taiwan participation.pdf

8/11/2019 Taiwan Participation.pdf

1/20


2/20

2

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 drivescustomer 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 OneResearch 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,


3/20

3

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-toconsumer 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

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.


4/20

4

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 participationrelational 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

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


5/20

5

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

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

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

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 creationand job satisfaction, and generates the following hypothesis.


6/20

6

H1

H2

H3

H4H6

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

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.

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


7/20

7

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 iscustomers 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

ones job as achieving or facilitating the achievement of ones 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


8/20

8

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.

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 thequestions 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 Cronbachs as criterion to measure the reliability in order to

let the questionnaires to be stable and consistency. we can clear know the Cronbachs


9/20

9

of every constructs are higher than 0.7 (Cronbachs 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.

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 fortunedepartment 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.

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.


10/20

10

Chapter FourEmpirical Analysis and Discussion

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).

Reliability AnalysisIn this part we do the reliability test, and according as Nunnally (1978) indicates

if value is under than 0.35, that represents the reliability is too low; between 0.5and

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 Cronbachs 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).

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


11/20

11

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.

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 VarianceExtracted (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.


12/20

12

Table 4.1 CR, AVE and SMC

Dimension () SMC t value

Customer Participation

(Cronbachs = 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

(Cronbachs = 0.950, CR = 0.95, AVE = 0.87 )

Customer relational value 1



0.94

0.95

0.91

0.89

0.91

0.83

13.39***

13.56***

13.18***

Customer Satisfaction

(Cronbachs = 0.742, CR = 0.81, AVE = 0.52 )

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

(Cronbachs = 0.821, CR = 0.84, AVE = 0.51 )

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

(Cronbachs = 0.973, CR = 0.97, AVE = 0.89 )

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

(Cronbachs = 0.943, CR = 0.94, AVE = 0.83 )

Employee relational value 1Employee relational value 2

0.910.90

0.830.81

22.66***22.51***


13/20

13

Employee relational value 3 0.92 0.85 22.95***

Employee Job Satisfaction

(Cronbachs = 0.924, CR = 0.93, AVE = 0.77 )

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

(Cronbachs = 0.980, CR = 0.98, AVE = 0.79 )

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

(Cronbachs = 0.963, CR = 0.96, AVE = 0.71 )

Organizational Commitment 1


0.84

0.89

0.71

0.79

19.82***

21.43***



Organizational Commitment 5Organizational Commitment 6






0.79

0.87

0.880.90

0.86

0.83

0.76

0.78

0.88

0.63

0.76

0.770.81

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***


14/20

14

Table 4.2 Discriminant Validity of Potential Constructs

Correlation Construct PhiStandard

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 0.018C.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 0.03C.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.VCustomer 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


15/20

15

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 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

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 jobsatisfaction 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.


16/20

16

Table 4.4 Structural Model Results

Hypothesis PathExcepted

Symbol

Standardized

Solutiont 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 Loyalty

Organizational Commitment

0.43

0.80

0.47

0.65

0.60

0.74

Note: *p


17/20

17

Table 4.5 Result of Moderating Effect of Customer


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


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

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


18/20

18

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.

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.

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.


19/20

19

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.

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.

Reference1. 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), 383406.5. 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),

500507.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 (78), 106114.

10. Gremler, Dwayne D. and Kevin P. Gwinner (2000), Customer-Employee Rapport in

Service Relationships,Journal of Service Research, 3 (August), 82104

11. Hartline, Michael D. and O.C. Ferrell (1996), The Management of Customer-Contact

Service Employees: An Empirical Investigation, Journal of Marketing, 60 (October),

5270.


20/20

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), 5270

13. King, M. & Gradner, D. (2006). Emotional intelligence and occupational stress among

professional staff in New Zealand. InternationalJournal of Organizational Analysis,14(3),

186-203.

14. Lovelock, Christopher H. (1983), Classifying Services to Gain Strategic Marketing

Insights,Journal of Marketing, 47 (Summer), 920

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), 2135.

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

and Relationship Outcomes inHealthcare Consultations, European Journal of Marketing,40(910), 10681086.

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),21333

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,InternationalJournal of Service Industry Management, 12 (2),90113

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), 109122.

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), 222.

taiwan participation.pdf

Documents