The Relationship between Service Recovery and Customer Satisfaction:
Introducing a new Service Recovery Action Plan
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The Relationship between Service Recovery and Customer Satisfaction:
Introducing a new Service Recovery Action Plan
Master’s Thesis
University of Groningen
Faculty of Economics and Business MSc Business Administration Department of Marketing July 23rd, 2013
Name: Wouter van der Hoek
Student number: 1697447
Address: Baltrasna House Apartment 61
Spencer Dock
North Wall Quay, Dublin 1 Ireland
E-mail: woutervanderhoek@gmail.com
Telephone: +31 (0)6 387 705 45
Supervisor: Drs. J. Berger
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MANAGEMENT SUMMARY
Several contradicting theories exist regarding how service failures could best be recovered in business-to-business settings. In this thesis, an empirical data research was performed in order to be able to determine what service recovery strategies would suit best for business-to-business (IT) services. In restoring service failures, the big question was whether B2B organizations should focus on rewarding duped customers with monetary compensations for experiencing the service failure (traditionalist service recovery perspective), or on providing duped customers with both monetary and non-monetary compensations for restoring the aggrieved customer satisfaction after service failures have happened (behavioral sciences perspective of service recovery).
Analyzing the data of 48 survey respondents yielded the following results:
Quantitative research revealed significant evidence can be found for the existence of a negative moderating role of service recovery quality on the negative relationship between service failure and customer satisfaction in the business-to-business IT service industry.
Quantitative research also revealed service failure frequency has a significant negative influence on service recovery quality and organizational explanation quality for the service failure to have happened has a positive significant influence on service recover quality. Service failure magnitude and customer participation and co-creation in the recovery process both have an insignificant influence on service recovery quality in the business-to-business IT service industry.
Based on the outcomes of the quantitative analysis it can be concluded that organizations should not only try to recover from the failed service by rewarding customers with monetary compensations, but organizations should also strive to explain to the customer why the service failure occurred in order to be able to gain customer acceptation for the service failure to have happened and maximize customer satisfaction.
Keywords: customer satisfaction, service failure, service recovery, service recovery quality, service failure magnitude, service failure frequency, customer participation, organizational explanation quality.
Research theme: business-to-business service recovery Mentor: Drs. J. Berger
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TABLE OF CONTENTS
1. INTRODUCTION ... 5
1.1 Objective…. ... 6
1.2 Academic and Management Contribution ... 9
1.3 Problem Statement and Structure of the Thesis... 11
2. CONCEPTUAL FRAMEWORK AND HYPOTHESES... 12
2.1. Theoretical Review and Hypotheses Drawing ... 12
2.3 Conceptual Model ... 24
3. RESEARCH DESIGN ... 26
3.1 Current trends in the IT industry ... 26
3.2 Research Methods of Sampling and Data Collection ... 26
3.3 Statistical Procedures ... 28
3.4 Variables…. ... 29
4. STATISTICAL HYPOTHESES TESTING ... 32
4.1 Testing hypotheses 3, 4, 5, and 6 ... 33
4.2 Testing hypothesis 1. ... 35
4.3 Testing hypothesis 2. ... 37
5. CONCLUSIONS AND THE INTRODUCTION OF A NEW SERVICE RECOVERY ACTION PLAN .... 39
6. REFERENCES ... 42
APPENDIX 1: CUSTOMIZATION AND CLOUD COMPUTING ... 54
APPENDIX 2: CUSTOMER SATISFACTION SURVEY ... 56
APPENDIX 3: DEMOGRAPHIC CHARACTERISTICS ... 61
APPENDIX 4: RELIABILITY ANALYSIS ... 62
APPENDIX 5: SIMPLE LINEAR REGRESSION ANALYSIS HYPOTHESIS 1 ... 64
APPENDIX 6: MULTIPLE LINEAR REGRESSION ANALYSIS HYPOTHESIS 2 ... 65
APPENDIX 7: MULTIPLE LINEAR REGRESSION ANALYSIS HYPOTHESIS 3 – 6 ... 66
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1. INTRODUCTION
Figure 1: POS terminal malfunctioning causing customer anger. (Source: www.nu.nl)
The concept of service recovery is an actual topic in the Dutch media. Figure 1 depicts a recent example of customer anger caused by lacking service recovery capabilities of a service provider. It seems that more and more people become dissatisfied and frustrated about the capabilities and capacities of service recovery systems of large organizations (Nguyen et al., 2003).
Like the service-profit chain model suggests, there’s a clear relationship between service recovery and customer satisfaction (Heskett et al., 2008). Nowadays, lots of articles about the relationship between service recovery and customer satisfaction are being published in prominent scientific journals, like the Journal of Marketing, Harvard Business Review and the Academy of Management
Journal. Although the relationship between service recovery and customer satisfaction is a ‘hot’
topic, most of the recently published articles are directed towards describing the relationship between service recovery and consumer satisfaction in business-to-consumer relationships. Still little is known about the influence of service recovery on customer satisfaction in business-to-business relationships.
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1.1
Objective
According to Reichheld & Sasser (1990), a service failure occurs when an organization is unable to meet or exceed previously set customer expectations about the performance of a service. From a service receiver view, a service failure is considered to be a service encounter that results in a dissatisfied customer (Kordupleski et al., 1993). Mattila (2008) argues service failures are unavoidable for service organizations and hard to prevent happening. Due to the possible negative financial impact service failures might have on firm profitability, it’s important service organizations have an appropriate service recovery system (Buzzell & Gale, 1987).
Service industries play a key role in modern society. In The Netherlands, around 73,7% of the gross domestic product (GDP) is generated by the service sector (CBS, 2012). Because of the large impact the service industry has on the functioning of civilization and its revenue generating capabilities, academicians, business managers and politicians have a particular interest in the service sector market-mechanism as a whole, and – more specific – the factors influencing service industry efficiency (Anupam et al., 2011).
It’s very difficult for service organizations to prevent service failure to happen due to the complex nature of service delivery processes. The complicatedness of business-to-business service delivery processes is (partly) caused by constantly changing customer expectations, simultaneous production and consumption, and by customer and employee uncertainty (Matilla, 2008).
Research of Keaveney (1995) about customer switching behavior in service industries indicates that 44% of all service company switches made by customers (service receivers) are caused by improperly solved core service failures. In order to be able to create sustainable revenue, service firms must prevent customer-switching behavior (Anderson et al., 2004). Although service failure is hard to predict and unavoidable, Chuang et al. (2012) are convinced that choosing the correct type of service failure recovery strategy can help to prevent and limit customer switching behavior.
Modern service recovery theories primarily focus on how service failure problems should ideally be solved in order to increase customer satisfaction (Chuang et al., 2012; Matilla, 2008; Heskett et al., 2008). Research of Chuang et al. (2012) reveals the existence of a positive relationship between customer satisfaction and firm profit. Daneher and Rust (1996) argue customer satisfaction is directly influenced by the way in which service failures are restored – the service recovery process.
According to Boshoff (2012), it’s important service providing organizations activate their service recovery process as quickly as possible after a service failure has been identified in order to limit the perceived customer service drawbacks and maximize customer satisfaction. A proper service recovery can even lead to a phenomenon called the ‘service recovery paradox’. According to the service recovery paradox, it is possible customer satisfaction is higher after a service failure is highly effective recovered than if the service failure had never happened (Krishna, 2011). Good service recovery systems create goodwill and can turn dissatisfied customers into loyal customers (Hart et
al., 1990).
7 Behavioral sciences and psychology theory suggest post service failure customer satisfaction is not only determined by the appropriateness of the compensation incentives for the service failure itself, but also by customer willingness to accept the service failure to have happened and the customer forgiveness ability (Pettitt, 1987).
In contradiction to business-to-consumer theories that state emotions play a role in the decision making process of (most) consumers, scientific business-to-business theories state company decision making processes are primarily based on rationality (Webster & Wind, 1972; Ho et al., 2006; Che et
al., 2007; Olavarrieta & Friedmann, 2008, and White et al., 2005). According to Webster and Wind
(1972), organizational decision making takes place in the context of a formal organization and involves many people in the decision making process. For example, the organizational buying decision is influenced by cost, budget and profit considerations.
Although, from a business perspective, business decisions should ideally be made on base of rationality, in practice this is not always the case (Strauss, 1962). By ‘zooming in’ till a micro-level on company decision making processes, helps explain why emotions might influence organizational decision making.
Contradicting the traditional views of organizational decision making, modern theorists state organizational decision making is a complex process (rather than a single, instantaneous act) carried out by individuals, in interaction with other people (Jabs, 2005). Singh (1986) states organizational decision making most often involves many persons. The number of people involved in the decision making process usually depends upon the importance and (possible) impact of the decision that has to be made, the structure of the company, and the company size (Singh, 1986).
Decision makers can have multiple individual, social, organizational and environmental motives in making decisions. According to Webster and Wind (1972), motives in decision making can be task related and non-task related. Task related motives are motives that relate to a specific decision making problem to be solved, are based on rationality and are ‘in the best interest for the organization’. Non-task related motives are the motives which relate to fulfilling the personal wishes and interests of the individual decision maker. Non-task related motives can be categorized into two broad categories: achievement motives and risk-reduction motives (Webster and Wind, 1972). Maitlis and Ozcelik (2004) argue that, due to the fact that organizational decision makers have task and non-task related motives, it can be concluded that organizational decision making is not only based on rationality, but emotions also play a role in the organizational decision making process. Combining the theory of Maitlis and Ozcelik (2004) with current service recovery theory (e.g. Heskett
et al., 2008; Nguyen et al., 2003; Matilla, 2008) would suggest that the service recovery strategies of
organizations in the business-to-business service industry should, besides focusing on restoring damaged task-related customer evaluations as efficient as possible, also focus on restoring damaged (personal) non-task related customer evaluations in order to maximize customer satisfaction.
8 The main hypothesis of this master’s thesis research sounds:
To elucidate the statement proposed above, this research concludes business-to-business service recovery strategies should not only focus on what monetary or non-monetary compensations should be assigned to duped customers for restoring customer satisfaction after service failures, but service recovery strategies should also pay particular attention to the way by which service failures are being restored and the personal impact these recovery processes would have on the duped customers.
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1.2
Academic and Management Contribution
This thesis and the accompanying developed service recovery action plan are quite distinctive compared with existing frameworks and theories about customer satisfaction and service recovery. Current business marketing theories mainly focuses on ways to restore service failure problems as quickly and efficient as possible (e.g. Matilla, 2008; Chuang et al., 2012; Reichheld & Sasser, 1990). Where contemporary literature mainly focuses on the restored solution and providing monetary and non-monetary rewards for compensating service failures, this thesis focuses on the service recovery process itself and the personal effects service failures have on organization members. In summary, it can be said that the in this thesis revolutionary developed service recovery theory not only focuses on the interests of companies in restoring service failures, but also takes personal interests of the duped organization members into consideration in finding ways to restore customer satisfaction after service failures in the business-to-business service industry.
This research has a positive contribution for both academia and business world in the sense that this thesis contributes to a better understanding of service recovery processes in business-to-business relationships. This thesis distinguishes itself from existing business-to-business service recovery theory by paying attention to the organizational impacts and the personal impacts service failures may have in the business-to-business service industry.
This thesis provides additional insights into the effectiveness of service recovery programs in the business-to-business service industry by stating that organizations should not only aim to solve service failures as quickly and efficient as possible, but should also aim to solve the service failure in a proper way in cooperation of the duped customer in order to maximize customer satisfaction.
1.2.1 Academic Contribution
From an academic point of view, this thesis contributes to marketing theory by introducing a new service recovery action plan and by providing additional insights into the effectiveness of service recovery systems by examining their impact on customer satisfaction. The outcome of the empirical research part of this thesis reveals that in business-to-business processes, the organizational decision making process is influenced by both rational decision making aspects and (personal) emotional decision making aspects. Current service recovery theory lacks a scope on personal interests in organizational processes. Contemporary business marketing theory aims to maximize firm revenue by focusing on fulfilling specific customer wishes and applying customization principles (Coelho and Henseler, 2012). This thesis suggests organizations can increase customer satisfaction by customizing service recovery programs and take personal interests of individual organization members into account in the recovery process itself.
10 1.2.2 Management Contribution
A contemporary development in the business-to-business service industry is the changing perspective on marketing budget allocations. Due to increased competition caused by the economic downturn and advent of service provider comparison websites on the internet, it’s harder for service companies to differentiate their services from the services provided by their competitors (Byrd et al., 2012). In order to be able to have a sustainable competitive advantage, many companies now focus on increasing customer lifetime value instead of focusing primarily on sales volume. An ongoing trend in services marketing is therefore the transition of business-to-business service companies from transaction-focused marketing strategies to relationship-oriented marketing strategies (Spencer and Cova, 2012).
From a business perspective, this thesis ads values for best practice business management by introducing a new customer intimacy enhancing strategy and by providing additional insights into the relationship between service recovery mechanisms and customer lifetime value by examining the impact of customer satisfaction on firm revenue. If implemented, the newly developed service recovery action plan will facilitate managers to transfer customer frustration into customer loyalty and customer satisfaction.
Research of Daneher and Rust (1996) revealed the importance of customer satisfaction on firm profitability. This research concludes that customer satisfaction influences firm profitability via customer retention and via word of mouth. Positive word of mouth can be a very effective manner to attract new customers, increase sales and profits, and improve a company’s advertising and promotion efficiency (Hogan et al., 2003; Luo and Homburg, 2007). Customer retention increases long-term firm profits by increasing Customer Lifetime Value (CLV) and customer equity (Anderson et
al., 1994; Reinartz and Kumar, 2000).
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1.3
Problem Statement and Structure of the Thesis
Besides trying to display the relationship between service recovery and customer satisfaction in business-to-business relationships, this research paper aims to develop a business-to-business service recovery action plan for effectively restoring customer satisfaction. The central research question to be answered on base of the outcomes of the quantitative analysis of this thesis sounds:
‘What is the relationship between service recovery and customer satisfaction in the business-to-business service industry?’
Given the limited scope of this study, the main focus in this thesis will lie on depicting the relationship between service recovery and customer satisfaction in the information technology (IT) business-to-business service industry.
Before turning to the empirical part of the thesis, a literature research will be performed. The aim of the literature research part in this thesis is to describe the concepts of customer satisfaction and service recovery, and to explain the relationship between these two concepts. It’s also envisaged that by performing a literature research, the relevant factors influencing the relationship between the service recovery and customer satisfaction, will be found.
Next, in the empirical part of this thesis, the ‘real’ influence of these factors on the above mentioned relationship will be tested on the basis of a quantitative data research.
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2. CONCEPTUAL FRAMEWORK AND HYPOTHESES
In this part of the thesis hypotheses are drawn and a conceptual model is drafted on base of a review of contemporary service recovery theory. This chapter begins with a theoretical review of relevant terms (service failure, service recovery, customer satisfaction and the service recovery paradox) and the accompanied drawing of hypotheses. After the hypotheses have been set up, a conceptual model will be developed in the second part of this chapter. In chapter three and four the veracity of the drafted conceptual model will be tested on base of an empirical study and by performing a quantitative data analysis.
2.1. Theoretical Review and Hypotheses Drawing
2.1.1 Business-to-business buying decisions and relationships in the service industry
Traditional views of organizational buying state organizational decision making processes are primarily based on rationality instead of on emotionality, which is the case in consumer buying situations (Webster & Wind, 1972; Ho et al., 2006; Che et al., 2007; Olavarrieta & Friedmann, 2008, and White et al., 2005). According to Webster and Wind (1972), organizational decision making takes place in the context of a formal organization and involves many people in the decision making process. Traditional views adhere the principle that organizational buying decisions are influenced by cost, budget and profit considerations and consider organizational buying decisions as complex processes. Bounded rationality theory suggest organizational decision making has a strong cognitive emphasis, since organizational buying actors strive to make rational choices (March, 1997).
Although, from a traditional view, business decisions should ideally be made on base of rationality, in practice this is not always the case (Strauss and Corbin, 1998). In their research, theorists like Ashkanasy et al. (2002) and Elsbach et al. (1998) emphasize the fact that organizations also have emotional areas. Although some organizational decisions may be primarily made on base of rationality, certain organizational buying decisions may provoke intensely emotional decision making processes (Ashforth and Humphrey, 1995). Some organizational decision making processes might even provoke intensely obnoxious feelings like ignominy, anxiety, apprehension, and anger among employees and other stakeholders (Diener et al., 1995). Maitlis and Ozcelik (2004) introduced the concept of toxic decision processes, which can best be defined as decision processes that generate extensive negative emotions in organizations. Toxic decision processes are activated by decision making situations regarding issues that are sensitive, equivocal and not urgent that trigger employees’ emotions and actions.
13 2.1.2 Customer Satisfaction
In the service industry, customer satisfaction is determined by the customization quality and standardization quality of the service: how well a service fulfills the needs of customers (customization quality) and how reliable the service is in fulfilling the needs of customers (standardization quality) (Juran, 1988).
Anderson et al. (2004) argue a positive relationship between customer satisfaction and shareholder value exists. Although customer satisfaction influences long-term profit, the effect of customer satisfaction on firm revenue is likely to differ across industries and firms due to industry and customer factors. Customer satisfaction performance measures can be used to predict future cash flows since customer satisfaction is an indicator of the strength of the organization’s customer relationships (Anderson et al., 2004). Many scientists confirm the presence of a positive correlation between customer satisfaction and customer retention (Anderson and Sullivan, 1993; Bearden and Teel, 1983; Bolton, 1998; Bolton and Drew, 1991; Boulding et al., 1993; Mittal and Kamakura, 2001; Oliver, 1980; Oliver and Swan, 1989; Yi, 1991).High customer retention indicates a stable customer base that provides a stable and predictable source of future revenue due to repeat buying behavior (Anderson and Sullivan, 1993). In turn, when customer satisfaction is high, negative customer outcomes (such as customer churn, complaints and negative word of mouth) and negative cash flow developments are less likely to occur (Luo et al., 2010).
High customer satisfaction might even enable service organizations to charge higher prices for their products or be better able to resist downward price pressures (Narayandas, 1998). Empirical research of Anderson et al. (1997) depicts the relationship between customer satisfaction and return on investment (ROI) is stronger for goods than for services. Customer satisfaction is, according to Anderson et al. (2004), not only determined by service quality, but also by market segmentation and customer selection by the via an organization’s marketing mix (product, price, distribution and communication channels).
Field research of Anderson et al. (1997) reveals the correlation between changes in customer satisfaction and changes in productivity is positive for goods and negative for services. This negative correlation between customer satisfaction and productivity exists because intangibility and inseparability of production and consumption make it hard to standardize the product quality of services without having to compromise on the customization ability of the service product (Baumol
et al, 1989). In practice, this implicates compromises between productivity and customer satisfaction
is more an issue for the services sector and less relevant for the goods manufacturing industry. In services, an organization with high customer satisfaction rates is likely to improve its productivity, reduce its operational costs and gain a competitive advantage by having to allocate fewer resources to restoring unsatisfying services, compensation schemes for service failures and complaint management compared to organizations with lower customer satisfaction rates (Deming, 1982). The attitude of contact service personnel has a large influence on customer satisfaction and customer retention in the service industry (Heskett, 1987; Parasuraman et al, 1991; Susskind et al., 2003). It is to be believed that the employee attitude which highly influences customer satisfaction, is determined by employee job involvement, employee job commitment and team design (Harrison et
al., 2006; Jernigan et al., 2002; Thoresen et al.,2003; Herzberg, 1966; Hirschfield, 2002; Harley, 2001;
14 Field research of Burns and Peterson (2012) among executive business directors reveals 93% of all responding executives consider customer experience to be on their company’s list of strategic priorities. Burns and Peterson (2012) argue this is an inevitable reaction to the forces of globalization, which has made it a difficult and risky proposition for organizations to compete on price or product alone. According to them, those companies that get ahead of customer expectations will continue to flourish. Those companies who react slowly, or devise service offerings that fail to take into account the particular demands of their audience, will face increasing marginalization (Bozalek, 2011). According to Luo et al. (2012), most organizations recognize this growing demand to move beyond ‘satisfactory’ experiences – with market share, loyalty and increased revenue awaiting those who deliver stand out customer satisfaction.
Luo (2004) argue customer satisfaction has a positive influence on an organization’s promotion efficiency and its human capital performance. Longitudinal research reveals customer satisfaction decreases organizational advertising and marketing costs due to the free spread of word-of-mouth communication (Rust et al., 2002). Outcomes of this research also evince the positive relationship between customer satisfaction and human capital performance since customer satisfaction increases employee work satisfaction and decreases employee churn (Ryan et al., 1996; Benson et al., 2004; Hitt et al., 2001; Hauser and Simester, 1996).
Customer satisfaction can also be linked towards the principles of customer loyalty. Bogomolova (2011) discovered the existence of a triangular relationship between customer satisfaction, service quality perceptions and loyalty. In general, satisfied customers evaluate the quality of a service more positive than unsatisfied customers and are, in turn, more loyal towards the service provider than non-satisfied customers. Besides, behaviorally loyal customers also tend to be attitudinally loyal and are therefore ideal for organizations in spreading word-of-mouth publicity (De Ruyter et al., 1998). Enterprise managers can use the customer satisfaction measurements in its strategic and operational policies. According to Lemmink and Kasper (1989), customer satisfaction quality measurements can be used at different ways and for different purposes, including:
Management and control purposes
Market segmentation
Evaluation of strategic strengths and weaknesses
Periodic evaluations
Competitor analysis
Improvements regarding the organizational service culture and the service delivery process
2.1.3 Services
The service sector becomes increasingly important throughout the world. To give an example, in 1960, around 62% of the gross domestic product of the United States was generated by the service sector. Nowadays, around 81% of all US national income is generated by the service industry and figures are still increasing (Bureau of Labor Statistics, 2005).
Congruous patterns can be found all over the world among first world countries (Sheram and Soubbotina, 2000). Even in developing countries (e.g. Mexico, Brazil, Indonesia, and Thailand), the transition from a manufacturing oriented focus to a more service oriented focus for generating revenue is gaining fast momentum (Krishna and Dangayach, 2012).
15 Although good organizational service climates can contribute towards creating competitive business advantages, service organizations must be careful for the occurrence of a phenomenon called ´service sweethearting´. Service sweethearting occurs when front office employees provide customers with extra, unauthorized free or discounted services. An inadequate policy and a lacking supervision on frontline service employees might cost a service organization millions of Euro´s in lost revenues every year due to service sweethearting (Brady et al., 2012).
Rust and Chung (2006) argue several areas of scientific organizational service research exist. According to them, it’s useful to classify modern service literature into separate categories. Rust and Chung (2006) distinguish the following four principal service domains:
Managing service
Customizing service
Customer satisfaction and relationships
Financial impact
The domain of managing services concerns tactical and strategic decision making processes regarding the optimalization of customer acquisition and customer retention. Customizing services implicates personalizing and customizing products and services to the personal wishes of (individual) customers. The customer satisfaction and relationships domain involves the organizational efforts that try to build long-term customer relationships and aim to maximize customer lifetime value and customer satisfaction. The service domain regarding the financial impact of customer relationships deals with the company efforts to quantify the profitability of customer relationships (Rust and Chung, 2006).
Managing services is completely different than managing products due to the fact that production and consumption happen simultaneously in services, the fact that services have a limited tangibility, evaluability and sustainability, and the fact that heterogeneity is high in services (Parasuraman et al., 1985).
With the term ‘intangibility’, Vargo and Lusch (2004) denote the restricted possibilities of inventorying services. Heterogeneity of services refers to impossibility to standardize services, so every single service encounter is unique. The inseparability of production and consumption of services implicates customers participate in the production process of the service. Finally, with the limited sustainability of services, Lovelock and Gummesson (2004) refer to aspect that customers can only consume services during a short time-scope, so services are perishable.
Chu et al. (1998) argue that, in general, perceived customer purchase risk will be higher for services than for goods because of higher intangibility, heterogeneity and perishability. According to Slotegraaf and Inman (2004), organizations can decrease perceived customer purchase risk by providing customers with service guarantees and applying generous no-questions-asked refund policies.
Customer evaluations regarding the quality of a service are based on performance rather than on objects due to the intangible nature of services (Berry et al., 2006). In determining the performance of a service, customers evaluate the quality of the service clues which appeared during the service encounter. Service clues influence both emotional and rational customer perceptions regarding the service performance. According to Berry et al. (2006), a distinction can be made between three types of service clues, namely: functional clues, mechanic clues, and humanic clues. Each clue type influences customer service experiences differently.
Functional clues are clues that form customer perceptions on base of the technical
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Mechanic clues are the tangible elements of a service that form customer perceptions
regarding the service performance.
Humanic clues are the behavioral and appearance clues of the service employees on which
customers form service performance perceptions.
Even small service clues can have a huge impact on customer service experiences. In order to optimize customer service experiences, organizations must carefully design and implement service clues into service encounters (Haeckel et al., 2003). Keaveney (1995) states functional clues predominantly influence customers’ cognitive evaluations regarding the service quality, while humanic and mechanic clues chiefly influence affective customer service quality evaluations.
Service clues affect customer satisfaction by influencing customers’ feelings and thoughts. In turn, the influenced thoughts and feelings determine customer behavior (Isen, 1987). Combining these statements together would suggest service clues indirectly influences both customer satisfaction and consumer purchase behavior (Poon, 2001). According to Mehrabian and Russel (1974), environmental clues directly trigger feelings regarding whether or not to stay or leave in a certain environment. For managers, it is therefore important to manage and head experience clues in such a way that these service clues positively contribute in provoking positive customer feelings to stimulate (re)purchase behavior (Gardner, 1985).
2.1.4 Service failure
IT-company Oracle conducted an independent research to gain detailed understanding of how European shoppers currently feel about their interactions with businesses. The associated survey explored customer preferences in terms of communicating with brands, issue resolution, the impact of poor customer service and what constitutes the ideal customer experience (Oracle, 2012). The results of the survey among online shoppers show that:
70% of shoppers have stopped buying goods or services from a company after experiencing poor customer service.
64% of shoppers have, after experiencing poor customer service, gone straight to a competing brand / organization to make a purchase.
81% of shoppers are willing to pay more for a better customer experience.
According to Burns and Peterson (2012), poor experiences begin when customer expectations are not met. Without a clear understanding of what customer expectations are, it can be extremely difficult for organizations to deliver the kinds of experiences that actually meet customer needs (Rufin et al., 2012).
According to Reichheld & Sasser (1990), a service failure occurs when an organization is unable to meet or exceed previously set customer expectations about the performance of a service. From a customer-perspective view, a service failure is considered to be a service encounter that results in a dissatisfied customer (Kordupleski et al., 1993). Service failure is a phenomenon driven by discrepancies between service expectations and perceptions of customers. The SERVQUAL model of Parasuraman et al. (1985) shows service failures are quite common in most service industries due to the possible existence of various kinds of perception gaps between customers and service organizations. Unsolved service failures might even lead to disloyalty towards the service provider (Anupam et al., 2011).
17 From a customer perspective, service failures might lead to customer dissatisfaction, negative word of mouth, customer switching behavior and damaged customer confidence and trust towards service providers. For service providers, service failure might lead to decreasing goodwill and firm profit, damaged employee spirit and performance, customer churn and a restricted acquaintance of new customers (Anupam et al., 2011). Given the fact that service failures are quite common in the business-to-business service industry and the possibility to lose business due to dissatisfied customers, it’s important for service organizations to try to recover dissatisfied customers through a service recovery process (McKoll-Kennedy et al., 2003).
On base of the theory stated above the following hypothesis can be stated:
2.1.5 Service recovery
Grönroos (1988) defines service recovery as the actions an organization takes in response to a service failure. Service recovery actions include all activities and efforts an organization executes in order to solve, purge and restore the customer losses caused by a faltering service performance. According to Grönroos (1988), a distinction between two dimensions in service recovery must be made. Every service recovery contains a technical dimension and a functional dimension. The technical dimension of a service recovery represents the tangible compensation or outcome of a service recovery. In contrast, the functional dimension of a service recovery represents the process of how the service recovery is done. Both technical and functional dimensions of a service recovery influence customer service failure recovery evaluations.
The way by which an organization deals with service failures has a major impact on customer satisfaction. Dissatisfied and disloyal customers, caused by a service failure of the service provider, can be turned into loyal and satisfied customers by a process called service recovery (Anupam et al., 2011). Empirical research of Brown and Tax (1998) in the service industry revealed service providers loose on average 10% to 15% of its customer base annually due to service failure. If customer churn was lower and these customers could be retained, they can contribute to a higher profit due to increased customer lifetime value (CLV). It is impossible for service organizations to deliver 100% error-free services (Dong et al., 2007). Therefore, it’s to be believed effective service recovery is crucial for service organizations in order to be able to create satisfied customers, positive word-of-mouth and improve service delivery performance (Fisk et al., 1993; Tax et al., 1998; Zeithaml and Bitner, 2003).
Service recovery is an effective manner to restrain failure (Andreassen, 1998). Brown and Tax (1998) conclude service organizations should not only focus on acquiring new customers, but should also focus on retaining its current customer base by implementing an effective service recovery program in order to maximize firm profitability.
Hypothesis 1:
18 Service recovery can be seen as a business processes directed towards restoring the negative effects of service failures (Brown and Tax, 1998). According to the recovery model of Bell and Zemke (1987), an effective business-to-consumer recovery process contains at least the following four recovery steps:
1. Problem acknowledgement
2. Explanation of the service failure reason 3. Apology to the aggrieved customer
4. Compensation for the experienced service failure
An important outcome of a successful service recovery process is that the customers feels sufficiently (monetary and non-monetary) compensated for his/her losses from experiencing a service failure (Bell and Zemke, 1987). Kelley et al. (1993) and Tax et al. (1998) state refunds, price discounts, apologies and upgraded services are possible tangible and non-tangible service recovery compensation methods organizations could consider in their service failure recovery processes. A successful service recovery process can even turn frustrated customers into loyal and satisfied once. A startling phenomenon is the existence of the service recovery paradox. In their field research, Elzel and Silverman (1981) discovered customers might be more satisfied after the execution of a successful service failure recovery process than they would have been if the service failure had never happened. Maxham and Netemeyer (2003) introduced the term service recovery paradox and performed further research about this phenomenon. They concluded implementing a service recovery process can help organizations build customer loyalty fast and in an effective way. So, service organizations may achieve higher customer satisfaction by achieving superior service recovery (Dong et al., 2007).
2.1.6 The relationship between service recovery and customer satisfaction
In their research towards customer evaluations of service complaint experiences, Tax et al. (1998) found sufficient evidence for the existence of a strong correlation between service recovery and customer satisfaction. On base of field research evidence, Bitner et al. (1990) and Smith et al. (1999) declare service recovery plays an important role in achieving post-service failure customer satisfaction, positive word of mouth and subsequent customer purchase decisions.
By performing field research on the relationship between negative critical incidents (NCIs) and customer satisfaction with public transport services, Friman et al. (2001) discovered customers generally tend to forget service failures that happened in the distant past, but tend to accurately remember the frequency at which service failures emerged.
Flanagan’s (1954) Critical Incident Theory (CIT) is a widely used method to inquire and measure service quality perceptions and customer satisfaction after service failure incidents took place and / or after service recovery processes have been completed (Bitner, 1990; Bitner et al., 1990; Edvardsson, 1992; Strauss, 1993).
Johnston (1995) states negative word-of-mouth is likelier to be spread by dissatisfied customers than by satisfied customers. Unsolved service failures and accompanying customer dissatisfaction could therefore have a major negative impact on brand image of service organizations due to the dissemination of negative word-of-mouth (Frisk and Schneider, 1984; Green, 1984; Woodley and Ellis, 1989).
19 outcome-related service failure can be better approached via tangible (monetary) recovery efforts than via psychological recovery efforts. In turn, customers suffering from process-related service failures can best be approached psychological recovery efforts instead of via tangible (monetary) recovery efforts.
On base of the above-mentioned service recovery theory, it can be stated the customer dissatisfaction caused by a service failure can (possibly) be restored via a service recovery process. Therefore, the following hypotheses can be drawn:
2.1.7 Service failure frequency, service failure magnitude and recovery quality
Brown et al. (1993) argue service recovery quality measures should be the cornerstone of performance metrics of every service provider. In their research towards investigating the moderating factors on the relationship between organizational explanation and post-recovery customer satisfaction, Bobocel and Zdaniuk (2005) discovered post-recovery customer satisfaction is also influenced by qualities and characteristics of the service failure itself. This field research reveals frequency, foresee-ability, intensity, timing and the nature of a service failure all affect customer evaluations of complacence with the service recovery outcome.
Post-recovery customer satisfaction and customer contentment evaluations of the recovery outcome seem to increase and become more favorable as the compensation amounts increase (Webster and Sundaram, 1998; Wirtz and Matilla, 2004).
Service failures can be allocated towards a big range of organizational causes, may emerge on a regular or irregular basis, might be stable or unstable, and can be controllable or uncontrollable (Conlon and Ross, 1997). If service failures emerge on a regular basis, are controllable (the organization could have avoided the service failure to have happened), and the cause of the failure can be attributed towards the service organization, it is likely customers develop negative perceptions regarding the organizational service quality (Weiner, 2000).
Several business and psychology scientists agree prior service failure experience and service failure magnitude both directly affect customer satisfaction (i.e. Adotte and Jenkins (1987), Parasuraman et
al. (1985), Hoffman et al. (1995), and Harris et al. (2006)).
Cadotte et al. (1987) state service quality perceptions are formed by customers on base of prior experienced service failures. These prior service encounter set quality expectations affect post-encounter service quality evaluations and, ultimately, customer satisfaction (Holloway et al., 2005; Parasuraman et al., 1985). According to Matilla et al. (1999), severity of the service failure is also related to customer satisfaction. The higher the perceived severity of loss due to the service failure, the more difficult it will be to restore the service failure and the lower customer satisfaction will be (Harris et al., 2006).
The magnitude of the service failure directly influences customer satisfaction and customer responses to service recovery (Smith and Bolton, 2002; Weun et al., 2004). Because of the direct influence of service failure magnitude on customer satisfaction and customer service recovery response, it’s important organizations understand this magnitude in order to be able to develop an appropriate service recovery strategy (Hart et al., 1990). Severe service failures cause customers to perceive larger losses. The greater the perceived loss, the lower customer satisfaction will be
Hypothesis 2:
20 (Magnini et al., 2007). Due to the fact that magnitude of service failure also influences the effectiveness of recovery strategies, it seems extreme losses are really hard to recovery (Thaler, 1985).
On base of the scientific theory of Cadotte & Jenkins (1987), Parasuraman et al. (1985), Hoffman et
al. (1995), Harris et al. (2006), Smith & Bolton (2002), Matilla et al. (1999), Weun et al. (2005), Hart et al. (1990), Magnini et al. (2007), and Thaler (1985) regarding service failure frequency, service failure
magnitude and recovery quality, the following hypothesis can be drawn:
Thus, customer satisfaction increases as effectiveness of the service recovery increases. But, prior service failure experience and service failure magnitude both negatively affect service recovery effectiveness. Therefore, the more severe the perceived loss due to a service failure is and the more frequent a service failure occurs, the less effective service recovery efforts will be in restoring damaged customer satisfaction.
2.1.8 Customer co-creation of service recovery solutions
Dong et al. (2007) define customer participation in service recovery as “the degree to which the customer is involved in taking actions to respond to a service failure.” Customers can participate in the service recovery process by applying their specific knowledge and skills in helping to develop and implement a service recovery solution with the service organization (Vargo and Lusch, 2004b). Prahalad and Ramaswamy (2000) argue involving customers in the service recovery process can be seen as a competitive strategy. Customers will be able to better fulfill their personal needs and maximize satisfaction by actively co-produce value with the service provider in the recovery process. Customer participation and co-creation in services can be highly beneficial for creating customer loyalty and building brand value for service organizations (Harwood and Garry, 2010). Due to the economic downturn, competition in most markets will become even fiercer (Wilson, 2010). Several scientists in the area of business- and marketing studies are convinced customer participation and customer co-creation in services will become increasingly important for (service) organizations for being able to create a sustainable competitive advantage in highly competitive markets (e.g. Claybomb et al., 2011; Bendapudi and Leone, 2003; Vargo and Lusch, 2004a). Service providers take advantage of customer co-creation of value in the service deliver process by being able to achieve higher operating efficiencies and greater service value. Due to the increased operating efficiencies of the service providers, customers, in turn, benefit from their participation and co-creation of value in service delivery process by the ability of service providers to deliver the service faster and being able to deliver the service at lower prices (Claybomb et al., 2001).
According to Anderson and Narus (1998), trust and commitment two key requirements for a successful working relationship. Empirical research of Dong et al. (2007) reveals that customers who
Hypothesis 3:
“A negative relationship between service failure frequency and service recovery quality
exists”
Hypothesis 4:
21 are engaged in service recovery processes and participate in designing and implementing a service failure recovery solution, experience a higher feeling of satisfaction and are more loyal towards the service organization after the service recovery process has ended than they would have been if they were not involved at all in the service recovery process.
Dong et al. (2007) developed a categorization of customer recovery participation. A classification is made between three groups based on customer efforts in the service recovery process. The groups to be distinguished are: firm recovery, joint recovery, and customer recovery. In a service recovery process, all three groups may occur.
Firm recovery: When a firm recovery takes place, customer participation in the service failure
recovery process is low. In the situation of a firm recovery, customers request the service organization to solve their problems and they might only provide basic, limited information regarding the service failure (Claycomb et al., 2001; Meuter and Bitner, 1998; Zeithaml and Bitner, 2003). The recovery efforts for restoring the service failure are solely or mostly performed by the service company and its organization members themselves. Although solving service failures via firm recovery might be quick and efficient, customers will probably have no understanding about the exact procedures and details of the by the service organization performed recovery process.
Joint recovery: In the case of a joint recovery, both organization and customer participate in
the service recovery process. Customers fulfill certain recovery tasks and actively help to solve the service failure. In the eyes of Schneider and Bowen (1995) customers can be viewed as ‘external employees’ in a joint recovery process because of the time, effort, skills and knowledge they allocate in the recovery. Since customers are partial recovery solution developers in a joint recovery, their expertise directly influences the quality of the recovery outcome (Meuter et al., 2005).
Customer recovery: Some service failure could be recovered by the customer without any
help from the service organization. A customer recovery exists when all recovery actions are initiated, directed and performed by customers without the interference of the service organization.
According to Claybomb et al. (2001), customer involvement in the recovery process can be seen as a manner for creating organizational socialization. The organizational socialization by involving customers in the recovery process starts by creating customer understanding of their roles as ‘external employees’ and the creation of customer awareness of how to act in the service encounter. Bandura (1977) argues the higher the customer participation in the service failure recovery process is, the higher the knowledge and ability of customers is likely to be due to an increased understanding of customer roles and procedures in the service recovery process. Bandura (1977) is convinced organizations can improve service recovery productivity and efficiency by increasing customer involvement in the recovery process. Applying Bandura’s customer socialization theory on the customer recovery participation categorization of Dong et al. (2007), would suggest joint recovery and customer recovery are likely to be more effective service recovery strategies compared with a firm recovery strategy due to surpassing service recovery efficiency and productivity on account of customer socialization.
A positive side effect of high levels of customer participation in the recovery process is that service organizations will be better able to shape customer perceptions about service quality and influence the overall customer service experience thanks to customer co-creation of service value (Prahalad and Ramaswamy, 2004).
co-22 creation, the increased perceived service quality due to enhanced customer participation will increase customer satisfaction (Kelley et al., 1990).
Because involving customers in creating service failure recovery solutions will increase the overall perception of service quality by customers and customer motivation, it can be stated that:
Thus, customer satisfaction increases as the level of customer participation in the service recovery increases.
2.1.9 Customer acceptation of the service failure
Bradley and Sparks (2012) dedicated a field research towards discovering the effect of service failure explanation on customer satisfaction. They discovered explanation type, explanation quality, failure magnitude and compensation all have a major influence on customer satisfaction. Both explanation type and explanation quality of the service failure reason directly affect customer satisfaction with the service recovery. Bradley and Sparks (2012) evince, in terms of explanation types, organizational apologies and excuses towards customers for the service failure to have happened, have a high impact on post-service recovery satisfaction. Their research also reveals justification type has a moderating role on the relationship between service recovery and customer satisfaction. This is in line with the theory of Shaw et al. (2003), which states excuses are more effective than justifications in restoring damaged customer satisfactions after organizational service failures have happened. McColl-Kennedy and Sparks (2003) state customers appreciate and attach value on receiving organizational explanations of why a service failed, what the reason for this failure was, and what the organization will do to solve this service failure. In their article, Bradley and Sparks (2012) mention four types of explanations service organizations can use on aggrieved customers to recovery from service failure:
Excuses. From a service recovery perspective, excuses could best be described as
explanations that call upon extenuating conditions in order for the service organization to acquit itself from responsibility for the negative outcomes caused by the service failure (Neugebauer et al., 2012)
Justifications are explanations that involve the acknowledgment of the service organization
of the liability for causing the service failure, but also justify and legitimize the reason for causing the service failure on the basis of higher organizational needs and/or goals (Medjoudj et al., 2012).
Reframing / referential accounts is the case when service organizations try to minimize the
customer perceptions of service failure damage by comparing the current situation of a duped customer with customers who are worse off due to the service failure. When this happens, downward comparisons are made by service organizations (Harris, 2008).
Apologies are explanations that involve a recognizance of inadequacy and decrepitude by
the service organization, followed by an expression of regret (Wirtz and Matilla, 2004). Customer evaluations about the correctness and suitability of the recovery outcome are heavily influenced by the specific features of the explanation they receive (Bradley and Sparks, 2012). Explanation quality directly affects attitudinal and behavioral post-recovery customer satisfaction outcomes (Shaw et al., 2003). Combining theories of Folger and Cropanzano (1998), Sitkin and Bies
Hypothesis 5:
23 (1993), and Bradley and Sparks (2012) with each other would suggest explanation quality is determined by two elements: explanation adequacy and explanation sincerity. While information adequacy concerns the level of detail, informativeness and clearness of the explanation, explanation sincerity is about the honesty and truthfulness of the explanation message itself (Bradley and Sparks, 2012). In order to have an effect on post-recovery customer satisfaction, explanations must be of a moderately high quality (Bies et al., 1988; Shapiro, 1991).
Adams (1965) argues explanation effectiveness on restoring customer satisfaction is influenced by customer perceptions of fairness and explanation justice. Post-recovery customer satisfaction is affected by customer perceptions of being treated fairly in the recovery process and perceptions of being justifiably compensated (Greenberg, 1993; Thibault and Walker, 1975).
Because customers attach value towards receiving organizational explanations for service failures and because field research of Bradley and Sparks (2012) shows that explanation type and explanation quality directly affect post-recovery customer satisfaction, it can be stated that:
Hypothesis 6:
24
2.3 Conceptual Model
In order to be able to statistically prove the existence of a moderating role of service recovery quality on the relationship between service failure and customer satisfaction in business-to-business settings, the two main research hypotheses of the quantitative analysis part of this thesis sound:
On base of the sub-hypotheses drawn in the theoretical research part of this thesis, the following conceptual model can be drawn:
Figure 1: Conceptual model of the relationship between service recovery and customer satisfaction in the business-to-business IT industry.
Whereby:
H1: A negative relationship exists between service failure and customer satisfaction.
H2: Service recovery quality has a negative (moderating) influence on the relationship between service failure and customer satisfaction.
H3: A negative relationship between service failure frequency and service recovery quality exists.
“Customer Satisfaction = Service Failure – Service Recovery Quality”
“Service Recovery Quality = Customer Participation in the Service Failure Recovery Process
+ Service Failure Explanation Quality – Service Failure Magnitude – Service Failure
25
H4: A negative relationship between service failure magnitude and service recovery quality exists.
H5: A positive relationship exists between customer participation and co-creation in the service failure recovery process and service recovery quality.
26
3 RESEARCH DESIGN
3.1
Current trends in the IT industry
Due to the restricted time and scope of this thesis, the empirical research of this assignment will focus on displaying the relationship between service recovery and customer satisfaction in the business-to-business IT industry.
The advent of the new information technology (IT) over the last three decades possibilities made it possible to collect, store, analyze and transmit huge amounts of information in an automated manner. Many firms now recognize new information technology offers organizations great possibilities for managing customer relationships. Customer relationship management (CRM) becomes increasingly popular in the business world and lots of companies are investing in the procurement of CRM software solutions, databases, business intelligence applications and IT infrastructure in order to be able to execute technology-driven marketing activities. New CRM software solutions allow organizations to maintain a continuous dialogue with its customers, to monitor and categorize customer profitability, to automate customer service requests, and to help maximize customer bonding and commitment towards the service organization (Shah et al, 2006). Currently, two major trends gain momentum in the IT service industry, namely customization and cloud solutions. Appendix 1 discusses the impact of these two trends on the structure and the business-to-business relationship characteristics of the IT service industry.
3.2
Research Methods of Sampling and Data Collection
The hypotheses that were formulated in the theoretical research part of this thesis will be tested on the basis of an empirical study. In order to be able to test the hypotheses on veracity, primary data from the business-to-business IT services industry is collected via a quantitative survey.
According to Cooper & Schindler (2007), two methods of data collection can be distinguished: data collection by means of monitoring and data collection data collection through processes of communication. In this study, data was collected using the communication process method. This implies the researcher conducted a survey among preselected individuals and had to collect their answers himself in order to generate data. A cross-sectional study was conducted in which the data was collected on base of a spot sample/instantaneous sample.
In order to be able to make correct statistical interferences regarding the moderating effect of service recovery quality on the relationship between service failure and customer satisfaction in the business-to-business IT service industry, a correct sampling process must be set up before executing the survey (Blyth and Marchant, 1996).
27 In making a sample distribution, researchers must decide whether the population size is finite or infinite (Malhotra, 2010). Because limited information regarding the total number of Dutch companies using IT service providers for outsourcing organizational IT processes is known, it’s impossible to make an accurate estimation about the total Dutch organizational population size using services from an (external) IT service provider. In determining the minimum number of respondents, the population size will therefore be considered to be infinite.
According to Moore and McCabe (2002), the minimum sample size for an infinite population can be calculated using the following formula:
Where:
n = sample size.
z = standard error at a certain confidence interval.
p = response distribution. P represents the chance someone answers a certain answer possibility.
In the quantitative research part of this thesis, the following parameters will be applied:
z: In this section, a confidence level of 95% will be used, since this is the most commonly used rate in quantitative business-to-business research (Cooper and Schindler, 2006). Fliess et al. (2003) state the standard deviation for a confidence level of 95% is 1,96.
p: In Likert scales, there are 7 answer possibilities, so the chance a respondent answers a particular answer possibility (p) is 1/7.
f: According to Cooper and Schindler (2006), a margin of error of 10% is often used in business-to-business quantitative research. Therefore, an error margin of 10% will be used in this research.
Which results in a sample size of n 47. The actual sample size on which the hypotheses were tested was 48 (valid) respondents. The demographic characteristics about the survey respondents are summarized in appendix 3.
Cooper & Schindler (2007) argue interviews can be either structured, unstructured or semi-structured in nature and may contain open and/or closed questions. Because the data collected in this study will also be used for giving management advice to a business unit manager of Oracle Corporation, the survey will exist of objectively interpretable closed questions containing semantic differential or Likert scales. The questionnaire can be found in Appendix 2.
As can be seen in Appendix 2, the questionnaire consists primarily out of closed questions. Closed questions were being used in order to limit the interpretation possibilities of the interviewees (and therefore also the risk of misinterpretation and research biases). The survey was conducted using the online research application ‘Thesistools.com’. Respondents had to answer the questions in a predetermined sequence, which made all interviews structured from nature. The in the quantitative data research participating companies were selected by applying convenience sampling. Two predetermined conditions that organizations had to meet before they were allowed to participate in the survey were that they the corresponding organizations had to exist for at least a period of five
28 years and that the corresponding organizations needed to outsource a substantial part of its IT processes.
Note: The most commonly used margins of error in quantitative business-to-consumer research are 2% and 5% (Thornton and Thornton, 2004). In quantitative business-to-consumer research, the applied margins of error are often higher than the in B2C research widely used 2% and 5% rates. Applying higher margins of error in calculating the ideal sample size in B2B research is (most often) justified due to the increased difficulty of finding survey respondents compared with B2C research (Cooper and Schindler, 2006). In quantitative research, it’s therefore widely accepted to work with higher margins of error.
3.3
Statistical Procedures
The interviews generated ordinal data. However, due to the fact the interview questions make use of a seven point Likert scale and the fact the sample size of the survey is sufficiently large, it is allowed to consider the by the survey generated primary data as interval data (Braunsberger & Gates, 2009). Before the hypotheses will be tested on truthfulness by means of a regression analysis, a reliability analysis will be carried out. A reliability analysis is carried out to determine the reliability of the in the survey used multi-item scales.
Jarvis et al. (2003) argue in reflective scales a variety of constructs measure a common factor. In order to be able to combine the different survey questions into one representative factor, the corresponding questions need to be sufficiently internal consistent. The internal consistency of these questions can be measured by means of a reliability analysis.
The validity and reliability of the reflective multi-item scales will be assessed via a Cronbach’s Alpha test. A Cronbach’s Alpha test is used since every single concept is tried to be measured on base of a combination of multiple interview questions and the underlying dimensions of the concepts are already known.
Cronbach’s Alpha is a measure for internal consistency of questionnaire items. The α-value will indicate the extent to which the research questions measure the same concept (Raykov, 1997). In this study, a threshold of α = 0,70 is applied. The questionnaire items that measure a particular concept will be considered to be sufficiently internally consistent if α is greater than or equal to 0,70. The validity and reliability of the formative multi-item scales will be assessed via a discriminant validity test. According to Campell and Fiske (1959), discriminant validity tests that constructs that should have no relationship do not have any relationship. By performing a discriminant validity test, it can be ensured the non-overlapping factors – the unrelated constructs – do not overlap with each other in the formative scale (Domino and Domino, 2006).
The α-value will indicate the extent to which the research questions of the formative scale overlap with each other in measuring the same concept. In this study, a threshold of r = 0,85 is applied. A value of r ≥ 0,85 would suggest the questions (constructs) of the formative scale overlap greatly and are likely to measure the same variable. The formative questionnaire items that measure a particular concept will be considered to be sufficiently internally consistent and have insufficient discriminant validity between them if r is greater than or equal to 0,85 (Struwig et al., 2001).
