Completion date: August 28, 2014 Nhat Quang LE Analyzing the Antecedents and the Effects of Employee-Customer Interface in a Customer-Contact Center

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Analyzing the Antecedents and the Effects of Employee-Customer

Interface in a Customer-Contact Center

Nhat Quang LE

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Analyzing the Antecedents and the Effects of Employee-Customer

Interface in a Customer-Contact Center

Completion date: August 28, 2014

Research Master Thesis

University:

RijksUniversiteit Groningen

Faculty:

Economics and Business

Department:

Marketing

Program:

Research Master in Economics and Business,

Marketing track

Author:

Nhat Quang LE

Address:

Esdoornlaan 626

9741MG, Groningen, the Netherlands

Phone Number:

+31 (0) 6 447 478 96

Email:

lequangnhatth@gmail.com

Student number:

2483084

First Supervisor:

Prof. Dr. J.E. (Jaap) Wieringa

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Abstract

The goal of this study is to examine the antecedents and the effects of the employee-customer interface on customer outcomes including satisfaction and word-of-mouth (WOM) intentions. Two aspects of the employee-customer interface have been investigated including the employee’s customer orientation (CO) and so-called efficiency orientation (EO). Using dyadic data collected from both customers (599 mystery callers and 208 real customers) and employees (51 employees) of a customer-contact center, the results suggest that CO have positive effects, whereas EO have negative effects on customer satisfaction and WOM intentions. In addition, we find that job empowerment and listening skills have positive effects on the employee’s CO, whereas social competence has a negative effect on the employee’s EO. Finally, the findings suggest that the discrepancies between perceptions of the customers and the employees about the customer-oriented and efficiency-oriented behaviors in a call center have significant impacts on customer outcomes.

Key words: Customer Orientation, Efficiency Orientation, Call Center, Customer-Contact

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To date, most managers are asking for the movement from a market segment to an individual customer approach when examining the impact of marketing activities (Ramani and Kumar 2008). Similarly, Kumar and Ramani (2006) suggest the new perspective on the future is to “think of customer as individuals, and not aggregates”. In fact, most of companies nowadays are possessing rich data on each individual customer. In addition, especially in personal service settings such as hotel, financial services, etc., the interaction between firms and individual customers is proved to be able to help firms better understand and refine their knowledge about customer tastes and interests (Srinivasan et al. 2002). Therefore, now seems to be the proper time to focus on service interactions, or in other words, to develop “interaction orientation”, which is defined by Ramani and Kumar (2008) as “a firm’s ability to interact with its individual customers and to take advantage of information obtained from them through successive interactions to achieve profitable customer relationships”, in order to build a lasting competitive advantage (Rayport and Jaworski 2004).

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1.2 Customer Orientation and Efficiency Orientation

As the person who directly interacts with customer, the contact employee is expected to fulfill the needs of the customers and satisfy them. Nevertheless, very frequently, the contact employee has to face the conflict between job performance expectations and performance evaluation criteria, which refers to “role conflict” (Tuten and Neidermeyer 2004). For example, the call center strategy is usually minimizing talk time, because the less talk time, the greater the number of consumers handled by a call center. Therefore, in a customer-contact center, the expectations of the managers stressing operational efficiency (e.g. short response time, short waiting time) may clash with the desire of customers who want problem resolution or satisfaction (de Ruyter et al. 2001). The question now is that should the contact employees try to satisfy the customers totally (CO) or should they try to get as many calls answered as quickly as possible (EO).

Previous research has found that this role stress may have negative impact on job satisfaction, organizational commitment, and employee performance (de Ruyter et al. 2001). Nevertheless, it is unclear that how the employee-customer interface (customer versus efficiency oriented) influences on customer outcomes such as customer satisfaction, gratitude, or WOM intention. Indeed, despite an existing rich body of literature concerning the role that frontline employees play in shaping customers’ service evaluations (Wieseke et al. 2012), previous research focuses primarily on the impacts of service-worker CO that represent only a subset of employee-customer interface. As far as it is concerned, empirical investigations of the simultaneous effects of the employee’s CO and the employee’s EO in service encounters are absent. Furthermore, under which circumstances the employee’s CO and/or EO are suitable has not been considered by a research (Grizzle et al. 2009). In addition, no previous study has investigated an inclusive set of their antecedents in terms of organizational beliefs, human resources management practices, and employee characteristics. This research therefore aims to fill these gaps by comprehensively examining the antecedents and the outcomes of the employees’ customer- and efficiency-oriented behaviors in a customer-contact center.

1.3 Research Context: Customer-Contact Center

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2018 (Britt 2012). In spite of a huge growth in contact centers to better provide customer services, CO in call centers has been analyzed by very few researchers including Rafaeli et al. (2008), Dean (2007), and de Ruyter et al. (2001). This study uses both customer data (collected from mystery callers and regular customers) and employee data sets to shed light on and confirm the discussed theoretical antecedents and impacts of employees’ customer- and efficiency-oriented behaviors.

1.4 Problem Statement and Research Questions

From the above perspective, the main research objective can be stated as follows:

“What are the antecedents and outcomes of the employee-customer interface in a customer-contact center?”

To be in line with the above objective, four main research questions are defined as: 1. How do the employee’s CO and EO affect customer satisfaction and WOM intention? 2. Which characteristics of the employee-customer interaction and relationship moderate the impacts of employee-customer interface on customer outcomes and how large are those moderating effects?

3. Which factors in a customer contact center influence the employee-customer interface? 4. Whether do the gaps between the employees’ and the customers’ perceptions about the employees’ behaviors influence customer satisfaction and WOM intention?

1.5 Theoretical Contribution

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Secondly, this study expands the previous research on moderating effects of relationship characteristics (e.g. relationship age) (Bell et al. 2005; Verhoef 2003; Verhoef et al. 2002) into the associations between employee-customer interface and customer outcomes. Moreover, the moderating effects of the characteristics of interaction between employees and customers (e.g. call length and topics of call) are also examined.

Thirdly, when examining the outcomes of the employee-customer interface, the data were collected from both regular customers and mystery callers. Although the data collected from mystery shoppers have been used widely in the marketing research context due to its cost effectiveness, only little academic research has addressed the reliability and validity of mystery shopping data (e.g. Finn 2001), and no existing research relates to mystery calling (MC) data (Ammeraal et al. 2013). Furthermore, the precise effects of some important aspects of employee-customer interface such as CO from a customer’s perspective are less clear (Dean 2007). As it is hard to capture the customer’s perception of service experience and the size of data set from regular customers is often limited, this study assesses the effects of CO and EO from a mystery caller’ perspective that are expected to bring managers some useful insights into the customer’s thinking.

Fourthly, previous studies have focused on different subsets of the antecedents of the employee-customer interface in a customer-contact center. It is therefore necessary to investigate all of these antecedents in one study. More specifically, this study attempts to contribute to the current literature by analyzing the impacts of the triad: 1) the organization as a whole, 2) the managers and their management practices, 3) the employees and their characteristics, on the employees’ behaviors.

Finally, this study builds the literature up by combining the data at the customer level and the data at the employee level to see whether the gaps existing between the perceptions of the customers and the employees about the customer-oriented and efficiency-oriented practices in a customer-contact center can influence the customer outcomes, namely customer satisfaction and word-of-mouth intentions.

1.6 Thesis Outline

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CHAPTER 2: THE EFFECTS OF EMPLOYEE-CUSTOMER INTERFACE ON CUSTOMER SATISFACTION AND WORD-OF-MOUTH INTENTIONS IN A CUSTOMER-CONTACT CENTER

2.1 Introduction

The employee-customer interface is one of three pillars in the employee management process, along with the manager-employee interface, and the employee-role interface. As stated by Hartline and Ferrell (1996), whereas the two latter interfaces deals with customer-contact employees only (i.e. employees’ responses, job satisfaction, self-efficacy…), the employee-customer interface deals with employee-customer interaction during the service encounter such as the relationship between customer perceptions of the service encounter and contact employees’ attitudinal and behavioral responses (Hartline and Ferrell 1996).

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We start with theoretical background of employee-customer interface, customer outcomes, characteristics of employee-customer interaction and relationship. After that, we depict the conceptual research framework made on a basis of the literature review. Thereafter, the research method is proposed after describing the data collected from a Dutch insurance company. We also present the reliability and validity of adopted measures in the methodology part. Finally, conclusions, recommendations, managerial implications, limitations and future research will conclude this chapter.

2.2 Theoretical Framework

2.2.1 Effect of Employee-Customer Interface

As mentioned above, two components of the employee-customer interface are the employee’s CO and EO. Whereas the employee’s CO is a familiar construct in service marketing literature, the employee’s EO is a relatively new concept that has not been empirically tested in any academic marketing research. Hereafter, these two concepts will be explained based on existing marketing literature.

2.2.1.1 Employee’s Customer Orientation

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The possible consequence of CO is a main issue that has been investigated by many scholars, especially the positive effect of customer-oriented behavior on customer satisfaction (e.g. Bettencourt and Brown 1997; Goff et al. 1997; Ramsey and Sohi 1997). There seems to be a consensus, which is that customer-oriented employees are more likely to deliver exceptional service quality and create satisfied customers (Stock and Hoyer 2005). The direct effect of employee’s CO on customer satisfaction has been found by some authors (e.g. Goff et al. 1997; Stock and Hoyer 2005). This leads to the first hypothesis:

Hypothesis 1: Employee CO will exert a positive influence on customer satisfaction.

In addition, Kelley (1992) proposes service firms with customer-oriented employees will not only “increase the satisfaction of their customers”, but also lead “to the development of long-term relationships”. For example, after being satisfied, the customer may strongly appreciate the contact employee, and reward those efforts with stronger loyalty intentions. Indeed, Homburg et al. (2011) find a strong positive main effect of functional CO, which is viewed as a set of task-oriented behaviors (e.g. describing products accurately or identifying customer needs), on customer loyalty. These authors further indicate that the growing emphasis on building long-term buyer-seller relationships strongly favors the development of customer loyalty. Jones et al. (2003) also posit that the salesperson’s CO plays an important role in retaining customers and inducing customer loyalty. Moreover, the research of Dixon et al. (2010) shows that 25% of customers are likely to say something positive about their customer service experience, 23% of customers who had a positive service interaction told 10 or more people about it, whereas 65% are likely to speak negatively, and 48% of customers who had negative experiences told 10 or more others. Hence, it is expected that a strong CO will lead to more favorable behavioral outcomes such as WOM intentions (e.g. Brady and J. Joseph Cronin 2001; Dean 2007). This leads to the second hypothesis:

Hypothesis 2: Employee CO will exert a positive influence on customers’ WOM intentions.

2.2.1.2 Employee’s Efficiency Orientation

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with conflicting objectives between service quality and quantity (bureaucratic efficiency), which are referred to “a customer-oriented bureaucracy” (Korczynski 2002) or “role conflict” (Tuten and Neidermeyer 2004). The employees who adopt efficiency-oriented approach will not wonder “How can I best solve this person’s problems?” (Saxe and Weitz 1982) but instead ask, “How can I handle the call in the most efficient way?” Therefore, the employee’s EO may be defined as the degree to which contact employees try to maximize the number of customers’ problems solved rather than concerning about the quality of solutions. Because the high level of EO often corresponds to a low level of CO in a salesperson and vice versa, the effects of EO on customer outcomes are expected to be opposite to those effects of CO. One can argue that, in some easy cases, customers may prefer a short and efficient procedure in order resolve their problems quickly. Nevertheless, nowadays, that kind of easy procedure is often designed to allow customers solve their problems by themselves or with the assistance of technology only such as looking at frequently asked questions (FAQ) or using handy forms in the company website. Thus, it is believed that customers only need aid of customer-contact employees when the problems cannot be solved too quickly. As a result, in those cases, customers will perceive the completion of the solution more important than the speed of handling procedure, as no one would like to call or meet again for the old problem. This study concerns the employee’s EO in which the contact employee focuses only on the speed of problem handling rather than a quality of problem solution. Hence, it is reasonable to expect a negative effect of the employee’s EO on customer outcomes. That leads to two following hypotheses:

Hypothesis 3: Employee EO will negatively affects customer satisfaction.

Hypothesis 4: Employee EO will negatively affects customers’ WOM intentions.

2.2.2 Moderating Effects

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apart from the length of the interaction, which is the type of problem or the topic motivating each call.

2.2.2.1 Characteristics of Service Interaction

Service interaction is the person-to-person interactive process occurring in a service encounter when the service is delivered (Surprenant and Solomon 1987). Building on previous research in this area (e.g. Donavan et al. 2004), this study examines two characteristics of service interaction including duration of call and topic of call.

The first characteristic of service interaction that is considered as an important moderator in this model is the duration of call. As proposed by Rafaeli et al. (2008), research in organizational behavior too often omits the critical role of context when examining the relationship between employee behaviors and organizational outcomes. These authors find a significant interaction effect of call duration and employees’ customer-oriented behavior on customer evaluations of service quality. The results imply that customers in longer service encounters may be more sensitive to the effect of CO (Rafaeli et al. 2008). For example, when the needs of those customers are finally met, they will experience a higher level of satisfaction compared to a customer with a shorter interacting duration. This may be explained that CO is very time-consuming, as the contact employees often need more time to identify and fulfil the customer needs. In contrast, with too short calls, the customers are more likely to underestimate the employees’ efforts, which are (intended to) put into solving the problem. Moreover, the contact time with customers can also strengthen the influence of service workers’ CO on job performance, job satisfaction, and commitment (Donavan et al. 2004) and as such, if the employees can perform better, there will be more customers satisfied. This leads to the following hypothesis:

Hypothesis 5: The positive influences of employee CO on (a) customer satisfaction and (b)

WOM intentions are stronger when call length is long than when call length is short.

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Hypothesis 6: The negative influences of employee EO on (a) customer satisfaction and (b)

WOM intentions are stronger when call length is short than when call length is long.

In contrast, previous research studying the effect of the second moderator, topic of call, is very limited. One exception is the research of Rafaeli et al. (2008) in which they stated that very complex calls are more likely to give employees more opportunities to perform their CO in comparison with simple calls. In this study, the complexity of the call is not measured. Instead, the calls are classified as new- versus existing-business calls, or financial versus non-financial calls. Although it is hard to determine which group of calls is more complex, with respect to the new-business call or financial call, the efforts of the employee in trying to find a suitable solutions or products are expected to be more highly evaluated by customers. Then, it is postulated that the effect of employees’ CO on customer satisfaction and WOM intentions will be stronger when the call is about new business or financial business.

In contrast, if the employee performs the efficiency-oriented behavior, the customer might sympathize because the topic of call is relatively new and complicated (such as financial issues). The customer could think that the employee probably does not know the issues well, causing the quick but incomplete solution. Thus, the negative effects of EO on satisfaction and WOM intentions could be reduced for these calls. That leads to the following hypotheses:

WOM intentions are stronger when the call is about new (financial) business than when the call is about existing (non-financial) business).

WOM intentions are weaker when the call is about new (financial) business than when the call is about existing (non-financial) business).

2.2.2.2 Length of Relationship

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experience higher effects of the employee’s CO on their satisfaction and loyalty than a customer with shorter relationship does. On the other hand, Bolton (1998) finds that customers with more experience weigh their prior cumulative satisfaction more heavily and new information (relatively) less heavily. This means that it is easier and quicker to make a customer dissatisfied when he/she is a less-experienced customer rather than when he/she is an experienced customer. Specifically, new customers with less experience will often feel more vulnerable (and more dissatisfied) with respect to the employee’ EO compared to old customers. These predictions are hypothesized as follows:

WOM intentions are stronger when relationship duration is long than when relationship duration is short.

WOM intentions are smaller when relationship duration is long than when relationship duration is short.

2.2.3 Customer Outcomes

This study analyzes two customer outcomes. The first customer outcome is customer satisfaction relating to “attitude toward organization,” whereas the second one is WOM intentions relating to “behavior toward organization” (Guo et al. 2009). Measuring the effect of the employee-customer interface on both of customer attitude and behavior is frequently implemented in customer-centric organizations (Ramani and Kumar 2008) and have also been applied in many earlier studies (e.g. Dean 2007; Goff et al. 1997; Ramsey and Sohi 1997; Stock and Hoyer 2005).

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satisfaction can create a high positive attitude toward the object (e.g. brand, service provider) and may consequently cause a true brand loyalty from a customer. Furthermore, satisfied customers who possess greater perceptions of an overall service quality will more likely to engage in behavioral intentions such as the likelihood of repurchasing and recommending (Boulding et al. 1993). As the effect of customer satisfaction on WOM intentions has been investigated extensively in the marketing literature (e.g. Lam et al. 2004; Verhoef et al. 2002; Wangenheim and Bayón 2007; Woisetschläger et al. 2011), it is expected that the influence of the employee-customer interface on WOM intentions will be partially mediated through satisfaction. Specifically, the employee-customer interface directly (and significantly) affects both customer satisfaction and WOM intention, but the latter effect will be weakened (but still significant) if the direct effect of customer satisfaction on WOM intention is taken into account. In other words, the indirect effect of customer/EO on WOM (through the mediation of satisfaction) does not equal its total effect (when satisfaction is not considered) but is smaller and of the same sign (Shrout and Bolger 2002). This leads to the following hypothesis:

Hypothesis 11: The influences of employee CO (a) and/or EO (b) on customers’ WOM

intention are partially mediated by customer satisfaction.

In addition, Verhoef et al. (2002) find evidences to support the moderating effect of relationship age on the association between satisfaction and relationship outcomes such as customer referrals. Thus, it is expected that the length of service provider-customer relationship will strengthen the impacts of satisfaction on WOM intentions. This leads to the hypothesis:

Hypothesis 12: The positive impact of customer satisfaction on WOM intentions is stronger

when relationship duration is long than when relationship duration is short.

2.2.4 Conceptual Model

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Figure 2.1: Conceptual Model

The consequences of the CO have been examined in various research. To sum up, Table 2.1 provides a summary of key impacts of the employee-customer interface (mainly CO), which supports the abovementioned rationales behind the variables and the corresponding effects in the proposed model.

Table 2.1: Literature Review on the Consequences of Employee-Customer Interface Author(s),

year

Context of the study Main impacts of employee-customer interfaces

Other variables

Bagozzi et al. (2012)

Various of firms across multiple industries

CO  Contextual knowledge formation, motivation to learn from customers, and buying knowledge formation (+) Bettencourt

and Brown

(1997)

Multi-state, western bank Contact employee extra-role

customer service  Customer satisfaction (+); Contact

employee role-prescribed

customer service  Customer H9a H9b H10b H10a H6b H8b H5a H7a H5b H7b H12 H11a H11b H6a H8a Employee’s CO Employee’s EO Customer satisfaction WOM intentions

* Characteristics of service interaction (call length, topic of call)

* Characteristics of relationship (relationship length)

Moderators

Employee-customer interface Customer outcomes

Relationship length

H4 H1

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satisfaction (+) Blocker et al.

(2011)

Global business-to-business firms in information and

communication technology

services operating in 19

industries across India,

Singapore, Sweden, the United Kingdom and the United States

Proactive CO  Customer value perceptions (+) (1); Responsive CO  Customer value perceptions (+) (2); Proactive CO*Responsive CO  Customer value perceptions (+)

M for (1), (2): Customer value change intensity (+); M for (1): Global relationship scope (+), Transnational

relationship structure (+) C: Offer Quality, service

support, personal

interaction and other

offering provider advantages Brady and J. Joseph Cronin (2001)

Various customers CO  Customer perceptions of

service employee performance, the service environment, and any physical goods included (+) CO  Customer perceptions of overall service quality, value, satisfaction, and behavioral outcomes (I+)

Brown et al. (2002)

Food services industry CO  Self- and supervisor

ratings of performance (+) Cross et al. (2007) Field business-to-business salespeople Salesperson CO  Salesperson performance (+)

Dean (2007) Two call centers: End consumers of insurance and business customers of online banking

Customer focus  Perceived service quality, customer loyalty (+); Customer feedback  Affective commitment (+) Donavan et

al. (2004)

Financial institution (a midsize bank) Service-worker CO  Job satisfaction (1), Commitment (2), and customer-oriented altruism (+) M for (1), (2): Contact time (+) Franke and Park (2006)

Meta-analysis Customer oriented selling 

Self-rated performance, job satisfaction (+)

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(1997) makes salesperson (+); CO  Satisfaction with dealer (P+);

Selling Orientation 

Satisfaction with salesperson (–) Grizzle et al. (2009) A chain of full-service restaurants Employee CO  Performance frequency of customer-oriented behaviors (+) M: Unit CO climate (+); C: tenure Harris et al. (2005)

Real estate industry CO  Work Satisfaction (+)

Homburg et al. (2011)

Various companies operating mainly in B2B markets Functional CO  Customer loyalty (+) M: Customer’s task orientation (+); Product importance (+); Brand strength (–) Jones et al. (2003)

National consumer goods

manufacturer’s sales force and retail trade customers

Salesperson’s CO  Role conflict, role ambiguity, and customer’s propensity to switch suppliers (–)

Kelley and Hoffman (1997)

Regional insurance company Customer-oriented behavior  Service quality (+); Sales-oriented behavior  Service quality (–)

Luo et al. (2008)

Chinese firms located in three metropolitan areas of China

CO  Customer trust and customer commitment (+) M: Channel networking (+); Governmental networking (P–) Rafaeli et al. (2008)

Service interaction in the call center

Customer-oriented behaviors  Customer evaluation of the quality of a service encounter (+)

M: Call Length (+)

Ramsey and Sohi (1997)

Car buyers A customer’s perception of

listening behavior  Trust in

the salesperson and the

anticipation of future interaction with the salesperson (+)

Rozell et al. (2004)

Medical devices nationwide company

CO  Emotional intelligence (+), CO  Performance (+)

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Hoyer (2005) Customer oriented behaviors

(+); Customer oriented

behaviors  Customer

satisfaction (+); Customer-oriented attitudes  Customer satisfaction (+)

Note: (+) Positive effects, (–) Negative effect, “”: Correlate with, P: Partial support for effect, M: Moderating effect, I: indirect effect, C: Control variable. Only significant effects were reported.

2.3 Research Design

2.3.1 Data Description

2.3.1.1 Two Individual Samples

This study examines the impacts of employee-customer interface using data obtained from a customer-contact center of a health insurance company in the Netherlands. The data provided by a marketing research agency were collected from two independent rating sources - “real” customers and mystery callers. On the one hand, a total number of 599 mystery callers were recruited via six rounds and they have assessed the performance of the customer-contact center on various aspects such as satisfaction, gratitude, and WOM intention. A number of 80 mystery callers in the first round were not asked about the new construct of EO. Thus, only 519 individuals had answered the questionnaire completely. On the other hand, the data of 208 customers were collected via a telephone survey conducted by a research team. To achieve the most accurate answers, only customers who had contacted the company in the past five months were selected and asked about their last telephone contact. Of 208 customers, only 105 answers had no missing value. Those missing values mainly belong to items of CO, EO, or satisfaction constructs.

Given that the number of missing observations in regular customer (RC) data is rather substantial compared to MC data, the first data set from mystery callers are used to test the proposed model and hypotheses. Nevertheless, as the mystery caller does not have a “real” relationship with the company, the second data set from the regular customers are used to analyze the moderating effect of relationship age.

2.3.1.2 Sample Characteristics

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more-experienced mystery callers. Whereas among 208 customers, 67.8% were female, 65.4% were in Friesland, and 75% of them had been with the company for five years or more.

2.3.1.3 Measures

Table 2.2: Measures for Examined Constructs Variable

Customer orientation (CO) (1 = fully disagree, 7 = fully agree) CO1 The employee did his/her best to figure out what my needs/wishes were. CO2 The employee did his/her best to figure out which product would help me best. CO3 The employee had my interests in mind.

CO4 The employee looked for the product that helped me solve my problem in the best way. CO5 The employee offered me a product that fit me the best in my situation.

Efficiency orientation (EO) (1 = fully disagree, 7 = fully agree) EO1 I had the impression that the employee took all their time to assist me.

EO2 The employee tried to finish the call without ensuring that I was satisfied with the service.* EO3 I felt that the staff wanted to keep the call as short as possible.*

EO4 I had the feeling that the employee was under time pressure.*

EO5 The employee tried to handle the conversation as efficient as possible rather than to offer quality to me.*

Satisfaction (SAT) (1 = fully disagree, 7 = fully agree) SAT1 I am very satisfied with this call.

SAT2 This call met my expectations completely. SAT3 I am not disappointed with this call. SAT4 My experiences with this call are excellent.

Gratitude (GRAT) (1 = fully disagree, 7 = fully agree) GRAT1 I feel thankful to the employee I spoke with.

GRAT2 I feel appreciative to the employee I spoke with. GRAT3 I feel grateful to the employee I spoke with.

WOM intentions (0 = certainly not recommend, 10 = certainly recommend) WOM Would you recommend the organization to friends or colleagues?

* EO2-EO5 were recoded inversely (1 = fully disagree, 7 = fully agree)

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higher the score, the lower efficiency-oriented level. Single-item construct of WOM intentions which was measured by 11 point scale ranged from 0 (certainly not recommend) to 10 (certainly recommend) is also reported in Table 2.2.

The constructs considered, which were developed by Ammeraal et al. (2013), were borrowed from previous studies and adapted to the specific situation. In particular, employees’ CO items used a subset of the items from SOCO scale developed by Saxe and Weitz (1982); satisfaction was measured with the scales adapted from items provided by Hennig-Thurau et al. (2002); gratitude was measured with the scales provided by Palmatier et al. (2009); and the WOM intentions item was a 11-point scale suggested by Reichheld (2003). In order to test whether measures of these constructs are consistent with previous understanding of their natures, confirmatory factor analysis (CFA) was performed. Specifically, CFA was used to examine whether these constructs except for WOM (due to its single-item operationalization) are underlying the corresponding items used.

2.3.1.4 Reliability and Validity of Measures

Table 2.3: Measures and Measurement Criteria (MC Data)

Variable Standardized Factor Loading Error Variance CRa _AVEb

CO .94 .77 CO1 .841 .293 CO2 .896 .198 CO3 .828 .314 CO4 .910 .173 CO5 .908 .175 EO .91 .67 EO1 .782 .388 EO2 .862 .256 EO3 .887 .213 EO4 .855 .268 EO5 .704 .505 Satisfaction .97 .88 SAT1 .960 .077 SAT2 .953 .091 SAT3 .865 .251 SAT4 .967 .065 Gratitude .94 .85 GRAT1 .896 .197 GRAT2 .920 .154 GRAT3 .948 .102

a_{CR: Composite Reliability;}b_{AVE: Average Variance Extracted. They are computed based on formulas given by} Fornell and Larcker (1981) as follows: CR = (∑𝑛𝑖=1𝜆𝑖)2⁄((∑𝑛𝑖=1𝜆𝑖)2+ (∑𝑖=1𝑛 𝛿𝑖)); AVE = (∑𝑖=1𝑛 𝜆2𝑖)⁄ ∑( 𝑛𝑖=1𝜆𝑖2+

∑𝑛𝑖=1𝛿𝑖) where λ = standardized factor loading; δ = error variance = 1- λ2; n = number of indicators.

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The scores in Table 2.3 and construct-level correlations for all constructs in Table 2.7 were results of CFA implemented in Mplus7 using MC data. The maximum likelihood (ML) estimator with standard errors and a scaled Chi-square test statistic that are robust to nonnormal and missing data were used. CFA provided the adequate global fit measures: Comparative Fit Index (CFI) = .940, Tucker-Lewis index (TLI) = .928, which are all above .9 (Jöreskog and Sörbom 1993), and Standardized Root Mean Square Residual (SRMR) = .066, which is less than .08 (Kline 2010). Nevertheless, the Satorra-Bentler’s (SB) scaled 𝜒2(113) = 487.885 (p-value = .000) indicates a poor fit between the covariance matrix generated by the model and the observed (actual) covariance matrix. Given the sensitivity to sample size of 𝜒2-value (Jöreskog and Sörbom 1993), this study does not rely on the significance of this Chi-square test. In contrast, the root mean square error of approximation (RMSEA) = .074, which is below the recommended cut-off value of .08 (Bloemer et al. 1999), indicates the good model specification.

The construct validity is examined based on four rules of thumb suggested by (Hair et al. 2010). Firstly, all items significantly loaded on the hypothesized latent variables and all standardized factor loadings were higher than .7, indicating that they converge on the latent constructs. In addition, composite reliability (CR) and average variance extracted (AVE) scores also demonstrated adequate convergent validity of constructs. This use of CR and AVE is in line with the two-step approach recommended by Anderson and Gerbing (1988), in which the measurement model is used in conjunction with the structural model to enable a comprehensive and confirmatory assessment of construct validity. As mentioned by Netmeyer et al. (1990), CR, as with coefficient alpha, assesses the internal consistency of a measure, or in other words, the reliability for the construct. Although there is no consensus on which of several alternative reliability estimates is best, CR was employed here as it is often used in conjunction with SEM. As shown in Table 2.3, the CR values were all greater than .9, and the AVE values were more than .65 that indicate a good construct reliability and adequate convergent validity, following Fornell and Larcker (1981) and Hair et al. (2010)1. Another component of construct validity is discriminant validity, which indicates how a construct is different from other constructs (Hair et al. 2010). A rigorous test used to assess discriminant validity is to compare the AVE values of latent constructs in CFA results with

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the squared correlation between all construct pairs following (Fornell and Larcker 1981; Hair et al. 2010; Luo et al. 2008). More specifically, the AVE values should be larger than the squared correlation estimates as a common point or latent construct should be able to explain more of the variance in the observed variables to which it is theoretically related than its share with another construct (shared variance) (Hair et al. 2010). From Table 2.3 and Table 2.7, it is shown that the AVEs for CO and EO (.77 and .67 respectively) both exceed their squared correlation (.31). This result supports the discriminant validity between the two constructs, leading to the conclusion that each of the two constructs CO and EO is unique and captures some aspects of employee-customer interface that other measures do not (Hair et al. 2010). In other words, these two constructs were truly distinct from each other.

In addition, by performing a principal component analysis with Varimax rotation method using SPSS20, Table 2.4 indicates that the five CO and the five EO items loaded highly on separate components. Moreover, all cross-loading were below .4, providing further evidence of convergent and discriminant validity of these scales (Schepers et al. 2012).

Table 2.4: CO and EO Component Matrix

Item Component* 1 2 CO1 .844 .278 CO2 .903 .224 CO3 .808 .350 CO4 .895 .239 CO5 .905 .211 EO1 .393 .737 EO2 .218 .868 EO3 .182 .891 EO4 .183 .870 EO5 .323 .703

Table 2.5: SAT and GRAT Component Matrix

Item Component 1 SAT1 .945 SAT2 .944 SAT3 .901 SAT4 .944 GRAT1 .859 GRAT2 .922 GRAT3 .909

Notes: Item numbering corresponds to sequence of items in Table 2.2; Extraction method: Principal Component Analysis; * Rotation method: Varimax

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Furthermore, by performing structural equation modeling (SEM) with the full sample, the path model without GRAT fit the data better than the nested model with GRAT (SB scaled 𝜒2

𝑑𝑖𝑓𝑓 =131.084, dfdiff = 49, p-value < .0001) (see Table 2.6). Hence, GRAT was excluded

from the estimation model.

Table 2.6: Model Comparison to Determine the Inclusion of GRAT (MC Data)

Path Model AICa _BICb _Scaled_𝝌𝟐_(df)

Model 1 (without GRAT) 40776.975 41128.596 534.786 (129)

Model 0 (with GRAT) 45163.202 45589.542 667.571 (178)

SB scaled Chi-square difference test*: 𝜒2

𝑑𝑖𝑓𝑓 =131.084, dfdiff = 49, p-value < .0001

a_{AIC: Akaike’s Information Criterion = -2LL + 2k;}b_{Bayesian Information Criterion = -2LL + klnN where LL:} likelihood value; k: number of parameters, N: sample size; *_{This is computed based on a formula given by (Satorra} and Bentler 2001): 𝜒2

𝑑𝑖𝑓𝑓= (𝑐0𝜒02− 𝑐1𝜒12)/[(𝑐0𝑑𝑓0− 𝑐1𝑑𝑓1)/(𝑑𝑓0− 𝑑𝑓1)] where c0 and c1 are scaling correction factors associated with the models 0 and 1 (1.2934 and 1.3050 respectively).

Table 2.7: Means, Standard Deviations, and Correlations (MC Data)

M SD 1 2 3 4 5 6 7 8 9 10 11 Focal constructs 1. CO 5.65a _1.55 ₁ 2. EO 5.71a _1.00 _.552*** ₁ 3. SAT 4.92a _1.48 _.806*** _.610*** ₁ 4. WOM 7.21 2.02 .670*** _.558*** _.761*** ₁ 5. CL 6.01 3.65 .319*** _.270*** _.242*** _.191*** ₁ 6. NBC N.A. N.A. -.086** _-.085* _-.108*** _-.137*** _.074* ₁ 7. FNC N.A. N.A. -.052 -.058 -.022 -.001 -.147*** _-.296*** ₁ Control variables 8. Age 41.52 10.87 -.020 .060 .034 -.018 .125*** _.036 _-.048 ₁

9. Gender N.A. N.A. .017 .036 .015 -.007 -.011 .009 -.029 -.159*** ₁

10. #jobs 61.17 44.69 -.074* _.014 _-.072 _.000 _-.092** _.026 _-.037 _.026 _-.022 ₁

11. #months 30.77 9.55 .000 .106** _.084** _.056 _-.038 _.069* _-.038 _.118** _-.047 _.203*** ₁

*p < .1; **p < .05; ***p < .01; Notes: N.A. = not applicable, CL: Call length; a _{Computed based on factor score weights.}

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Table 2.8: Operationalization of Moderators and Control Variables (MC Data)

Variable Operationalization

Moderator Call

length

Duration of call in minutes

NBC Topics of call dummy (0 = existing business; 1 = new business) FNC Topics of call dummy (0 = non-financial; 1 = financial) Control variable

Age Age in years

Gender Gender dummy (0 = male; 1 = female)

#jobs Total number of jobs that the mystery caller has performed #months Total number of months since the mystery caller was employed

The results of CFA using RC data are not reported here, as they were similar to those with MC data. Regarding to RC data, one can find the tests of measures reliability and validity in Appendix 2, the descriptive analysis of study variables is in Appendix 3. As mentioned above, the moderator included in the model using RC data is relationship length, whereas gender and region were included as control variables. The operationalization of study variables can be found in Table 2.9.

Table 2.9: Operationalization of Moderators and Control Variables (RC Data)

Variable Operationalization

Moderator Relationship length

Length of relationship between a customer and the company in months

Control variable

Gender Gender dummy (1 = female; 0 = male)

Region Region dummy (1 = Friesland; 0 = Others)

2.3.2 Analysis

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reduced compared to the one with interaction terms. Furthermore, another reason is that the main effects of call length and relationship length are not much interesting in this area. Finally, as stated before, three hypotheses H9, H10, and H12 predict the moderating effects of relationship duration. Nevertheless, mystery callers are not “real” customers of the company, thus, relationship length does not exist for the MC data. Therefore, to examine these hypotheses, a multi-group SEM analysis was implemented using the second data set from regular customers.

2.3.3 SEM Assumption

As with all statistical methodologies, to ensure accurate inferences, structural equation modeling requires that certain underlying assumptions be satisfied such as sufficiently large sample size, completely random missing data, correct model specification, multivariate normality, continuously distributed endogenous variables (Kaplan 2008). Main assumptions are discussed hereafter.

2.3.3.1 Sample Size

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2.3.3.2 Missing Data

Kline (2010) suggested that a few missing values, such as less than 5% on a single variable, in a large sample may be of little concern or listwise deletion may be acceptable. Nevertheless, the proportions of cases with missing data in this study are 13.4% (=80/599) for MC data and 49.5% (=103/208) for RC data, which are much larger than the recommended value of 5%. In this case, listwise deletion method for dealing with incomplete data could lead to two major problems consisting of: 1) the decrease in statistical power, and 2) the risk of nonconvergent solutions, incorrect standard errors (Byrne 2009). Therefore, other methods should be considered. A number of researchers have asserted that full information maximum likelihood (FIML) outperform traditional methods of handling missing data such as listwise deletion, pairwise deletion, or similar response pattern imputation (e.g. Enders and Bandalos 2001). Specifically, in comparison with other recent and sophisticated methods such as expectation maximization (EM), mean substitution (Mean), and regression imputation (Regression), Olinsky et al. (2003) indicated that FIML is a superior method in the estimation of most different types of parameters in a SEM format. Summing up, FIML seems to be the most preferred and reliable method to deal with missing data. Hence, this study employed FIML technique to handle incomplete data problems.

2.3.3.3 Multivariate Normality

A critically important assumption in the conduct of SEM analyses is that the data are multivariate normal (Byrne 2009; Kaplan 2008; Kline 2010). This means that: 1) all the individual univariate distributions are normal; 2) the join distribution of any pair of the variables is bivariate normal or each variable is normally distributed for each value of every other variable; 3) All bivariate scatterplots are linear and the distribution of residuals is homoscedastic (Kline 2010). Hence, it is necessary to check for univariate normality first, before assess the multivariate normality, as the former is a necessary, although not sufficient, condition for multivariate normality.

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Table 2.10: Assessment of Normality (MC Data)

Variable Skewness C.R.* _Kurtosis _C.R.

CO1 -1.058 -9.836 .055 .255 CO2 -.805 -7.487 -.657 -3.055 CO3 -1.434 -13.342 1.454 6.762 CO4 -.976 -9.079 -.226 -1.053 CO5 -.908 -8.445 -.464 -2.156 EO1 -2.339 -21.757 5.355 24.902 EO2 -1.897 -17.640 2.409 11.205 EO3 -1.755 -16.320 1.840 8.554 EO4 -2.013 -18.724 3.004 13.969 EO5 -1.319 -12.268 .489 2.274 SAT1 -1.190 -11.064 .728 3.387 SAT2 -1.051 -9.775 .154 .716 SAT3 -1.230 -11.442 .565 2.627 SAT4 -1.164 -10.826 .467 2.170 WOM -1.040 -9.677 1.062 4.937 Age -.061 -.564 -.227 -1.057 Gender -.921 -8.562 -1.153 -5.360 #jobs 1.406 13.075 1.726 8.026 #months -.720 -6.698 -1.074 -4.993 Multivariate 193.065 77.849

* C.R.: critical ratio. Note: This test was implemented in Amos21 using non-missing data

Regarding to MC data, as shown in Table 2.10, all univariate kurtosis values ranged from .055 to 5.355 are less than 7, indicating no item to be substantially kurtotic. Nevertheless, the critical ratio of multivariate kurtosis (Table 2.10), which implicitly represents normalized estimate of multivariate kurtosis developed by Mardia (1970), is rather large (77.325) indicating nontrivial positive kurtosis. Further, Bentler (2006) has suggested that, in practice, values larger than 5 provide evidence of nonnormality in the sample. Hence, this data are not normally distributed. Moreover, to identify multivariate outliers, which have extreme scores on two or more variables, the squared Mahalanobis distance (D2) for each case was computed. Typically, an outlying case will have a D2_{that is substantially different from all}

the other D2 values (Byrne 2009). Results showed a list of 100 cases farthest away from the centroid (see Appendix 4 for first 10 and final ranked scores). Given the small gap in Mahalanobis D2 values between any two cases, no evidence of serious multivariate outliers was detected.

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or CFI are modestly underestimated (Byrne 2009). One way to the analysis of nonnormal data is to implement asymptotic distribution-free (ADF) estimation. Nevertheless, a problem with the ADF estimator is that it can only perform well when sample sizes are extremely large (1000 to 5000 cases) (West et al. 1995).

An alternative approach is to use the robust approach developed by Satorra and Bentler (1994); Satorra and Bentler (2001); Satorra and Bentler (1988) that can work very well with smaller sample sizes (Byrne 2009). By using Satorra-Bentler (SB) procedure, the maximum likelihood Chi-square statistic as well the standard errors of parameter estimates are corrected to adjust for potential multivariate nonnormality (Morhart et al. 2009). This study employed this approach for dealing with multivariate nonnormal data.

2.4 Results

2.4.1 Direct Effects of Employee-Customer Interface on Customer Outcomes

Table 2.11: CO and EO as Antecedents of Satisfaction and WOM (MC Data)

Hypothesis Full sample βa _t-value _Supported Hypothesized Paths CO  Satisfaction H1 .677*** 16.591 Yes CO  WOM H2 .131** 2.347 Yes EOb  Satisfaction H3 .227*** 4.697 Yes EOb  WOM H4 .139*** 2.965 Yes SAT  WOM .578*** _9.432

Control Variable Paths

Age  Satisfaction .028 1.108 Age  WOM -.048* -1.809 Gender  Satisfaction .001 .053 Gender  WOM -.030 -1.082 #jobs  Satisfaction -.038 -1.288 #jobs  WOM .053* _1.913 #months  Satisfaction .065** 2.391 #months  WOM -.013 -.485 R2_Satisfaction _.693 R2_WOM _.605

* p < .1; ** p < .05; *** p < .01; a _{Table reports standardized coefficients;}b _{EO scale was recoded inversly in} which low rating score corresponds to high level of EO.

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CFI = .933, TLI = .914, SRMR = .054 and RMSEA = .072. Overall, the model explained 69.3% of the variance in Satisfaction and 60.5% in WOM.

A large positive effect of CO on satisfaction was found (β = .677, p < .01), in strong support of H1. The negative effect of EO on satisfaction was much smaller in absolute value but still significant (β = .227, p < .01), in support of H3. It is noted that EO scale was recoded inversely in which low rating score corresponds to high level of EO. Hence, the positive value of its coefficient indicates a negative effect from the focus on efficiency of employees and vice versa. H2 also received support as the effect of CO on WOM was positive and significant (β = .131, p < .05). The negative effect of EO on WOM was slightly larger (β = .139, p < .01), in strong support of H4.

2.4.2 Moderating Effects of Call Duration and Topic of Call

To examine the moderating effects of call duration or topics of call on the relationships between employee-customer interface and customer outcomes, the multiple-group analysis was employed with MC data (Schepers et al. 2012), applying scaled SB procedure and FIML approach. First, the sample was split into two (longer versus shorter) call groups at the median call duration value (5 minutes) (Aiken and West 1996). Analogously, the sample splitting was repeated based on the dummy variables of call topics (new business versus existing business, financial versus non-financial). Second, a base (unconstrained) model was calibrated in which the structural path to be moderated was freely estimated. An alternative (constrained) model in which this path was fixed was also calculated. A moderation effect would exist if a significant SB scaled chi-square change in the comparison of these two models was observed (Schepers et al. 2012).

2.4.2.1 Call Duration

H5 predicts that the association between CO and customer satisfaction/WOM may be stronger with a relatively longer call than a shorter one. As shown in Table 2.12, the path loading from CO to customer satisfaction was .766 in the longer call group, which is larger in magnitude than its path loading of .629 in the shorter call group. Furthermore, results from SB scaled Chi-square difference test between these two groups indicated this difference is significant at 10% significance level (∆𝜒2_{(𝑑𝑓) = 3.788 (1), p = .051 < .1). Thus, H5a is}

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Table 2.12: The Moderating Effects of Call Duration (MC Data)

Longer call Shorter call SB scaled Chi-square difference testc

βa _t-value _βa _t-value _Moderating

hypotheses

∆𝝌𝟐_(𝒅𝒇) _p-value _Supported

Structural Paths

CO  SAT .766*** _11.623 _.629*** _13.033 _H5a _3.788*₍₁₎ _.0516 _Yes

CO  WOM .106 1.410 .163** _2.199 _H5b _{.117 (1)} _.7327 _No

EOb_{ SAT} _.119 _1.352 _.297*** _5.541 _H6a _{1.128 (1)} _.2883 _No

EOb_{ WOM} _.072 _1.017 _.185*** _3.066 _H6b _{.495 (1)} _.4818 _No

SAT  WOM .672*** _8.456 _.488*** _5.729

Age  SAT -.001 -.020 .060 1.699 Age  WOM -.099*** _-2.781 _-.011 _-.269 Gender  SAT -.006 -.154 .001 .031 Gender  WOM -.033 -.862 -.031 -.779 #jobs  SAT -.031 -.676 -.059 -1.562 #jobs  WOM .050 1.205 .043 1.216 #months  SAT .090** _2.118 _.057 _1.468 #months  WOM -.064 -1.487 .021 .558

* p < .1; ** p<.05; *** p<.01; a _{Table reports standardized coefficients;}b _{EO scale was recoded inversly in which low rating} score corresponds to high level of EO; c_{The formula is the same as in Table 2.6.}

Notes: Overall fit measures: 𝜒2_{(280) = 706.645, CFI = .929, TLI = .916, SRMR = .066, RMSEA = .071.}

In contrast, the difference in size between the path loadings from CO to WOM in the longer call and the shorter call group is not significant (∆𝜒2_{(𝑑𝑓) = .117 (1), p > .1). Thus, H5b is}

not supported by the data. Moreover, although CO significantly affected WOM in the full sample, for the longer call group, its effect was insignificant (β = .106, p > .1). In contrast, this effect was significant for the shorter call group (β = .163, p < .05).

H6 predicts that the relationship between EO and customer satisfaction/WOM may be stronger with a relatively short call than with a long call. As shown in Table 2.12, the effect of EO on satisfaction is insignificant for the longer call group (β = .119, p > .1) but strongly significant for the shorter call group (β = .297, p < .01). Similarly, the effect of EO on WOM is only significant for the shorter call group (β = .185, p < .01), not for the longer call group (β = .072, p > .1). The common finding is that, when comparing the two group, there was no significant change of Chi-square for both the path from EO to satisfaction (∆𝜒2_{(𝑑𝑓) = 1.128}

(1), p > .1) and the path from EO to WOM (∆𝜒2_{(𝑑𝑓) = .495 (1), p > .1). Hence, H6a and H6b}

were not supported by the data.

2.4.2.2 Topic of Call

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call, all the loading between employee-customer interface and customer outcomes were strongly significant. In addition, when comparing these two groups, there was no significant change of SB scaled Chi-square for all these following paths: CO  satisfaction (∆𝜒2 = .023,

df = 1, p > .1), CO  WOM (∆𝜒2 = .549, df = 1, p > .1), EO  satisfaction (∆𝜒2 = 1.309, df

= 1, p > .1), EO  WOM (∆𝜒2 = .742, df = 1, p > .1). Thus, the effects of CO and/or EO on

satisfaction and WOM are equal in magnitude between groups of existing and new calls. Table 2.13: The Moderating Effects of Business-Dummy Variable for Call Topics (MC Data)

New business Existing business SB scaled Chi-square difference testc

hypotheses ∆𝝌𝟐_{(𝒅𝒇) p-value Supported} Structural Paths CO  SAT .690*** _9.651 _.658*** _12.922 _H7ad _{.023 (1)} _.8805 _No CO  WOM .068 .572 .168*** _2.693 _H7b _{.549 (1)} _.4589 _No EOb_{ SAT} _.152 _1.563 _.261*** _4.633 _H8a _{1.309 (1)} _.2526 _No EOb_{ WOM} _.082 _.998 _.165*** _2.848 _H8b _{.742 (1)} _.3891 _No SAT  WOM .613*** _5.689 _.545*** _7.073

Age  SAT -.023 -.387 .038 1.429 Age  WOM -.054 -.983 -.039 -1.252 Gender  SAT .110* _1.955 _-.024 _-.820 Gender  WOM -.069 -1.181 -.025 -.748 #jobs  SAT .074 1.288 -.073** _-2.045 #jobs  WOM .062 1.061 .052* _1.666 #months  SAT -.001 -.020 .087*** _2.637 #months  WOM .042 .655 -.022 -.656

* p < .1; ** p<.05; *** p<.01; a _{Table reports standardized coefficients;}b _{EO scale was recoded inversly in which low rating score}

corresponds to high level of EO; c_{The formula is the same as in Table 2.6;}d_{This is Wald Test of Parameter Constraints used to replace SB}

scaled Chi-square difference test as MLR estimator results a negative Chi-square difference values. Notes: Overall fit measures: 𝜒2_{(280) = 727.059, CFI = .930, TLI = .917, SRMR = .059, RMSEA = .073.}

Table 2.14: The Moderating Effects of Financial-Dummy Variable for Call Topics (MC Data) Financial call Non-financial call SB scaled Chi-square difference testc

hypotheses ∆𝝌𝟐_(𝒅𝒇) _p-value _Supported Structural Paths CO  SAT .765*** _10.998 _.645*** _14.255 _H7a _{1.162 (1)} _.2811 _No CO  WOM .064 .461 .144** _2.387 _H7b _{.349 (1)} _.5548 _No EOb_{ SAT} _.172** _2.162 _.248*** _4.512 _H8a _{1.24 (1)} _.2655 _No EOb_{ WOM} _.178* _1.761 _.126** _2.334 _H8bd _{.138 (1)} _.7101 _No SAT  WOM .647*** _3.783 _.570*** _8.684

Age  SAT -.019 -.611 .035 1.148 Age  WOM -.091 -1.615 -.037 -1.211 Gender  SAT -.090* _-1.868 _.024 _.788 Gender  WOM .019 .307 -.041 -1.297 #jobs  SAT -.050 -.941 -.034 -1.007 #jobs  WOM .077 1.386 .049 1.563 #months  SAT .138*** _2.691 _.051 _1.620 #months  WOM .012 .199 -.020 -.631

* p < .1; ** p<.05; *** p<.01; a _{Table reports standardized coefficients;}b _{EO scale was recoded inversly in which low rating score corresponds}

to high level of EO; c_{The formula is the same as in Table 2.6;}d_{The Wald Test of Parameter Constraints was used here as MLR}

estimators using Mplus7 results “negative” SB scaled Chi-square difference statistic.

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Similarly, when comparing financial and non-financial call groups, there was no significant change of Chi-square for all the structural paths: CO  satisfaction (∆𝜒2 = 1.162, df = 1, p > .1), CO  WOM (∆𝜒2 = .349, df = 1, p > .1), EO  satisfaction (∆𝜒2 = 1.24, df = 1, p > .1), and EO  WOM (∆𝜒2_{= .138, df = 1, p > .1) (Table 2.14). Therefore, the effects of CO}

and/or EO on satisfaction/WOM are indifferent between financial and non-financial calls. Hence, there was no evidence to support H7a, H7b, H8a, and H8b by the data.

2.4.3 Partial Mediating Effect of Satisfaction

H11 predicts that the significant effects of employee-customer interface on WOM found will be shrunk upon the addition of the direct association between satisfaction and WOM. To examine this hypothesis, a bootstrap method was used in which the mediating effect of the mediator (satisfaction) was tested directly and the original sample was replaced and re-estimated for 5000 times. After that, the distribution of the obtained estimates was examined and determined with a 10%, 5%, and 1% significance level (Shrout and Bolger 2002). A FIML approach was still used to deal with missing data problem whereas a scaled SB procedure could not be used together with bootstrapping. Nevertheless, bootstrapping can also be used to estimate non-biased standard errors for nonnormal data (Kline 2010).

Table 2.15: The Mediating Effect of SAT on Relationships between CO, EO, WOM (MC Data)

Relationship

Direct effect Indirect effect Total effect

Hypotheses Supported Βa _2.5th_pctile _Βa _97.5th_pctile _Βa CO  SAT .677*** _--- _--- _--- _.677*** H11a Yes CO  WOM .131** _.293 _.391*** _.489 _.522*** EOb_{ SAT} _.227*** _--- _--- _--- _.227*** H11b Yes EOb_{ WOM} _.139*** _.071 _.131*** _.191 _.270*** SAT  WOM .578*** _.578***

* p < .1; ** p < .05; *** p < .01; a _{Table reports standardized coefficients;}b _{EO scale was recoded inversly in which low rating score}

corresponds to high level of EO.

Notes: Overall fit measures: 𝜒2_{(129) = 697.874, CFI = .940, TLI = .923, RMSEA = .086}

Completion date: August 28, 2014 Nhat Quang LE Analyzing the Antecedents and the Effects of Employee-Customer Interface in a Customer-Contact Center

Analyzing the Antecedents and the Effects of Employee-Customer

Interface in a Customer-Contact Center

Nhat Quang LE

Analyzing the Antecedents and the Effects of Employee-Customer

Interface in a Customer-Contact Center

Completion date: August 28, 2014

Research Master Thesis

University:

RijksUniversiteit Groningen

Faculty:

Economics and Business

Department:

Marketing

Program:

Research Master in Economics and Business,

Marketing track

Author:

Nhat Quang LE

Address:

Esdoornlaan 626

9741MG, Groningen, the Netherlands

Phone Number:

+31 (0) 6 447 478 96

Email:

lequangnhatth@gmail.com

Student number:

2483084

First Supervisor:

Prof. Dr. J.E. (Jaap) Wieringa

Abstract

Table of Contents

List of Figures and Tables

List of Appendices