, AND THEIR INFLUENCE ON CUSTOMER AND ORGANIZATIONAL PERFORMANCE T HE EFFECT OF ORGANIZATIONAL CULTURE AND CUSTOMER METRICS ON CUSTOMER CENTRICITY

Sicco Hempenius

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The effect of organizational culture and customer metrics

on customer centricity, and their influence on customer

and organizational performance

Name Student: Sicco A. Hempenius Student Number: s1811452

E-mail: s.a.hempenius@student.rug.nl

Address: Tuinbouwdwarstraat 10a, 9717 HV Groningen Telephone: +31 6 21943465

University: University of Groningen

Faculty: Faculty of Economics and Business Department: Marketing

Module: Master Thesis Marketing Management 1st supervisor: Dr. J.C. Hoekstra

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M

ANAGEMENT

S

UMMARY

This study empirically examines the effect of organizational culture and customer metrics on customer centricity, and their influence on customer and firm performance. In order to do so, this study takes the model of Shah et al. (2006) as a starting point, but focuses only on organizational culture and customer metrics. Customer metrics is divided into ‘measurement of customer metrics’ and ‘having objectives in terms of customer metrics’. The antecedents of customer centricity (e.g. organizational culture, measurement of customer metrics and objectives in terms of customer metrics) are expected, and hypothesized, to positively influence customer centricity, customer performance and organizational performance. Subsequently, customer centricity is expected to positively influence customer and organizational performance. Lastly, customer performance is expected to positively influence organizational performance.

In order to test these hypotheses, an online survey was developed, consisting of statements for all variables of the study. The control variables firm size, type of industry, B2B/B2C, competitive intensity and environmental dynamism were added as well. Where possible, existing measurement scales were adopted. In other cases, existing scales were modified or developed on the basis of literature. The data collection was done through the network of the PvKO, who invited Dutch marketing managers to contribute to this study. This resulted in 162 respondents. In order to analyze the data, linear regression analysis was performed.

The results show that organizational culture has a significant positive effect on customer centricity and customer performance, but no effect on organizational performance. The measurement of customer metrics does not contribute to customer centricity, but has a positive effect on customer and organizational performance. Having objectives in terms of customer metrics does not have an influence on customer centricity, nor customer or organizational performance. Also, no significant relation was found between customer centricity and customer and organizational performance. Lastly, this study found a strong significant relationship between customer performance and organizational performance.

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focus on improving the customer performance, since this study revealed that customer performance is a decent predictor for organizational performance.

Keywords: customer centricity, customer performance, organizational performance, organizational

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P

REFACE

With handing in this thesis, officially an end comes to my student life. In six and a halve years I have graduated a Bachelor in International Business & Management, went on exchange in Mexico, and during my Master Marketing Management I conducted market research in South Africa with IBR (EBF). Besides, I gained experience in boards and committees, followed courses in many fields of study, and developed valuable skills which make me, as they say, ready for a career.

Above all, my student life at the University of Groningen has resulted in one of the nicest times of my life so far, in which I have laid a solid foundation for the rest of my life. In the first year, I had doubts about my own capabilities, and almost made the switch to HBO. After all, I’m proud and glad that I have continued and succeeded. Therefore I would like to thank my parents, Lieuwe and Henny, which always supported me, and above all gave me the freedom to make my own decisions. This probably resulted in late graduation, but with a mind full of great memories and experiences, which I will never forget and regret. Secondly, I would to thank my friends, which made studying easier and more joyful. Thirdly, I would like to thank Tomas Geerts and Lisette Klaver for the pleasant time and constructive feedback during the period of writing this thesis. Fourth, I would like to thank Dr. Janny Hoekstra, for the useful and pleasant feedback sessions and guidance during this research. Lastly, I would like to thank the members of the Platform voor Klantgericht Ondernemen (PvKO) which gave us the opportunity to write a thesis for them. Besides, their input and way of data collection has certainly contributed enormously to this study.

Kind regards,

Sicco Hempenius

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T

ABLE OF

C

ONTENTS

2.3 Customer & Organizational Performance ... 11

2.4 Organizational Culture ... 13

2.5 Customer Metrics ... 15

3. Methodology ... 18

3.1 Data Collection and Sample Characteristics ... 18

3.2 Measurement of Constructs ... 19

3.3 Method of Analysis ... 22

4. Results ... 24

4.1 Model Fit ... 24

4.2 Results Customer Centricity Model ... 25

4.3 Results Customer Performance Model ... 26

4.4 Results Organizational Performance Model ... 26

4.5 Mediation Analyses ... 26

4.6 Summary of Results ... 30

5. Discussion, Conclusion, Limitations & Implications ... 31

5.1 Discussion & Conclusion ... 31

5.2 Limitations & Future research ... 34

5.3 Managerial Implications... 35

References ... 36

Appendices... 41

Appendix I – Descriptives of Sample ... 41

Appendix II - Questionnaire ... 43

Appendix III – Measurement of Constructs ... 49

Appendix IV – Correlations Table ... 51

Appendix V – Regression Results Control variables ... 52

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1. I

NTRODUCTION

For many years, the concept of customer centricity has been high on the agenda for many firms. In the 1950s, Peter Drucker was the first one to stress its importance, by stating “it is the customer who determines what a business is, what it produces, and whether it will prosper.” However, until recently, the customer centricity concept was “no more than a business philosophy or an ideal policy statement” (Gummesson, 2008; Lamberti, 2013). This business philosophy has turned into a marketing strategy nowadays and customer centric organizations are no longer serving the mass markets, but focus on market segments, and preferably the individual customer (Sheth et al., 2000). Thereby, a customer centric focus of an organization is a prerequisite in order to remain competitive (Thalmann & Brettel, 2012).

The importance of customer centricity is indisputable, and therefore in most annual reports organizations claim to be customer centric, while in fact behaving product centric (Galbraith, 2005). For many organizations it is too much of a struggle to transform from a product centric to customer centric organization. One of the reasons that firms struggle with being customer centric, might be that a comprehensive instrument for measuring the degree to which an organization is customer-centric, is lacking. This study aims at developing such an instrument, whereby the antecedents and consequences of customer centricity will be empirically assessed.

Shah et al. (2006) provide a general road map for firms to understand and overcome the key challenges in order to achieve customer centricity. Their model describes organizational culture, financial and performance metrics, structure and processes as key drivers, which can either help or hinder a firm become customer centric. Besides, Shah et al. (2006) state that customer centricity leads to superior customer and organizational performance. This model is used as a reference point for this study, whereby the main focus is on the influence of organizational culture and financial metrics on the degree of customer centricity of organizations, and the influence on customer- and organizational performance. 1

When focusing on organizational culture, much literature discusses the central norms and values within an organization which typify a specific organizational culture (Desphandé & Webster, 1989 and Flamholtz, 2001). However, this study’s objective is not to discuss organizational cultures, but to identify the characteristics of customer centric organizational cultures, and how these might lead to, or have an effect on the degree of customer centricity within the organization, and simultaneously to better customer and organizational performance. Several empirical studies (e.g. Jayachandran, 2005;

1

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Peltier et al., 2013; Peelen et al., 2009 and Iriana et al., 2013) have linked organizational culture and Customer Relationship Management (CRM) (success) and its influence on customer centricity and customer- and organizational performance. This seems to be an interesting field of study, and simultaneously a decent link with the second antecedent of customer centricity to be discussed in this paper, namely customer metrics, which are in turn outcomes of CRM. According Reimann et al., (2010) organizations who want to become more customer centric have to implement CRM systems, in or order to get to know their customers. Besides, according to Shah et al, (2006) “customer metrics are not only important in motivating individual employees to be more customer centric, they also are useful in helping marketing managers measure the financial implications of their decision making.”

In addition, we will also measure if organizations have specific objectives in terms of customer metrics, since specific goal setting influences task performance (Locke et al., 1981), and hence we expect a positive relation among the degree of customer centricity and customer and organizational performance. Therefore, in order to determine whether or not an organization is behaving customer centric, and to make the results of a customer centric organization visible, the following research question is formulated:

‘Does organizational culture and measurement and objectives in terms of customer metrics influence the degree of customer centricity and do they lead to better customer- and organizational

performance?’

In order to answer this question, scales are developed for every construct of the Shah et al. (2006) model, and combined in a questionnaire. Subsequently, around 4000 marketing managers of Dutch firms have received the survey through a database of the PvKO. In order to get diverse data, the selected managers were working in a large number of industries.

10 Control variables: - Firm size - Industry - B2B/B2C - Environmental dynamism - Competitive intensity

2. L

ITERATURE

R

EVIEW

In this chapter, the conceptual model (2.1) will be displayed. Following, an extensive literature study of customer centricity (2.2), customer- and organizational performance (2.3), organizational culture (2.4) and customer metrics (2.5) will be provided. Thereby, definitions and relationships among these variables will be explained, accompanied by hypotheses.

2.1

C

ONCEPTUAL

M

ODEL

Figure 1 displays the conceptual model of this study. The model shows the theorized relationships between the antecedents of customer centricity, and their influence on the degree of customer centricity, customer performance and organizational performance. We assume that organizational culture and customer metrics foster organizations to become more customer centric. Besides, there is hypothesized that a higher degree of customer centricity leads to better customer performance and in addition to better organizational performance too. Likewise, a direct effect between the degree of customer centricity and organizational performance is incorporated. As control variables, firm size, type of industry, B2B/B2C or both, competitive intensity and environmental dynamism were added.

Figure 2.1: Conceptual model

H4,7,8 H2 H3 H1 H6,11,12 H5,9,10 Antecedents of customer centricity O r g a n i z a t i o n a l c u l t u r e C u s t o m e r c e n t r i c i t y C u s t o m e r p e r f o r m a n c e O r g a n i -z a t i o n a l p e r f o r m a n c e M e a s u r e m e n t o f c u s t o m e r m e t Organizational Culture Organizational Performance Measurement of customer metrics Customer metrics M e a s u r e m e n t o f c u s t o m e r Customer metrics objectives Customer

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2.2 C

USTOMER

C

ENTRICITY

Two terms which are closely related, but slightly different are customer centricity and customer orientation. The main difference is that customer centricity uses the individual consumer as a starting point, whereas customer orientation focusses more on customer segments. According to Sheth et al. (2000), “customer centric marketing emphasizes understanding and satisfying the needs, wants, and resources of individual consumers and customers rather than those of mass market segments.” Similarly, Wagner and Majchrzak (2007) make the “needs and resources of individual customers the starting point for planning new products and services or improving existing ones.” Customer orientation, as one of the components of market orientation, is defined as “the firm’s sufficient understanding of its target buyers in order to be able to create superior value for them continuously (Narver and Slater, 1990).” Another related concepts is CRM, which attempts to maximize value of the customer for the organization (Reimann et al., 2010), by using marketing strategies and information technology in order to establish, maintain and enhance long-term relationships with the customer (Srivastava et al., 1999, Peelen et al., 2009). The difference between CRM and customer centricity is in terms of scope, whereas CRM is a collection of processes that manage relationships with customers and customer centricity is an organization wide philosophy.

An extensive literature research of Geerts (2015) has led to the following definition of the concept. “Customer centricity is the extent to which an organization uses information of individual customers to profitably serve the needs and wants of customers, and the extent to which information of individual customers is used as a starting point in decision making throughout the organization.”

2.3 C

USTOMER

&

O

RGANIZATIONAL

P

ERFORMANCE

Following the literature (e.g. Zhu and Nakata, 2007; Brady and Cronin 2001; Homburg and Pflesser, 2000 and Peltier et al., 2013), performance can be divided into two categories, namely customer performance and organizational performance or business or firm performance.

In general, customer performance focusses on the strength of the customer relationship, expressed in terms such as satisfaction and retention, cross-selling, share-of-wallet, Customer Lifetime Value (CLV) and customer equity (Peltier et al., 2013). On the other hand, organizational, business or firm performance focusses on the financial performance, and is operationalized as return on sales (Homburg and Pflesser, 2000), gross profit margin, return on equity and other traditional accounting-based measures (Zhu and Nakata 2007).

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As displayed in our conceptual model, we also expect a relationship between customer centricity and customer performance. Ramani and Kumar (2008) have found that the greater the interaction orientation of a firm, the greater is the customer performance. In addition, Kirca et al., (2005) have found similar results, namely, market orientation positively affects customer loyalty and customer satisfaction. Although interaction orientation, nor market orientation are not completely similar as customer centricity, it has many similarities, and therefore we generalize the findings to assume a positive relation among the two concepts. Therefore we draw the following hypotheses:

H1: Customer centricity positively influences customer performance

In addition, we expect a direct relationship between customer centricity and organizational performance, although this relation is to a great extent mediated by customer performance. For the direct effect among the two concepts, significant results are provided by several authors (e.g. Narver & Slater, 1990; Brady & Cronin, 2001 and Zhu & Nakata, 2007). Besides, Reinartz et al., (2004) and Reimann et al., (2010) have ascertained a positive relation between CRM processes and organizational performance. Therefore, we hypothesize:

H2: Customer centricity positively influences organizational performance.

Lastly, a direct effect between customer performance and organizational performance was expected. Many authors have found significant results for this relation, although different elements of customer performance were used in analysis to predict organizational growth. In the early days, focus was upon customer satisfaction and customer retention, however word of mouth is the metric linked to growth (Keiningham et al., 2007). Net Promoter on the other hand, seems to be less accurate predicting the behavior of the individual customer, but far more accurate in estimating the growth of the entire organization (Keiningham et al., 2007 and Reichheld, 2003).

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namely that customer performance, or parts of customer performance lead to better organizational performance. Therefore, we hypothesize:

H3: Customer performance positively influences organizational performance

2.4 O

RGANIZATIONAL

C

ULTURE

Among researchers and scientists, organizational culture has received a lot of attention over the past decades, with over 4600 articles examining the subject (Pinho et al., 2004). Although the definition has been, or is, subject to change, the central notion is that the term ‘culture’ refers to core organizational values and norms (Flamholtz, 2001). In 1975, Haggett defined the concept as “patterns of behavior that form a durable template by which ideas and images can be transferred from one generation to another, or from one group to another”. However, in the late 80s, the definition was tightened to “the pattern of shared values and beliefs that help individuals understand organizational functioning and thus provide them norms for behavior in the organization” (Deshpandé and Webster, 1989). To date, this definition is still very commonly used in marketing literature.

For this study, it is valuable to find a link between organizational culture and the degree of customer centricity of a firm. Conceptual work has highlighted the characteristics of a typical customer centric organizational culture. According to Shah et al. (2006), a typical customer centric organizational culture sees the customer as the central value, and every decision made begins with that. A common norm within customer-centered organizations is that “employees are customer advocates.” Therefore, a “customer oriented culture will help employees understand that the customer’s interest always comes first, ahead of those of owners, managers or employees (Peelen et al., 2009; Hoekstra et al., 1999 and Webster, 1992).”

Besides, Shah et al. (2006) state that information sharing among employees is a true indicator of a customer centric organization, as long as the information sharing has the goal to be better able to meet customer needs. Likewise, within customer centric cultures marketing is seen as an investment rather than an expense (Shah et al., 2006).

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affect the transformation to customer centricity. One of the factors influencing this transformation is based on the ability of the leaders to implement change (Sheth et al., 2000). Other conceptual work (e.g. Day, 1994; Narver & Slater, 1990) stresses that top management’s emphasis on market orientation (as an element of customer orientation) has a positive impact on the level of an organization’s market orientation. Kirca et al. (2005) build on this statement and found a significant positive relation in their meta-analysis on empirical market orientation literature.

In order to become more customer centric, organizations cannot ignore Customer Relationship Management (CRM) anymore. Successful CRM enables organizations to get insight in their consumers, and hence to improve an organization’s ability to learn and adept (Reimann et al. 2010; Peelen et al, 2009). However, implementation of CRM have high failure rates (29%-70% in 2013) (Iriana et al., 2013), which is merely due to culture (van Bentum & Stone, 2005), and the organization’s ability (and willingness) to learn (Peltier et al., 2013). More specifically, CRM failure has been attributed to “the inability to facilitate and enhance organization wide transfer of customer information” (Jayachandran, 2005; Peltier et al., 2013) and that effective organizational learning depends on the degree of information dissemination and interpretation (Slater and Narver, 1995). On the other hand, Peelen et al. (2009) found significant proof that formulation of CRM in a vision, and the commitment of top management has a positive impact on CRM success, and thus enables organizations to become more customer centric. Besides, incorporation of CRM in a vision provides a stable basis for employees, and reflects a company’s future state (Peelen et al., 2009). Although not many empirical research has linked organizational culture with the degree of customer centricity, based on the provided literature regarding organizational culture and its relating concepts, we expect a positive relationship between organizational culture and customer centricity. Therefore, we hypothesize:

H4: Customer centric organization culture positively influences customer centricity.

Jayachandran et al. (2005) found significant proof that customer relationship orientation, which is rooted in an organization’s overall culture, has a positive association with relational information processes, whereas the latter is defined as ‘the specific routines that a firm uses to manage customer information to establish long-term relationships with customers’ (Jayachandran et al., 2005). Besides, customer relationship orientation guides organizations toward CRM, which enables firms to better manage the newly established long-term relationships.

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H5: Customer centric organization culture positively influences customer performance.

Numerous studies have elaborated, and have found a positive significant relationship between organizational culture and organizational performance (e.g. Pinho et al., 2013; Rashid et al., 2003 and Flamholtz, 2001). However, Pinho et al., (2013) and Rashid et al., (2003) used different types of organizational culture, but found similar results. Namely, each type of organizational culture has a different effect on organizational performance. In that perspective, Homburg and Pflesser (2000) found empirical proof that firms with strong customer oriented organizational cultures have higher long term performances, and thus better organizational performance.

Iriana et al., (2013) linked organizational culture with CRM financial outcomes, and found for only two of the four culture types significant proof. However, the importance of effective use of CRM is indisputable, and leads to improved organizational performance (e.g. Krasnikov et al., 2009 and Ramani & Kumar, 2008). Therefore, Peltier et al. (2013) use data quality, as an outcome of effective CRM implementation, as a mediator between organizational culture and organizational performance. This relation proved to be positively significant. Following the literature, we hypothesize the following:

H6: Customer centric organization culture positively influences organizational performance.

2.5 C

USTOMER

M

ETRICS

The use of customer metrics within organizations has received more attention over the years. One of the reasons is that it enables firms to assess marketing’s productivity and its accountability. However, as Shah et al (2006) propose the use of financial metrics, this study focusses on non-financial measures too, since existing financial metrics have proven to be inadequate in order to relate marketing’s productivity to performance (Rust et al., 2004). Therefore, instead of using the term financial metrics, the term customer metrics is used for all customer related metrics, either financial or non-financial.

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directing how firms adapt their behaviors through new information and insights.’ The knowledge gained of the customer, helps organizations to get a single view of the customer, and to make sound marketing decisions (Peltier et al., 2013).

One specific customer metric, namely Customer Lifetime Value (CLV), has proven to help organizations to become more customer centric. Firstly, CLV helps organizations to select the right customers. (Gupta and Zeithaml, 2006) Secondly, it helps organizations to better allocate the marketing budget (Berger et al. 2002). The help of CLV models has shown that a lot variety exist between customers in terms of profitability, which makes selection critical (e.g. Gupta & Zeithaml, 2006; Niraj et al, 2001; Reinartz & Kumar, 2003; Venkatesan & Kumar, 2004). Thereby, focusing and devoting the marketing budget on the right customers enables firms to cut costs, but simultaneously become more customer centric by getting a single view of the customer (Gupta & Zeithaml, 2006).

In addition, the reward system in place can direct an organization towards customer centricity, since it is an important driver of employee behavior. When evaluation of managers/employees are based on for example market share instead of customer metrics it is more likely that they are focused on these criteria (Jaworski and Kohli, 1993). The study of Jaworski and Kohli (1993) showed that a reward system focused on customer metrics, led to a higher degree of customer centricity. Building on the work of Jaworski and Kohli (1993), Ramani and Kumar (2008) found that the “greater the firm’s reliance on customer metrics is for evaluating and rewarding managers, the greater is its interaction orientation.” The latter is the organization’s ability to take advantage of the information obtained of its individual customers, and to interact with them in order to achieve profitable customer relationships (Ramani and Kumar, 2008).

Thus, the choice of reward systems motivates employees, and so is the effect of goal setting. A study of Locke et al. (1981) reviewed the relation between specific goal setting and task performance, and found that 90% of the tasks were performed better, when specific goals or objectives were in place. This, in turn leads to higher organizational performance. Therefore, we will not only test the measurement of customer metrics, but also if organizations have objectives/targets in terms of customer metrics, since we expect positive relationships with respectively the degree of customer centricity, customer- and organizational performance.

In sum, we assume that organizations which measure customer metrics are better able to serve its customers and behave customer centric. Subsequently, having organizational objectives in terms of customer metrics positively influences the degree of customer centricity, and customer and organizational performance. Following this logic, we draw the following hypotheses.

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H8: Organizational objectives in terms of customer metrics positively influences customer centricity Customer performance metrics evaluate the strength of the customer relationship, and include satisfaction and retention, cross-selling, share-of-wallet, customer ROI, CLV, and customer equity (Peltier et al., 2013). According Jayachandran et al. (2005), the use of CRM technology to measure customer metrics enhances the influence of CRM processes on customer relationship performance. Also, a direct positive effect between CRM processes and customer relationship performance was found. Literature on the link between the use of, and having objectives in terms of customer metrics, are scant. Nonetheless, we expect a positive relationship among the concepts and hypothesize the following:

H9: Measurement of customer metrics positively influences customer performance.

H10: Organizational objectives in terms of customer metrics positively influences customer performance Similarly, we expect a direct effect of the use of customer metrics on organizational performance. A growing stream of research show that effective implementation of CRM (from which customer metrics are generally obtained) and use contribute to improved organizational performance. (Homburg et al., 2008; Krasnikov, et al., 2009; Ramani & Kumar 2008)

Literature about the relationship between customer metrics and organizational performance is not widespread. However, Reinartz et al. (2004) found that successful CRM positively contributes to organizational performance. In addition, Seddon et al. (2010) and Peltier et al. (2013) have found a positive relationship between data quality and organizational performance. Data quality is determined as high quality when the information is collected across multiple transactions, touch points and channels, accurately reflects the behavior and sentiments of customers, both collectively and individually (Peltier et al. 2013). Besides, Peltier et al.’s (2013) study showed that data quality mediates the effect on organizational performance. This stresses the need for a focus on customer metrics, and more specifically, to collect the customer data accurately across multiple channels and touch points to ensure its quality. Therefore, we hypothesize the following:

H11: Measurement of customer metrics positively influences organizational performance.

H12: Organizational objectives in terms of customer metrics positively influences organizational

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3. M

ETHODOLOGY

This chapter describes the data collection and sample characteristics (3.1), the measurement of constructs (3.2) and the method of analysis (3.3).

3.1 D

ATA

C

OLLECTION AND

S

AMPLE

C

HARACTERISTICS

In order to answer the research question and to test the hypotheses, an online questionnaire was developed. The questionnaire consists of questions about customer centricity, customer centric structures and processes, organizational culture, customer metrics, and customer and organizational performance. For customer and organizational performance, three variants were measured. The respondents had to answer the same statements with respect to their main competitor, with respect to the previous year and with respect to their ambitions. The format and questioning was discussed and approved in several meetings with the PvKO and the supervisor. After completion of the questionnaire it was pretested among three unknowing members of the PvKO, to make sure the line of reasoning and questioning was clear and not ambiguous.

The data collection was organized and performed by the PvKO. Via the internal networks of the PvKO and its members, around 4000 Dutch marketing managers were invited to contribute to this study by e-mail. The selected organizations had over 30 employees. In exchange, the respondents can receive a benchmark of their firm’s degree of customer centricity, compared to other contributing firms within the same industry. Besides, the respondents will be invited for a seminar of the PvKO, in which the results of this study will be presented.

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3.2 M

EASUREMENT OF

C

ONSTRUCTS

For customer centric organizational culture, a reflective scale was developed, since no existing measurement scales were available. The scale development was done following the first steps of Churchill (1979). At first, the domain of the construct, in this case customer centric organizational culture was specified. Secondly, items were gathered, or adjusted in order to capture the domain precisely as specified (e.g. Shah et al., 2006; Peelen et al., 2009; van Bentum & Stone, 2005; Lamberti, 2013).

For the customer metrics, two formative scales were developed, based on the most common metrics described in literature in relation to customer centricity and CRM outcomes (e.g. Shah et al., 2006; Gupta and Zeithaml, 2006; Ramani and Kumar, 2008, Peltier et al., 2013 and Zahay and Griffin, 2004, Rust et al., 2004). There was chosen to develop formative scales, because we assume, that the more you know of an individual consumer, the better you are in serving their specific needs and wants. (Melnyk et al., 2004) Therefore, the first scale measures how many customer metrics an organization registers, whereas the second measures how many objectives in terms of customer metrics the organization has. In line with the proposed method of Diamantopoulos & Winklhofer (2001), the customer metrics were carefully selected with regard to customer centricity. In addition, more or less similar metrics were stated as being one answer opportunity, in order to decrease collinearity among the items.

The other scales for structures, processes, customer centricity, customer and organizational performance and the control variables environmental dynamism and competitive intensity were developed by Geerts (2015) and Klaver (2015), and adopted in this research. The subdivision of industries and the amount of employees at location and in the Netherlands was adopted from MarketResponse, the Dutch organization specialized in market research and responsible for the data collection of this study. The complete questionnaire can be found in Appendix II.

Factor Analysis Customer Centric Organization Culture

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After the first factor analysis of customer centric organizational culture, KMO was 0.891, and Bartlett’s Test of Sphericity was significant (.000). However, one item, (Within our organization, marketing expenditures are seen as investment rather than costs) scores a communality of 0.417 after PCA, and is therefore excluded from further research.

After the second factor analysis, KMO was 0.888, Bartlett’s Test of Sphericity significant (.000) and all communalities >0.5. The PCA found two factors, both with an Eigenvalue >1.0, at least 5% of variance explained individually and cumulative 66.5% (>60%) of variance explained. Varimax rotation method with Kaiser Normalization was chosen in order to prevent that variables load on one factor. The factor loadings have to be higher than 0.6, and simultaneously not load higher than 0.3 on other factors (Malhotra, 2009). For customer centric organizational culture, five of the nine remaining items had cross loadings exceeding the range. However, in order to not lose all valuable information at once, the items were deleted one by one, and the factor analysis redone. The items which did not load on one factor at all, and were furthest away from the limit, were removed first.

In total, the process was repeated six times. Based on communalities or cross loadings one item was deleted at a time. Finally, the factor analysis found one factor for customer centric organizational culture, consisting of four items, with a KMO of .708 and a significant Bartlett’s Test of Sphericity (.000). Thereby, the factor has an Eigenvalue >1.0 and the cumulative percentage of variance explained is >60.0. Likewise, communalities are higher than 0.5 and factor loading all exceed 0.6 (See Table 3.1). At last, in order to check the reliability of the found factor, a Cronbach’s Alpha test was performed. In this case, Cronbach’s Alpha was 0.779, which is reliable (>0.6) (Malhotra, 2009).

Validity and reliability of reflective scales can be tested with factor analysis, but this is not appropriate for formative scales when the ‘items are formed as a linear sum of measurements’, which is the case for the customer metrics scales (Diamantopolous & Winklhofer, 2001; Coltman et al., 2008). The respondents had to answer ‘yes’, ’no’, or ‘don’t know’. For the measurement of customer metrics, eight metrics (or group of similar metrics) were proposed. With regard to objectives in terms of customer metrics, five metrics (or group of similar metrics) were proposed. Subsequently, two new variables had to be created, which indicated how often the respondent answered ‘Yes’ with regard to the measurement of, and having objectives in terms of customer metrics. These newly computed variables were used in subsequent analysis.

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Table 3.1: Measurement of Constructs

Construct Item Source Cronbach’s

α Factor Loading Customer Centric Organizational Culture

The vision of our organization describes what we want to do for the individual customer *

Based on Peelen et al.(2009) .779 -

Everyone within the organization is aware of the importance of our customers.

Peelen et al.(2009) 0.839

Within our organization, marketing expenditures are seen as investment rather than costs. *

Based on Srivastava et al. (1998) and Shah et al. (2006)

- Top management stresses the importance of

customer centricity within our organization. *

Based on Shah et al. (2006) and Lamberti (2013)

- Top management spends time with individual

customers on a regular basis.

Based on Shah et al. (2006) and van Bentum & Stone (2005)

.766 Top management shows concerns about the

problems of individual customer.

Based on Lamberti (2013) .736

Top management stresses the importance of celebrating customer successes. *

Based on Cook & Macaulay (1997)

- Everyone within the organization has the goal of

helping every customer as quick and correct as possible. *

Based on Lamberti (2013) -

Our employees put the needs and wants of the individual customer first.

Based on Shah et al. (2006) .775

Our employees find it important to share customer information among each other *

Based on Shah et al. (2006) and Lamberti (2013)

- Registration of

Customer Metrics

Customer satisfaction and/or Net Promoter Score (NPS) and/or Customer Effort Score and/or Customer Loyalty

e.g. Gupta & Zeithaml, (2006), Ramani & Kumar, (2008), Reichheld, (2003) Rust et al. (2004), Dixon et al., (2010) and Wong, (2010)

NA** NA**

Customer touch points Shah et al., (2006)

Customer Feedback Shah et al., (2006)

Revenues per customer e.g. Peltier et al., (2013) and Rust

et al., (2004)

Costs per customer Rust et al (2004)

Share of wallet Zahay & Griffin, (2004)

Customer Lifetime Value e.g. Gupta & Zeithaml, 2006;

Peltier et al. (2013) and Rust et al. (2004)

Churn rate Wong, (2010)

Objectives in terms of Customer Metrics

Customer satisfaction and/or Net Promoter Score (NPS) and/or Customer Effort Score and/or Customer Loyalty

e.g. Gupta & Zeithaml, (2006), Ramani & Kumar, (2008), Reichheld, (2003) Rust et al. (2004), Dixon et al. (2010) and Wong, (2010)

NA** NA**

Revenues per customer e.g. Peltier et al., (2013) and Rust

et al., (2004)

Share of wallet e.g. Zahay & Griffin, (2004)

Customer Lifetime Value e.g. Gupta & Zeithaml, (2006);

Peltier et al. (2013) and Rust et al., (2004)

Customer Retention e.g. Peltier et al., (2013); Rust et

al. (2004) and Wong, (2010) Optimal use of

Customer Metrics

Do you feel like your organization optimally uses the registered customer metrics?

Own contribution NA*** NA***

* Item was deleted after Factor Analysis

22

3.3 M

ETHOD OF

A

NALYSIS

The statistical analyses are performed with IBM SPSS Statistics, version 22. In order to test our hypothesized relationships, linear regression was used.

Estimating the significance of control variables

Since this research includes a range of control variables, only control variables which had a significant effect on the dependent variables were used in subsequent analysis. In order to perform regression, dummy variables were created for firm size, industry and type of business. Environmental dynamism and competitive intensity, multiple item scales, were subjected to a factor analysis. Subsequently, new variables were created for these control variables with the remaining items. Thereafter, a linear regression analysis was performed in order to assess the individual effect of each control variable in relation to our dependent variables (i.e. customer centricity, customer and organizational performance).

The result of the linear regression left us with a reduced number of control variables. When assessing organizational performance, with respect to the competitor, construction industry should be included in the regression. For organizational performance, with respect to ambitions, construction industry, health and welfare industry and sports, culture and tourism should be included. The results of the other models proved to be insignificant. An overview of the effects of the control variables can be found inAppendix V.

Model specification

Each dependent variable (customer centricity, customer performance and organizational performance) will be used in linear regression. In a later stage, based on the model fit (Adjusted R2), there will be decided which variant of performance will be used to draw conclusions. Therefore, for all possible models, the equations are estimated in order to test our hypotheses:

23

Model 5: Hypothesis: 2, 3, 6, 11, 12

OPcomi = ∝ + β1Ci + β2MRi + β3MOi + β4CCi + β5CPcomi + β6CIi+ εi

Model 6: Hypothesis: 2, 3, 6, 11, 12

OPpyi = ∝ + β1Ci + β2MRi + β3MOi + β4CCi + β5CPpyi + εi

Model 7: Hypothesis: 2, 3, 6, 11, 12

OPambi = ∝ + β1Ci + β2MRi + β3MOi + β4CCi + β5CPambi + β6CIi + β7HWIi + β8SCTIi + εi

Dependent variables: CC = Customer Centricity CP = Customer performance OP = Organizational performance Independent variables: C = Organizational Culture

MR = Registration of Customer Metrics MO = Objectives in terms of Customer Metrics

Control Variables:

CI = Construction Industry HWI = Health/Welfare Industry

SCTI = Sports/Culture/Tourism Industry

Other:

i = Respondent 1-155

24

4. R

ESULTS

This section provides the result of the linear regressions performed for this study. First, a decision with regard to the performance measure will be made, based on the model strength (4.1). Subsequently, the models for the main effects for customer centricity (4.2), customer (4.3) and organizational performance (4.4) will be analyzed. Thereafter, the results of mediation analyses will be discussed (4.5). The chapter will close with an overview of the hypotheses and its found effects (4.6).

4.1 M

ODEL

F

IT

In the questionnaire, respondents were asked to rate their customer and organizational performance with respect to their competitor, the previous year and their ambitions. This was done in order to see which variant provided the strongest model. Therefore, six regression analyses (Model 2-7 from previous chapter) were performed, and on the basis of Adjusted R2, the model with the best fit was selected. As can be seen in Table 4.1, customer performance, with respect to previous year (Model 3), provides the highest adjusted R2 (.230). For organizational performance, the variant with respect to ambitions (Model 7) provides the highest adjusted R2(.505), meaning that these specific models explain the most. Since we want to choose one of the variants, we select with respect to ambitions, since organizational performance, with respect to ambitions (Model 7), provides a sufficiently higher adjusted R2 _{than organizational performance, with respect to previous year (Model 6). Therefore, for}

subsequent analysis, the variables ‘Customer Performance, with respect to ambition’ (Model 4) and ‘Organizational Performance, with respect to ambition’ (Model 7) are used as ‘Customer Performance’ and ‘Organizational Performance’.

Table 4.1: Adjusted R2 of performance measures.

25

4.2 R

ESULTS

C

USTOMER

C

ENTRICITY

M

ODEL

Table 4.2 shows the regression results for the hypotheses regarding the relationships between organizational culture, customer metrics, customer centricity, customer and organizational performance. Firstly, we will focus on model 1, taking customer centricity as the dependent variable.

H4 hypothesizes a positive association between customer centric organization culture and customer

centricity. This hypothesis is supported (p<.01), meaning that organizational culture has a positive relationship with customer centricity (β = .420). For the hypothesis regarding the measurement of customer metrics (H7), and having objectives in terms of customer metrics (H8), no significant

relationships were found (H7: p>.10 and H8: p>.10). This implies that measurement of customer

metrics, and having objectives in terms of customer metrics does not influence customer centricity.

Table 4.2: Regression results of econometric models Model 1 (CC) Model 4 (CP) Model 7 (OP) Main variables Hypothesis (effect) Standard. coefficients Hypothesis (effect) Standard. coefficients Hypothesis (effect) Standard. coefficients Customer Centric Organizational Culture (C) 4 (+) .420a 5 (+) . 292a 6 (+) -.003 Customer Metrics, Registration (CR) 7 (+) .111 9 (+) .152c 11 (+) .183b Customer Metrics, Objectives (CO) 8 (+) .112 10 (+) .049 12 (+) -.093 Customer Centricity (CC) 1 (+) .124 2 (+) -.064 Customer Performance(CP) 3 (+) .687a Control variables

Construction Industry (CI) -.100c

Health & Welfare(HWI) -.043

26

4.3 R

ESULTS

C

USTOMER

P

ERFORMANCE

M

ODEL

In order to elaborate on the results of customer performance, please see Table 4.2, model 4, which takes customer performance as the dependent variable.

H1 hypothesizes a positive relationship between customer centricity and customer performance.

However, the regression analysis did not provide support for this hypothesis (p>.10). There is support for hypothesis H5 (p<.01) andH9 (p<.10), confirming that organizational culture and the measurement

of customer metrics positively influence customer performance, whereas organizational culture (β = .292) has a stronger influence than measurement of customer metrics (β = .152). Lastly, no support was found for H10 (p>.10). Therefore, we cannot state that having objectives in terms of customer metrics

have an influence on customer performance.

4.4 R

ESULTS

O

RGANIZATIONAL

P

ERFORMANCE

M

ODEL

In order to elaborate on the results of organizational performance, please see Table 4.2, model 7, which takes organizational performance as the dependent variable.

A positive relation was hypothesized between customer centricity and organizational performance in H2. However, this was not supported after the regression analysis (p>.10). The test for H3 gives support

for the positive relationship between customer performance and organizational performance (p<0.01). The found effect was even very large (β = .687). The positive influence of organization culture on organizational performance, hypothesized in H6 was not supported (p>.10). Likewise, having

objectives in terms of customer metrics does not influence organizational performance, as hypothesized in H12 (p>.10). However, measurement of customer metrics positively influences

organizational performance, as hypothesized in H11 (p<.05, β = .183). The control variable construction

industry shows a negative significant relation (p<.10, β = -.100).

4.5 M

EDIATION

A

NALYSES

27 Figure 4.1: Mediation Analysis Customer Centricity

The model specification of the relations displayed in Figure 4.1 is shown below.

(C) CP

= ∝ + β1C

+ β2MR

+ β3MO

+ ε

(a) CC

= ∝ + β1C

+ β2MR

+ β3MO

+ ε

(b) CP

= ∝ + β1CC

(c’) CP

= ∝ + β1C

+ β2MR

+ β3MO

+ β4CC

ε

Table 4.3: Results of mediation analysis customer centricity

Model C Model a Model b Model c’

Main variables Standard.

coefficients Standard. coefficients Standard. coefficients Standard. coefficients Customer Centric Organizational Culture (C) .344a .420a .292a

Customer metrics, Registration (MR) .166c .111 .152c

Customer Metrics, Objectives (MO) .063 .112 .049

Customer Centricity (CC) .322a .124 R2 (Adjusted R2) .203 (.187) .267 (.252) .104 (.098) .215 (.194) F-value (Sig.) 12.841 (.000) 18.302 (.000) 17.719 (.000) 10.247 (.000) Notes: a p-value<.01; b p-value < .05; c p-value < .10

The results of the mediation analysis of customer centricity are shown in Table 4.3. We see that, in Model C and a, organizational culture has a significant positive relation with respectively, customer performance and customer centricity. Model b also shows a significant, positive relation between customer centricity and customer performance. However, when we add customer centricity - the expected mediator - to the model, we see a decrease in the effect of organizational culture on

28

customer performance. Therefore, it indicates partial mediation of customer centricity between customer centric organizational culture and customer performance.

Next, a mediation analysis for customer performance was performed. Figure 4.2 shows the tested relations.

Figure 4.2: Mediation Analysis Customer Performance

The model specification of the relations displayed in Figure 4.2 is shown below.

(C) OP

= ∝ + β1C

+ β2MR

+ β3MO

+ β4CC

+ β5CI

+ β6HWI

+ β7SCTI

+ ε

(a) CP

= ∝ + β1C

+ β2MR

+ β3MO

+ β4CC

+ ε

(b) OP

= ∝ + β1CP

β2CI

+ β3HWI

+ β4SCTI

+ ε

29 Table 4.4: Results of mediation analysis customer performance

Model C Model a Model b Model c’

Main variables Standard. coefficients Standard. coefficients Standard. coefficients Standard. coefficients Customer Centric Organizational Culture (C) .189 b .292 a -.003

Customer Metrics, Registration (MR) .286 a .152 c .183 b

Customer Metrics, Objectives (MO) -.061 .049 -.093

Customer Centricity (CC) .001 .124 -.064

Customer Performance (CP) .706 a .687 a

Control variables

Construction Industry (CI) -.125 -.093 -.100 c

Health & Welfare Industry (HWI) -.089 -.048 -.043

Sports, Culture and Tourism Industry (SCTI) -.136 c .006 -.004

R2 .178 .215 .508 .531

Adjusted R2 (.139) (.194) (.495) (.505)

F-Value 4.542 10.247 38.759 20.625

(Sig) (.000) (.000) (.000) (.000)

Notes: a p-value<.01; b p-value < .05; c p-value < .10

30

4.6 S

UMMARY OF

R

ESULTS

To get an overview of the found effects, Table 4.5 shows the hypothesis and the found effect.

Table 4.5: Overview of hypotheses and its effect.

Hypothesis Result

H1: Customer centricity positively influences customer performance Not Supported H2: Customer centricity positively influences organizational performance. Not Supported H3: Customer performance positively influences organizational performance Supported a H4: Customer centric organization culture positively influences customer centricity. Supported a H5: Customer centric organization culture positively influences customer performance. Supported a H6: Customer centric organization culture positively influences organizational performance. Not Supported H7: Measurement of customer metrics positively influences customer centricity. Not Supported H8: Organizational objectives in terms of customer metrics positively influences customer

centricity

Not Supported

H9: Measurement of customer metrics positively influences customer performance. Supported c H10: Organizational objectives in terms of customer metrics positively influences customer

performance

Not Supported

H11: Measurement of customer metrics positively influences organizational performance Supported b H12: Organizational objectives in terms of customer metrics positively influences

organizational performance

31

5. D

ISCUSSION

,

C

ONCLUSION

,

L

IMITATIONS

&

I

MPLICATIONS

This chapter discusses and concludes the results of this study (5.1). In addition, eventual limitations of the study will be provided, followed by some directions for future research (5.2). The chapter finishes with some managerial implications (5.3).

5.1 D

ISCUSSION

&

C

ONCLUSION

This research focused on answering the following research question: ‘Does organizational culture and

measurement and objectives in terms of customer metrics influence the degree of customer centricity and do they lead to better customer- and organizational performance?’ In order to answer this

question, the model of Shah et al. (2006) was taken as a starting point. The focus of this study was on the influence of customer centric organizational culture and customer metrics on the degree of customer centricity, customer performance and organizational performance. In addition, customer metrics was measured in two constructs, namely ‘measurement of customer metrics’ and ‘having objectives in terms of customer metrics’. Based on literature, there were positive relationships expected, and thus hypothesized among all variables.

32

practice are presented to the managers, which in turn might have had an effect on the absence of the relation.

The second regression model focused on the influences of the antecedents of customer centricity on customer performance, while customer centricity itself was incorporated too. Entirely contrary to expectations, no relationship was found between customer centricity and customer performance, while many authors previously found these existing relation (e.g. Jayachandran et al., 2005; Peelen et al, 2009; Shah et al., 2006; Lamberti, 2013). However, Sørensen (2011) did not find a relation between customer centricity and customer performance. He states that focusing too much on the outdated preferences of customers, hinders innovativeness and competitiveness of an organization, which in turn hinders customer performance.

In line with the findings of Jayachandran et al., (2005) and Homburg & Pflesser (2000), this study found proof for the positive relationship between customer centric organizational culture and customer performance. However, mediation analysis proved that customer centricity functions as a partial mediator between customer centric organizational culture and customer performance. We also expected a positive relation between the measurement of customer metrics and customer performance. This relationship was found. Lastly, we expected a positive relationship between having objectives in terms of customer metrics and customer performance, but no significant results were found to proof these relations. A reason for the absence of these relations might be that objectives can be seen as static and for a longer period of time, whereas today’s customers’ are characterized by ‘never satisfied’ with ever changing needs and demands. Therefore, metric systems need to be flexible in recognizing and responding to changing demands, and so should the objectives set by top management be (Melnyk et al., 2004).

33

which leads to the neglect of other stakeholders, and simultaneously to worsening of organizational performance.

In literature, (e.g. Gupta & Zeithaml, 2006; Ittner & Larcker, 1998; Reichheld & Sasser, 1990) many positive relationships between customer performance, or customer performance metrics were related with organizational performance. This study found a very strong relation among the concepts. However, the expected positive relation between customer centric organizational culture and organizational performance, was not found. This is remarkable, since several authors (e.g. Pinho et al., 2013, Rashid et al., 2003 and Flamholtz, 2001) have researched and found this relation. Therefore, a mediation analysis was performed in order to determine if customer performance functions as a mediator between customer centric organizational culture and organizational performance. As it turns, customer performance functions as a full mediator between the two variables, which explains why no significant results were found.

The relationship between the measurement of customer metrics and organizational performance was expected to be positive, and this study supports this claim, although the mediation analysis proved that customer performance functions as a partial mediator. Lastly, the expected positive relationship between having objectives in terms of customer metrics and organizational performance has not been found. In contrast, a negative but not significant effect was found. Although it is notable, some arguments might clarify the reversed relation. As discussed earlier, measuring everything is more detrimental than measuring nothing (Brown, 1996). This counts for organizations’ abilities to process larger sets of metrics as well. When an organizations has several objectives in terms of customer metrics, it needs to measure and analyze all the metrics too, in order to achieve the objectives. However, an increasing number of metrics increases confusion and complexity (Melnyk et al. 2004). Thereby, half of our sample consists of organizations with fewer than hundred employees. For these organizations, it is most likely that too many objectives in terms of customer metrics are simply not achievable due to a lack of resources, and thus causes a negative effect on organizational performance.

34

5.2

L

IMITATIONS

&

F

UTURE RESEARCH

In order to interpret the results of this study, some limitations should be taken into consideration. First of all, although the sample size meets Malhotra & Grover’s (1998) requirements, 155 respondents are somewhat low. A substantial larger response might have influenced the results of this study. Secondly, the survey was sent to marketing managers working in all sorts of industries in the Netherlands, in an attempt to generalize the results for all industries, or to be able to make industry specific statements. However, as a result of a somewhat low response, we cannot conclude that some of the findings are to be generalized for all industries, since some of the industries participating had fewer than ten contributions. Thirdly, the contributing marketing managers to this study are all Dutch. Therefore, cultural and social factors might have influenced the outcomes of this study, and make it hard to generalize the outcomes for all countries. Fourth, the fact that only marketing managers received the survey, reduces the reliability of this study, especially with regard to the outcomes of customer and organizational performance. As it appears, many marketing managers answered ‘don’t know’ (sometimes up to 45%) when answering their firms customer and/or organizational performance. Especially when asked to rate their performance with regard to Net Promoter Score (NPS) and financial performance measures as Return of Equity, Assets and Investments (ROE, ROA, and ROI respectively). Therefore, the measures of these items are sometimes substantially lower than others, which might have influenced the outcomes. Lastly, literature argues that the objectives set at higher levels, should be aligned with the metrics measured at lower levels. In this study, it is hard to distinguish specific measured customer metrics from the metrics in which an organization has objectives in, since somewhat similar metrics (e.g. customer loyalty and Net Promotor Score) are one answer option.

This last point, can be an interesting starting point for future research in customer metrics and its relations with customer centricity, customer and organizational performance. In addition, when time and resources are available, the customer performance and moreover, the organizational performance measures stated in the questionnaire could be asked to a managing or financial director instead of a marketing manager. This study has shown that in general many marketing managers lack knowledge in this field, which might have had its effect on the outcomes. A more complex direction for future research can be to study which specific metric contributes most to achieving customer centricity, instead of focusing on the number of metrics, as this study has done.

35

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ANAGERIAL

I

MPLICATIONS

36

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