The role of Customer knowledge management for the strategic value disciplines.

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Master Thesis

The role of Customer Knowledge Management for the Strategic Value Disciplines University of Amsterdam Master Thesis

Rick van der Vlugt, 10282688

Date: January 26, 2014 Supervisor: Drs. F. Slisser Second supervisor: Dr. E. Peelen

University of Amsterdam MSc Business Studies

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Abstract

Loyalty becomes more and more important these days, because of the low switching costs for consumers due to the technological innovations. To create loyal customers, firms should provide superior offerings to meet or surpass the customer’s needs and wants. So, in order to provide superior offerings, firms need to know the customer’s needs and wants. This study provides insights in what role Customer Knowledge Management can play for the strategic value disciplines of Treacy and Wiersema (1993), (1) Customer Intimacy, (2) Operational Excellence, and (3) Product Leadership, in order to create more loyal customers and reduce their switching behaviour. Based on this study it can be stated that Customer Knowledge Management does not contribute to the loyalty of customers, for all strategies. However, for firms who pursue a Customer Intimacy or Product Leadership strategy, Customer Knowledge Management has a positive influence on the loyalty of customers towards these firms. The Customer Knowledge Management practises that contribute most to the loyalty of customers are Information Integration and Information Access. So, firms need to integrate all the collected information of customers and make it easy to access for all employees to improve the loyalty of their customers and reduce their switching behaviour.

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Acknowledgement

First of all I would like to thank my supervisor Drs. F. Slisser for his guidance and

enthusiasms during the writing of this thesis. The clear and constructive feedback kept me sharp and motivated me to get the best out of this master thesis.

Thereby I would like to thank my employer Kega who made it possible for me to follow this study and distribute the questionnaire of this thesis. In particular I would like to thank Dhr. K. Verkade and Dhr. B. Zoet, for their feedback and practical experiences about the subject of this thesis.

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1. Introduction

Because of technological innovations the way of shopping is changed radically the last decades (Carlton & Chevalier, 2001). With these technological innovations, like the Internet, Smartphones and Tablets, customers are nowadays able to shop everywhere at anytime. In other words there are multiple points of sale which customers can choose to fulfill their needs and wants. As a result of these technological innovations, markets become more and more transparent (Carlton & Chevalier, 2001). Customers have the ability to check product specifications, competitor’s websites, product reviews and price comparisons. This market transparency may result in lower switching costs and increasing buying power for customers (Carlton & Chevalier, 2001). When switching costs for customers are low, loyal customers are more important than ever. This is supported by the study of Sharma (2007), who found that transactional customers are more likely to switch than loyal customers. In addition to this, in the field of marketing, research shifts from transactional marketing towards more relational marketing (Sheth & Parvatiyar, 2002). Historically firms are more internal oriented and fully focussing on advantages of their products, instead of focussing on the end user of these products (Levitt, 1960). In addition to this finding, Levitt (1960) is proposing that firms should not only focus on selling their products, but on fulfilling the customers needs. Shah et al. (2006) are outlining five trends that drive firms to a more customer oriented perspective, and focussing on fulfilling the customers needs: (1) the pressure to improve marketing

productivity, (2) increasing market diversity, (3) intensifying competition, (4) demanding and well-informed customers, (5) technological innovations. These trends make firms realising that the need for a customer centric strategy is needed to develop a close and sustainable relationship with customers (Shah et al., 2006). Shah et al. (2006) are defining the core of a customer centric strategy as creating value for customers and subsequent creating value for

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the firm, instead of focussing on how to sell products to customers. So, also in the academic literature the focus on increasing loyalty of customers towards firms becomes more important. To switch from transactional to relational marketing, a firm needs to capture individual

customer data and apply this data in order to develop or maintain the customer relationship (Payne & Frow, 2006). The capability of translating the customer data into creating value for the customer can be seen as a knowledge-based resource (Alavi & Leidner, 2001).

Knowledge-based assets are considered as intangible resources, which are often difficult to copy (Teece, 2003). Barney (1991) argues that this inimitability contributes to sustained competitive advantage of firms. However, it is not just the existence of knowledge-based assets in firms that results in sustainable competitive advantage, but it is about applying this knowledge (Alavi & Leidner, 2001). So, firms should be able to apply customer knowledge in order to create loyal customers, and subsequently achieve sustainable competitive advantage. But is this suitable for every firm independent from the executing strategy? Treacy and Wiersema (1993) proposed three value discipline strategies for competitive advantage, (1) Customer Intimacy, (2) Operational Excellence, and (3) Product Leadership. These strategies all have different performance indicators that are focussed on (1) the customer, (2) efficiency, and (3) innovation and quality.

1.1 Problem Statement

Since, the three value discipline strategies of Treacy and Wiersema (1993) all have different performance indicators, it remains interesting if applying customer knowledge increases loyalty of customers towards firms for all strategies?

The objective of this research is to shed light on to what extent Customer Knowledge Management can impact customers to become more loyal towards firms who pursue one or more of the three value discipline strategies of Treacy and Wiersema (1993), in order to

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decrease the switching behaviour of these customers. Therefore, the research question this study proposes is:

“To what extent can Customer Knowledge Management increase the loyalty of customers for all value discipline strategies?”

1.2 Contribution

There is a lack of academic research in the moderator effect of Customer Knowledge Management, as part of Knowledge Management, on Customer Loyalty towards firms who execute one or more value discipline strategies of Treacy & Wiersema (1993).

Lots of studies are focussing on the effect of Knowledge Management on organizational performance (Andreeva & Kianto, 2012), measured by multiple factors. Some of these studies are measuring the importance of Knowledge Management for organizational performance in terms of innovation (Darroch & McNaughton, 2003; Gloet & Terviovski, 2004; Kiessling et al., 2009), in terms of organizational creativity (Lee & Choi, 2003), in terms of product and employee improvement (Kiessling et al., 2009) and others in terms of financial outcomes (Tanriverdi, 2005; Darroch, 2005; Marques & Simon, 2006; Zack et al., 2009). None of them are focusing on the organizational performance in terms of customer loyalty, which becomes more important in the dynamic and transparent environment of these days.

Zack et al. (2009) argues that Knowledge Management is often used to improve the value discipline strategies, so they link Knowledge Management practices to the three value disciplines as indicators of strategic organizational performance. They (Zack et al., 2009) measure the organizational performance of a Customer Intimacy strategy by customer satisfaction and retention, an Operational Excellence strategy by operating costs and a Product Leadership strategy by innovation and quality. In their study (Zack et al., 2009) they find support for the positive impact of Knowledge Management on organizational

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performance by all the value disciplines. However, they find no significant support for the relationship between the Knowledge Management practice “Experimenting/learning about customers” and organizational performance by the value discipline strategies Product Leadership and Operational Excellence (Zack et al., 2009). So the effect of Customer Knowledge Management on the value disciplines Product Leadership and Operational Excellence needs further investigation. At the same time, Zack et al. (2009) are focussing on the financial output instead of the output in customer loyalty.

Besides this, the study of Zack et al. (2009) is based on organizations from North America and Australia, and does not represent other geographic, cultural or economic environments. Like this study Langerak and Verhoef (2003) are focussing on the Knowledge Management practice of customer knowledge in relation to the value discipline strategies. Langerak and Verhoef (2003) are in their study representing the effect of Customer Relationship

Management (CRM) for two value discipline strategies, (1) Operational Excellence, and (2) Customer Intimacy. They (Langerak & Verhoef, 2003) argue that CRM contributes to the daily business processes and activities for firms with an Operational Excellence strategy. For firms with a Customer Intimacy strategy, CRM contributes to long-term customer

relationships (Langerak & Verhoef, 2003). Also Langerak and Verhoef (2003) do not measure if Customer Knowledge Management positively moderates on customer loyalty by executing different value discipline strategies. So it is still unclear if Customer Knowledge Management can increase loyalty in order to reduce switching behaviour of customers for all the value discipline strategies of Treacy and Wiersema (1993). This remains an interesting question because loyal customers, as said, are becoming more important to firms these days. This study also contributes to managerial implications, in that way that it gives managers understanding of the importance of customer loyalty these days independent from which

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strategy their firms pursue. Thereby it sets a list of Customer Knowledge Management practices for the different value discipline strategies that managers can execute.

1.3 Structure of Research

In the following sections of this paper it will come up with the theoretical background and interaction between the main constructs: Loyalty, the Strategic Value Disciplines (Customer Intimacy, Operational Excellence, Product Leadership) and Customer Knowledge

Management. Based on this a conceptual framework and hypotheses are formulated.

Thereafter the methodology of this study to test the hypotheses will be discussed. In addition to this, this study provides the results of the research. Then, based on the results the

conclusion and discussion of this research are provided. Finally, it comes up with some managerial implications, limitations of this study and implications for further research.

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2. Literature Review

In this section the existing literature about the constructs Loyalty, the Strategic Value

Disciplines (Customer Intimacy, Operational Excellence, Product Leadership) and Customer Knowledge Management is been reviewed. Thereafter the interactions between these

constructs are presented to formulate the hypotheses and come up with a theoretical framework.

2.1 Loyalty

Lots of research is done about the relation between firms and customers. For instance Chaudhuri and Holbrook (2001) are defining loyalty as a variable that consists of two

disciplines (1) behavioural or purchase loyalty, and (2) attitudinal loyalty. Behavioural loyalty is focussing on the repeated purchases of the brand, whereas attitudinal loyalty includes the unique value association with the brand (Chaudhuri & Holbrook, 2001). This study is focussing on the behavioural loyalty of customers to firms.

Some authors argue that the relationship is a continuum ranging through different stages and others are arguing that the relationship between firms and customers are fully based on the customer’s willingness to step in a relationship with the firm (Garbarino & Johnson, 1999). Dwyer et al. (1987) are arguing that there are different stages in developing buyer–seller relationships. These relationship stages are: (1) awareness, (2) exploration, (3) expansion, (4) commitment and (5) dissolution (Dwyer et al., 1987). In addition to this, Dwyer et al. (1987) state that customers can be evolved through these relationship stages by activities of the firm. Garbarino and Jonhson (1999) are explaining that Jackson (1985) is arguing that the potential of evolving a buyer-seller relationship is fully based on the customer’s orientation towards the relationship with the firm. In addition to Dwyer et al. (1987), Garbarino and Johnson (1999)

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are describing that Macneil (1980) argues that a transactional relationship exists of minimal personal relationship with no engagement to future exchanges. In contrast to this, they (Garbarino & Jonhson, 1999) state that relational exchanges are characterized by

collaborative activities and reciprocal adaption to the relationship. Hence, Garbarino and Johnson (1999) are distinguishing two types of customers, the relational customer and the transactional customer.

Dwyer et al. (1987) are arguing that trust and commitment are the factors that make loyal customers in contradiction to single purchase customers. Trust stands for confidence and reliability of the firm, whereby commitment stands for the enduring willingness of psychological attachment to the firm (Garbarino & Johnson, 1999). Thereafter, Rust and Zahorik (1993) are arguing that overall satisfaction, which can be explained as the degree of satisfaction based on all experiences with the firm, is necessary for retention and is therefore key in relational marketing.

However, Garbarino and Jonhson (1999) found that between customers with high relational behaviours and customers with more transactional patterns there are significant differences in trust and commitment. According to Garbarino and Johnson (1999), trust and commitment do not play a significant role in the future intentions of the transactional customer. They

(Garbarino & Johnson, 1999) found that, the decision of transactional customers to repurchase is driven by the overall satisfaction, while the decision of relational customers is driven by trust and commitment. Hence, Garbarino and Johnson (1999) are arguing that satisfaction, trust and commitment play different roles in the prediction of future intentions for low and high relational customers. To achieve this trust, commitment and overall satisfaction in order to increase loyalty (Dwyer et al., 1987; Rust & Zahorik, 1993; Garbarino & Johnson, 1999) firms need to provide resources, opportunities and benefits that are superior to the offerings of alternative firms (Morgan & Hunt, 1994).

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2.1.1 Switching costs

Due to the Internet, markets become more transparent which may result in lower switching costs for customers. Customers can easily switch to products of competitors because they can shop anywhere and anytime. Besides that, customers have more information about product specifications, experiences of other customers and comparisons in pricing. Burnham et al. (2003) define switching costs as: “the onetime costs that customers associate with the process of switching from one provider to another”. Thereby, Burnham et al. (2003) propose in their study three types of switching costs: (1) Procedural switching costs, (2) Financial switching costs, and (3) Relational switching costs. Procedural switching costs are about the time and effort of customers to stay at their incumbent firm (Burnham et al., 2003). In addition to this, Financial switching costs involves the loss of financial resources for customers, and

Relational switching costs consists of losing psychological attachment of customers to the firm (Burnham et al., 2003).

According to Sharma (2007), transactional customers are more likely to switch than loyal customers. Besides, Reinartz and Kumar (2000) found that there is a strong positive customer lifetime-profitability relationship and so customer profits increase over time. Thereby they state that the costs of serving new customers are higher than serving existing customers (Reinartz & Kumar, 2000). In addition to this, Chaudhuri and Holbrook (2001) are stating that greater customer loyalty will result in higher market share, because of repurchases. Hence, it is important for firms to have loyal customers with low switching behaviour.

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2.2 Strategic Value Disciplines

Treacy and Wiersema (1993) are outlining three value discipline strategies that firms can pursue to become industry leaders and achieve competitive advantage, (1) Customer

Intimacy, (2) Operational Excellence and (3) Product Leadership. These value disciplines can also be seen as strategic performance capabilities that may lead to competitive advantage (Zack et al., 2009). Customer Intimacy stands for competing based on understanding, satisfying and retaining customers. In other words Customer Intimacy is about tailoring the offerings to precisely create value and match the demands of customers (Treacy & Wiersema, 1993). This tailoring of offerings and fit the demand of customers can be expensive, but firms who pursue a Customer Intimacy strategy are willing to invest and build customer loyalty for the long term (Treacy & Wiersema, 1993). These firms are not looking at the value of a single transaction but on the customer lifetime value to the firm (Treacy & Wiersema, 1993). To deliver the best fit of customer demands, firms with a Customer Intimacy strategy are highly skilled in segmenting customer groups to tailor advertising, merchandising and offerings (Treacy & Wiersema, 1993).

Operational Excellence is focussed on competing through efficiency and providing customers reliable products or services to competitive prices, delivered with minimum obstructions and difficulty (Treacy & Wiersema, 1993). The goal of firms who pursue an Operational

Excellence strategy is becoming industry leader in price and convenience (Treacy &

Wiersema, 1993). To become an industry leader by Operational Excellence firms are seeking ways to minimize overhead costs, reduce transaction costs, eliminate intermediate production steps and optimize their business processes (Treacy & Wiersema, 1993).

Firm who pursue a Product Leadership strategy are focussed on competing by product and service innovation (Treacy & Wiersema, 1993). Product Leadership is about making rival’s goods obsolete by offering superior products and services that enrich the usability and

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application for customers (Treacy & Wiersema, 1993). To continuously produce these superior products and services, firms must challenge themselves in three ways: (1) they must be creative, (2) commercialize ideas quickly and (3) pursue solutions for the problems of their latest product to sustain competitive advantage (Treacy & Wiersema, 1993). Fast reaction to situations that occur and avoiding bureaucracy are strengths of Product Leadership firms (Treacy & Wiersema, 1993).

2.3 Customer Knowledge Management

Grant (1996) states that knowledge is strategically the most significant resource of the firm. In addition to this statement, Grant (1996) also argues that the integration of knowledge, also known as Knowledge Management, is the primary role of firms and the essence of

organizational capability in sustaining competitive advantage in dynamically markets. Salomann et al. (2005) are explaining that Knowledge Management has the same goal as Customer Relationship Management: “the delivery of continuous improvement towards customers”. Initiatives to achieve this objective have been labelled to ‘Customer Knowledge Management’ (CKM) or ‘knowledge-enabled CRM’ (Salomann et al., 2005). Gibbert et al. (2002) are stating that Customer Knowledge becomes more and more accepted as resource that can be useful to support Research & Development. In addition to this, Darroch and McNaughton (2003) arguing that Customer Knowledge can also facilitate sensing of emerging markets, improve innovation and long-term customer relationships.

To come up with a definition of CKM, Rollins and Halinen (2005) are stating that Customer Knowledge Management is an integrated management approach that exists of integrating Knowledge Management into Customer Relationship Management processes. Stefanou et al. (2003) are supporting that Customer Relationship Management (CRM) is related to the discipline of Knowledge Management. This, because CRM enables the possibility to not only

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acquiring customer knowledge, but also use this knowledge in order to develop and maintain successful customer relationships (Bradshaw & Brash, 2001). According to Stefanou et al. (2003) Customer Relationship Management is about knowing your customer and using this knowledge. CRM gives firms the opportunity to customize the firm’s services and fully arrange their activities with a customer centric orientation, through the development of a sophisticated Customer Relationship Management system (Stefanou et al., 2003). However, Gibbert et al. (2002) are defining some differences between Customer Knowledge

Management and Customer Relationship Management. The biggest difference is that

Customer Knowledge Management is about knowledge from the customer, rather than about the customer (Gibbert et al., 2002).

Knowledge Management is the foundation of Customer Knowledge Management, but is fully focussing on the internal perspective of knowledge creating, sharing and leveraging between employees or companies (Gibbert et al., 2002). In contrast to this Customer Knowledge Management is focussing on the customer in the value creation process (Gibbert et al., 2002). Rollins and Halinen (2005) are complementing this by arguing that Customer Knowledge Management is both inside en outside the organisation.

Gibbert et al. (2002) are defining Customer Knowledge Management as “the process of gaining, sharing and expanding the knowledge residing in customers, to both customer and corporate benefit”. Gibbert et al. (2002) are also proposing five styles of Customer

Knowledge Management: (1) Prosumerism, (2) Team-based Co-learning, (3) Mutual Innovation, (4) Communities of creation and (5) Joint Intellectual Property.

Rollins and Halinen (2005) are supporting the definition of Gibbert et al. (2002) that

Customer Knowledge Management (CKM) is an ongoing process within a firm and between the firm and its customers of generating, disseminating and using customer knowledge. CKM

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is a learning process whereby knowledge exchange is essential and customers can help firms to mention the strengths and weaknesses (Hennestad, 1999).

To effectively implement Customer Knowledge Management, Rollins and Halinen (2005) are stating that firms should have a CKM competence. Firms who possess a CKM competence have the ability to implement customer information and knowledge into a firm’s CRM system (Rollins & Halinen, 2005).

Because Customer Knowledge Management in terms of interacting with customers is becoming more important to achieve competitive advantage and built a relationship with customers (Prahalad & Ramaswamy, 2004; Gibbert et al., 2002; Rollins & Halinen, 2005), Jayachandran et al. (2005) are conceptualizing relational information processes for

relationship marketing. They (Jayachandran et al., 2005) are distinguishing five processes with different scales: (1) Information reciprocity, which is focussing on reciprocal

communication between the firm and the customer, (2) Information capture, that is about the ongoing acquisition of customer information, (3) Information integration, which determines the efforts of the firm to bring the collected information on a customer basis together, (4) Information access, which is focussing on accessibility of the integrated customer data for relevant employees, and (5) Information use, which indicates to what extent a firm uses the customer information for their business processes. In other words, the relational information process of Jayachandran et al. (2005) conceptualizes the Customer Knowledge Management construct of an ongoing process of gaining/generating, sharing/disseminating, expanding and using customer knowledge (Gibbert et al., 2002; Rollins & Halinen, 2005).

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2.4 Interaction between Strategic Value Disciplines and Loyalty

Treacy and Wiersema (1993) are arguing that selecting the category of customers that firms will serve can be a result of the chosen value discipline strategy. They (Treacy & Wiersema, 1993) consider the selection of a value discipline strategy and customer segment as one single choice. Different customers have different value perceptions. One may define value within price and convenience while others may prefer high service and tailor-made offerings (Treacy & Wiersema, 1993). To achieve loyalty from customers in order to reduce switching

behaviour, firms need to built trust, commitment and overall satisfaction (Dwyer et al., 1987; Rust & Zahorik, 1993; Garbarino & Johnson, 1999). Morgan and Hunt (1994) are arguing that if overall satisfaction, trust and commitment are the main drivers for nurturing a relationship with customers, firms can develop satisfaction, trust and commitment by

providing resources, opportunities and benefits that are superior to the offerings of alternative firms.

By choosing one or two of the value discipline strategies, firms can serve superior offerings over competitors to meet the most valuable preferences of customers (Treacy & Wiersema, 1993). Important is that Treacy and Wiersema (1993) state that firms need to master two value disciplines or perform sufficient on the other two value disciplines for sustainable competitive advantage. Firms who execute a Customer Intimacy strategy will create loyalty by providing excellent service and match the demands of customers with tailored offerings, while Operational Excellence create loyalty by price and convenience (Treacy & Wiersema, 1993). In contrast to these two strategies, Product Leadership firms create loyalty by superior products in usability and/or application (Treacy & Wiersema, 1993). This study also connects the value discipline strategies to the types of switching costs defined by Burnham et al. (2003). As stated earlier, a firm who pursues a Customer Intimacy strategy is trying to build a sustainable relationship with the customer, so relational switching costs seem more important.

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Besides, customers of Operational Excellence firms will be more restrained by procedural or financial switching costs. Based on the existing literature about the strategic value disciplines of Treacy and Wiersema (1993) and customer loyalty, this study proposes three hypotheses. These hypotheses suggest that firms who score high on the items of the three strategic value disciplines have more loyal customers than firms who score low on these items. Since one firm can score high on multiple value disciplines, every individual value discipline has his own hypothesis.

H1a: Firms who pursue a Customer Intimacy strategy have more loyal customers. H1b: Firms who pursue an Operational Excellence strategy have more loyal customers. H1c: Firms who pursue a Product Leadership strategy have more loyal customers.

2.5 Interaction between CKM and Loyalty

Morgan and Hunt (1994) are arguing that if satisfaction, trust and commitment are the main drivers for nurturing a relationship with customers, firms can develop satisfaction, trust and commitment by some activities who enable firms to achieve sustainable competitive

advantage. One activity to develop trust and commitment is providing resources,

opportunities and benefits that are superior to the offerings of alternative firms (Morgan & Hunt, 1994). As stated earlier trust stands for confidence and reliability of the firm and commitment for the enduring willingness of psychological attachment to the firm (Garbarino & Johnson, 1999). So, to improve this confidence, reliability, willingness of psychological attachment and degree of satisfaction based on all experiences with the firm, firms need to provide resources, opportunities and benefits that are superior to the offerings of alternative firms (Morgan & Hunt, 1994).

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In order to provide these superior offerings, firms need to know their customers and meet or surpass the customer’s needs and wants. Hence, Customer Knowledge Management is an important activity whereby customers can mention the strengths and weaknesses of the offerings from these firms (Hennestad, 1999). By anticipating to the mentioned strengths and weaknesses firms can provide superior offerings in all terms of products, services, efficiency and customization. For instance, firms can use Customer Knowledge Management for new product development, aligning the business processes, or make personalized offerings to customers.

This customer centric view is also supported by the study of Payne and Frow (2006) who are arguing that applying individual customer data in the daily business processes contributes to the customer relationship. As said earlier, firms should not only focus on their products, but on fulfilling the customer needs (Levitt, 1960). Based on the existing literature about Customer Knowledge Management and loyalty of customers, this study proposes the following hypothesis:

H2: Customer Knowledge Management has a positive relationship with loyalty from customers.

2.6 Interaction between Strategic Value Disciplines and CKM

The three value discipline strategies of Treacy and Wiersema (1993) have all different perspectives of becoming industry leader and achieving sustainable competitive advantage. Customer Intimacy is focussing on long-term relationships by a customer centric point of view, whereby Operational Excellence and Product Leadership are more internally focussed (Treacy & Wiersema, 1993). So, firms who pursue a Customer Intimacy strategy will interact more with customers than firms who pursue an Operational Excellence or Product Leadership

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strategy. However, Prahalad and Ramaswamy (2004) are stating that a new approach of value creation is the future of competition. This new approach brings consumers and companies together whereby consumers are engaged in the process of defining and creating value (Prahalad & Ramaswamy, 2004). For companies who pursue a Product Leadership strategy, consumers can interact in the new product development process. The development of

customer knowledge is the main influence on new product success (Cooper & Kleinschmidt, 1995). This development of customer knowledge should be a learning process acknowledged by three characteristics: (1) it is running in the pre launch stages of new product development, (2) every new stage in the product development process arises from customer interaction, (3) there is trial and error of organizational learning about customer preferences (Joshi & Sharma, 2004). Joshi and Sharma (2004) found support in their study that customer knowledge

development is positively related to new product performance. In other words customer knowledge management is valuable to firms who execute a Product Leadership strategy. In addition to this, Langerak and Verhoef (2003) representing the effect of Customer

Relationship Management for the other two value discipline strategies, Customer Intimacy and Operational Excellence. Given the theoretical foundations that Customer Knowledge Management is an integrated approach of Knowledge Management into Customer

Relationship Management (Rollins & Halinen, 2005), the study of Langerak and Verhoef (2003) gives more understanding about the effect of Customer Knowledge Management by pursuing a Customer Intimacy or Operational Excellence strategy. Langerak and Verhoef (2003) found that CRM contributes to firms with an Operational Excellence strategy because it serves the daily business processes and activities to become more efficient. This better performance of an Operational Excellence strategy by Customer Knowledge Management can result in loyal customers who perceive price and convenience as valuable. Firms who pursue a Customer intimacy strategy also benefit form CRM, and so Customer Knowledge

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Management, because it contributes to a long-term relationship with customers (Langerak & Verhoef, 2003). This better performance of a Customer Intimacy Strategy by Customer Knowledge Management can result in loyal customers who perceive tailored advertising, merchandising and offerings as valuable. Darroch and McNaughton (2003) are supporting these findings and argue that Customer Knowledge can increase long-term customer relationships. The reason that Customer Knowledge can increase long-term customer relationships is because it facilitates suiting the customer requirements (Darroch and

McNaughton, 2003). Based on the existing literature about the strategic value disciplines of Treacy and Wiersema (1993), Customer Knowledge Management and loyalty of customers this study proposes the following hypotheses:

H3: Customer Knowledge Management moderates the positive relationship between the value discipline strategies and customer loyalty, so that this relationship is stronger for higher values of Customer Knowledge Management.

H4a: Firms who pursue a Customer Intimacy strategy use Customer Knowledge Management to provide personal offerings to customers.

H4b: Firms who pursue an Operational Excellence strategy use Customer Knowledge Management to align the business processes.

H4c: Firms who pursue a Product Leadership strategy use Customer Knowledge Management for new product development.

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H2 2.7 Theoretical Framework H4c H4b H4a H3 Customer Intimacy Loyalty Customer Knowledge Management H1a Operational Excellence Product Leadership H1b H1c

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3. Methodology

In the following section the research design is described in order to find an answer on the research question: “To what extent can Customer Knowledge Management increase the loyalty of customers for all value discipline strategies?”. This section consists of the strengths and weaknesses of our research design, sample characteristics and data collection, and the measurement scale of the different variables.

3.1 Research strategy

This study, which can be termed as an explanatory research (Saunders et al., 2009), establishes a causal relationship between the strategic value disciplines of Treacy and Wiersema (1993), Customer Knowledge Management and loyalty. The earlier mentioned hypotheses are formulated to examine the relationship between the independent variables the strategic value disciplines and dependent variable loyalty, moderated by customer knowledge management. The research design to answer the research question consists of a

cross-sectional survey research method, which is usually associated with the deductive approach (Saunders et al., 2009). This method is characterized by a structured collection of data from a sizeable population (Saunders et el., 2009). The collection of data is done by use of a

standardized questionnaire (see Appendix A). This survey strategy allows us to statistically analyse our data and produce relationships between the variables (Gable, 1994). Using a survey strategy also gives generalizable statements for the whole population about the research objective when a representative sample of organizations is studied (Gable, 1994). A weakness of the survey strategy is that it is less wide-ranging than data collected by other research strategies, because there is a limitation in questions that the questionnaire can contain to get goodwill from respondents (Saunders et al., 2009). Thereby, the survey strategy often

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consists of a cross-sectional study whereby the data is collected at one single point in time (Gable, 1994). Besides, a survey questionnaire is inflexible in adapting to discoveries made during data collection (Gable, 1994). When the questionnaire is sent, there is no possibility to add crucial items or change ambiguous or misunderstood questions (Gable, 1994). Therefore, the questionnaire is pilot tested to assess the validity. Six specialists in the field of marketing and strategy, who are employees of marketing agency Kega, completed this pilot test.

Respondents are asked to comment and provide recommendations and suggestions for any adjustments to the questionnaire. Thereby, respondents need to complete an additional

checklist about the clarity of instructions and questions, time need to complete the survey, and layout.

The survey will be self-administrated and internet-mediated which is very efficient, low cost and there is no data entry after the survey. However, disadvantages of this research design are the barrier of spam filters, computer literacy, crashes and consulting respondents.

To eliminate participant errors and participant bias, control variables about the industry and function are added and anonymity is guaranteed.

3.2 Sample characteristics and data collection

Executing a value discipline strategy or implementing customer knowledge management is independent from the industry in which a firm competes. Therefore, this study is not constrained to specific industries, but is fully focussing on the Dutch market. Based on

specialists in the field of marketing and strategy from marketing agency Kega, key informants for this study are Marketing- or Customer Relationship Managers and executives.

This study is conducted by a self-administrated and internet-mediated survey (Appendix A). So, all measures including the performance measure loyalty were based on the respondent’s perception. The final survey will be launched on the Internet and sent to 2073 e-mail

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addresses of Marketing- or Customer Relationship Managers and executives. These

Marketing- or Customer Relationship Managers and executives are representing their firms, which are competing in different Business to Consumer industries. The different industries are: Retail, Fast Moving Consumer Goods, Durable Goods, Online pure players, Publishing, and Service companies. The Relationship Management System of Kega provides these e-mail addresses to distribute the survey. This Relationship Management System is built to execute the core business (developing joint promotions) of Kega’s department Promotional Partners. Table 1 gives an overview of the sample characteristics.

Table 1: Sample characteristics

Frequency Percent Industry

Retail 61 37

Fast Moving Consumer Goods 19 11,5

Durable goods 12 7,3

Online pure players 5 3 Publishing companies 11 6,7 Service companies 57 34,5

Total 165 100

Number of employees

0 -‐ 10 16 9,7 11 -‐ 50 39 23,6 51 -‐ 250 47 28,5 251 -‐ 1000 26 15,8 > 1001 37 22,4 Total 165 100

Purchase frequency of customers

More times a week 34 20,6

Weekly ₁₄ _8,5

More times a month 17 10,3

Monthly ₁₆ _9,7

More times a year 38 23

Annually ₄₆ _27,9

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3.3 Measures

Loyalty in this study is proposed as determinant to lower switching behaviour of customers. Hence, the construct loyalty will be measured in terms of total retention indexes in reference of all purchases of the firm. This index will be provided by the respondent.

To measure the value discipline strategy firms pursue, this study makes use of the

measurement scale for organizational performance by value discipline of Zack et al. (2009). They (Zack et al., 2009) measure the degree of a Customer Intimacy, Operational Excellence or Product Leadership strategy by asking respondents to rank their organizations performance relative to other organizations in the industry on a five-point likert scale (one of the lowest, below average, average, above average, one of the highest). For Customer Intimacy, respondents need to rank their organization in terms of customer satisfaction and retention. Besides, to measure the degree of Operational Excellence respondents rank their organisation in terms of operating costs. Important for this measurement is that operating costs are reverse coded. Finally, Product Leadership should be ranked by product innovation and quality. Important for this study is that this measurement gives the opportunity to score high on more than one strategy. This fits the theory of Treacy and Wiersema (1993) that firms need to master two value disciplines or perform sufficient on the other two value disciplines for sustainable competitive advantage.

To measure the degree of implementing Customer Knowledge Management this study makes use of the measurement scale for relational information processes of Jayachandran et al. (2005). As stated earlier in the Literature Review this measurement scale for relational information processes of Jayachandran et al. (2005) conceptualizes the Customer Knowledge Management construct of an ongoing process of gaining/generating, sharing/disseminating,

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expanding and using customer knowledge (Gibbert et al., 2002; Rollins & Halinen, 2005). This measurement scale exists of 5 practises with different items. The first practise is Information Reciprocity, which indicates to what degree a firm communicates with the customers. Next to that, Information Capture measures the performance of collecting customer information. The third practise, Information Integration, gives insights to what extent a firm integrates the collected information by different channels and individualizes customers. Thereafter, Information Access measures to what degree employees can access and use the collected information. Finally, Information Use gives more understanding about the performance of using the information for different purposes. Thereby, this study added some items based on the literature review. For the measurement scale Information Capture, following the purchase process of individual customers is added. For Information Use, the use of customer information to make personal offerings, align the business processes, and new product development are added. Thereby, the item efficiency is added to the measurement scale for Operational Excellence.

This study differs from Jayachandran et al. (2005) in using a five-point likert scale unlike a seven-point likert scale. However, Dawes (2008) shows that there is no significant difference between using five- and seven-point likert scales.

3.4 Analysis of data

To test the hypotheses and answer the research question the data must meet different criteria. In the following section the data is checked for normality, tested for reliability and validity.

3.4.1 Data cleaning

The data conducted by online survey software Qualtrics is imported in SPSS. With the statistical program SPSS the data is prepared for the analysis. First of all, the incomplete and

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test surveys are deleted, which resulted in a total of 165 surveys. These final 165 surveys are 68,5% of all 241 originally registered surveys. Thereby the data has been analysed with a boxplot in order to find outliers in the dataset. This study reduces the effect of the outliers by transforming the data to computed variables (see chapter 3.4.3 Computing Variables)(Field, 2009).

3.4.2 Recoding counter-indicative items

When respondents perceive the score of their organisation in terms of Operating Costs as bad they indicate the item as “one of the highest”. To analyse the data properly compared with the other items and examine whether firms are pursuing an Operational Excellence strategy, the item Operating Costs is recoded to counter-indicative items. So, all scores of 5 become 1, 4 become 2 and reversed.

3.4.3 Computing variables

To reduce the effect of the outliers and interpret the items of Customer Knowledge

Management and the Strategic Value Disciplines, the scale means are computed by averaging the underlying items of the construct. The Customer Knowledge Management items are: Information Reciprocity, Information Capture, Information Integration, Information Access, and Information Use. After that, these scale means are computed to the item CKM to test the hypotheses. To interpret the strategic value disciplines, customer satisfaction and retention are computed to Customer Intimacy, operating costs and efficiency are computed to Operational Excellence, and product innovation and quality are computed to Product Leadership.

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3.4.4 Checking normality

To test whether the data is normally distributed, in order to analyse the dataset properly, the frequency distribution gives more information (Appendix B). The central limit theorem helps to indicate if the sampling distribution is normal based on the normality of the sample data (Field, 2009). Thereby Field (2009) states that the assumption of normality is important for regression analysis. First of all, this study looks for normality visually by use of histograms. The data is normally distributed when it shows a bell-shaped curve in the histogram (Field, 2009). Because the normality can deviate, this study looks at the values that quantify aspects of a distribution and compares these values to a normal distribution (Field, 2009). These values that quantify aspects of a distribution are skewness and kurtosis (Field, 2009). In a normal distribution the values of skewness and kurtosis are zero, so the higher the values of skewness and kurtosis are away from zero, the higher the chance that the sample data are not normally distributed (Field, 2009).

By looking at the histograms of the dataset it can be assumed that for the variables Customer Intimacy, Operational Excellence, Product Leadership and Customer Knowledge

Management the data looks normally distributed (Appendix B). However, the data of the variable Retention index seems to lack pointyness. The data of the variable Retention index is a bit positively skewed and has a platykurtic distribution.

Due to subjectivity of the histograms, this study is also checking the quantifiable values for distribution (Table 8, Appendix B). Table 8 (Appendix B) shows that all values of skewness and kurtosis deviate from zero. However, all values of skewness still seem to fall in the acceptable range to claim approximate symmetry of a distribution for values between [-1, 1]. Also the values of kurtosis for the variables Customer Intimacy, Product Leadership and Customer Knowledge Management fall in the same acceptable range to claim approximate symmetry of a distribution. In contrast to this, the values of kurtosis for Retention index and

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Operational Excellence are not falling in this acceptable range. The variable Retention index has a negative kurtosis and is a so-called platykurtic (flat) distribution. The variable

Operational Excellence has a positive kurtosis and is a so-called leptokurtic (pointy) distribution.

3.4.5 Testing assumptions

This study also conducts a Kolmogorov-Smirnov and Shapiro-Wilk test (K-S test) to indicate whether the distribution is normal. By looking at table 9 (Appendix B) it can be stated that all variables are highly significant (p < .001) with an exception of Customer Knowledge

Management. The variables Retention Index D(165) = 0.10, p < .001, Customer Intimacy D(165) = 0.18, p < .001, Operational Excellence D(165) = 0.29, p < .001, and Product Leadership D(165) = 0.17, p < .001, are all significantly non-normal. However, the variable Customer Knowledge Management D(165) = 0.06, p > .05 can be recognized as normal. The K-S test confirms the deviations discovered in the previous descriptive statistics. Though, Field (2009) states that for large samples the significance tests can be conducted but are not recommended, because these tests can be significant even when the scores are only slightly different from a normal distribution. Hence for this study (N = 165), checking the shape of the distribution by histograms and inspecting the descriptive statistics skewness and kurtosis makes more sense.

Next to the assumption of normal distribution, this study is checking the assumption of homogeneity of variance for the different industries in which respondents operate, by means of Levene’s test. Because respondents operate in six different industries it is important to know whether the variables are measured in the same way. So the variance of the variables should be stable or the same for each industry (Field, 2009). Table 2 shows an overview of the group size for the different industries. The assumption of homogeneity is violated if

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Levene’s test is significant at p < .05. For the variables Customer Intimacy F(5, 159) = 1.43, ns, Product Leadership F(5, 159) = 0.65, ns, and Customer Knowledge Management F(5, 159) = 0.80, ns, the variances are equal for the different industries. For the variables Retention Index, F(5, 159) = 2.67, p < .05, and Operational Excellence F(5, 159) = 3.22, p < .05 the variances are significantly different in the six groups (Table 10, Appendix B).

Table 2: Group size

Industry Cases

Valid

N

Retail 61

Fast Moving Consumer Goods 19 Durable goods 12 Online pure players 5 Publishing companies 11 Service companies 57

3.4.6 Reliability analysis

To know whether the questionnaire is able to come up with consistent findings across different conditions the reliability of the measurements can be tested through a reliability analysis (Field, 2009). This study makes use of the Cronbach’s Alpha (α) to measure the scale reliability. To measure the reliability of the questionnaire Field (2009) recommends applying Cronbach’s Alpha individually to the multi-item scales of the questionnaire. Hence, this study runs separate reliability analysis for all scales. According to Field (2009), the data of all variables should correlate with the total in a reliable scale. Except for the variable Operational Excellence, all data have item-total correlations above .30, which can be stated as good (Field, 2009). Table 3 shows the values of Cronbach’s Alpha for all measures. The measures

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Information Use, Customer Intimacy and Customer Knowledge Management have all higher reliabilities than the recommended lower threshold of .70 for Cronbach’s Alpha (Field, 2009). The measures Operational Excellence and Product Leadership have lower values for

Cronbach’s Alpha than the recommended threshold of Cronbach’s α = .70. Compared to the other scales, the measurement scales Operational Excellence and Product Leadership have fewer items, which can affect the reduced reliability (Field, 2009). Finally to improve reliability this study looks at the values of Cronbach’s Alpha if Item is Deleted. Only the measures Information Capture and Information Use can be slightly improved by deleting items. However, due to this minimum improvement and existing reliability compared to the loss of data this study decided to not delete any items. The original scales of the

measurements Information Capture and Information Use consisted of 5 and 7 items (Jayachandran et al., 2005). Deleting the items added by this study does not improve the reliability of these measures.

Table 3: Reliability analysis

Measures Cronbach's Alpha N of Items

Retention Index -‐ 1 Information Reciprocity 0,813 4 Information Capture 0,827 6 Information Integration 0,905 4 Information Access 0,915 4 Information Use 0,942 10 Customer Intimacy 0,728 2 Operational Excellence -‐0,612 2 Product Leadership 0,54 2 CKM 0,867 5

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3.4.7 Validity analysis

To determine whether the measurement error is kept to a minimum one can test the validity of the measurement scale. Validity refers to whether an instrument actually measures what it sets out to measure (Field, 2009). In addition to this, Field (2009) states that it is a necessary however not a sufficient condition of an instrument. Validity consists of three items, content validity, criterion validity and construct validity (Field, 2009). Content validity concerns the degree to which individual items represent the full range of the construct (Field, 2009). This study provided content validity by a literature review and pilot test under 6 specialists in the marketing and strategy field. Besides, criterion validity is about how well one item predicts an outcome based on information of other items (Field, 2009). To test criterion validity, this study discusses the correlations in a subsequent chapter. To test the construct validity of the different scale items, this study uses a factor analysis (also named principal component analysis)(Table 4). For each item the factor loadings are provided. Thereby separate factor analyses are conducted for each scale. To determine the reliability of this factor analysis, this study uses the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (Field, 2009). All KMO values for the Customer Knowledge Management construct are above .70, which are considered good (Field, 2009). The KMO values for the strategic value disciplines are above .05, which are considered mediocre (Field, 2009). All loadings of the scale items are highly significant (p < .01). Thereby all the loadings of the individual scale items are higher than the recommended lower threshold of .40 (Field, 2009).

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Table 4: Factor analysis results

Items whithin constructs _loadingsFactor

Information Reciprocity*

1. We enable our customers to have interactive communications with us. 0,79 2. We provide our customers with multiple ways to contact the organization. 0,80 3. We focus in communicating periodically with our customers. 0,79 4. We maintain regular contact with our customers. 0,84

Information Capture*

1. We collect customer information on an ongoing basis. 0,84 2. We capture customer information form internal sources within the organization. 0,81 3. We collect customer information using external sources (such as market research agencies,

syndicated data sources, and consultants). 0,52 4. The information collected from customers is updated in a timely fashion 0,84 5. We use customer interactions to collect information 0,78 6. We follow the purchase process of individual customers 0,62

Information Integration*

1. We integrate customer information from the various functions that interact with customers

(such as marketing, sales, and customer service). 0,92 2. We integrate internal customer information with customer information from external sources. 0,85 3. We integrate customer information from different communication channels (such as telephone,

mail, e-‐mail, the Internet, fax, and personal contact). 0,87 4. We merge information collected from various sources for each customer. 0,89

Information Access*

1. In our organization, relevant employees find it easy to access required customer information _0,89 2. In our organization, relevant employees can access required customer information even when

other departments/functional areas have collected it. 0,90 3. In our organization, relevant employees always have access to up-‐to-‐date customer information. _0,92 4. In our organization, relevant employees are provided the information required to manage

customer relationships. 0,86

Information Use*

1. We use customer information to develop customer profiles. 0,81 2. We use customer information to segment markets. 0,86 3. We use customer information to assess customer retention behaviour. 0,88 4. We use customer information to identify appropriate channels to reach customers. 0,87 5. We use customer information to identify our best customers. 0,87 6. We use customer information to assess the lifetime value of our customers. 0,80 7. We use customer information to make personal offerings. 0,67 8. We use customer information to align our business processes. 0,76 9. We use customer information to customize our offers. 0,85 10. We use customer information for new product development. 0,73

Customer Intimacy**

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the industry

2. Rank your organisations performance in customer retention relative to other organizations in the

industry 0,89

Operational Excellence**

1. Rank your organisations performance in operating costs relative to other organizations in the

industry -‐0,79

2. Rank your organisations performance in efficiency relative to other organizations in the industry 0,79

Product Leadership**

1. Rank your organisations performance in product innovation relative to other organizations in the

industry 0,83

2. Rank your organisations performance in product quality relative to other organizations in the

industry 0,83

Loyalty

1. What is to your product/organization the percentage of repurchases compared with overall

sales? -‐

* 5-‐Likert scale: 1 = not at all, 5 = excellent

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3.5 Correlations

Given the results of the tests for the assumption of normal distribution the variables can, by the underlying condition of near-normal distribution, be considered as normal distributed. Besides the data is measured at the interval level. Therefore, this study runs a bivariate correlation analysis for the preliminary investigation of the pattern of relationships, by use of the parametric Pearson’s correlation coefficient (Field, 2009). In Table 5 the means, standard deviations and correlations can be found. Significant positive correlations between the main constructs Loyalty and Customer Intimacy, Loyalty and Product Leadership, Loyalty and Customer Knowledge Management, Customer Intimacy and Customer Knowledge Management, and Product Leadership and Customer Knowledge Management can be recognized.

Table 5: Means, Standard Deviations, Correlations

Correlations M SD 1 2 3 4 5 6 7 8 9 10 1. Retention index 48,32 25,18 1 0,065 ,188* ,236** ,229** ,163* ,208** 0,087 ,192* ,221** 2. InfoReciprocity 3,65 0,79 0,065 1 ,521** ,343** ,397** ,462** ,243** 0,061 0,119 ,655** 3. InfoCapture 3,05 0,80 ,188* ,521** 1 ,650** ,568** ,715** ,330** -‐0,026 ,157* ,848** 4. InfoIntegration 2,59 0,92 ,236** ,343** ,650** 1 ,684** ,663** ,332** 0,02 ,261** ,838** 5. InfoAccess 2,94 0,93 ,229** ,397** ,568** ,684** 1 ,636** ,417** 0,089 ,280** ,825** 6. InfoUse 2,85 0,93 ,163* ,462** ,715** ,663** ,636** 1 ,306** 0,005 ,244** ,867** 7. CustomerIntimacy 3,65 0,70 ,208** ,243** ,330** ,332** ,417** ,306** 1 -‐0,015 ,265** ,405** 8. OperationalExcellence 3,06 0,48 0,087 0,061 -‐0,026 0,02 0,089 0,005 -‐0,015 1 0,071 0,038 9. ProductLeadership 3,67 0,71 ,192* 0,119 ,157* ,261** ,280** ,244** ,265** 0,071 1 ,267** 10. CKM 3,02 0,71 ,221** ,655** ,848** ,838** ,825** ,867** ,405** 0,038 ,267** 1 * Correlation is significant at the 0.05 level (2-‐tailed).

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

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4. Results

The principal objective of this study is to examine to what extent a customer centric strategy, based on customer knowledge, can impact customers to become more loyal towards firms who pursue one or more of the three value discipline strategies of Treacy and Wiersema (1993), in order to decrease the switching behaviour of these customers. The model testing process constitutes of four parts: (1) the dimension of Loyalty of customers as outcome variable with the strategic value disciplines as predictors, (2) the dimension of Loyalty of customers as outcome variable with Customer Knowledge Management as predictor variable, (3) the moderation effect of Customer Knowledge Management on the association between the strategic value disciplines and Loyalty of customers, and (4) the dimension of different Customer Knowledge Management practices as outcome variable with the strategic value disciplines as predictors.

To examine whether the constructs, which exist of different items, are cohesive this study conducts a reliability analysis. As stated earlier in the Methodology section, all multi-item scales are acceptable based on Cronbach’s Aplha, except for the variable Operational Excellence.

4.1 Hypothesis testing

The outcome variable Loyalty of customers is continuous even as the predictor variables, which are represented by the strategic value disciplines and Customer Knowledge

Management. To test the proposed hypotheses, this study conducts simple and multiple regression analyses. Table 7 gives an overview of all hypotheses and results. First of all the individual relationship between the predictor variables (1) Customer Intimacy, (2)

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customers in terms of retention is discussed. Next to that, the individual relationship between predictor variable Customer Knowledge Management and outcome variable Loyalty of customers in terms of retention is discussed. Thereafter, this study discusses the moderator effect of Customer Knowledge Management on the association between the strategic value disciplines and Loyalty of customers in terms of retention. Finally, this study discusses the relationship between the strategic value disciplines (1) Customer Intimacy, (2) Operational Excellence, (3) Product Leadership, and three different Customer Knowledge Management practises.

4.1.1 Strategic value disciplines – Loyalty

To test hypotheses 1.a, 1.b, and 1.c a simple linear regression analysis for each predictor variable is conducted (Appendix C). Based on the simple linear regression analysis it can be recognized that for the strategic value disciplines Customer Intimacy and Product Leadership the regression models overall predict loyalty of customers significantly well (p < .05). For the strategic value discipline Operational Excellence this study does not find significant evidence that this variable predicts loyalty of customers. Therefore H1.b is not supported.

Concerning the strategic value disciplines Customer Intimacy and Product Leadership, the R2

explains between > 3 % and < 5% of variance in the outcome variable Loyalty of customers. To determine the strength of the relationship between the predictor value and the outcome value, this study uses the (unstandardized) coefficient B. Based on the (unstandardized) coefficient B it can be recognized that there is a positive relationship between the predictor variables Customer Intimacy and Product Leadership, and outcome variable Loyalty of customers. Both predictor variables have a significant relationship (p < .05) with the outcome variable. In addition to this, the standardized coefficient Beta of Customer Intimacy indicates a significant effect on Loyalty of customers (β = .21, p < .01). Besides, this predictor variable

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accounts for 4.3 % of the variation in Loyalty of customers. The standardized coefficient Beta of Product Leadership also indicates a significant effect on Loyalty of customers (β = .19, p < .05). Thereby, the predictor variable Product Leadership accounts for 3.7 % of the variation in Loyalty of customers. Hence, based on the simple linear regression analysis it can be

recognized that hypotheses 1.a and 1.c find support. Although the effect is significant, there is room for other predictor variables that have influence on loyalty of customers that are not embodied in this study.

4.1.2 Customer Knowledge Management – Loyalty

To test hypotheses 2 a simple linear regression analysis for the predictor variable Customer Knowledge Management and the outcome variable Loyalty is conducted (Appendix C). Based on the simple linear regression analysis it can be recognized that for Customer Knowledge Management the regression model predicts the loyalty of customers significantly well (p <

.01). When looking at the value of R2, it can be recognized that Customer Knowledge

Management explains 4.9% of the variation in the outcome variable Loyalty of customers. As mentioned earlier, to determine the strength of the relationship between the predictor value and the outcome value, this study uses the (unstandardized) coefficient B. Based on the (unstandardized) coefficient B it can be recognized that there is a positive relationship between the predictor variable Customer Knowledge Management and outcome variable Loyalty of customers. The predictor variable Customer Knowledge Management has a significant relationship with the outcome variable (p < .01). Besides, the standardized coefficient Beta of Customer Knowledge Management indicates a significant effect on Loyalty of customers (β = .22, p < .01).

However, there is still a lot of variance for the outcome variable Loyalty of customers that cannot be explained by the single predictor variable Customer Knowledge Management.