exploring customer participation in value creation from...

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2012 TOPCO 崇越論文大賞

論文題目

Exploring Customer Participation in Value Creation from Customers’ and

Employees’ Perspective: for Professional Financial Service

報名編號： _______D0093________________

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Exploring Customer Participation in Value Creation from Customers’

and Employees’ Perspective: for Professional Financial Service

Abstract This study delineates and empirically tests hypotheses regarding the effect of

customer participation on value creation and satisfaction for both customers and employees with different customer ability and employee emotional intelligence in the context of professional financial services. Using data collected from 383 pairs of customers and professional financial advisors from several public sectors banks and private banks in Taiwan, this research examines how (1)customer participation drives customer satisfaction and employee job satisfaction, through the relational values creation, and (2)the moderating effect of customer ability on customer relational values creation and customer satisfaction; and (3) the moderating effect of emotional intelligence on employee relational values creation and employee job satisfaction.

The research findings are as follows: first, customer participation can drive customer satisfaction through relational value creation. Second, customer participation not entirely creates positive employee relational value, also increases employees’ job stress and hampers job satisfaction. Third, the result of moderating effect states customer ability can facilitate customer satisfaction, but employees emotional intelligence not completely affect the relationship between value creation and job satisfaction. Keywords: customer participation, employee job satisfaction, organizational commitment, emotional intelligence, common method variance

Chapter One、Research Background and Motivation

With the changes in economic structure, gradual growth of the global bank services industry. However, due to the low barriers to entry of the bank service industry, resulting in a highly competitive market for services. To maintain competitive advantage, businesses must be innovative and provide a variety of products and services to meet customer needs. Therefore, firms must learn from and collaborate with customers to create values that meet their individual and dynamic needs.

Value co-creation is a central tenet of the service-dominant logic and the main premise of customer participation. Customer participation should deliver value to both customers and firms (Auh et al. 2007; Lovelock and Young 1979), and customers who perceive more value from their service encounters tend to be more satisfied (Ouschan,

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Sweeney, and Johnson 2006; Patterson and Smith 2001). Such customer participation should benefit customers through improved service quality, more customization, and better service control (Dabholkar 1990; Xie, Bagozzi, and Troye 2008), and it should benefit firms through increased customer satisfaction and productivity gains.

However, little empirical research has examined or confirmed the value co-creation process in the business-to–consumer context, particularly from a dyadic (i.e., customers and employees) perspective. In this research, we empirically test how customer participation drives service outcomes (i.e., customer satisfaction, employee job satisfaction, and employee organizational commitment) through the creation of relational values for both customers and service employees in the business-to-consumer context of Taiwance professional financial service.

Chapter Two、Literature Review and Hypothesis Development

一、 About Common Method Variance (CMV)

Then before to understand what is common method variance, we must to know 「method variance」, according to Campbell and Fiske (1959) defined method variance as because of the error due to measurement tools, they explained the result of psychometric measurement can divided into random error variance and systematic variance. And systematic variance also can divided into trait variance and method variance, among this random error variance and method variance are all the error from measurement method, and all effect the validity of measurement tools. Be further inferred, if simultaneous detection of two or more constructs, the result of correlation between the constructs is very high, while in fact, the result of high correlation may be not real high correlation between the constructs, but due to measure tools, it is result of method variance appear in both the constructs, brings common method variance, which led to inflation of correlation between the constructs.

However, the two common ways to avoid common method variance are separation approach of data collecting and design approach of instrument developing. And scholars considers that for reduce the effect of common method variance, the most basic practice is the arrangement of the questionnaires (i.e reverse question and random arrangement) and feasible isolation method as the first step of data collection. On this research we use design approach of instrument developing and the way of isolation customer and employee to avoid common method variance occur.

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二、 Effect of Customer Participation on Satisfaction Through Relational Value Creation In regard to create on the value of customer participation, this study focuses on

relational value creation for customer and employees. Prior studies have suggested that participation can be intrinsically attractive (Bateson, 1985) and enjoyable (Dabholkar and Bagozzi 2002). Parasuraman, Zeithaml, and Malhotra (2005) call for more research on the experiential aspects of enjoyment and fun in service encounters. Therefore, customer participation may increase communication and relationship building between customers and employees (Claycomb, Lengnick-Hall, and Inks, 2011). In addition, on the service provider side, employees may fulfill their social needs for approval when they co-create service with customers, similar to the way their perceptions of being valued by the organization enable them to satisfy their social needs for approval, affiliation, and esteem (Eisenberger et al, 1986). Thus, every interaction between employees and customers symbolize an opportunity to create relational value for both parties (Fleming, Coffman, and Harter 2005). And then we define customers and employees could co-create relational value through their sense of enjoyment and by building relationships.

Based on the above literature, this customer participation－relational value－satisfaction link, for both customers and employees, is particularly evident when the service is long term, customers depend heavily on credence qualities for their service evaluation, and employees have more personal connections with customers (Fleming, Coffman, and Harter, 2005), such as in the professional services context. Therefore, we hypothesized customer participation have positive relationship satisfaction through relational value creation. The assumptions as follow :

H1: Customer participation positively influences customer relational value creation H2: Customer participation positively influences employee relational value creation H3: Customer relational value creation positively influences customer satisfaction H4: Employee relational value creation positively influences employee job

satisfaction 三、 Effect of customer satisfaction on customer loyalty

Oei et al, (2006) empirical prove satisfaction will effect coproduction with direct or indirect short-term loyalty is that when the other better alternative choices come up, the customers will change the other one without any hesitation. However, customer satisfaction has been proven to be a key factor to affect the long-term relationships between customers and suppliers (Geyskens et al, 1999). Many studies have shown that customer satisfaction will affect customer loyalty, and shape of long-term relationship (Ganesan, 1994; Mittal and Kamakura, 2001; Mittal et al, 1998). When customers are satisfied with the service provider, customer will patronize again or

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recommended the service provider to other customers. Heskett et al, (1997) also believe that customer loyalty will be rapid increase after customer satisfaction. Based on above, we can find that have a strong correlation between customer satisfaction and customer loyalty, and we hypothesize as follows:

H5: Customer satisfaction positively influences customer loyalty

四、 Effect of job satisfaction on organizational commitment The research basis of Heskett et al, (1994) proposed the Service-Profit Chain,

points out employees’ intention of stay have affection with job attitude, and employee organization commitment will be the impact of internal service quality and employee satisfaction. Furthermore, Loveman, (1998) proposed to simplify the Service-Profit Chain model, empirical research in the bank sector to explore the relationship between employee satisfaction, organizational commitment and financial performance. Based on above, we can find that have a strong correlation between employee satisfaction and organizational commitment, and we hypothesize as follows:

H6: Employee job satisfaction positively influences organizational commitment 五、 The Moderator Role of Customer Ability

Schneider and Bowen (1995) advocated that when customer have high ability and be able to provide information immediate, will lead to higher quality process of co-production. Auh et al. (2007) use the expertise of customer to as a representative of customer, because expertise can promote effectively implement of service, and customer can provide accurate and appropriate information to employees. Based on above, we find ability and result of co-production have significant relationship, and generates the following hypothesis.

H7: Customer ability positively moderate the relationship between customer relational value creation and customer satisfaction 六、 The moderator role of emotional intelligence

In the early 1980, similar conceptual of emotional intelligence had begun. And we know that high emotional intelligence individual tries to understand self emotions and adapts appropriate emotional management instead of avoiding or accusing negative emotions. In many vocations, especially high-contact service, emotional competencies are essential to facilitate performance (Stough and De Guara, 2003; King and Gradner, 2006). The higher emotional intelligence employees, the more likely they are satisfied their jobs (Dong and Howard, 2006). Based on above, we find high emotional intelligence person is likely able to control some interference or at least moderate them to an acceptable degree, further affect performance (i.e. satisfaction), however, very few studies as it the role of moderator. Thus, in this study, we expects emotional intelligence as moderator of employee relational value creation and job satisfaction, and generates the following hypothesis.

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H1

H2

H3

H4 H6

H5

H7

H8

H8: Emotional intelligence positively moderate the relationship between employee relational value creation and employee job satisfaction

Chapter Three、Research Design and Methodology

一、 Research Framework According to the result of literature review in Chapter Two, this study develops

the framework which is illustrated as below (Figure 3.1). In this study the examining variables are including customer participation, relational value creation of customer and employee respectively, satisfaction of customer and employee, customer loyalty, employee organizational commitment, customer ability and emotional intelligence.

The research framework describes the relationship between customer participation, value creation and satisfaction. That contains customer participation will have an influence on the value creation of customer and employee respectively then affects satisfaction of customer and employee, finally makes an impact on customer loyalty and employee organizational commitment for professional financial services in Taiwan. Moreover, it also investigates moderating effect of customer ability and emotional intelligence. 二、 Conceptual Development and variable measurement

In this study the examining variables are including customer participation, customer relational value creation, customer satisfaction, customer loyalty, customer ability, employee relational value creation, job satisfaction, organizational commitment and emotional intelligence. The operational definition and each variable

Figure 3. 1Framework

Customer

Participation

Customer Relational

Value Creation

Employee Relational

Value Creation

Customer

Satisfaction

Employee Job

Satisfaction

Customer

Loyalty

Organizational

Commitment

Customer

Ability

Emotional

Intelligence

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measurement as follow. Because our research purpose is to understand the value creation process when customers participation and interact with employees in service, and this research with particular emphasis on the value creation of the relationship between customers and employees, so we do not consider firm and customer production (e.g., self-service technologies). Based on previous research we adapt definition s of customer participation to our research context (i.e., professional financial service) by conceptualizing customer participation as a behavioral construct that measures the extent to which customers provide or share information, make suggestions and become involved in decision making during the service value creation and delivery process (Auh et al, 2007; Bettencourt, 1997; Bolton and Saxena-Iyer, 2009; Hsieh, Yen, and Chin, 2004), and measurement scales from Auh et al, (2007).

In this study the relational value creation is divided into the customer side of the relationship value and employee side of the relational value. And in this research we defined customer relational value comprises items that represent an enjoyable interaction with and relational approval from the providers. Similar measures assess employee’ relational value perception. The measurement scales and definition are quoted from Hartline and Ferrell (1996), Zeithaml (1988). And this questionnaire designed is the same question exchange the subject of customer side and employee side. For example, in customer side the question is “my participation helps me build a better relationship with the service provider”; in employee side the question is “customers’ participation helps me build a better relationship with the customer”.

Based on view of Oliver (1999), we define customer satisfaction as the result of customer co-production, feeling pleasurable and to recognize its right decision. The measurement scales and definition are quoted from Lam et al, (2004), Oliver and Swan (1989). For employee satisfaction, we based on view of Locke (1969) defines job satisfaction as “the pleasurable emotional state resulting from the appraisal of one’s job as achieving or facilitating the achievement of one’s value”. Thus, we rely on two four-item scale to measure customers’ satisfaction with the service provided and employees’ job satisfaction. The employee of measurement scales and definition are quoted from Hackman and Oldham (1975), Hartline and Ferrell (1996).

For customer satisfaction, we according to Zeithaml et al. (1996) defined loyalty as customer will continue to buy the co-production service provider's products, and recommend it to others, and develop this questionnaire. However, about organizational commitment, we according to Mowday and Steers (1997) proposed the normative point of organization commitment to development organization commitment questionnaire (OCQ), defined commitment as the relative degree of individual recognition and participation organization, due to past studies have shown

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that the organization commitment questionnaire (OCQ) is high reliability and validity, and it is the most popular piece measuring tool to measure organizational commitment currently. Thus we measure employees' organizational commitment with organization commitment questionnaire (OCQ).

Regarding to customer ability, Meuter et al, (2005) advocate customer readiness will affect whether the customer use self-service technology, that is status and extent of whether the customer is ready to use innovative. Therefore, we according to Meuter (2005) defined customer ability as customer participation to co-creation value must to have skills and expertise, and under it to develop questionnaire. Furthermore, In this research we defined emotional intelligence as individual competence to aware, regulate, and utilize emotions effectively in self and others (Salovey and Mayer, 1990 1997; Goleman, 1995). This self-report emotional intelligence test (SREIT) was developed by Schutte et al, (1998) comprises 33 items. It is one of best-known tests and widely used in the literature. So we adapt SREIT which is one factor to measure emotional intelligence. And we selected from the 33 questions 13 questions to measure emotional intelligence. 三、 Questionnaire Design and Pretest

In this study, the design of the questionnaire is divided into two versions, one version for customers to fill in, and it contains the questions of customer participation, customer relational value creation, customer satisfaction, customer loyalty, and customer ability; another one is for financial advisors to fill in, and it contains the questions of employee relational value creation, employee job satisfaction, organizational commitment, emotional intelligence. The measure were assessed in five-point scale (ranging from 1 = strongly disagreement, to 5 = strongly agreement ). And the control variable of customer aside is bank category, sex, age, level of education, career, salary, the years of trade with financial advisor, the average times of to the bank in a month, the average times of consult with the financial advisor in a month. Then the control variable of employee aside is bank category, sex, age, the years of engaged in financial advisor, the average times of consult with the customer in a month, the years of work in the bank. Furthermore, for avoiding common method variance we use reverse question, random arrangement and feasible isolation method as the first step of data collection.

The pretest is researched from February 3, 2012 to February 20, 2012. We collected on 50 valid samples which focus on the financial advisors who had done the professional financial investment services, for example personal loans, insurance, financial planning, and asset/fund management ,etc. After recovering the questionnaires, we use Cronbach’s αas criterion to measure the reliability in order to let the questionnaires to be stable and consistency. we can clear know the Cronbach’s

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αof every constructs are higher than 0.7 (Cronbach’s α of customer participation is 0.90; customer relational value is 0.97; customer satisfaction is 0.75; customer loyalty is 0.77; customer ability is 0.97; employee relational value is 0.97; employee job satisfaction is 0.91; emotional intelligence is 0.99; organizational commitment is 0.92), which demonstrate the reliability of this measurement scale is good. Therefore, we can develop the formal questionnaire. 四、 Population Information and Data Collection

We started to do the formal questionnaire investigating from February 25 to May 30 in 2012. The data for this study come from 383 pairs of customers and professional financial advisors of the public sectors banks and private banks. We sample respondents from eight public sectors banks and seven private banks and focus on professional financial services such as personal loans, insurance, financial planning, and asset/ fund management. The employee respondents bear job titles such as financial advisors. Furthermore, the questionnaire is through executives to grant to every professional financial advisor who have to pick out a customer from all customer lists to finish the questionnaire, and form the relationship of pairs. We selected professional financial advisor respondents whose titles are representative and high frequency contact with customer in process of service to disseminate 450 pairs of questionnaires in Taiwan. After selecting ineffective questionnaires, we have 383 effective samples of paper questionnaires. And respond rate is 85.7%.

The target population of this study focuses on financial advisors in the fortune department of top 15 Taiwan domestic retailing banks, including seven public and eight private banks. According to Banking Bureau, Financial Supervisory Commission, Executive Yuan, R.O.C, top 15 Taiwan domestic retailing banks this study selected, except industrial banks, were ranked by equities of 2011 in Taiwan. Financial advisors are defined as professional financial consultants providing customers with investment planning, insurance, loan project, fortune management and so on. Because it must to form pairs, it increase the difficulty of the data collection, so we total spent three months to collection samples. In process, we was constantly remind and request by telephone, even to more banks visited financial advisors in person, it is very hard and difficult. The final we withdrew 450 pairs of customer and employee, and have 383 pairs effective samples of paper questionnaires. 五、 Analytical Method

After the questionnaire recovering, we exanimate and encode the data artificially and then login the database. In this research we use SEM to do sampling statistic examination and adopt the Amos 18.0 software to analyze the data. The data analysis uses descriptive statistic analysis to understand the population statistic description of respondents.

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Chapter Four、Empirical Analysis and Discussion

一、 Sample Structure Description The data for this study come from 383 pairs of customers and financial advisors

from public and private banks in Taiwan. And about financial advisors sample structure is as follows: the percentage of male gender in all respondents is 35.2% (135), and the female in all respondents is 64.8% (248); the level of age of most respondents is between 31-year-old and 35-year-old is 36.6% (140); as to the most years of engaged in financial advisors is between 1-year and 3-years is 52.7% (202), the most service times of within one month is between five and seven times is 44.1% (169); the most years of service in this bank is between 1-year and 3-years is 45.4% (174). Furthermore, about customer sample structure is as follows: the percentage of male gender in all respondents is 39.9% (153), and the female in all respondents is 60.1% (230); the level of age of most respondents is between 36-year-old and 40-year-old is 32.6% (125); as to the level of education most of the respondents are colleges 71.8% (275); the career of most respondents is business is 43.1% (165); the salary of most respondents is between 50001 and 60000 is 48% (184); the years of trade with this financial advisors of most respondents is between 1-year and 3-years is 58.5% (224); the most discuss times with this financial advisors within one month is between two and four times is 60.8% (233). 二、 Reliability Analysis

In this part we do the reliability test, and according as Nunnally (1978) indicates if α value is under than 0.35, that represents the reliability is too low; between 0.5 and 0.7 shows the acceptable measurement scale; higher than 0.7 indicate the measurement items have consistency. From the result of this research, we can know all Cronbach’s α are higher than 0.7 (customer participation is 0.85; customer relational value is 0.95; customer satisfaction is 0.75; customer loyalty is 0.82; customer ability is 0.97; employee relational value is 0.93; employee job satisfaction is 0.92; emotional intelligence is 0.98; organizational commitment is 0.96), which means the items correspond high reliability and reach the measurement standards. The result of implies that this scale item shows the good reliability and further we can do the Confirmatory Factor Analysis (CFA). 三、 Confirmatory Factor Analysis

This study is based on the confirmatory factor analysis method to evaluate the measured model. And the common indexes of evaluating the confirmatory factor analysis are Composite Reliability (CR), Average Variance Extracted (AVE), and Squared Multiple Correlation (SMC). A high composite reliability implies that there are high relationship between indicators and indicators. Hair et. al. (1998) think if the

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composite reliability indicator is higher than or equal to 0.5, and other scholar think the composite reliability indicator is higher than or equal to 0.6 (Fornell and Larck, 1981; Bagozzi and Yi, 1988), the observe variable can measure latent variable effectively. Average Variance Extracted (AVE) computes the variances of latent variable. Bagozzi and Yi (1988), Fornell and Larch (1981) suggest that value should higher than 0.5 that implies the scale is stable. About Squared Multiple Correlation (SMC) can reflect the percentage of measuring item variable which can be explained by latent variable, and the value should higher than 0.3 (Chin, 1998).

From Table 4.1, we can see the CR values are above 0.5, even all construct are higher than 0.7. Although some of AVE values are reluctantly higher than 0.5 but most of CR value are still higher in presupposition. Based on above description the reliability is in the acceptable area. The results demonstrate that all the measurement items have acceptable reliabilities. 四、 Validity Analysis

In this study, execute confirmatory factor analysis with Amos 18.0 to testing validity of questionnaires. Construct Validity can be separated to convergent validity and discriminant validity that means the level of measuring the constructs or traits in questionnaire or scale. And then convergent validity must meet three standards: (1) the factor loading of items must between 0.5 and 0.95, and T-test significant (Anderson and Gerbing, 1988); (2) Composite Reliability (CR) must higher than 0.6 (Fornell and Larck, 1981; Bagozzi and Yi, 1988); (3) and every Average Variance Extracted (AVE) must higher than 0.5 (Fornell and Larck, 1981). From Table 4.1 we can see every factor loading, CR and AVE are fits the standard of good validity.

According to Anderson and Gerbing (1988), with Confidence Interval test discriminant validity, and it means construct correlation coefficient plus or minus two standard errors to from upper and lower limit, as long as the confidence interval of the correlation coefficient does not contain a "1", the constructs to each other with good discriminant validity (Smith and Barclay, 1997). The result is under below in Table 4.2, and we can see every confidence interval of the correlation coefficient does not contain a "1", which fits the standard of validity.

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Table 4.1 CR, AVE and SMC

Dimension (λ) SMC t value

Customer Participation

(Cronbach’s α = 0.853, CR = 0.86, AVE = 0.55 )

Customer participation 1





0.68

0.75

0.70

0.78

0.79

0.47

0.57

0.49

0.61

0.63

14.50***

16.37***

14.95***

17.44***

17.65***

Customer Relational Value


Customer relational value 1



0.94

0.95

0.91

0.89

0.91

0.83

13.39***

13.56***

13.18***

Customer Satisfaction


Customer satisfaction 1




0.65

0.71

0.72

0.79

0.43

0.51

0.52

0.63

10.36***

11.40***

12.95***

14.96***

Customer Loyalty


Customer loyalty 1

Customer loyalty 2

Customer loyalty 3

Customer loyalty 4

Customer loyalty 5

0.64

0.77

0.83

0.71

0.56

0.41

0.59

0.69

0.51

0.32

10.54***

11.53***

11.99***

11.16***

8.98***

Customer Ability


Customer ability 1

Customer ability 2

Customer ability 3

Customer ability 4

0.90

0.95

0.97

0.97

0.81

0.90

0.94

0.94

22.87***

24.98***

25.94***

26.03***

Employee Relational Value


Employee relational value 1

Employee relational value 2

0.91

0.90

0.83

0.81

22.66***

22.51***

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Employee relational value 3 0.92 0.85 22.95***

Employee Job Satisfaction


Employee Job Satisfaction 1




0.85

0.83

0.86

0.97

0.73

0.69

0.74

0.94

17.95***

17.45***

18.17***

20.30***

Emotional Intelligence


Emotional Intelligence 1













0.88

0.86

0.88

0.91

0.91

0.89

0.85

0.91

0.93

0.93

0.89

0.81

0.88

0.78

0.74

0.77

0.83

0.83

0.79

0.72

0.83

0.86

0.86

0.79

0.66

0.77

21.99***

21.12***

21.67***

23.32***

23.11***

22.22***

20.84***

23.39***

23.95***

23.83***

22.60***

21.44***

21.83***

Organizational Commitment


Organizational Commitment 1


0.84

0.89

0.71

0.79

19.82***

21.43***










0.79

0.87

0.88

0.90

0.86

0.83

0.76

0.78

0.88

0.63

0.76

0.77

0.81

0.74

0.69

0.58

0.61

0.77

18.09***

20.94***

21.33***

22.03***

20.44***

19.18***

16.89***

17.48***

21.18***

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Table 4.2 Discriminant Validity of Potential Constructs

Correlation Construct Phi Standard Error

Confidence Interval Lower Upper

C. R.V <--> E. J. S -0.096 0.029 -0.154 -0.038 C. R.V <--> C. S 0.129 0.013 0.103 0.155 C. R.V <--> C. L 0.47 0.022 0.426 0.514 C. R.V <--> C. A. B 0.854 0.013 0.828 0.88 C. R.V <--> E. I -0.078 0.036 -0.15 -0.006 C. R.V <--> O. C -0.059 0.032 -0.123 0.005 C. R.V <--> E. R.V -0.056 0.037 -0.13 0.018 C.P <--> C.R.V 0.898 0.037 0.824 0.972 C.P <--> E.R.V -0.052 0.016 -0.084 -0.02 C.P <--> O.C -0.018 0.018 -0.054 0.018 C.P <--> E.R.V -0.039 0.02 -0.079 0.001 C.P <--> E.I -0.041 0.02 -0.081 -0.001 C.P <--> C.S 0.224 0.008 0.208 0.24 C.P <--> C.L 0.525 0.013 0.499 0.551 C.P <--> C.A.B 0.901 0.038 0.825 0.977 C.S <--> E.J.S -0.022 0.009 -0.04 -0.004 C.S <--> O.C -0.117 0.01 -0.137 -0.097 C.S <--> E.I 0.000 0.011 -0.022 0.022 C.S <--> E.R.V 0.008 0.011 -0.014 0.03 C.S <--> C.L 0.735 0.009 0.717 0.753 C.S <--> C.A.B 0.132 0.013 0.106 0.158 C.L <--> E.JS -0.028 0.012 -0.052 -0.004 C.L <--> O.C -0.108 0.013 -0.134 -0.082 C.L <--> E.I 0.009 0.014 -0.019 0.037 C.L <--> E.R.V 0.016 0.015 -0.014 0.046 C.L <--> C.A.B 0.476 0.023 0.43 0.522 E.J.S <--> C.A.B -0.087 0.03 -0.147 -0.027 C.A.B <--> E.I -0.077 0.037 -0.151 -0.003 O.C <--> C.A.B -0.043 0.033 -0.109 0.023 E.R.V <--> C.A.B -0.064 0.038 -0.14 0.012 E.J.S <--> E.I 0.866 0.036 0.794 0.938 Note: C.P – Customer Participation; C.R.V–Customer Relational Value

C.S – Customer Satisfaction; C.L – Customer Loyalty; C.A.B – Customer Ability

E.R.V – Employee Relational Value; E.J.S – Employee Job Satisfaction

E.I – Emotional Intelligence; O.C – Organizational Commitment

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五、 Hypotheses Testing and Structure Model Analysis The common indexes of evaluating the overall fitness of model are Chi-square

test, GFI (goodness-of-fit index), AGFI (adjusted GFI), RMR (root mean square residual) et al. From Table 4.3, we can discover the Chi-square/df is 2.39 which is smaller than 5, the result is acceptable. And others values of goodness of freedom in this model such as GFI = 0.826, AGFI = 0.802 which are larger than 0.8, the result is acceptable; RMR = 0.038, RMESA = 0.06 which are smaller than 0.08, the result is acceptable; NFI = 0.912, CFI = 0.929 which are larger than 0.9, the result all is acceptable. That are all fit the standard so that overall measured model is good enough to do the validity test.

Table 4.3 Goodness of Freedom Standard and the Value of this Model

Fit index Standard Result Resource Chi-square χ² As small as

possible 1325.6

P = 0.00 df = 554

2.39

Bagozzi &Yi (1988) Hair et. al (1998)

Tanaka (1993) Bollen (1989)

p P > 0.05

χ²/df 2~5

Goodness of fit

GFI > 0.8 0.83 Doll et. al. (1994) AGFI > 0.8 0.81 CFI > 0.9 0.93 Bagozzi &Yi (1988)

NFI > 0.9 0.91 Bentler &Bonett (1980)

PGFI > 0.5 0.726 Hair et. al. (2006)

Alternative index RMSEA < 0.08 0.06 Hair et. al. (2006) Residuals analysis RMR < 0.05 0.038 Hu & Bentter (1999)

Furthermore, according to result in Table 4.8 we can know H1: customer participation positively influences customer relational value creation; H3: Customer relational value creation positively influences customer satisfaction; H4: Employee relational value creation positively influences employee job satisfaction; H5: Customer satisfaction positively influences customer loyalty; H6: Employee job satisfaction positively influences organizational commitment, above all are support. However, only H2: Customer participation positively influences employee relational value creation is not support. Most of SMC are above 0.5 except SMC of employee relational value and customer satisfaction. Although the value of 0.43 and 0.47, which is close to the standard, does not reach the value of 0.5, we still accept it. The detail data is under below on Table 4.4.

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Table 4.4 Structural Model Results

Hypothesis Path Excepted Symbol

Standardized Solution

t value Reslut

H1 C.P → C.R.V + 0.899 10.52*** Support

H2 C.P → E.R.V + -0.053 -0.95 Not

Support

H3 C.R.V → C.S + 0.267 4.20*** Support

H4 E.R.V → E.J.S + 0.805 13.08*** Support

H5 C.S → C.L + 0.774 8.68*** Support

H6 E.J.S → O.C + 0.524 9.31*** Support

SMC Employee Relational Value Customer Relational Value Customer Satisfaction Employee Job Satisfaction Customer Loyalty Organizational Commitment

0.43 0.80 0.47 0.65 0.60 0.74

Note: * p<0.05, **p< 0.01, ***p< 0.001

C.P – Customer Participation; C.R.V–Customer Relational Value C.S – Customer Satisfaction; C.L – Customer Loyalty; C.A.B – Customer Ability

E.R.V – Employee Relational Value; E.J.S – Employee Job Satisfaction

E.I – Emotional Intelligence; O.C – Organizational Commitme

六、 Result of Moderating effect From Table 4.5, we can see the interaction term of customer aspect of customer

relational value and customer ability is significant (H7 is support). It means that customer ability have moderating effect in the relationship between customer relational value and customer satisfaction, and the factor of interaction term is positive which states that the higher individual customer ability, the strong positive relationship between customer relational value and customer satisfaction.

From Table 4.6, we can see the interaction term of employee aspect of employee relational value and emotional intelligence is not significant (H8 is not support). It means that emotional intelligence have not moderating effect in the relationship between employee relational value and employee job satisfaction.

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Table 4.5 Result of Moderating Effect of Customer

Customer Satisfaction Model 1 Model 2 Model 3 Model 4

Control variables Bank classification -0.382*** -0.384*** -0.398*** -0.397***

Sex 0.013 -0.004 0.002 -0.003 Number of trading years -0.003 0.008 0.017 0.02

Independent variable Relational Value 0.139* -0.282 -0.25

Moderating variable Customer ability 0.440 0.465*

Interaction term Relational Value * ability 0.130**

Table 4.6 Result of Moderating Effect of Employee

Employee Job Satisfaction Model 1 Model 2 Model 3 Model 4

Control variables Bank classification -0.112 -0.137*** -0.095** -0.097**

Sex 0.120* 0.03 -0.004 -0.006 Number of trading/ month -0.131* -0.046 -0.042 -0.041

Independent variable Relational Value 0.756*** 0.225*** 0.156

Moderating variable Emotional intelligence 0.635*** 0.662***

Interaction term Relational Value * Emotional

intelligence -0.054

Chapter Five、Discussion and Suggestion

一、 Discussion of the Insignificant Path In this model we discover have one path is not significant such as customer

participation to employee relational value, and one moderating effect also not support, in this section we will discuss the part of insignificant and provide some empirical evidences explain it. Although we understand that value co-creation is a central tenet of the service – dominant logic and the main premise of customer participation. However, customer participation may not unequivocally create positive value; customers’ increased involvement in the service process may shift more power from

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service employees to customers and thereby increase employee workloads and role conflict, thus the shift of power and control away from employee could lead to job stress.

Furthermore, in this case, employee job satisfaction may be affected by bank classification has a different strength, because in private bank the financial advisors have more performance pressure than in public bank, that will affect the job satisfaction of financial advisors. On the other hand, in high customer-contact service, employees must not only provide services but also engage in “emotional labor” (Hochschild 1983), by demonstrating polite and pleasant manners, regardless of customers’ behaviors. Emotional labor is a key employee job stressor that causes burnout and hampers work performance. Therefore, emotional intelligence as a precondition factor of job satisfaction is better than as a moderating factor. 二、 Theoretical and Managerial Implication

In this study we find customer participation adds a new dynamic to the customer and employee relationship that engages customers directive in the co-creation of value. Therefore, understanding how companies can harness the benefits and circumvent the drawbacks of customer participation is of great importance. The findings have several implications for firms that are considering or have engaged their customers in co-creation of value in the service process. And we suggest manager in professional financial service may motivate customers to be co-creators, and customers also need to be trained to know what to expect and how to behave in given situations, particularly in professional services in which the service is more complex and customers are usually less familiar with the situations. Furthermore, we can cultivate a customer participation culture. Just as customers need to learn their co-creation roles, employees must adjust to their new roles. The view of customers as co-creators dictates that employees include customers’ new roles and expectations in their planning and execution of daily operations. Employees also must recognize the business value of the new approach, their responsibilities, and the way it might bring them personal benefits. 三、 Study Limitation

The interpretation and application of the findings in the research are constrained by some limitations and they will be as the suggestions for future research. There are several limitations in this research. First, limitation of this study is related to special workplaces of the sample. The study explored the role of co-creation value among financial advisors and customer in Taiwan. As a result, one should be extremely cautious while generalizing the results. Second, limitation of the study sample of this research for the professional financial advisors in the banking industry, therefore, financial advisors in the insurance industry is not included in the sample of this study.

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In addition, we also limit financial advisors and customers must have the experience of consulting. Therefore, this study does not consider the relationship less than one year. 四、 Future Research

Based on the research findings and limitations in this study, several suggestions for future research are as follows. First, future research should explore other industry, or compare the differences between western organizations and eastern organizations to generalize the results of this study. Second, according to result of this study that we can find the control variable” bank classification have significant affect on value creation and satisfaction for both customer and employee. Therefore, further research might to test it as a moderate variable, whether there are have difference result from public sectors bank and private bank.

Reference 1. Auh, S., S. Bell, C. McLeod, and E. Shih (2007), “Co-production and customer loyalty in

financial services,” Journal of Retailing, 83(3): 359-370.

2. Auh, Seigyoung, Simon J. Bell, Colin S. McLeod, and Eric Shih (2007), “Co-Production and Customer Loyalty in Financial Service,” Journal of Retailing, 83 (3), 359-70.

3. Bateson, John E.G (1985), “Self-Service Customer: An Exploratory Study,” Journal of Retailing, 61 (3), 49-76

4. Bettencourt, Lance A. (1997), “Customer Voluntary Performance: Customers as Partners in Service Delivery,” Journal of Retailing, 73 (3), 383–406.

5. Campbell, D.T., & Fiske, D.W, (1959).Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix, Psychological Bulletin, 56(2)：81-105.

Competition Fourm, 4 (2), 381-388

6. Dabholkar, Pratibha A.(1990), “How to Improve Perceived Service Quality by Increaseing Customer Participation,” in Developments in Marketing Science, Vol13, B.J Dunlap, ed.

Cullowhee, NC: Academy of Marketing Science, 438-487.

7. Dong, Q. & Howard, T. (2006). Emotional intelligence, trust and job satisfaction.

8. Eisenberger, Robert, Robin Huntington, Steven Hutchison, and Debora Sowa (1986), “Perceived Organizational Support,” Journal of Applied Psychology, 71 (3),

500–507.Experience”, Journal of Marketing, Vol. 56, No. 1, pp: 6-12.

9. Fleming, John H.,Curt Coffman, and James K. Harter (2005)“Manage Your Human

Sigma,” Harvard Business Review, 83 (7–8), 106–114.

10. Gremler, Dwayne D. and Kevin P. Gwinner (2000), “Customer-Employee Rapport in Service Relationships,” Journal of Service Research, 3 (August), 82–104

11. Hartline, Michael D. and O.C. Ferrell (1996), “The Management of Customer-Contact Service Employees: An Empirical Investigation,” Journal of Marketing, 60 (October),

52–70.

20

12. Hartline, Michael D. and O.C. Ferrell (1996), “The Management of Customer-Contact Service Employees: An Empirical Investigation,” Journal of Marketing, 60 (October), 52–70

13. King, M. & Gradner, D. (2006). Emotional intelligence and occupational stress among professional staff in New Zealand. International Journal of Organizational Analysis, 14(3),

186-203.

14. Lovelock, Christopher H. (1983), “Classifying Services to Gain Strategic Marketing Insights,” Journal of Marketing, 47 (Summer), 9–20

15. Lovelock, Christopher. and R. Young(1979), “Look to Consumers to Increase to increase Productivity,” Harvard Business Review

16. Lovelock, Christopher. and R. Young(1979), “Look to Consumers to Increase to increase Productivity,” Harvard Business Review

17. Meuter, M. L., Ostrom, A. L, Roundtree, R. I., and Bitner, M. J.(2000), “Self-Service Technologies: Understanding Customer Satisfaction with firms

18. Oei, Fuk Jin and Ogunlana, Stephen O. (2006), Assessment of the Development role of a statutory body from a customer perceptive :A relationship Marketing Approach, Journal of

construction in Developing Countries , Vol.11 (2), pp.31-52

19. Oliver, Richard L. and John E. Swan (1989), “Consumer Perceptions of Interpersonal Equity and Satisfaction in Transactions: A Field Survey Approach,” Journal of Marketing, 53

(April), 21–35.

20. Ouschan, Robyn, Jillian Sweeney, and Lester Johnson (2006),“Customer Empowerment

and Relationship Outcomes inHealthcare Consultations,” European Journal of Marketing,

40(9–10), 1068–1086.

21. Parasuraman, A.,Valarie A. Zeithaml, and Arvind Malhotra(2005), “E-S-QUAL: A Multiple-Item Scale for Assessing Electronic Service Quality,” Journal of Service Research,

7 (3),213–33

22. Patterson, Paul G., Elizabeth Cowley, and Kriengsin Prasong-sukarn (2006),and Tasman Smith (2001), “Modeling RelationshipStrength Across Service Types in a South-East Asian

Context,”International Journal of Service Industry Management, 12 (2),90–113

23. Stough, C. & De Guara (2003). Examining the relationship between emotional intelligence and job performance, paper presented at the 5th Industrial/ Organizational Psychology

Conference, Melbourne, July.

24. Xie, Chunyan, Richard P. Bagozzi, and Sigurd V. Troye (2008),“Trying to Prosume:

Toward a Theory of Consumers as Co-Creators of Value,” Journal of the Academy of

Marketing Science, 36 (1), 109–122.

25. Zeithaml, Valarie A. (1988), “Consumer Perceptions of Price,Quality, and Value: A Means-End Model and Synthesis of Evidence,” Journal of Marketing, 52 (July), 2–22.

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