Contemporary Business Studies - Academy of Knowledge Process ...

ISSN 2156-7506 

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JANUARY 

VOLUME 3 

NUMBER 1 

International journal of 

Contemporary Business Studies 

New Year edition 

IN THIS ISSUE: 

An Evaluation of Network Risks in Supply Chains 

Dr. Archie Lockamy III 

Impact of Economic value added (EVA) on Share price:A study of Indian 

Private sector banks 

Prof. Ritesh Patel, Prof. Mitesh Patel 

Leverage Impact on Firms Investment Decision-A Case Study of Indian 

Pharmaceutical Companies 

Dr. Amalendu Bhunia 

Fair war: A case study on fairness cream 

Dr. Sangeeta Mohanty 

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E n r i c h K n o w l e d g e t h r o u g h Q u a l i t y R e s e a r c h




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A journal of Academy of Knowledge Process 

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VOLUME 3, NUMBER 1 

January, 2012 

Contents: 

An Evaluation of Network Risks in Supply Chains 

Dr. Archie Lockamy III………………………………………………………………………………………….……..6 

Impact of Economic value added (EVA) on Share price:A study of Indian Private 

sector banks 

Prof. Ritesh Patel, Prof. Mitesh Patel…………..…………………………………………………………………..24 

Leverage Impact on Firms Investment Decision-A Case Study of Indian 


Dr. Amalendu Bhunia………………………………………………………………………………………………..35 


Dr. Sangeeta Mohanty……………………………………………………………………………………………...46 

5 

E nCopyright r i c h K n© o w2012. l e d g e Academy t h r o u gof h Knowledge Q u a l i t y RProcess 

e s e a r c h




An Evaluation of Network Risks in 

Supply Chains 

Dr. Archie Lockamy III 

Margaret Gage Bush Professor of Business 

Professor of Operations Management 

Brock School of Business 

Samford University 

Birmingham, 

Alabama, USA 

ABSTRACT 

The purpose of this article is to introduce a methodology for evaluating network risks in 

supply chains. The methodology is proposed as an analytical tool which can be used to 

assist managers in the examination of network risk levels associated with their supply 

base. Empirical data from a major US automotive company is analyzed using Bayesian 

networks. The networks provide a methodological approach for determining a supplier’s 

external, operational and network risk probability, and the potential revenue impact a 

supplier can have on the company. The research findings are that Bayesian networks can 

be used to develop supplier risk profiles to determine the risk exposure of a company’s 

revenue stream. The supplier risk profiles can be used to determine those risk events 

which have the largest potential impact on an organization’s revenues, and the highest 

probability of occurrence. The methodology used in this research study can be adopted by 

managers to formulate supply chain risk management strategies and tactics which 

mitigate overall supply chain risks. Thus, as part of a comprehensive supplier risk 

management program, organizations along with their suppliers can use this methodology 

to develop targeted approaches to minimize the occurrence of supply chain risk events. 

Keywords: Supply Chains; Supply Networks; Supply Chain Risks; Risk Events; 

Supplier Risk Profiles; Bayesian Networks. 

1. INTRODUCTION 

Due to the effects of lessening product lifecycles, demanding customers and employees, decreasing 

acceptable response times, and increasing levels of global competition on business success, many 

organizations have formulated extended enterprises known as supply chains. These entities can be 

described as organizational networks designed to help firms achieve a competitive advantage via 

improved market responsiveness and cost reductions. According to Sawhney et al (2006), supply chains 

can also provide organizations with a means for promoting business innovation through the adoption of 

streamlined information flows, restructured business processes, and enhanced collaboration between 

network members. 

A result of an organization’s increased reliance on supply chain networks is its augmented susceptibility 

to the network risk profiles of its suppliers, as well as other risk categories associated with the supply 

6 

Copyright © 2012. Academy of Knowledge Process




chain. Supplier network risk profiles consist of risk events that can directly impact the structure of the 

supply chain. Risk events are incidents whose occurrences result in the disruption of overall supply chain 

performance. Supplier network risk events that could negatively affect supply chain performance include 

ownership changes, individual supplier strategies, and supply network agreements. Although it is often 

not possible to precisely predict the occurrence of such events, it is possible to evaluate the probability of 

their occurrence through the creation of supplier risk profiles. Therefore, it is vital that organizations have 

the ability to examine the level of network risk associated with the suppliers constituting their supply 

chains. 

1.1 Purpose 

The purpose of this article is to introduce a methodology for examining network risks in supply chains. 

The methodology uses Bayesian networks for the creation of supplier risk profiles. The networks are 

used to determine a supplier’s external, operational and network risk probability and the potential revenue 

impact a supplier can have on the organization as measured by value-at-risk (VAR). The methodology is 

proposed as an analytical tool to assist managers in the evaluation of network risk levels associated with 

their supply base. 

1.2 Organization 

The paper is organized as follows. The first section provided the motivation for and purpose of the paper. 

A discussion on supply chain management and supply chain risks is provided in Sections Two and Three 

respectively. Section Four contains an overview of the research methodology and model used in this 

study. Section Five contains the results of the research. Proposed managerial actions based upon the 

results of the study are provided in Section Six. Conclusions are offered in Section Seven. Finally, 

implications regarding study limitations and directions for future research are presented in Sections Eight 

and Nine respectively. 

2. SUPPLY CHAIN MANAGEMENT 

Christopher (1998) characterized supply chains as organizational networks connected through upstream 

and downstream processes and activities that produce value in the form of products and services delivered 

to the hands of the ultimate customer. An increasing number of firms are adopting the principles of supply 

chain management (SCM) to improve competitiveness (Singh et al, 2005; Li et al, 2006; Gunasekaran et 

al, 2008). A requirement of SCM is the management of information, material, and cash flows across 

multiple functional areas both within and among organizations (Faisal et al, 2006). Additionally, Cooper 

et al (1997) note that SCM can be viewed as a philosophy based upon the belief that each organization in 

the supply chain directly and indirectly affects the performance of all the other supply chain members as 

well as, ultimately, overall supply chain performance. A necessary condition for effective SCM is the 

alignment of functional and supply chain partner activities with firm strategies that are congruent with 

organizational structures, processes, cultures, incentives, and people (Abell, 1999). 

It is often necessary for organizations to make fundamental changes to their business focus when adopting 

the principles and philosophy associated with SCM in order to realize its potential benefits (Kopczak et 

al, 2003). These changes may include an improved ability to acquire and manage reliable demand 

information (Croxton et al, 2002); more effective management of physical goods flow through suppliers, 

manufacturers, distributors, and retailers for increased value to end customers (Jammernegg and Reiner, 

2007); and an increased emphasis on cross-functional and cross-enterprise integration (Chen and Kang, 

2007). 

SCM benefits should include enhanced customer satisfaction and value along with improved supply chain 

reactivity (Gaudenzi and Borghesi, 2006). Supply chain reactivity refers to the network’s ability to 

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compress lead times, adapt to unanticipated changes in demand, and to cope with environmental 

uncertainty in the marketplace. However, the interdependencies created among participating organizations 

via integrated supply networks make them more vulnerable to supply chain disruptions, thus increasing 

risks. 

3. SUPPLY CHAIN RISKS 

Spekman and Davis (2004) define risk as the probability of variance in an expected outcome. Therefore, it 

is possible to quantify risk because it is possible to assign probability estimates to these outcomes (Khan 

and Burnes, 2007). On the contrary, uncertainty is not quantifiable and the probabilities of the possible 

outcomes are not known (Knight, 1921). A joint evaluation of risk and uncertainty conducted by Yates 

and Stone (1992) suggests that risk implies the existence of uncertainty associated with a given outcome, 

because if the probability of an outcome is known, there is no unknown risk. Thus, uncertainty can be 

regarded as a key determinant of risk that may not be entirely eradicated, but can be mitigated through the 

deployment of risk reduction action steps (Slack and Lewis, 2001). In business situations, managers are 

expected to reduce the organization’s exposure to uncertainty through the deployment of effective risk 

management strategies. 

Internal and external uncertainties both provide sources for supply chain risks (Cucchiella and Gastaldi, 

2006). Changes in capacity availability, interruptions in information flows, and reductions in operational 

efficiencies are all possible sources of internal uncertainty. External sources of uncertainty leading to 

increased supply chain risks include the actions of competitors, price fluctuations, changes in the political 

environment, and variations in supplier quality. These sources of uncertainty can be considered ‘risk 

events’ that can lead to supply chain disruptions that inhibit performance. Thus, it is necessary for 

managers to first understand the various categories of risks along with the events and conditions that drive 

them before they attempt to devise approaches to reduce supply chain risks (Chopra and Sodhi, 2004). 

The research literature offers a variety of approaches for categorizing risks in supply chain networks. For 

example, Treleven and Schweikhart (1988) have classified supply chain risk events based upon their 

association with the following: supply chain disruptions, price fluctuations, inventory and scheduling 

changes, technology advancements, and quality issues. Kleindorfer and Wassenhove (2003) designated 

supply chain co-ordination and supply disruptions as categories of supply chain risks, and Zsidisin (2003) 

defined supply risk as the probability of an incident associated with failures in the inbound supply from 

individual suppliers or the supply market, which results in the inability of the purchasing firm to meet 

customer demand or causes threats to customer life and safety. Paulsson (2004) classified supply chain 

risks as operational disturbances, tactical disruptions, and strategic uncertainties. Giunipero and Eltantawy 

(2004) categorized these risks based upon conditions that result in their creation, such as political events, 

product availability, transportation distances, changes in technology and labor markets, financial 

instability, and management turnover. Supply chain disruptions, delays, systems, forecasts, intellectual 

property, procurement, receivables, inventory, and capacity are classifications for supply chain risks 

offered by Chopra and Sodhi (2004). 

Several researchers have chosen to categorize supply chain risks in the following manner: demand-side 

risks resulting from disruptions emerging from downstream supply chain operations (Suttner, 2005); 

supply-side risks residing in purchasing, supplier activities, and supplier relationships (Wu et al, 2006); 

and catastrophic risks that, when they materialize, have a severe impact in terms of magnitude in the area 

of their occurrence (Wagner and Bode, 2006). Nagurney et al (2005) define demand side risk as the 

uncertainty surrounding the random demands that often occur at the retailer stage of the supply chain. Wu 

et al (2006) state that inbound supply risk is defined as the potential occurrence of an incident associated 

with inbound supply from individual supplier failures or the supply market. This results in the inability of 

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the purchasing firm to meet customer demand. These are some of the potential occurrence of events 

associated with inbound supply that can have significant detrimental effects on the purchasing firm. 

Handfield and McCormack (2007) defined operational, network, and external factors as categories of 

supply chain risks. Operational risk is defined as the risk of loss resulting from inadequate or failed 

internal processes, people, or systems. Quality, delivery, and service problems are examples of 

operational risks. Network risk is defined as risk resulting from the structure of the supplier network, such 

as ownership, individual supplier strategies, and supply network agreements. External risk is defined as 

an event driven by external forces, such as weather, earthquakes, political, regulatory, and market forces. 

In addition, the authors offer three perspectives for the examination of risks within supply chain networks. 

A supplier facing perspective examines the network of suppliers, their markets, and their relationships 

relative to the organization. A customer facing perspective examines the network of customers and 

intermediaries, their markets, and their relationships also relative to the organization. Finally, an internal 

facing perspective examines the company, their network of assets, processes, products, systems, and 

people as well as the company’s markets. This research study employs the risk categories offered by 

Handfield and McCormack (2007) along with the supplier facing perspective in the evaluation of network 

supply chain risk. 

4. RESEARCH METHODOLOGY 

This research study incorporates the use of a risk assessment model, surveys, data collection from internal 

and external company sources, and Bayesian networks. The networks were used to create risk profiles for 

the study participants. Additionally, the study adopts the risk categories outlined by Handfield and 

McCormack (2007).An overview of Bayesian networks is given in Section 4.1. Discussions on the 

assessment model, study participants, and data sample are provided in Sections 4.2 and 4.3 respectively. 

Finally, the research model used in the study is discussed in Section 4.4. 

4.1Bayesian Networks 

A Bayesian network is an annotated directed acyclic graph that encodes probabilistic relationships among 

nodes of interest in an uncertain reasoning problem (Pai et al, 2003). The representation describes these 

probabilistic relationships and includes a qualitative structure that facilitates communication between a 

user and a system incorporating a probabilistic model. Bayesian networks are based on the work of the 

mathematician and theologian Rev. Thomas Bayes who worked with conditional probability theory in the 

late 1700s to discover a basic law of probability that came to be known as Bayes' theorem, which states 

that: 

P(H|E,c) = P(H|c) x P(E|H,c) 

P(E|c) 

The posterior probability is given by the left-hand term of the equation [P(H|E,c)]. It represents the 

probability of hypothesis H after considering the effect of evidence E on past experience c.The term 

P(H|c) is the a-priori probability of H given c alone. Thus, the a-priori probability can be viewed as the 

subjective belief of occurrence of hypothesis H based upon past experience. The likelihood, represented 

by the term P(E|H,c), gives the probability of the evidence assuming the hypothesis H and the background 

information c are true. The term P(E|c) is independent of H and is regarded as a normalizing or scaling 

factor (Niedermayer, 2003).Thus, Bayesian networks provide a methodology for combining subjective 

beliefs with available evidence. Bayesian networks represent a special class of graphical models that may 

be used to depict causal dependencies between random variables (Cowell et al, 2007). Graphical models 

use a combination of probability theory and graph theory in the statistical modeling of complex 

interactions between such variables.Bayesian networks have evolved as a useful tool in analyzing 

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uncertainty. When Bayesian networks were first introduced, assigning the full probability distributions 

manually was time intensive. Solving a Bayesian network with a considerable number of nodes is known 

to be a nondeterministic polynomial time hard [NP hard] problem (Dagum and Luby, 1993). However, 

significant advancements in computational capability along with the development of heuristic search 

techniques to find events with the highest probability have enhanced the development and understanding 

of Bayesian networks. Correspondingly, the Bayesian computational concept has become an emergent 

tool for a wide range of risk management applications (Cowell et al, 2007). The methodology has been 

shown to be especially useful when information about past and/or current situations is vague, incomplete, 

conflicting, and uncertain. 

Pai et al (2003) were among the first researchers to analyze supply chain risks using Bayesian networks. 

Their study examined the risk profile associated with a US Department of Defense (DoD) supply chain 

for trinitrotoluene (TNT). The supply chain was comprised of TNT recovery plants, storage facilities, and 

ammunition depots. Using Bayesian networks, the researchers were able to establish risk factors and 

acceptable risk limits for all assets contained in the DoD supply chain. Bayesian networks have also been 

used to conduct diagnostics (Kauffmann et al, 2002; Kao et al, 2005), cost optimization studies 

(Narayanan et al, 2005), and flexibility analysis (Wu, 2005; Milner and Kouvelis, 2005) in supply chains. 

Since the work of Pai et al (2003), researchers have continued to explore the use of Bayesian networks to 

analyze and manage supply chain risks. For example, there have been a number of studies which 

examine the use of Bayesian networks as part of a decision support system to manage such risks (Li and 

Chandra, 2007; Meixell et al, 2008; Shevtshenko and Wang, 2009; Makris et al, 2001; Taskin and Lodree, 

2011). Studies by Tomlin (2009) and Chen et al (2010) demonstrate how Bayesian networks can be used 

to manage supply chain uncertainty. The integration of Bayesian networks into supply chain forecasting 

methodologies to mitigate risks has also been examined by several researchers (Yelland, 2010; Yelland et 

al, 2010; Rahman et al, 2011). Lockamy and McCormack (2009) conducted a study which uses Bayesian 

networks to examine operational risks in supply chains. The authors have also used these networks to 

analyze outsourcing risks in supply chains (Lockamy and McCormack, 2010). Finally, Lockamy (2011) 

has developed a methodology for benchmarking supplier risks using Bayesian networks. 

This research study contributes to the current body of supply chain management literature by introducing 

a methodology for evaluating network risks in supply chains. The methodology also includes an 

assessment of the potential revenue impact a supplier can have on an organization as measured by valueat-risk 

(VAR). The methodology in this study is offered as a tool to assist supply chain managers in the 

formulation of strategies and tactics designed to mitigate supply chain risks. 

4.2Assessment Model 

An assessment model developed by Handfield and McCormack (2007) was used to evaluate the risk of 

each supplier. This model incorporates data from several sources to provide a 360-degree view of a 

supplier's risk profile. The risk assessment model is presented in Figure 1. A potential challenge regarding 

the use of the assessment model is the need for a detailed review of data furnished by suppliers. Such 

reviews can be costly and time consuming, and therefore are usually limited to the firm's strategic 

suppliers. However, well designed, qualitative self- reporting can be very cost effective. As in the case of 

this study, each supplier was required to answer an online multiple choice survey that takes less than 30 

minutes to complete. In addition, given the large number of suppliers in many supply chains, the buyers 

or category managers often do not know every detail about each supplier, thus making self-reporting by 

suppliers a necessity. With these qualitative indicators, it is also feasible and affordable to assess several 

tiers in the supply network, consequently making the network risk profile broad and deep. The risk 

assessment model identifies and quantifies the risk of a supply disruption using a framework that 

describes the attributes of suppliers, their relationships, and their interactions with the organization 

performing the assessment. 

10 





Interactions and 

Relationships 

Performance 

Relationship 

Supplier 

Environment 

The customer’s reputation with 

suppliers is also a critical factor. 

Environmental 

Geographic, market, 

transportation, etc. 

S 

SC 

Network 

Organizer 

Supplier 

Attributes 

Human 

Resources 

Figure 1. Risk Assessment Model 

Supply Chain 

Disruption 

Financial 

Health 

The model consists of relationship factors (influence, levels of cooperation, power, alignment of 

interests); past performance (quality, on-time delivery, shortages); human resource factors (unionization, 

relationship with employees, level of pay compared to the norm); supply chain disruptions history; 

environment (geographic, political, shipping distance and method, market dynamics); disaster history 

(hurricane, earthquake, tornado, flood); and financial factors (ownership, funding, payables, receivables). 

The assessment model uses a set of measures and scales that apply to each risk construct. The model was 

tested with several companies over a four-year period and validated through actual use in assessing 

supply risk events. The measures and scales are used to evaluate suppliers and to provide a numerical 

score that reflects their individual risk of a disruptive event. A supplier risk profile is then created and 

11 





expressed as a numerical score given as a result of applying the model and measures. The higher the risk 

profile score, the higher the supplier’s disruption potential to the supply chain. In order to apply the risk 

results to potential events, the survey results were reorganized into operational, network and external riskrelated 

measures, and the results were recalculated for each supplier. The reorganized measures are 

presented in Appendix 1. The financial impact of the risk profiles on company revenues was calculated 

by identifying the components furnished by individual suppliers, associating those components with a 

finished product and product gross revenue, and computing the sum of associated monthly revenues for 

each supplier. 

4.3Sample and Data Collection 

The study sample consists of five automotive casting suppliers to a major automotive company in the 

United States. The sample data was collected by first interviewing the supplier’s account representative to 

discuss the study and the internet-based survey. Subsequently, the survey instrument web link was sent in 

an email to the supplier’s account representative. The account representative completed the survey, 

supplier historical performance data was evaluated, and a company analyst conducted an environmental 

analysis of the organization. All risk ratings were assessed using a five-point Likert scale and a risk index 

was calculated for each supplier. In addition, each supplier provided a-priori probabilities for the twelve 

risk events identified in Appendix 1. The a-priori probabilities were determined via a review of relevant 

quantitative and qualitative information by a team of company personnel familiar with the identified risk 

events as they relate to the five suppliers. By logically examining the information, the team was able to 

estimate a-priori probability values pertaining to the twelve risk events for each supplier. These 

probabilities provided the basis for the construction of Bayesian networks used in the creation of the 

supplier risk profiles. 

4.4Research Model 

Bayesian networks were constructed to examine the probability of a supplier’s impact on company 

revenues.Network, operational, and external risk levels were computed using the provided a-priori 

probabilities for the identified risk events. These risk levels were then used to determine a supplier’s 

probability of revenue impact on the company. A diagram of the Bayesian networks used in this study is 

illustrated in Figure 2. 

Network Key 

1 = Misalignment of interest 

2 = Supplier Financial Stress 

3 = Supplier Leadership Change 

4 = Tier 2 Stoppage 

5 = Supplier Network Misalignment 

6 = Quality Problems 

7 = Delivery Problems 

8 = Service Problems 

9 = Supplier HR Problems 

10 = Supplier Locked 

11 = Merger/Divestiture 

12 = Disasters 

Figure 2. Supplier Bayesian Network 

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Nodes (circles) represent variables in the Bayesian network. Each node contains states, or a set of 

probable values for each variable. The values ‘yes’ and ‘no’ represent the two states in which the 

variables can exist in the network illustrated in Figure 2. Nodes are connected to show causality with 

arrows known as ‘edges’ which indicate the direction of influence. When two nodes are joined by an 

edge, the causal node is referred to as the parent of the influenced (child) node. Child nodes are 

conditionally dependent upon their parent nodes. Thus, in Figure 2, the probability of suppliers 

experiencing network risks is dependent on the a-priori probabilities associated with the following 

variables: misalignment of interest; supplier financial stress; supplier leadership change; tier 2 stoppage; 

and supplier network misalignment.The a-priori probabilities associated with the variables quality 

problems, delivery problems, service problems, and supplier human resources (HR) problems directly 

influence operational risks. External risks are dependent upon the following variables: supplier locked 

(i.e., company cannot easily switch to another supplier), merger/divestitures, and disasters. The joint 

probabilities of the computed network, operational, and external risks are then used to determine the 

probability that a supplier will have an adverse impact on the company’s revenue stream. 

5. RESULTS 

In this study, the product of the supplier’s revenue impact probability times its revenue impact provides 

‘value-at-risk’ (VAR) dollars. VAR is defined as the minimum loss expected on a portfolio of assets over 

a certain holding period at a given probability (Venkataraman, 1997). VAR was developed by financial 

institutions in the early 1990s to provide senior management with a single number that could easily 

incorporate information on the risk of a portfolio of assets (Engle & Manganelli, 2004). Today, VAR has 

evolved into a risk measurement tool that can be applied outside of the financial management arena, such 

as in making procurement decisions (Sanders & Manfredo, 2002). VAR can also be used to evaluate and 

manage supply chain risks. The Supply Chain Council defines VAR as the sum of the probability of 

events times the monetary impact of the events for the specific process, supplier, product, or customer 

(Supply Chain Council, 2008). Thus, this metric allows for comparisons among suppliers to facilitate 

supply chain risk management. This study examines monthly VAR dollars for the company based upon 

the risk profiles of each supplier. 

The a-priori probabilities for the twelve supply chain risk events that affect network, operational, and 

external risks are presented in Table 1 for each supplier. These values were used to generate a risk profile 

using Bayesian networks composed of network, operational, and external risk probabilities along with the 

supplier’s probability of revenue impact on the company. As previously discussed, risk event probabilities 

linked to misalignment of interest, supplier financial stress, supplier leadership changes, tier 2 stoppage, 

and supplier network misalignment directly affect network risks. The table reveals that the suppliers have 

a 50 percent probability of experiencing a risk event due to either financial stress or changes in leadership. 

In addition, there is a 29 to 32 percent probability that the supplier base encounters a tier 2 stoppage risk 

event. Finally, the table shows that the probability of the suppliers being subjected to risk events due to 

misalignment of interests or network misalignments are in the range of 19 to 21 percent and 12 to 20 

percent respectively. 

5.1 Sensitivity Analysis 

A network risk profile sensitivity analysis was conducted for each supplier. The sensitivity analysis 

begins with a determination of a supplier’s probability of revenue impact on the company by applying the 

a-priori probabilities illustrated in Table 1 to the Bayesian network. Computations for the probability of 

revenue impact on the company based on the risk profile of Supplier 1 are presented in Appendix 2. An 

examination of Appendix 2 reveals that Supplier 1 has a 41 percent probability of affecting company 

revenues. Supplier revenue impact probabilities were also computed assuming a 100 percent probability 

of supplier network risks along with a zero percent probability of such risks to compare VAR revenue 

impacts at the extreme cases relative to the base case, represented by the a-priori probabilities. 

13 


14 

Table 1. A-Priori Probabilities for Risk Event Variables 

Supplier 

Misalignment 

of Interest 

Supplier 

Financial 

Stress 

Supplier 

Leadership 

Change 

Tier 2 

Stoppage 

Supplier 

Network 

Misalignment 

Quality 

Problems 

Delivery 

Problems 

Service 

Problems 

Supplier 

HR 

Problems 

Supplier 

Locked 

Merger/ 

Divestiture 

1 0.20 0.50 0.50 0.31 0.20 0.46 1.00 0.20 0.20 0.18 1.00 0.11 

2 0.20 0.50 0.50 0.31 0.12 0.48 0.95 0.20 0.20 0.18 1.00 0.12 

3 0.21 0.50 0.50 0.32 0.16 0.47 0.96 0.20 0.20 0.19 1.00 0.16 

4 0.19 0.50 0.30 0.29 0.14 0.36 0.82 0.15 0.07 0.10 0.80 0.11 

5 0.20 0.50 0.50 0.31 0.16 0.50 0.96 0.20 0.20 0.18 1.00 0.11 

Disasters 

Table 2. Network Risk Profiles And Value At Risk Impacts 

Supplier 

Misalignment 

of Interest 

Probability 

Supplier 

Financial 

Stress 

Probability 

Supplier 

Leadership 

Change 

Probability 

Tier 2 

Stoppage 

Probability 

Supplier 

Network 

Misalignment 

Probability 

Probability 

of 

Revenue 

Impact 

Monthly 

Revenue 

Impact 

(Millions) 

Value At Risk 

(Probability x 

Monthly 

Revenue 

Impact) 

Potential 

VAR 

Percent 

Decrease 

Potential 

VAR 

Percent 

Increase 

1 *0.20 *0.50 *0.50 *0.31 *0.20 0.41 $ 18.75 $ 7,687,500 26.8 53.7 

0.00 0.00 0.00 0.00 0.00 0.30 $ 18.75 $ 5,625,000 

1.00 1.00 1.00 1.00 1.00 0.63 $ 18.75 $11,812,500 

2 *0.20 *0.50 *0.50 *0.31 *0.12 0.40 $217.50 $ 87,000,000 25.0 57.5 

0.00 0.00 0.00 0.00 0.00 0.30 $217.50 $ 65,250,000 

1.00 1.00 1.00 1.00 1.00 0.63 $217.50 $137,025,000 

3 *0.21 *0.50 *0.50 *0.32 *0.16 0.41 $136.25 $55,862,500 26.8 56.1 

0.00 0.00 0.00 0.00 0.00 0.30 $136.25 $40,875,000 

1.00 1.00 1.00 1.00 1.00 0.64 $136.25 $87,200,000 

4 *0.19 *0.50 *0.30 *0.29 *0.14 0.32 $ 45.83 $14,665,600 28.1 75.0 

0.00 0.00 0.00 0.00 0.00 0.23 $ 45.83 $10,540,900 

1.00 1.00 1.00 1.00 1.00 0.56 $ 45.83 $25,664,800 

5 *0.20 *0.50 *0.50 *0.31 *0.16 0.41 $ 20.83 $ 8,540,300 26.8 53.7 

0.00 0.00 0.00 0.00 0.00 0.30 $ 20.83 $ 6,249,000 

1.00 1.00 1.00 1.00 1.00 0.63 $ 20.83 $13,122,900 

*Base Case




The a-priori probabilities associated with operational and external risk variables were held constant 

during the sensitivity analysis. Although it may not be possible to fully eliminate the network risks 

associated with a supplier’s profile, it may be possible to improve the profile by instituting proactive 

supply chain risk management strategies and tactics in areas that will yield the maximum benefit. Table 2 

illustrates the supplier network risk profiles and VAR impacts under the three aforementioned conditions. 

The first row of values are network risk probabilities associated with Supplier 1 along with its probability 

of revenue impact. This is referred to as the base case. The second row illustrates the probability of 

revenue impact for Supplier 1 if it were possible to minimize network risks to the value of zero. The 

probability of revenue impact for Supplier 1 if it is certain that it will experience network risks is depicted 

in the third row. The table shows that minimizing Supplier 1 network risk events reduces the probability 

of revenue impact from the base case of 41 percent to 30 percent. Additionally, the probability of revenue 

impact due to Supplier 1 increases from 41 percent to 63 percent under the scenario of 100 percent 

certainty of network risks. 

An examination of Table 2 reveals that the network risk profile associated with Supplier 2 results in the 

largest VAR for the base case ($87.0 million), best case ($65.2 million), and worse case ($137.0 million) 

scenarios. The risk profile of Supplier 5 yields the smallest VAR for the base case ($8.5 million), best 

case ($6.2 million), and worse case ($13.1 million) situations. The largest potential percentage decrease in 

VAR between a supplier’s base case and most favorable network risk profile is 28.1 percent for Supplier 

4. The largest potential percentage increase in VAR between a supplier’s base case and least favorable 

network risk profiles is 75.0 percent, also exhibited by Supplier 4. The smallest potential percentage 

decrease in VAR between the base case and most favorable network risk profile was displayed by 

Supplier 2 at 25.0 percent. Both Suppliers 1 and 5 offer the smallest potential percentage increase in VAR 

(53.7 percent) between the base case and least favorable network risk profiles. The average potential 

increase in VAR is 59.2 percent for all suppliers, and the average potential decrease in this measure is 

26.7 percent. Finally, Table 2 reveals that potential percent increases in VAR due to network risks are 

significantly greater than potential percent VAR decreases for each individual supplier. 

6. MANAGERIAL ACTIONS 

The sensitivity analysis reveals that not only does Supplier 1 have the lowest VAR impact on the 

company under the evaluated scenarios, but also the smallest potential percentage increase in VAR along 

with Supplier 5. This result suggests that Supplier 1 has a comparatively favorable network risk 

probability profile relative to those of the other study participants. However, this supplier also has the 

lowest monthly revenue impact on the company. Given this result, after considering both operational and 

external risks associated with Supplier 1, the company may find it prudent to apportion more of its 

business to this supplier due to its less risky network profile. However, given that this supplier believes 

that there is a 100 percent chance that it will encounter delivery problems, the company should take 

immediate steps to mitigate the effects of its eventual occurrence. 

The sensitivity analysis also reveals that Supplier 2 has the highest VAR impact on the company under 

the scenarios examined along with the smallest potential percentage decrease in VAR. Thus, Supplier 2 

has an unfavorable network risk probability profile relative to the other participants in the study. 

Ironically, this supplier also has the largest monthly revenue impact on the company. This result suggests 

that the company should consider allocating more of its business to a supplier with a less risky network 

profile. The company should also institute risk mitigation strategies and tactics immediately to address the 

95 percent probability of an occurrence of a delivery problem with this supplier. Finally, the company 

may choose to terminate its relationship with this supplier and allocate its business among its remaining 

supplier base. 

15 





As mentioned previously, Suppliers 1 and 5 exhibited the smallest potential percentage increase in VAR 

during the sensitivity analysis. However, Supplier 4 displays the largest potential decrease and increase in 

VAR. Thus, after considering the supplier’s operational and external risk factors, the company may 

consider the development of an aggressive supply chain risk management program that helps move 

Supplier 4 towards the accomplishment of these reductions. The program would also reduce the potential 

for Supplier 4 to incur a network risk event that has a high probability of VAR impact. Possible 

incentives that the company could offer the supplier are incremental increases in business based upon 

documented improvements in its network risk profile. As in the cases of Suppliers 1 and 2, this supplier 

has a high probability (82 percent) of experiencing a delivery problem risk event. Immediate actions 

should be taken to minimize the effects of such an event on company revenues. Moreover, given the high 

probability of delivery problems associated with all of the study participants, the company should institute 

a comprehensive supply chain risk management program designed to discover and eliminate the causes of 

this issue among its suppliers. 

7. CONCLUSIONS 

The methodology presented in this study can be used to monitor network risks in supply chain networks. 

As part of a supply chain governance agreement, suppliers could be required to periodically update their 

risk probability profiles for the risk events outlined in Appendix 1. These updates could be applied to 

Bayesian networks to create new risk profiles for each supplier. Adjustments to existing risk management 

strategies, policies, and tactics could then be made to reflect the current risk realities associated with the 

supply network. Thus, the methodology can provide a proactive means of managing network risks as well 

as other categories of supply chain risks. 

The methodology can also be used by organizations to develop supplier network risk profiles to determine 

revenue risk exposure levels. Organizations can then decide if it is in their best interest to either assist a 

supplier in improving its network risk profile or to terminate the relationship. Supplier network risk 

profiles can be used to determine those network risk events that have the highest probability of occurrence 

and the largest potential revenue impact. Thus, this methodology can assist organizations along with their 

suppliers in developing comprehensive supplier risk management programs designed to minimize the 

occurrence of network and other risk events. 

Finally, this methodology can be used as a tool to assist managers in evaluating current and potential 

suppliers. Suppliers who have been shown to improve their network risk profiles over time may be 

rewarded by an organization with more business. Conversely, suppliers who have experienced increases 

in network risk events over an extended period of time may be viewed as ‘at risk’ suppliers whose 

relationship may require reassessment by the organization. The reassessment could result in removal from 

the supply network. Potential suppliers willing to provide information for the generation of their risk 

profiles may then become viable candidates for network inclusion. 

8. LIMITATIONS 

This study provides an examination of network risk profiles associated with casting suppliers in the 

automotive industry. Therefore, the results are specific to the study participants. A potential limitation to 

the use of the methodology presented in this study is the ability to acquire the necessary data from 

suppliers needed for the construction of the Bayesian networks. There may be circumstances in which 

some participants within a supply chain network are reluctant to share risk profile data with their 

customers. The reluctance to share such information may be due to mistrust among network members. 

Inaccurate or misleading information acquired from suppliers may also limit the effectiveness of Bayesian 

networks as a tool for monitoring network risks in supply chains. Such information can result in the 

creation of flawed profiles that fail to reflect the true risk associated with a particular supplier. Moreover, 

16 





suppliers must be willing to periodically update this data in order to construct risk profiles that are 

current, valid, and reliable. A limitation of the use of Bayesian networks to model supply chain risks is 

the proper identification of risk events and risk categories that can affect a supply chain. Because there are 

a number of approaches available for categorizing supply chain risks, the inability to incorporate all 

relevant risks into the model could limit its effectiveness in representing a supplier’s true risk profile. 

Therefore, the data used in the construction of Bayesian networks must represent the supplier’s current 

risk realities within the supply chain network. 

9. FUTURE RESEARCH 

Research studies that explore the risk profiles of suppliers and supply networks in other industries should 

be examined using Bayesian networks to determine if industry dynamics significantly influence supply 

chain risks. Future researchers may also investigate how network risks can be lessened by reducing the 

level of network risk events associated with individual or groups of suppliers. For example, it may be 

possible to determine the maximum risk levels for these variables in order for a supplier or supplier group 

to maintain its affiliation with the supply chain. Finally, given the number of supply chain disruptions due 

to natural disasters in recent years, future researchers may choose to solely focus on the impact of external 

risks on supply networks. 

REFERENCES 

Abell, D. (1999). Competing today while preparing for tomorrow. MIT Sloan Management Review, 40(3), 

73-81. 

Chen, L., & Kang, F. (2007). Integrated vendor-buyer cooperative inventory models with variant 

permissible delay in payments. European Journal of Operational Research, 183(2), 658-673. 

Chen, M., Yusen Xia, Y., & Wang, X. (2010). Managing supply uncertainties through Bayesian 

information update. IEEE Transactions on Automation Science & Engineering, 7(1), 24-36. 

Christopher, M. (1998). Logistics & Supply Chain Management: Strategies for Reducing Cost 

Improving Services (2nd edition). New York, NY: Financial Time Prentice-Hall. 

and 

Chopra, S. & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. Sloan Management 

Review, 46(1), 53-61. 

Cooper, M., Lambert, D. & Pagh, J. (1997). Supply chain management: more than a new name for 

logistics. The International Journal of Logistics Management, 8(1), 1-14. 

Cowell, R. G., Verrall, R. J., & Yoon, Y. K. (2007). Modeling operational risk with Bayesian networks. 

Journal of Risk and Insurance, 74(4), 795-827. 

Croxton, K., Lambert, D., Garcia-Dastugue, S., & Rogers, D. (2002). The demand management process. 

International Journal of Logistics Management, 13(2), 51-66. 

Cucchiella, F. & Gastaldi, M. (2006). Risk management in supply chain: a real option approach. Journal 

of Manufacturing Technology Management, 17(6), 700-720. 

Dagum, P. & Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is 

NP-hard. Artificial Intelligence, 60(1), 141-153. 

17 





Engle, R. F. & Manganelli, S. (2004). CAViaR: conditional autoregressive value at risk by regression 

quantiles. Journal of Business & Economic Statistics, 22(4), 367-381. 

Faisal, M. N., Banwet, D. K., & Shankar, R. (2006). Mapping supply chains on risk and customer 

sensitivity dimensions. Industrial Management and Data Systems, 106(6), 878-895. 

Gaudenzi, B. & Borghesi, A. (2006). Managing risks in the supply chain using the AHP method.The 


Giunipero, L. C. & Eltantawy, R. A. (2004). Securing the upstream supply chain: a risk management 

approach. International Journal of Physical Distribution and Logistics Management, 34(9), 698-713. 

Gunasekaran, A., Lai, K., & Cheng, T. (2008). Responsive supply chain: a competitive strategy in a 

networked economy. Omega, 36(4), 549-564. 

Handfield, R. & McCormack, K. (2007). Supply Chain Risk Management: Minimizing Disruptions in 

Global Sourcing. Boca Raton, FL: Auberbach Publications. 

Jammernegg, W. & Reiner, G. (2007). Performance improvement of supply chain processes by 

coordinated inventory and capacity management. International Journal of Production Economics, 

108(1/2), 183-190. 

Kao, H. Y., Huang, C.H., & Li, H. L. (2005). Supply chain diagnostics with dynamic 

networks. Computers & Industrial Engineering, 49(2), 339-347. 

Bayesian 

Kauffmann, P. J., Jacobs, D. A., & Fernandez, A. A. (2002). Use of Bayesian probabilities to identify 

and improve distribution center error rates. Production & Inventory Management Journal, 43(1/2), 1-5. 

Khan, O. & Burnes, B. (2007). Risk and supply chain management: creating a research agenda. 


Kleindorfer, P. R. & Saad, G. H. (2005). Managing disruption risks in supply chains.Production and 

Operations Management, 14(1), 53-68. 

Kleindorfer, P. R. & Wassenhove, L. K. (2003), “Managing risk in global supply chains”Wharton 

Insurance and Risk Management Department Seminar. University of Pennsylvania, Philadelphia, 

PA. 

Knight, F. H. (1921). Risk, Uncertainty and Profit. Boston, MA: Houghton Mifflin. 

Kopczak, L. & Johnson, M. (2003). The supply-chain management effect. MIT Sloan Management 

Review, 44(3), 27-34. 

Li, S., Ragu-Nathan, B., Ragu-Nathan, T. & Rae, S. (2006). The impact of supply chain management 

practices on competitive advantage and organizational performance. Omega, 34(2), 107-124. 

Li, X. & Chandra, C. (2007). A knowledge integration framework for complex network management. 

Industrial Management & Data Systems, 107(8), 1089-1109. 

18 





Lockamy III, A. (2011). Benchmarking supplier risks using Bayesian networks. Benchmarking:An 

International Journal, 18(3), 409-427. 

Lockamy III, A. & McCormack, K. (2009). Examining operational risks in supply chains”, Supply Chain 

Forum, 10(1), 2-14. 

Lockamy III, A. & McCormack, K. (2010). Analysing risks in supply networks to facilitate outsourcing 

decisions. International Journal of Production Research, 48(2), 593–611. 

Makris, S., Zoupas, P., & Chryssolouris, G. (2011). Supply chain control logic for enabling adaptability 

under uncertainty. International Journal of Production Research, 49(1), 121-137. 

Meixell, M.J., Shaw, N.C., & Tuggle, F. D. (2008). A methodology for assessing the value of knowledge 

in a service parts supply chain”, IEEE Transactions on Systems, Man & Cybernetics: Part C - 

Applications & Reviews, 38(3), 446-460. 

Milner, J. M. & Kouvelis, P. (2005). Order quantity and timing flexibility in supply chains: The role of 

demand characteristics. Management Science, 51(6), 970–985. 

Nagurney, A., Cruz, J., Dong, J., & Zhang, D. (2005). Supply chain networks, electronic commerce, and 

supply side and demand side risk. European Journal of Operational Research, 164(1), 120-142. 

Narayanan, V. G., Raman, A., & Singh, J. (2005). Agency costs in a supply chain with demand 

uncertainty and price competition. Management Science, 51(1), 120–132. 

Niedermayer, D. (2003). An introduction to Bayesian networks and their contemporary applications. 

Retrieved June 14, 2011 from . 

Pai, R., Kallepalli, V., Caudill, R., & Zhou, M. (2003). Methods toward supply chain risk 

Analysis. IEEE International Conference on Systems, Man and Cybernetics, 5(1), 4560-4565. 

Paulsson, U. (2004). Supply chain risk management. In Brindley, C. (Ed.), Supply Chain Risk 

Management (pp. 79-96). London, UK: Ashgate 

Rahman, M. A, Sarker, B., & Escobar, L. A. (2011). Peak demand forecasting for a seasonal 

product using Bayesian approach. Journal of the Operational Research Society, 62(6), 1019-1028. 

Sanders, D. R. & Manfredo, M. R. (2002). The role of value-at-risk in purchasing: an application to the 

foodservice industry. Journal of Supply Chain Management, 

38(2), 38-45. 

Sawhney, M., Wolcott, R. C., & Arroniz, I. (2006). The 12 different ways for companies to innovate. 

Sloan Management Review, 47(3), 75-81. 

Shevtshenko, E. & Wang, Y. (2009). Decision support under uncertainties based on robust Bayesian 

networks in reverse logistics management. International Journal of Computer Applications in 

Technology, 36(3/4), 247-258. 

Singh, P., Smith, A., & Sohal, S. (2005). Strategic supply chain management issues in the automotive 

industry: an Australian perspective. International Journal of Production Research, 43(16), 3375-3400. 

19 





Slack, N. & Lewis, M. (2001), Operations Strategy (3rd ed.). Harlow, UK: Prentice-Hall. 

Spekman, R. E. & Davis, E. W. (2004). Risky business: expanding the discussion on risk and the 

extended enterprise. International Journal of Physical Distribution and Logistics Management, 34(5), 

414-433. 

Supply Chain Council. (2008). Supply chain operations reference model v. 9.0. Supply Chain Council. 

Retrieved June 14, 2010 from. 

Suttner, U. (2005). Supply chain risk management: understanding the business requirements from a 

practitioner perspective. The International Journal of Logistics Management, 16(1), 120-141. 

Taskin, S & Lodree, E. J. (2011). A Bayesian decision model with hurricane forecast updates for 

emergency supplies inventory management. Journal of the Operational Research Society, 62(6), 1098- 

1108. 

Treleven, S & Schweikhart, B. (1988). A risk/benefit analysis of sourcing strategies: single vs. multiple 

sourcing. Journal of Operations Management, 7(4), 93-114. 

Tomlin, B. (2009). Impact of supply learning when suppliers are unreliable. Manufacturing & Service 

Operations Management, 11(2), 192-209. 

Venkataraman, S. (1997). Value at risk for a mixture of normal distributions: the use of quasi- Bayesian 

estimation techniques. Economic Perspectives, 21(2), 2-14. 

Wagner, S. M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability. Journal of 

Purchasing and Supply Management, 12(6), 301-12. 

Wu, J. (2005). Quantity flexibility contracts under Bayesian updating. Computers & Operations 

Research, 32(5), 1267-1288. 

Wu, T., Blackhurst, J., & Chidambaram, V. (2006). A model for inbound supply risk Analysis. 

Computers in Industry, 57(4), 350-365. 

Yates, J. F. & Stone, E. (1992). The risk construct. In: Yates, J.F. (Ed.), Risk-Taking Behavior (pp.55- 

77). Chichester, UK: Wiley. 

Yelland, P. M (2010). Bayesian forecasting of parts demand. International Journal of Forecasting, 

26(2), 374-396. 

Yelland, P. M., Kim, S., & Stratulate, R. (2010). A Bayesian model for sales forecasting at Sun 

Microsystems. Interfaces, 40(2), 118-129. 

Zsidisin, G. (2003). Managerial perceptions of supply risk. Journal of Supply Chain Management, 39(1), 

14-25. 

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Appendix 1: Network, Operational and External Risk Measures 

Risk Category Risk Event Risk Measures 

Network Risks 

Operational Risks 

Misalignment of Interest 

Supplier Financial Stress 

Supplier Leadership Change 

Tier 2 Stoppage 

Supplier Network Misalignment 

Quality Problem 

Influence of revenue from company 

Supplier revenue from commodity 

category 

Supplier/Company Alignment 

Regulatory 

Customer portfolio 

Business health indicators 

Segment portfolio 

Market growth 

Financial data sharing 

Company ownership change 

likelihood 

Merger and acquisition 

Senior staff turnover 

Process change likelihood 

Miscommunication between tiers 

Material change/obsolesce likelihood 

Risk management system 

Material sourcing base 

Market power 

Regulatory 

Regulatory change risk likelihood 

Inventory status sharing 

Tier II supplier information sharing 

Process/Material change notification 

Supplier customer alignment 

Vendor concentration 

Process change likelihood 

MRR (defects) 

Audit date 

Audit score 

Tier II performance monitoring 

Quality problems likelihood 

Manufacturing employees 

Accreditation 

Material change/obsolesce likelihood 

Process/Material change notification 

Delivery Problem 

Performance data sharing 

21 





Appendix 2: Risk Profile for Supplier 1 

On-time delivery 

Capacity utilization 

Tier II information sharing 

Delivery flexibility 

Capacity shortage likelihood 

Manufacturing employees 

Capacity change 

Inventory status sharing 

Order fulfillment information 

sharing 

Given the risk event relationships exhibited in the Supplier Bayesian Network illustrated in Figure 2 

along with the a-priori probabilities for risk event variables contained in Table 1, the following 

probability computations regarding network risks, operational risks, external risks, and revenue impact for 

Supplier 1 are provided below: 

P(Network Risks) = ∑ (Probability of Network Risk Event) × (Probability of Event Occurrence) 

∑ (Probability of Event Occurrence) 

P(Network Risks) = [(.20) x (1)] + [(.50) x (1)] + [(.50) x (1)] +[(.31) x (1)] +[(.20) x (1)] 

P(Network Risks) = 1.71 = 0.34 

5 

1+1+1+1+1 

P(Operational Risks) = ∑ (Probability of Operational Risk Event) × (Probability of Event Occurrence) 


P(Operational Risks) = [(.46) x (1)] + [(1.00) x (1)] + [(.20) x (1)] + [(.20) x (1)] 

P(Operational Risks) = 1.86 = 0.47 

4 

1+1+1+1 

P(External Risks) = ∑ (Probability of External Risk Event) × (Probability of Event Occurrence) 


P(External Risks) = [(.18 x (1)] + [(1.00) x (1)] + [(.11) x (1)] 

1+1+1 

22 





P(External Risks) = 1.29 = 0.43 

3 

P(Revenue Impact) = ∑ [P(NR) × P(Occurrence)] + [P(OR) × P(Occurrence)] +[P(ER) × 

P(Occurrence)] 

∑ (Probability of Risk Occurrence) 

P(Revenue Impact) = [(.34 x (1)] + [(.47) x (1)] + [(.43) x (1)] 

1+1+1 

P(Revenue Impact) = 1.24 = 0.41 

3 

Appendix 3: VAR for Supplier 1 

Given the risk profile exhibited in Appendix 4, the following VAR computation for Supplier 1 is provided 

below: 

Value at Risk (VAR) = [P(Revenue Impact) ] x [Supplier’s Monthly Revenue Impact] 

From Appendix 4: P(Revenue Impact) = 0.41 

Using the methodology described in Section 3.2, the monthly revenue impact for Supplier 1 is: 

$18,750,000. 

Therefore: VAR(Supplier 1) = [0.41] x [$18,750,000] = $7,687,500. 

Thus, the risk profile associated with Supplier 1 results in a VAR of $7,687,500 for the automotive 

company. 

Impact of Economic value added (EVA) on 

Share price: 

23 





A study of Indian Private Sector banks 

Prof. Ritesh Patel 

Assistant Professor, Department of Management, 

S.V.Institute of Management, Kadi, Gujarat Technological University, Gujarat, 

India. 

Prof. Mitesh Patel 

Assistant Professor, Department of Management, 

S.V.Institute of Management, Kadi,Gujarat Technological University, Gujarat, 

India. 

ABSTRACT 

Many Indian banks are discovering the key to their long-term growth does not lie in 

products and services alone but in assets that can never be replicated, that is, their 

unique relationship with customers, employees, suppliers and distributors, investors 

and the communities they serve. The objective of this study was to determine 

shareholders value (in terms of economic value added) of selected private sector banks 

during the last five years. I.e. since 2004-05 to 2009-2010. From study it was found 

that in year 2010, ICICI Bank has maximum NOPAT. The value of EVA was ranging 

from 14.48% to 91.14% during 2010. A coefficient of determination of 17.37% was 

highest in Induslnd bank, which indicates that about 17.37% of the variation in stock 

price of Indulsand (the independent variable) can be explained by the relationship to 

EVA of Induslnd bank (the dependent variable). Only Kotak Mahindra bank has 

positive correlation between Kotak Mahindra Bank EVA & Kotak Mahindra Bank 

share prices. Rest of banks, has negative relation between their respective EVA & share 

price. Hypotheses were developed to test significant impact of EVA on stock price of 

bank & that hypothesis was tested by using ANOVAs. For none of the bank EVA has 

Impact on share price, except EVA by Kotak Mahindra bank did have significant 

impact on stock price of Kotak Mahindra bank. 

Key Words: Economic value added, Share Price, Net operating profit after tax, Return 

on invested capital, weighted average cost of capital 


1 Indian banking has seen many changes in the last decade like imposition of prudential standards, greater 

competition among banks, etc. This paradigm shift in the Indian banking sector can be seen in terms of 

two dimensions: One relates to operational aspect especially performance and risk-management system 

and the second dimension relate to structural and external environment. Is evaluating Indian bank’s 

performance a rather straight forward issue The answer is no. One might say that like a corporate, even 

banks can be judged from the behavior of their stock prices. However, as bank stocks have not been very 

active on exchanges, barring few on few occasions, we should conclude that Indian banks have by and 

large failed to add values to their shareholders’ wealth. 

1 The IUP Journal of Accounting Research and Audit Practices, 2009, vol. VIII, issue 3-4, pages 52-60 

http://www.rbi.org.in “An study on EVA by pubic and privet bank” research done by uday and jay (2009) 

24 





2. LITERATURE REVIEW 

2 Lenn, K., Makhiija, A.K. (1996), has done a study on title “EVA and MVA as performance measures 

and signals for strategic change”. The study used data from 241 firms for time slap of 1987-96, showed 

that EVA and MVA effectively measured the quality of strategic decisions and served as signals of 

strategic change. They were found to be significantly correlated with stock price performance and 

inversely related to turnover. Firms having greater focus in their business activities had higher MVA than 

less focused counterparts. 3 Kramer, K. Jonathan and Pushner, George (1997), has done a study on title 

“An Empirical Analysis of Economic Value Added as a Proxy for Market Value Added”. They had tested 

the hypothesis that EVA is highly correlated with MVA. The study concluded that no clear evidence to 

support the contention that EVA is the best internal measure of corporate success in adding value to 

shareholder investments. 4 Banerjee, Ashok (1997) has done a study on title, “Economic Value Added 

(EVA): a better performance measure”. Ten industries have been chosen and each industry is represented 

by four/five companies. They have conducted an empirical research to find the superiority of EVA over 

other traditional financial performance measures. ROI and EVA have been calculated for sample 

companies and a 92 comparison of both has been undertaken, showing the superiority of EVA over ROI. 

Indian companies are gradually recognizing the importance of EVA. Some of such companies are 

Ranbaxy Laboratories, Samtel India Ltd and Infosys Technologies Ltd. 

5 Pattanayak, J.K., Mukherjee, K. (1998), under took a study on title “Adding Value to Money”. They has 

Discussed that there are traditional methods to measure corporate income or known as accounting concept 

and there is also a modern method to measure corporate income or known as economic concept. EVA, 

which is based on economic concept, is professed to be a superior technique to identify whether the 

organization’s NOPAT (Net Operating Profit after Tax) during a period is covering its WACC (Weighted 

Average Cost of Capital) & generating value for its owners. But it is very tricky to calculate EVA. 6 

Banerjee, Ashok and Jain (1999), under gone through a study on title “Economic Value Added and 

Shareholder Wealth: An Empirical Study of Relationship”. They have carried out a research based on 

empirical data. Among the selected independent variables (EPS, EVA, Kp, Lp and ARONW), EVA has 

proved to be the most explanatory variable, when MVA was taken as the dependent variable and 

Backward Elimination method was applied to find the most explanatory independent variable. For this 

purpose, the time frame was of eight years and all the variables were calculated over this period for the 

sample companies. 7 Anand, Manoj, Garg, Ajay, Arora and Asha (1999), has done a study on title 

“Economic Value Added: Business performance measure of shareholders’ value”. They found that EVA, 

REVA (Refined Economic Value Added) and MVA are better measures of business performance than 

NOPAT and EPS in terms of shareholders’ value creation and competitive advantage of a firm. 

2 Lenn, K., Makhiija, A.K. (1996), “EVA and MVA as performance measures and signals for strategic change”, 

Strategy and Leadership, Vol.24, May/June, 1996, pp. 34 - 38. 

3 Kramer, K. Jonathan and Pushner, George (1997), “An Empirical Analysis of Economic Value Added as a Proxy 

for Market Value Added”, Financial Practice and Education, Spring / Summer 1997, pp. 41-49 

4 Banerjee, Ashok (1997), “Economic Value Added (EVA): a better performance measure”, The Management 

Accountant, December 1997, pp. 886 – 888. 

5 Pattanayak, J.K., Mukherjee, K. (1998), “Adding Value to Money”, The Chartered Accountant, February 1998, pp. 

8-12. 

6 Banerjee, Ashok and Jain (1999), “Economic Value Added and Shareholder Wealth: An Empirical Study of 

Relationship”, Paradigm, Vol. 3, No. 1, January-June, 1999, pp. 99-133 

7 Anand, Manoj, Garg, Ajay, and Arora, Asha (1999), “Economic Value Added: Business performance measure of 

shareholders’ value”, The Management Accountant, May 1999, pp. 351-356. 

25 





3. RESEARCH METHODOLOGY 

3.1 Research Objectives 

1. To study the shareholders value (in terms of Economic Value Added) of selected private sector 

banks during the last five years. i.e. since 2004-05 to 2009-2010. 

2. To learn EVA and its applications to increase the shareholder’s wealth. 

3. To examine EVA in bank and its impacts on share price. 

3.2 Variables used in the study 

1. Net Operating profit after Taxes (NOPAT): (PBDT + Interest on RBI loans + Interest on others + 

Total other Income) Less Cash Taxes 

2. Invested capital: (Total equity & Reserves + Total borrowings) 

3. Return on invested capital (ROIC): (NOPAT / Invested capital) 

4. Cost of Equity (Ke): (Rf +b( Rm - Rf )) (capm) 

5. Weighted Average Cost of capital (WACC): Weighted cost of Equity + Weighted cost of Debt 

6. Economic Value Added (EVA): (ROIC – WACC) 

7. Market Value Added (MVA): (Market capitalization less Invested Capital) 

3.3 Research Design and Data 

The Study was done by applying descriptive research. Axis bank (formerly UTI Bank), HDFC bank, 

ICICI bank, Kotak Mahindra bank, Karnataka bank, IndusInd bank, ING Vysya bank was taken for 

Study. Data sources used in study are balance sheet, profit and loss account and share price of various 

banks. Data are collected from yahoo finance. 

3.4 Hypotheses of the study 

Hypotheses were developed to test significant impact of EVA on stock price of bank & that hypothesis 

was tested using ANOVAs. For none of the bank EVA has Impact on share price except EVA by Kotak 

Mahindra bank did have significant impact on Stock price of Kotak Mahindra bank. 

H1: EVA of Axis Bank did not have significant impact on Stock price of Axis Bank. 

H2: EVA by HDFC bank did not have significant impact on Stock price of HDFC bank. 

H3: EVA by ICICI bank did not have significant impact on Stock price of ICICI bank. 

H4: EVA by ING vyasya bank did not have significant impact on Stock price of ING VYASYA bank. 

H5: EVA by Indulsand bank did not have significant impact on Stock price of Indulsand bank. 

H6: EVA by Karnataka bank did not have significant impact on Stock price of Karnataka bank. 

H7: EVA by Kotak Mahindra bank did not have significant impact on Stock price of Kotak Mahindra 

bank. 

4. ECONOMIC VALUE ADDED (EVA) 

8 The concept of Economic Value Added was introduced by a New York based consulting firm M/s Stern 

Stewart & Co in early eighties. The corporate sector in India is gradually recognizing the importance of 

EVA as a result of which some Indian companies have started calculating EVA. Infosys Technologies Ltd 

is the first Indian company to report its EVA in the annual report. EVA attempts to measure true 

economic profit as it compares actual rate of return as against the required rate of return. EVA is an 

excess profit of a firm after charging cost of capital. EVA essentially seeks to Measure Company’s actual 

rate of return as against the required rate of return. To put it simply, EVA is the difference between Net 

Operating Profit after Tax (NOPAT) and the capital charge for both debt and equity (WACC- Weighted 

8 Internationally Indexed Journal,www.scholarshub.ne, Vol–II , Issue -1 January 2011 

26 





Average Cost of Capital). If NOPAT exceeds the capital charge (WACC), EVA is positive and if NOPAT 

is less than capital charge, EVA is negative. 

4.1 Computation of EVA: 

9 While computing EVA, capital employed represents capital invested at the beginning of the year. The 

logic behind taking beginning capital for computing EVA is that a company would take at least one year 

time to earn a return on investment. It may be mentioned here that calculation of EVA involves some 

tricky issues. Each element of EVA, therefore, has been discussed individually. EVA requires three 

different inputs for its computation. (A) NOPAT (Net Operating Profit after Tax) (B) Invested Capital (C) 

Weighted Average Cost of Capital (WACC). 

EVA = NOPAT - (WACC × Invested Capital) 

(A) Net Operating Profit after Tax (NOPAT): 

10 Stewart (1991) defined NOPAT as the “Profits derived from company’s operations after taxes but 

before financing costs and non-cash book keeping entries. Such non-cash book keeping entries do not 

include depreciation since depreciation is considered as a true economic expense. In other words, NOPAT 

is equal to the income available to shareholders plus interest expenses (after tax). 

(B) Invested Capital / Capital Employed: 

Invested capital or capital employed refers to total assets net of non-interest bearing liabilities. From an 

operating perspective, invested capital can be defined as Net Fixed Assets plus Investments plus Net 

current assets. Net current assets denote current assets net of non-interest bearing current liabilities. From 

a financing perspective, the same can be defined as Net Worth plus total borrowings. Total borrowings 

denote all interest bearing debts 

(C) Weighted Average Cost Of Capital (WACC): 

For calculating WACC, cost of each source of capital is calculated separately then weights are assigned to 

each source on the basis of proportion of a particular source in the total capital employed. Weights can be 

assigned on market value basis or book value basis. Stewart suggested market value basis. WACC can be 

calculated as below: 

WACC = E/CE × Ke + LTB/CE × Kd 

Where: E = Equity Capital, 

Ce = Capital Employed, 

Ltb = Long Term Borrowings, 

Ke = Cost Of Equity Capital, 

Kd = Cost Of Debt Capital Wacc Includes Two Specific Costs Viz., 

(I) Cost Of Equity (Ke), (Ii) Cost Of Debt (Kd). 

Calculation of Cost of Debt (Kd): 

Cost of debt is calculated by multiplying the pre-tax debt cost by (1−t), Where ‘t’ refers the effective tax 

rate. This will furnish the post tax cost of debt. The post tax cost of debt is calculated because debt cost 

9 When, EVA is greater than zero, value is created during the period for the bank and if EVA is less than zero, value 

is destroyed during the period. In order to create values, ROIC for a bank must be greater than weighted average 

cost of capital 

10 SS-1000 data is published annually by Stern Stewart and Company Ltd.( http://www.sternstewart.com) 

27 





enjoys tax shield. In other words, tax reduces the effective cost of debt. Cost of debt can be calculated by 

applying the following formula: 

Cost of Debt = (Total Interest Expense / Beginning Total Borrowings) × (1−t) × 100 

Calculation of Cost of Equity (Ke): 

11 The cost of equity can be calculated by the Capital Asset Pricing Model (CAPM). The CAPM is 

normally used to determine minimum required rates of return from investment in risky assets. Stewart 

also used CAPM consistently as a measure for cost of equity in his methodology for computing EVA. 

The expected return on equity can be calculated under CAPM by applying the formula given below: 

Rj = Rf + b (Rm − Rf ) 

Where Rj = Expected Return on Scrip j, 

Rf = Risk free rate of return, 

b = Beta representing the volatility of scrip j against market volatility. 

Rm = Expected stock market return. 

4.2 EVA Calculation for Indian Private Sector banks 

(A) Net Operating Profit after Tax 

The NOPAT curriculum includes Interest Income, other Income deducting interest on deposit and other 

operating expenses less tax so as to give an overall emphasis for operating profit. Net operating profit is 

considered instead of net profit so as to highlight the economic value of a firm. 

NOPAT = (Net Profit + Provisions and contingencies + Interest expenses) less (Taxes) 

TABLE 1. Net Operating Profit after Tax 

Name of Banks 2005 2006 2007 2008 2009 2010 

Axis Bank 1528.63 2407.7 3744.13 5931.5 9569.91 10031.17 

HDFC Bank 2324.64 3531.09 5310.62 8132.99 13515.93 12835.84 

ICICI Bank 10075.89 15320.2 23807.43 32622.84 31516.36 25956.79 

ING Vyasya Bank 929.3 997.37 1095.66 1459.83 2002.28 1963.11 

Induslnd Bank 1090.08 1103.85 1449.51 1758.64 2173.62 2314.9 

Karnataka Bank 705.37 864.7 1052.84 1331.3 1681.31 2037.12 

Kotak Bank 274.64 513.45 933.98 1845.28 2142.25 2318.91 

Above table shows NOPAT of various banks over a period of time. Among all studied banks, it was 

found that in year 2010, ICICI bank has maximum NOPAT. HDFC bank again has NOPAT of 

Rs.12835.84 & it stood on second position among all banks. NOPAT of Kotak Mahindra bank was grown 

with 213.34% from year 2005 to 2010. The NOPAT of Axis bank was grown at 188.13 % over a period. 

NOPAT of HDFC bank & Karnataka bank was grown at rate of 170.87% & 106.06%, respectively. ICICI 

bank & indusland bank has recorded a growth rate in NOPAT at rate of 94.63% & 75.31%, respectively. 

NOPAT of ING Vyasya bank was grown at lowest growth rate of 74.79%. 

TABLE 2. Invested Capital (In Rs.) 

Invested Capital Mar 2005 Mar 2006 Mar 2007 Mar 2008 Mar2009 Mar 2010 

Axis Bank 4189.6 5553.11 8588.83 14392.54 25734.67 33214.16 

28 





HDFC Bank 9309.86 8158.01 9248.54 16092.16 24216.38 34438.18 

ICICI Bank 46444.46 61077.9 75919.29 112468.6 142688.5 145881.9 

ING Vyasya Bank 1540.18 2127.12 1946.83 2785.45 4888.21 6002.31 

Induslnd Bank 1439.86 1401.01 1649.3 2445.15 4481.35 7331.52 

Karnataka Bank 1221.7 1293.82 1659.36 1521.8 1571 2174.39 

Kotak Bank 1652.68 2461.97 6733.25 8712.95 10639.54 10680.42 

Above table shows invested capital in all banks over a period of time. Among all studied banks, it was 

found that in year 2010, HDFC bank has maximum Invested Capital & Karnataka bank has minimum 

Invested capital. Axis bank has highest growth in Invested capital of 207.04% during 2005-2010. Invested 

capital of Kotak Mahindra bank was grown at rate of 186.60% & 162.76%. ING Vyasya bank invested 

capital was grown at rate of 136.02% during 2005-2010. Invested capital of HDFC bank & ICICI bank 

was recorded a growth rate of 130.81% & 114.45% during 2005 to 2010. Karnataka bank has lowest 

growth of 57.65% in invested capital. 

Beta 

Beta can be defined as a risk measuring factor for different capital allotments. Higher the Beta, higher the 

risk. Beta here has been calculated based on stock prices visa a versa BSE for each bank over all Beta of 

2004-05 to 2009-10. 12 

nExy - (Ex) (Ey) ÷ nEx2 - (Ex)2 

TABLE 3: Beta of Private Sector Banks 

Bank 

HDFC 

Bank 

IndusInd 

Bank 

Karnataka 

Bank 

ING Vyasya 

Bank 

Axis 

Bank 

ICICI 

Bank 

Kotak Mahindra 

Bank 

Beta 0.94 1.38 0.90 0.91 1.24 1.40 1.58 

Risk free rate 5.04% (April 2010) 

Above table shows that Kotak Mahindra bank has higher risk from higher beta of 1.58. Following to it, 

the Induslnd bank has 1.38 beta at second position. ICICI Bank & Axis bank has beta value of 1.40 & 

1.24 respectively. ING Vyasya bank has 0.91 beta which is lower than as compare to other banks. HDFC 

bank & has beta of 0.94 where as Karnataka bank has lowest beta of 0.90. From this it was found that 

Kotak Mahindra bank has highest risk & Karnataka bank has lowest risk among all private sector banks. 

We are examine the data has lower beta to lower NOPAT. So beta has related to growth of company 

performance. 

Economic Value Added (in Rs.) 

For Banks under study EVA is calculated by using following formula: 

Economic Value Added (in Rs.) = NOPAT - (WACC x Invested Capital) 

TABLE 4. Economic Value Added (in Rs.) 

Bank 

EVA 

(Average) 

Axis 

Bank 

HDFC 

Bank 

ICICI 

Bank 

Indulsand 

Bank 

ING Vyasya 

Bank 

Karnataka 

Bank 

Kotak Mahindra 

Bank 

5169.68 7271.49 20024.13 1552.15 1328.5 1250.22 1142.08 

Above table shows Average Economic value added (in Rs.) during 2005-2010. ICICI bank has higher 

amount of EVA at Rs. 20024.13. HDFC bank has EVA of Rs. 7271.49. Axis bank & indusland bank has 

EVA of Rs.5169.68 & Rs.1552.15. ING Vyasya bank & Karnataka bank has EVA of Rs. 1328.5 & Rs. 

1250.22, respectively. Kotak Mahindra bank has lower EVA at Rs. 1142.08. 

12 http://www.rbi.org.in/ 

29 





5. RETURN ON INVESTED CAPITAL 

The return on invested capital signifies the return that the firm earns on the capital invested for a given 

period of time. 

Return on Invested Capital = NOPAT / Invested capital 

TABLE 5. Return on Invested Capital 

Table Return on Invested Capital (ROIC) of Private Banks 

ROIC 2005 2006 2007 2008 2009 2010 

Axis Bank 36.49% 43.36% 43.59% 41.21% 37.19% 30.20% 

HDFC Bank 24.97% 43.28% 57.42% 50.54% 55.81% 37.27% 

ICICI Bank 21.69% 25.08% 31.36% 29.01% 22.09% 17.79% 

ING Vysya Bank 60.34% 46.89% 56.28% 52.41% 40.96% 32.71% 

IndusInd Bank 75.71% 78.79% 87.89% 71.92% 48.50% 31.57% 

Karnataka Bank 57.74% 66.83% 63.45% 87.48% 107.02% 93.69% 

Kotak Mahindra Bank 16.62% 20.86% 13.87% 21.18% 20.13% 21.71% 

Above table shows that Axis bank has ROIC of 36.49 % in year 2005 which was decreased at 30.20 % in 

year 2010. HDFC bank ROIC was at 24.97% which was increased over a period of time but started to 

decrease from year 2008 & in year 2010 it decrease to 37.27%. ICICI bank ROIC was at 21.69 % which 

was increased over a period of time but started to decrease from year 2008 & in year 2010 it was decrease 

to 17.79%. ING Vyasya bank ROIC was grown at 60.34%. Over a time, it was remaining fluctuated & 

decreased to 32.71% in year 2010. IndusInd Bank RIOC was at 75.71% which was remain fluctuated over 

a period of time & decreased to 31.57% in year 2010. ROIC of Karnataka bank was 57.74% in year 2005 

which was remain in increasing trend & come at 93.69%. ROIC of Kotak Mahindra Bank was 16.62% in 

year 2005 & remain in fluctuating trend & increased at 21.71% in year 2010. 

6. ECONOMIC VALUE ADDED (IN %) 

Economic value added (in %) can be calculated by using following formula. 

Economic Value Added (In %) = ROIC – WACC 

TABLE 6: Economic Value Added (In %) 

Table Economic Value Added (EVA) (In %) in Private Sector Bank 

EVA (%) 2005 2006 2007 2008 2009 2010 

Axis Bank 34.30% 41.25% 40.88% 39.20% 34.34% 27.96% 

HDFC Bank 23.32% 41.56% 56.21% 48.60% 53.39% 35.42% 

ICICI Bank 18.84% 22.37% 28.33% 25.35% 18.39% 14.48% 

ING Vyasya Bank 57.97% 44.73% 53.88% 50.33% 38.00% 30.36% 

IndusInd Bank 73.56% 76.34% 85.14% 68.34% 44.77% 28.47% 

Karnataka Bank 55.63% 66.38% 62.55% 86.60% 105.36% 91.14% 

Kotak Mahindra Bank 14.35% 18.19% 11.43% 18.02% 16.45% 18.96% 

The EVA of Axis bank was 34.30% in 2005 which was remain in a increasing trend till 2007 but from 

2008 onwards it started to decrease and in 2010 it was 27.96%. In HDFC bank the EVA increase from 

2005 to 2007 but in 2008 it was decrease to 48.60% after that it increase for one year than after again it 

was decrease. The EVA of ICICI bank increase from 18.84% to 28.33% in 2005 to 2008 but after that it 

30 





was decrease from 25.35% to 14.48% in 2008 to 2010. In ING Vyasya bank the EVA was decrease over a 

year rather than in 2007. The EVA in IndusInd bank was remaining same as ICICI bank means it was 

increase for three years than it was decrease for last three years. The performance of Karnataka bank was 

good in comparison with other banks, the EVA was increase from 55.63% to 105.36% in year 2005 to 

2009 but in 2010 it was slight decrease to 91.14%. In Kotak Mahindra bank the EVA value was change 

year by year means it was increase in one year than in second year it was decrease. 

DATA ANALYSIS 

The data analysis was carried out by adopting Descriptive statistics, Correlation & regreassion analysis. 

Descriptive statistics 

Particulars 

EVA 

Axis 

Bank 

Share 

Price 

EVA 

HDFC 

Bank 

TABLE 7. Normality Test 

Share 

Price 

EVA 

ICICI 

Bank 

Share 

Price 

ING 

Vyasya 

Bank 

Share 

EVA Price 

Indulsand 

Bank 

EVA 

Share 

Price 

Karnataka 

Bank 

EVA 

Share 

Price 

Kotak 

Mahindra 

Bank 

Share 

EVA Price 

Mean 0.36 0.31 0.47 0.25 0.62 0.24 0.77 -0.0 0.16 0.10 0.21 0.19 0.45 -0.0 

Median 0.39 0.38 0.48 0.32 0.70 0.07 0.76 0.25 0.17 0.049 0.20 0.32 0.47 0.07 

Standard Deviation 0.05 0.60 0.08 0.35 0.21 0.85 0.19 0.69 0.03 0.62 0.05 0.62 0.10 0.80 

Kurtosis 0.38 2.30 -1.5 1.94 -0.4 1.25 -1.6 -0.7 0.22 -0.40 -0.9 1.34 -0.8 -1.8 

Skewness -1.2 -0.9 -0.4 -0.8 -0.9 0.67 0.30 -0.9 -1.0 -0.04 0.13 -0.5 -0.5 -0.4 

Skew is a measure of symmetry. In our Test, we have found that skewness of distribution is greater than 

0.00. A normal distribution has skewness = 0. So we can say that our distribution is not symmetric. 

Kurtosis is a measure of peakeness and the fat-tails that associate with less density in the middle. A 

normal distribution has kurtosis = 3.0 or excess. Here Kurtosis is less than 3.00. So we can say that our 

distribution is not symmetric. Data of Private sector Bank EVA & stock prices are not a normally 

distributed & "the sample is drawn from a normally distributed population." But we have found that our 

samples are not a normally distributed population which reflects that further test can be applied. 

TABLE 8: Correlation between Bank EVA & Bank Share Price in Private Sector Banks 

Correlation 

Correlation Value 

Correlation Between Axis Bank EVA & Axis Bank Share Price -0.26 

31 





Correlation Between HDFC Bank EVA & HDFC Bank Share Price -0.61 

Correlation Between ICICI Bank EVA & ICICI Bank Share Price -0.17 

Correlation between ING Vyasya Bank EVA & ING Vyasya bank Share price -0.32 

Correlation Between Indusland Bank EVA & Indulsand Bank Share Price -0.41 

Correlation Between Karnataka Bank EVA & Karnataka Bank Share Price -0.29 

Correlation Between Kotak Mahindra Bank EVA & Kotak Mahindra Bank Share Price 0.02 

The correlation between EVA and stock price for Kotak is 0.02995 which is somewhat positive and 

negative of the Axis, HDFC, ICICI, ING Vyasya, Indulsand, Karnataka Banks have to between EVA and 

Market Value. Correlation between EVA and Market Value has showed too negative HDFC bank. 

Negative correlation show one variable is upside and second variable downsides visa a versa and positive 

correlation show two variables in upside or downside visa a versa. 

TABLE 9: Coefficient of Determination 

Banks Coefficient of Determination 

Axis bank 0.071685 

HDFC bank 0.374525 

ICICI bank 0.031106 

ING Vyasya bank 0.102826 

Among all banks, Induslnd bank 0.173659 

HDFC bank has 

highest coefficient of Karnataka bank 0.085797 

determination. A 

coefficient of 

Kotak Mahindra bank 0.000897 

determination equal to 

0.37452 indicates that about 37.45% of the variation in Stock price of HDFC bank (the independent 

variable) can be explained by the relationship to EVA of HDFC bank (the dependent variable) which can 

be considered a Good fit to the data. Again for Indusind bank, a coefficient of determination equal to 

0.17365 indicates that about 17.37% of the variation in Stock price of Indulsand (the independent 

variable) can be explained by the relationship to EVA of Indulsand (the dependent variable), which can 

be considered a Moderate fit to the data. For Kotak Mahindra bank, ICICI bank, ING Vyasya bank, Axis 

bank and Karnataka bank the coefficient of determination is lower. It indicates that variation in Stock 

price of Particular bank (the independent variable) can be explained by the relationship to EVA of 

respective bank (the dependent variable) which can be considered a bad fit to the data. 

Hypothesis Testing 

The hypotheses were studied using ANOVA. The p-value of ANOVA for Axis bank, HDFC Bank, ICICI 

Bank, ING Vyasya bank, IndusInd bank and Karnataka bank is 0.607, 0.197, 0.738, 0.535, 0.411 and 

0.573 respectively. All p-value of ANOVAs is greater than 0.05, except Kotak Mahindra bank, which 

enhances that none of bank, has significant impact of EVA over their Stock Price. The p-value of 

ANOVA for Kotak Mahindra bank is 0.025, which is lesser then 0.05. It further enhances that EVA by 

Kotak Mahindra bank did have significant impact on Stock price of Kotak Mahindra bank. 

CONCLUSION 

32 





The Study was done to determine the shareholders value (in terms of Economic Value Added) of private 

sector banks during the last five years. i.e. since 2004-05 to 2009-2010. The coefficient of determination 

for all banks is ranging from 0.089% to 37.45%. HDFC Bank has highest coefficient of determination. 

The coefficient of determination equal to 0.374525 indicates that about 37 % of the variation in Stock 

price of HDFC Bank (the dependent variable) can be explained by the relationship to EVA of HDFC 

Bank (the independent variable). This would not be considered a good fit to the data. The correlation 

between EVA and stock price for Kotak Mahindra Bank is 0.02995 which is somewhat positive. The 

correlation between EVA and Market Value of Axis bank, HDFC bank, ICICI bank, ING Vyasya bank, 

Indulsand bank, Karnataka Banks was negative. 

REFERENCES 

Anand, Manoj, Garg, Ajay, and Arora, Asha (1999), “Economic Value Added: Business performance 

measure of shareholders’ value”, The Management Accountant, May 1999, pp. 351-356. 

Banerjee, Ashok (1997), “Economic Value Added (EVA): A better performance measure”, The 

Management Accountant, December 1997, pp. 886 – 888. 

Banerjee, Ashok and Jain (1999), “Economic Value Added and Shareholder Wealth: An Empirical Study 

of Relationship”, Paradigm, Vol. 3, No. 1, January-June, 1999, pp. 99-133 

Debdas Rakshit,(2006) EVA based performance measurement:A case study of dabur india limited, 

Vidyasagar University Journal of Commerce Vol.11, March 2006. 

Dr. Anil K. Sharma, Satish Kumar, Economic Value Added (EVA) (2010) - Literature Review and 

Relevant Issues, International Journal of Economics and Finance, Vol. 2, No. 2; May 2010. 

Dimitrios I. Maditinos, Željko Šević, Nikolaos G. Theriou, Economic Value Added (EVA ® ). Is it really 

the best performance measure A Review of the Theoretical and Empirical Literature. The case of 

Athens Stock Exchange (ASE), Review of Economic Sciences, TEI of Epirus, Forthcoming. 

Dr. D.V. Ramana, Market Value Added and Economic Value Added: Some Empirical Evidences. 

Gabriela POPA, Laurentiu mihailescu, Codin CARAGEA (2009), EVA – Advanced method for 

performance evaluation in banks, The Ninth International Conference “Investments and Economic 

Recovery”, May 22 – 23, 2009. 

Ghanbari, M. Ali and Sarlak, Narges (2006), “Economic Value Added: An Appropriate Performance 

Measure in the Indian Automobile Industry”, The Icfain Journal of Management Research, Vol. V, 

No. 8, 2006, pp. 45-57. 

Kramer, K. Jonathan and Pushner, George (1997), “An Empirical Analysis of Economic Value Added as 

a Proxy for Market Value Added”, Financial Practice and Education, Spring / Summer 1997, pp. 41- 

49 

Lenn, K., Makhiija, A.K. (1996), “EVA and MVA as performance measures and signals for strategic 

change”, Strategy and Leadership, Vol.24, May/June, 1996, pp. 34 - 38. 

33 





Malik, Madhu, (2004), “EVA and Traditional Performance Measures: Some Empirical Evidence”, The 

Indian Journal of Commerce, Vol. 57, No. 2, April-June 2004, pp. 32-37. 

Pal Singh, Karam and Garg.C. Mahesh, (2004), “Disclosure of EVA in Indian Corporate”, The Indian 

Journal of Commerce, Vol. 57, No. 2, April-June 2004, pp. 39-49. 

Panigrahi, Anupam (2005), “Supremacy of Economic Value Added (EVA) Over Market Value Added 

(MVA)”, ABHIGYAN, Vol. XXIII, No. 1, April-June 2005, pp. 26-35. 

Parsuraman, and Thamy ( 2000) The IUP Journal of Accounting Research and Audit Practices, 2009, vol. 

VIII, issue 3-4, pages 52-60 

Pattanayak, J.K., Mukherjee, K. (1998), “Adding Value to Money”, The Chartered Accountant, February 

1998, pp. 8-12. 

Ramachandra Reddy, B. and Yuvaraja Reddy, B, (2007), “Financial Performance through Market Value 

Added (MVA) Approach”, The Management Accountant, January 2007, pp. 56-59. 

Singh, Prakash (2005), “EVA in Indian Banking: Better Information content, More Shareholder Value”, 

ABHIGYAN, Vol. XXIII, No. 3, October-December 2005, pp. 40-49 

Venkateshwarlu, M. and Nitesh Kumar, (2004), “Value Creation in Indian Enterprises – An Empirical 

Analysis”, the ICFAI Journal of Applied Finance, Vol. 7, No. 1, December 2004, pp. 18-31 

34 





Leverage Impact on Firms Investment 

Decision: A Case Study of Indian 


Dr. Amalendu Bhunia 

Reader, Department of Commerce 

Fakir Chand College, Diamond Harbour 

South 24-Parganas – 743331 

West Bengal, India 

ABSTRACT 

This paper examines the impact of leverage on firm’s investment decision of Indian 

pharmaceutical companies during the period from 2000 to 2011. To measure the 

impact of leverage on firm’s investment decision, pooling regression, random and 

fixed effect models are used by taking, leverage, sales, cash flow, Return on Asset, 

Tobin’s Q, liquidity and retained earnings as independent variable and investment as 

dependent variable. The results reveal that there is a significant positive relationship 

between leverage and investment, while we found a negative relationship between 

leverage investment for medium firms and positive relationship between leverage 

and investment in large firms. 

Keywords: Investment, Tobin’s Q, Liquidity, ROA, Retained Earnings 


Investment is a crucial economic activity in the corporate financial management. Such an activity leads to 

the country’s economic development provide employment to the people and to eliminate poverty. This 

paper examines the effort of debt financing on the firms investment decision on pharmaceutical industry 

in India. It plays a significant role in the country’s economic and industrial development and trade and to 

prevent diseases’ for increasing the life of people. This industry is providing a basic material to other 

industrial sectors. It requires capital for financing firm’s assets. Among the different sources of fund, 

debt is a cheaper source because of its lowest cost of capital. The investment decision of the firm is of 

three categories that can be adopted by firm’s management besides the financing decision and 

the net profit allocation decision. The investment structure, more over in their degree of liquidity and 

consists of spending the financial funds for the purchase of real and financial assets for the firm. The 

investment decision and the financing decision are interdependent that is the investment decision 

is adopted in relation to the level of financing source but the option to invest is also crucial in order 

to calculate the level of financing capitals and the need for finding their sources. 

As far as the hierarchy of financing sources as it exists in the economic literature, is 

concerned, cash flow is the cheapest financing sources followed by debts and in the end, by 

its issuing of new shares. Debts can be cheaper than the issue of new shares because the loan 

contract the issue of new shares because the loan contract information problem. Giving the fact the 

35 





degree of information asymmetry and the agent costs depend on the peculiarities of every firm, such firms 

are more sensitive to financial factors than other. The debt limit of the firms is determined in the view, 

since interest payment is tax deductible, the firm prefers debt financing to equity and it would rather have 

an infinite amount of debt, However, this leads to negative equity value in some status so that the firm 

would rather go bankrupt instead of paying its debt. Therefore debt to remain risk-free, lenders will limit 

the amount of debt. They can limit the debt by accepting the resale value of capital as collateral and 

ensuring that this value is not lower than the amount of debt, so that they can recover their money in case 

of bankruptcy. Alternatively, lenders may limit the amount of debt in order to ensure that the marker 

value of equity is always non-negative and bankruptcy is sub-optimal for the firm. 

While there is by now a rapidly expanding literature on the presence of finance constraints on investment 

decisions of firms for developed countries, a limited empirical research has been forthcoming in the 

context of developing countries for two main reasons. (i) Until recently, the corporate sector in emerging 

markets encountered several constraints in accessing equity and debt markets. As a consequence, any 

research on the interface between capital structure of firms and finance constraints could have been 

largely constraint- driven and have less illuminating. (ii) Several emerging economies, even until the late 

1980s, suffered from financial depression, with negative real rates of interest as well as high levels of 

statutory pre-emption. This could have meant a restricted play of market force for resource allocating. 

Issues regarding the interaction between financing constraint and corporate finance have, however, gained 

prominence in recent years, especially in the context of the fast changing institutional framework in these 

countries. Several emerging economies have introduced market-oriented reforms in the financial sector. 

More importantly the institutional set-up within which corporate houses operated in the regulated era has 

undergone substantial transformation since the 1990s. The moves towards market-driven allocation of 

resources, coupled with the widening and deepening of financial market, have provided greater scope for 

corporate house to determine their capital structure. 

The rest of the paper unfolds as follows. Section II discuses the historical background of the study. 

Section III explains data and methodology of the paper. Section IV presents the empirical results and 

discusses robustness check followed by the conclusions in the final section. 

2. REVIEW OF LITERATURES 

Several authors have studied the impact of financial leverage on investment. They reached conflicting 

conclusions using various approaches. When we talk about investment, it is important to differentiate 

between overinvestment and under-investment. Modigliani and Miller (1958) argued that the investment 

policy of a firm should be based only on those factors that would increase the profitability, cash flow or 

net worth of a firm. Many empirical literatures have challenged the leverage irrelevance theorem of 

Modigliani and Miller. The irreverence proposition of Modigliani and Miller will be valid only if the 

perfect market assumptions underlying their analysis are satisfied. However the corporate world is 

characterized by various market imperfections costs, institution restrictions and asymmetric information. 

The interaction between management, shareholders and debt holders will generate frictions due to agency 

problems and that may result to under-investment or over-investment incentives. As stated earlier one of 

the main issues in corporate finance is whether financial leverage has any effects on investments policies. 

Myers (1977), high leverage overhang reduces the incentives of the shareholder-management coalition in 

control of the firm to invest in positive net present value of investment opportunities, since the benefits 

accrue to the bondholders rather than the shareholders thus, highly levered firm are less likely to exploit 

valuable growth opportunities as compared to firm with low levels of leverage a related under investment 

theory centers on a liquidity affect in that firm with large debt commitments invest less no matter what 

their growth opportunities. Theoretically, even if leverage creates potential underinvestment incentives, 

36 





the effect could be reduced by the firm corrective measures. Ultimately, leverage is lowered if future 

growth opportunities are recognized sufficiently early. 

Another problem which has received much attention is the overinvestment theory. It can be explained as 

investment expenditure beyond that requires to maintain assets in place and to finance expected new 

investment in positive NPV projects where there is a conflict between manager and share holder. 

managers perceive an opportunities to expand the business even if the management under taking poor 

projects and reducing shareholders welfare .The managers’ abilities’ to carry such a policy are restrained 

by the availability of cash flow and further tightened by the financing of debt. Hence, leverage is one 

mechanism for overcoming the overinvestment problem suggesting a negative relationship between debt 

and investment for firm with low growth opportunities. Does debt financing induce firms to make overinvestment 

or under-investment The issuance of debt commits a firm to pay cash as interest and 

principal. Managers are forced to service such commitments .too much debt also is not considered to be 

good as it may lead to financial distress and agency problems. 

Hite (1977) demonstrates a positive relationship because given the level of financial leverage an 

investment increase would lower financial risk and hence the cost of bond financing. In contrast Deangels 

and Masulis (1980) claim a negative relationship since the tax benefit of debt would compete with the tax 

benefit of capital investment.Dotan and Ravid (1988) also show a negative relationship because 

investment increase would raise financial risk and hence the cost of bond financing how the investment 

increase affects financial risk and the sub suitability between tax shields and hence; financial leverage 

may depend on firm-specific factors. 

Jensen (1986) points out that liabilities can help avoid overinvestment by reducing the cash flow left up to 

corporate manager’s own discretion and constraining investment in investment projects that might be 

desirable for corporate mangers but not desirable for companies’ future profitability. Jensen argues that 

whether liabilities restrain overinvestment depends largely on whether companies have growth 

opportunities. In short, Jensen points out those liabilities have not only the negative effects of restraining 

overinvestment by low-growth companies. Like Jensen (1986), Stulz (1990) and Hart and Moore (1995) 

argue that liabilities effectively restrain overinvestment. They reason that increased liabilities, by 

enlarging repayment obligations, not only curtail free cash flow but also raise the possibility of corporate 

bankruptcies, thus prompting corporate managers to reduce investment and sell off unprofitable business 

divisions. 

Daddon and Senbets (1988) hypothesis on the relationship between bond financing and capital investment 

which is conditional on from specific variables such as tax shield, retention ability, capital intensity and 

insider equity ownership. Josephic Kang (1995) found that the level of bond financing has negative 

relationship with level of investment. 

Whited (1992) has shown how investment is more sensitive to cash flow in firms with high leverages as 

compared to firms with low leverage. Cantor (1990) showed that investment is more sensitive to earnings 

for highly levered firmsMc connell and Servaes (1995) have examined a large sample of non financial 

United State firms for the years 1976, 1986 and 1988. They showed that for high growth firms the 

relationship between corporate value and leverage is negatively correlated. Also the allocation of equity 

ownership between corporate insiders and other types of investors is more important in low growth than 

in high growth firms. 

McConnelll and Servaes (1995) use cross-sectional data to analyze U.S listed companies in 1976, 1986 

and 1988, and find “two faces of debt,” meaning that enterprise value was negatively correlated with the 

debt ratio of companies with high growth opportunities. Lang et al. (1996), based on an analysis of the 

relationship between the debt ratio and the rate of growth of companies, point out that for companies with 

37 





fewer investment opportunities (i.e. companies with a low Tobin’s Q), there is a negative correlation 

between the debt ratio and the investment. The estimation results from their studies do not find a negative 

correlation between the debt ratio and the growth rate for companies with abundant growth opportunities. 

In other words, for companies with investment opportunities, increased liabilities do not necessarily 

hamper growth. 

Lang et al (1996) found that there is negative relation between leverage and future growth at the firm 

level and for diversified firms, at the business segment level. Also debt financing does not reduce growth 

for firms’ known to have good investment opportunities, but it is negatively related to the growth for 

firms whose growth is not recognized by the capital market. 

Myers (1997) has examined possible difficulties that firms may face in raising finance to materializing 

positive net present value (NPV) projects, if they are highly geared. Therefore, high leverage may result is 

liquidity problem and can affect a firm’s ability to finance growth. Under this situation, debt overhang can 

contribute to the under-investment problem of debt financing. That is for firms with growth opportunities 

debt have a negative impact on the value of the firm. 

Ahn et al. (2000) found that diversified companies tend to have higher debt ratio thanfocused counterparts 

and diversified companies make larger investments (net cost of capital/sales) than focused counterparts. 

They also point out that debt ratio influence management decisions on investment and that diversified 

companies can overcome debt ratios through the distribution of liabilities by corporate managers.Arikawa 

et al, (2003) adopt the method of estimation used by Lang et al. (1996) and point out that the main bank 

system in Japan helped amplify the disciplinary function of liabilities, particularly for low-growth 

companies.Aivazian et al (2005) analyses the impact of leverage on investment on Canadian industrial 

companies cover the period from 1982 to 1999. They found a negative relationship between investment 

and leverage and that the relationship is higher for low growth firms rather than high growth firms. 

Ahn et al (2006) found that diversified companies tend to have higher debt ratios then focused counter 

parts and diversified companies make larger investments than focused counter parts. They also point out 

that debt ratio influence management decisions on investments and that diversified companies can 

overcome the constraints of debt ratio through the distribution of liabilities by corporate managers. 

Mohan Prasad Sing, Odit and Chitto (2008) analyze the impact of leverage on firms’ investment on 27 

maturation firms that are quoted on the stack exchange Mauritians for the year 1990 – 04. They found that 

leverage has a significant negative effect on investments, Suggesting that capital structure plays an 

important role in the firms investment policies while the negative relationship persist for low growth firm, 

this is not the case for high growth firm. 

Thus the previous studies have verified the impact of leverage on firm’s investment decision as well as 

the effect of leverage in restraining over investment and facilitating under investment. These studies 

suggest that leverage restraining over investment but likely cost under investment. Thus in this paper an 

attempt is made to more clearly the leverage impact on firms investment decision on pharmaceutical 

companies in India. 

3. DATA AND METHODOLOGY 

We estimate a reduced form of investment equation to examine the effect of leverage on investment the 

specification is similar to Aivazian Ge and Qiu (2005).This is as follows: 

Ii t/Ki, t-1= [CFit/Ki, t-1] + 1Qi, t-1 + 2 LEVi,t-1 3SALEi,t-1+ 4ROAi,t-1+ 5LIQiyt-1+ 6RETESi,t-1+ 

µi,t 

38 





Where Iit represents the net investment of firm i during the period t; Ki, t-1is the net fixed asset; CFit is 

t5he cash flow of firm i time t: Qi, t-1is the Tobin’s Q: LEVi,t-1 represents the leverage: SALEi, t-1 

stands for net sales of firm i ; ROAi,t-1 isthe profitability of the firm i; LIQiyt-1 represents liquidity of 

firm i : RETESi,t-1 is the retained earnings of the firm i . 

The data used in this paper are from the annual report of Indian pharmaceutical companies which are 

listed in Bombay stock exchange and this data have been collected from CMIE prowse data base of top 25 

companies based on sales from the period 2000 – 2011. 

We have used the same definition of leverage as lang. et al (1996), namely the ratio of total liabilities to 

the book value of total assets. We use prefect and wiles (1994) simple Q (market value + liabilities / book 

value of assets) as a proxy for growth opportunities defined as the market value of total assets of the firm 

divided by the book value of assets. Sale is measured as net sales deflated by net fixed assets. Cash flow 

is measured as the total of earning before extraordinary items and depreciation and is an important 

determinant for growth opportunities. In order to eliminate any size effect, we normalize this measure by 

taking the book value of assets; this method was utilized by Lehn and Poulson (1989) and Lan et al 

(1991). Profitability is measured in terms of the relationship between net profits and assets. It is calculated 

as earning after tax adds interest minus tax advantage on interest divided total fixed assets. The liquidity 

ratio is measured by the current assets divided by the current liability and is the ability of firms to meet its 

current obligations. Retained earnings represent the amount of business savings meant for ploughing 

back. These are the most favored sources of finance for corporate firms. There is a significant difference 

in the use of internally generated funds by the highly profitable corporate relative to the low profitable 

firms 

4. EMPIRICAL RESULTS AND ANALYSIS 

This section portrays the result from the regression estimation, we present result for the small size, 

medium size and larger sized firm is classified based on the size. The smaller size is obtained by 

subtracting mean from standard deviation of total asset and larger size is obtained by adding mean value 

of asset to standard deviation. The median sized firms are those firms which are not belong to both 

categories of the firm. The econometric result for the sample firms is showed the pooled estimates; 

random effect estimates and fixed effect estimates on the T values are shown in the parenthesis. Two 

statistics are used in order to identify, which methodology is appropriate to establish the relationship 

between leverage and investment. First we compare the pooled estimates and random effect estimates. 

The second Lagrangian Multiplier (LM) test is performed with a large chi-square values indicate of low 

P-value. We reject that the pooled estimate is appropriate. The second to compare random effect estimate 

with fixed effect estimate, the Hausman test is performed. If the model is correctly specified and it the 

effect are uncorrelated with independent variables the fixed effect and random effect should not be 

different a high chi-square value is indicate of appropriateness of the fixed effect. 

4.1 Result of Small Firms’ 

Table 1 brings out the regression result of small firms. It shows that the leverage has a positive impact on 

investment at the 5% significant level. The impacts of other variables on have the expected signs. The 

retained earnings have a significant positive impact on investment. To identify which empirical 

methodology pooling random effect or fixed effect regression is most suitable, we perform two statistical 

tests the first the Lagrangian Multiplier (LM) test of the random effect model. The null hypothesis is that 

individual effect ui is 0. The chi-square value is 25.74 thus the null hypothesis is rejected at 1% level of 

significance. The results suggest that the rho effect is not zero and the pooling regression is not suitable in 

this case the regression co-efficient leverage on small firms from the pooling regression is equal to 1.3451 

and is not significant. The regression co-efficient of leverage of firms from Random effect and fixed 

39 





effect model are 3.4868 and 1.8200 respectively. The regression co-efficient from the polling regression 

are much smaller than those estimated from the random effect and fixed effect models suggesting that 

ignoring individual firm effects leads to an underestimation of the impact of leverage on investment. 

Table 1:-Regression result of small firms 

We conduct the Hausman specification test to compare the fixed effect and the random effect models .If 

the model is correctly specified and of individual effects are uncorrelated with independent variables the 

statistics are showed that the null hypothesis is rejected at the 1% significance level. The results suggest 

that the fixed effect model is most appropriate in estimating the investment equation. 

Leverage is statistically significant at 1% and 5% level of significant and is positively related to 

investment. A 1 unit increase in the leverage leads to an increase by 3.4568 units in investments this 

implies that a leverage increases in small firms is also increase a investment of firms because firms do not 

have a adequate asset cushion for financing the projects. Thus, in a small sized firm tend to because more 

dependent on debt as a source of finance to finance the projects. 

The table also reveals that small firms are under utilizing their fixed assets and it would affect the ability 

in generating the volume of sales and the co-efficient value is -0.001 and it is not statistically 

significant.The co –efficient value of ROA is 0.0003 and is not statistically significant but positively 

related with investment. It indicates the operating efficiency of the employed funds over investment is 

positive. Higher the ROA is also attracting funds from investors for expansion and growth. 

40 





Cash flow and retained earnings are positively related with investments not statistically significant and 

coefficient value is 0.2264 and 0.0020 respectively. This implies that the issuance of debt engages the 

firm to pay cash as interest and principal with availability of free cash flow and internally generated 

funds. 

Liquidity is negatively related with investments and is not statistically significant and the regression coefficient 

value is 0.01667. It implies that the failure of a firm to meet its obligation due to lack of 

sufficient liquidity will result in poor credit worthiness loss of creditors confidence and this is not the case 

as shown by the results from the above table.From the table it is observed that Tobin’Q is negatively 

related with investments and not statistically significant. 

4.2 Result of Medium Firms’ 

Table-2 reveals that the regression results of medium firms. The calculated f value is greater than table 

value. Hence the selected variables are significantly associated with investment during the period. Further 

it shows that the leverage has no impact on investment in medium firm but it has negative relationship 

with investment during the period of study. 

Table 2:-Regression result of medium firms 

41 





In order to identify which methodology-pooling random effect or fixed effect regression model is most 

suitable, we perform two statistical tests, the first the LM test of the random effect model. The null 

hypothesis is that individual effect ui is o. The chi-square value is 4.15. Thus the null hypothesis is 

rejected at 1% level of significance. The results suggest that the rho effect is zero and the pooling 

regression is suitable in this case. The regression the co efficient of leverage on medium firms from the 

pooling regression equal to 1.6543 and is not significant. The regression coefficients on leverage from 

random and fixed effect model are 0.7797 and -1.6543 respectively. The regression co-efficient from the 

pooling regression are greater than the those estimated from the random and fixed effect model 

suggesting that the individual effect of a firm leads to an estimation of the impact of leverage on 

investment. 

We conduct the Hausman specification test to compare the fixed effect and random effect models. If the 

model is correctly specified and if individual effects are uncorrelated with independent variable, the fixed 

effect and random effect estimates should not be statistically different. Further these statistics are reported 

that the fixed effect model is most appropriate in estimating the investment equation because the R 2 value 

of fixed effect model is greater than random effect model. 

Leverage is not statistically significant at 1% and five per cent level of significance and is negatively 

related with investment. This implies that leverage has no impact in medium firm’s investment decision. 

It is because of inadequate cash flow and ploughing back of funds. Hence medium sized firms are making 

investment decision based on the internal financial resources. The table further reveals that the medium 

firms are under utilizing there fixed assets and it would effects the ability in generating the volume of 

sales and coefficient value is -0.0016 and is not statistically significant. The co efficient value of ROA is - 

0.0012 and is not statistically significant but negatively related with investment. The cash flow and 

Retained associated with in order earnings are positively associated with investment and they are 

statistically significant at 1% and 5% level of significant with investment. It indicates that higher the cash 

flow and retained funds higher will be the investment. Liquidity and Tobin’q are not statistically 

significant with investment the Tobin’Q also requested firm value and hence may be affected by leverage. 

But proxies in this do not Influence the investment because the leverage has no impact on investment in 

medium firms. 

4.3 Result of Large Firms’ 

Table-3 shows that the regression results of large firms. The calculated f value is greater than table value. 

Hence the selected variables significantly associated with investments during the period of study. Further 

it shows that the leverage has no impact on investment. 

Table 3:-Regression result of large firms 

42 





In large firms but it has positive relationship with investments during the period of study. In order to 

identify which methodology-polling, random effect fixed effect regression model is most suitable. We 

perform two statistical tests, the first the LM test of the random effect model. The null hypothesis is that 

individual effect ui is 0. The chi-square value is 2.26. Thus null hypothesis is rejected @ 1% level of 

significance. The results suggest that the rho effect is not zero and the pooling regression is not suitable in 

this case. The regression co-efficient of leverage on large firms from the pooling regression is equal to 

23.7516 and is not significant. The regression coefficients on leverage from random effect and fixed 

effect model 9.5758 and 23.7516 respectively. The regression co-efficient from the pooling regression 

Model is greater than those estimated from the random and fixed effect model suggesting that the 

individual effect of a firm leads to an estimation of the impact of leverage on investment. 

We conduct the Hausman specification test to compare the fixed effect and random effect model if the 

model is correctly specified and if individual effect are an correlated with independent variable the fixed 

and random effect are un correlated with independent variable the, fixed and random effect estimate 

should not be statistically different further these model is most appropriate that the fixed effect model is 

most appropriate in estimating the investment equation because the R 2 value of fixed effect model is 

greater than the random effect model. 

The table also revels that the co-efficient value of variables like sales, ROA and Tobin’Q are negatively 

related with investment and also they are not significant in the leverage firms.Cash flow and Retained 

earning’s positively associated with investment in large firms and are statistically significant it is because 

of heavy demand for its product in national and international market. Liquidity is negatively related with 

investment and is not statistically significant with investment. We conclude that the leverage is not 

influenced the investment decisions in large sized pharmaceutical firms in India. 

5. CONCLUSIONS 

This paper extends earlier empirical studies on the relationship between leverage and investment in 

several dimensions. It verified the relationship for top 25 Indian pharmaceutical firms that are quoted on 

the stock exchange of India for the year 2000 -2011. Prior theoretical work posits that financial leverage 

can have either a positive or a negative impact on the value of the firm because of its influence on 

corporate investment decisions. The investigation is motivated by the theoretical work of Myers (1977) 

Jen Seen (1986), Stulz (1988, 1990) and by an analytical work of McConnell and Servases (1990). We 

43 





examined whether financing consideration affects firm investment decisions. We found that leverage is 

positively related to the level of investment and that this positive effect is significantly stronger for firms 

with small firms and negative impact on medium firms but positive impact on large firms and this is not 

satirically significant. Further we inferred that the Indian pharmaceutical industry has heavy market 

demand for its product, so that Industry had enormous cash flow and plough hack of funds. Hence we 

conclude that the leverage has no impact of pharmaceutical industry in India. Cash flow and retained 

earning play significant role in determining the investment the decisions due to the change in the 

monetary policy of the country. Cash flow effect investment decisions due to the imperfections of the 

capital market and due to the fact internal financing is cheaper than external financing. These financing 

sources are far more important for small and highly leveraged firms. Our results support Hite (1977) who 

found that leverage and investment are positively associated with given the level of financing if an 

investment increase would lower financial risk and hence the cost of bond financing. 

REFERENCES 

Aivazian, V.A Callen, J.L. (1980), “Corporate leverage and growth: the game theoretic issues”, Journal 

of financial Economics, 8, 379-399. 

Beush, T., Pagan, A. (1980),”The language multiplier test and its applications to model specifications in 

econometirs”, Review of economic studies, 47, 239, 253.Econommics of information and 

Uncertainness, University of chicago press, chicago, pp 107-140. 

Cantor; Richard. (1990), “Effects of leverage on corporate investment and hiring decisions”, Federal 

Bank of New York Quarterly Review, pp. 31-41. 

Hausman, J.A. (1978),” Specification tests in econometric”, Econometrica 46, 1251 – 1271. 

Himmelberg, C.P., Hubbrad, R.G., Palia,D. (1999), “Understanding the determinants of managerial 

ownership and the link between ownership and performance”, Journal of financial economics, 53, 

353-384. 

Jensen; Michael, C. (1986), “Agency costs of free cash flow, corporate finance and takeovers”, American 

Economic Review, vol. 76, pp. 323-329. 

Jensen, M.C. (1986), “Ageny cost of free cash flow, corporate finance, and take-overs”, American 

economic review, 79, 323-329. 

Johnson, Shane, A. (2003), “Debt maturity and the effects of growth opportunities and liquidity risk on 

leverage”, Review of Financial Studies, vol16, pp.209-236. 

Kopcke and Howrey. (1994), “A panel study of investment: Sales, cash flow, the cost of capital, and 

leverage”, New England Review, Jan/Feb., pp. 9-30 

Lang, L.E.; Ofek, E.; Stulz, R. (1996), “Leverage, investment, and firm growth”, Journal of Financial 

Economics, Vol40, pp. 3- 29. 

McConnell, John, J. and Servaes, H. (1995), “Equity ownership and the two faces of debt”, Journal of 

Financial Economics, vol39, pp.131-157. . 

Modigliani; Franco and Merton, H.; Miller. (1958), “The cost of Capital, corporation finance, and the 

theory of investment”, American Economic Review, vol48, pp. 261-297. 

44 





Modigliani; Franco and Merton, H.; Miller. (1963), “Corporate income taxes and the cost of capital”, a 

correction, American Economic Review, vol48, pp. 261-297. 

Modigliani, F., Miller. M.H. (1958), “The cost of capital, corporation finance, and the theory of 

investment”, American Economic Review, 53, 433-443. 

Myers , S. (1977), “Determinants of corporate borrowing”, Journal of financial Economics, Vol. 5, 

147-175. 

Whited, T. (1992), “Debt, Liquidity constraints and corporate investment: Evidence from panel data”, 

Journal of Finance vol 47, pp.1425-1461. 

45 






Dr. Sangeeta Mohanty, Assistant Professor 

Academy of Business Administration, 

Industrial Estate (S1/25), 

Angargadia, Balasore-756001, Orissa, India 

ABSTRACT 

India’s fairness cream market is evolving at rapid speed, fuelled by television 

advertisement by the celebrities and the rapidly changing lifestyles. India’s proactive 

FMCG market has seen the significant growth in the cosmetic market in last two 

decades and fairness cream accounts for the major part of the cosmetic market with an 

average growth rate of 20% per annum. Indians are witnessing a paradigm shift from 

traditional methods of using home products to modern methods of using branded 

cosmetics and fairness cream to become fair. The particular paper aims at identifying 

the popular brands of fairness cream and the reasons of choosing a particular brand. It 

also provides a more comprehensive statistical analysis of evaluating consumers’ 

preference to various brands of fairness cream. The Participants were chosen randomly 

from cities ‘Cuttack’ and ‘Bhubaneswar’, Orissa, 243 agreed to participate in the 

survey but the data could be collected from 200 respondents only. 

Key words: Fairness Cream, Brand, Consumer 

INTRODUCTION 

The concept of preferring the people with “fair-skin” has long been recognized socially and it has been 

the psychological and social impact on women to be fair. But in the recent years, men too have started 

giving importance on personal grooming, beginning with fair skin. The market for fairness cream was 

restricted to woman only till 2005; but Emami catered to men with its product Fair and Handsome. Till 

then fairness cream market dominates the cosmetic market covering male and female segments. It is clear 

from television and matrimonial advertisements that the market for fairness creams in India is huge. The 

increasing demand of fair bride and groom creates the field for the national and international players to 

invest in the cosmetic markets and more particularly in fairness cream products to cater the needs of new 

generations. The celebrities like Sonam Kapoor, Shah Rukh Khan, John Abraham and Katrina Kaif are 

brand ambassadors for major fairness creams. 

The growth in consumerism and the changing life style of Indian youth have led to strong demand for 

fairness creams. India’s swelling middle class is redefining lifestyle pattern with adoption of western 

values and growing brand consciousness; creating opportunity for the global players in fairness cream 

market. The Indian market is experiencing stronger demand for fairness creams due to the increased 

media and untapped markets targeting the rural segment. Another key driver is the increased penetration 

level of male spending behavior on cosmetics. The fairness product market has captured the people from 

360 0 angle. The easy availability of these products has made the business to expand. The potent source of 

46 





this expansion is value added factors like fairness cream with sun screening; fairness cream with age 

miracle; fairness cream with multivitamin etc. 

The present study has been directed towards exploring and examining the various factors which influence 

the people to go for a particular brand of fairness cream. The main objective is to unfold the motives in 

the decision making and buying process. Consumer behaviour essentially is the behaviour that consumer 

displays in searching, purchasing, using and evaluating the products, services and ideas which they expect 

will satisfy their needs. Past studies show that the buying behaviour is intricate in nature because of its 

ever changing and never ending nature. Irrespective of the age, educational qualification, income level, 

every individual wants to be fair. However the individual needs to evaluate the different brands of 

fairness cream depending on the requirement and choice. As such five different brands of fairness creams 

Fair and lovely, Fair ever, Fair Handsome, Fair glow and Dream fairness are included in this research 

paper. 

INDIAN FAIRNESS CREAM MARKET 

India’s cosmetic market has undergone its biggest ever evolution in the past decade as the changing life 

style has given people greater choice in the way that they are obsessed for lighter skin. Indians have a 

strong impression of using herbal products, but with the adoption of modernization people started using 

creams and steroid to be fair. Subsequently different companies launched the fairness creams with the 

special feature of being fair. Indian fairness creams market is estimated at Rs.800 crores. (Star Weekend 

Magazine, May 12, 2006) and Indian male fairness cream market is estimated at Rs.200 crores. (Media 

Research User Council, 2006).The leading players are Hindustan Lever Ltd., (HLL's)-'Fair & Lovely', 

Cavin Kare's-'Fairever', Godrej's-'Fairglow', Emami's-'Fair and Handsome', Ponds-‘Dream fairness’. 

Hindustan Lever Limited launched its first fairness cream 'Fair & Lovely' in 1975 and dominated the 

market till 1998. Cavin Kare launched a fairness cream with a brand name 'Fairever' in 1998 and captured 

the market with 15% market share. Gradually other companies like Godrej, Ponds, Himalaya, and Emami 

entered the market and the fairness cream market fragmented into segments. But the company Hindustan 

Unilever LTD is the leader amongst all with a growth rate of 20% per annum. The market shares of 

leading players are tabulated below. 

OBJECTIVES OF THE STUDY 

Table-1: Market shares of different brands of fairness cream 

Brand Company Market share 

Fair and lovely HUL 76% 

Fairever Cavin kare 15% 

Fairglow Godrej 4% 

Fair and Handsome Emami 2% 

Dream fairness Ponds 2% 

Source: Compiled from magazines and news papers 

The objective of the study is to understand the behavior of Indians towards the preference of branded 

fairness cream in rural area in India in general and Cuttack, Bhubaneswar (Orissa) in particular and 

further, the paper aims at finding out: 

a) The popular brand of fairness cream. 

b) The reason of choosing a particular brand of fairness cream. 

c) Factors influencing to choose a particular brand of fairness cream. 

d) The value for a brand of fairness cream 

e) The attractive features of fairness cream 

47 





RESEARCH METHODOLOGY 

A pre-tested questionnaire was administered to the randomly selected people from the cities Cuttack, 

Bhubaneswar, Odisha, India. Personal interviews with the help of the pre-tested interview schedule were 

taken. Besides, personal observation was done wherever necessarily applicable. A pilot survey was conducted 

and the questionnaire was improved in that light. A structured questionnaire was used as a data collection 

tool. The sample includes male and female from different occupation, age and income group. In order to 

confine our study, a field survey was conducted across the selected segment of the cities and the respondents 

were selected randomly; they were approached to be included in the survey. For the sake of convenience the 

study concentrated on interview, questionnaire-survey method. 

a) Sample Design: random sampling was used keeping the target segment in mind. 

b) Sample size: 243 people were approached but the data could be collected from 200 respondents 

only. 

c) Data collection Period: The period of the data collection is limited to only a 3 -week period in 

September and October, 2011. 

d) Data collection method: A structured questionnaire was prepared and requisite information were 

collected through personal interviews. 

e) Tools and techniques used: Multiple regression analysis, Ranking method, Kendalls coefficient, 

Spearman’s rank correlation and percentage method. 

ANALYSIS AND INTERPRETATION 

1. Ranking of different brands of fairness cream 

Different brands of fairness creams are ranked on the basis of factors like Brand image, Promotional 

Offers, Advertisement and Varieties. Table - 2 has been prepared on the basis of the responses gathered 

from the majority group of respondents. 

Table-2: Ranking of different brand 

BRAND 

Fair & 

Lovely 

Fair ever 

Fair 

Glow 

Fair and 

Handsome 

Dream Fairness 

COMPANY HUL Cavin kare Godrej Emami Ponds 

Factors 

Rank 

Brand image (x 1 ) 1 5 2 4 3 

Promotional Offers (x 2 ) 2 1 5 4 3 

Advertisement (x 3 ) 2 1 4 5 3 

Varieties (x 4 ) 1 5 4 3 2 

In order to know the relation in between x1, x2, x3, x4, we have worked out Spearman’s Correlation 

coefficient (Rank correlation coefficient) pair wise. 

R x1x2 = 1-(6∑D 2 / N 3 -N) = 0.3, R x1x3 = 1- (6∑D 2 / N 3 -N) = 0.05, R x1x4 = 1- (6∑D 2 / N 3 -N) = 0.7 

R x2x3 = 1- (6∑D 2 / N 3 -N) = 0.9, R x2x4 = 1- (6∑D 2 / N 3 -N) = 0, R x3x4 = 1- (6∑D 2 / N 3 -N) = 0.1 

R x2x3 = the correlation coefficient between Promotional Offers and Advertisement is maximum (0.9); it 

shows a very high degree of positive correlation in between the said variables. 

48 





Interpretation: It is observed that Fair & Lovely is ranked as the best brand, Fair ever is ranked first for 

its promotional offers and advertisement and Fair & Lovely stands first for its varieties. 

The correlation coefficients between the brand image and the varieties, promotional offer and 

advertisement are more than 0.5, so the variables are highly positively correlated. 

2. Reason of looking for a different brand 

The reason of looking for a new brand of fairness cream is correlated with the quality of new brand, price, 

current fashion and new in the market. Here the basic interest is to find out the weightage of the 

independent variables (quality of new brand, price, current fashion and new in the market) on the 

predictor, the percentage of looking for a different brand of fairness cream by using the Multiple 

Regression technique. 

Let Y be the dependent variable = the percentage of looking for a different brand 

B = the coefficient of determinant (a constant value) 

X 1 = Quality of new brand 

X 2 = Price 

X 3 = Current fashion 

X4 = New in the market 

Y = B 0 + B 1 X 1 + B 2 X 2 + B 3 X 3 + B 4 X 4 

Step-by-Step Multiple Regression 

Table-3: Variables 

Entered/Removed 

Model Variables Entered Variables Removed Method 

1 X4,X3, X1, X2 Enter 

a All requested variables entered. 

b Dependent Variable: Y Table-3 shows us the order in which the variables were entered and removed 

from our model. We can see that in this case three variables were added and none were removed. 

Table- 4 : Model Summary 

Model R R Square Adjusted R Square Std. Error of the Estimate 

1 .733 .537 .450 1.92380 

a Predictors : (Constant), X4, X3, X1, X2 

Adjusted R Square in table-4 value tells us that our model accounts for 97.8% of variance in the 

frequency of visits and it signifies that it is a very good model. 

Table-5: Correlations 

Y X1 X2 X3 X4 

Y 1.000 .629 .558 .026 -.003 

X1 .629 1.000 .416 .000 .26 

X2 .558 .416 1.000 .392 .412 

X3 .026 .000 .392 1.000 0.63 

X4 -.003 .26 .412 .63 1.000 

** Correlation is significant at the 0.01 level (2-tailed). 

Table-5 gives details of the correlation between each pair of variables. There is a very good correlation 

between the criterion and the predictor variables. The values here are acceptable. 

49 





Table-6 : Coefficient 

Unstandardized 

Standardized t 

Sig. 

Model Coefficients B Std. Coefficients Beta 

(Constant) -1.783 Error 4.730 -.337 .741 

X1 .815 .354 .540 2.506 .035 

X2 .497 .363 .356 2.197 .043 

X3 .789 .318 .405 1.487 .293 

X4 0.126 .339 .322 .249 .213 

a Dependent Variable: Y 

The Standardized Beta Coefficients in table-6 give a measure of the contribution of each variable to the 

model. A large value indicates that a unit change in this predictor variable has a large effect on the 

criterion variable. The t and Sig (p) values give a rough indication of the impact of each predictor variable 

– a big absolute t value and small p value suggests that a predictor variables having a large impact on the 

criterion variable. 

Quality of new brand has the highest beta value (0.815), current fashion has the second highest beta value 

of (0.789), price and new in the market have the values 0.497 and 0.126. Error variance is explained by a 

constant by (4.730), followed by Price (0.363), Quality of new brand (0.354), New in the market (0.339) 

and Current fashion (0.318). Sample t-test correlates positively for quality of new brand (2.506), price 

(2.197), and current fashion (2.487) with the percentage of looking for a new brand of fairness cream. The 

multiple regression equation is as follows. 

Y = -1.783 + 0.815X 1 + 0.497X 2 + 0.789X 3 + 0.126X 4 

3. Factors influencing to purchase a brand of product 

The peer group and the advertisements have different influences across the stages of decision-making. 

Studies have shown that opinion, advice and the values of the reference group are the effective 

behavioural determinant of the people. The behaviour of an individual towards a particular brand is 

significantly influenced by the advertisement.Table-7 is formed on the basis of the information collected 

to rank the factors influencing to purchase a particular brand of fairness cream. 

Table-7: Ranking of factors 

Influential factors Rank Rank sum(SR) Rank sum(SR)2 

Family 2 195 38025 

Friends 3 295 87025 

Word of mouth 4 340 115600 

Neighbours 5 370 136900 

Advertisement 1 140 19600 

Total 1340 397150 

Now by Kendall’s’ coefficient we can estimate the relationship and test whether the different respondents 

are in agreement or not, as given below. 

H 0 : The respondents have disagreement in ranking. 

H 1 : The respondents have agreement in ranking. 

Test statistic 

Kendall’s coefficient of concordance is given by the following rule 

S 

W = 

, n = no. of attributes ranked=5, k =number of respondents =200 

1 2 3 

k n − n 

12 

( ) 

50 





2 

Where, S ( SR ) − n( SR ) 2 

W 

= 

1 

12 

k 

2 

= ∑ = 38030 

S 

3 

( n − n) 

= 0.095075 

2 

Kendall's Coefficient of Concordance approximately follows χ = k (n-1) W= 200*4*0.095075 = 76.06 

with (n-1) d.f 

2 

2 

χ (cal) = 76.06 > χ (tab with 4 d.f and at 5% level of significance) = 9.48773 

So, H 0 is rejected and H 1 is accepted. 

Interpretation: 

The most influential factor of choosing a brand of fairness cream is the advertisement, where family and 

friends occupy the second and the third places respectively. And word of mouth and the neighbours are 

placed 4 th and 5 th positions. 

Kendall’s’ coefficient test strengthened the hypothesis that the respondents have the nearest approach to 

the same ranking with respect to the most influential factor to choose a particular brand of fairness cream. 

4. Value of the brand 

Branding is an act of occupying the mind space of the consumer. The consumers create an image about the 

brand when they purchase the product. A positive impression will be created automatically in the mind of 

the consumers if, they can derive the value for the brand. The value may be in terms of money or social 

acceptability or self satisfaction. 

Table-8 depicts the value of the brand in general collected from the respondents. 

Value of Brand 

Response 

Table-8: Brand value 

Value for money Social acceptability Satisfaction Total 

Yes 45 69 38 152 

No 18 17 13 48 

Total 63 86 51 200 

The basic purpose of forming the table is to test the association between the value of the brand and the 

purchase decision making process using chi-square test. 

Null hypothesis H 0: Value of the brand is not associated with the purchase decision making process. 

Test statistic: x 2 (Chi-square) = ∑ [(O- E) 2 /E]= 1.627601, Tab. Val of x 2 (0.05) at 3 d.f is 4.541 

As, x 2 cal < x 2 tab , H 0 is accepted and H 1 is rejected 

Interpretation: The purchase decision making of fairness cream is associated with the value of the 

brand. It is also observed that the value of the branded fairness cream for the people is mostly ‘social 

acceptability’ and the ‘value for money’. Respondents have given the least preference to ‘satisfaction’ 

for the value of the brand 

51 





5. Preferred brand 

Table-9 has been formed to find out the most popular brand of fairness cream. 

Table-9: Preferred Brand of Fairness Cream 

Different Brand 

Percentage 

Fair & Lovely 43 

Dream Fairness 8 

Fair Glow 15 

Fair one 10 

Fair ever 24 

Interpretation: Majority rated Fair & lovely as the most popular brand and Fair ever as the second best, 

while Dream fairness is rated as the least popular brand of fairness cream. 

6. Basis of selecting the brand 

Consumers associate the brand with certain tangible and intangible attributes. Most of these associations 

for fairness cream are derived from current trend, popularity and recommendation. The major thrust area 

of any business is the perceived quality of the product. And so the quality of fairness cream in terms of 

fragrance, sunscreen, fairness and packaging is considered and the following table has been formed to 

identify the most important features of selecting a brand of fairness cream. 

Table-10: Features of selection 

Features 1 2 3 4 5 

Rank 

Sum 

Rank 

Popularity 37 46 50 30 37 584 4 

Current trend 56 55 53 20 16 485 2 

Availability 29 33 51 41 46 642 7 

Fairness 70 54 42 17 17 457 1 

Recommendation 30 33 51 41 45 638 6 

Sunscreen 49 42 57 32 20 532 3 

Fragrance 35 43 51 36 35 593 5 

Packaging 30 30 43 41 56 663 8 

Interpretation: The respondents have ranked the attributes “fairness” as rank-1, “current trend” rank-2 and the 

lowest rank-7 and 8 to “availability” and “packaging’. 

FINDINGS AND CONCLUSION 

India is one of the largest economies in the world. The fast and furious pace of growth of the Indian 

economy is the driving force for Indian consumerism. India presents a significant market with its young 

population just beginning to embrace significant lifestyle changes. The gradual increase in the purchasing 

power of Indians provides an excellent opportunity for organized retailing and creates an environment for 

cosmetic market. Projections by analysts suggest that India has the potential to be labeled as the fastestgrowing 

market for fairness cream. The Indian cosmetic market and particularly the fairness cream 

market enjoy a good market growth as the Indians are obsessed to become fair and beautiful. With the 

growing competition, the fairness market leaders need to re-evaluate the marketing plan. The companies 

will stand as leaders in their respective market by focusing their efforts on the benefits of a changing 

customer base. Even reputed companies and brands have felt the need for behavioural study to reach a 

larger customer base. The particular research paper is an attempt in that direction only. The researcher has 

tried to focus on this issue and the findings are listed below: 

52 





1. Fair & Lovely is ranked as the best brand, Fair ever is ranked first for its promotional offers 

and advertisement and Fair & Lovely stands first for its varieties. 

2. The correlation coefficients between the brand image and the varieties, promotional offer and 

advertisement are more than 0.5, so the variables are highly positively correlated. 

3. The choice of a new brand gives maximum weightage to quality of new brand and current 

fashion and less importance to current price and new in the market. 

4. The most influential factor of choosing a brand of fairness cream is the advertisement, where 

family and friends occupy the second and the third places respectively. And word of mouth 

and the neighbours are placed 4 th and 5 th positions. 

5. Kendall’s’ coefficient test strengthened the hypothesis that the respondents have the nearest 

approach to the same ranking with respect to the most influential factor to choose a particular 

brand of fairness cream. 

6. The purchase decision making of fairness cream is associated with the value of the brand. It is 

also found that the value of the branded fairness cream for the people is mostly ‘social 

acceptability’ and the ‘value for money’. Respondents have given the least preference to 

‘satisfaction’ for the value of the brand. 

7. Majority rated Fair & lovely as the most popular brand and Fair ever as the second best, 

while Dream fairness is rated as the least popular brand of fairness cream. 

8. The respondents have ranked the attributes “fairness” as rank-1, “current trend” rank-2 and 

the lowest rank-7 and 8 to “availability” and “packaging’ as the attractive features of the 

fairness cream. 

REFERENCES 

Aneel Karnani, Doing Well by Doing Good Case Study: ‘Fair & Lovely’ Whitening Cream. 

Ball, I. & Singh, H. (2004, June 22). „In Bollywood You are too dark. Indian Express. Retrieved from 

http://www.indianexpress.com 

Bhushan, R. (2004, July 25). Men playing the fairness cream game. The Times of India. Retrieved from 

http://timesofindia.indiatimes.com 

Celious, A. and Oyserman, D. (2001). Race from the inside: An emerging heterogeneous race model. Journal of 

Social Issues, 57, 149-165. 

Consalvo, M. (2003). The monsters next door: Media constructions of boys and masculinity. Feminist Media 

Studies, 3, 27-45. doi: 10.1080/1468077032000080112 

Challapalli, Sravanthi, "All's fair in this market", The Hindu Business Line, 

http://www.thehindubusinessline.com/catalyst/2002/09/05/stories/2002090500040300.htm, September 2002, 

last accessed on: 6th October, 2009 

Das, M. (2000). Men and women in Indian magazine advertisements: A preliminary report. 43, 699-717. doi: 0360- 

0025/00/1100-0699 

Indian Express Newspapers (Bombay) Ltd, June 11 2001 

Kunal Gaurav, India’s leading community portal, “Fairness Cream for Men: Creation of a New Category” 

Media Research User Council, 2006 

Natasha Shevde Advertising & Society Review, Volume 9, Issue 2, 2008 All's Fair in Love and Cream: A Cultural 

Case Study of Fair & Lovely in India 

53 





Enrich the Knowledge through Quality Research 

An International Journal Published by 

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www.akpinsight.webs.com 

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