Empirical investigation of customers’ channel extension behavior: Perceptions shift toward the online channel

Computers in Human Behavior 27 (2011) 1688–1696

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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Empirical investigation of customers’ channel extension behavior: Perceptions shift toward the online channel Shuiqing Yang a, Yaobin Lu a,⇑, Ling Zhao a, Sumeet Gupta b a b

School of Management, Huazhong University of Science and Technology, Wuhan 430074, PR China Shri Shankaracharya Institute of Technology and Management, Bhilai 490020, India

a r t i c l e

i n f o

Article history: Available online 17 March 2011 Keywords: Customer channel extension Behavioral intention Perceived service quality Self-efficacy Online channel

a b s t r a c t An increasing number of traditional (offline) firms are opening up their online sales channels. However, a number of them are finding it difficult to increase the utilization of their online channels from their existing customers. The purpose of the current study is to identify factors that influence customers channel extension from the offline to online channel and to understand how these factors influence customers’ behavior towards online channel extension. Drawing on the theory of entitativity formulated by Campbell, we propose a research model of customers channel extension by focusing on the shift of perception from offline to the online channel. The data for the study is collected from a large bank in China. The structural equation modeling analysis results indicate that perceived offline service quality influences perceived online service quality both directly as well as indirectly through perceived entitativity. Perceived online service quality, in turn influences customers’ behavior towards the online channel extension. The results also demonstrate that self-efficacy for change directly influences behavior towards the online channel extension, and it also has an important moderating influence on relationship between perceived offline service quality and perceived online service quality. Theoretical and practical implications of the findings are discussed. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction The widespread diffusion of the Internet has boosted sophistication of firms’ marketing approaches and improved customer relationship management. A number of traditional brick-andmortar firms (i.e., those organizations that have a physical presence and conduct face-to-face transactions) are opening up their online sales channels by leveraging upon the relationships with their existing customers. Kumar and Venkatesan (2005) argue that by having multiple sales channels firms can improve customer retention because switching costs increase with increased number of channels. Indeed, customers’ experiences with the established channel, which provide customers with the relevant information about the new channel, may help reduce the uncertainties and perceived risks associated with the newly launched channels. A number of previous studies (Kumar & Venkatesan, 2005; Neslin & Shankar, 2009; Wallace, Giese, & Johnson, 2004) also confirm that customers who shop across multiple channels from a firm are a greater source of revenue to the firm, are more loyal and more active than other customers.

⇑ Corresponding author. Tel.: +86 027 87556448. E-mail addresses: [email protected] (S. Yang), [email protected] (Y. Lu), [email protected] (L. Zhao), [email protected] (S. Gupta). 0747-5632/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2011.02.007

The multi-channel strategies give companies an opportunity to obtain additional revenue from their existing customers who purchase from their other channels. The present study defines such transfer of customers to additional channels for conducting business with the firm as customer channel extension. Customer channel extension refers to the use of services or purchases made by the customers by utilization of additional channels of the firm in addition to the ones they currently use. Companies employ various strategies to drive their customers from offline channel to online channel because online channel is known to be more cost efficient than the traditional channel. Still companies find it difficult to increase the utilization of their online channels by their existing customers (Falk, Schepers, Hammerschmidt, & Bauer, 2007). This phenomenon poses an interesting question for the current study: what are the drivers of customers channel extension behavior, and how do customers evaluate the channels extension process? Understanding the factors that influence customers’ channel extension from the offline to the online channel would offer managers important guidelines in the competitive environment. In academia, most of the empirical studies on e-commerce have investigated customer online behavior including adoption (Pavlou & Fygenson, 2006), satisfaction (Kim, Ferrin, & Rao, 2009), and loyalty (Cyr, 2008). While the existing literature has made great strides in terms of understanding customer online behavior and associating it with long-term customer profitability, a majority of

S. Yang et al. / Computers in Human Behavior 27 (2011) 1688–1696

these studies tend to examine the drivers of customer profitability within a single channel. In recent years, although a number of studies, especially in marketing, have discussed online customer behavior (Chiu, Hsieh, Roan, Tseng, & Hsieh, 2010; Falk et al., 2007; Kumar, 2010; Kuruzovich, Viswanathan, Agarwal, Gosain, & Weitzman, 2008; Neslin et al., 2006) in the context of multiple channels, most of them separately examine the influence of online and offline channel on consumers’ behavior. Only a few studies (e.g., Kim & Park, 2005) have investigated the customer channel extension behavior by focusing on the internal association between online and offline channel. Kim and Park (2005) reported that a customer’s attitude toward a traditional retailer positively affect his evaluation of the retailer’s online channel. However, they did not answer the questions: why and how a customer’s perception of an offline channel influences his evaluation of the corresponding online channel. The literature still lacks a systematical investigation into the internal mechanisms of customer channel extension behavior. To fill the gap, the current study investigates customers channel extension behavior by focusing on the shift of customers’ perception about offline channel to the online channel. Based on the theory of entitativity and previous research on innovation adoption, the current study investigates (1) how does the customers’ perceived service quality about a firm’s offline channel influences their perceived service quality about the firm’s online channel? (2) what is the role of perceived entitativity (degree to which a collection of individual entities is perceived as belonging to one group) on customers’ behavior toward online channel extension? (3) how does customers’ self-efficacy for change influence their behavior towards online channel extension? This research thus contribute to a better theoretical understanding of customer channel extension behavior as well as offer practical insights to organizations for managing such channel extension process. The present study is organized as follows. The research theoretical background and hypotheses are presented in Section 2. Next, in Section 3 the research material and methods is discussed, followed by results in Section 4. The results are discussed in Section 5, along with their theoretical and practical implications in Section 6. Finally, the conclusions of the present study are presented in Section 7. 2. Theoretical background and research hypotheses The current study seeks to develop a research model of customer channel extension by drawing on the extant literature on innovation adoption and consumer behavior. A review of the literature suggest that perceived entitativity, perceived services quality, and self-efficacy for change might offer an explanation why consumers of one channel extend their consumption to another channel. This section first discusses the theory of entitativity. Then, the research model is proposed and hypotheses are discussed based on the main constructs of perceived entitativity, perceived services quality, and self-efficacy for change. 2.1. Entitativity Campbell (1958) defines entitativity as the degree to which a collection of individual entities is perceived as belonging to a group. The concept of entitativity has been examined in various fields such as social psychology (e.g., Johnson & Queller, 2003; McConnell, Sherman, & Hamilton, 1997), marketing (e.g., Franziska & Sattler, 2006) and information systems (e.g., Peijian, Cheng, Wenbo, & Lihua, 2009; Stewart, 2003). These studies indicate that the degree of perceived entitativity determines the information impression formation, representation, and processing (Johnson & Queller, 2003; Peijian et al., 2009). For example, Crawford,

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Sherman, and Hamilton (2002) reported that the transference of traits from one group member to other group members is stronger in case of groups with high entitativity than in groups with low entitativity because in low entitativity groups cohesion among members is low. In the information systems literature, the conception of entitativity has been applied to study the transfer of trust and usage among different entities. For example, Stewart (2003) examined the trust transfer in the world wide web. His research demonstrated that the perceived interaction and similarity (components of entitativity) play a causal role in transfer of consumers’ initial trust from trusted websites to unknown websites via hypertext links. This research empirically validated that high perceived entitativity between an unknown target and a trusted target will lead to strong initial trusting belief about the unknown target. Similarly, by empirically examining transfer of usage from one instant messenger, QQ, to its portal, QQ.com, Peijian et al. (2009) found that perceived entitativity has an important moderating effect on the transfer behavior. Particularly, when the perceived entitativity is high, the impact of perceived usefulness and perceived ease of use of an incumbent technology product on perceived usefulness and perceived ease of use of new technology product will be stronger than when the perceived entitativity is low. Thus, the theory of entitativity provided an ideal theoretical background for the current study to investigate the complex customer’s channel extension behaviors. 2.2. Research model and hypotheses Drawing on the theory of entitativity and prior research related to service quality and self-efficacy for change, the current study proposes a research model that examines factors that influence customers channel extension behavior (Fig. 1). It depicts that customers’ perceived service quality of an offline channel influences perceived service quality of the extended online channel both directly and indirectly through perceived entitativity, which in turn influences customers’ online channel extension behavior. Customers’ self-efficacy for change has a moderating effect on the relationship between perceived service quality of an offline channel and perceived service quality of the extended online channel. Customers’ self-efficacy for change also has a direct effect on their online channel extension behavior. Theoretical justifications of the hypotheses are as follows. 2.2.1. Perceived service quality Perceived service quality is defined as a customer’s assessment of the overall excellence or superiority of a service (Zeithaml, Parasuraman, & Berry, 1988). As companies are expanding their service channels by going online, customers are also becoming multichannel customers from being single channel customers. Before going to a firm’s online channel, a customer has already developed an impression about the service quality of a firm’s offline channel. Since the extended online channel shares the same brand name as the existing offline channel, a customer evaluates it on the basis of his evaluation of the existing channel. When a customer has a favorable impression of service quality in the offline channel, he tends to develop a favorable perception of service quality in the firm’s online channel, and vice versa. Empirical evidence also reveals that there is a positive influence of perception about a firm’s offline channel to the firm’s extended online channel (Bhatnagar, Lurie, & Zeithaml, 2003; Kim & Park, 2005; Kwon & Lennon, 2009b; Montoya-Weiss, Voss, & Grewal, 2003). For example, Kim and Park (2005) found that customer’s attitude toward a traditional retailer influenced their evaluation of the retailer’s online channel. Similarly, Bhatnagar et al. (2003) developed an expectation-transfer model and examined whether consumers’ experiences in one

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Perceived Entitativity

Hypothesis 3

Hypothesis 4

Hypothesis 2

Hypothesis 1 Perceived Offline

Hypothesis 5

Perceived Online Service Quality

Service Quality

Behavior toward Online channel Extension

Hypothesis 6a

Hypothesis 6b Self-efficacy for change

Fig. 1. The research model.

domain (offline or online) can influence their evaluations about the other domain. They found that customers’ offline service experiences indeed have a strong influence on their perception of online service quality (reliability, responsiveness, assurance, empathy and tangibles). Therefore, it is reasonable to expect that a favorable perception of service in the offline channel will be transferred as a favorable perception about the service in the online channel. Thus: Hypothesis 1. Customers’ perceived service quality of an offline channel will have a positive impact on their perceived service quality of the firm’s online channel. 2.2.2. Perceived entitativity between the offline and extended online channels The successful extension from one channel to another channel depends on the unknown target being grouped with original known target. According to the theory of entitativity, the perceptions of entitativity would strongly affect information impression formation and processing (Crawford et al., 2002). If the perceived entitativity between two targets is high, the group will be perceived as a coherent unit, and the new extension target will be more easily reconciled with the original target in people’s memory. This cognitive consistency information retrieved from the memory may evoke a categorization process that leads to transferring of perception of original known target to the new extended target. If the perceived entitativity is low, customers may question the rationale behind the firm’s extending to the new target. Empirical studies have shown that perceived entitativity has a positive influence on perceptions of the transferred or extended target (e.g., Peijian et al., 2009; Stewart, 2003). In the context of the current study, it is reasonable to expect that perceived entitativity will exert a positive influence on customers’ perceived service quality of the extended online channel. Hence: Hypothesis 2. Customers’ perceived entitativity between the offline channel and the extended online channel has a positive impact on their perceived service quality of the extended online channel. Since most multichannel companies use the same brand name for both their offline and online channels, the brand becomes an observable signal that indicates strong tie between an offline channel and an online channel. In other words, the brand name is an index of entitativity. When the brand is associated with high service quality, customer may form a positive attitude towards the new extended online channel. The underlying mechanism is that customers tend to expect the similarity, proximity, consistency and common fate of the targets in processing information formation when perceived entitativity between two targets is high (Campbell, 1958). The higher the perceived entitativity, the more

positive is the attitude of the customer towards the new extended target. In the context of the current study, when customers in a bank’s offline channel perceive high entitativity between the bank’s offline and the online channels, they will develop a relatively favorable attitude towards the bank’s online channel. Hence: Hypothesis 3. Customers’ perceived entitativity between the offline channel and the extended online channel has a positive impact on their intention to use extended online channel. A brand, a product, or an event in memory is depicted as consisting of a set of nodes and links (Keller, 1993). Nodes are stored information joined by links that vary in strength. The strength of association between the activated node and all linked nodes determines the particular information that can be retrieved from memory and the extent to which it can be retrieved (Keller, 1993). Most banks use the same brand name in both their offline and online channels. The overall brand evaluation may be stored and retrieved in an individual’s memory separately from specific attribute information (Aaker & Keller, 1990). The perceived service quality of the offline channel can influence perceived entitativity through the retrieval of memory. In fact, when customers’ have a positive perception of service quality in the offline channel, they may expect the service provider to have more capability and skills to launch an extension channel. Consequently, the perceived entitativity will be high. Thus: Hypothesis 4. Customers’ perceived service quality of an offline channel will have a positive influence on their perceived entitativity. According to the theory of reasoned action (Ajzen, 1991), behavioral intention is a function of attitude which is a combination of evaluative judgments and feelings towards the behavior. In the context of present study, the online channel offers better banking services (more accurate, prompt, and personal) than traditional channels. When the online channel is perceived to offer high service quality, customer’s intention to use the extended online channel will also be higher. The positive relationship between the perceived service quality and behavior intention has been validated in both the offline and online context (Bauer, Falk, & Hammerschmidt, 2006; Cronin, Brady, Brand, Hightower, & Shemwell, 1997; Kumar & Grisaffe, 2004; Montoya-Weiss et al., 2003). Thus: Hypothesis 5. Customers’ perceived service quality of the extended online channel will have a positive influence on their intention to use extended online channel. 2.2.3. Self-efficacy for change During the process of customer online channel extension, some customer may experience difficulty in using online channels and

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may not feel comfortable using a digital interface (Falk et al., 2007). Self-efficacy for change is defined as an individual’s confidence in his own ability to adapt to the new situation (Kim & Kankanhalli, 2009). It is considered an internal factor which reflects the extent to which an individual can master the difficulties during IS-related change (Kim & Kankanhalli, 2009). This ability is dependent on customers’ skills in using computer and specifically Internet. Customers with a low level of self-efficacy for change do not completely notice the features provided by a service provider’s website due to lack of confidence in their computer skills (Lee, Choi, & Kang, 2009). Consequently, they will not form a high service quality perception towards the digital interface and may be more inclined to resist the change. In contrast, customers with a high level of self-efficacy will develop a favorable perception towards the digital interface and will be more inclined to embrace the change. In fact, self-efficacy has been found to moderate the relationship between individual’s evaluation and cognitive effort (e.g., Lee et al., 2009; Schaubroeck, Lam, & Xie, 2000). For example, Lee et al. (2009) found that computer self-efficacy has significant moderating effect on the relationship between the appraisal of a website and cognitive efficiency. Similarly, in the context of the present study, the ability to adapt to the new situation can be recognized as perceived cognitive effort associated with online channel extension. Therefore, it can be argued that self-efficacy moderates the relationship between the evaluation of offline channel service quality and the cognitive effort (service quality of online channel) perceived by an online channel extension customer. Specifically, the relationship between perceived service quality of an offline channel and the perceived service quality of the extended online channel will be stronger for customers’ with a high level of self-efficacy. On the other hand, customers with a high level of self-efficacy for change face the change confidently and are inclined to adopt new and innovative online channel. Indeed, according to the social cognitive theory (SCT), customers with high self-efficacy will have more positive expectations toward behavioral consequences (Bandura, 1986). These arguments lead to the following hypotheses: Hypothesis 6a. The positive relationship between perceived service quality of an offline channel and the perceived service quality of the extended online channel will be stronger for individuals with higher self-efficacy for change.

Hypothesis 6b. Customers’ self-efficacy for change will have a positive impact on their intention to use extended online channel.

Mobile number was also useful for preventing repeat responses in the dataset. All responses were scrutinized and those that used the same answer for all questions (like all seven or all four) were dropped. Those responses where respondents did not have any online banking experience were also dropped. This is because to answer the questions for online channel service quality, it is necessary that the respondent has some online banking experience. Thus, a total of 441 valid responses were obtained in the study. Table 1 presents a description of the demographic information of the respondents. The demographic statistics show that 56% of the participants were male and 44% were female. 3.2. Research setting To test the research model banking services were chosen primarily for three reasons. First, banking services are among the most critical and common business affairs in modern society. This ensures the availability of respondents and high relevance of the present study to practitioners. Second, online banking is relatively new compared to offline banking at least in China. This evolution ensures the temporal order of channel extension. Third, the usage of online channel complements offline channel in case of banks. In multichannel grocery stores there is a possible cannibalization of offline sales, in case the online sales picks up. In case of banks, it is desirable that customers use their online channel because this reduces the load on their offline bank counters and the employees could be deployed on other important works. 3.3. Measurement The survey instrument for the present study was developed from existing reliable and valid scales, wherever possible. Five items of perceived service quality, developed by Cronin and Taylor (1992), were used to measure perceived offline service quality and perceived online service quality. The items of perceived entitativity were adapted from Peijian et al. (2009) to reflect individuals’ perceptions on integration level of a bank’s offline channel and online channel. The items for self-efficacy for change were adapted from Kim and Kankanhalli (2009). Two items of behavior towards online channel extension (i.e., intention to use the online channel) were adapted from Venkatesh and Davis (2000). All items were

Table 1 Demographics of the research sample (n = 441). Measure

Item

Frequency

Percentage (%)

3. Material and methods

Gender

Male Female

247 196

56.0 44.0

3.1. Sample

Age (years old)

<18 18–24 24–30 >30

3 180 191 67

0.7 40.8 43.3 15.2

Education

High school or below Two-year college Four-year college Graduate school or above

69 183 168 21

15.6 41.5 38.1 4.8

Occupation

Corporate Government Education Student Others

227 21 41 116 36

51.5 4.8 9.3 26.3 8.1

Internet experience

<1 year

27

6.1

131 283

29.7 64.2

The data for the present study was collected from users who had bank accounts with a large nationwide bank in China. A survey hyperlink was placed on the homepage of the bank’s website and subjects were informed that they should have experience of both offline and online banking with the bank. Respondents were first asked to recall and assess their perceptions of the bank’s offline channel. They were then asked to answer the questions related to the bank’s online channel in relation to its offline channel. All participants were informed that there was a 10% chance of winning a prize by completing the survey. In order to prevent a subject from participating in the survey more than once, a user could enter the data from the same IP address only once. In addition, participants were also asked to provide their mobile phone number which was used for drawing lots and distributing prizes to the winners.

1–3 years >3 years

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measured on a seven-point Likert scale, ranging from strongly disagree (1) to strongly agree (7). As the questionnaire was in Chinese, a back to back translation procedure was conducted to ensure translation validity. Three professors from information system domain were invited for suggestions on the instrument. Based on their feedback, some necessary changes were made to make items clearer and understandable. A pilot test was also conducted to validate the instrument. The final items in each scale are listed in Appendix A. 4. Results The current study adopted a two-step Structural Equation Modeling analysis approach as recommended by Anderson and Gerbing (1988). In this approach the measurement model and the structural model were tested separately. First the measurement model was examined for reliability and validity followed by the structural model for establishing significance of hypothesis. For determining whether a construct is reflective or formative, the criterion suggested by Petter, Straub, and Rai (2007) was followed. In the present study, perceived offline service quality and perceived online service quality were treated as formative constructs in that they reflected different dimensions of service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy). The remaining constructs (perceived entitativity, self-efficacy for change, behavior towards online channel extension) were treated as reflective constructs. 4.1. Measurement model The convergent and discriminant validity of the instruments was examined using both principal components factor analysis and confirmatory factor analysis. The Bartlett’s Test of Sphericity resulted in a Kaiser–Meyer–Olkin (KMO) statistic of 0.849 (significant at 0.01 level), indicating the appropriateness of using the principle components factor analysis on the data. As shown in Table 2, three factors with eigen-values above one were extracted and they altogether explained 80.344% of the variance. All indicators loaded on the expected factors and were all higher than 0.70, while loading on other factors were all lower than 0.30, suggesting good validity of the instruments. The confirmatory factor analysis (CFA) was conducted to further examine the measurement model. As shown in Table 3, the Cronbach’s alpha and composite reliability for all the reflective constructs in the model was above the recommended threshold of 0.70. All average variance extracted (AVE) values of the reflective constructs exceeded 0.50. As for the formative constructs, reliability in the form of very high internal consistency of constructs is actually undesirable (Petter et al., 2007). The variance inflation factor (VIF) statistical test was conducted to ensure that multicollinearity was not present in the data. The results indicated that

4.2. Structural model

Table 2 Loadings and cross loading for reflective measures. Factor SEC1 SEC1 SEC1 PEN1 PEN2 PEN3 BEX1 BEX2 Eigen-values Variance% Cumulative ***

p < 0.001.

SEC ***

0.850 0.916*** 0.885*** 0.175 0.063 0.220 0.275 0.230 2.557 31.961 31.961

none of the VIF values were greater than 3.30, suggesting that multicollinearity was unlikely to be a problem in this dataset. The convergent validity of measurement items was further established by examining the factor loadings and weights. As shown in Table 3, the item loadings of the three reflective constructs were all greater than 0.70 and statistically significant at the 0.001 level, suggesting adequate convergent validity. As for formative constructs, similar to the statistical results of service quality construct displayed in the study of Cenfetelli and Bassellier (2009), one of the indicators of perceived offline channel service quality (assurance) and two of the indicators of online channel service quality (tangibles and responsiveness) were not significant (Table 3. Because the statistical significance and magnitude of indicator’s weight of formative constructs are closely related to the number of indicators used for formative measures, there is a greater chance that many of the indicator weights will be low in magnitude and not significant if the number of indicators is large (Cenfetelli & Bassellier, 2009). Although formative indicator weights are important for determining their relative contribution to the assigned construct, it is also important to evaluate the absolute importance of a formative indicator to its construct. Following the procedure suggested by (Cenfetelli & Bassellier, 2009), both the relative and absolute contribution of the indicators was assessed. Contrary to what the present study found from the indicator weight results alone, these indicator loadings were all significant at least at 99% significance (p < 0.01) level, indicating their importance in an absolute sense. The present study further examined the theoretical overlap of the indicators and found no such effects in the measures. Thus, these indicators were kept in the subsequent data analysis for their respective constructs. To examine the discriminant validity, the square root of the AVE of each construct and its correlation coefficients with other constructs were compared. Table 4 shows that the square roots of the AVEs were larger than their corresponding correlation coefficients, thus establishing discriminant validity. As the self-reported data from a single source was used, two statistical analyses were performed to assess the possible severity of common method bias. First, a Harman’s one factor test as suggested by Podsakoff and Organ (1986) was conducted. Three factors with eigenvalue greater than 1 are extracted. The results show that no factor accounted for most of the covariance in the variables, indicating that common method bias was unlikely a problem of the results. Second, following the procedure used by Podsakoff, MacKenzie, Lee, and Podsakoff (2003), a new measurement model with all indicators loading on a common method factor was constructed and compared with the original measurement. The results of statistical analyses demonstrated that the principal variables loading were all significant at the p < 0.001 level, while the common method factor loadings were all not significant. This indicated that the common method bias, again, was unlikely a threat to the results of the present study.

PEN

BEX

0.181 0.174 0.162 0.787*** 0.879*** 0.731*** 0.211 0.172 2.091 26.136 58.097

0.215 0.188 0.229 0.212 0.209 0.036 0.867*** 0.897*** 1.780 22.247 80.344

Partial least squares software (PLS-Graph version 3.01060) was used to test the structural model and the corresponding hypotheses. Fig. 2 presented the path coefficient estimation results along with R square. As shown in the Fig. 2, path analysis results in the model provided strong support for all the hypotheses, except Hypothesis 3. More specifically, two hypothesized paths from perceived service quality of offline channel to perceived service quality of the online channel and perceived entitativity were both significant at p < 0.001, supporting Hypotheses 1 and 4. The path from perceived entitativity to perceived service quality of the online channel was found to be significant, supporting Hypothesis 2. As hypothesized, the relationship between perceived service

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S. Yang et al. / Computers in Human Behavior 27 (2011) 1688–1696 Table 3 Psychometric features of the measurement model. Construct

Item

Weight

St. error

t-value

Loading

St. error

t-value

Perceived offline service quality (formative) VIF = 1.380 OFQ1 VIF = 2.511 OFQ2 VIF = 1.687 OFQ3 VIF = 2.516 OFQ4 VIF = 1.606 OFQ5

0.342 0.377 0.260 0.106 0.217

0.074 0.107 0.088 0.127 0.102

4.616 3.522 2.942 0.834 2.163

0.737 0.839 0.728 0.806 0.724

0.048 0.037 0.047 0.052 0.063

15.359 22.654 15.494 15.753 11.503

Perceived online service quality (formative) VIF = 1.368 ONQ1 VIF = 2.605 ONQ2 VIF = 1.503 ONQ3 VIF = 3.259 ONQ4 VIF = 1.901 ONQ5

0.220 0.340 0.034 0.376 0.332

0.117 0.158 0.117 0.135 0.137

1.863 2.147 0.294 2.785 2.416

0.651 0.814 0.607 0.843 0.687

0.081 0.054 0.065 0.047 0.081

8.095 15.445 9.463 18.255 8.445

Perceived entitativity (reflective) CR = 0.869 PEN1 AVE = 0.689 PEN2 Cronbach a = 0.771 PEN3

0.835 0.894 0.742

0.021 0.014 0.032

40.739 65.139 22.925

Self-efficacy for change (reflective) CR = 0.947 SEC1 AVE = 0.857 SEC2 Cronbach a = 0.917 SEC3

0.897 0.947 0.932

0.022 0.011 0.018

39.494 87.123 51.519

0.942

0.010

89.952

0.937

0.012

79.308

Behavior toward online channel extension (reflective) CR = 0.938 BEX1 AVE = 0.884 Cronbach a = 0.868 BEX2

Table 4 Factor correlation coefficients and the square root of the AVEs. OFQ OFQ ONQ PEN SEC BEX

NA 0.468 0.358 0.344 0.263

ONQ NA 0.474 0.343 0.578

PEN

0.906 0.396 0.421

Table 5 The results of mediating effects.

SEC

BEX

0.879 0.505

IV

M

OFQ PEN 0.940

*

DV

PEN ONQ

IV ? DV

0.505*** 0.252***

ONQ BEX

IV ? M

0.390*** 0.515***

IV + M ? DV IV

M

0.343*** 0.094*

0.376*** 0.408***

p < 0.05. p < 0.001.

Note: Diagonal elements are the square root of AVE. These values should exceed the inter-construct correlations for adequate discriminant validity.

***

quality of online channel and behavior toward online channel extension was found to be significant, thus supporting Hypothesis 5. The moderating effect of self-efficacy for change on relationship between perceived service quality of the offline channel and perceived service quality of the online channel as well as the direct effect of self-efficacy for change on behavior toward online channel extension were both found to be significant, thus supporting Hypotheses 6a and 6b. The model explained 39.20% of variance of behavior toward online channel extension.

Following the procedures suggested by Baron and Kenny’s (1986), the mediating effects of perceived entitativity and perceived service quality of online channel in the proposed model was also tested. As shown in Table 5, the relationship between perceived service quality of offline channel and perceived service quality of online channel was partially mediated by perceived entitativity; the relationship between perceived entitativity and behavior towards online channel extension was also partially mediated by perceived service quality of online channel.

Perceived Entitativity

0.375 (7.487) ***

Perceived Offline

0.080 (1.380)

0.363 (6.474) ***

0.326 (5.783) *** Perceived Online

Service Quality

R2=0.140

0.395 (8.721) ***

Behavior toward Online channel Extension

Service Quality R2=0.327

R2=0.392

0.273 (3.632) ***

0.318 (6.854) *** Self-efficacy R2=0.327

for change Fig. 2. The results of path analysis. p < 0.05;



p < 0.01;



p < 0.001.

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5. Discussion 5.1. Interpretation of results Based on the theory of entitativity and prior research related to service quality and self-efficacy for change, the current study examines the customer channel extension behavior by focusing on shift of perception from offline to online channels in the context of banking services. The structural equation modeling analysis reveals interesting findings. The discussion below is structured around the study’s three research questions. The first research question examined the influence of customers’ perceived service quality about a firm’s offline channel on their perceived service quality about the firm’s online channel. The results of the present study reveal that perceived offline service quality influences perceived online service quality both directly as well as indirectly through perceived entitativity. This is consistent with several previous studies (e.g., Kim & Park, 2005; Kwon & Lennon, 2009a) which reported that customers’ perceptions or beliefs shift from the offline channel to the online channel. This finding suggests that it is possible for firms to enter into additional new Internet marketing channels by leveraging existing favorable perceptions of their traditional channel service quality. Creating and enhancing customers’ perceived service quality of the offline channel may not only have a positive effect on a firm’s existing offline channel operations, but they also have a positive cross-channel impact onto the firm’s new online channel practice. In conformation with the prior research (e.g., Bauer et al., 2006; Kumar & Grisaffe, 2004; Montoya-Weiss et al., 2003), the present study found that perceived online service quality positive influences customers’ behavior towards the online channel extension. Thus, the current study further validated the association between perceived services quality and behavior intention in the multiple channels context. The second research question examined the role of perceived entitativity on customers’ behavior toward online channel extension. The present study found that perceived entitativity has an important mediating effect on the evaluation process of customers’ perceptions shift from offline channel to online channel. The result indicated that the theory of entitativity can not only be applied to study trust transfer (e.g., Stewart, 2003) and usage transfer behavior (e.g., Peijian et al., 2009) in single channel context, but it can also be used to explain the channel extension behavior in a multiple channel environment. In the past, partly due to the substantial differences in the fulfillment process and merchandising techniques required for each channel, many firms with multichannel operations, especially in grocery retailing industry, maintain a different organizational structure (Zhang et al., 2010). This structure could increase the challenges in achieving cross-channel synergies. Although banking services may be different from grocery retailing because in grocery retailing there is a possible cannibalization of sales whereas in banking industry the online channel complements the offline channel. The cross-channel synergies are also important to banks with multichannel operations. In a multichannel service context, the service providers may implement a range of strategies across the channel mix because the offline and online services may differ (Montoya-Weiss et al., 2003). In the context of the present study, banks can offer mutual funds, stock trading facilities, and home mortgages. Customers may prefer to conduct transactions for mutual funds and stocks online and obtain home mortgages through a salesperson (Kumar & Venkatesan, 2005). Our findings suggest that maintaining a high level of perceived entitativity between channels can greatly facilitate the effect of cross-channel synergies. Perceived entitativity did not have a significant effect on behavior toward online channel extension. One plausible explanation is

that the effect of perceived entitativity on behavioral intention is mediated by perceived service quality of online channel. Indeed, the direct effect of perceived entitativity on behavior intention is significant at the 99.9% (p < 0.001) significance level. This finding further strengthened the important role of perceived entitativity on customers’ behavior toward online channel extension. The multichannel firms can implement a range of strategies to enhance the level of perceived entitativity between channels. For instance, firms could link databases between offline and online channel, and inform offline customers about interesting and similar online offers (Verhagen & van Dolen, 2009). The third research question examined the role of customers’ self-efficacy for change on their behavior toward online channel extension. The present study found that self-efficacy for change directly influences behavior towards the online channel extension, and it also has an important moderating influence on relationship between perceived offline service quality and perceived online service quality. The significant and positive moderating affect shows that the effect between perceived offline service quality and perceived online service quality is stronger for individuals with a high level of self-efficacy for change compared to those with a low level of self-efficacy for change. This may be because individuals with high self-efficacy for change tend to have higher computer operation skills and higher confidence in managing the difficulties during IS-related change. Thus, they will easier form a positive service quality perception of the new online channel by fully noticing its innovational features. The present study did not detect a direct influence of selfefficacy for change on perceived online service quality in both original model and interaction model. This indicates that selfefficacy for change, depending upon individual’s traits may have more salient indirect effect on perceived online service quality. Indeed, individuals who developed a favorable attitude toward the online channel may not necessary without using the offline channel because of their favorable perception on the online channel service quality. Perhaps, this may just because they have higher computer operation skills and have higher confidence in managing the difficulties during the channels extension. Self-efficacy for change also displayed a strong direct effect on behavior toward online channel extension. This result is consistent with previous arguments (Bandura, 1986; Compeau & Higgins, 1995) suggesting that the effect of self-efficacy is salient in explaining behavioral consequences. This result indicates that customers with a high level of self-efficacy will have more positive expectations toward online channel extension in addition to the indirect moderating effect on the evaluation of cross-channel synergies.

5.2. Limitations The current study suffers from some limitations. First, as the biases inherent in most web survey-based research, the possibility of repeat responses in the dataset may exist in that the IP address could be replaced. However, this is not a serious limitation because we scrutinized all responses and found no repeat responses in our dataset. Further study can adopt more rigorous design to full preventing repeat responses in their study. Second, the current study did not incorporate actual usage behavior in the research model. Although the use of intentions has been generally accepted as a proxy of technology acceptance, different measures of behavioral intention can affect the predictive power of the construct (Kim & Malhotra, 2005). Further study could therefore make more insightful claims by including the actual use.

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Third, in order to faithfully capture the complex online channel extension process, an ideal empirical design would be a longitudinal analysis of the transfer process from the offline channel to the corresponding online channel. However, such temporal analyses are restricted by the cross-sectional nature of the current study. Future research can use a longitudinal design to examine the dynamic online channel extension process. Finally, in accordance with the goal of the present study of examining the customer channel extension behavior, the current study only examined the cross-channel synergies effect by focusing on customers’ perceptions shift from offline channel to online channel. Although online channel can display synergies with offline channels, these multiple channels may also has a cross-channel cannibalization effect (Falk et al., 2007), particularly in case of firms’ selling products through their offline and online channels. Therefore, future research can examine customer’s online channel extension by focusing on the cross-channel synergies and cannibalization effect simultaneously.

6. Theoretical and practical implications The present study has both theoretical and practical implications. From a theoretical perspective, considering that the research on customer multichannel behaviors is still in its early stages (Verhoef, Neslin, & Vroomen, 2007), the current study adds valuable empirical finding to the literature and broaden our understanding of customer multichannel behavior. In particular, unlike previous studies that regard the offline and online channel as a single and separate channel, the current study explores the customer channel extension from offline to online channel considering that online channel complements offline channel. In addition, the present study applied the theory of entitativity into a multi-channel context, and validated perceived entitativity as the important factor that affected customers’ channel extension behaviors. Moreover, individual’s self-efficacy is also applied in a multi-channel setting. Our findings further substantiate the role of individual’s self-efficacy and add insight to the literature, which states that individual’s self-efficacy is salient in explaining customer behavior. Thus, the present study provided important insights on understanding the customer channel extension behaviors. From the practical perspective, the results yield interesting implications for management of multiple-channel extension. First, in line with recent findings, the current study find that perceived offline service quality has a strong cross-channel synergies effect on perceived online service quality (Van Birgelen, de Jong, & de Ruyter, 2006; Wallace et al., 2004). For firms who want to shift their customers to the online channel, they need to maintain a high level of service quality in the traditional offline channel. By doing so, they can leverage their existing offline service quality to produce similar positive perceptions of their online service quality. Second, management should be aware of the critical effect of perceived entitativity on customer channel extension. Management can attempt to enhance entitativity between the offline and online channels by increasing multichannel integration. For instance, multichannel organizations can share the common assets, such as brand, symbol, regulation, among different channels; they can build an integrated information technology infrastructure so that data across channels can be linked and analyzed in a holistic manner (Zhang et al., 2010); they can also implement a cross-channel promotion strategy such that coupons offered in one channel are redeemable in other channels (Verhoef et al., 2007). Finally, based on the strong effect of self-efficacy for change on customer channel extension, firms should analyze their target customer groups, and adopt different strategies to target them. It is relatively easier for firms to drive customers with a high level of self-efficacy into the

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Internet channel. But for firms having customer subgroups with a low level of self-efficacy, a viable strategy would be to enhance their confidence toward the Internet channel by providing special introduction programs. 7. Conclusion The goal of the present study was to identify factors that influence customers channel extension from the offline to the online channel and to understand how they affect the customers’ behavior towards online channel extension. Toward that goal, the theory of entitativity was adapted from the consumer behavior literature and integrated with theoretical and empirical findings from prior innovation adoption related research to theorize a model of customers channel extension. Data collected from a web based survey of banking customers provided empirical support for the proposed model. The results indicate that perceived offline service quality has both direct and indirect influence through perceived entitativity on perceived online service quality, which in turn affects customers’ behavior towards the online channel extension. In addition, customers’ self-efficacy for change not only has a direct influence on behavior towards the online channel extension, but also has an important and positive moderating influence on relationship between perceived offline service quality and perceived online service quality. Noteworthy contributions of the research include theorizing and validating one of the earliest models of customer channel extension in the multiple channels context, integrating perceived entitativity, self-efficacy for change, and perceived services quality constructs within our current understanding of customer channel extension behavior, and offering practical insights to organizations for managing customers channel extension.

Acknowledgements This work was partially supported by the grants from the NSFC (70971049, 70731001), a grant from NSFC/RGC (71061160505) and a grant from the Modern Information Management Research Center in HUST.

Appendix A. Measurement scales Perceived offline service quality (adapted from Cronin and Taylor (1992)).  OFQ1: Offline bank presents visually attractive physical facilities.  OFQ2: Offline bank’s service is dependable.  OFQ3: Offline bank’s employees can respond to customer requests promptly.  OFQ4: You can feel safe in your transactions with offline bank’s employees.  OFQ5: Offline bank has your best interests at heart. Perceived online service quality (adapted from Cronin and Taylor (1992)).  ONQ1: Online bank presents visually attractive web appearance.  ONQ2: Online bank’s service is dependable.  ONQ3: Online bank can respond to customer requests promptly.  ONQ4: You can feel safe in your transactions with online bank’s information system.  ONQ5: Online bank have your best interests at heart.

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Perceived entitativity (adapted from Peijian et al. (2009)).  PEN1: Both offline channel and online channel are bank’s important channels.  PEN2: Bank’s offline channel and online channel have a strong relationship with each other.  PEN3: The integration level of bank’s offline channel and online channel is very high. Self-efficacy for change (adapted from Kim and Kankanhalli (2009)).  SEC1: based on my own knowledge, skill and abilities, changing to use online banking would be easy for me.  SEC2: I am able to change to use online banking without the help of others.  SEC3: I am able to change to use online banking reasonable well on my own. Behavior intention toward online channel extension (adapted from Venkatesh and Davis (2000)).  BEX1: Assuming I have access to the online banking services, I intend to use it.  BEX2: Given that I have access to the online banking services, I predict that I would use it.

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