Predicting the level of banks´ customer trust and forgiveness ...

The European Institute of Retailing and Services Studies
Recent Advances in Retailing and Consumer Service Science
Philadelphia, USA
July 7-10, 2013
Predicting the level of banks´ customer trust and
forgiveness following a banking crisis: The case of
Iceland
Authors:
Fridrik Eysteinsson, University of Iceland (fridrike@hi.is)
School of Business, Gimli v/Saemundargata, 101 Reykjavik, Iceland
Thorhallur Gudlaugsson, University of Iceland (th@hi.is)
School of Business, Gimli v/Saemundargata, 101 Reykjavik, Iceland

ABSTRACT
In October 2008 Iceland experienced the deepest and most rapid financial crisis recorded in
peacetime history. In a matter of days three of the country´s biggest retail banks, representing
about 85% of total banking assets, collapsed along with the biggest savings and loans and
passed into government hands. Customer trust towards the banks was lost.
Business organizations and consumers were hard hit. Many customers of the Icelandic
banks suffered additional harm because they lost part of the money they had invested in the
banks´ money market funds, had taken out loans in foreign currencies which increased
dramatically as a result of the fall of the value of the Icelandic krona and lastly they lost the
lion´s share of their investment in the banks´ shares. The Icelandic banks seem to have had a
hard time earning customer forgiveness for the direct and indirect harm they suffered as a
consequence of their collapse.
But how can banks repair customer trust and elicit their forgiveness? Xie and Peng put
forth a model that has eight dimensions. Three kinds of repair efforts (affective, functional
and informational) are posited to have an effect on three trustworthiness dimensions
(integrity, competence and benevolence). The trustworthiness dimensions are then posited to
have an effect on forgiveness and trust. Finally, forgiveness is posited to have an effect on
trust.
To the best of the authors´ knowledge no papers investigating which dimensions of Xie
and Peng´s (2009) model predict the level of customer trust and forgiveness have been
published in scholarly journals let alone following a banking crisis. The authors´ research
therefore aims to answer the four research questions:
1. Which dimensions in Xie and Peng´s (2009) model predict the level of banks´
customer trust following a banking crisis?
2. Which dimensions in Xie and Peng´s (2009) model are the most important in
predicting the level of banks´ customer trust following a banking crisis?
3. Which dimensions in Xie and Peng´s (2009) model predict the level of banks´
customer forgiveness following a banking crisis?
4. Which dimensions in Xie and Peng´s (2009) model are the most important in
predicting the level of bank´s customer forgiveness following a banking crisis?
1

INTRODUCTION
Iceland is an isolated island located in the North Atlantic Ocean with a population of about
320.000. Throughout the 19 th and the first decades of the 20 th century, Iceland was one of the
poorest countries in Europe but at the turn of the last century it was one of the most
prosperous countries in the world. In 1995 its banking sectors´ level of foreign debt was 6%
of GDP. In 2004 that level was 138% and had grown to about ten times GDP by the end of
2007.
In October 2008 following serious liquidity problems, three of the biggest Icelandic
banks, representing about 85% of total banking assets, collapsed along with the biggest
savings and loans. The banks had become too big for the Central Bank (or the government for
that matter) to rescue. As a consequence Iceland experienced the deepest and most rapid
financial crisis in peacetime history. Customer trust towards the Icelandic banks went down
considerably (Eysteinsson and Gudlaugsson, 2011; Friðrik Eysteinsson and Þórhallur
Guðlaugsson, 2011; Gudlaugsson and Eysteinsson, 2010, 2012a, 2012b; Þórhallur
Guðlaugsson and Friðrik Eysteinsson, 2010).
After the crisis the price of real estate went down and the bottom fell out of the
Icelandic stock market. The value of the Icelandic krona fell dramatically. Public and private
debt went up because of indexation and disposable income and purchasing power decreased.
Business organizations and consumers were hard hit. Many customers of the Icelandic banks
suffered additional harm because they lost part of the money they had invested in the banks´
money market funds, had taken out loans in foreign currencies which increased dramatically
as a result of the fall of the value of the Icelandic krona and lastly they lost the lion´s share of
their investments in the banks´ shares. The Icelandic banks seem to have had a hard time
earning customer forgiveness for the direct and indirect harm they suffered as a consequence
of their collapse (Brynjar Smári Rúnarsson and Friðrik Eysteinsson, 2011).
The paper is split into four sections. The first section deals with the state of knowledge.
The second one details the methodology. The third presents the findings and finally the
implications are discussed in the discussion section.
1 THEORETICAL BACKGROUND
Moorman, Zaltman and Despande (1992, page 315) define trust as “a willingness to rely on
an exchange partner in whom one has confidence”. Sirdeshmukh, Singh and Sabol (1992,
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page 17) define it as “the expectations held by the consumer that the service provider is
dependable and can be relied on to deliver on its promise”. Trust applies to both individuals
and business organizations (Jones, Wilkens, Morris and Masera, 2000). A lack of trust can
lead to a competitive disadvantage (Barney and Hansen, 1994).
Researchers have defined forgiveness in a variety of ways, so there is no consensus on
its definition (Finkel, Rusbult, Kumashiro and Hannon, 2002). In this paper the authors adopt
Xie and Peng´s (2009, page 578) definition and define customer forgiveness as “customer´s
willingness to give up retaliation, alienation, and other destructive behaviors, and to respond
in constructive ways after an organizational violation of trust and the related recovery
efforts.” According to Tsarenko and Gabbot (2006) forgiveness has the potential to accelerate
the restoration of a strained relationship between a service provider and customer. Chung and
Beverland (2006) maintain that forgiveness may not only restore a relationship to its original
state but also enhance its quality. Little is known, however, about customer forgiveness
(Zourrig, Chebat and Toffoli, 2009).
Xie and Peng (2009) conducted a scenario-based experiment which revolved around
how to regain customer trust and elicit their forgiveness. They put forth a model that has
eight dimensions. Three kinds of repair efforts (affective, functional and informational) are
posited to have an effect on three trustworthiness dimensions (integrity, competence and
benevolence). The trustworthiness dimensions are then posited to have an effect on
forgiveness and trust. Finally, forgiveness is posited to have an effect on trust. Their research
confirmed, among other things, that affective, functional and informational repair efforts all
had an effect on trustworthiness and that all the trustworthiness dimensions had a positive
effect on customer forgiveness and trust. Finally, there was a positive relationship between
forgiveness and trust.
Brynjar Smári Rúnarsson and Friðrik Eysteinsson (2011) did a study among the
customers of the Icelandic banks following the banking crisis. Their conclusion was that there
was a positive relationship between the repair efforts and the perceived trustworthiness
dimensions and the perceived trustworthiness dimensions and both forgiveness and trust.
Finally, there was a positive relationship between forgiveness and trust.
To the best of the authors´ knowledge no papers investigating the dimensions in Xie
and Peng´s (2009) model that predict the level of customer trust and forgiveness have been
published in scholarly journals let alone following a banking crisis. The authors´ research
therefore aims to answer the four research questions:
3

1. Which dimensions in Xie and Peng´s (2009) model predict the level of banks´
customer trust following a banking crisis?
2. Which dimensions in Xie and Peng´s (2009) model are the most important in
predicting the level of banks´ customer trust following a banking crisis?
3. Which dimensions in Xie and Peng´s (2009) model predict the level of banks´
customer forgiveness following a banking crisis?
4. Which dimensions in Xie and Peng´s (2009) model are the most important in
predicting the level of bank´s customer forgiveness following a banking crisis?
2 METHODOLOGY
The research is quantitative and based on a survey done in November 2012. In the following
section the methodology will be discussed.
The population of interest consisted of the customers of the Icelandic banks but a
convenience sample was used. The findings are therefore only generalizable to the sample.
The participants were students at the University of Iceland in the fall 2012. The total number
of answers was 341.
Perceived affective repair was measured by three items, perceived functional repair by
three items and finally perceived informational repair by two items (Xie and Peng, 2009).
Measures of competence, integrity, and benevolence were adapted from Sirdeshmukh, Singh,
and Sablo (2002) and Kim, Ferrin, Cooper and Dirks (2004) to this research context. Four
questions, adapted from Finkel et al. (2002) to this research context, were used to assess the
level of forgiveness (α = 0,892). Finally, trust was measured by 6 items. All the questions use
a five point Likert scale where 1 = strongly disagree and 5 = strongly agree. In addition three
questions asked about gender, age and the bank with which the participants mainly did their
banking business.
The survey was web-based. It commenced on November 16 th 2012 and ended two
weeks later on November 29 th when 341 students had participated. One remainder was sent
out. When the data gathering was complete the data was imported into the SPSS program. To
predict the level of customer trust and forgiveness a traditional regression analysis was
performed. To improve the quality of the regression outliners were deleted from the data set.
Other assumptions of regression analysis were met.
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3 RESULTS
In this section the results of the authors´ research is detailed. It is split into five parts. The
first part describes the participants. The second part deals with the dimensions in Xie and
Peng´s (2009) model which predict the level of customer trust following a banking crisis. The
third part deals with the dimensions that are the most important in predicting the level of
customer trust. The forth part deals with the dimensions in Xie and Peng´s (2009) model
which predict the level of customer forgiveness following a banking crisis. Finally, part five
deals with the dimensions that are the most important in predicting the level of customer
forgiveness.
Participants
The majority of the participants were women, or just over 65%, which is in line with the
gender split at the University of Iceland. 11.1% of the participants were 21 years of age or
younger, 36.1% were between the ages of 22 and 30, 20.2% between the ages of 31 and 40
and 32.6% were 41 years or older. The market shares of the banks, among those who did
answer the survey, are in line with their market shares among the population at large.
Which dimensions predict the level of customer trust?
The coefficients of three out of six dimensions intended to measure the three kinds of repair
efforts and the three dimensions of trustworthiness in the regression model were statistically
significant. They explained 74.1% of the variability in customer trust (R 2 = 0.741). The
results can be seen in table 1.
Table 1: A predictive regression model for the variability in customer trust
M odel S ummary
Model
R
R Square
Adjusted R
Square
Std. Error of
the Estimate
1 ,861 a ,741 ,738 ,48434
a. Predictors: (Constant), Benevolence, Affective repair efforts, Competence
The three dimensions with the statistically significant coefficients are benevolence
(trustworthiness), affective repair (repair efforts) and competence (trustworthiness). Each
dimension is measured by a number of questions. The average score for benevolence is 2.37,
for affective repair 2.03 and competence 2.43.
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Which dimensions are the most important in predicting the level of customer trust?
It is important to mention that the different dimensions do not all have the same relative
importance in predicting customer trust. Table 2 shows the results for the predictive
regression model and the three dimensions which had statistically significant coefficients.
Table 2: Coefficients in a regression equation with three dimensions
Coefficients a
Model
Unstandardized Coefficients
Standardized
Coefficients
Collinearity Statistics
B Std. Error Beta t
Sig. Tolerance VIF
(Constant) ,932 ,074 12,546 ,000
Affective
repair efforts
,101 ,040 ,106 2,528 ,012 ,463 2,159
Competence ,466 ,054 ,477 8,564 ,000 ,264 3,782
Benevolence ,301 ,052 ,333 5,815 ,000 ,250 3,996
a. Dependent Variable: Trust
Because the multiple regression equation has the form Y = α + β 1 *x 1 +β 2 *x 2 …..β n *x n it can be
stated as:
Y = 0,932+0,101* Affective repair efforts +0,466* Competence +0,301* Benevolence
Where Y is the predictive value for customer trust. To judge the goodness of the model it is
possible to put the averages for the three dimensions which predict the level of customer trust
into the equation and compare the result to the result for the research itself. The predictive
value for trust is 2.99 which is exactly the same value obtained for customer trust in this
research.
The regression equation´s coefficients (B) are a measure of the contribution of each
dimension to the predictive value of the regression equation and since all the dimensions are
measured using the same scale they can be used to calculate the dimensions´ relative
importance. The conclusion is that competence has a 52.1% weight in predicting the value of
customer trust, benevolence 36.3% and affective repair 11.6%.
Which dimensions predict the level of customer forgiveness?
The coefficients of four out of six dimensions intended to measure the three kinds of repair
efforts and the three dimensions of trustworthiness in the regression model were statistically
significant. They explained 78.5% of the variability in customer trust (R 2 = 0.785). The
results can be seen in table 3.
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Table 3: A predictive regression model for the variability in customer forgiveness
M odel S ummary
Model
R
R Square
Adjusted R
Square
Std. Error of
the Estimate
1 ,886 a ,785 ,782 ,46409
a. Predictors: (Constant), Benevolence, Affective repair efforts, Competence,
Integrity
The four dimensions with the statistically significant coefficients are benevolence
(trustworthiness), affective repair (repair efforts), competence (trustworthiness) and integrity
(trustworthiness). Each dimension is measured by a number of questions. The average score
for benevolence is 2.37, for affective repair 2.03, competence 2.43 and integrity 2.38.
Which dimensions are the most important in predicting the level of customer forgiveness?
As before it is important to mention that the different dimensions do not all have the same
relative importance in predicting customer forgiveness. Table 4 shows the results for the
predictive regression model and the four dimensions which had statistically significant
coefficients.
Table 4: Coefficients in a regression equation with four dimensions
Coefficients a
Model
Unstandardized Coefficients
Standardized
Coefficients
Collinearity Statistics
B Std. Error Beta t
Sig. Tolerance VIF
(Constant) ,457 ,072 6,313 ,000
Affective repair
efforts
,099 ,040 ,099 2,466 ,014 ,435 2,298
Integrity ,257 ,059 ,267 4,359 ,000 ,187 5,360
Competence ,375 ,056 ,366 6,715 ,000 ,236 4,240
Benevolence ,208 ,059 ,219 3,551 ,000 ,184 5,430
a. Dependent Variable: Forgiveness
Because the multiple regression equation has the form Y = α + β 1 *x 1 +β 2 *x 2 …..β n *x n it can be
stated as:
Y = 0,457+0,099* Affective repair efforts +0,257* Integrity +0,375* Competence +0,208* Benevolence
Where Y is the predictive value for customer forgiveness. To judge the goodness of the
model it is possible to put the averages for the four dimensions which predict the level of
customer forgiveness into the equation and compare the result to the result for the research
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itself. The predictive value for trust is 2.67 which is exactly the same value obtained for
customer trust in this research.
The regression equation´s coefficients (B) are a measure of the contribution of each
dimension to the predictive value of the regression equation and since all the dimensions are
measured using the same scale they can be used to calculate the dimensions´ relative
importance. The conclusion is that competence has a 38.5% weight in predicting the value of
customer forgiveness, integrity 28.1%, benevolence 23% and lastly affective repair 10.4%
4 DISCUSSION
The answer to the first research question, “Which dimensions in Xie and Peng´s (2009)
model predict the level of banks´ customer trust following a banking crisis?” is that three
dimensions explain 74.1% of the variability in customer trust. Two of them, benevolence and
competence, are trustworthiness dimensions and the last one affective repair is a repair effort.
The answer to the second research question, “Which dimensions in Xie and Peng´s
(2009) model are the most important in predicting the level of banks´ customer trust
following a banking crisis? is that three dimensions seem to be most important for banks in
their quest to regain customer trust following a banking crisis. Competence is most important
in predicting the level of customer trust, with a weight of 52.1% followed by benevolence
with a weight of 36.3% and lastly affective repair with 11.6%. The trustworthiness
dimensions therefore seem to be much more important in predicting the level of customer
trust then the repair efforts.
The answer to the third research question, “Which dimensions in Xie and Peng´´s
(2009) model predict the level of banks´ customer forgiveness following a banking crisis?”
is that four dimensions explain 78.5% of the variability in customer trust. Three of them,
benevolence, competence and integrity are trustworthiness dimensions and the last one
affective repair is a repair effort.
The answer to the forth research question, “Which dimensions in Xie and Peng´s
(2009) model are the most important in predicting the level of banks´ customer forgiveness
following a banking crisis? is that four dimensions seem to be most important for banks in
their quest to elicit customer forgiveness following a banking crisis. Competence is most
important in predicting the level of customer forgiveness, with a weight of 38.5% followed
by integrity with a weight of 28.1%, benevolence with a weight of 23% and lastly affective
repair with 11.6%. The trustworthiness dimensions therefore seem to be much more
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important in predicting the level of customer forgiveness than the repair efforts. Just like in
the case of customer trust, competence has the most weight in predicting customer
forgiveness. It should therefore get special attention in bank´s quest to regain trust and elicit
forgiveness following a banking crisis.
The research contributes to theory by showing which dimensions predict the level of
banks´ customer trust and forgiveness following a banking crisis and which are the most
important. The findings are in line with Xie and Pang’s (2009) findings that customer trust
and forgiveness requires trustworthiness. The authors´ research findings, though, have more
external validity than Xie and Pang’s findings because they are based on the attitudes of real
customers towards certain business organizations whereas Xie and Pang’s research findings
were based on a scenario-based experimental design.
One managerial implication of this research is that improving the perceptions of the
dimensions of trustworthiness with an emphasis on competence is the most promising route
to increase the level of customer trust and forgiveness.
One limitation of the current research is that a convenience sample was used. Another
limitation is that the research was only done in one country following one banking crisis.
Both of these limit the research findings´ generalizability. Future research could use more
representative samples and could be done among customers in more countries. It would also
be interesting to examine which of the repair efforts predict the variability in the dimensions
of trustworthiness.
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