"The Social Identity of Customer Loyalty in a B2B Service Environment"

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Master Thesis

"The Social Identity of Customer Loyalty in a B2B Service Environment"

Avi Ovadiah 6389740

Universiteit van Amsterdam Faculty Economics

Master Business Studies

Date: March 2014

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“The social identity of Customer Loyalty in a B2B service environment” 2

Acknowledgements

This research and the writing of my thesis has not been easy for the people in my

environment and myself; the switch of a new job, the new born baby girl and all the hobby’s in the middle of such an important step in my life. This journey of writing my thesis has been a great learning curve and surprised me in all essence. The experience of tunneling thoughts to a concept and combining theory, data and analysis can deliver such an interesting paper. The result would not have been possible without the help and support of wonderful people.

First of all, I want to thank all the clients who took the time to answer my survey, I was surprised with the amount of people that took the time for this research. Many thanks to my employer Meijers Assurantiën BV and the colleagues which supported me during the

research and writing of the thesis. They were always interested during the whole project and were available to give feedback at all stages.

Then my gratitude must go to my supervisor, Dr. Karin Venetis. Your help and understanding during the process gave me self-confidence. Your guidance to focus on what was important and the feedback encouraged me to deliver and finish this thesis.

Mark and Dan thank you very much that you took the time to look at this paper and provided me the needed feedback, making it what I was looking for.

Last but not least, I want to thank my wife who is my best friend who supported me in the moments that I really needed it.

“ Education is an unfinished symphony”

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Abstract

Although neuroscience suggests that in the processing of sensory information, cognitive functions of the brain and feeling can affect each other, evidence on this reciprocal relationship has not been garnered in marketing research as described by Vakratsas and Ambler (1999).

Customer value from the holistic view is linked to other concepts like customer loyalty. The general understanding amongst managers/organization is that loyal customers are valuable for the company, especially in a service providing firm.

In this research, data was collected through a survey that was sent by e-mail to 860 customers of Meijers; a broad selection to ensure a diverse target population. In total 109 questionnaires were returned.

Our study showed that the direct relationship between customer-company identification and customer loyalty is significant, as well as the direct relation between customer satisfaction and customer loyalty. We also compared both variables and find support for the relatively strong effect of customer-company identification on customer loyalty, compared to the effect of customer satisfaction. We propose that this conclusion can be drawn especially in a business to business service environment, because a service is an experience compared to tangible products that should match physical expectations.

Keywords: customer loyalty, customer-company identification, corporate identity, relationship, customer satisfaction, business to business

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Introduction

‘Merely satisfying customers will not be enough to earn their loyalty. Instead, they must experience exceptional service worthy of their repeat business and referral. Understand the factors that drive this customer revolution.’ (Rick Tate)

Delivering value to the customer is most important to managers, shareholders and entrepreneurs all around the world. There are various interpretations of what is meant by customer value and how it is created. Regarding customer value two theoretical perspectives could be identified: The rational perspective and the holistic perspective. From the rational perspective customer value is a transaction and a concept on its own. Within the holistic perspective customer value is relational and connected to or influenced by other concepts, for example customer loyalty.

From the rational perspective, the buyers' perception of value represents a tradeoff between quality and benefits they perceive in the product/service relative to the sacrifice they perceive by paying the price (Monroe 1990). The consumer's overall assessment of the utility of a product or service based on the perceptions of what is received and what is given can be seen as customer value according to Zeithaml (1988). It can also been seen as a comparison of weighted "get" attributes to "give" attributes following Heskett et al. (1994). Anderson (1993) mentions that value in business markets is the perceived worth in monetary units of the set of economic, technical, service and social benefits received by a customer firm in exchange for the price paid for a product/service, taking into consideration the available suppliers' offerings and prices. In addition Gale (1994) describes that customer value is market perceived quality adjusted for the relative price of your product.

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More recent research emphasizes the holistic perspective of customer value. Customer loyalty has become a dominant factor in customer relationship management.

Right now, for most companies in a service orientated environment, customer loyalty is the key to future profitability and growth. Just like many other organizations, the Dutch insurance broker Meijers is transforming rapidly, driven by global competition, more demanding customers, and economic downturn. Meijers continuously seeks to achieve and retain competitive advantage. It is important to have the internal process of an organization in order, to deliver an outstanding organizational performance. This performance can be influenced by the loyalty of customers towards a company. This aspect of customer relationships is of growing interest to managers and organizations, because they believe that customer loyalty leads to a long term and sustainable competitive advantage. Palmatier et al. (2007) and Woodruff (1997) support this thought.

Looking at most organizations, many decisions are based on the idea that there is a relation between customer value, customer relationships and customer loyalty. Within Meijers we believe that this can positively influence the performance of the company. This is also supported by Palmatier et al. (2007) who have described that a positive effect of a good relationship with clients is increasing seller performance in a business to business market. A good relationship can be the result of meeting expectations and satisfy customers by delivering good performance. In this research, we will focus on excellent service, which we will define as “exceeding expectations”. Delivering outstanding results that will spread by word-of-mouth.

The basis of this research is the assumption that there is a different approach of customer loyalty for business-to-consumer relationships and business-to-business relationships in a service environment. Individuals (consumers) are strongly influenced by ‘sameness’ or ‘substance’ against a backdrop of change and ‘outside’ elements, according to Cheney and

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Tompkins (1987). Entrepreneurs in a business-to-business relationship are acting more independently, not being part of a social group. Thus, current research doesn’t focus on the holistic perspective of loyalty in a business-to-business relationship.

Research on customer loyalty is often conducted in the business to consumer market (Lam, 2004). However the concepts that are also used by managers in the business to business market, even though the environment is different, these managers use a similar meaning of the concept. Due to the fact that this research will focus on a business to business service environment, the interaction between the company and its representatives and the customer are a dominant factor for customer loyalty. For this thesis, we will research customer-company identification as an indicator of customer loyalty in a business to business environment using research from the business to consumer market as well as business to business market. This leads to the following research question:

“Has Customer–Company Identification in general more effect on customer loyalty than Customer Satisfaction in a service organization in a business-to-business context?”

In order to answer this question, the following sub questions will need to be answered as well:

■ What is customer-company identification?

■ What is customer satisfaction?

■ What is customer loyalty in a business-to business service industry context? Research Goal

According to the existing literature, further research could focus on the incremental explanatory power of customer–company identification beyond the influence of customer satisfaction. For most companies, it is helpful to establish customer loyalty in different ways.

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The relevancy for practice lies within the need for business to business service organizations to increase or maintain revenue by creating a unique corporate identity, with the help of modern media.

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Theoretical Framework

In this chapter the Customer loyalty, customer-company identification and customer satisfaction will be explained theoretically, this way giving the reader an opportunity to distinguish the main factors of the conceptual model.

Customer Loyalty

Although neuroscience suggests that in the processing of sensory information, cognitive functions of the brain and feeling can affect each other, evidence on this reciprocal relationship has not been garnered in marketing research as described by Vakratsas and Ambler (1999).

As stated earlier, customer value from the holistic view is linked to other concepts like customer loyalty. The general understanding amongst managers/organizations is that loyal customers are valuable for the company, especially in a service firm (Gwinner, Gremler, Bitner, 1998). To find more information about the link between customer value and customer service we will focus on customer loyalty in this thesis. Therefore I will outline this concept in the theoretical framework. The different definitions I have found so far, all have a similarity, which is that customer loyalty is a behavior. Below customer loyalty will be further enlightened.

Following Oliver (1999) customer loyalty is a buyer's overall attachment or deep commitment to a product, service, brand or organization. The loyalty concept is similar in meaning to relationship commitment, which is described by the relationship marketing literature as an enduring desire to be in a valued relationship according to Anderson and Weitz (1992). In the research of Fornell (1992) is described that customer loyalty manifests itself in a variety of behaviors, the more common ones being recommending a service provider to other customers (word-of-mouth) and repeatedly patronizing the provider.

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Customer satisfaction

Prior research has found qualified support for a positive relationship between satisfaction and customer retention (Rust and Zahorik, 1993). Bolton (1998) found that the duration of a relationship between a customer and a service provider is longer when the customer is satisfied. Crosby and Stephens (1987) found that prior satisfaction increases the likelihood of a customer renewing their insurance policy. Rust et al. (2000) propose that the relationship between satisfaction and loyalty is positive. The more satisfied customers are with a service provider, the more loyal they are. Reicheld (1996), however, suggests that satisfaction is significantly related to loyalty only at very high levels of satisfaction.

Customer loyalty and customer satisfaction

Customers’ loyalty to a service provider is influenced by their overall satisfaction with that provider. From a more holistic perspective Butz & Goodsstein (1996) describe that by customer value, they mean the emotional bond established between a customer and a producer after the customer has used a salient product or service produced by that supplier and found the product to provide an added value. Heskett et al. (1997) explains that there is an argument in the service management literature that customer satisfaction is the result of a customer's perception of the value received in a transaction or relationship. According to this study Heskett et al. discuss how companies can deliver high-value services to their customers and thereby satisfying their customers' needs well. Therefore the following hypothesis is stated:

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Customer-company identification

The theoretical foundation of explaining why customers identify with companies and how this identification fosters their loyalty is the social identity approach (e.g. Tajfel and Turner 1979). The main theories of this approach are social identity theory (e.g. Tajfel 1978; Tajfel/Turner 1986) and self-categorization theory (e.g. Turner et al. 1987). Both complement each other insofar as the first theory explains the consequences of identification, whereas the latter is concerned with the antecedent conditions that lead to identification.

Cheney and Tompkins (1987) state that identification is "the appropriation of identity, either (1) by the individual or collective in question or (2) by others. Identification includes "the development and maintenance of an individual's or group's 'sameness' or 'substance' against a backdrop of change and 'outside' elements."

In an indirect response to the success of companies such as Apple or Harley-Davidson, they have managed to build a fervently loyal customer base. Recent studies have shown how identification may enable companies to turn their customers into loyal apostles (Belk and Tumbat, 2005, Schouten and McAlexander 1995, Bhattacharya and Sen (2003).

When corporate identity can satisfy one of consumers’ three basic self-definitional needs, consumers will be likely to be attracted by the identity. Corporate identity attractiveness depends on how similar it is to consumers’ identity (identity similarity), its distinctiveness in traits consumers evaluate (identity distinctiveness), and its prestige (identity prestige) (Bhattacharya and Sen, 2003). Identity similarity can satisfy self-continuity need

(Bhattacharya and Sen, 2003; Pratt, 1998); identity distinctiveness can satisfy distinctiveness need (Ashforth and Mael, 1996); identity prestige can satisfy self-enhancement need (Ashforth & Mael, 1989; Bhattacharya & Sen, 2003 ;Dutton, Dukerich, & Harquail, 1994). When corporate identity meets the outsiders’ self-definitional needs, the company will be perceived as attractive (Mukherjee and He, 2008). When consumers

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recognize traits of the company similar to theirs, the difference from other rivals and the prestige, they will be attracted to the company.

Most research on customer-company identification is focusing on products and not on (business to business) services. In general, it’s easier to identify with a product rather than with a service. Services are simply less visible and tangible compared to products. The holistic/emotional aspect needs more attention of managers in a service environment. Word-of-mouth is essential (as mentioned by Hong and Yang, 2009).

Customer loyalty and Customer-company identification

Bhattacharya and Sen (2003) describe the social identity approach into the customer domain and have developed a conceptual framework for customer-company identification. In their study the core suggestion is that customers can identify with a company. Einwiller (2006) describes the emotional part of customer-company identification as follows: "Strong identification occurs when a company becomes personally relevant for consumers, and personal relevance creates the potential for emotional reactions." Thus, we propose the following:

H2: Customer-company identification will have a positive relation to customer loyalty

The literature review shows that studies on customer satisfaction in relation to customer loyalty are largely written in the nineties of the last century. Customer-company identification and it’s relation to customer loyalty are described in several articles over the last fifteen years, the era of internet and social media. Social and corporate identity has become the main focus of marketeers in this new era (Simmons, 2008).

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This thesis will emphasize the importance of customer-company identification, in comparison to customer satisfaction. While most research describes both aspects of customer loyalty without measuring it’s individual effect, in this research we will take it a step further and compare both aspects, with the length of customer relationship as a moderator. Thus, we propose the following hypothesis:

H3: The effect of customer-company identification on customer loyalty is stronger in comparison with customer satisfaction

A satisfied customer expects the continuation of a good service. A customer who identifies with a company will also promote it after a while.

Moderation of length of customer relationships

In addition to the comparison of both aspects on customer loyalty, we conduct research on the moderating effect of the length of the customer relationship on the strength of the relation between customer company identification / customer satisfaction and customer loyalty.

H4: The relevance of customer company identification in comparison with customer satisfaction will increase when the length of the customer relationship is longer

In this research we focus on two drivers of customer loyalty: customer-company identification and customer satisfaction. Both aspects are described separately in different studies in a business-to-consumer environment. There is no scientific evidence yet that one of the two aspects has a stronger effect on customer loyalty in a business-to-business environment. In this study we try to identify the strength of the effect of each variable on customer loyalty.

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Control variables - Age - Gender

- Company tenure

Company tenure can affect the variables, as the perception of the customer can change due to seniority and experience in the broker industry. The same could be true for age.

We assume that there might be a difference between male and female respondents in the way they look at rational and holistic aspects of a company relationship.

The conceptual model

Below a graphic display of the complete model in which this research is based on, providing the reader an overview of all the relations and hypotheses provided in the literature review.

Control variables - Age, Gender and Company tenure

Customer Satisfaction Customer-company Identification Customer Loyalty Length of customer relationship H4 H3 H2 H1

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Methodology

In this chapter, the research method of this paper is described. First the sample and procedure will be described, followed by a description of the measures and instruments.

Sample and procedure

For this research the survey strategy will be used and will have a deductive approach.

“A deductive approach is concerned with developing a hypothesis (or hypotheses) based on existing theory, and then designing a research strategy to test the hypothesis” (Wilson, 2010) Monette et al (2005) further explain deductive approach by the means of hypotheses, which can be derived from the propositions of the theory. “Deduction begins with an expected pattern that is tested against observations, whereas induction begins with observations and seeks to find a pattern within them” (Babbie, 2010).

The data will be gathered and analyzed following quantitative research methods. The quantitative research method is also known as “ex post facto research”, which means that the testing of the hypotheses follows the data collection. This avoids intervention during the collection of the data (Jonker and Pennink, 2010). The surveys will be collected via a sample population of customers in a business-to-business service context.

The sample population measured in this research was randomized and performed in the client portfolio of Meijers Risk Brokers. This company provides financial services.

For this research we will focus on the Netherlands and the surveys will be held amongst customers located in the Netherlands.

The company was chosen due to the relationship with the researcher. Meijers is the employer of the researcher and is well willing to cooperate with this research.

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For the collection of the data of this research a web-based survey will be used, this is also because of the time efficiency in the collection of the data as well as being cost friendly. A valid sample, according to Pfleeger and Kitchenham (2002), is a representative subset of the target population. In this case data was collected through a survey that was sent by e-mail to 860 customers of Meijers; a broad selection to ensure a diverse target population. The surveys were anonymously returned online by using Qualtrics. In total 109 questionnaires were returned.

For analyzing the collected data the statistical software SPSS has been used, the results are being formulated regarding the output of SPSS.

Measures

The questionnaire will be designed considering Saunders et al (2009) with steps to maximize the response rates, validity and reliability, such as careful design of individual questions, clear pleasing layout, lucid explanation of the purpose of the questionnaire and pilot testing.

The three constructs in this thesis are: customer loyalty, customer-company identification and customer satisfaction. Before the constructs were presented to the respondents, descriptive questions were presented, such as gender, age, company tenure and role in decision making process.

For the survey the seven-point Likert-type scale (1=strongly disagree to 7=strongly agree) will be used, considering questions that will measure the customer value perception and the customer loyalty with respect to the company.

The design of the individual questions constructs will be adapted from previous studies such as Homburg (2009) with the question “ All in all I am very satisfied with Meijers Assurantiën BV” for customer satisfaction and then we have used the question “ I strongly identify with Meijers Assurantiën BV” for the variable customer identification. For customer loyalty the

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question asked is “ The likelihood of my repurchase with Meijers Assurantiën BV in the future is ….”. A complete overview of the survey used in this research can be found on appendix A.

For this research, three control variables were included in the survey. The questionnaire asked respondents to fill out their gender, age and company tenure. Also, we included questions about the role in decision making, the nature of the business (family business or not) and function.

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Results

In this chapter, the four previously mentioned hypotheses are tested and analyzed. When creating hypotheses certain relationships are expected beforehand, however by using linear regression the true relationships will be proved.

We will provide an overview of the results provides by SPSS after the analysis of the data was performed. It includes a sample overview of the descriptive questions asked, analysis and review of the construct reliability and the statistical results of the different hypotheses proposed.

Descriptive overview

In this research there were a total of 109 respondents divided into 83 male and 26 female. 11.9% of the sample was between 20 and 30 years, 22% between 31 and 40 years and 66.1% above 40 years of age. 91.7% of the respondents are playing a role in the decision making process of the company. This is relevant for the research because only decision makers are responsible for the development of the relationship (resulting in loyalty). 47.7% answered to be the owner of the company, while 8.3% and 9.2% are respectively CFO or CEO. 32.1% of the respondents work in a family business.

The above percentages are in line with the total population of 860 customers to which the survey has been sent. Of the population about 700 were male and 160 female. Over 90% of the population is decision maker. In conclusion we can state that the sample is random.

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N Minimum Maximum Mean Std. Deviation

What is your gender? 109 1 2 1,24 ,428

What is your age? 109 2 4 3,54 ,701

How many years have you been working for your company?

109 1 4 2,21 ,851

Do you play a role in the decision making process at your company?

109 1 2 1,08 ,277

What is your function at your company? 109 1 5 2,51 1,653

Is your organization a family business? 109 1 2 1,68 ,469

Valid N (listwise) 109 Frequency Percent Valid Percent Cumulative Percent Male 83 76,1 76,1 76,1 Female 26 23,9 23,9 100,0 Total 109 100,0 100,0 Frequency Percent Valid Percent Cumulative Percent 20 - 30 years 13 11,9 11,9 11,9 31 - 40 years 24 22,0 22,0 33,9 > 40 years 72 66,1 66,1 100,0 Total 109 100,0 100,0 Frequency Percent Valid Percent Cumulative Percent Yes 100 91,7 91,7 91,7 No 9 8,3 8,3 100,0 Total 109 100,0 100,0 Frequency Percent Valid Percent Cumulative Percent Owner 52 47,7 47,7 47,7 CFO 9 8,3 8,3 56,0 CEO 10 9,2 9,2 65,1 Administrator 16 14,7 14,7 79,8 Other 22 20,2 20,2 100,0 Total 109 100,0 100,0 Frequency Percent Valid Percent Cumulative Percent Yes 35 32,1 32,1 32,1 No 74 67,9 67,9 100,0 Total 109 100,0 100,0

Do you play a role in the decision making process at your company? Descriptive Statistics

What is your gender?

Valid

What is your age?

Valid

What is your function at your company?

Valid

Is your organization a family business?

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Reliability

The measurement scales have been tested on internal validity. This means that the study is done in such a way that it is investigating what it is supposed to investigate. To measure the consistency in the response we calculated the Cronbach’s alpha. For the Customer Loyalty (dependent variable) we obtained a Cronbach’s alpha of 0.956, for this reason we maintain all questions. Cronbach´s alpha for the construct Customer-company identification and Customer satisfaction (Independent variables) were 0.933 and 0.958, in these constructs no questions were removed and the analysis was done by not changing any scale of the questions provided to our correspondents.

Variables correlations

A Pearson correlation analysis was performed for all variables to study if there were any high and significant correlations between the studied independent and dependent variables. This way we can observe if there is any multicollinearity in the variables that can affect any regression estimates that will be studied in our hypotheses analysis.

According to the correlation table below, there are significant correlations between the variables studied in this research. This provides an initial understanding of multicollinearity.

Zscore(Cust.sat) Zscore(Cust.ide) Zscore(Cust.loy)

Zscore: How many years are you a client of Meijers Assurantiën BV?

Pearson Correlation 1 ,821** _,826** _-,168 Sig. (2-tailed) ,000 ,000 ,080 Pearson Correlation 1 ,875** _-,218* Sig. (2-tailed) ,000 ,023 Pearson Correlation 1 -,157 Sig. (2-tailed) ,103 Pearson Correlation 1 Sig. (2-tailed)

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Correlations

Zscore(Cust.sat) Zscore(Cust.ide) Zscore(Cust.loy) Zscore: How many years are you a client of Meijers Assurantiën BV?

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Customer loyalty and customer satisfaction

The first hypothesis of our study reflects the direct relation between customer loyalty and customer satisfaction controlled by age, gender and company tenure, which explained by theory, the customer loyalty will have a positive relation to customer satisfaction (H1). For H1 an SPSS linear regression analysis will be performed including our control variables.

In this analysis the customer satisfaction is our independent variable which for analysis purposes we centralized, where the mean of the variable is subtracted to each value and the new variable will have mean zero. This centralized new variable is called Z-score of the variable. This centralization is also done to our dependent variable customer loyalty.

This regression results, showed that the direct relation between above variables is significant (β=0.790, SE=0.053, p-value=0.000); and that the variation in customer satisfaction explains the variation in customer loyalty in a 71.7% given by the R² of this first analysis.

In conclusion we see that hypothesis 1 is supported.

R Square Change F Change df1 df2 Sig. F Change 1 ,847a _,717 _{,706 ,54231160} _,717 _65,805 ₄ ₁₀₄ _,000 Control Age Gender Company tenure Standardiz ed Coefficient s

B Std. Error Beta Tolerance VIF

(Constant) ,000 ,052 ,000 1,000 Zscore(Cust.sat) ,790 ,053 ,790 14,774 ,000 ,953 1,050 Sig. Collinearity Statistics 1 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Coefficientsa Model Unstandardized Coefficients t

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Customer loyalty and customer-company identification

The second hypothesis of our study reflect the direct relation between customer loyalty and customer-company identification controlled by age, gender and company tenure, which explained by theory, the Customer loyalty will have a positive relation to Customer-company identification (H2). For H2 an SPSS linear regression analysis will be performed including our control variables.

In this analysis the Customer loyalty is our dependent variable which for analysis purposes we centralized, where the mean of the variable is subtracted to each value and the new variable will have mean zero. This centralized new variable is called Z-score of the variable. This centralization is also done to our independent variable customer-company identification.

Our regression results, showed that the direct relation between customer loyalty and customer-company identification is significant (β= 0.842, SE=0.047, p-value=0.000); and that the variation in Customer-company identification explains the variation in Customer loyalty in a 78.2% given by the R² of this first analysis.

R Square Change F Change df1 df2 Sig. F Change 1 ,884a _,782 _{,774 ,47574482} _,782 _93,293 ₄ ₁₀₄ _,000 Control Age Gender Company tenure Standardiz ed Coefficient s

(Constant) ,000 ,046 ,000 1,000 Zscore(Cust.ide) ,842 ,047 ,842 17,742 ,000 ,930 1,076 Change Statistics Coefficientsa Model Unstandardized Coefficients t Sig. Collinearity Statistics Model Summary Model R 1

a. Dependent Variable: Zscore(Cust.loy)

R Square Adjusted R Square Std. Error of the Estimate

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Customer satisfaction versus Customer-company identification

To analyze the effects of the two variables on customer loyalty we performed an integral analysis with both centralized independent variables (Customer company identification and customer satisfaction) in one linear regression, together with our centralized control variables (Gender, Age and tenure) as part of its control effect in this relationship. Here the direct relation was measured between customer-company identification and customer loyalty and customer satisfaction and customer loyalty.

In this analysis both customer-company identification (β=0.568, SE=0.075, p-value=0.000) and customer satisfaction (β=0.333, SE=0.074, p-value=0.000) stayed significant. Comparing the beta values of both variables gives an indication of the relative effect of both variables.

Model Summary

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 ,904a _,817 _,809 _,43751981

a. Predictors: (Constant), Zscore(Cust.ide), Zscore: What is your gender?, Zscore: How many years have you been working for your company?, Zscore: What is your age?, Zscore(Cust.sat)

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“The social identity of Customer Loyalty in a B2B service environment” 24 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2,093E-16 ,042 ,000 1,000

Zscore: What is your gender? ,015 ,042 ,015 ,357 ,722

Zscore: What is your age? -,102 ,046 -,102 -2,220 ,029

Zscore: How many years have you been working for your company?

-,053 ,045 -,053 -1,176 ,242

Zscore(Cust.sat) ,333 ,074 ,333 4,468 ,000

Zscore(Cust.ide) ,568 ,075 ,568 7,536 ,000

To study the multicollinearity of this analysis, we reviewed the variance of inflation factors (VIF) of both variables in this linear regression, controlled by age, gender and tenure of the correspondent.

Theory highlight that there is no multicollinearity if the VIF of the relation is lower than 5. For the relations customer-company identification / customer satisfaction and customer loyalty the results were lower than 5 (VIF customer satisfaction = 3.127 & VIF customer-company identification = 3.204), concluding that there is no multicollinearity in these relations.

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Multicollinearity Relation: VIF Customer satisfaction, customer company identification and customer loyalty Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std.

Error Beta Tolerance VIF

1 (Constant) 2,093E-16 ,042 ,000 1,000

Zscore: What is your gender? ,015 ,042 ,015 ,357 ,722 ,989 1,012

Zscore: What is your age? -,102 ,046 -,102

-2,220

,029 ,844 1,185

-,053 ,045 -,053

-1,176

,242 ,886 1,128

Zscore(Cust.sat) ,333 ,074 ,333 4,468 ,000 ,320 3,127

Zscore(Cust.ide) ,568 ,075 ,568 7,536 ,000 ,312 3,204

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Moderator variable – Length of customer relationship

The fourth hypothesis of our study reflects the effect of the moderator variable (length of customer relationship) on the relevance of customer-company identification. In order to correctly analyze this relationship we performed several regression analyses. We initiated this study by dividing the population into 2 groups, as defined below:

Group 1: Length of relationship between 0 – 2 years

Group 2: Length of relationship greater or equal than 5 years

Group 1 consists of 31 respondents. For this analysis we chose a maximum length of relationship of 2 years to establish a significant amount of respondents. Contracts in a business-to-business environment normally last for one year, but a client who just renewed their contract for the first time could still be considered as a new client.

Group 2 consists of 64 respondents, all being client of the company for 5 years or more. We did not discover any relevant differences in the background of the respondents in both groups, based on the control variables.

These selected groups together account for 87% of the total of respondents in this study.

First we will perform 2 separate analyses, one with each group selected above, controlled by age, gender and tenure of the correspondent.

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Analysis 1 – Linear regression Customer satisfaction/Customer company identification and customer loyalty for Group 1 (0-2 years)

Model Summary

Model

R

R Square Adjusted R Square Std. Error of the Estimate Tenure2 = 1 (Selected)

1 ,928a _,860 _,832 _,33698417

a. Predictors: (Constant), Zscore(Cust.ide), Zscore: How many years have you been working for your company?, Zscore: What is your gender?, Zscore: What is your age?, Zscore(Cust.sat)

Coefficientsa,b Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) -,037 ,070 -,538 ,595

Zscore: What is your gender? -,038 ,075 -,040 -,504 ,619

,025 ,063 ,031 ,393 ,698

Zscore(Cust.sat) ,316 ,107 ,372 2,944 ,007

Zscore(Cust.ide) ,494 ,117 ,549 4,224 ,000

a. Dependent Variable: Zscore(Cust.loy) b. Selecting only cases for which Tenure2 = 1

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Analysis 2 – Linear regression Customer satisfaction/Customer company identification and customer loyalty for Group 2 (≥5 years)

Model Summary

Model

R

R Square Adjusted R Square Std. Error of the Estimate Tenure2 = 2 (Selected)

1 ,906a _,821 _,805 _,47872444

a. Predictors: (Constant), Zscore(Cust.ide), Zscore: What is your gender?, Zscore: How many years have you been working for your company?, Zscore: What is your age?, Zscore(Cust.sat)

Coefficientsa,b Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) ,032 ,064 ,503 ,617

Zscore: What is your gender? ,024 ,057 ,024 ,423 ,674

-,101 ,068 -,086 -1,482 ,144

Zscore(Cust.sat) ,310 ,103 ,309 2,995 ,004

Zscore(Cust.ide) ,579 ,105 ,565 5,501 ,000

a. Dependent Variable: Zscore(Cust.loy) b. Selecting only cases for which Tenure2 = 2

In this analysis both customer-company identification for length of customer relationship between 0 and 2 years (Group 1) (β=0.549, SE=0.117, p-value=0.000) and customer satisfaction (β=0.372, SE=0.107, p-value=0.007) were significant.

The variation in Customer-company identification / customer satisfaction explains the variation in Customer loyalty in an 86% given by the R² of this analysis.

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Also customer-company identification for length of customer relationship of greater or equal than 5 years (β=0.565, SE=0.105, p-value=0.000) and customer satisfaction (β=0.309, SE=0.103, p-value=0.004) were significant.

The variation in Customer-company identification / Customer satisfaction explains the variation in Customer loyalty in an 82.1% given by the R² of this analysis.

Comparing the betas, we can conclude that the relation between customer-company identification / customer loyalty increases for long term customer-company relationships compared to short term relationships.

We can also conclude that the relation between customer satisfaction / customer loyalty decreases for long term customer-company relationships compared to short term relationships.

Furthermore the relation between customer-company identification and customer loyalty is stronger for both short and long term relationships.

Moderation test using Regression

To analyze the moderating effect of the length of customer relationship (tenure) we used linear regression analyses with interaction terms. In this analyses we included the centralized variables of the interaction terms between tenure and customer satisfaction as well as tenure and customer-company identification.

In contrary with the previous analysis we used the complete sample of respondents, without dividing it into two groups, to reduce possible multicollinearity in this relationship. Furthermore, we use the complete sample to avoid losing important data.

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“The social identity of Customer Loyalty in a B2B service environment” 30 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .907a .823 .811 .43529706

a. Predictors: (Constant), Zscore(interact_tenure_satisfaction), Zscore(Cust.ide), Zscore: What is your gender?, Zscore: How many years have you been working for your company?, Zscore: What is your age?, Zscore(Cust.sat),

Zscore(interact_tenure_identification) Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std.

1 (Constant)

1.174E-16

.042 .000 1.000

Zscore(Cust.ide) .494 .109 .494 4.516 .000 .147 6.813

Zscore(Cust.sat) .380 .096 .380 3.965 .000 .191 5.232

Zscore: What is your gender? .017 .042 .017 .400 .690 .974 1.027

Zscore: What is your age? -.118 .047 -.118

-2.535

.013 .811 1.234

-.055 .045 -.055

-1.234

.220 .877 1.140

Zscore(interact_tenure_identification) .280 .258 .280 1.086 .280 .026 38.017

Zscore(interact_tenure_satisfaction) -.219 .260 -.219 -.841 .402 .026 38.574

In this analysis the results are not significant. Because both interaction variables are included in one regression and in this way could possibly cause multicollinearity. This is supported by the analysis below:

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“The social identity of Customer Loyalty in a B2B service environment” 31 Correlations interact_ztenure _zidentification interact_ztenure _zsatis interact_ztenure_zidentificati on Pearson Correlation 1 .818** Sig. (2-tailed) .000 N 109 109

interact_ztenure_zsatis Pearson Correlation .818** ₁

Sig. (2-tailed) .000

N 109 109

**. Correlation is significant at the 0.01 level (2-tailed).

To further analyze the moderating effect of length of customer relationship, in the next step we used two separate interaction terms instead of combining them in one model. In the first analysis we perform a regression with the interaction between tenure and customer satisfaction and in the second between tenure and customer-company identification.

Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .906a .821 .810 .43567752

a. Predictors: (Constant), Zscore(interact_tenure_satisfaction), Zscore(Cust.ide), Zscore: What is your gender?, Zscore: How many years have you been working for your company?, Zscore: What is your age?, Zscore(Cust.sat)

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“The social identity of Customer Loyalty in a B2B service environment” 32 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std.

1 (Constant)

2.304E-16

.042 .000 1.000

Zscore(Cust.ide) .580 .076 .580 7.674 .000 .308 3.246

Zscore(Cust.sat) .315 .075 .315 4.193 .000 .311 3.218

-2.416

.017 .824 1.214

-.058 .045 -.058

-1.295

.198 .880 1.137

Zscore(interact_tenure_satisfaction) .060 .044 .060 1.369 .174 .927 1.079

Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .906a .822 .811 .43467256

a. Predictors: (Constant), Zscore(interact_tenure_identification), Zscore: What is your gender?, Zscore(Cust.ide), Zscore: How many years have you been working for your company?, Zscore: What is your age?, Zscore(Cust.sat)

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“The social identity of Customer Loyalty in a B2B service environment” 33 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std.

1 (Constant)

2.059E-16

.042 .000 1.000

Zscore(Cust.ide) .560 .075 .560 7.471 .000 .311 3.218

Zscore(Cust.sat) .329 .074 .329 4.443 .000 .319 3.131

-2.468

.015 .819 1.222

-.058 .045 -.058

-1.297

.197 .881 1.135

Zscore(interact_tenure_identification) .066 .043 .066 1.534 .128 .940 1.063

We also performed equal regression analyses with interaction terms with the two predefined groups:

Group 1: Length of relationship between 0 – 2 years

Group 2: Length of relationship greater or equal than 5 years

In all analyses the VIF of the relation was higher than 5 concluding that there is multicollinearity in these relations. Results are included in appendix D.

In conclusion we can state that in none of the performed analyses we can identify a moderating effect between the dependent and independent variable with length of customer relationship as a moderator. All analyses showed insignificant results. In conclusion we see that hypothesis 4 is not supported.

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Summary of results

Control variables: Age, Gender and Tenure

Customer Satisfaction Customer-company Identification Customer Loyalty Length of customer relationship H4 – Not Supported H3 - Supported H2 - Supported H1 – Supported

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Discussion

For this research, the relative strength of the relationship between customer-company identification and customer loyalty and between customer satisfaction and customer loyalty the length of customer relationship in relation to customer loyalty has been analyzed.

To identify the social identity of customer loyalty in a business-to-business service environment we applied four steps:

The first two steps are about proving the relationship between both customer satisfaction/ customer-company identification and customer loyalty. The third step is included to measure the individual effect of the two variables in relation to customer loyalty in one model. The logical fourth step is to measure the moderating effect of the length of customer relationship to identify the relevance of customer-company identification in long term relationships compared to short term relationships. We used the length of customer relationship as a moderator instead of a mediator, because we specifically want to measure the relevance of customer-company identification, instead of measuring the mediating effect of time on customer loyalty. The moderator length of customer relationship measures the interaction between the dependent and independent variables.

In our study we found support for the four hypotheses we proposed and found that in fact there is a relationship between customer-company identification/ customer satisfaction and customer loyalty. We found support for the hypothesis that the relationship between customer-company identification and customer loyalty is stronger than the relationship between customer satisfaction and customer loyalty. There is no support for the positive moderating effect of the length of the relationship, but analyzing the moderating effect of the length of relationship provided additional support for the relevance of customer-company identification compared to customer satisfaction.

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The results we found in our study are in line with our theoretical framework suggesting that strong identification occurs when a company becomes personally relevant for consumers, and personal relevance creates the potential for emotional reactions (Einwiller, 2006).

In our view customer-company identification is indeed very relevant, because in the business to business service environment, when a customer feels identified with its broker then the trust between both companies (customer and broker) is higher which translates into higher loyalty to the broker company.

Hypotheses discussion

Hypothesis 1: In the first hypothesis we propose the relationship between customer satisfaction and customer loyalty. Prior research has found qualified support for a positive relationship between satisfaction and customer retention (Rust and Zahorik, 1993). Bolton (1998) found that the duration of a relationship between a customer and a service provider is longer when the customer is satisfied. Crosby and Stephens (1987) found that prior satisfaction increases the likelihood of a customer renewing their insurance policy. Rust et al. (2000) propose that the relationship between satisfaction and loyalty is positive. The results of our study showed that the direct relation between customer satisfaction and customer loyalty is significant (β=0.790, SE=0.053, p-value=0.000). The variation in customer satisfaction explains the variation in customer loyalty in a 71.7% given by the R² of this first analysis. Thus, we see that the results regarding hypothesis 1 are in line with the existing theory.

Hypothesis 2: In the second hypothesis, the relationship between customer-company identification and customer loyalty has been researched. Belk and Tumbat (2005), Schouten and McAlexander (1995) and Bhattacharya and Sen (2003) found that companies focusing on identity have managed to build a loyal customer base. Our research supported the

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existing theory that there is a positive relation between customer-company identification and customer loyalty, also in a business to business environment. Our first regression results, showed that the direct relation between customer loyalty and customer-company identification is significant (β= 0.842, SE=0.047, p-value=0.000); and that the variation in Customer-company identification explains the variation in Customer loyalty in a 78.2% given by the R² of this first analysis.

Hypothesis 3:

In our research question we compare the effect of customer-company identification and customer satisfaction on customer loyalty in one model. In order to make an accurate comparison we will focus on the beta of both correlations (the p-value of both relations is not differentiated by SPSS).

Results show the following betas in the integrated model:

1. Customer-company identification and customer loyalty: β = 0.568 2. Customer satisfaction and customer loyalty: β = 0.333

With this difference, we clearly see that the relation between customer-company identification and customer loyalty is much stronger than the relation between customer satisfaction and customer loyalty. Research shows that studies on customer satisfaction in relation to customer loyalty are largely written in the nineties of the last century. Customer-company identification and it’s relation to customer loyalty are described in several articles over the last fifteen years, the era of internet and social media. Simmons (2008) indicates that social and corporate identity has become the main focus of marketers in modern times.

Hypothesis 4:

The last hypothesis focuses on the length of the relationship in relation to the strength of customer-company identification and customer satisfaction. In support of this view, Homburg,

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Koschate, and Hoyer (2005) find that the impact of customer satisfaction (as an important factor of customer loyalty) on willingness to pay is stronger for longer relationships than for shorter relationships. In the regression analysis both customer-company identification for length of customer relationship between 0 and 2 years (β=0.856, SE=0.079, p-value=0.000) and customer satisfaction (β=0.800, SE=0.082, p-value=0.006) stayed significant.

Also customer-company identification for length of customer relationship of longer than 5 years (β=0.819, SE=0.063, p-value=0.000) and customer satisfaction (β=0.777, SE=0.071, p-value=0.000) stayed significant. By analyzing the moderating effect of the length of customer relationship results did not show significant evidence, not by including both independent variables with interaction terms, nor by using both variables alone in separate analyses.

Our results indicate that the corporate identity of a company in a business to business service environment is not moderated by the duration of the customer relationship. Bhattacharya and Sen (2003) describe that corporate identity attractiveness depends on how similar it is to consumers’ identity (identity similarity), its distinctiveness in traits consumers evaluate (identity distinctiveness), and its prestige (identity prestige). When corporate identity meets the outsiders’ self-definitional needs, the company will be perceived as attractive (Mukherjee and He, 2008). When consumers recognize traits of the company similar to theirs, the difference from other rivals and the prestige, they will be attracted to the company. This attraction is not necessarily a post-customer effect, but could be realized in an earlier stage (in accordance with the AIDA model as described by Verhage, Cunningham, 2004). In support of this theory, we find that customers at the early stage of the relationship are experiencing more identification compared to customers with a longer customer-company relationship.

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Conclusion

Delivering value to the customer is most important to managers, shareholders and entrepreneurs all around the world. There are various interpretations of what is meant by customer value and how it is created. Palmatier et al. (2007) described that a positive effect of a good relationship with clients is increasing seller performance in a business to business market. A good relationship can be the result of meeting expectations and satisfying customers by delivering good performance. In this study, we researched three important variables of customer value: customer-company identification, customer satisfaction and customer loyalty, with the length of the relationship as a moderator. Ahearne, Bhattacharya, and Gruen 2005 and Bhattacharya and Sen 2003 suggested to move beyond customer satisfaction or other rational perspectives to find other ways to build a more holistic bond between customers and companies. We focused on the variable customer-company identification and found that recent research support our holistic approach.

Our study showed that the direct relation between customer-company identification and customer loyalty is significant, as well as the direct relation between customer satisfaction and customer loyalty. The research question of this study was intended to compare both variables and find support for the relatively strong effect of customer-company identification on customer loyalty, compared to the effect of customer satisfaction. We find support for this in the comparison of the beta, which is stronger for the relationship between customer-company identification and customer loyalty than it is for customer satisfaction versus customer loyalty. We propose that this conclusion can be drawn especially in a business to business service environment, because a service is an experience compared to tangible products that should match physical expectations.

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Our research shows that there is no evidence that length of customer relationship has a positive moderating effect on the relationship between customer-company identification and customer loyalty. A longer relationship doesn’t mean that the relevance of customer-company identification will increase in relation to customer loyalty. Our results indicate that the corporate identity of a company in a business to business service environment is not positively moderated by the duration of the customer relationship. However, the effect of customer-company identification is stronger than the effect of customer satisfaction for both short and long term relationships.

Managerial implications

This research provides managers with more insight in the different aspects of customer loyalty and customer value. Delivering value to the customer is most important to managers all around the world. A holistic perspective could help managers to understand the needs of (potential) customers. Furthermore, managers should ask themselves the following questions: Why do we exist? What is our added value to the customer? It is much easier for managers to measure satisfaction than it is to establish a corporate identity. But, a corporate identity is the direct result of managerial policy, while satisfaction is the sum of several internal and external factors.

Secondly, firms should actively stimulate customer-company identification in a business to business environment. According to Homburg (2009), the main instruments to do this are actions to raise the salience of the company as a group category. For example, by stressing favorable comparisons between the “ in-group “ and “ out-groups “ and by organizing events that stimulate a group feeling among customers. Ideally, a company brand is consistent with the personality of the key target group. In this respect, it is important to understand that managing customer-company identification requires largely different means than the customer satisfaction approach, helped by the fact that in a business to business environment companies are doing business with their ‘equals’: entrepreneurs.

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Furthermore, a social identity should be established and maintained for the long term, as we find that the effect of customer-company identification is not positively moderated by the length of the relationship. It is common knowledge that loyal clients have an important contribution to the company’s profit. To establish certain sustainability in the loyalty, identification is of key importance.

The particular relevance of customer-company identification for short term relationships (less than 2 years) could possibly be explained by the frequency of contact moments between customers and companies in a service environment. New clients experience a high level of service due to the process of establishing the new relationship and all paperwork involved. Long term client relationships, in a service environment, normally don’t need much

‘maintenance’. An important managerial suggestion derived from this research is to keep the frequency of contact moments high in a long term relationship.

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Limitations and suggestions for further research

This study could be repeated with a larger sample size in order to increase its significance and validity. We believe that this study should be repeated using qualitative research methods like interviews in combination with quantitative research methods. Combining the two research methods will expand the scope of this study (Sandelowki, 2000). The qualitative research typically involves purposeful sampling to enhance understanding of the information-rich case (Patton, 1990). Quantitative research ideally involves probability sampling to permit statistical inferences to be made according to Sandelowski (2000).

We used the length of customer relationship as a moderator instead of a mediator, because we specifically want to measure the relevance of customer-company identification, instead of measuring the mediating effect of time on customer loyalty. It is possible that a mediating effect exists for length of customer relationship between these relations, controlled by age, gender and company tenure. To measure the possible mediation an analysis could be performed to three direct relations using linear regression and a fourth regression combining two independent variables, customer-company identification and length of customer relationship, with our dependent variable customer loyalty.

All respondents of the survey were clients of one company. The tested variables are thus influenced by the specific characteristics of this company. Further research could include research on clients of different companies in a business to business service environment. Furthermore, Homburg (2009) suggests including the concept of company identification in related concepts, such as the customer equity (e.g., Rust, Lemon, and Zeithaml 2004) or return-on-quality (e.g., Rust, Moorman, and Dickson 2002; Rust, Zahorik, and

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In our research we found remarkable results related to the decision making role of the respondent in the connection between customer-company identification and customer loyalty the relationship between the two variables increase if the respondent does not have a decision making role. We neglected these results due to the small number of non-decision makers among the respondents (9 out of 109). Further research on these particular aspects could be valuable.

Standardiz ed Coefficient

s

(Constant) -,002 ,050 -,030 ,976

Zscore(Cust.ide) ,868 ,050 ,868 17,286 ,000 1,000 1,000

1

b. Selecting only cases for which Do you play a role in the decision making process at your company? = Yes

Coefficientsa,b Model Unstandardized Coefficients t Sig. Collinearity Statistics Standardiz ed Coefficient s

(Constant) -,095 ,101 -,943 ,377

Zscore(Cust.ide) 1,044 ,113 ,961 9,220 ,000 1,000 1,000

1

b. Selecting only cases for which Do you play a role in the decision making process at your company? = No

Coefficientsa,b Model Unstandardized Coefficients t Sig. Collinearity Statistics

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"The Social Identity of Customer Loyalty in a B2B Service Environment"

Master Thesis