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The Influence Of Industry Trust And Customer Involvement On Customer
Based Corporate Reputation
Master Thesis Business Studies Kevser Altintas (10278591) 30th November 2016
2 Statement of Originality
This document is written by Student Kevser Altintas who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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Preface
Grateful and proud are words that first come to my mind. Grateful for getting the opportunity to learn and proud to have been able to finish my thesis.
I am very thankful for the love and support that I have received during this journey. To begin with my parents, especially my mother for her non-stop prayers. My partner for his
unconditional support during my studies, and our son who has been my inspiration to complete this final work.
My supervisor Dr. Karin Venetis has been incredibly professional, supportive and
motivational to keep on going. I have learned a great deal during her guidance, as a student and as a person for which I am very thankful for.
Last but not least, I would like to thank everyone that has participated in my survey.
Utrecht, November 30th, 2016
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Table of content
Abstract ... 5 Introduction ... 6 Theoretical background ... 9 Corporate reputation ... 9 CBR ... 9 Trust ... 11 Consumer involvement ... 13 Research model ... 15 Research design ... 17 Procedure ... 17Overview of the data collection... 18
Data analysis ... 19
Measurement ... 20
Results ... 22
Participants ... 22
Reliability of the scales ... 24
Manipulation check ... 24
Descriptive statistics ... 25
Correlation analyses ... 27
Regression analyses ... 28
Discussion ... 33
Implications for practice ... 36
Limitations and direction for future research ... 37
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Abstract
Theaim of this study is to have a closer look at industry characteristics and how different people perceive sentiment and the influence it has on the reputation perception building process of consumers towards single corporations. This study examines the influence of industry trust and consumer involvement on the relationship between the CBR dimensions and corporate reputation. In a partially manipulated study environment, two online
questionnaires have been distributed among consumers who were most likely negative about the banking industry or positive about the telecom industry. The questionnaires were used as a manipulation check for industry trust as well as to measure the variables perceived corporate reputation, the CBR dimensions, and consumer involvement. The findings confirm that the CBR dimensions indeed all positively affect the perceived corporate reputation. Additionally, it is concluded that in the banking and telecom industries the product and service quality has the biggest influence on reputation. Additionally, the importance of the dimensions good employer and product and service quality on corporate reputation become greater in an industry with high trust versus low trust. Further, it was found that consumer involvement does not influence the effect of industry trust. This study concludes that the importance of the CBR dimensions change under different circumstances within specific industries, and it is important to consider these changes for enhancing corporate reputation. This study adds to the current literature by providing empirical findings for the effects of the CBR dimensions on corporate reputation and the influence of industry trust.
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Introduction
“What is the robbing of a bank compared to the FOUNDING of a bank?” (Bertolt Brecht)
“I think I’m a nice person. People that know me, like me” (Donald J. Trump)
Not a lot of people really get to know Donald Trump, but a whole lot of them probably have formed an opinion about him. Just like with people, consumers form some kind of an opinion about a company, without having any experience with them directly. Over the past decades many studies have shown the relevance of corporate reputation and it is no secret that reputation is really important; customer’s overall evaluation of a company is an intangible asset and when perceived positively, it can lead to many competitive advantages for various stakeholders (Fombrun 1996).
According to Walsh et al. (2007) corporate reputation is a multidimensional construct that affects customers reactions to the firm. Walsh et al. (2007) identified five dimensions of customer based corporate reputation (CBR) and developed scales to measure these dimensions. These dimensions are: Customer Orientation, Good Employer, Reliable and Financially Strong Company, Product and Service Quality, and Social and Environmental Responsibility. While all of the dimensions are important for reputation, Customer
Orientation is believed to be overall the most important one in the CBR measure. However, is this always the case or could this change under different circumstances?
Benjamin Franklin once said that it may take many good deeds to build a good reputation, and only one bad one to lose it. Trump today is not the only one suffering from past deeds though. The context of the recent financial crisis that has brought the lowest levels of trust in
corporations in history as well as corporate reputational decline (Eisenegger, 2009). The trust in banks has eroded since the financial meltdown and consumers distrust banks more than any other industry (Edelman Trust Barometer 2015). According to Hansen (2012) trust does not only relate to customer trust towards single corporations (i.e., narrow-scope trust, or
interpersonal trust) but also to the broader business context in which customer–seller
relationships may develop (i.e., broad-scope trust, or generalized trust). Industry trust depends on an individual’s perception of the characteristics of the industry environments (Zucker, 1986). In other words, a person’s trust in a bank exists within a larger context of
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trustworthiness as the current trust level in the industry is low. Till this day the banking industry is still coping with a lot of backlash, which is affecting the industry sentiment. According to Skowronski and Carlston (1987) negative publicity has a larger effect on consumer perception than positive publicity. However the way information is processed varies, and is dependent on the personal interest or relevance (Petty and Cacioppo, 1981). According to Petty and Cacioppo (1981) consumer involvement acts as an moderator in the way negative information is processed and whether or not to put more thought to the given information. When an industry such as banking is perceived with a lack of trust, do consumers still really care most about how a company and/or its employees treats customers? Or are they going to look for cues that signal trust and find it more important that a company is reliable and trustworthy first before anything? Theaim of this study is to have a closer look at
industry characteristics, and the influence it has on the reputation perception building process of consumers towards single corporations.
Research question and sub questions
This study is about corporate reputation that extents prior work from Walsh et al. (2007), who have developed a five-dimensional Customer Based Reputation scale (CBR-scale). According to Walsh et al. (2007), corporate reputation consist of five dimensions, with
Customer Orientation having the strongest influence in the CBR-measure. However this could change when the circumstances are different, such as the level of trust there is in an industry and how consumer’s involvement can strengthen the trust. Industry trust and consumer involvement are important influences on how people interpret information and behave (Hansen, 2012; Petty & Cacioppo, 1981; Skowronski & Carlston,1987).Therefore extending prior research from Walsh et al. (2007) on how these factors influence the importance of the underlying dimensions of CBR is an important area of research.
The research question is formulated as follows:
“Does industry trust and customer involvement influence the relative importance of the underlying dimensions of Customer Based Corporate Reputation?”
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In order to answer the main question, the following sub questions will be answered in the literature review:
• What is Reputation and why is it important in Marketing? • What are the CBR dimensions?
• How does Industry Trust impact the reputation building process?
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Theoretical background
Corporate reputation
The term corporate reputation stems back to the 1950’s (Bennet and Kottasz, 2000). Since then here have been many studies that have defined corporate reputation. It was defined by Weiss et al. (1999) as the extent to which a company is held in high esteem in the eyes of the consumer. Consumers judgement about a company as good or bad based on their direct experiences with the company or merely just by the reputation information that comes to their attention. In the end it is an accumulation of their perceptions on how well a company meets their demands and interests (Abratt & Kleyn 2012). Yoon et al. (1993) defined corporate reputation as a corporations result from past actions. According to Rose and Thompson (2004) it is the general perception of people, simply based on what they think they know about the company. According to Fombrun (1996) corporate reputation is the overall
evaluation of a company by various stakeholders and an intangible asset for any company. An asset that should be well managed by the corporation (Dowling, 2001). In this study, the definition of corporate reputation defined by Abratt & Kleyn (2012) is considered adequate as it is from the consumers perspective and reputation is not only based on the direct experience. The impact of corporate reputation on customer behavior has received much interest in
previous literature. Publilius Syrus cited: “A good reputation is more valuable than money”. It is a key factor for corporate success. Research has shown that there is a positive relationship between reputation and performance (Weiss, Anderson, & MacInnis, 1999; Roberts &
Dowling, 2002). When reputation is perceived positively, it can lead to competitive
advantages such as attracting more customers lower costs, gaining price premiums, positive product and services responses among others (Gardberg and Fombrun; 2002).
CBR
To sustain a long term competitive advantage a good reputation is essential (Mahon, 2002). For instance, the reputation of an organization can directly or indirectly influence the financial performance (Rose & Thomsen, 2004). However, the financial performance can influence the reputation as well. In order for a company to know how to enhance its corporate reputation and apply the right strategy, a company must understand how it stands on the reputation scale (Walsh et al. 2007). Walsh and Beatty (2007, p. 129) define customer-based reputation (CBR) as “the customer's overall evaluation of a firm based on his or her reactions to the firm's goods, services, communication activities, interactions with the firm and/or its representatives
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or constituencies (such as employees, management, or other customers) and/or known
corporate activities.” Walsh et al. (2007) argue that corporate reputation is a multidimensional construct that affects customers reactions to the firm. They have identified a five-dimensional customer based reputation scale (CBR). The five CBR dimensions are: customer orientation, good employer, reliable and financially strong company, product and service quality, and social and environmental responsibility.
Customer orientation refers to the customers perception of the willingness of company employees to satisfy customer needs (e.g. Brown et al., 2002). The good employer dimension is concerned with customers perceptions about how the company and its management treats its employees and pays attention to their interests, and customer expectations that the
company has competent employees. The reliable and financially strong company dimension is about customers perception of the company in terms of competence, solidity and
profitability. Moreover, it measures customer expectations that the company uses financial resources in a responsible manner and that investing in the company would involve little risk. The product and service quality dimension refers to customers perceptions of the quality, innovation, value and reliability of the firm’s goods and services. Finally, the social and environmental responsibility dimension captures customers beliefs that the company has a positive role in society and towards the environment in general. Walsh et al. (2007) conducted their study in various countries and service firms and found support for the predictive validity of the CBR scale for corporate reputation.
This study expects the same outcome such that that all five CBR dimensions indeed have an significant effect on corporate reputation. Therefore the following hypotheses is proposed: H1: a. The CBR dimension customer orientation has a positive effect on corporate
reputation. b. The CBR dimension good employer has a positive effect on corporate reputation. c. The CBR dimension reliable and financially strong company has a positive effect on corporate reputation. d. The CBR dimension product and service quality has a positive effect on corporate
reputation e. The CBR dimension social en environmental responsibility has a positive effect on
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If CBR is used effectively it can lead to competitive advantages such as lowering transaction costs, more loyalty from customers and making it harder for competitors to enter the market (Rose and Thomsen, 2004). Recognizing the difference in the relative importance between the CBR dimensions that determines the overall corporate reputation should lead managers to develop more targeted strategies to enhance their positioning and communication strategies to improve their reputation. The study by Walsh et al. (2007) was conducted across multiple industries and it was found that customer orientation overall is relatively the most important dimension predicting corporate reputation.
The importance of customer orientation is also widely recognized, Brown et al. (2002) found empirical evidence that customer oriented selling is linked to sales performance. It is defined by Narver and Slater (1990) as “the sufficient understanding of one’s target buyers to be able to create superior value for them continuously”. According to Saxe and Weitz (1982)
customer orientation is the extent to which a company and its employees focus their efforts in understanding and satisfying the customers. Much has been written about the importance of customer orientation for each company in relation to its market area (Deshpande, Farley & Webster, 1993; Kohli & Jaworski, 1990; Leeflang, 2011). Customer orientation is important for the business performance. Companies that make it their mission to create customer value, generate a higher degree of satisfaction, loyalty, innovation and performance (Kirca,
Jayachandran & Bearden, 2005).
In this study the same outcome is expected from the CBR scale and therefore the following hypotheses is proposed:
H2: From the CBR dimensions, customer orientation overall has the highest positive effect on corporate reputation.
Trust
Corporate reputation is associated with key marketing outcomes, such as trust (Fombrun and Van Riel, 1997) For maintaining well-functioning customer-seller relationship, trust is
important (Morgan and Hunt 1994). It can lead to customer loyalty and commitment and other benefits (e.g., Driscoll, 1978; Grayson, Johnson, & Chen, 2008). Past research has shown that trust not only relates to individual companies, but also relates to the industry the business is in where customer seller relationships may develop. According to Hansen (2012) there are two kinds of trust: one is narrow-scope trust and the other one is broad-scope trust.
Narrow-12
scope trust is defined by Sirdeshmukh, Singh, and Sabol (2002) as “the expectation held by the customer that the service provider is dependable and can be relied on to deliver on its promises”. Broad scope, also known as generalized trust or industry trust is defined as the expectation held by the customer that companies within a certain business type are generally dependable and can be relied on to deliver on their promises.
The context of the recent financial crisis has brought the lowest levels of trust in corporations in history (Eisenegger, 2009), and corporate reputation decline (Alsop, 2004). Sometimes at the hands of a single company that may or may not have purposely made mistakes which then caused major consequences for an entire industry such as the banking sector, which has received a tremendous amount of backlash in the recent years. One serious outcome is that the global financial crisis has led many to believe that the relationships between customers and financial service providers have become badly damaged. For instance, recent research and commentaries have suggested that ‘‘financial services providers are struggling to maintain consumer trust’’ (Costa 2010) and that ‘‘the economic crisis of 2008 led to a loss in
confidence in financial institutions’’ (Uslaner 2010, p. 110). The years following the crisis, banking has been viewed as the most distrusted industry. Industry trust depends on an individual’s perception of the characteristics of the industry environments (Zucker, 1986). In other words, a person’s trust in a bank exits within a larger context of trustworthiness as the current trust level in the industry is low.
According to the CBR construct by Walsh et al. (2007), all five dimensions have an significant effect on the overall reputation of an individual company within the banking sector, and with the expectation that customer orientation relatively is the most important determinant for corporate reputation. For consumer’s trust means the willingness to accept vulnerability in the exchange and the belief that the business will keep its promise and will not exploit that vulnerability for its own benefit (Chouk & Perrien, 2004; Ranaweera,
McDougall, & Bansal, 2005). In the past, some banks have exploited this trust. Products were sold that did not benefit the customer at all and banks went bankrupt with enormous
consequences for customers.
So far, research has paid little attention to the moderating influence of industry trust in the reputation perception building process of consumer towards single corporations. This study expects that particularly in a low trust industry consumers will first look for cues of trust, such as being a trustworthy company that offers only reliable products. This study proposes that
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consumers are to some extent influenced by the industry sentiment and it is therefore proposed that the relative importance of the CBR dimensions change under different circumstances and therefore the following hypotheses are proposed:
H3: a. The effect of the CBR dimension reliable and financially strong company is negatively moderated by industry trust such that it has a higher positive effect on corporate reputation when the industry trust is low versus high.
b. The effect of the CBR dimension product and service quality is negatively moderated by industry trust such that it has a higher positive effect on corporate reputation when the industry trust is low versus high.
c. The effect of the CBR dimension customer orientation is positively moderated by industry trust such that it has a lower positive effect on corporate reputation when the industry trust is low versus high.
Consumer involvement
Involvement is a cross-disciplinary concept derived from social psychology. There has been considerable discussion about how best to define and measure the construct customer involvement (Zaichkowsky, 1985; Andrews, Durvasula & Akhter, 1990; Sherif and Cantril, 1947). It was defined by Krugman (1965) from the communication persuasion framework as “the number of connections, conscious bridging experiences or personal references per minute that the subject makes between the content of the persuasive stimulus and the content of his own life” (p. 584). By Zaichkowsky (1985, p. 32) involvement was later defined as “a person’s perceived relevance of the object based upon inherent needs, values, and interests.” Eventually Laaksonen (1994) categorized the various definitions into three groups, namely: cognitive-based approaches, individual-state approaches, response-based approaches. According to the cognitive-based approach, involvement is the experienced personal importance and relevance of a product or activity. With the individual-state approaches involvement concern is aroused with the person through a potential situation that has
occurred. This situation does not require any personal relevance and is temporary. According to the response-based approaches, these involve the views regarding information processing. This study focuses on the cognitive-based approach and response-based approaches. It is interested in investigating what the influence is of the level of personal interests towards an industry and the manner of how information is then processed. This does not involve situational occurrence such as getting involved in banking for the need of a mortgaged.
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One theory that explains the effects of involvement is the Elaboration Likelihood Model. The Elaboration Likelihood Model (ELM) of persuasion, explains the formation of individual attitudes that are prompted by central and peripheral cues. ELM suggest that consumers that are highly involved use central signals and when consumers are less involved they use the peripheral signals. In other words, more involved people are more motivated to put more thought towards a message in contrary to less involved people whom are less motivated. According to Skowronski and Carlston (1987) negative publicity has an larger effect on consumer perception than positive publicity. However there is a difference in how information is processed, dependent on the personal interest or relevance (Petty and Cacioppo, 1981).
Till this day the banking industry is still coping with an amount of negative publicity. According to Petty and Cacioppo (1981) consumer involvement acts as an moderator in the way negative information is processed and whether or not to put more thought to the given information. Based on the ELM model and the level of distrust in the banking industry this could lead to consumer’s involvement strengthening the effect of industry trust. In this study it is expected that less involved consumers are more susceptible to the industry sentiment, such that low involved consumers will put more emphasis on the negative information and make less distinction towards individual companies versus high involved customers. Therefore it is likely that the level of consumer involvement has an influence on the previously proposed hypotheses. Therefore the following hypothesis are proposed:
H4: a. Industry involvement has a negative moderating effect on the effect that industry trust has on reliable and financially strong company, such that the effect of industry trust is stronger among low involved consumers versus high.
b. Industry involvement has a negative moderating effect on the effect that industry trust has on product and service quality, such that the effect of industry trust is stronger among low involved consumers versus high.
c. Industry involvement has a negative moderating effect on the effect that industry trust has on customer orientation, such that the effect of industry trust is stronger among low involved consumers versus high.
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Research model
In figure 1. the construct measured in this study is visualized. It contains the independent variables (the five CBR dimensions), the moderators (industry trust and consumer
involvement) and the dependent variable (corporate reputation). This section also includes the proposed four hypotheses.
Hypotheses:
H1. a. The CBR dimension customer orientation has a positive effect on corporate reputation.
b. The CBR dimension good employer has a positive effect on corporate reputation.
c. The CBR dimension reliable and financially strong company has a positive effect on corporate reputation.
d. The CBR dimension product and service quality has a positive effect on corporate reputation
e. The CBR dimension social en environmental responsibility has a positive effect on corporate reputation
Industry trust
Industry involvement
Customer Orientation
Good Employer
Reliable and Financially Strong Company Corporate Reputation
Product and Service Quality
Social and Environmental Responsibility
Figure 1. Research model
H1b: (+) H2 H1c: (+) H1d: (+) H1e: (+) H1a: (+) H4a: (-) H4b.: (-) H4c.: (-) H3c. : (+) H3b. : (-) H3a. : (-) Moderators
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H2. From the CBR dimensions, customer orientation has the highest positive effect on corporate reputation.
H3: a. The effect of the CBR dimension reliable and financially strong company is negatively moderated by industry trust such that it has a higher positive effect on corporate reputation when the industry trust is low versus high.
b. The effect of the CBR dimension product and service quality is negatively moderated by industry trust such that it has a higher positive effect on corporate reputation when the industry trust is low versus high.
c. The effect of the CBR dimension customer orientation is positively moderated by industry trust such that it has a lower positive effect on corporate reputation when the industry trust is low versus high.
H4: a. Industry involvement has a negative moderating effect on the effect that industry trust has on reliable and financially strong company, such that the effect of industry trust is stronger among low involved consumers versus high.
b. Industry involvement has a negative moderating effect on the effect that industry trust has on product and service quality, such that the effect of industry trust is stronger among low involved consumers versus high.
c. Industry involvement has a negative moderating effect on the effect that industry trust has on customer orientation, such that the effect of industry trust is stronger among low involved consumers versus high.
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Research design
Procedure
To perform this study a situation is needed where there is a variance in industry trust and consumer involvement to measure the moderating effects. To do this the study will be
partially manipulated and first a pre-test is performed to verify the industries that are used for this study. According to the Edelman Trust Barometer (2015) the most and least trusted industries are technological and banking. To verify this for the purpose of this study, a small pre-test was performed with twenty respondents around the circle of the researcher. They were asked to rate the eight industries mentioned in the Edelman trust barometer in order of most and least trusted ones. With (8) for most trusted and (1) for least trusted industries. The result of the pre-test for the least trusted was the same as Edelman’s, namely banking. The most trusted however was different. The most trusted industry according to the pre-test was the telecom industry. The results of the pre-test are found in figure 2.
Figure 2 : Pre-test
After the pre-test, two questionnaires were developed accordingly to the industries tested from the pre-test. The questionnaires are distributed on anti-industry sites or pro-industry sites to collect as many positive or negative response for the industries. There needs to be two
industries that clearly vary in industry trust, high versus low. Within each group there needs to be an equal amount of high versus low involved respondents. The questionnaires are used to test the variance in the groups and measure the effects on corporate reputation.
The questionnaire is adopted from four different studies: CBR, industry trust, consumer involvement, corporate reputation. Each respondent was asked to whether they had ever heard of the organization from a list of well-known brands in the industry, with a side note that they do not need to have any experience with them directly. Then they were asked to answer some questions for three different organizations randomly picked from the organizations that they
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have heard off. This part consists of the CBR scales, corporate reputation and trust and is looped three times in the questionnaire. They ended with answering their trust level in the industry and involvement as well as some demographic variables: gender, age and education. Overview of the data collection
This is an quantitative study that examines the relations between the independent variables, the CBR dimensions and corporate reputation and the moderating effects of industry trust and consumer involvement. This study is conducted over a short period of time and therefore only provides cross-sectional insight on the moderating effects of industry trust and consumer involvement. The study environment is partially manipulated to target the right respondents groups to create the variance in industry trust. To collect the data, two questionnaires are distributed online is via Qualtrics.com. The questionnaires will first serve as a manipulation check. To study the results, the sample size must contain two respondents groups whom significantly differ from each other, such that one group perceives a high industry trust and the other one low industry trust. Within these two groups there must be an equal amount of variance in consumers involvement. The two online questionnaires are identical, one for the banking sample group and another for the telecom group. The respondents will either fill in the telecom or banking survey, dependent of the manipulated environment where the survey is initially distributed. Part of the questionnaire consist of questions that are only asked once, and another part is looped three times where respondents are asked to evaluate three banks or three telecom provider services.
To target the right audience for the questionnaire for low trust industries, the link of the questionnaire is shared in the comments box or forum section of several news sites where negative articles surrounding banking were published such as FD, Nu.nl, Ad.nl, as well as Facebook shared news articles. On Radar.nl the link was distributed under the writers that had left a complaint about a specific bank as well as readers that had left their comments for them. The link was also distributed on Klachten.nl, where people can leave their complaint for specific companies.
For the telecom sector the same news sites were used where the link was distributed in the comments box or forum section of published new articles with positive news concerning telecom. The link was further distributed on the forum site of tweaker.nl, which is a forum for technology and telecom as well as Facebook shared new articles about telecom that were positive.
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The link of the survey was first distributed on 24 January till 15 February 2016. Every two to three days the link of the survey was re-distributed on the sites. Either under new published or shared articles or the old ones to approach new commentators. When opening the survey, the respondents were guaranteed full anonymity and the promise that the collected information is only to be used for this study purposes only. The link to the survey was posted online and the contact information of the researcher was available.
It is unknown how big the target audience initially was as there is no information available about the amount of online visitors on these sites. The survey itself takes up to approximately 8-10 minutes. The survey is formulated in Dutch as the study is conducted in the Netherlands where the majority of the consumers are Dutch native speakers.
Data analysis
To analyze the data the following steps have been taken. First the data from both surveys collected in the survey tool Qualtrics was exported to SPSS 22 into one file. Then the data was restructured where each respondent was split into three rows instead of one, as they each measured three separate banks or telecoms organizations. The control variables and the variables on industry trust and involvement were copied three times. Three items were recoded as they were reversed in the survey. The text column was cleaned for answers, such as “helping an acquaintance with a telecom contract” was changed into the standard answer knowing the organization from my network. The validity of cases were checked and
incomplete surveys were deleted. In total 86 respondents, most of them which had only opened the survey before canceling their contribution to the study. Fourteen outliers have been removed. A Pearson chi-square analysis was performed to determine the independency of the control variables and a t-test is run to verify the variances in industry trust and
consumer involvement. To test the reliability of the questionnaire the Cronbach’s alpha was calculated. A correlation analyses was run for the CBR dimensions, Industry Trust,
Involvement en Corporate Reputation. The add-on macro module in SPSS, PROCESS by Andrew Hayes is used to perform the regression analyses on the direct effects of the CBR dimensions and for the interactional effects of the moderators.
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Measurement
The two questionnaires for banking and telecom were both administered in Dutch, translated from the original English scales and was pre-tested with five acquaintances of the researcher. The questionnaires starts with a word of appreciation and introduction explaining the goal of the survey, the average time needed to fill the survey, and the contact information of the researcher. All measured validated scales and derived from prior research. In the appendix there is a copy of both questionnaires distributed to the respondents. Except for the control questions, all scales are measured on a seven-point Likert scale ranging from 1: strongly disagree to 7: strongly agree. For the 3-item consumer involvement scale, a semantic differential scale with a 7-point rating is used.
Customer-based corporate reputation was measured with the 15-item measure of the CBR-scale adopted from Walsh, Beatty and Shiu (2007). The CBR-CBR-scale consist of five dimension. Each dimension consists of a three-item scale. All items are measured on a seven-point scale, ranging from 1: strongly disagree to 7: strongly agree.
An example of the Customer orientation measure is: “Has employees who treat customers courteously”.
An example of the Good employer measure is, “Looks like a good company to work for”. An example of the Reliable and financially strong company measure is, “Tends to outperform competitors”.
An example of the Product and service quality measure is: “Offers high quality products and services”.
An example of the Social and environmental responsibility measure is:, “Would reduce its profits to ensure a clean environment”.
Corporate reputation was measured with a 3-item scale measure adopted from Nguyen & Leblanc (2001). All items are measured on a seven-point scale, ranging from 1: strongly disagree to 7: strongly agree. An example of the measure is: “This bank/telecom provider has a good reputation”.
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Industry trust was measured with a 3-item scale measure adopted from Tax, Brown and Chandrashekaran (1998). All items are measured on a seven-point scale, ranging from 1: strongly disagree to 7: strongly agree. An example of the measure is: “In general, I believe that banks/telecom providers cannot be relied upon to keep their promises”.
Consumer involvement was measured with a 3-item scale measure adopted from Shiv, Britton and Payne (2004). All items are measured on a seven-point scale, with words that are
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Results
Participants
In total there were 199 unique respondents for both questionnaires. For banking there were 117 respondents and 82 for Telecom. Each respondent answered questions about three different companies, either banking or telecom. The questionnaires consisted of three control variables, gender, age and education. In table 1. the respondents descriptives are to be found.
From the 199 respondents, n=99 (49,7%) were male, almost equal to the amount of women, n= 100 (50,3%). The educational level of the respondents is mostly higher educated, n=95 (47,7%) has a Bachelor’s degree level and n=75 (37,7%) has a Master’s degree level. Only n=6 (3%) has reached High School level and 23 (11,6%) has reached MBO also known as intermediate vocational education. The last control variable is age, this was also the last question in the questionnaire. The age of the respondents is divided as follows: 18-24 n=1 (0,5%), 25-34 n=48 (24,1%), 35-44 n=54 (27,1%), 45-54 n=66 (33,2%), 55-64 n=30 (15,1) and none of the respondents were above 64.
A total of 595 companies are rated by 199 respondents. For banks that were 349 and telecom providers 246. For each industry, eight brands were selected. The brands include the market leaders and a few smaller ones. In Qualtrics the brands were randomized, and the respondents had to mark the ones they had ever heard of. Subsequently three brands were randomly selected from the selected list and a list of questions was presented for each brand. This was
N % Age 18-24 1 0,5 25-34 48 24,1 35-44 54 27,1 45-54 66 33,2 55-64 30 15,1 Gender Male 99 49,7 Female 100 50,3
Education High school 6 3
MBO 23 11,6 HBO 95 47,7 WO 75 37,7 Industry Banks 117 58,8 Telecom 82 41,2 N=199 Table 1. Descriptives respondents
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repeated three times. It can be seen from the results in table two, that the major brands are better known among the respondents. These brands were offered more often through the randomization selection in Qualtrcis probably because they were selected more often in the previous question by the respondents. Table 2. gives an insight about the companies that were rated.
The larger banks are: Rabobank, ABN AMRO, ING and SNS and the remaining smaller banks in this study are Regiobank, ASN, KNAB and Triodos. The larger companies for telecom providers are KPN, Vodafone, T-Mobile and Telfort and the smaller providers are Hollandse Nieuwe, Ben, Tele-2 and SIMYO. It seems that the larger companies are selected more often, which is not surprising as the chances are greater that respondents have ever heard of them.
A Pearson Chi-square test is performed to test whether the demographic variables age, gender and education are associated with the industries. To test the goodness of fit between the demographic variables and industries, three requirements have to be met. These are the validity of cases, whether the variables are equally exclusive and there must be a minimum 5 One of the criteria is that there needs to be a minimum of five occurrences between each
Table 2. Descriptives corporations per industry
N % Banks Rabobank 50 8,4 ASN bank 35 5,9 ABN AMRO 45 7,6 Triodos 39 6,6 Regiobank 46 7,7 ING 59 9,9 SNS 40 6,7 KNAB 35 5,9 Total banks 349 58,8 Telecom KPN 32 5,4 Vodaphone 35 5,9 T-Mobile 33 5,5 Tele-2 27 4,5 Hollandse Nieuwe 24 4,0 Ben 34 5,7 SIMYO 21 3,5 Telfort 40 6,7 Total telecom 246 41,2 Total 595 100
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category. For the variables education and age these were initially not met. The categories that were less than five were merged with the following category. There is no significant
association found between gender (Pearson Chi- square value 1,194 and p = 0.274, 2- sided), age (Pearson Chi- square value 1,189 and p = 0.756, 2- sided) and education (Pearson Chi- square value ,077 and p = 0.962, 2- sided) and the industry. In other words the control variables are independent from the industry.
Reliability of the scales
Cronbach’s alpha is used to measure the internal consistency of the questionnaires and a reliability score of .70 or higher is required to continue with the use of the questionnaires (Pallant, 2010). The reliability of the used scales in this study can be assumed as they all are higher than .70. The results of the reliability analyses is listed in table 3.
Table 3: Cronbach’s Alpha
Manipulation check
Further, an independent-samples t-test was conducted to compare the means in industry trust and consumer involvement between the two industries. There was a significant difference in the scores for industry trust for banking (M=2,617, SD=,757) and industry trust for telecom (M=4,380, SD=1,048) conditions; t(593)=-22,555, p =0.000. There is not a significant difference in the scores for consumer involvement banking (M=3,972, SD=1,563) and
consumer involvement telecom(M=4,069, SD=1,464) conditions; t(593)=-,764, p =0.445. The results are good and as expected for the purpose of this study to carry on with the tests.
α N of items
Corporate Reputation .908 3
Customer Orientation .971 3
Good Employer .950 3
Reliable and Financially Strong Company .932 3
Product and Service Quality .938 3
Social and Environmental Responsibility .904 3
Industry Trust .913 3
25 Descriptive statistics
All the variables in the construct are measured on a seven-point Likert scale. In table 3. the means and standard deviations are divided into three columns presenting the mean and standard deviation for each industry and a total column. The rows include the dependent variable corporate reputation, the five dimensions from the CBR-scale, and the two moderators industry trust and industry involvement.
Table 4. shows the results of 595 companies measured by 199 respondents. Except for the industry involvement variable, overall the total means are below the middle point of the Likert scale. This is probably due to the fact that there are more banking (n=349) responses than telecom (n=246) while the average mean score for telecom is just above the middle point. The highest mean score is industry involvement (M=4,012) and the lowest is the CBR dimension social and environmental responsibility (M= 2,625). The lowest standard deviation is social and environmental responsibility (SD=1,175) and the highest standard deviation is customer orientation (SD=1,518). Within the two industries the variances between the variables are more evident. For banking the lowest standard deviation is industry trust (SD=0,757), the highest standard deviation is industry involvement (SD=1,563) and for telecom, the lowest standard deviation is social and environmental responsibility (SD=0,734) and the highest standard deviation is industry involvement (SD=1,464).
Comparing the five dimensions from the CBR-scale separately the highest scoring mean is customer orientation (M=3,287) and the least is social and environmental responsibility (M=2,625). This order is the same in both industries. For banking the second highest scoring mean from the CBR-dimensions is reliable and financially strong company (M=2,264)
whereas for telecom this is good employer (M=4,350). The second lowest scoring mean in the CBR-dimensions for banking is good employer (M=2,210) whereas for telecom this is
Table 4. Mean and standard deviation total and divided per industry
Variable Total Mean SD Banking Mean SD Telecom Mean SD
1. Corporate Reputation 3,007 1,358 2,193 0,940 4,161 0,967
2. Customer Orientation 3,287 1,518 2,389 1,152 4,561 0,960
3. Good Employer 3,095 1,465 2,210 1,105 4,350 0,881
4. Reliable and Financially Strong Company 3,089 1,409 2,264 0,980 4,260 1,049
5. Product and Service Quality 3,096 1,465 2,245 1,091 4,304 1,009
6. Social and Enviremental Responsibility 2,625 1,175 1,959 0,951 3,569 0,734
7. Industry Trust 3,346 1,242 2,617 0,757 4,379 1,048
8. Industry Involvement 4,012 1,522 3,972 1,563 4,069 1,464
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reliable and financially strong company (M=4,260). For both industries the mean for product and service quality is ranking third of the total of five dimensions, for banking (M=2,245) and for telecom (M=4,304).
As expected there is a large difference in trust between the two industries. Banking has the lowest industry trust of the two industries (M=2,617) and telecom the highest (M=4,379). While there is a difference, the industry trust level for telecom is not as high as expected. It is just above the middle point, indicating a rather neutral trustworthiness than high industry trust. The mean score in Industry involvement (M=4,012) shows there is an equal variance of involvement in both industries.
Table 5. Results mean scores divided per industry and company
In table 5. the results of the average mean scores per company are shown. For both industries, the result indicate that the larger companies have a higher score on corporate reputation. Within banking, ING has the highest score on corporate reputation and it also scores the highest on the dimensions good employer, reliable and financially strong company and product and service quality. Rabobank has the highest score on customer orientation. Not entirely surprising, the highest scoring on socially and environmental responsibility is Triodos. Triodos is a relatively small bank in the Netherlands, but one that distinguishes in sustainability. The lowest scoring bank on corporate reputation is Regiobank, which also has
Industry Company N Corporate
Reputation
Customer
Orientation Good Employer
Reliable and Financially Strong Company Product and Service Quality Social and Evironmental Responsibility
Industry Trust Industry Involvement Rabobank 50 2,420 2,860 2,007 2,193 2,567 2,073 2,617 3,972 ASN bank 35 1,876 1,924 1,667 1,914 1,771 1,771 2,617 3,972 ABN AMRO 45 2,326 2,689 2,630 2,452 2,519 2,104 2,617 3,972 Triodos 39 2,239 2,316 2,299 2,197 2,009 2,393 2,617 3,972 Regiobank 46 1,862 2,080 1,812 1,928 1,804 1,616 2,617 3,972 ING 59 2,492 2,768 2,802 2,723 2,836 2,130 2,617 3,972 SNS 40 2,150 1,992 2,058 2,117 1,933 1,817 2,617 3,972 KNAB 35 1,943 2,095 2,105 2,381 2,114 1,638 2,617 3,972 Total banks 349 2,193 2,389 2,210 2,264 2,245 1,959 2,617 3,972 KPN 32 4,542 5,073 4,833 4,375 4,875 3,979 4,379 4,069 Vodaphone 35 4,990 5,152 5,019 5,171 5,210 3,733 4,379 4,069 T-Mobile 33 4,232 4,727 4,374 4,545 4,606 3,505 4,379 4,069 Tele-2 27 3,914 4,136 4,062 4,148 4,086 3,481 4,379 4,069 Hollandse Nieuwe 24 3,514 3,889 3,806 3,958 3,569 3,486 4,379 4,069 Ben 34 3,980 4,392 4,176 3,706 3,882 3,392 4,379 4,069 SIMYO 21 3,556 4,063 3,873 4,000 3,746 3,460 4,379 4,069 Telfort 40 4,100 4,592 4,275 4,000 4,042 3,467 4,379 4,069 Total Telecom 246 4,161 4,561 4,350 4,260 4,304 3,569 4,379 4,069 Total 595 3,007 3,287 3,095 3,089 3,096 2,625 3,346 4,012 Telecom Banking
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the lowest score on social and environmental responsibility. What is striking is that ASN scores the lowest at four out of the five dimensions, however it is not the lowest scoring bank on corporate reputation. Surprisingly Triodos scores higher than SNS. While SNS is the fourth largest bank in the Netherlands and compared to Triodos it is much bigger.
Among the telecom providers, respondents are most positive about Vodafone. Vodafone not only scores the highest on reputation, but also on four out of the five dimensions. The Dutch company KPN scores the highest on social and environment responsibility. The least-scoring on reputation within telecom is Hollandse Nieuwe, that has the lowest scores on customer orientation, good employer and product and service quality. Surprisingly, the telecom provider BEN scores the lowest on two dimensions, reliable and financially strong company and social and environmental responsibility, but is not even the second-lowest scoring company on reputation, which is SIMYO.
Correlation analyses
To describe the strength and direction of a linear relationship between variables a Pearson correlation analyses is performed in SPSS. The result of the correlation analyses are shown in table 6. The first column gives an overview of all the variables, followed by the average total means and SD, Pearson correlation and ends with the reliability scores.
The correlation analysis shows that there is a significant positive relationship between the CBR dimensions and reputation. These values are extremely high, suggesting that the dimensions are very strongly associated and almost interchangeable. This is unfortunate and may have been caused by the repetition of the questions which have led respondent to answer in a more automated way.
Table 6. Mean, standard deviation, correlation and reliability scores
Variable Total Mean SD 1 2 3 4 5 6 7 8
1. Corporate Reputation 3,007 1,358 (.908)*
2. Customer Orientation 3,287 1,518 .872** (.971)*
3. Good Employer 3,095 1,465 .867** .893** (.950)*
4. Reliable and Financially Strong Company 3,089 1,409 .860** .817** .838** (.932)*
5. Product and Service Quality 3,096 1,465 .898** .875** .881** .891** (.938)*
6. Social and Enviremental Responsibility 2,625 1,175 .805** .768** .768** .745** .771** (.904)*
7. Industry Trust 3,346 1,242 .669** .640** .656** .622** .643** .584** (.913)*
8. Industry Involvement 4,012 1,522 -.021 -.031 -.001 -.026 -,020 -,090* .054 (.859)*
*Reliability is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed) ***Correlation is significant at the 0.05 level (2-tailed).
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In the proposed model industry trust is the moderator between the CBR dimensions and corporate reputation. The correlation analyses shows high correlation values between the dimensions and industry trust which also makes it difficult to predict the contribution of each dimension on corporate reputation with the moderating influence of trust.
In the model industry trust is acts as a moderator, moderating the relationship between industry trust and the CBR dimensions on corporate reputation, at levels of industry
involvement. Consumer involvement does not seem to correlate with industry trust, and there seems to be enough spread which is fortunate and paves the way to perform the three-way moderation analysis. The only direct relationship with industry involvement variable is the social and environmental responsibility dimension. Although significant, this is a very low correlation and therefore negligible.
The correlation analysis shows that the dimensions mutually have very strong relationships which makes it difficult to determine the unique contribution of each dimension. It suggests that the dimensions are evaluated in the same manner and are interchangeable. It makes it difficult to interpret whether one is really more important than the other. To determine the unique contribution of each to dimension that is recognized as the independent variable, the other dimensions are included in as a control variable in the model used in PROCESS. Thus, the unique contribution of the independent dimension is estimated, given that the other dimensions have already had their effects. To further explore the interrelationship among the set of variables and a regression analyses is run through SPSS add-on macro PROCESS by Andrew Hayes.
Regression analyses
The research model is based on a construct of eight variables. It consist of the five CBR dimensions (independent variables), corporate reputation (dependent variable) and the
(moderated) moderators industry trust and industry involvement. First the results of the direct effects of all the independent variables on corporate reputation will be presented. Then the moderating effects by industry trust on the relationship between the CBR dimensions and corporate reputation. And finally the results of three way moderation where the moderating effects are shown of industry trust on the CBR dimensions at values of industry involvement. The regression analyses is performed in SPSS through the add-on model PROCESS
developed by Andrew Hayes. By using PROCESS mean centering or standardization will not be required.
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To test how much variance in corporate reputation is accounted for by all of the variables
including the interactions it is important to look at the R2 score. The R2 (.868, p<0.01)
indicates that 85% of the variance in corporate reputation is explained by this model, in other words 85% of the variance is greater than chance and is significant. The results of the
regression analyses are to be found in table 7.
The first hypotheses stated that all of the five CBR dimensions predict corporate reputation such that is has an positive effect on this dependent variable. To test the first two hypotheses this model was run 5 times for each dimension. In each run, one dimension acted as the independent variable, while the others were included in the model as control variables such that the unique contribution of each dimension was determined. The results in table 5 indicates that indeed all five models have an significant positive effect on corporate
reputation. For the dimension customer orientation (β=.182, p<.01), good employer (β=.082, p<.01), reliable and financially strong company (β=.154, p<.01), Product and Service Quality (β=.292, p<.01) and finally social and environmental responsibility (β=.198, p<.01).
Therefore H1 is supported.
The second hypotheses stated that in accordance with the literature by Walsh et al. (2007) that overall customer orientation is the most important predictor from the CBR scale with the highest direct effect on corporate reputation. The results of this study found in table 5 indicates that product and service quality in the banking and telecom industries has the strongest effect on corporate reputation (β=.292, p<.01) whereas customer orientation is (β=.182, p<.01). Therefore H2 is not supported.
Surprisingly, given the high correlation between the dimensions, there is a significant unique contribution from each dimension. Although the effects are very close in range of each other, the difference between the dimensions Good Employer (β=.082, p<.01) and Product and
Table 7.
Results Regression Analysis PROCESS Model 1: (standardised coefficients)
coeff (β) se t LLCI ULCI
1.Customer Orientation .182** .038 4.751 .107 .257
2.Good Employer .082** .037 2.217 .009 .155
3.Reliable and Financially Strong Company .154** .039 3.996 .078 .230 4.Product and Service Quality .292** .043 6.761 .207 .376 5.Social and Environmental Responsibility .198** .033 5.979 .133 .263 **(p<0.01)
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Services Quality stand out the most (β=.292, p<.01), which is more than three times the equivalent. It suggests that from the CBR dimensions being a good employer has the least effect on reputation in these industries. Which is understandable as being a good employer is not necessarily a direct interest from a consumers perspective whereas getting a good product and/or service quality is.
Next, the moderating effects of industry trust on the relationship between the CBR dimensions and corporate reputation are to be found in table 8.
Hypotheses 3a. stated that in a low trust industry the CBR dimension reliable and financially strong company is negatively moderated such that it has a stronger positive effect on
corporate reputation in comparison to when industry trust is high. The results in table 6 suggest that there is no significant moderating effect of industry trust on reliable and financially strong company. Therefore H3a. is not supported.
Similarly to hypotheses 3a., hypotheses 3b. stated that the CBR dimension product and service quality is negatively moderated by industry trust such that it has a stronger positive effect on corporate reputation when the trust in the industry is low versus when it is high. The results in table 6. suggests that there is indeed a significant moderating effect from industry trust on, however this is in the opposite direction as proposed (β=.039, p<.05). Therefore H3b. is not supported.
Hypotheses 3c. stated that the effect of customer orientation on corporate reputation is lower in a low industry trust environment versus a high one. Table 6. indicates that there is no
Table 8.
Effect se t LLCI ULCI
1.Customer Orientation .031 .016 1.909 -.001 .063
2.Good Employer .035* .015 2.292 .005 .065
3.Reliable and Financially Strong Company .031 .017 1.826 -.002 .063
4.Product and Service Quality .039* .016 2.488 .008 .071
5.Social and Environmental Responsibility -.022 .020 -1.140 -.061 .016
Corporate reputation Dimension x Industry Trust
Results Moderation Analyses PROCESS Model: 1 (5000 bootstrap resamples; unstandardised coefficients)
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significant moderating effect of industry trust on customer orientation. Therefore H3c. is not supported.
The results in table 6 further indicate that the dimension good employer is positively
moderated by industry trust suggesting that the effect of good employer on overall corporate reputation is stronger in a high trust industry versus low trust (β=.035, p<.05).
Although industry trust appears to have a moderating effect on two out of the five CBR dimensions, it is just barely significant and close in rage of one another. Due to the high correlation between the dimensions and industry trust the results statistically are not quite convincing.
The results of the three-way moderation analyses are shown in table 9.
Hypotheses 4 proposed a moderated moderation effect from industry involvement on industry trust on the relationship between the CBR dimensions and corporate reputation. The results in table 7 indicate that there is no significant interactional effect with either dimension.
Therefore H4 is not supported.
Additional results indicate that consumer involvement has a significant positive moderation effect on the relationship between customer orientation and corporate reputation. It suggest that the effect of customer orientation becomes significantly stronger when consumer involvement is high versus when it low.
The results show that the CBR dimensions, in line with Walsh et al. (2007), are significant predictors of corporate reputation. This study found that there is a moderating effect of industry trust within these industries although limited to two dimensions and not in the same
Table 9.
Effect se t LLCI ULCI
1.Customer Orientation .007 .010 .700 -.012 .025
2.Good Employer .007 .010 .699 -.013 .027
3.Reliable and Financially Strong Company .009 .012 .803 -.014 .032 4.Product and Service Quality .012 .010 1.175 -.008 .031 5.Social and Environmental Responsibility .001 .013 .109 -.024 .027 Results Moderated Moderation analyses PROCESS Model: 3 (5000 bootstrap resamples; unstandardised coefficients)
Corporate reputation
**(p<0.01), *(p<0.05)
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direction as proposed in the hypotheses. Furthermore this study has not found empirical evidence that industry involvement has an moderated moderation effect on industry trust.
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Discussion
Theaim of this study is to have a closer look at industry characteristics and the influence it
has on the reputation building process of consumers towards single corporations. This study investigated the moderating influence of industry trust and consumer involvement on the relationships between the five CBR dimensions and corporate reputation. Understanding the impact of industry trust and consumer involvement on these relationships can help to better anticipate on corporate reputation.
The first two hypotheses were proposed to test some basic assumptions from the CBR measure, namely that all the CBR dimensions have a positive effect on corporate reputation and overall customer orientation is relatively the most important dimension.
The mean score on corporate reputation overall in these industries was below the midpoint of the Likert scale (M=3.007). Respondents scored corporate reputation of telecom companies (M=4,161) higher than banks (M=2,193). This suggests that consumers are more positive about the corporate reputation for telecom companies than banks (Eisenegger, 2009). The results of this study indicate that in accordance with the literature (Walsh, 2007) that the CBR dimensions indeed all have an positive effect on corporate reputation. This suggest that the consumers perceived corporate reputation increases as the mean scores on these dimensions increase. The support from the first hypotheses was expected and therefore not surprising. Next, was to test the relative importance of the CBR dimensions. In the regression analyses the two industries were merged and it did not reveal that overall customer orientation is the most important dimension. Contrary to the literature (Walsh et al, 2007), the results of this study indicate that the strongest effect on corporate reputation is from the product and service quality dimension. Product and service quality was the was the third highest mean scoring dimension in both industries, while it has the highest effect on reputation. In this study, there were more banking (n = 349) responses than telecom (n = 246). This difference could
possibly explain the importance of the product and service quality dimension. According to Quyet, Nguyen and Taikoo (2015) many consumers have difficulty understanding of financial products and when it comes to quality selection rely on brand names which is inherently linked with reputation.
The respondents lowest mean scores in both industries was social and environmental
responsibility. This dimension however has the second highest effect on corporate reputation. It is very likely that the companies are active in CSR related activities. Apparently, this is not yet visible for the consumer. Thus, this dimension could possibly use more attention from
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both industries to enhance reputation. According to Cheney (2010) CSR is increasingly becoming an integral part of the business industry and it seems that the financial executives have just come to the realize that. The results further indicate that good employer has the lowest effect on corporate reputation, suggesting that the consumers perceived reputation is least affected by how well companies treat their employees. This might not be that surprising as being a good employer is not of direct interest from consumers perspective. The high correlation values between the dimensions are of concern in this study, however nevertheless the results indicate that each dimension has a unique contributes on corporate reputation. Further it appears that the effect of product and service quality is stronger in the banking and telecom industry compared to the literature. The biggest difference between the effects of the dimensions on reputation is the impact of product and service quality and good employer. The effects of social and environmental responsibility, customer orientation and reliable and financially strong company effects are close together, suggesting that they are not very distinctive from each other.
The extent to which industry trust plays a role on the relationship between the dimensions and corporate reputation was the next question. The variances in the mean scores of industry trust between banking M=3,346) and telecom (M=4,379) indicated a difference in trust. The trust level for banking is below the midpoint of the Likert scale and this was as expected (Edelman Trust Barometer 2015). However, the trust level in the telecom industry is just above de midpoint and not a high score for a high trusted industry. Nevertheless, there is a significant difference in trust between the industries to add the moderation in the construct. The results of the moderated regression analyses indicates that industry trust does not have a moderated influence on the relationship between reliable and financially strong company. Nor does it have an influence on customer orientation. There is a very low interactional effect found on the dimensions product and service quality and good employer. It suggests that the mean scores of these dimensions have a stronger effect on the reputation of single companies in a high trust environment versus low trust. In other words, when industry trust is not a concern
the valuation of products & servicesand employership will have a greater impact on the
reputation perception. However, as mentioned before, although the interactional effects of industry trust on these two dimensions is significant, the effect is very small. The probable cause here might also be the high correlation values of the dimensions mutually and also with industry trust.
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The last test, the three-way moderation investigated the influence of consumer involvement on the effect of industry trust. In other words the influence of how different people perceive industry sentiment and the impact on the CBR measure. There was no interactional effect found between industry trust and consumer involvement on the relationships between the CBR dimensions and reputation. Zaichkowsky’s (1985) Personal Involvement Inventory (PII) is a more commonly used scale in measuring involved with products, advertisement and purchases and perhaps more appropriate for this study.
In order to hypothesize that trust industry has an effect on the CBR measure, we tested two basic assumption first. It was confirmed that the CBR dimensions all have an positive effect on reputation. Within the research industries however, product and service quality has the strongest effect on reputation. Indicating that product and service quality and good employer seem to have a greater effect within the telecom industry under the influence of industry trust.
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Implications for practice
There will be no doubt about how important reputation is for the success of businesses. Starting point of the research has been among others, that the reputation in the banking world and the distrust that prevails there. It is for managers in this industry important to know how consumers look at their reputation. For this, the CBR measure can be used as a standard to assess the dimensions, considering the significant effects on reputation in this study. Given the distrust in banks and the mistakes made in this sector, it might be easy now to focus primarily on customer orientation and reliability because this seems logical. As a response to the corporate reputational decline (Eisenegger, 2009), banks have invested in new marketing strategies. For instance "Transparency" and "Simplicity" is a term that is often used by banks. The results of this study however, suggests that product and service quality is still of
importance for consumers. Attractive products and prompt customer service can therefore be highlighted more in communications. Social and environmental responsibility also had a relatively large effect on reputation. While this dimensions scored the lowest in both
industries. There could be more emphasis on this dimension by actively promoting the CSR related activities of the bank or make CSR an integrated part of the business strategy. The larger banks probably already report their CSR related activities on their company website. However, according to Lee (2014), the best effective channels for forming a CSR reputation is through press releases or the companies Facebook page.
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Limitations and direction for future research
The research design of this study was an experiment, because the research environment was manipulated. In a pre-test the most and least trusted industries were rated. Then respondents were approached, from which it was likely that they were positive or negative in relation to the industries. The results are therefore not reflection of the reality. For that several industries will have to be examined and an objective approach for respondent selection must be applied. In addition, there have been more responses for banks than telecom companies. This may have had an impact on the importance of the dimensions, in contrary to the literature (Walsh et al, 2007). The biggest limitation or setback of this investigation, unfortunately is the high correlation between trust and the dimensions and the dimensions mutually. The reason for this is most probably the length of the questionnaire. This was due to the repetition of the
questions, which was looped three times. It seems that the respondents may not have answered with full attention and care but rather automated and quick. It would have been better to have asked their opinion towards one bank and/or telecom instead. Also, it might have been better to recode more questions and to divide the CBR questions randomly in a view blocks rather than all at once. This could have led to more awareness from the
respondents while answering. Further, the telecom industry was not really high on trust but rather a neutral level, despite the statistical significant difference with banking.
For further research, it is interesting to research multiple industries, and really seek for a difference between a high and low trust industry and use an more objective respondents selection. In addition, there could be more in-depth research done towards the moderating effects of large versus small companies on the CBR measure. It may also be considered, what the influence is between companies that present themselves as sustainable and businesses that do not.
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