The influence of organizational structure and
processes on the level of customer centricity,
and their effects on customer performance and
firm performance
2
The influence of organizational structure and
processes on the level of customer centricity,
and their effects on customer performance and
firm performance
University of Groningen
Faculty of Economics and Business
Master Marketing Management
Master thesis
12 January, 2015
Supervisor: dr. J.C. Hoekstra
External supervisor: drs. J. Berger
Lisette Klaver
Jan van Goyenstraat 1-a,
9718 NX Groningen
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Management summary
Building on conceptual work of Shah et al. (2006), this study empirically explores the path from product centricity to customer centricity, together with its consequences. Herein, certain relationships are expected. It is proposed that a firm’s organizational structure, indicated by the constructs (1) centralization, (2) alignment and (3) customer integration, positively influence the firm’s level of customer centricity. It is also proposed that a firm’s processes, indicated by the constructs (4) collection of customer information and (5) the use of customer information positively influence the firm’s level of customer centricity. Subsequently, those constructs of organizational structure and processes are expected to positively influence both customer performance and firm performance in an organization. Customer centricity is also expected to positively influence both customer performance and firm performance. Lastly, a positive influence of customer performance on firm performance is expected.
The purpose of this study is to provide organizations with insights in how to become customer-centric. To come to this answer, the aforementioned expected relationships were assessed. First, an online survey was established, in which all constructs were measured, including questions regarding control variables. The items measuring the constructs were adopted from existing measurement scales or developed based on the beforehand established definition of the construct. Data was collected via the network of the PvKO, resulting in 155 usable responses from Dutch marketing managers.
A regression analysis was used to test the proposed relationships. Findings show that the customer centricity of a firm is positively influenced by having an aligned organizational structure, by integrating the customer and by making use of customer information. No significant evidence was found for a relationship between the other constructs and customer centricity. Findings also reveal that customer performance will increase by having an aligned organizational structure, by integrating the customer and by making use of customer information. No proof was found for the other constructs to positively influence customer performance. As expected, the collection of customer information is positively influencing firm performance. The remaining constructs were not found to positively influence firm performance. Contrary to expectations, customer centricity does neither seem to influence customer performance nor firm performance. Evidence was found for the positive influence of customer performance on firm performance.
4 information between and within functional units needs to be facilitated. To integrate the customer, a focus towards the needs and requirements of customers should be of high regard, to translate this into suitable products. Also, customer information should be accurately used to establish long-term valuable relationships. The aforementioned constructs alignment, customer integration and the use of customer information will also contribute to a better customer performance for the organization. To increase the firm performance, organizations should focus on extensively collecting customer information along all customer touch points.
Keywords:
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Preface
The master thesis that lies in front of you, is the result of nearly five months of hard work. It is also the last step in gaining my Master’s degree and therewith, my student life comes to an end. I completed my Bachelor’s degree in International Business & Languages, where the topic marketing already had my special interest. After I completed my Bachelor’s degree, I felt that I was neither done studying nor ready to participate in working life, and decided to specialize in the field of marketing. Without a doubt, I can say that it was one of the best decisions in life I had made up to that day. Although there were some struggles, I am proud of myself to be at this point. I really enjoyed the courses I followed during my time in the Marketing Management program and I think that this will be of great use in further working life.
During my Bachelor’s degree, I already gained some practical experience while I finished two internships. The fact that this thesis would be of practical relevance for the Platform voor Klantgericht Ondernemen (PvKO), caught my attention. Also, the growing importance of the concept customer centricity was reason for me to write this thesis.
I want to use this opportunity to thank some people who supported and guided me during this thesis. First of all, I would like to thank dr. Janny Hoekstra for all the useful and valuable feedback sessions, and for being a pleasant supervisor throughout the process. Second, I would like to thank my fellow students Tomas Geerts and Sicco Hempenius for the pleasant cooperation, useful feedback sessions and the joyful time working together. Third, I would like to thank my family and friends who have always supported me, not only during the last months working on this thesis, but throughout my entire student life. Lastly, I would like to thank the members of the Platform voor Klantgericht Ondernemen (PvKO), for giving us the opportunity to write our thesis on this very interesting topic. Their input during our meetings was of great help, as well as their assistance in the data collection.
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Table of contents
Management summary ... 3 Preface ... 5 1. Introduction ... 10 2. Theoretical Framework ... 12 2.1 Conceptual model ... 12 2.2 Customer centricity ... 13 2.2.1 Customer orientation ... 142.2.2 Customer relationship management ... 14
2.3 Customer performance ... 14
2.4 Firm performance ... 15
2.5 Organizational structure ... 16
2.5.1 Conceptualization of organizational structure ... 16
2.5.2 Organizational structure and customer centricity ... 17
2.5.3 Organizational structure and performance ... 19
2.6 Processes ... 21
2.6.1 Conceptualization of processes ... 21
2.6.2 Processes and customer centricity ... 21
2.6.3 Processes and performance ... 22
3. Research Methodology ... 24
3.1 Data collection and sample characteristics ... 24
3.2 Survey design and measurement of constructs ... 24
3.2.1 Measure development – Organizational structure... 25
3.2.2 Measure development – Processes ... 25
3.2.3 Factor analysis – Organizational structure ... 26
3.2.4 Factor analysis - Processes ... 27
3.3 Method of analysis ... 29
3.3.1 Control effects ... 29
3.3.2 Model specification ... 29
4. Results ... 31
4.1 Model fit performance ... 31
4.2 Customer centricity model ... 31
4.3 Customer performance model ... 32
4.4 Firm performance model ... 32
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4.5 Mediation analysis ... 34
4.5.1 Mediating role of customer centricity ... 34
4.5.2 Mediating role of customer performance ... 35
5. Conclusions and discussion ... 38
5.1 Discussion ... 38
5.1.1 Additional factor analysis ... 39
5.1.2 Theoretical implications ... 39
5.2 Managerial implications ... 42
5.3 Limitations and suggestions for future research ... 42
References ... 44
APPENDIX A – Sample descriptives ... 50
APPENDIX B – Survey ... 51
APPENDIX C – Measurement scales ... 59
APPENDIX D - Correlations ... 62
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List of tables
Table 3.1: Measurement information Table 4.1: Results model fit performance Table 4.2: Regression results
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List of abbreviations
SC = Structure - Centralization SA = Structure - Alignment
SI = Structure - Customer integration
PC = Processes - Collection of customer information PU = Processes - Use of customer information CC = Customer centricity
CPcomp = Customer performance relative to the main competitor CPyear = Customer performance relative to previous year CPamb = Customer performance relative to the ambitions FPcomp = Firm performance relative to the main competitor FPyear = Firm performance relative to previous year
FPamb = Firm performance relative to the ambitions CI = Construction industry
HWI = Health/welfare industry
SCTI = Sports/culture/tourism industry
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1.
Introduction
It is beyond dispute that the importance of the customer in marketing literature is widely acknowledged. Throughout the years, the customer has gained an ever more important place in the decision making of organizations (Sheth et al., 2000). A shift emerged from the traditional product orientation to a market orientation, focusing on markets and segments, towards a customer orientation (Sheth et al., 2000). Although the belief about the importance of being customer focused seems to be adopted, little attention has been given to the implementation (Lamberti, 2013), causing firms to struggle in becoming customer focused (Shah et al., 2006).
To become customer oriented, it is suggested that managers need to engage in segmenting the customers, in two-way communications and in building long-term and close relationships with the customer (Day, 2006; Rust et al., 2010). Simultaneously, it is suggested that organizations should redesign their structure, processes, culture, and their metrics around the customer to become customer focused (Johnson et al., 2012; Shah et al., 2006; Thalmann and Brettel, 2012). The concept of customer centricity takes it one step further, as Wagner and Majchrzak (2007, p. 18) state that “customer centric business makes the needs and resources of individual customers the starting point for planning new products and services or improving existing ones”. Other research points out that, in order to remain competitive, firms should shift toward a more customer-centric management (Thalmann and Brettel, 2012). As described, several authors underpin the relevance of a customer-centric approach, although no empirical evidence was performed to prove this. This study herewith contributes to existing conceptual work on customer centricity by empirically exploring the relationship between antecedents on customer centricity, and subsequently on both customer performance and firm performance.
The model of Shah et al. (2006), describing the path from product centricity to customer centricity, is used as a reference point in this study1. The model describes four interrelated organizational barriers: culture, structure, processes, and financial metrics. These need to be overcome to achieve customer centricity. To contribute to existing theory, this study will perform an in-depth research into organizational structure and processes and examine the influence of organizational structure and processes on customer centricity. This study will further look into the effects of organizational structure and processes on customer performance and firm performance.
1This study takes part in a project in association with the Platform voor Klantgericht Ondernemen (PvKO), which is
11 Hence, we try to find an answer to our research question: 'To what extent do organizational
structure and processes influence the level of customer centricity of a firm, and how do they affect customer performance and firm performance?'
To find an answer to our research question, a thorough literature study finding relevant information concerning our variables was performed. Thereafter, measurement scales were developed for our variables to subsequently create a survey. With the results of our quantitative research we aim to answer our research question. The relevancy of this study is twofold. Besides the academic relevancy, the results of this study will be used for designing an instrument that is highly relevant for organizations by providing the necessary conditions to steer their firm towards becoming customer-centric.
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2.
Theoretical Framework
This chapter discusses the theoretical framework of this study. The conceptual model that is used as a reference point throughout this study is presented in section 2.1. This section also covers the reasoning behind the model. In the subsequent sections, all concepts within this framework and their hypothesized relations will be discussed. Customer centricity will be defined in section 2.2. The consequences of customer centricity, customer performance and firm performance, are defined in section 2.3 and 2.4 respectively2. Hereafter, the antecedent organizational structure, in section 2.5, and the antecedent processes, in section 2.6, will be discussed extensively. Their subsections will elaborate on their relationship to customer centricity, customer performance and firm performance.
2.1 Conceptual model
Our conceptual model, based on the work of Shah et al. (2006), illustrates the antecedents in the path to becoming customer centric and their effects on customer performance and firm performance. The conceptual model is presented in figure 2.1. Reasoning behind the model is that organizational structure and processes serve as building blocks that facilitate customer centricity of organizations. Organizational structure is indicated by the constructs (1) centralization, (2) alignment and (3) customer integration. Processes is indicated by the constructs (4) collection of customer information and (5) use of customer information. Subsequently, those five constructs are expected to positively influence both customer performance and firm performance. The model expects customer centricity to influence customer performance and firm performance as well. Lastly, the model assumes that customer performance positively affects firm performance. Firm size, industry, competitive intensity (Jayachandran et al., 2005), environmental dynamism (Jayachandran et al., 2005) and B2B/B2C were added as control variables.
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Figure 2.1: Conceptual model.
2.2 Customer centricity
Although the concept of customer centricity is gaining more ground in today's marketing literature, a shared definition on customer centricity is missing. In order to define customer centricity, prior literature on customer centricity is used, together with literature on the closely related concepts customer orientation and CRM. These related concepts will aid in specifying customer centricity, to come to an accurate definition.
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2.2.1 Customer orientation
A concept closely related to customer centricity is customer orientation. Customer orientation is concerned with putting the customer first and to understand their needs and preferences (Deshpandé et al.,1993; Eggers et al., 2013). Herewith, organizations aim to create and maintain value for the customer (Olson et al., 2005). To create customer value, the information concerning the customer should be used to identify possible segments, to subsequently develop products/services for those segments (Gatignon and Xuereb, 1997; Ruekert, 1992). Customer orientation and customer centricity have the same underlying philosophy, namely fulfilling the needs and desires of customers. However, customer centricity goes beyond customer orientation by focusing on the individual customer rather than focusing on customer segments.
2.2.2 Customer relationship management
The second concept closely related to customer centricity is customer relationship management (CRM). CRM is an organizational process with the objective to obtain customer-related knowledge in order to establish, maintain and improve long-term customer relationships (Peelen et al., 2009; Srivastava et al., 1999; Stefanou et al., 2003). CRM also enables the organization to distinct profitable customers from less profitable customers (Parvatiyar and Sheth, 2001). Both CRM and customer centricity are concerned with obtaining customer information and share the goal to create value for the customer and the firm. But there is a difference to make. CRM is a collection of processes to facilitate long-term valuable customer relationships, whereas customer centricity is a firm’s strategy and philosophy that focuses on creating value for the individual customer.
This research takes the definition of Geerts (2015) by stating that 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 Customer performance
15 customer-centric performance measures and make a distinction between customer-based relational performance measures, those that assess attitudes, and on the other hand customer-bases profit performance measures, that assess customer behavior. To evaluate customer performance, measures such as customer satisfaction, customer retention indicating customer loyalty, word-of-mouth, customer lifetime value and share of wallet are often used in today's marketing literature (Hooley et al., 2005; Kasim and Minai, 2009; Peltier et al., 2013; Ramani and Kumar, 2008; Zahay and Griffin, 2004).
Customer centricity is expected to have a positive influence on customer performance. Customer centric firms can create more loyal customers by having a better understanding of the customer’s needs and wants (Shah et al., 2006) and are better able to respond to those customer needs by translating this in customized offers, hereby increasing the customer performance (Lamberti, 2013). This is confirmed in research of Singh and Ranchhod (2004), stating that organizations becoming customer-focused have a higher need to meet the customers’ expectations and to retain their loyalty, which can obtained by offering customization. Although no prior work was found directly relating customer centricity to customer performance, comparable relationships were found. Brady and Cronin (2001) related customer orientation, stressing the customer as the focal point of the firm’s total operations, to customer perceptions and found that being oriented enhances customer perceptions, increasing customer loyalty and word-of-mouth recommendations. Mithas et al. (2005) and Reimann et al. (2010) related CRM to customer satisfaction and found that CRM has a positive effect on the level of customer satisfaction. Based on the above theory we therefore state the following:
H1: Customer centricity positively influences customer performance
2.4 Firm performance
16 Several authors found a positive relation between customer orientation and firm performance (Narver and Slater, 1990; Brady and Cronin, 2001). As illustrated in our conceptual model, we expect both a direct effect of customer centricity on firm performance and an indirect effect, mediated by the aforementioned customer performance. The direct effect can be explained by CRM processes having a positive effect on firm performance, since firms that successfully practice CRM processes are better able to select highly profitable customers while neglecting unprofitable customers herewith enhancing firm performance (Reinartz et al., 2004).
Since the concepts CRM and customer centricity are closely related, we expect a direct relationship between customer centricity and firm performance. Therefore, we hypothesize the following:
H2: Customer centricity positively influences firm performance
Looking into the relationship between customer performance and firm performance, two underlying factors play a role. Since satisfied customers spend more and stay longer with a firm, indicating their loyalty to the firm, research shows that customer satisfaction is a good predictor for financial performance (Anderson and Sullivan, 1993). On the other hand, customer performance leads to higher profitability by declining acquisition costs and higher price tolerance of customers (Chang et al., 2014; Jayachandran et al., 2005; Reichheld, 1996). Building on this literature, we hypothesize the following:
H3: Customer performance positively influences firm performance
2.5 Organizational structure
2.5.1 Conceptualization of organizational structure
Organizational structuring is concerned with the division of labor into a number of distinct tasks, the coordination of these, and the design of the organization’s decision-making system (Mintzberg, 1980). More concrete, organizational structure refers to the grouping of organizational activities and functions (Gebauer and Kowalkowski, 2012). Several authors state that a structure is formed by its level of centralization, formalization and specialization (Mintzberg, 1979; Thalmann and Brettel, 2012). In a centralized organization, the decision-making authority is assigned to top management and members at lower levels within the organization take less participation in the decision-making process. Formalization refers to the degree of strict rules and standardized procedures that need to be obeyed. Lastly, specialization refers to what extent the worker has a broad task or a specialized task and next to that, to what extent the worker has control over the task (Jaworski and Kohli, 1993; Mintzberg, 1979)3.
17 As Mintzberg (1980) states, the coordination of tasks is part of organizational structuring. Coordination can be achieved by having informal coordination activities, and the alignment of these. Alignment is defined as the shared understanding of organizational goals and objectives at all levels and within functional units of the organization (Kathuria et al, 2007). Moreover, alignment reflects the recognition by functional units of being interdependent in order to benefit the organization (Luca and Atuahene-Gima, 2007). Interdepartmental connectedness, indicating the degree of contact among employees across functional units, facilitates the interaction, exchange, and utilization of information (Jaworski and Kohli, 1993). An aligned organizational structure therefore prevents functional silos, reduces conflict, it contributes to the sharing of information within and among functional units, and it contributes to the firm-wide vision to share, use and enhance customer information in order to facilitate the level of responsiveness (Eng et al., 2006; Kohli and Jaworski, 1990; Peltier et al., 2013; Shah, 2006).
The enhancement of customer information is facilitated by integrating the customer. Customer integration refers to the coordination and collaboration with critical customers in order to understand their needs. Subsequently, this information about the customer’s requirements can be beneficial in the decision-making when developing products and/or services (Flynn et al., 2010; Lamberti, 2013). We define a customer integrated organizational structure as a structure that facilitates the dialogue with the customer, herewith involving the customer, to discover their desires and requirements. This fosters the development of products and/or services around these needs (Day, 1994; Flynn et al., 2010). Based on the above, we will use centralization, alignment and customer integration as elements in our analysis on organizational structure.
2.5.2 Organizational structure and customer centricity
In a decentralized organization, as opposite to a centralized organization, the decision-making authority is pushed down to lower levels, making the organization more adaptive and responsive to the environment which enhances customer-interactions (Lin and Germain, 2003; Thalmann and Brettel, 2012). The interaction with the individual customer increases the knowledge about the customer's preferences and can be used to achieve profitable customer relationships. (Ramani and Kumar, 2008; Srinivasan et al., 2002). Moreover, customer-interactions, which are enhanced by a customer-centric approach, generate customer loyalty and profitability (Kasim and Minai, 2009; Sheth et al., 2000). Consistent with these findings, centralization is found to be serving as a barrier for firms in getting to know their customers and herewith continuously create superior value (Auh and Menguc, 2007; Jaworski and Kohli, 1993; Narver and Slater, 1990). Centralizing the
18 making authority at the top rather than empowering employees in lower levels impedes the firm’s ability to collect and respond to the customers’ needs and preferences (Auh and Menguc, 2007; Jaworksi and Kohli, 1993; Lin and Germain, 2003). Hence, it prevents the organization to maintain continuous interaction with customers to understand their explicit and hidden needs, a prerequisite in order to be customer-centric (Lamberti, 2013). Drawing on those arguments, we propose that a centralized organizational structure has a negative effect on the customer-centric approach of a firm and therefore firms with a decentralized structure will be better able to be customer-centric. Accordingly, we hypothesize:
H4: Centralization negatively influences customer centricity.
As mentioned, aligned organizations are better able to share, use and enhance information throughout the organization since functional silos withholding information from other functional units are inhibited (Eng et al., 2006; Lamberti, 2013; Peltier et al., 2013). Customer information can therefore be brought deeper into functional areas of the firm and is not isolated among marketing managers (Reinartz et al., 2004). Organizational alignment is also expected to positively affect the firm's ability to adapt to the needs of customers and their ability to develop and manage customer relationships (Homburg et al., 2008; Johnson et al., 2012; Reinartz et al., 2004). Employees of different departments working together and their willingness of intra-departmental sharing of customer information will improve customer information systems (Johnson et al., 2012; Zahay and Peltier, 2008). The collection and sharing of customer information within a coordinated, aligned organizational structure is an important determinant of customer centricity (Lamberti, 2013). Moreover, the focus on integrating all customer-related activities by aligning all organizational activities around customer value-adding activities will enhance the customer centricity of the organization (Sheth et al, 2000). Therefore, we propose the following:
H5: An aligned organizational structure positively influences customer centricity.
19 In addition, customers might be wrongly valuated when the organization would not engage them (Verhoef et al., 2010). Therefore we propose the following:
H6: Customer integration positively influences customer centricity
2.5.3 Organizational structure and performance
Centralization was found to be negatively related to interaction orientation (Thalmann and Brettel, 2012). Responsiveness, the customer concept, and lower levels of bureaucracy are important determinants for interaction orientation and are lacking in a centralized organization (Ramani and Kumar, 2008; Thalmann and Brettel, 2012). Interaction orientation was found to be positively related to the firm’s customer-based relational performance, indicating higher customer satisfaction levels (Ramani and Kumar, 2008). High customer satisfaction levels will contribute to creating superior customer performance (Hooley et al., 2005). These findings indicate that centralization hinders customer performance by disabling the firm to interact with their customers in order to create satisfied customers. Moreover, centralization impedes employees’ flexibility and willingness to satisfying the customer needs and demands (Boles et al, 2001). This inflexibility leads to employees giving less consideration to the customer’s point of view and therefore employees are not willing to go to extra lengths to reach customer satisfaction (Boles et al., 2001), which is paramount for customer performance (Hooley et al., 2005). Based on the above theory we expect the following concerning the relation between centralization and customer performance:
H7: Centralization negatively influences customer performance.
Centralization was found to not only impede customer orientation, it also hinders the effect of customer orientation on firm performance (Auh and Menguc, 2007; Thalmann and Brettel, 2012). In several studies, firm performance was found to be positively influenced by customer orientation (Narver and Slater, 1990; Brady and Cronin ,2001) and centralization is an obstructing factor in this relationship. Since little research exists about the relation between centralization and customer centricity, our hypothesis is based on customer orientation literature since customer orientation is also focused on putting the customer's interest first (Deshpandé et al., 1993). Therefore, the following hypothesis on the relation between centralization and firm performance is formulated:
20 Kathuria et al. (2007) examined the relation between alignment and performance and found evidence for the existence of this relationship. The configuration of objectives and decisions throughout various levels within the firm, referring to alignment, was found to be related to performance. Evidence was also found for the relationship between cross-functional coordination and firm performance in the supply chain (Eng, 2006). As was already stated, organizational alignment is concerned with the sharing of information within and across functional units. This sharing of knowledge within the organization contributes to a learning orientation which in turn positively affects a firm's performance (Calantone et al., 2002). The sharing and utilization of customer related information within various functions in the organization improves the firm's customer information systems (Eng et al., 2006; Zahay and Peltier, 2008), and leads to improved customer performance when effectively managed (Zahay and Griffin, 2004). Based on this information, we expect a positive association between alignment and customer performance and we assume a positive association between alignment and firm performance. Due to these expectations, the following hypotheses are formulated:
H9: Alignment positively influences customer performance H10: Alignment positively influences firm performance
Maintaining close relationships with the customer enables the organization to be more responsive to the customer requirements for products/services. Subsequently, it enables the organization to be more accurate in designing suited products/services. Looking at empirical research on the relation between customer integration and performance, Swink et al. (2007) found that integration has a positive effect on both customer satisfaction, as part of customer performance, and on market performance, measured by financial performance measures. This is consistent with research results of Frohlich and Westbrook (2001), showing that, in terms of performance improvement, it is important to maintain a customer integration strategy. Using customer needs and preferences and translating this later on in required products/services might be costly for organizations, but in the long run it has the potential to generate positive outcomes through the creation of customer engagement value (Kumar et al., 2010). This is confirmed by the finding that it pays off to empower the customer (i.e. connect and collaborate with the customer) (Fuchs and Schreier, 2011). This customer empowerment leads to stronger behavioral intentions, reflected in purchase intentions and loyalty. Resulting from the above findings, we state the following:
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2.6 Processes
2.6.1 Conceptualization of processes
Organizational processes are concerned with the designing and execution of work practices to create and sustain customer value (Srivastava et al., 1999). Customer value can be created by collecting and analyzing customer information, which are essential functions for customer relationship management (CRM), a core organizational process (Jayachandran et al., 2005; Shah et al., 2006). CRM has gained much attention in academic marketing literature (e.g. Payne and Frow, 2005; Peelen et al., 2009; Reinartz et al., 2004; Srivastava et al., 1999; Verhoef, 2003) and is concerned with identifying customers, creating their knowledge, building profitable long-term relationships with customers, and shaping customer perceptions of the organization and its products (Payne and Frow, 2004; Peelen et al., 2009). Reinartz et al. (2004) provide a clearer conceptualization of CRM processes, focusing on the customer-facing level, getting a single view of the customer across all customer touch points and the diffusion of the customer data to all customer-facing functions. Organizational processes, and specifically CRM processes, can be used to discriminate between profitable and unprofitable customers in order to maximize the value of the relationship portfolio (Jayachandran et al., 2005; Reinartz et al., 2004). Based on the above theory, we will define customer-centric processes as the design of the organizational activities in such a way that the information from the individual customer along all touch points is captured, stored and utilized to create a single and comprehensive view of the customer, in order to build a profitable long-term relationship. Therefore, the closely related concepts collection of customer information, and the use of customer information will be used as the two constructs in our analysis on processes. The next section will provide a link between organizational processes and customer centricity and will discuss outcomes of research.
2.6.2 Processes and customer centricity
22 organizations need to collect customer information. To take decisions at an individual level, more customer interactions and the therefrom arising customer information is needed. This is supported by the finding that organizations with a higher level of customer knowledge generation are better able to meet the customer needs (Jayachandran et al., 2004). Based on these findings, we expect the following:
H13: The collection of customer information positively influences customer centricity
The collection of customer information enables organizations to get insights in the individual customer needs and preferences and to act upon this. The use of customer information enables the organization to actually act upon those needs in order to build long-term valuable customer relationships. An interaction orientation can enhance customer relationships when firms interact with their customers and use this information to their advantage (Ramani and Kumar, 2008). When using CRM processes, organizations can effectively identify its customers by consulting the stored information, and build long-term relationships (Peelen et al., 2009). CRM processes play an important role in enhancing the firm's ability to use individual customer information and enable customer interactions, to positively influence the customer experience (Peelen et al., 2009). Jayachandran et al. (2005) found a positive association between customer-centric systems and relational information processes. Relational information processes are concerned with the use of customer information to appropriately respond to customer needs in order to build long-term relationships. Based on the above, we hypothesize the following:
H14: The use of customer information positively influences customer centricity
2.6.3 Processes and performance
23 that the implementation of CRM within an organization is positive for relationship maintenance and relationship initiation. Based on the above research results on the collection of customer information, and the use of customer information on both types of performance, we hypothesize the following:
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3.
Research Methodology
This chapter describes the research design of this study. In section 3.1 the data collection process is explained and a description of the sample is given. Thereafter, the development of our survey instrument and the measurement of our constructs are explained in section 3.2. Lastly, in section 3.3, all methods needed to enable us to test our hypotheses are discussed.
3.1 Data collection and sample characteristics
Data were collected via the network of PvKO. Our focus was on Dutch organizations with above 30 employees in which the marketing manager would be invited to fill out the survey. Approximately 4000 organizations were approached via email. The email included our study purpose, a confidentiality guarantee of the response and the incentive offering. The offered incentive for participation in our study was threefold: the respondent was offered a management summary, a benchmark of their organization relative to other participating organizations, and an invitation for a seminar to present our research results. A follow up email, stressing the importance of the research, was used to improve the response rate. Out of the approximately 4000 organizations contacted, a total of 162 respondents participated in our study: a response rate less than 5%. From these 162 responses, 155 responses were usable for further analysis since 7 responses were deleted due to a straight-line in response. The sample can be described as follows. More than half of the marketing managers (51,6%) work at firms that are primarily seen as B2B. Just over a fifth (21,3%) of the marketing managers work at firms that are primarily seen as B2C. The remainder is seen as both B2B and B2C. The majority (63,3%) of the marketing managers work at firms with between 50-499 employees, at their location. The proportion of respondents working at a firm also having between 50-499 employees, but within the Netherlands, is practically equal (63,2%). The most notable difference is that the share of firms with 1000 or more employees is larger within the Netherlands (16,8%), than at the location (6,5%). This suggests that some organizations have more than one location within the Netherlands. The largest proportion of the marketing managers work in the business/professional services sector (22,6%), followed by wholesale & retail (16,8%) and the industrial sector (15,5%). Appendix A shows the sample descriptives.
3.2 Survey design and measurement of constructs
25 constructs, items were adjusted or deleted. Based on the specification of our constructs, some new measures were developed. This was realized by using relevant literature and by using the PvKO members’ expertise during meetings on the survey instrument. Since the scales drawn from the literature were in English, the preliminary survey was translated to Dutch. The generated items were carefully reviewed on their wordings and understanding. Thereafter, the resulting list of items was pre-tested by three contacts of the PvKO to eliminate any ambiguities. Based on their suggestions to alter some wordings, potential misunderstandings were eliminated to ensure that the items were understandable for our respondents. The items are measured on a 7-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree. Appendix B shows the final survey.
3.2.1 Measure development – Organizational structure
We measured organizational structure using 16 items that capture the facets of centralization, alignment and customer integration. The items capturing the construct centralization were based on the reflective scale by Thalmann and Brettel (2012). The alignment items were based on existing scales of Luo et al. (2006) and Fisher et al. (1997). Lastly, for customer integration no existing scale was found from which we could derive relevant items. Therefore we developed our own scale, based on relevant literature (Jayachandran et al, 2005; Peelen et al., 2009; Safizadeh et al., 2009; Thalmann and Brettel, 2012).
3.2.2 Measure development – Processes
Processes, capturing the collection of customer information and the collection of customer information, was measured by a total of 20 items. The construct collection of customer information is comprised of 13 items, mainly derived from the Jayachandran et al. (2005). The remainder was derived from research of Peelen et al. (2009) and Beltman (2004). The construct use of customer information consisted out of seven items, of which six items were also derived from the measurement scale of Jayachandran et al. (2005), the other item was derived based on literature of Safizadeh et al. (2003). The final measures regarding organizational structure and processes, by means of reflective scales, and the sources are shown in table 3.1.
26 environmental dynamism and competitive intensity were both measured by reflective scales (Jayachandran et al., 2005). The measurement of the other control variables firm size, industry, and B2B/B2C were provided by MarketResponse. For firm size, the respondents had to indicate the number of employees for both their location and within the Netherlands. Concerning industry, the respondents were given 11 industries to choose from in which their firm is active. Appendix D presents the means, standard deviations and the correlations of the constructs, where the correlations between all variables show significant results.
3.2.3 Factor analysis – Organizational structure
27
3.2.4 Factor analysis - Processes
For the second driver, processes, a total of twenty items were included in the factor analysis to find uncorrelated factors. At first we looked at the KMO statistic (.915) and Bartlett’s Test of Sphericity (.000), indicating that factor analysis was appropriate to proceed with. The first factor analysis gave three factors, all having an eigenvalue above one, an individual variance of >5% and a cumulative variance of >60%. When looking at the communalities, four items did not appear to meet the threshold of >0.5 (Malhotra, 2010) and since we deleted items one-by-one, we first deleted the item with the lowest value and proceeded with factor analysis with the remainder. We needed to perform three iterations before all items met the communalities threshold indicating that the items share enough variance with all other items included. The analysis still indicated three factors, all meeting the variance requirements. Thereafter we looked at the loadings and one item did not meet the critical level to load on any factor (>0.6). After performing a new factor analysis with the remainder fifteen items, seven items appeared to load on more than one factor (>0.3). We first deleted the item with the highest cross-loading. Four iterations were performed after which two other items needed to be, one-by-one, excluded based on the communalities. At this moment, factor analysis was still appropriate based on the KMO and significance statistic, and two factors remained. Still, two items had cross-loadings and were separately deleted from the factor analysis. In total, thirteen iterations were needed to arrive at the final two factors with a remainder of six items, meeting all thresholds, together explaining 74,6% of the variance. The reliability analysis indicated, based on the Cronbach’s alpha (.785 and .865), that the factors were strong enough for further analysis. The loadings and reliability results of the final constructs can be found in table 3.1.
TABLE 3.1
MEASUREMENT INFORMATION
Construct Item Source Cronbach’s α Loadings
Customer integration
Our organization is designed to enable us to respond to the
wishes and needs of the individual customer Based on Peelen et al., 2009
α . 818
.856 Our supply chain allows us to provide customization to the
individual customer Safizadeh et al., 2003 .906 Our organizational results are internally considered as the
sum of the customer(group) results (rather than the sum of the products(group) results)*
Based on
Jayachandran et al., 2005
-
In our organization, for every customer touch point (any place where customer contact occurs) one or more person(s) is (are) responsible*
Based on Peelen et al., 2009
-
In our organization, for (the) entire customer journey(s)
one or more person(s) is (are) responsible* Based on Peelen et al., 2009
- Customer contact (for example in the contact center) is
assessed on revenues rather than costs*
Based on Thalmann & Brettel, 2012
-
Centralization
In our firm, decisions related to the individual customer
tend to be made at lower levels* Thalmann & Brettel, 2012
α . 788
- Our managers are allowed flexibility in solving customer
problems* Thalmann & Brettel, 2012
28
Employees are encouraged to take customer decisions
themselves Thalmann & Brettel, 2012 .821 Small matters are never referred to someone higher in our
firm for a decision Thalmann & Brettel, 2012 .657 Our middle- and lower-level management have substantial
autonomy when taking customer decisions Thalmann & Brettel, 2012 .820 In our firm, the employees have wide leeway in the choice
of means they utilize to accomplish their goals Thalmann & Brettel, 2012 .748
Alignment
In our firm, it is common that frequent customer matters
are discussed between all departments Luo et al., 2006
α .860
.892 Customer decisions are taken by mutual agreement
between all departments involved
Luo et al., 2006 .852
Marketing personnel share close professional ties with
people in other departments* Luo et al., 2006
- The sharing of customer information between departments
is strongly encouraged Fisher et al., 1997 .798
Collection of customer information
In our firm, we have appointed an owner for customer
processes* Peelen et al., 2009
α .785
- Our customer processes have been implemented across the
departments* Peelen et al., 2009
- When designing our processes, we put the customer first* Beltman, 2004 - We provide our customers with multiple ways to contact
the organization*
Jayachandran et al., 2005
- We maintain regular contact with our customers* Jayachandran et al.,
2005
- We collect customer information on an ongoing basis Jayachandran et al.,
2005 .727
We know the needs and wishes of our individual
customers* Beltman, 2004
- The information collected from customers is updated in a
timely fashion* Jayachandran et al., 2005
- We use customer interactions to collect individual
customer information
Jayachandran et al., 2005
.846 We integrate customer information from the various
functions that interact with customers (such as marketing sales, and customer service)*
Jayachandran et al., 2005
-
We integrate customer information from different
communication channels (such as telephone, mail, e-mail, the Internet, fax, and personal contact)*
Jayachandran et al., 2005
-
We merge the customer information collected from various
sources (intern and extern, like market research)* Jayachandran et al., 2005
- In our organization, relevant employees can access
required customer information even when other departments have collected it
Jayachandran et al.,
2005 .850
Use of customer information
We use customer information to develop customer
profiles* Jayachandran et al., 2005
α .865
- We use customer information to predict the churn of our
individual customers Jayachandran et al., 2005 .888 We use customer information to predict the loyalty of our
individual customers Jayachandran et al., 2005 .875 We use customer information to identify appropriate
channels to reach customers*
Jayachandran et al., 2005
- We use customer information to customize our offers* Jayachandran et al.,
2005
- We use customer information to identify our best
customers Jayachandran et al., 2005 .814 Our production, supply, and service processes are to a
great extent determined and shaped by individual customer requirements*
Based on Safizadeh et al., 2003
-
29
3.3 Method of analysis
The first step was to determine whether the control variables actually effected our outcome variables. Before we could determine this, dummy coding was performed for the categorical control variables to enable regression analysis. After determining which control variables show significant effects and should thus be included in further analysis, we could proceed with establishing the model specification for testing our hypotheses. Both steps are described below.
3.3.1 Control effects
To determine whether our control variables should be included in further analysis, our first step was to check if these controls had a significant effect. All control variables were regressed on customer centricity, customer performance (relative to main competitor, previous year, and ambitions) and firm performance (also relative to main competitor, previous year, and ambitions). Results show that for firm performance, relative to the main competitor, the construction industry should be included in the regression analysis. For firm performance, relative to ambitions, the controls construction industry, health and welfare industry, and the industry for sports, culture and tourism should be included based on significant results. The results of the effects of the control variables are shown in Appendix E.
3.3.2 Model specification
Multiple regression analysis was performed to test our hypothesized relationships between the variables. In our survey, respondents were asked to answer several performance measures and to compare these results with their main competitor, previous year and with their ambitions. Based on the regression output, we determined which of the three components of performance (i.e. relative to main competitor, previous year or ambitions) had the most explanatory power (adjusted R²) when interpreting the results. The best model fit, thus having the highest explanatory power, would be used as a basis for our results. This will be determined in chapter 4. We estimated the following equations using multiple regression analysis to test our hypothesized effects:
Model 1: (H4, H5, H6, H13, H14)
CCᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + εᵢ
Model 2: (H1, H7, H9, H11, H15, H17)
30 Model 3: (H1, H7, H9, H11, H15, H17)
CPyearᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + εᵢ
Model 4: (H1, H7, H9, H11, H15, H17)
CPambᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + εᵢ
Model 5: (H2, H3, H8, H10, H12, H16, H18)
FPcompᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + β7CCᵢ*SCᵢ + β8CPᵢ + β9CIᵢ + εᵢ
Model 6: (H2, H3, H8, H10, H12, H16, H18)
FPyearᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + β7CCᵢ*SCᵢ + β8CPᵢ + εᵢ
Model 7: (H2, H3, H8, H10, H12, H16, H18)
FPambᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + β7CCᵢ*SCᵢ + β8CPᵢ + β9CIᵢ + β10HWIᵢ + β11SCTIᵢ + εᵢ
where
CC = customer centricity
CPcomp = customer performance relative to the main competitor CPyear = customer performance relative to previous year CPamb = customer performance relative to the ambitions FPcomp = firm performance relative to the main competitor FPyear = firm performance relative to previous year FPamb = firm performance relative to the ambitions SC = structure - centralization
SA = structure - alignment
SI = structure – customer integration
PC = processes – collection of customer information PU = processes – use of customer information CI = construction industry
31
4.
Results
This chapter presents the results from testing the eighteen hypotheses. In section 4.1 we first determine which of the three components of both customer and firm performance provides the best model fit to continue with for testing our hypotheses. Section 4.2 presents the results of our customer centricity model, section 4.3 presents the results of our customer performance model and in section 4.4 the results of our firm performance model are presented. Lastly, section 4.5 gives the results of mediation analysis.
4.1 Model fit performance
As aforementioned, before interpreting the results of testing our hypotheses, we determined which of the three components of performance (i.e. relative to main competitor, previous year or ambitions) had the most explanatory power. As described in section 3.3, model 2,3 and 4 represent the customer performance models. Based on the adjusted R2 statistic, indicating the explanatory power of the model, we determined the best fit. Model 4, customer performance relative to ambitions, appeared to be the strongest variant of these three models (.296). To determine the best model fit for firm performance, we look at model 5, 6 and 7.
Model 7, firm performance relative to ambitions, indicates the highest explanatory power (.503) of the three. Based on these results, we included customer performance relative to ambitions (i.e. model 4) and firm performance relative to ambitions (i.e. model 7) as dependent variables in our regression analysis to test our hypotheses. Hereafter, with customer performance and firm performance we are referring to the ‘relative to ambitions’ component. The outcomes of the model fit is presented below in table 4.1.
TABLE 4.1
RESULTS MODEL FIT PERFORMANCE Model 2 CPcomp Model 3 CPyear Model 4 CPamb Model 5 FPcomp Model 6 FPyear Model 7 FPamb R2 (Adjusted R2) .236 (.205) .318 (.290) .323 (.296) .418 (.382) .467 (.438) .539 (.503)
4.2 Customer centricity model
32 information and the use of customer information as the processes constructs (H13 and H14). The overall model was found to be significant (.000) and explains 33,4% of the variance of customer centricity. Since centralization does not significantly influence customer centricity, H4 was rejected. We find support for H5 (<.05) and H6 (<.01) that alignment and customer integration positively affect customer centricity, respectively. No support was found for H13, hypothesizing a positive influence of the collection of customer information on customer centricity. The assumed positive influence of the use of customer information on customer centricity on the other hand, is significant (p <.05). Therefore, H14 is supported.
Customer integration seems to have the strongest effect on customer centricity (β = .334), followed by alignment (β = .215) and the use of customer information (β = .161). The results of the multiple regression analysis of the customer centricity model are reported in table 4.2.
4.3 Customer performance model
The influence on the dependent variable customer performance was tested in model 4 by performing multiple regression analysis. We now included customer centricity as an independent variable, hypothesizing a positive effect of customer centricity on customer performance (H1). As independent variables we again added centralization (H7), alignment (H9) and customer integration (H11), the collection of customer information (H15) and the use of customer information (H17). The overall customer performance model was found to be significant (.000), explaining 29,6% of the variance of customer performance. Again, the constructs alignment, customer integration and the use of customer information appeared to positively influence customer performance, and therefore H9 (p <.05), H11 (p <.01) and H17 (p <.10) are supported. The other relationships resulted to be non-significant, thus H1, H7 and H15 were rejected. In comparison with the use of customer information (β = .146) and alignment (β = .188), customer integration also has the strongest effect on customer performance (β = .315). The outcomes of testing the customer performance model are shown in table 4.2.
4.4 Firm performance model
33 all three models, namely 50,3%. The results of measuring the firm performance model resulted in significant effects of customer performance (<.01) and the collection of customer information (<.05). Therefore, we find support for H3, the positive relationship between customer performance, also relative to ambitions, and firm performance. In H8, we predicted that centralization inhibits the positive influence of customer centricity on firm performance, but did not find support for this moderating effect. We accept H16, indicating a positive relationship between the collection of customer information and firm performance. Customer performance shows a rather stronger effect (β = .696) on firm performance, compared to the collection of customer information (β = .179). The results of testing the firm performance model can be found in table 4.2.
Table 4.5 gives a clear overview of all stated hypotheses of this research, related to customer centricity, customer performance, and firm performance. This table shows that only H3, H5, H6, H9, H11, H14, H16 and H17 were supported, the remaining hypotheses were rejected.
4.4.1 Moderator effect
34
TABLE 4.2
REGRESSION RESULTS (standardized coefficients) Hypothesis Model 1 (CC) Model 4 (CP) Model 7 (FP) β β β Main variables Structure – centralization (SC) 4, 7 .076 .080 -.007 Structure – alignment 5, 9, 10 .215b .188b -.078
Structure – customer integration 6, 11, 12 .334a .315a .014
Processes – collection of customer Information
13, 15, 16 -.008 .046 .179b
Processes – use of customer information 14, 17, 18 .161b .146c .050
Customer centricity (CC) 1, 2 -.024 .011 Customer performance 3 .696a Moderator CC*SC 8 -.117 Control variables Construction industry -.079 Health/welfare industry -.017 Sports/culture/tourism industry .026 R2 (Adjusted R2) .356 (.334) .323 (.296) .539 (.503) F-value 16.441a 11.775a 15.195a
Notes:a p-value<.01; b p-value < .05; c p-value < .10
4.5 Mediation analysis
Mediation analysis was performed to assess the two mediators in our conceptual model. First, we tested whether customer centricity serves as a mediator in the relationship between the antecedents of customer centricity and customer performance. Secondly, we tested whether customer performance serves as a mediator in the relationship between customer centricity and firm performance. The procedure suggested by Baron and Kenny (1968) is followed, by performing regression analyses, to determine to what extent the mediator accounts for the relationship between the independent and dependent variables.
4.5.1 Mediating role of customer centricity
To determine whether customer centricity mediates the relationship between the antecedents of customer centricity and customer performance, certain conditions must hold and will be tested in four new models of which the results are presented in table 4.3.
35 of customer information (β = .142, p<.10) show significant results. Second, the antecedents of customer centricity should be related to customer centricity. Alignment and customer integration reveal significant results in model 2. Third, customer centricity must have a significant impact on customer performance, which is confirmed by results in model 3. Fourth, the significant effect of the antecedents on customer performance must disappear when we include the mediator customer centricity in the model for full mediation. For partial mediation, the strength of the effect must be diminished. Model 4 shows that the significant effect of alignment, customer integration and the use of customer information remained with practically the same effect (β = 1.88, p<.05; β = .315, p<.01; β = .146, p<.10). Therefore, we can conclude that customer centricity does not mediate the relation between the antecedents of customer centricity and customer performance.
TABLE 4.3
RESULTS OF MEDIATION ANALYSIS CUSTOMER CENTRICITY
Model 1 Model 2 Model 3 Model 4
Main variables
Structure – centralization .078 .076 .080
Structure – alignment .182b .215b .188b
Structure – customer integration .307a .334a .315a
Processes – collection of customer information
.046 -.008 .046
Processes – use of customer
information .142 c .161b .146c Customer centricity .322a -.024 R2 (Adjusted R2) .323 (.300) .356 (.334) .104 (.098) .323 (.296) F-value 14.201a 16.441a 17.719a 11.775a
Notes:a p-value<.01; b p-value < .05; c p-value < .10
Model 1: CPᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + εᵢ Model 2: CCᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + εᵢ Model 3: CPᵢ = ∝ + β1CCᵢ + εᵢ
Model 4: CPᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + εᵢ
4.5.2 Mediating role of customer performance
36 centricity, and customer centricity are regressed on customer performance. Significant results are found for alignment (β = .188, p<.05), customer integration (β = .315, p<.01) and the use of customer information (β = .146, p<.10). The third step requires that customer performance is related to firm performance, again controlling for the construction industry, the health/welfare industry and the sports/culture/tourism industry. Model 3 shows that customer performance is indeed significantly related to firm performance. Lastly, in model 4 we regress the antecedents of customer centricity, and customer centricity itself on firm performance, controlling for the construction industry, the health/welfare industry and the sports/culture/tourism industry, and we include the mediating variable customer performance. When we include customer performance in the model, the relationship between customer integration and firm performance becomes insignificant and the effect is reduced (β = .015). Also for the use of customer information, the relation becomes insignificant together with a reduced effect (β = .051). These results suggest that customer performance functions as a mediator in the relationship between customer integration and firm performance, and in the relationship between the use of customer information and firm performance. The results are shown below in table 4.4.
TABLE 4.4
RESULTS OF MEDIATION ANALYSIS CUSTOMER PERFORMANCE
Model 1 Model 2 Model 3 Model 4
Main variables
Structure – centralization -.017 .080 -.078
Structure – alignment .062 .188b -.080
Structure – customer integration .229b .315a .015
Processes – collection of customer
information .181
c .046 .178b
Processes – use of customer
information .153 c .146c .051 Customer centricity -.096 -.024 -.059 Customer performance .696a .696a Control variables Construction industry -.140c -.093 -.078 Health/welfare industry -.044 -.048 -.018 Sports/culture/tourism industry -.086 .006 .025 R2 (Adjusted R2) .225 (.177) .323 (296) .508 (.495) .539 (.507) F-value 4.675a 11.775a 38.759a 16.814a
Notes:a p-value<.01; b p-value < .05; c p-value < .10
Model 1: FPᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + β7CIᵢ + β8HWIᵢ+ β9SCTIᵢ + εᵢ Model 2: CPᵢ = ∝ + β1SCᵢ + β2SAᵢ + β3SIᵢ + β4PCᵢ + β5PUᵢ + β6CCᵢ + εᵢ
Model 3: FPᵢ = ∝ + β1CPᵢ + β2CIᵢ + β3HWIᵢ+ β4SCTIᵢ + εᵢ
37
TABLE 4.5
OVERVIEW HYPOTHESES AND RESULTS
Hypothesis Sig.
H1: Customer centricity positively influences customer performance ns
H2: Customer centricity positively influences firm performance ns
H3: Customer performance positively influences firm performance p <.01
H4: Centralization negatively influences customer centricity ns
H5: An aligned organizational structure positively influences customer centricity p <.05 H6: Customer integration positively influences customer centricity p <.01
H7: Centralization negatively influences customer performance ns
H8: Centralization negatively moderates the positive relationship between ns customer centricity and firm performance
H9: Alignment positively influences customer performance p <.05
H10: Alignment positively influences firm performance ns
H11: Customer integration positively influences customer performance p <.01
H12: Customer integration positively influences firm performance ns
H13: The collection of customer information positively influences customer ns centricity
H14: The use of customer information positively influences customer centricity p <.05 H15: The collection of customer information positively influences customer ns performance
H16: The collection of customer information positively influences firm p <.05 performance
H17: The use of customer information positively influences customer performance p <.10 H18: The use of customer information positively influences firm performance ns
38
5.
Conclusions and discussion
This chapter will discuss the results from this study. Section 5.1 concludes the study results and will discuss the theoretical implications. Subsequently, the managerial implications will be explained in section 5.2. Lastly, section 5.3 will present some study limitations and suggestions for future research.
5.1 Discussion
Although customer centricity has gained attention in conceptual research (Lamberti, 2013; Sheth et al., 2006), no empirical research has been performed on the concept customer centricity. Thus, an important contribution of this research is the measurement of the road map to achieve customer centricity, developed by Shah et al. (2006). This study aims to answer the research question: ‘To
what extent do organizational structure and processes influence the level of customer centricity of a firm, and how do they affect customer performance and firm performance?’
To answer this, we explored the effects of centric organizational structure and customer-centric processes on customer customer-centricity, customer performance and firm performance. We hypothesized a positive relation between customer centricity and both customer performance and firm performance. Lastly, a positive relationship between customer performance and firm performance was hypothesized and measured.
