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The Spill-over Effect Of Customer Experience In

Innovative Industries On Customer Expectations, Customer

Satisfaction And Customer Loyalty

In The Consumer Banking Industry

MASTER THESIS

WIEBE LUITZEN HOEKSTRA

Student Number: 10998497 Supervisor: Dr. H. Güngör Version: Final Amsterdam Business School Executive Programme in Management Studies

Digital Business Track June 29th, 2018

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Statement of Originality

This document is written by Student Wiebe Luitzen Hoekstra 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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Table of Contents

STATEMENT OF ORIGINALITY ... 1 TABLE OF CONTENTS ... 2 LIST OF TABLES ... 3 LIST OF FIGURES... 3 ABSTRACT ... 4 1. INTRODUCTION ... 5 2. LITERATURE REVIEW ... 7 2.1.CUSTOMER EXPERIENCE ... 7 2.2.CUSTOMER EXPECTATIONS... 8 2.3.CUSTOMER SATISFACTION ... 9 2.4.CUSTOMER LOYALTY ... 10 2.5.INNOVATION ... 11

2.6.CONSUMER BANKING INDUSTRY ... 12

2.7.HYPOTHESIS ... 13

2.8.CONCEPTUAL MODEL ... 15

3. DATA AND METHOD ... 16

3.1.INTRODUCTION ... 16 3.2.RESEARCH DESIGN ... 16 3.3.VALIDITY ... 20 3.4.SAMPLE COLLECTION ... 22 3.5.COLLECTION METHOD ... 22 3.6.ETHICS ... 23 3.7.DATA ANALYSIS ... 23 4. RESULTS ... 25

4.1.DESCRIPTIVE DATA ANALYSIS ... 25

4.2.CORRELATION ANALYSIS ... 27

4.3.REGRESSION ANALYSIS ... 30

5. DISCUSSION ... 40

5.1.DISCUSSION OF RESULTS ... 40

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5.3.THEORETICAL AND PRACTICAL IMPLICATIONS ... 45

5.4.STRENGTHS AND LIMITATIONS ... 45

5.5.FURTHER RESEARCH ... 47

6. CONCLUSION ... 48

7. REFERENCES ... 50

APPENDIX ... 55

List of tables

Table 1: Characteristics of the participants ... 26

Table 2: Means, Standard Deviations and Correlations of Totalized Scales ... 28

Table 3: Results from the regression analysis of Hypothesis 1 ... 30

Table 4: Effects of the model of Hypothesis 1... 30

Table 5: Hierarchical Regression Model of Customer Expectations in Consumer Banking Industry ... 32

Table 6: Hierarchical Regression Model of Customer Satisfaction In Banking Industry ... 33

Table 7: Hierarchical Regression Model of Customer Expectations in Banking Industry ... 34

Table 8: Hierarchical Regression Model of Total Customer Loyalty in Consumer Banking Industry .... 35

Table 9: Results from the regression analysis of the conceptual model ... 37

Table 10: Total indirect effect of the model at levels of Customer Experience with Innovative Companies ... 37

Table 11: Results from the regression analysis of the conceptual model ... 39

Table 12: Total indirect effect of the model dependent variable is Customer Loyalty in Consumer Banking Industry ... 39

List of figures

Figure 1: The Conceptual Model ... 15

Figure 2: Conceptual Model and the Results ... 36

Figure 3: Revised Conceptual Model and the Results ... 38

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Abstract

In academic literature a gap is determined for the spillover effect of customer experience in one industry to another industry and the consequences of this effect on customer satisfaction and customer loyalty. This research investigates the spillover effect of Customer Experience in Innovative Industries on Customer Expectations in the Consumer Banking Industry and the subsequent effect on Customer Satisfaction and Customer Loyalty in the Consumer Banking Industry.

The purpose of this research is to find out the existence of this cross industry spillover effect and the subsequent effects of this spillover on Customer Satisfaction and Customer Loyalty in the Consumer Banking Industry. The following research question is formed to investigate this subject: “Do customer experiences in innovative industries spill over towards customer expectations in the consumer banking industry? And what is the effect of this construct on customer satisfaction and subsequently on loyalty in the consumer banking industry?”

In order to answer the research question quantitative research is performed under 208 Dutch inhabitants. The respondents are acquired by social media and resulted in a varied sample based on age, gender, level of education and their main bank. By analysing the results statistically, it is found that a spillover effect between Customer Experience in Innovative Industries and Customer

Expectations in the Consumer Banking Industry occurs. However, the subsequent effect on Customer Satisfaction and Customer Loyalty in the Consumer Banking Industry was different than expected, a positive interaction was measured instead of a negative interaction.

Based on these results further research is proposed for determining industries and industry

characteristics where spillover effects of customer experience on customer expectations occurs. For managers in the consumer banking industry it is proposed to move from monitoring experiences with competitors towards monitoring experience with innovative industries.

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1. Introduction

“Customer expectations? Nonsense. No customer ever asked for the electric light, the pneumatic tire, the VCR, or the CD. All customer expectations are only what you and your competitor have led him to expect. He knows nothing else.” (Deming, 1994)

Dr. W.E. Deming suggests that competitors form customer expectations. If we take this into a broader perspective, would it also be possible that expectations are formed in other industries? If this is the case, there would be a spillover effect between industries regarding customer

expectations.

In academic literature a need for further research is suggested for examining the spillover effect of customer experience in one industry on customer expectations in another industry. For example: “Do superior perceived customer experiences at Apple transfer to customer expectations of mobile telecom operators, clothing retailers, or restaurants?” (Lemon & Verhoef, 2016).

Customer expectations are formed by previous experiences (Deliza & MacFie, 1996) and as customer expectation is an antecedent of satisfaction (Johnson & Fornell, 1991) it is a crucial element for firms (Patterson, Johnson, & Spreng, 1996). The customer experience is an personal experience, and expresses the involvement of the customer (Gentile, Spiller, & Noci, 2007). Customer experience is important for companies, the experience should meet the formed expectations in order to prevent a negative impact in satisfaction from these expectations (Rust, Inman, Jia, & Zahorik, 1999). The importance of customer satisfaction is highlighted by its role in establishing longer lasting customer relationships (Patterson et al., 1996) and its relation to profitability (Bolton, 1998; Hallowell, 1996). Customer loyalty and customer satisfaction are linked to each other, satisfactory experiences can lead to customer loyalty (Oliver, 1999).

This writing tends discover the extent of which customer experiences in one industries spill over into customer expectations in other industries (Lemon & Verhoef, 2016) and the effect of this spill over towards customer satisfaction, which in turn is an antecedent of Customer Loyalty (Bitner, 1990). Research concerning spillover-effects on customer experience and customer expectations, between industries, is subjected to scarcity. There is research available that indicates the possibility for such an effect, however the research is conducted in a different context. The research identified spillover effects of expectations across multiple categories within the same umbrella brand (Erdem, 1998) and as many brand portfolio’s operate in multiple industries (Morgan & Rego, 2009), there is reason to support the suggestion for further research on this topic.

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As the example stated by Lemon and Verhoef entails an innovative firm (Jaruzelski, Staack, & Chwalik, 2017), we will look at the moderating effect of experience with innovative firms. The focus on innovation is supported by the relation between innovation and firm performance, a positive relation between innovation and firm performance has been found in revenue growth (Thornhill, 2006).

By researching the effect of the spillover effect on Customer Satisfaction the importance of this spillover effect for business can be determined on an academic level, by measuring the impact on Customer Loyalty the importance and impact for businesses can be determined.

In order to increase the feasibility of this research one disrupted industry is selected to measure the spillover effect on customer expectations, customer satisfaction and customer loyalty. An industry facing a high degree of disruption is the banking industry. The introduction of open banking in Europe (Arnold, 2018), blockchain technology reinventing the banking ecosystem (Swan, 2015) and new European data collection and processing legislations (Tankard, 2016) are changing the industry. Due to these disruptions the boundaries around industries are fading: open banking invites third parties to facilitate payments, blockchain technology might make traditional banking obsolete and the new data legislations smothers the bank potential for data processing and utilization.

The combination of the need for further research on spillover effects of customer experience on customer expectation between industries, the importance of customer satisfaction and the disruption in the banking industry leads to the following research question:

“Do customer experiences in innovative industries spill over towards customer expectations in the consumer banking industry? And what is the effect of this construct on customer

satisfaction and subsequently on loyalty in the consumer banking industry?”

This research will have both academic and managerial implications. On an academic level this

research will partially answer the research question stated by Lemon and Verhoef, specifically for the Dutch consumer banking industry. Depending on the result and the effect on customer satisfaction, further importance will be assigned or deprived for this gap in the literature. In a managerial perspective, managers in the consumer banking industry will learn the importance of monitoring customer experiences in other industries.

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2. Literature Review

In order to answer the research question a few important constructs and concepts are defined. The first element that is defined is Customer Experience, which is the independent variable in this study. The second element described is Customer Expectations, this is the first mediating variable. Next Customer Satisfaction is defined, this the second mediating variable. The fourth definition is

Customer loyalty, this is the dependent variable in the research model. Furthermore some definitions are defined that are used within the mentioned variables.

2.1. Customer Experience

The concept of customer experience is a frequently used element in academic literature. The importance of customer experience is growing because differentiating on traditional elements like price and delivery is no longer a sustainable business strategy, customer experience is claimed to be a new differentiator and a sustainable business strategy (Shaw & Ivens, 2002).

Customer Experience definitions tend to overlap with satisfaction, especially with cumulative satisfaction where satisfaction is defined as the overall experience of a customer to date (Johnson & Fornell, 1991).

In 2007 a conceptual definition of customer experience was derived based on the most relevant academic and managerial articles: ‘‘The Customer Experience originates from a set of interactions between a customer and a product, a company, or part of its organization, which provoke a reaction” (Gentile et al., 2007). Gentile et al. further describe customer experience as a personal experience, expressing involvement of the customer at a rational, emotional, sensorial, physical and spiritual level. The customer evaluates the experience by comparing prior expectations with stimuli resulting from interactions with the company and the offering of the company.

Customer experience should be seen from two perspectives (Holbrook & Hirschman, 1982), the information-processing perspective and the experiential perspective (Frow & Payne, 2007). The information-processing perspective sees the consumer as a logical thinker, it is a cognitive view which suggests goal-directed activities in the process like information search, comparing alternatives and decision making (Holbrook & Hirschman, 1982). The experiential view has hedonic

characteristics, Holbrook et al. (1982) suggest that the value is not within the consumption itself but in the experience of the consumption. Customer Experience is not handled as cumulated in order to distinct this variable from Customer Satisfaction.

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The definition of customer experience that is used in this writing is based on the conceptual definition from Gentile et al. (2007): Customer Experience originates from a set of interactions between a customer and a product or a company.

2.2. Customer Expectations

The essence of customer expectations is the word ‘expectation’, an expectation is “a strong belief that something will happen or be the case” (Oxford English Dictionary, 2018). There is not an exact definition of ‘customer expectations’ in the academic literature and the number definitions is endless. Many definitions are based on customer experiences with the same firm (Johnson, Gustafsson, Andreassen, Lervik, & Cha, 2001) and no literature is available concerning customer expectations based experiences in other industries.

In the academic literature there are different explanations of customer expectations and related concepts. The process of expectation formation starts with the previous information and experiences that form prior expectations (Deliza & MacFie, 1996). Expectations can be seen as beliefs about expected performance, where satisfaction by (dis)confirmation is the difference between expectation and perception (Peter & Olson, 1996).

Two levels of customer expectation are determined for service expectations (V. A. Zeithaml, Berry, & Parasuraman, 1993). The levels of customer expectations are distilled by Zeithaml et al. (1993), a desired level and an adequate level. Both levels are influenced by predicted expectations which are based on promises, word of mouth and past experiences. The desired level is the expectations that the customer hopes for, but the customer is aware that this is not always possible. The adequate level can be seen as the threshold at which the customer accepts the perceived service. The distance between the two levels is called the zone of tolerance.

The constituted customer expectations and perceived performance are primary antecedents of satisfaction. (Johnson & Fornell, 1991). And in the perspective of customer satisfaction as a crucial element for establishing longer-term client relationships (Patterson et al., 1996), the importance of customer satisfaction is emphasized.

Based on the essence of the academic literature, it can be stated that customer expectations are formed by prior consumptions of product or services.

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2.3. Customer Satisfaction

For organizations customer satisfaction is an important indicator for firm performance (Heskett et al., 1994), crucial for establishing longer-term client relationships (Patterson et al., 1996) and positively related to profitability (Bolton, 1998; Hallowell, 1996).

Customer satisfaction is an antecedent of customer retention, it is estimated that acquiring new customer is five to ten times more expensive than it cost retaining current customers (Slater & Narver, 2000). Especially for retail banking customer satisfaction and retention are critical (Levesque & McDougall, 1996).

Laboratory experiments suggest that the level of satisfaction might be positively related to the amount of effort spent to obtain the product and that customer satisfaction might be lower when expectations are not met (Cardozo, 1965).

Other research shows that extremely dissatisfied customers are more likely to result in greater word of mouth than highly satisfied customers (Anderson, 1998). As the impact of negative word of mouth on cognition and attitude is higher than the impact of positive word of mouth (Lutz, 1975), the importance of customer satisfaction for organizations is emphasized.

Literature defining satisfaction and customer satisfaction is almost endless, one of the most described construct is the disconfirmation effect. The disconfirmation effect is modelled as the outcome of the comparison between expectations (or prior attitude) and perceived performance (Oliver, 1980; Peter & Olson, 1996). As perceived performance is based on previous experiences, Customer Experience is a variable in the process of generating Customer Satisfaction, these two variables are not an equivalent to each other. The disconfirmation effect is in line with theories stating that customer expectation is an antecedent of satisfaction (Johnson & Fornell, 1991). Two types of satisfaction can be distinguished: transaction-specific satisfaction and overall

satisfaction (Jones & Suh, 2000). Overall satisfaction is seen as an overall attitude whilst transaction-specific satisfaction may vary for every experience, it is possible that the customer experience was dissatisfying in one experience and still a positive overall satisfaction consists. Consumer Satisfaction is an dynamic process, the positive and negative transaction-specific satisfactions have impact on the overall satisfaction (Fournier & Mick, 1999).

In this writing Customer Satisfaction is based on overall satisfaction which is formed by the

comparison between expectations (or prior attitude) and perceived performance of a company or an industry as a whole.

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2.4. Customer Loyalty

It is well known that customer loyalty and profitability have a positive relation, research shows that retaining customer with an extra five percent results in an profit increase up to one hundred percent (Reichheld & Sasser, 1990).

In academic and business literature customer loyalty and customer satisfaction are linked (Oliver, 1999). Satisfaction (or dissatisfaction), as a result of matching expectations and perceived

performance, is an antecedent of loyalty (Bitner, 1990). However loyal customers tend to be satisfied, satisfied customers do not always turn into loyal customers (Bowen & Chen, 2001; Oliver, 1999).

A frequently used definition in academic literature of customer loyalty is: “A deeply held

commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behaviour” (Oliver, 1999).

Customer loyalty as a concept is more than simple purchase repetition behaviour, it consists of two dimensions: behaviour and attitude (Beerli, Martín, & Quintana, 2004). Based on these dimensions Beerli et al. (2004) distinguished two types of customer loyalty concepts. The first concept is ‘loyalty based on inertia’ which refers to purchases out of habit due to minor efforts, however the consumer is willing to switch to other brands for more convenience. The second concept is ‘true brand loyalty’, this concept is explained as a conscious decision for repeat purchasing the same brand and is

affected by a positive attitude and high commitment towards the brand.

Indicating loyalty solely by measuring behaviour (repeat purchases) might not be reliable due to happenstance buying and convenience purchases (Jacoby & Chestnut, 1978). Loyalty measurement should be measured by three phases: Cognitive loyalty, Affective loyalty and Conative Loyalty (Oliver, 1999). Cognitive loyalty is loyalty based on brand attribute information indicating preference towards a brand, it can be described as loyalty based on brand belief. Affective loyalty is the second phase and is a cumulation of satisfactory experiences that has resulted in an attitude or liking towards the brand. The Conative loyalty phase, which can be described as the behavioural intention phase, is the commitment to repurchase from the same brand and is derived from cumulating positive affect towards the brand.

In this research we will utilize the definition by Oliver (1999) and in order to utilize the focus on attitudinal loyalty we use the perspective of ‘true brand loyalty’ by Beerli et al. (2004).

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2.5. Innovation

Innovation is part of both the independent variable as the moderating variable in this research, respectively innovative industries and innovative companies. Before deriving the concepts of innovative industries and innovative companies the definition of innovation is explained. Applying new ideas to products and processes of a firm can be defined as innovation and an important part of innovation consist of extracting value from ideas (Feeny & Rogers, 2003). Within the definition of innovation two types of innovation can be derived: product innovations and process innovations (Abernathy & Utterback, 1978; Kotabe & Murray, 1990; Rowley, Baregheh, & Sambrook, 2011). This research will focus on product innovations and in particular on service innovations (Rowley et al., 2011). Service innovations can be defined as “the introduction of new services to the existing or new clients and offer of existing services to new clients” (Damanpour, Walker, &

Avellaneda, 2009).

The definition of innovation, according to the Online Oxford English Dictionary is: “Make changes in something established, especially by introducing new methods, ideas, or products” (Oxford English Dictionary, 2018).

A common mentioned type of innovation in the current era is digital innovation. Digital innovation can be defined as the carrying out of new combinations of digital and physical components to produce novel products (Yoo, Henfridsson, & Lyytinen, 2010). This is in line with current literature stating that new products and services can be created by combining components in innovative ways (Zammuto, Griffith, Majchrzak, Dougherty, & Faraj, 2007).

Innovation is referred to as: The introduction of new digital services to the existing or new clients and offer of existing digital services to new clients.

2.5.1. Innovative Companies

Academic literature refers often to innovative firms or innovative companies, without stating a definition of this concept. One way of defining the concept of innovative companies is the

perspective of reputation, firm reputation is a set of characteristics that are attributed to the firm based on prior actions of the firm (Weigelt & Camerer, 1988). Due to a lack of academic research on this topic, this research will not go in further detail about which characteristics lead to an innovative reputation.

Being perceived as an innovative firm has some effects on customer expectations and customer loyalty. The perception of a firm’s innovativeness tends to lead to rising expectations for the firm’s

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products. And the perception of a high reputation for product innovation leads to consumer excitement and higher loyalty to the innovative firm (Henard & Dacin, 2010).

The selection of innovative companies is based on the ‘Global Innovation 1000’ which is published by PricewaterhouseCoopers on an annual basis. This publication reports the annual spending on R&D and shows the thousand biggest R&D spenders. As innovative activity is broader than R&D activity (Napolitano, 1991), the results based on R&D spending will not be utilized for this purpose. Part of the ‘Global Innovation 1000’ is the composition of ‘The 10 Most Innovative Companies’ based on “an online survey of 562 innovation leaders around the world” (Jaruzelski et al., 2017), the respondents tend to perceive companies like Alphabet (Google), Apple, Samsung and Tesla as most innovative. The ‘Global Innovation 1000’ provides an up-to-date set of companies and industries that is used in this research to select innovative companies.

2.5.2. Innovative Industries

In both academic literature as managerial literature there is little attention for defining innovative industries. In management literature there is more focus on determining firm innovativeness than on industry innovativeness, a simple test for this statement can be done in a search engine like Google Scholar. A search query on “firm innovativeness” returns over 5.000 results, a search query on “industry innovativeness” returns 340 results.

In order to identify innovative industries, the ‘The State of Innovation Report 2017’ is used. This annual report is composed by Clarivate Analytics, formerly known as Thomson Reuters Intellectual Property. The report ranks twelve major industries, based on data from the Derwent World Patents Index (DWPI) that is collected from over 50 patent authorities from around the world. This ranking is based on the number of patents and is quantitative, no qualitative aspects are considered.

2.6. Consumer Banking Industry

In the literature there are various banking typologies, retail banking is one of the most common banking terms in academic writing. Retail banking focusses on both individuals and small businesses (‘Financial Times Lexicon’, 2018a). Especially the element considering small businesses does not fit in this research. This writing will refer to the consumer banking industry. Despite that the definition of consumer banking is the same as for retail banking (‘Financial Times Lexicon’, 2018b), the emphasis on individuals is more clear. Consumer banking is referred to as banking services for individuals.

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2.6.1. Consumer and Customer

In this research we will refer to consumers as individuals that are able to have a bank account. In the stated literature the terms customer and consumer a both used in various ways. The term

‘consumer’ is preferably used due to the emphasized relation to individuals. The term customer is often related to business in literature, however only references are used that relate to customers as individuals. Both consumer as customers are referred to as individuals that are able to have a bank account.

2.7. Hypothesis

In order to increase the readability, the industries are not mentioned in the main hypothesis. Customer Experience in Innovative Industries is referred to as Customer Experience. Customer Expectations, Customer Satisfaction and Customer Loyalty all be referred to with regard to the Consumer Banking Industry.

As the suggestion for further researching the cross industry spillover effect of customer experience on customer expectations example stated by Lemon and Verhoef (2016) entails an example of an innovative firm (Jaruzelski et al., 2017), the scope of the independent variable will consist of innovative industries and the moderating effect is scoped to innovative companies.

Due to the high degree of disruption within the industry, both the mediating variables and the dependent variable are scoped to the consumer banking industry.

2.7.1. Hypothesis I

As customer expectations are formed by prior consumptions of product or services (Deliza & MacFie, 1996; Deming, 1994) and spillover effects of expectations across multiple categories occur (Erdem, 1998), it can be stated that there is ground for further research on spillover effects from customer experience to customer expectations in different industries.

As customer expectation is an antecedent of customer satisfaction (Johnson & Fornell, 1991) and customer satisfaction is an important construct for firms (Bolton, 1998; Hallowell, 1996; Heskett et al., 1994), customer satisfactions is part of the hypothesis.

Hypothesis I: The relationship between Customer Experience and Customer Satisfaction is mediated by Customer Expectations. Customer Experience leads to higher Customer

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2.7.2. Hypothesis IA

The definition of Customer Experience, based on the definition of (Gentile et al., 2007), states that Customer Experience originates from a set of interactions between a customer and a product or a company. The perception of a firm’s innovativeness tends to leads to rising expectations for the firm’s products (Henard & Dacin, 2010). Spillover effects of expectations across multiple categories within the same umbrella brand have been identified (Erdem, 1998) and many brand portfolio’s operate in multiple industries (Morgan & Rego, 2009). Combining this information results in the believe that interactions (Customer Experience) with industries that are perceived as innovative lead to higher Customer Expectations in other industries. And as the scope of this research is the

Consumer Banking Industry, the hypothesis is stated as follows:

Hypothesis IA: Customer Experience in Innovative Industries leads to higher Customer Expectations in the Consumer Banking Industry.

2.7.3. Hypothesis IB

Customer Expectation is an antecedent of satisfaction (Johnson & Fornell, 1991). As satisfaction is modelled as the outcome of the comparison between expectations and perceived performance (Oliver, 1980; Peter & Olson, 1996) we expect that the increased Customer Expectations leads to decreased Customer Satisfaction.

Hypothesis IB: The increased Customer Expectations in Consumer Banking Industry leads to decreased Customer Satisfaction in Consumer Banking Industry.

2.7.4. Hypothesis II

It can be expected that consumers that have experienced innovative companies will have higher customer expectations (Henard & Dacin, 2010). As expectations are formed by prior information and experiences (Deliza & MacFie, 1996), encounters with one of the ten most innovative companies might positively relate to higher customer expectations. At the same time this hypothesis acts as a validity check and should further confirm hypothesis IA.

Hypothesis II: The relationship between Customer Experience and Customer Expectations is positively moderated by the level of Customer Experience with Innovative Companies. Customer

Expectations will be higher when the level of Customer Experience with Innovative Companies increases.

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2.7.5. Hypothesis III

As Customer Satisfaction is an antecedent of Customer Loyalty (Bitner, 1990) it is expected that decreased Customer Satisfaction leads to decreased Customer Loyalty.

Hypothesis III: The decreased Customer Satisfaction, as a result of the first hypothesis, leads to a decrease in Customer Loyalty.

2.8. Conceptual Model

The conceptual model is based on the research question and the underlying hypothesis. The conceptual is presented in figure 9.

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3. Data and Method

This section describes the data, the sample, how the data is collected, the validity of the data and the steps that are taken to analyse the data.

3.1. Introduction

This research tends to find out whether or not there is a spillover effect of Customer Experience in Innovative Industries on Customer Expectations in the Consumer Banking Industry and if the changed Customer Expectations have a mediating effect on Customer Satisfaction and Customer Loyalty in the Consumer Banking Industry.

The following research question is formulated to research this topic:

“Do customer experiences in innovative industries spill over towards customer expectations in the consumer banking industry? And what is the effect of this construct on customer

satisfaction and subsequently on loyalty in the consumer banking industry?”

The nature of this deductive study is exploratory, this research tends to answer whether or not customer experience spills over in to customer expectations in different industries. The scope of the research will set to inhabitants of The Netherlands aged between 20 and 65 years, this results in a large population to draw a sample from. Due to the high accessibility of respondents, and the relative low costs of collecting data from a large sample, this research is a survey by questionnaire.

The conducted research is cross-sectional, the study does not tend to describe a mechanism over time. During the literature review no implications are found which imply the use of a longitudinal research design. Adding up the timeframe of this research to these arguments a cross-sectional study is the best fit for this research.

The design of the survey will consist of two major themes: experiences and expectations. Experiences is referring to customer experiences in innovative industries, expectations will refer to customer expectations in consumer banking industry.

3.2. Research Design

The measures and scales that are used, are derived from existing research and existing frameworks or from extensive research by reputable firms. The questionnaire included questions in order to make sure that the respondents meet the sample criteria for this research and in order to measure the quota of the sampling method.

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The questionnaire is divided in multiple sections: Customer Experience, Customer Expectations, Customer Satisfaction, Customer Loyalty and Demographic information about the respondents. Customer experience is measured based on the perceived level of experience within the selected industries and companies. Customer expectations is questioned based on the level of which the respondent agrees that their main bank should offer a specified service. Customer satisfaction and Customer loyalty is derived by questioning selected questions that are common practiced in general and in the banking industry.

The innovative industries are selected from ‘The State of Innovation Report 2017’ as reported by Clarivate Analytics, this annual report was for previous years published by Thomson Reuters. To research the mediating effect of experience with innovative industries, the respondents is questioned to what extent they agree to have experience with the given industry. The innovative companies are selected companies from ‘The 10 Most Innovative Companies’ as reported by PriceWaterhouseCoopers. To research the moderating effect of experience with innovative companies, the respondents is questioned to what extent they agree to have experience with the given company.

Customer Expectations are measured with the E-S-QUAL scales (Parasuraman, Zeithaml, & Malhotra, 2005), this can be seen as an online orientated alternative for the traditional SERVQUAL method. The SERVQUAL model is based on five dimensions (Reliability, Responsiveness, Assurance, Empathy, Tangibles) as drivers of service quality (Parasuraman, Zeithaml, & Berry, 1988). As there are less or even no physical touchpoints for many innovative companies, questions about physical facilities and employee appearance seem less suitable. The QUAL method consist of two scales: The basis E-S-QUAL scale and the E-RecS-Qual which accounts for service recovery actions. Service recovery actions are not within the scope of this research, the research is limited to the basic E-S-QUAL method. The E-S-QUAL method consists of four dimensions: Efficiency, Fulfilment, System Availability and Privacy. The E-S-QUAL items often refer to ‘the website’, ‘the site’ or other terms, for each scale small alterations are made to these terms in order to fit the purpose of this research. The questions will be stated for both expectations as perceptions, conform the SERVQUAL method.

The constructs that are researched are customer experience, customer satisfaction and customer loyalty. These constructs are measured on a seven-point Likert scale in order to provide ordinal data.

3.2.1. Customer Experience

Two types of customer experience are measured, customer experience with innovative industries and customer experience with selected innovative companies.

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3.2.1.1. Customer Experience with innovative industries

Customer Experience with innovative industries is derived as an ordinal variable, respondents are asked to which extent they agree to have experience with the given industry. This is asked on a seven-point Likert scale ranging from (1) “Strongly disagree” to (7) “Strongly agree”. Each industry consists of multiple subsectors, subsectors that are expected to be familiar for consumers in general are selected. For each listed subsector in an innovative industry, as derived from ‘The State of Innovation Report 2017’, respondents had to answer to what extent they agree to have Customer Experience in the mentioned industry. As Customer Experience is based on interactions between a customer and a product or a company (Gentile et al., 2007) respondents are instructed that experience is based on interactions, few interactions indicates little experience and many interactions indicate much experience. Each industry and subsector is clarified with an example. The following industries are selected: Automotive, Cosmetics and Wellbeing, Home appliances, Information technology, Telecommunications and Food, beverage & tobacco.

In the Automotive Industry the selected subsectors are ‘Alternative powered vehicles’,

‘Entertainment systems’ and ‘Self driving vehicles’. The selected subsector in the Cosmetics and Wellbeing Industry are ‘Skin’ and ‘Make-up’. The subsectors in the Home Appliances Industry are ‘Kitchen’, ‘Household cleaning’ and ‘Heating/air conditioning’. From the Information technology industry ‘Computing’, ‘Intelligent Domotics’ and ‘Smart media’ are selected. The Telecommunications industry results in ‘Mobile telephony’ and ‘Data transmission networks’. The subsectors selected from the Food, beverage & tobacco industry are ‘Brewing’, ‘Sugar’, ‘Tobacco’ and ‘Meat’. 3.2.1.2. Customer Experience (Interactions) with innovative companies.

A set of innovative companies is shown based on the ‘Global Innovation 1000’ by

PriceWaterhouseCoopers and the personal evaluation of the researcher. As Customer Experience is based on interactions between a customer and a product or a company (Gentile et al., 2007) respondents are instructed that experience is based on interactions, few interactions indicates little experience and many interactions indicate much experience. The respondents can answer if they agree having customer experience with the selected company. Each company is defined with an example of their products and/or services. To measure the extent of this Customer Experience the respondents are asked to which extent they agree with the statement “I have experience with the given industry”, this is asked on a seven-point Likert scale ranging from (1) “Strongly disagree” to (7) “Strongly agree”.

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The selected innovative companies are: Alphabet (Google), Apple, Amazon, Tesla, Microsoft, Samsung, Facebook and Alibaba.

3.2.2. Customer Expectations and Perceptions

Customer Expectations and Perceptions are measured by adopting the E-S-QUAL scales and

measures (Parasuraman et al., 2005). It is common to use the SERVQUAL method to measure the gap between expectations and perception (Johns & Howard, 1998; Lewis, 1991). The E-S-QUAL method can be seen as an alternative for the traditional SERVQUAL method with a focus on online

(Parasuraman et al., 2005). It is not intended to measure satisfaction by measuring the gap between expectations and perceptions, satisfaction is measured as a separate variable. Some of the items of the E-S-QUAL method are refined or removed in line with (Akinci, Atilgan-Inan, & Aksoy, 2010) to fit the measurement of Customer Expectations from consumers towards Consumer Banks. The

researcher slightly altered the statements by adding a reference towards mobile apps next to the references to web sites. All 8 items are plotted against agreement (Akinci et al., 2010) on a seven-point Likert scale ranging from (1) “Strongly disagree” to (7) “Strongly agree”.

The respondents are instructed to answer the question based on the expectations they have for their main bank, the features their bank should (Parasuraman et al., 1988) offer.

3.2.3. Customer Satisfaction

The general measures for Customer Satisfaction are inspired by existing research in the banking industry (Beerli et al., 2004) which covers the three most used aspects of satisfaction in literature: general satisfaction, confirmation of expectations and the hypothetical distance to the customer’s ideal product (Fornell, 1992).

The questions that are used in the questionnaire are the following:

1. To what extend does your bank live up to your general expectations of it?

2. Imagine the perfect bank. How far and/or close does your bank come to this ideal bank? 3. Based on your experience with your bank, how satisfied are you with this bank?

The questions are plotted on a seven-point Likert scale ranging from (1) “Completely dissatisfied” to (7) “Completely satisfied”.

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3.2.4. Customer Loyalty

As measuring Customer Loyalty by behaviour might not be reliable (Jacoby & Chestnut, 1978) this variable is measured by attitude, as done before by Beerli et al. (2004). The measures of Beerli et al. (2004) are based on the research of Oliver (1999). Conative loyalty is measured by the level of intent to maintain the current relationship with the bank and affective loyalty is measured by attitude towards the bank and attitude by recommendation. As cognitive loyalty as a customer state is superficial and often related to routine transactions (Oliver, 1999), this phase will not be measured. The questions, based on Beerli et al. (2004), that are used in the questionnaire are the following:

1. I do not like to change to another bank because I value my bank. 2. I am a customer who is loyal to his bank.

3. I would always recommend my bank to someone who ask me for advice.

The next question is added to measure loyalty from a repurchase perspective (Yang & Tsai, 2007): 4. I would consider this bank as my first choice for future financial transactions.

The following question is added to measure loyalty, also from a repurchase perspective (Valarie A. Zeithaml, Berry, & Parasuraman, 1996):

5. I intend to continue doing business with this bank in the future.

The questions are plotted on a seven-point Likert scale ranging from (1) “Strongly disagree” to (7) “Strongly agree”.

3.2.5. Demographics and General Information

The demographics that are included in the questionnaire are Age, Gender and Highest level of education. Also some general question are asked like which banks they are customer of and what their main bank is. The main bank is used for quota sampling purposes. As the survey is anonymous, no further personal details are retrieved.

3.3. Validity

3.3.1. Reliability (Internal validity)

The questions within the questionnaire are based on existing literature (Expectations, Perceptions, Satisfaction and Loyalty) or based on extensive research by reputable institutes or firms (Innovative industries and Innovative companies). By reusing the questions from this existing literature the reliability of the questions can be assumed. By constructing new scales based on research by

reputable companies, no academic reliability can be stated. Due to the lack of academic literature on innovative companies and industries the research of these companies is used. The research of

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innovative industries is based on Research and Development expenditures and the research on innovative companies is based on an online survey which was conducted among more than 500 ‘innovation leaders’ around the world. To ensure scale reliability the Cronbach’s Alpha is calculated for all variables which are measured with the questionnaire.

Before launching the questionnaire, a pilot test was conducted. Twenty people of varying age, gender and educational level were asked to participate in the survey, not knowing it was a pilot. Both stages of the pilot consisted of ten participants. The respondents were all asked to reflect on the survey afterwards, one remark that was made several times in the first stage of the pilot concerned the second part of the SERVQUAL method: the perceptions. The participants all had to think twice, at first they assumed that there was an error and the same question was shown again. In order to make the difference between expectations and perceptions more clear the researcher added a question where the participants confirm that they understand that they have to answer the questions based on their perception instead of expectation. During this pilot no extreme outliers were detected, which subsequently did not lead to suspicion for ambiguous questions.

In order to measure the reliability and validity of the scales used in the questionnaire the reliability of the scales is tested. For Customer Experience in Innovative Industries, Customer Satisfaction and Customer loyalty in Consumer Banking Industry factor analysis is performed. The Cronbach's alpha was calculated to test the reliability, this led to the exclusion of two variables within the scale of Customer Experience with Innovative Industries. The scale Customer Experience in Food, Beverage and Tobacco Industry is excluded due to low reliability as a scale itself. Customer Experience in Cosmetics and Wellbeing Industry is excluded due to low correlation with the total score of the Customer Experience with Innovative Industries scale. The scales that are used to measure Customer Experience with Innovative Industries are analysed with Factor Analysis, the four remaining

dimensions on the sublevel were distilled and the measures all loaded on the expected dimensions. The scales that are used to measure Expectations and Perceptions are based on the E-S-QUAL-method and are tested by the authors by exploratory factor analysis (Parasuraman et al., 2005). Factor analysis has been performed for both the Satisfaction- and Loyalty scale and resulted in two dimensions as expected and the measures were assigned to the expected dimensions.

By using existing literature and reports from reputable institutions and companies the study the study can be reproduced in two ways: with the same innovative industries and companies, and with an updated set of innovative industries and companies.

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3.3.2. External Validity

The Skewness and Kurtosis of all variables is measured and normality of the data is checked, this is reported in the analysis. Overall the scores for Skewness and Kurtosis were non-problematic. The sample is varied on age, gender, level of education and main bank. Two age groups, younger than 21 years and older than 60 years, are less represented in this research. The sample is slightly overrepresented on high education. The representativeness of the main banks is approximately equal to the market shares of deposits in the Netherlands. The sample is not fully representative, however it is sufficient for the purpose of this study.

A point of attention is privacy, privacy is part of both the expectations as the perceptions which are measured by the SERVQUAL method. The survey was conducted just after the introduction of the new European data collection and processing legislations (GDPR) and the testifying of the CEO of Facebook in front of the European parliament. Both events generated a lot of media coverage regarding the topic privacy and might lead to overestimated results for privacy by the respondents.

3.4. Sample Collection

The sampling method of this research is a combination of convenience and quota sampling. The quota sampling is based on the market shares of Dutch banks in consumer banking activities. The four largest consumer banks in The Netherlands have a total market share of almost 90 percent (De Nederlandsche Bank, 2015). The goal of the sampling method is to roughly reflect the market shares of the Dutch consumer banks within the sample, which was the case approximately and did not lead to any exclusions.

3.5. Collection Method

The data is collected by digital questionnaires via a mobile-friendly website. The hyperlink to this questionnaire is shared via e-mail with students of the Amsterdam Business School and via social networks (Facebook, Twitter and LinkedIn). Leveraging the sharing-options of the social networks gives the opportunity to reach further than the researchers’ social network. Next to this the survey is promoted in Facebook groups which are not part of the social network of the researcher, the

incentive was the chance of winning a set of chocolate bars.

The survey was available for eight days, starting at the 17th of May 2018. The survey resulted in 208 participants, which was sufficient as the pursued sample size was 200 respondents.

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3.6. Ethics

The questionnaire is anonymous, questions regarding demographic information like age, gender and level of education are included. This demographic information is insufficient to compromise the anonymous nature of the questionnaire.

The participants are notified that they are participating in a master’s thesis research, it is mentioned that the data will only be used for use in academic work. The respondent that participated to win the set of chocolate bars had to leave a response on Facebook and therefore their anonymity is

compromised. However, it is virtually impossible to match these participants with their responses.

3.7. Data Analysis

The main goal of the data analysis is to find out if there is significant support for the hypotheses, therefore the findings are statistically analysed for each hypothesis. The answers on the hypothesis lead to an answer on the research question.

3.7.1. Analytical strategy

The data is analysed with the help of IBM SPSS Statistics version 25 and different statistical tests, which are reported in this section.

The gathered data, from 208 respondents, did not include any counter indicative measures, so no counter indicative items are recoded. The sample did have variables that were not suitable for regression analysis. The tool that that was used for offering the online survey coded gender to 1 and 2 instead of 1 and 0, this measure was recoded into a new variable where Male is coded as 1 and Female is coded as 0. Another measure that was not suitable for regression analysis was the level of education, the seven measures were recoded into three dummy variables: Academic Education, University of Applied Sciences and Secondary Vocational Education or Lower.

After this the data names and labels and value labels were checked. Also the type of variable and the measure type of the variable were checked. The data was screened with frequency tables for missing values and errors in data entry, of which none were found in this study.

Scale means, often referred to as scale totals or totalized scales in this study, were calculated multiple times. This is due to the data cleaning methods that influenced the scale means after excluding responses. The scale means for Experience with Innovative Companies, Customer

Expectations in the Consumer Banking Industry and Customer Perceptions in the Consumer Banking Industry were calculated at both the sublevel as the main level. On the main level the scale mean was calculated based on the value of the sublevel, so each sublevel had the same importance.

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Univariate outliers were measured based on scale means, the scale means are standardized and responses that where classified as outliers are excluded from the sample. Responses with a scale mean that had a standardized values larger than three (Z>3) was classified as an outlier and excluded. After this step the scale means were updated.

As the number of measures was more than one hundred, normality checks have not been performed for all individual items. The Skewness and Kurtosis were first measured on the sublevel of the scale means. For example Customer Expectation in the Consumer Banking Industry consisted of four scales, each consisting of multiple values. For each scale within the Expectations scale, for example Efficiency, the Skewness and Kurtosis was tested. No items were excluded as a result of this step. After this the Skewness and Kurtosis were tested on the scale means on a main level. This did not lead to the exclusion of items.

Scale reliability has been analysed on both the main as the sublevels of the scale. For each subscale the scale reliability was tested. After excluding variables the reliability was tested for the scale on the main level, which led to the exclusion of one item. Due to the exclusion of item the scale means of both the sublevel and the main level were recalculated.

During the next step exploratory factor analysis is performed on Customer Satisfaction and Customer Loyalty in the Consumer Banking Industry to confirm that two different constructs were measured. The rotation method used is Varimax with Kaiser Normalisation. In order to validate that there is a difference between Customer Expectation and Customer Perceptions in the Consumer Banking Industry a paired samples t-test is performed.

The resulting data (n=191) is subsequently plotted into a correlation matrix. Due to the readability the correlation matrix is split into two matrixes, one with regard to the scales on the main level and one with regard to the scales on a sublevel. The matrix with the scales in the sublevel is included in Appendix 3. The results of both correlation matrixes are analysed and described in the corresponding section.

The next step was testing the hypothesis. Hypothesis were tested with regression analysis,

hierarchical regression analysis or the PROCESS Macro by Hayes was used depending of the nature of the hypothesis. Assumptions for regression analysis were tested checking the normality of the distribution of the standardized residual, the multicollinearity between variables and

homoscedasticity of the standardized residual. After testing the hypothesis the conceptual model as a whole was tested with the PROCESS Macro. Based on the results of both the hypothesis as the conceptual model, the conceptual model was revised and tested with the PROCESS Macro.

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4. Results

This part covers the results of the analysis of the data which is collected with the survey. The first part is the analysis of the descriptive data which describes the handling of outliers, covers the

normality and summarizes the profiles of the respondents. Second the reliability of the data is tested, correlation is shown and the hypotheses are tested with the help of regression analysis. In conclusion of the analysis the conceptual model as a whole is tested with the PROCESS Macro.

4.1. Descriptive data analysis

Responses were received from 208 participants, after deleting outliers 191 participants remained. The outliers for the experiences and expectations were found based on the means of the scale instead of individual scale items.

Due to the tendency of answering the expectations in the strongly agree area, a moderate negative skewness was applicable. Due to the non-problematic nature of both the skewness as the kurtosis at the totalized level of the scales (values between -1 and +1) the data is not transformed by

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4.1.1. Profile of participants

The table below (table 1) shows the characteristics of the participants. In total 191 participants are included in this analysis (n=191).

Table 1: Characteristics of the participants

Variable Value Frequency Percent

Age ≤ 20 7 4% 21-30 68 36% 31-40 57 30% 41-50 32 17% 51-60 22 12% ≥ 61 5 3% Gender Male (1) 90 47% Female (0) 101 53%

Level of education High school (Voortgezet onderwijs) 15 8%

Secondary vocational education (MBO) 52 27%

Associate degree in college (2-year) (HBO Associate Degree) 11 6% Bachelor's degree in college (4-year) (HBO Bachelor Degree) 56 29%

University Bachelor (Universitaire Bachelor) 16 8%

University Master (Universitaire Master) 40 21%

Doctoral degree (Doctoraat) 1 1%

Main bank ABN AMRO 36 19%

ASN Bank 3 2% ING 64 34% Rabobank 79 41% RegioBank 1 1% SNS 5 3% Other 3 2%

The background of the researcher, combined with the collection method could have resulted in a disproportionate number of customers for a specific bank (Rabobank). Based on the deposits of Dutch households Rabobank has a market share of 36% (De Nederlandsche Bank, 2015), this differs slightly from the share among the participants (41%). Market share of deposits and main bank market shares cannot be compared directly, however it does indicate that there is no

disproportionate number of customers for this specific bank.

In order to use the education level in the analysis, the level of education is recoded into ‘Academic Education’, ‘University of Applied Sciences’ and ‘Secondary vocational education or lower’.

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4.2. Correlation analysis

The correlation analysis is performed on two levels, the main scale level and one level deeper on the sublevel of the scales.

4.2.1. Correlation analysis on the main level

The correlation analysis showed some correlation between the control variables which will not be explained in further detail. With regard to the variables in the conceptual model, the control variables do not show any relevant correlations ( -.20 < r < .20 ).

Within the measures of the conceptual model Experience with Innovative Companies does not lead to unexpected findings, the variable has high positive correlations with Total Experience With Innovative Industries (r > .50) and a tendency for a positive correlation with Total Expectations in the Consumer Banking Industry (r > .20).

As expected Total Experience with Innovative Industries measures a tendency for positive correlation with Total Expectations in the Consumer Banking Industry (r > .20).

Total Expectations in the Consumer Banking Industry shows a high positive correlation with Total Perception in the Consumer Banking Industry (r > .50), a moderate tendency for a negative correlation with the Total Gap Score in the Consumer Banking Industry (r < .20), a moderate tendency for a moderate positive correlation with Total Satisfaction in the Consumer Banking Industry (r > .20) and a tendency for a positive correlation with Total Loyalty in the Consumer Banking Industry (r > .20). It was expected that when the Expectations rise the Gap score would increase as well, which would reflect a part of the disconfirmation theory (Oliver, 1980; Peter & Olson, 1996). Another expectation was a decrease in Satisfaction when Expectations increase (Cardozo, 1965), it is unexpected that this correlation is positive.

Total Satisfaction in the Consumer Banking Industry shows a high positive correlation with Total Loyalty in the Consumer Banking Industry (r > .50).

Interesting finding is the lack of correlation between Total Gap Score in Consumer Banking Industry and Total Satisfaction in the Consumer Banking Industry. A high negative correlation was expected as the gap should reflect the construct of the disconfirmation theory (Oliver, 1980; Peter & Olson, 1996).

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ts 28 : Mea n s, St a n d a rd Dev ia ti o n s a n d Co rr el a ti o n s o f T o ta liz ed Sca les bl e Me an SD 1 2 3 4 5 6 7 8 9 10 11 12 e 3 5 .7 8 1 1 .5 1 - n d e r .47 .50 -.06 - co n d ar y Vo ca ti o n al Ed u ca ti o n o r l es s .35 .48 .19 ** -.08 - iv er sity o f A p p lie d S cie n ce de gr ee .35 .48 .00 .08 -.54 ** - iv er sity de gr e e .30 .46 -.20 ** .00 -.48 ** -.48 ** - ta l ex p e rie n ce w ith In n o va ti ve C o mpa n ie s 4 .87 .88 -.13 .04 -.21 ** .05 .16 * (.6 1 ) ta l ex p e rie n ce w ith in n o va ti ve in d u str ie s 4 .33 1 .04 -.10 .15 * -.12 .01 .11 .60 ** (.6 8 ) ta l ex p e cta ti o n s c o n su mer b an king in d u str y 6 .06 .56 .02 -.07 -.03 -.12 .16 * .27 ** .26 ** (.7 3 ) ta l pe rc e p ti o n c o n su mer b an king in d u str y 5 .83 .58 -.05 -.09 -.02 -.13 .16 * .16 * .13 .62 ** (.7 2 ) o ta l G ap S co re c o n su mer b an king in d u str y -.23 .50 -.08 -.02 .01 -.01 .00 -.11 -.14 -.40 ** .47 ** - o ta l S ati sfa ct io n c o n su mer b an king in d u str y 5 .96 .58 -.07 -.15 * .13 -.15 * .02 .10 .03 .41 ** .57 ** .20 ** (.7 8 ) o ta l L o ya lty c o n su mer b an king in d u str y 5 .59 .93 -.08 -.17 * .10 -.04 -.06 .17 * .08 .27 ** .42 ** .19 ** .56 ** (.8 7 ) = 191. Re liab iliti es a re rep o rte d a lo n g t h e d iag o n a l. elat io n is si g n ific a n t a t th e 0.05 leve l (2 -ta iled ). rr elat io n is s ign ific a n t a t th e 0.01 leve l (2 -ta iled ).

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4.2.2. Correlation Analysis on the sublevel

The correlation analysis showed some correlation between the control variables which will not be explained in further detail. Due to the high number of variables in this correlation matrix on the sublevel of the scales, only variables with high correlation ( -.50 < r < .50 ) and remarkable correlations with a tendency for correlation ( -.20 < r < .20 ) will be discussed. Variables with

correlations within the same scale are excluded from this discussion. The full correlation analysis on the sublevel of the scales can be found in Appendix 3.

A tendency for a positive correlation between gender and two industries is found for the Automotive Industry and the Information Technology Industry, on average men obtain a 0.24 points higher score than women. A tendency for a negative correlation is found between respondents with Secondary Vocational Education or Less and Experience in the Information Technology Industry, lower educated people score on average 0.26 points lower.

A high positive correlation is found between Customer Experience with Innovative Companies and Experience with the Information Technology Industry. Respondents with a higher level of Customer Experience with Innovative Companies score on average 0.53 points lower on experience with this industry.

An interesting finding is the lack of high correlations between industry experience scales and experience scales. The most tendencies for correlations (r > .20) are with the Telecommunications Industry. Of the three remaining scales only the expectations concerning efficiency had a tendency for a positive correlation (r > .20).

The correlation between the expectation scales and the gap scores was just below the threshold for a high negative correlation (r > -.50 ). Fulfilment expectations and the fulfilment gap score noted the lowest correlation with -0.36 points.

For efficiency and availability the correlation between the perception scales and the corresponding gap scores scored just below the threshold of high positive correlation (r > .50 ). Fulfilment and privacy scored just above this threshold.

Interesting is the lack of high correlation between Customer Satisfaction in the Consumer Banking Industry and the perception scales, which was noticed for the main scales as well. Another interesting finding is the difference in correlation between the perception scales and Customer Satisfaction in the Consumer Banking Industry, the availability perceptions score is 0.23 points lower than the highest score.

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4.3. Regression analysis

The regression analysis is performed on both the hypothesis as on the conceptual model as a whole. Depending on the nature of the hypothesis the hypothesis is analysed by hierarchical regression or the PROCESS macro (version 3.0) for SPSS, written by Andrew F. Hayes.

The analysis starts with the analysis of the hypothesises, subsequently a graphical summary of the results and the analysis of the conceptual model as a whole is presented. The regression analysis will end with the revised conceptual model and the statistical analysis of this revised conceptual model. The control variables that are used for the regression analysis, unless mentioned otherwise, are: Gender (1 = Male), Age, Level of education (Academic Education and University of Applied Sciences). Tables and figures obtained during the statistical analysis are included in Appendixes 7-15.

4.3.1. Hypothesis I

The mediating effect of Customer Expectations on the effect of Customer Experience on Customer Satisfaction is analysed with the PROCESS macro, the model which is chosen to analyse the

conceptual model is model 4. The results are presented in the tables 3 and 4 below. In this analysis only the indirect effect is described, as relation between the remaining variables are not part of this hypothesis.

Table 3: Results from the regression analysis of Hypothesis 1

Consequent

Expect_T (M) Sat_Tot (Y)

Antecedent Coeff. SE p Coeff. SE p

Exper_In (X) .144 .225 <.001 -.036 .439 .352 Expect_tot (M) --- --- --- -.444 .071 <.001 Constant 5.362 .225 <.001 3.809 .439 <.001

R2 = .104 R2 = .220

F(5,185) = 4.31, p<.01 F(6,184) = 8.640, p<.001

Table 4: Effects of the model of Hypothesis 1

Effect SE p LLCI ULCI

Direct effect -.036 .038 >.05 -.113 .040

Total effect .028 .041 >.05 -.052 .108

Boot SE Boot LLCI Boot ULCI Indirect effect .064 .019 <.05 .028 .103

Two persons who differ by one unit in their Customer Experience in Innovative Industries are estimated to differ 0,064 units in Satisfaction, due to the level of expectations. The chosen

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confidence level 95%, as both the upper- (ULCI = ,03) and lower bootstrap (LLCI = ,10) are positive (the confidence interval does not include zero), the result significant (p<.05).

The direct effect of Customer Experience in Innovative Industries on Customer Satisfaction in Consumer Banking Industry, which is not part of the hypothesis, is not significant as the interval of bootstraps contains zero (LLCI=-,11; ULCI=,04). The total effect of the model, which includes both the direct as the indirect effect, is not significant (LLCI=-,05; ULCI=,11).

As the total effect also incorporates the direct effect, which is not part of the hypothesis, an insignificant total effect does not mean that the indirect effect is not significant.

Hypothesis I is rejected. It is shown that the relationship between Customer Experience and Customer Satisfaction is mediated by Customer Expectations, however in the opposite direction of the hypothesis. Mediated by Customer Expectations in The Consumer Banking Industry, a higher level of Customer Experience in Innovative Industries leads to a higher Customer Satisfaction in The Consumer Banking Industry.

4.3.2. Hypothesis IA

For hypothesis IA hierarchical regression was performed to research the ability of Customer Experience in Innovative Industries to predict the level of Customer Expectations in Consumer Banking Industry.

The first step of hierarchical multiple regression explained 3.6% of variance in Total Expectations in the Consumer Banking Industry, however the result is not statistically significant, F (4, 186) = 1.73; p=.15. The entry of Customer Experience in Innovative Industries, at step 2, resulted in a significant effect F (1, 185) = 4,30; p <.001 and explained 10.4% of variance in Customer Expectations in the Consumer Banking Industry.

Only one of the variables was statistically significant, Total Experience With Innovative Industries has an effect size of .27 (β = .27, p < .001). In other words, if a person’s Customer Experience in

Innovative Industries increases for one, their Customer Expectations for Consumer Banks will increase for 0.27.

Assumptions for regression analysis are met, the standardized residue has a normal distribution there is no multicollinearity for all the variables (VIF < 4) and the standardized residual is approximately homoscedastic.

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Table 5: Hierarchical Regression Model of Customer Expectations in Consumer Banking Industry R R2 R2 Change B SE β t Step 1 .19 .04 Age .00 .00 .04 .59 Gender (1 = Male) -.07 .08 -.07 -.92

Education: Academic Education .18 .10 .15 1.79

Education: University of Applied Sciences -.05 .10 -.05 -.55

Step 2 .32 .10** .07**

Age .00 .00 .06 .87

Gender -.12 .08 -.10 -1.47

Education: Academic Education .14 .10 .12 1.44

Education: University of Applied Sciences -.07 .09 -.06 -.76 Total Customer Experience in Innovative

Industries

.14 .04 .27** 3.76

Note. Statistical significance: *p <.01; **p <.001

It can be stated that hypothesis IA is supported, Customer Experience in Innovative Industries has an effect on Customer Expectations in Consumer Banking Industry. When Customer Experience in Innovative Industries increase, Expectations in the Consumer Banking Industry will increase as well.

4.3.3. Hypothesis IB

For hypothesis IB hierarchical multiple regression was performed to research the ability of Customer Expectations in Consumer Banking Industry to predict the level of Customer Satisfaction, after controlling for age, gender and level of education of the participant.

The first step of hierarchical multiple regression was statistically significant F (4, 186) = 2,56; p < .05 and explained 5.2% of the variance in Customer Satisfaction in the Consumer Banking Industry. After entry of Customer Expectations, at step 2, the total variance explained by the model as a whole is 21.6%. The variance explained by adding Customer Expectations in Consumer Banking industry is 16.4%, F (1, 185) = 10.20; p < .001.

At the first step only Gender had a significant effect (β = .08, p ≤ .05). At the second step of the model only Customer Expectations in Consumer Banking Industry had a significant effect (β = .41, p < .001).

In other words, if Customer Expectations increases for one, their Customer Satisfaction will increase for 0.41. Assumptions for regression analysis are met, the standardized residue has a normal

distribution, there is no multicollinearity for all the variables (VIF < 4) and the standardized residual is approximately homoscedastic.

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