International Journal of Bank Marketing

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wang2018- customer value-Self-efficacy-advisory

H1(H3a)

H2

H3(H3a)

Switching

costs

Customer

value

Customer

participation

Self-efficacy/

adviser-efficacy

Figure 1.

Research model

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interval of two by student interviewers as they exited the banks. The respondents received the

questionnaire items translated into Chinese. The analysis described was based on the data

from 220 subjects whose complete model-related information was available. The common

method variance features potential bias due to the self-reported nature of the data.

Thus, the Harman one-factor test was conducted to determine the extent of bias.

The unrotated factor analysis showed the first factor that accounted for only 29.16 percent

of the variance, indicating that the common method bias was not a serious threat in the study

(Podsakoff et al., 2003). The demographics of the sample were 52 percent female, 21 percent

below 30 years old, 31 percent between 31 and 40 years old, 30 percent between 41 and

50 years old, 18 percent under 50 years old, and 69 percent college graduates. These outcomes

are similar to the work on wealth management in Taiwan (Yu and Ting, 2011) and survey by

Wealth Magazine (2016).

Reliability and validity

To reduce the data into a smaller and more meaningful set of components, several

purification steps (confirmatory factor analyses and item-to-total) were ran. All items, except

for one item from customer participation, were retained for subsequent analysis. This work

dropped one item from customer participation (

“I always provide suggestions to the staff for

improving the service outcome

”) because the item shows a low correlation (o0.5).

Table I lists the composite reliabilities, average variances extracted (AVE), items and

loadings for the final multi-item measures. AMOS 17.0 software was used and confirmatory

factor analysis was performed to assess the measurement model consisting of all items

designed to measure the five constructs. The values for composite reliability and Cronbach

’s

α are higher than 0.7 (Nunnally, 1967) for all constructs. The AVE for all constructs range

Constructs and items (AVE, composite reliability, Cronbach

’s α)

Customer self-efficacy (0.67, 0.89, 0.90)

I have confidence in my ability to participate effectively

0.95

I do not doubt my ability to participate effectively

0.92

I have excellent participation skills and ability

0.68

I am proud of my participation skills and ability

0.69

Customer adviser-efficacy (0.79, 0.94, 0.94)

I have confidence in the staff

’s ability to respond to my participation effectively

0.89

I do not doubt the staff

’s ability to respond to my participation effectively

0.85

The staff

’s excellent skills and ability in responding to my participation

0.91

I am proud of the staff

’s skills and ability in responding to my participation

0.90

Customer participation (0.72, 0.91, 0.95)

I spent a lot of time sharing information about my needs and opinions with the staff during the

service process

0.77

I put a lot of effort into expressing my personal needs to the staff during the service process

0.92

I have a high level of participation in the service process

0.77

I am very much involved in deciding how the services should be provided

0.91

Customer value (0.83, 0.90, 0.90)

Overall, the value of the bank

’s services to me is high

0.89

Compared to what I had to give up, the overall ability of the bank to satisfy my wants and needs is high

0.93

Switching costs (0.84, 0.94, 0.94)

In general, it would be a hassle changing the bank

0.87

It would take a lot of time and effort changing the bank

0.96

For me, the costs in time, money and effort to switch the bank are high

0.93

Table I.

Overview of the

multi-item measures

Customer

participation

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from 0.67 to 0.84, which is greater than 0.5, and thus demonstrates convergent validity

(Fornell and Larcker, 1981). The correlation of a construct with its indicators (i.e. the square

root of the AVE) should exceed the correlation between that construct and any other

construct. The square roots of the AVE for all constructs range from 0.82 to 0.92, which

exceeds the correlation between that construct and any other ranging from 0.39 to 0.78, so

that five constructs have adequate discriminant validity (Fornell and Larcker, 1981).

In summary, the overall measure properties are acceptable (Table II).

Results

For the purpose of moderated mediation effects tests, the PROCESS model (Hayes, 2013) is

employed. PROCESS is a computational procedure for SPSS and SAS that implements

moderation or mediation analysis as well as their combination in an integrated conditional

process model (i.e. mediated moderation and moderated mediation). In addition to

estimating the coefficients of a model using OLS regression (for continuous outcomes) or

maximum likelihood logistic regression ( for dichotomous dependent variables), PROCESS

generates direct and indirect effects in mediation and mediated moderation models,

conditional effects in moderation models, and conditional indirect effects in moderated

mediation models with a single meditator or multiple ones. Hayes

’s (2013) Model 12 was

used wherein X is the independent variable, Y is the dependent variable, M

is the mediator

variable, and W and Z are two moderator

–mediator variables. The equation to assess effects

between the mentioned variables is represented by: (a) conditional indirect effect of X on Y

through Mi

¼ (a1i + a4iW + a5iZ + a7iWZ) bi and (b) conditional direct effect of X on

¼ c1’+ c4’W+c5’Z+c7’WZ. Hayes’s (2013) model 12 enables the specific examination of

these moderation effects, namely, of self-efficacy (W) and of adviser-efficacy (Z ) via

customer value

’s mediator effect (M) between customer participation (X) and

switching costs (Y ). Following Hayes (2013), this work used a bias-corrected 5,000

bootstrap analysis. The results showed that the customer value

“outcome” model was

significant (F(7,212)

¼ 87.87, po0.05) (see Table III). In the model, customer participation

Outcome: customer value

Constant

4.54*

Customer participation

0.28*

Self-efficacy

0.11

Adviser-efficacy

0.37*

Customer participation

× Self-efficacy

0.03

Customer participation

× Adviser-efficacy

−0.02

Self-efficacy

× Adviser-efficacy

−0.02

Customer participation

× Self-efficacy × Adviser-efficacy

0.05*

Notes: F(7, 212)

¼ 87.87*, R

¼ 0.74. *po0.05

Table III.

Regression analyses

testing for moderated

mediation (customer

value as the

dependent variable)

2

3

1. Customer participation

0.85

2. Customer self-efficacy

0.42*

0.82

3. Customer adviser-efficacy

0.78*

0.51*

0.89

4. Customer value

0.76*

0.59*

0.78*

0.91

5. Switching costs

0.72*

0.39*

0.74*

0.75*

0.92

Notes: The values in the diagonal are the square roots of AVE. *p

o0.05

Table II.

Correlations among

latent constructs

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(

β ¼ 0.28, po0.05) is statistically significant in predicting customer value, and the

result supports H1. Besides, the significant (customer participation

× self-efficacy ×

adviser-efficacy) interaction (

β ¼ 0.05, po0.05) implies that the direct effect of customer

participation on customer value is moderated by both self-efficacy

× adviser-efficacy.

Following Aiken and West (1991), this work used unstandardized regression coefficients

to plot the relationship between customer participation and value at low levels (one standard

deviation below the mean) and high levels (one standard deviation above the mean) of

customer self-efficacy and adviser-efficacy. Figures 2 and 3 show that customer

participation is positively related to customer value when both customer self-efficacy and

adviser-efficacy are high. However, customer participation is negatively related to customer

value when both customer self-efficacy and adviser-efficacy are low. These results support

H4a. This work also hypothesizes about the moderating effects of incongruent customer

self-efficacy and adviser-efficacy. Figure 2 reveals that customer participation is positively

related to customer value when customers have low customer self-efficacy and high

customer adviser-efficacy, in support of H5a. However, Figure 3 reveals that customer

participation has no effect on customer value when customers have high customer

self-efficacy and low customer adviser-efficacy. This finding implies that incongruence

reduces the effect of customer participation to the point that customers derive no value from

their participation in the service process. Thus, H6a cannot be confirmed. High customer

self-efficacy may have motivated customers to exert more effort in their participation and

evoked high customer value, which offset the negative effect on customer value due to the

role-expectancy violation of the employees.

The overall switching costs

“outcome” model is also significant (F(8,211) ¼ 52.76, po0.05)

(see Table IV ). Customer participation is not positively related to switching costs (

β ¼ 0.09,

W0.05), and this result does not support H2. Customer value ( β ¼ 0.45, po0.05) is

statistically significant in predicting switching costs, and the result supports H3. Moreover,

the criterion for mediation was the identification of a significant indirect effect indicated by the

Low customer

participation

High customer

participation

High self-efficacy

Low self-efficacy

Customer value

Figure 2.

High customer

adviser-efficacy

(customer value as the

dependent variable)

Low customer

participation

High customer

participation

High self-efficacy

Low self-efficacy

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4.1

4.2

4.3

Customer value

Figure 3.

Low customer

adviser-efficacy

(customer value as the

dependent variable)

Customer

participation

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95% confidence interval that does not include the zero value. The mediation results show that

customer value fully mediates the relationship between customer participation and switching

costs (ab

¼ 0.12; BootLLCI ¼ 0.06, BootULCI ¼ 0.22). Therefore, customer value fully mediates

the (customer participation

× self-efficacy × adviser-efficacy) → switching costs. Finally,

regarding the moderating effects of self-efficacy and adviser-efficacy on the relationship

between customer participation and customer value, the significant (customer

participation

× self-efficacy × adviser-efficacy) interaction ( β ¼ 0.04, po0.05) implies that

the indirect effect of customer participation on switching costs through customer value is

moderated by both self-efficacy

× adviser-efficacy.

Following Aiken and West (1991), this work used unstandardized regression coefficients

to plot the relationship between customer participation and switching costs at low levels

(one standard deviation below the mean) and high levels (one standard deviation above the

mean) of customer self-efficacy and adviser-efficacy. Figures 4 and 5 show that customer

Outcome: switching costs

Constant

2.30*

Perceived value

0.45*

Customer participation

0.09

Self-efficacy

−0.04

Adviser-efficacy

0.23*

Customer participation

× Self-efficacy

0.16*

Customer participation

× Adviser-efficacy

−0.13*

Self-efficacy

× Adviser-efficacy

0.00

Customer participation

× Self-efficacy × Adviser-efficacy

0.03*

Notes: F(8, 211)

¼ 52.76*, R

¼ 0.67, *po0.05

Table IV.

Regression analyses

testing for moderated

mediation (switching

costs as the dependent

variable)

Low customer

participation

Switching costs

High customer

participation

High self-efficacy

Low self-efficacy

Figure 4.

High customer

adviser-efficacy

(switching costs as the

dependent variable)

Low customer

participation

3.3

3.4

3.5

3.6

3.7

3.9

4.1

Switching costs

3.8

High customer

participation

High self-efficacy

Low self-efficacy

Figure 5.

Low customer

adviser-efficacy

(switching costs as the

dependent variable)

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participation is positively related to switching costs when both customer self-efficacy and

adviser-efficacy are high. However, customer participation is negatively related to switching

costs when both customer self-efficacy and adviser-efficacy are low. These results support

H4b. This work also hypothesizes about the moderating effects of incongruent customer

self-efficacy and adviser-efficacy. Figure 4 reveals that customer participation is positively

related to switching costs when customers have low customer self-efficacy and high

customer adviser-efficacy. Thus, H5b is supported. Figure 5 reveals that customer

participation negatively affects switching costs when customers have high customer

self-efficacy and low customer adviser-efficacy. Thus, H6b is supported.

Discussion

The focus of customer participation research on the co-creation of customer values and the

sole effect of self-efficacy fails to recognize other benefits associated with switching costs

and the interplay of customers

’ self-efficacy and adviser-efficacy (e.g. Chen and

Wang, 2016; Dong et al., 2014; Mustak et al., 2016; Yim et al., 2012). This oversight

precludes the exploration of strategies for managing and/or influencing the efficacy

beliefs of customers and employees toward an effective co-creation of customer values and

switching costs through customer participation. This study aims to complement extant

research by ascertaining the effectiveness of customer participation for customers from a

switching cost perspective. The study provides empirical evidence in support of the extant

premise that value creation is a prerequisite for the success of a firm

’s strategic efforts to

increase switching costs by encouraging customer participation. Furthermore, this study

adopts the theoretical framework of relational efficacy beliefs (Lent and Lopez, 2002) to

guide the development of hypotheses to test value co-creation and switching costs for

customers through customer participation conditional on the joint efficacies (self-efficacy

and adviser-efficacy) of participation. The current study contributes to the customer

participation and efficacy literature streams by exploring the synergistic effects of

self-efficacy and adviser-efficacy, including their congruence and incongruence levels, in

close relationships (Lent and Lopez, 2002). The findings indicate that customer value

mediates the relationship between customer participation and switching costs. Although

customer participation does not directly influence switching costs, it can influence

switching costs through customer value. The reason may be that customer participation

does not always result in positive outcomes. Haumann et al. (2015) pointed out that

perceived co-production intensity may negatively affect the customers

’ evaluation of a co-

production process, given that consumers generally view effort and time as cost factors to

minimize in the process of obtaining good service. In addition, these efficacy beliefs, as

boundary conditions, jointly affect the co-creation of customer value and switching costs

for customers and can be used to identify strategies that can enhance the benefits of

customer participation. This attempt to enrich the existing service-dominant logic

(Vargo and Lusch, 2004) literature pertaining to customer participation and provide

clarification about the effects of customer participation reveals several key findings for

further discussion.

The adviser-efficacy beliefs of customers explain the unique variance in their

participation

’s creation of customer value and switching costs beyond what is explained

solely by their self-efficacy. The clients who believe that their financial advisers have a high

level of efficacy to respond to their participation perceive an additional amount of value and

switching costs in the service process. The perceived efficacy of clients on their financial

advisers also complements the clients

’ own self-efficacy in determining whether they

perceive value and switching costs from their participation. These circumstances highlight

the importance of assessing adviser-efficacy beliefs of customers when customer

participation is examined.

Customer

participation

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Customers perceive a high amount of value and switching costs from customer

participation when the customers and their advisers possess high congruent levels of

self-efficacy and adviser-efficacy. In contrast, customers perceive a less amount of value and

switching costs from customer participation when the customers and their advisers possess

low congruent levels of self-efficacy and adviser-efficacy. Given that the co-creation of value

is a universal key benefit of customer participation (Chan et al., 2010), the maximum value

and switching costs of customer participation arise when customers have confidence in

themselves and their advisers, and when both work together in service production and

delivery. Compared with those with low self-efficacy, the customers who are satisfied with

themselves and their advisers

’ capabilities to participate in the service process perceive

additional value and switching costs from customer participation, feel more comfortable,

and are more willing to exert effort to overcome obstacles.

The incongruent appraisals of self-efficacy and adviser-efficacy can function in some

cases. The incongruent but complementary efficacy beliefs, such as when customers

perceive low self-efficacy and high adviser-efficacy, can enable customers to derive value

and switching costs from customer participation. Clients expect professional guidance and

advice from their financial advisers in the context of financial services. In this case, the

perception of clients on the efficacy of their partner is critical in determining their perceived

value and switching costs. By contrast, when customers perceive high self-efficacy and

low adviser-efficacy, the customers may derive less value and switching costs from

customer participation. These customers may leave their financial adviser when they

discover better alternatives.

Managerial implications

The significant role of customer value and switching costs in customer participation and the

importance of managing customers

’ self-efficacy and adviser-efficacy offer new

opportunities to increase value co-creation and switching costs by engaging customers

and employees in service co-production. The findings present several implications for

service providers to maximize the co-creation of values and switching costs through

customer participation.

Customer participation is a new task for both customers and employees whose perceived

values and switching costs can be cultivated through training or education for their new

roles and responsibilities. This study also bears implications for recruitment and job design.

An increase in customer participation initiatives requires capable and responsive employees

to cope with the queries and needs of customers. In addition, recruiting customer-oriented

employees can aid in customer participation because these individuals are inclined to show

commitment in responding to customer participation for their own sake (Brown et al., 2002).

Helping customers recognize the success of their participation can be an effective

strategy because personal performance accomplishments can reinforce the self-efficacy

beliefs of people (Bandura, 1977; Lent and Lopez, 2002). Firms should find the best fit

between what their customers expect to do and what the firms believe they can actually do.

Firms should comprehensively study the co-production requirements of the service delivery

process and the knowledge, skills and abilities of customers (Ford and Dickson, 2012).

Customers need to be trained to understand what to expect and how to behave in certain

situations, particularly in professional services in which the service is complex and

customers are less familiar with the situations (Bitner et al., 1994). Firms may invest in

training to strengthen the scripts of their customers and aid them in developing subscripts

for dealing with obstacles and errors (Mohr and Bitner, 1991). McKee et al. (2006) asserted

that self-efficacy can be increased by managing performance attainment, vicarious

experience, verbal persuasion and the physiological states of customers. In the context of

financial service, investment advisers may find it useful to introduce novice investors to a

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“beginner-level” investment (e.g. stock mutual funds) before moving them to an

“intermediate level” (e.g. laddered bonds). The emphasis is on incrementally increasing

the service customers

’ sense of self-efficacy when engaging in a particular service. Service

providers can increase the self-efficacy level of their customers by providing them with print

or video portrayals of similar customers engaged in certain services or by allowing them to

observe actual customers. Companies can hold regular investment seminars to provide their

novice clients with opportunities to learn from experienced investors and gradually take on

a value co-creator role (Yim et al., 2012). Employees can also help bolster the self-efficacy of

customers through positive verbal praise because self-appraisals are often formed in

response to the evaluative reactions of significant others (Bandura, 1982; Lent and

Lopez, 2002). Companies should provide their clients with skills in maintaining

communications and dialogues with their customer participation partners. People often

perceive their level of efficacy from their own physiological cues, reading their stress or

fatigue as negative predictors of successful task completion (Bandura, 1982, p. 127).

Organizations should offer services in a relaxing environment.

Customers perceive less value and switching costs from working with employees they

perceive as inefficacious. A high level of customer adviser-efficacy can help compensate

for the customers

’ own perceptions of inefficacy. Bitner et al. (1994) highlighted that

approximately half of satisfying customer encounters result from a contact employee

’s

ability to adjust the service delivery system to cater to specific customer needs and

requests. Service companies can adopt three possible strategies to increase the

adviser-efficacy level of their customers. First, the companies can convey efficacy-related

messages about their employees to customers, such as displaying certificates or

performance awards to increase the customers

’ perceptions of the efficacy of their

employee partners ( Jackson et al., 2008). Second, employees need to be trained to

understand what they should expect. For example, the employees should know how

to provide a logical explanation for a service failure to customers. The employees can also

offer useful and real-time information to clients and should always be prepared to answer

the queries of their clients in a non-technical language. Finally, employees should present

strong motivation and psychological factors (e.g. levelheaded and prophetical) to

co-produce with customers because people depend on these cues to form perceptions on

the efficacy of their partners ( Jackson et al., 2008).

Companies should ideally match clients and employees with high self-efficacy and

adviser-efficacy to produce maximum participation value and switching costs. Identifying

customer self-efficacy levels of different customer segments requires systematic marketing

research by service providers. One method is directly surveying customers about

their self-efficacy for a particular service. The companies can then facilitate continuous

collaborations between these matched customer

–employee dyads and avoid job rotations

that can cause the collaboration to disintegrate. Assigning employees with high levels of

self-efficacy to serve actively participating customers, regardless of their level of efficacy,

can be another strategy that can enhance the participation value and switching costs among

customers. For example, firms can assign tasks that require active customer participation,

such as asset/fund management, to efficacious employees.

Limitations and further research

The paper concludes by noting the limitations and presenting recommendations for future

research. This work collects data from a single service, but its generalizability can be

increased by replicating its proposed model across additional services, including medical

services, travel agencies, and fast-food restaurant services (e.g. services that involve low

contact vs services that involve high contact). Dong et al. (2014) indicated that firms may

need to conduct further fine-grained segmentation analysis to determine the exact effect of

Customer

participation

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customer participation on their respective service contexts. Future studies can explore the

similarities and differences from results across different types of services. Although

customer value and switching costs can be created through customer participation,

other variables, such as service quality (Dong et al., 2014), may emerge and require further

exploration in the service context. Dong et al. (2014) concluded that when customer

participation readiness is high, the increasing customer participation enhances service

outcomes, including customer satisfaction and perceived service quality. Instead of

customer self-efficacy and adviser-efficacy, customer participation readiness can be adopted

as a moderator to test the model of the current work for future research.

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