale: Highly reduces =5, Reduces=4, Neutral=3, Increases=2, Highly increases=1
32.Does the strategic supplier help you to reduce your sales cost?
Highly
reduces
Reduces Neutral Increases Highly
increases
Scale: Highly reduces =5, Reduces=4, Neutral=3, Increases=2, Highly increases=1
154

33.Does your strategic supplier also provide you with intelligence on the markets in
which you operate?
Always
provides
Sometime
provides
Neutral
Sometimes
does not
provide
Often does
not provide
Scale: Highly reduces =5, Reduces=4, Neutral=3, Increases=2, Highly increases=1
34. To what extent does your strategic supplier influence your inventory management?
Highly
positively
Positively Neutral Negatively Highly
negatively
Scale: Highly positively =5, Positively=4, Neutral=3, Negatively=2, Highly
negatively=1
35.Does your strategic supplier offer you flexible payment schedules?
Highly
flexible
Flexible Neutral Inflexible Highly
inflexible
Scale: Highly flexible =5, Flexible=4, Neutral=3, Inflexible=2, Highly
inflexible=1
36.To what extent does your supplier help you manage your inventory?
Highly
helps
Helps Neutral Problematic Highly
problematic
Scale: Highly helps =5, Helps=4, Neutral=3, Problematic=2, Highly
problematic=1
155

37.To what degree does your strategic supplier influence your cash conversion cycle?
A definitional clarification: The cash conversion cycle is the time duration in which a
firm is able to convert its resources into cash. It is actually the total time period required
to first convert resources into inventories, then inventories into finished goods, then
goods into sales, and then sales into cash.
Highly
positively
Positively Neutral Negatively Highly
negatively
Scale: Highly positively =5, Positively=4, Neutral=3, Negatively=2, Highly
negatively=1
38.To what extent have your strategic suppliers tried to simplify your
supply chain processes?
Highly
simplified
Simplified Neutral Increased
complexity
Highly
negatively
Scale: Highly simplified =5, Simplified=4, Neutral=3, Increased complexity=2, Highly
negatively=1
39. To what extent does using your strategic supplier influence your ability to minimize
the time needed for your firm to provide solutions for your customers?
Highly
reduces
Reduces Neutral Increases Highly
increases
Scale: Highly reduces =5, Reduces=4, Neutral=3, Increases=2, Highly increases=1
40. To what extent do your strategic suppliers correctly invoice your company?
Always
correctly
Moderately
correctly
Neutral
Moderately
incorrectly
Always
incorrectly
Scale: Highly Correctly =5, Moderately correctly =4, Neutral=3, Moderately
incorrectly =2, Always incorrectly=1
156

41.To what degree do those specific investments make it difficult for the strategic
supplier to end their relationship with you?
Very
difficult
Moderately
Difficult
Neutral
Moderately
easy
Highly
easy
Scale: Very difficult =5, Moderately difficult=4, Neutral=3, Moderately easy=2, Highly
easy=1
Buyer Satisfaction
42. How satisfied are you with the profits generated from carrying the supplier’s
products lines, if any?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
43. How satisfied are you with the overall manner with which the strategic
supplier’s representatives (regional or head office) treat you?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
44. How satisfied are you with the sales support provided by the manufacturer's
local sales representative?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
45. How satisfied are you with the new product market opportunities the
supplier provides you?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
157

46.How satisfied are you with the sales growth potential from carrying the supplier’s
products lines, if any?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
47.How satisfied are you with this supplier’s overall fairness and honesty
while dealing with you?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
48.How satisfied are you with the interest and concern that this supplier has in
helping you meet your objectives?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
49. What is your overall satisfaction with this supplier?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
50.How satisfied are you with the sales growth potential from carrying the supplier’s
product lines, if any?
Highly
satisfied
Satisfied Neutral Dissatisfied Highly
dissatisfied
Scale: Highly Satisfied =1, Satisfied=2, Neutral=3, Satisfied=4, Dissatisfied=5
158

Supplier Brand Value
51.How likely are you to repurchase from these suppliers?
Extremely
likely
Likely Neutral Unlikely Extremely
unlikely
Scale: Extremely likely =1, Likely=2, Neutral=3, Unlikely=4, Extremely
unlikely=5
52.How likely are you to tolerate a price increase by your strategic supplier?
Extremely
tolerant
Tolerant Neutral Intolerant Extremely
intolerant
Scale: Extremely tolerant =1, Tolerant=2, Neutral=3, Intolerant=4, Extremely
intolerant=5
53. To what extend does being associated with the strategic supplier’s brand give you
leverage with your customers?
Highly
positive
Positive Neutral Negative Highly
negative
Scale: Highly positive =1, Positive =2, Neutral=3, Negative =4, Highly negative=5
Buyer Firm Value
54.To what extent will your strategic supplier influence your future cash flow growth?
Extremely
positive
influence
Positive
influence
Neutral
Negative
influence
Extremely
negative
influence
Scale: Extremely positive influence =1, Positive influence=2, Neutral=3, Negative
influence=4, Extremely negative influence=5
159

55. To what extent do you think your strategic supplier will influence the stability of your
future cash flows?
Extremely
positive
influence
Positive
influence
Neutral
Negative
influence
Extremely
negative
influence
Scale: Extremely positive influence =1, Positive influence=2, Neutral=3, Negative
influence=4, Extremely negative influence=5
160

Appendix B
Scatter Plot Diagrams for Chapter 2:
161

162

163

SKEWNESS AND KURTOSIS CHAPTER 2
N Min Max Mean Std.Dev Skewness Kurtosis
Ratio
Ratio Statistic Std.
Error
Statistic Statistic Statistic Statistic Statistic Statistic Std.
Error
SIQ 164.00 1.00 5.00 1.95 0.84 0.78 0.19 4.11 0.55 0.38 1.46
CUSTNPD 164.00 1.00 4.00 2.07 0.87 0.76 0.19 4.03 0.14 0.38 0.37
CF 164.00 1.00 5.00 2.10 0.77 0.55 0.19 2.89 0.66 0.38 1.76
RP 164.00 1.00 5.00 1.76 0.79 0.98 0.19 5.17 1.16 0.38 3.08
Fairness 164.00 1.00 5.00 1.95 0.79 0.70 0.19 3.67 0.70 0.38 1.86
Sales 164.00 1.00 5.00 2.06 0.81 0.51 0.19 2.72 0.27 0.38 0.72
IM 164.00 1.00 5.00 3.72 0.87 -0.39 0.19 -2.04 0.37 0.38 0.99
CCC 164.00 1.00 5.00 3.71 0.77 -0.04 0.19 -0.22 0.01 0.38 0.02
AssBrand 164.00 1.00 4.00 2.10 0.83 0.21 0.19 1.08 -0.75 0.38 -2.00
BFV 164.00 1.00 4.00 2.17 0.71 0.08 0.19 0.42 -0.36 0.38 -0.96
SIQ.Sqr 164.00 1.00 2.24 1.36 0.30 0.32 0.19 1.70 -0.45 0.38 -1.19
CUST.NPD.sqr 164.00 1.00 2.00 1.40 0.30 0.32 0.19 1.69 -0.37 0.38 -0.99
CF.Sqr 164.00 1.00 2.24 1.42 0.27 0.03 0.19 0.15 -0.13 0.38 -0.34
Fair.Sqr 164.00 1.00 2.24 1.36 0.28 0.23 0.19 1.21 -0.43 0.38 -1.14
Sales.Sqr 164.00 1.00 2.24 1.40 0.29 0.07 0.19 0.37 -0.53 0.38 -1.41
RP.Sqr 164.00 1.00 2.24 1.29 0.29 0.54 0.19 2.87 -0.35 0.38 -0.92
As we can see from the Table above the following items are skewed: SIQ, Cust.NPD, CF, Fair, Sales
RP. To correct for the skewness we took a square root of the above variables. After the squareroot transformation the skewness to standard error
ratio is acceptable. The new variables are labeled as follows: SIQ.Sqr, CUST.NPD.sqr, CF.Sqr, Fair.Sqr, Sales.Sqr,
RP.Sqr.
164