The interplay of counter-conformity motivation, social influence, and trust in customers' intention to adopt Internet banking services: The case of an emerging country

Journal of Retailing and Consumer Services 28 (2016) 209–218

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The interplay of counter-conformity motivation, social influence, and trust in customers' intention to adopt Internet banking services: The case of an emerging country Walid Chaouali a, Imene Ben Yahia b, Nizar Souiden c,n a

Faculty of Economics and Management of Sfax, B.P. 289, Bousalem 8170, Tunisia High Institute of Finance and Taxation of Sousse, Chedli Kallala street, 5012 Sahline, Tunisia c Marketing Faculty of Business Administration, Laval University, Quebec, Quebec, Canada G1V 0A6, Canada b

art ic l e i nf o

a b s t r a c t

Article history: Received 5 June 2015 Received in revised form 21 September 2015 Accepted 22 October 2015

The aim of this paper is to shed light on the roles of counter-conformity motivation, social influence, and trust in explaining customers' intention to adopt Internet banking services. Data is collected from 245 respondents and analyzed using SmartPLS 2.0 M3. Results show that the intention to adopt Internet banking is mainly influenced by trust in the Internet banking services, followed by customers' counterconformity motivation and performance expectancy. Social influence and trust in the physical bank, however, have indirect impacts on customers’ intention to adopt Internet banking. Effort expectancy has no effect on it. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Internet banking adoption Counter-conformity motivation Social influence Trust Performance expectancy Effort expectancy

1. Introduction The banking sector is one of the fastest industries that have adopted the Internet as a delivery channel for their services (Laukkanen, 2007). However, despite the benefits of Internet banking (IB) (Liao et al., 1999; Sundarraj and Wu, 2005) and huge expenditures invested by retail banks, offline bank clients have not used the online service as expected (Yap et al., 2010). For example, Çelik (2008) and Yousafzai and Yani-De-Soriano (2012) argue that Turkish and English banks did not succeed in generating enthusiasm among their customers for adopting and accepting IB. The center of attention of most past studies has been largely on the factors that motivate customers to adopt Internet banking services. Among these are convenience, reduced costs, accuracy, information availability, perceived usefulness, security and privacy, consumer awareness, and trust, to mention a few (Aladwani, 2001; Pikkarainen et al., 2006; Grabner-Kräuter and Breitenecker, 2011; Jalal et al., 2011; Safeena et al., 2013). To foster the implementation and adoption of IB, some authors (e.g., Cheng et al., 2006; Nasri and Charfeddine, 2012) call on n

Corresponding author. Fax: þ 1 418 656 2624. E-mail addresses: [email protected] (W. Chaouali), [email protected] (I. Ben Yahia), [email protected] (N. Souiden). http://dx.doi.org/10.1016/j.jretconser.2015.10.007 0969-6989/& 2015 Elsevier Ltd. All rights reserved.

academics and practitioners to deeply understand the phenomenon by further exploring its determinants. To the best of these authors’ knowledge, no study has investigated the interplay between counter-conformity motivation (CCM), social influence (SI), and trust in explaining customers’ adoption of Internet banking services. Two main reasons can be provided to justify our motivation behind the consideration of these factors. First, Venkatesh et al. (2012) incite researchers to expand the nomological network associated with online self-service adoption by incorporating additional core concepts into the original unified theory of acceptance and use of technology (UTAUT) model. UTAUT follows a holistic view integrating factors from eight technology adoption models (Venkatesh et al., 2003; Oliveira et al., 2014) and provides the highest predictive power relative to other models (Venkatesh et al., 2012; Oye et al., 2014). Venkatesh et al. (2003) posit that three key factors, that are performance expectancy (PE), effort expectancy (EE), and social influence (SI), affect behavioral intention. In response to the call of Venkatesh et al. (2012), the present study uses the UTAUT theoretical framework, but augments it with a set of salient factors, namely, counter-conformity motivation (CCM), trust in the Internet banking services (TRIB), and trust in the physical bank (TRPB). Second, we argue that counter-conformity motivations and social influence might have a great impact on customers' behavior, particularly in emerging countries. For instance, in their

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study about Indian customers' choice of mobile phones, Parashar et al. (2008) conclude that customers' counter-conformity motivations, and their need for uniqueness and being differentiated from other people significantly explain their adoption of new products or services. Similarly, social influence in emerging countries is found to have a strong impact on customers’ overall consumption as well as their consumption of soft drinks (Dholakia and Talukdar, 2004), green products (Kaman, 2008), and luxury products (Shukla, 2011). Individuals in emerging countries might be ambivalent between their need for uniqueness and their need to be in harmony with their society. On the one hand, they are raised to be compliant with tradition, and to respect standards and social norms. On the other hand, they are taught to follow their own conscience and make their own choices in order to assert their personality and enhance their self-image (Barone and Jewell, 2012; Kim and Markus, 1999; Fiske et al., 1998). Also, the majority of these societies are considered to be risk averse. Agarwal et al. (2009) find that security is a significant obstacle to the adoption of online banking in India. In addition, Souiden et al. (2011b, p. 363) report that “consumers in emerging countries are ambivalent, torn between their risk aversion and their aspiration to purchase high-tech products”. Thus, counterconformity, social influence, and trust might be at the same time inhibiting and promoting factors for customers’ adoption of new products and services. Additionally, the offer of innovative products and services in these countries can be confined to basic new features, particularly at the introductory stage. For example, Internet banking is often limited to basic services such as bank account consultations, credit simulations, check order, and other informative services. Transaction and operation services (e.g., money transfers, payment of bills), for instance, might not be extensively offered, if any. This particular context may discourage customers from adopting Internet banking (Nasri and Charfeddine, 2012). The objective of this study is therefore to shed light on the roles of counter-conformity motivation, social influence, and trust in explaining customers' intention to adopt Internet banking services in the context of an emerging country. The remainder of this paper is structured as follows. First, the literature related to UTAUT, counter-conformity motivation, and trust theory will be reviewed and hypotheses developed. Second, the methodology will be described, followed by the analysis and results. Then, discussion and research implications will be presented. The paper ends with research limitations and recommendations.

2. Literature review 2.1. Social Influence Venkatesh et al. (2003, p. 451) define SI as “the degree to which an individual perceives that important others believe he or she should use the new system”. According to Kelman’s (1958) social influence theory, individuals’ attitudes, beliefs, and behaviors can be affected by three processes: compliance, internalization, and identification. Compliance means that the individual behaves in a certain way in order to either gain rewards or avoid punishment (Venkatesh and Davis, 2000; Ryan et al., 2011). It is defined as a “normative responsiveness based on the need for approval” (Tsai and Bagozzi, 2014, p. 146). Thus, an individual who believes that important others (e.g., family and friends) approve his usage of new products/services will be more inclined to trust and use these products and services. With respect to internalization, it transpires when an individual consciously or unconsciously embraces others’ opinions

and acts in harmony with them. It is defined as the “congruence of one's values or goals with group members” (Tsai and Bagozzi, 2014, p. 147). When an individual believes that significant others think that the adoption of the new technology has positive outcomes, he will be inclined to adopt the same opinion and share the same convictions (Venkatesh and Davis, 2000; Venkatesh and Bala, 2008). The opposite, however, is true. Finally, identification occurs when an individual seeks to establish a relationship with a social group by adopting a similar behavior. In the literature on new technology adoption, social influence represents the social pressure exerted on a person to adopt a new technology (Martins et al., 2014). Zhou et al. (2010) argue that SI has a positive and significant impact on user adoption of mobile banking. Also, Dwivedi et al. (2011) conclude that SI is the second most influential determinant of behavioral intention. Considering the context of emerging countries, we expect that with the slow penetration rate of Internet and Internet banking, individuals will gradually trust the online channel while continuing to trust the offline (i.e., traditional) one. Since societies in these countries exert a significant influence on customers to adopt new products and services (Dholakia and Talukdar, 2004; Kaman, 2008; Parashar et al., 2008; Shukla, 2011; Souiden et al. 2011b), we expect SI to have not only a significant and direct impact on consumers’ intentions to adopt Internet banking but also a significant and positive impact on customers’ trust in the online channel as well as in the offline (i.e., traditional) one. H1. : Social influence has a significant and positive impact on customers' trust in the physical bank. H2. : Social influence has a significant and positive impact on customers' trust in the Internet banking services. H3. : Social influence has a significant and positive impact on the intention to adopt Internet banking. 2.2. Counter-conformity motivation CCM is considered as a key concept that shapes behaviors (Cheema and Kaikati, 2010; Liang and He, 2012) and is defined as “the trait of pursuing differentness relative to others through the acquisition, utilization, and disposition of consumer goods for the purpose of developing and enhancing one's self-image and social image” (Tian et al., 2001, p. 52). CCM refers to the adoption and consumption of innovative, rare, and uncommon (or loss of interest in familiar and popular) products, services, and styles in order to search for social status and avoid similarity to others (Tian et al., 2001). The literature reports that the acceptance of an innovation may be driven by counter-conformity motivation (CCM) (Hong and Tam, 2006; Arbore et al., 2014). CCM reflects the symbolic meanings behind the use of a new technology (Arbore et al., 2014). Indeed, the transfer of symbolism from the product/service to the self may enhance self-esteem (Irmak et al., 2010). Parsons et al. (2014, p.91) find that consumers' online purchase decisions are in conjunction with their desire for uniqueness. In line with this view, Arbore et al. (2014, p. 91) contend that customers use innovations “not only for what they do but also because of what they mean”. In order to enhance their self-esteem, customers are motivated by the need for counter-conformism to social pressures when they adopt an innovation (Hong and Tam, 2006). Bellezza et al. (2014) demonstrate that signals of nonconformity to conventional behaviors are regarded as manifestations of conspicuous consumption, and lead to status and competence enhancements in the eyes of the community. During the decision making process, a person is motivated by the need to stand out from the crowd and have a unique identity (Arbore et al., 2014). In the

W. Chaouali et al. / Journal of Retailing and Consumer Services 28 (2016) 209–218

case of emerging countries, Souiden et al. (2011a) find that customers strive to buy symbolic products (or services) such as luxury and novelty items. These products/services can be easily noticed (Schiffman and Kanuk, 2004) and consumers purchase them because they significantly influence or reflect their status. Thus, in emerging countries, adopting novelty services (e.g., the Internet banking) could be perceived by consumers as a way to impress others. Therefore, we expect counter-conformity motivations in emerging countries to play a positive and significant role in influencing consumers’ trust and adoption of the Internet banking. Those displaying low counter-conformity motivations, however, are less likely to try new things. Thus, they tend to trust traditional or familiar products and services (i.e., offline banking services) and distrust new ones (i.e., Internet banking services). Building on the findings of Boush et al. (1994) that conformity and trust are positively related, we expect non-conformity motivations to be negatively related to trust in traditional banking services but positively related to trust in online ones. H4. : Counter-conformity motivation is negatively related to customers' trust in the physical bank. H5. : Counter-conformity motivation is positively related to customers' trust in the Internet banking services. H6. : Counter-conformity motivation is positively related to customers' intention to adopt Internet banking services. 2.3. Trust According to Mayer et al. (1995, p. 712), trust is defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party”. For Barney and Hansen (1994, p. 176) trust can be considered as a “source of competitive advantage”. Jarvenpaa et al. (2000) emphasize the need to account for the concept of trust in the context of e-commerce. By adding trust to the UTAUT model, Riffai et al. (2012) find strong evidence for the role of trust in affecting intention. According to Gefen et al. (2008), trust serves as a basis for adopters’ decisions to use new technologies. Indeed, trust is considered as a focal concept in uncertain and risky situations (Zhou, 2011) such as Internet banking because of the “spatial and also temporal separation” between the customer and the online bank (Grabner-Kraeuter, 2002, p. 43) and the lack of “physical cues” (Lee et al., 2007, p. 729). The reluctance of customers to adopt and use IB is attributable to the absence of trust (Yousafzai et al., 2005; Yap et al., 2010). Though prior studies have focused on the online trust as an important factor in determining the uptake of innovations (Yousafzai et al., 2005), the offline trust (or trust in the physical environment) is rarely addressed (Bock et al., 2012). The fact that researchers do not account for offline trust, in a multi-channel context, constitutes a gap in the literature (Yap et al., 2010). Recent studies, however, consider trust as a multi-dimensional concept (Schoorman et al., 2007; Luo et al., 2010). Indeed, a growing body of research assumes that trust in the organization (i.e., the physical entity that provides the online service) and trust in the channel through which the service is offered are two salient aspects of trust, specifically in the adoption stage (Tan and Thoen, 2001; Teo et al., 2009; Schaupp and Carter, 2010; Carter et al., 2011; Powell et al., 2012). When customers do not experience IB, they do not have enough information to form high initial online trust (Lin, 2011). Trust in the physical bank (TRPB), developed on the basis of precedent exchange relationships between customers and their physical bank, and trust in the online channel (i.e., Internet

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banking services (TRIB)) emerge as key factors that affect IB adoption (Lee et al., 2007, 2011; Schoorman et al., 2007; Bock et al., 2012). In line with this view, Lee et al. (2007) recommend studying the relationships between elements related to both traditional and electronic channels. Also, Chiou and Shen (2012) emphasize the need to combine offline and online factors in order to study the determinants of intention to adopt Internet banking (ITAIB). In the context of the present study, two types of trust are considered: trust in the offline or physical bank (TRPB) and trust in the online or Internet banking services (TRIB). Trust in the Internet banking services derives from sociology (Shapiro, 1987) and is associated with “the general web environment” rather than the bank or the transactional website (McKnight et al., 2002, p. 314). The online environment should be trusted in order to ensure the uptake of e-payments (Tan and Thoen, 2001). McKnight et al. (1998) posited that institution-based trust is a critical antecedent to trust in the early stages of its development. Due to the risks related to the transactions via the Internet (e.g., hackers’ attacks), security and privacy issues are of primary concern (Lee et al., 2007, Lin et al., 2011). Thus, customers should believe that legal and technical structures exist on the web in order to conduct IB transactions safely (Luo et al., 2010). When customers perceive the online channel as a safe and secure medium, then they will intend to use IB. Yousafzai et al. (2005) find that top online banks, ranked in terms of their perceived trustworthiness attributes (i.e., ability, integrity, and benevolence), were present in the marketplace with the same brand name. This implies that an offline presence may influence online trust (Yousafzai et al., 2005). Kuan and Bock (2007) and Bock et al.(2012) posit that TRPB serves as a basis for the formation of online trust, even without any prior experience with the transactional website. According to Bock et al. (2012), trust is transferred from the offline to the virtual channel, based on prior offline interactions. Lee et al. (2007) find that TRPB affects online banking in terms of flow, structural assurance, perceived website satisfaction, and perceived extent of future use. In addition, Lee et al. (2011) show that offline trust motivates customers to switch from the traditional banking delivery channels to Internet banking services. A non-user of IB might only have an offline experience with the physical bank. Therefore, TRPB will be a heuristic cue used by an offline bank customer in order to form an initial TRIB. These customers think that a trusted offline bank which is successful in providing its services through the traditional channel should likely have the technical ability to use a reliable, efficient, and secure system when offering online services (Luo et al. 2010; Lee et al., 2007). Hence, we hypothesize that: H7. : Trust in the physical bank has a positive and significant impact on trust in the Internet banking services. 2.4. Performance expectancy and effort expectancy The consideration of performance expectancy and effort expectancy in this study can be argued by the fact that both of them are reported to be determined by trust and by their expected influence on intention to adopt Internet banking. In this section, we will first explain the role of these two concepts in shaping customers’ adoption of Internet banking. Then, their relationships with trust will be clarified. Drawing upon previous models and theories (e.g., the theory of reasoned action (Fishbein and Ajzen, 1975), the theory of planned behavior (Ajzen, 1991; Taylor and Todd, 1995), the technology acceptance model (Davis, 1989; Venkatesh and Davis, 2000), the innovation diffusion theory (Moore and Benbasat, 1991; Rogers, 2003), Venkatesh et al. (2003) introduce the UTAUT model in

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H6

H4

CCM H5

TRPB

H7

PE

H8

H10

ITAIB

H1 SI

H9 H2

TRIB

H11

EE

H12 H3 CCM: Counter-conformity motivation; SI: Social influence; TRPB: Trust in physical bank; TRIB: Trust in Internet banking; PE: Performance expectancy; EE: Effort expectancy; ITAIB: Intention to adopt Internet banking Fig. 1. Conceptual Model.

order to get a deeper understanding of the salient factors that determine technology adoption. The UTAUT was first developed to study technology adoption in work settings (Venkatesh et al., 2012). Then, it was expanded to explore factors that affect the uptake of innovations at the individual level in different contexts (Venkatesh et al., 2012). For example, the UTAUT model was used to identify the antecedents to the decision of adopting online family dispute resolution services (Casey and Wilson-Evered, 2012), e-filling (Carter et al., 2011), health information technology (Kijsanayotin et al., 2009), mobile banking (Zhou et al., 2010), as well as online banking (Riffai et al., 2012). According to the original UTAUT model, performance expectancy (PE) and effort expectancy (EE) affect behavioral intention. PE is considered by Venkatesh et al. (2003, p. 447) as “the degree to which an individual believes that using the system will help him or her to attain gains in job performance”. PE is by far the most influential factor that explains behavioral intention (Venkatesh et al., 2003; Anderson et al., 2006; Kijsanayotin et al., 2009; Wang and Shih, 2009; Zhou et al., 2010; Carter et al.,2011; Casey and Wilson-Evered, 2012; Riffai et al., 2012). In Zhou et al.’s (2010) study, a positive and significant relationship was found between task technology fit and PE in the context of mobile banking. Similarly, in their cross-cultural study, Imet al. (2011) demonstrate that PE is a key determinant of technology adoption. In line with the UTAUT and despite that online banking in emerging countries is not quite implemented, the provided online services can be regarded as an enhancement of the total offer. Thus, customers' performance expectancy will encourage them to adopt online banking. Therefore, we stipulate that: H8. : Performance expectancy has a positive and significant impact on the intention to adopt Internet banking. With respect to effort expectancy (EE), Venkatesh et al. (2003, p. 450) define it as “the degree of ease associated with the use of the system”. Dwivedi et al. (2011) conducted a meta-analysis on the UTAUT and found strong evidence that EE is an underpinning factor in technology adoption. Accordingly, EE predicts the intention to adopt online banking (Riffai et al., 2012; Martins et al.,

2014). Thus, individuals who believe that online banking is effortless are likely to use it. Furthermore, when the system is userfriendly, customers are more likely to enhance their perceptions regarding its performance (Venkatesh and Bala, 2008). In other words, when the system is not difficult to use, customers save efforts and can do other activities (Venkatesh and Davis, 2000). In the context of emerging countries, we expect that the simplicity of the tasks to be performed would motivate customers to adopt Internet banking. Thus, the easier the efforts to make, the easier the adoption of the Internet banking services. Hence, the following hypothesis: H9. : Effort expectancy has a positive and significant impact on the intention to adopt Internet banking. Consumers' perceptions of the advantages and ease-of-use of Internet banking are strongly tied to a specific level of trust (Gefen et al., 2003; Guo and Barnes, 2007; Pavlou, 2003; Rotchanakitumnuai and Speece, 2009; Wu and Chen, 2005). If the technology is subject to errors or the system breaks down, the service fails in achieving its objectives and thus does not efficiently provide the utility expected by customers (Lee and Wan, 2010; Lu et al., 2008). In other words, if customers do not trust the online system, then they will not perceive its benefits (Pavlou, 2003). In the literature on technology adoption, both performance expectancy and effort expectancy are reported to be determined by trust. In line with this view and in relation to the UTAUT model, Guo and Barnes (2007) conceptualize trust as a determinant of PE and EE. Lee and Song (2013) add that trust is positively related to both EE and PE. Similar results are reported by Pavlou (2003) who finds that trust has a positive impact on perceived usefulness (similar to PE) and perceived ease of use (similar to EE). For instance, trust has a significant impact on EE as it lessens the resources of customers to control and check all the details of each transaction and thereby reduces the complexity of the task (performing Internet banking) (Pavlou, 2003). Stated differently, customers’ trust in the online system facilitates the transaction and makes it “effortless” (Pavlou, 2003). Hence, we propose the following hypotheses:

W. Chaouali et al. / Journal of Retailing and Consumer Services 28 (2016) 209–218

H10. : Trust in the Internet banking services has a positive and significant impact on customers' performance expectancy. H11. : Trust in the Internet banking services has a positive and significant impact on customers' effort expectancy. H12. : Trust in the Internet banking services has a positive and significant impact on customers' intention to adopt Internet banking. Fig. 1 shows the research framework and hypotheses.

3. Methodology 3.1. Data collection

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sample in countries where the access to the population is limited. Furthermore, Lassar et al., 2005, p. 184 contend that “While the use of a student sample limits the generalizability of our research, this group does represent web-educated and computer-skilled customers. And, both of these characteristics are requirements for Internet and online banking use. Computer users who are not experienced in using their browser or feel uncomfortable with the Internet will be less likely to use the web for commercial purposes.” The final sample is composed of 245 respondents who are aged between17 and 26. Of the sample, 37% are male and 63% are female. Amongst respondents, 93.5% reported that they have been using the Internet for more than 5 years and 85% are using it on a daily basis. 3.2. Measurement instruments

The main focus of IB-related studies has been on the US and Australia in addition to European and Asian countries (Yuen et al., 2010; Im et al., 2011). However, little attention has been paid to explain IB adoption in emerging countries, such as North African ones (Nasri and Charfeddine, 2012). Therefore, Tunisia is selected as the field study site. A self-administered survey was used to collect data. Since the dependent variable is the intention to adopt Internet banking (ITAIB), we aimed non-users of IB, students who were randomly intercepted and briefed about the aim of this study. The adoption of a student sample was deemed appropriate to overcome some issues (e.g., external validity, access to the population, etc.). We argue that the main purpose of the present study is not to generalize its findings, but to test the model relationships. Zhao et al. (2010) state that a convenience sample of university students is appropriate because they are very familiar with computers, the Internet, and have great potential to adopt technological or innovative services such as Internet banking. They add that it is quite a challenging task to define a meaningful

This study uses scales validated in prior research. As shown in Table 1, PE and EE are adapted from Oliveira et al. (2014) and Martins et al.(2014), respectively. SI is adapted from Venkateshet al. (2012) and CCM is derived from Arbore et al. (2014). For TRIB, we used the scale of Carter et al. (2011) and for TRPB, we used the scale of Lee et al. (2011). Finally, for ITAIB, we adopted the scale of AbuShanab and Pearson (2007). All items are measured using seven-point Likert scales ranging from totally disagree (1) to totally agree (7).

4. Analysis and results In order to test the proposed model, the present research uses Partial Least Squares (PLS) path modeling technique with SmartPLS 2.0 M3 (Ringle et al., 2005). Prior research argue that PLS is less restrictive than covariance-based structural equation modeling (e.g., LISREL) with respect to small sample size, distributional

Table 1 Measurement Items. Constructs Performance expectancy

Items

PE1: I gain time using IB. PE2: IB optimizes my financial operations. PE3: IB allows me to make my payments quicker. PE4: I will improve my earnings using IB. Effort expectancy EE1: My interaction with IB would be clear and understandable. EE2: It would be easy for me to become skillful at using IB. EE3: I would find IB easy to use. EE4: I think that learning to operate IB would be easy for me. Social influence SI1: People who are important to me think that I should use IB. SI2: People who influence my behavior think that I should use IB. SI3: People whose opinions that I value prefer that I use IB. Counter-conformity motivation CCM1: I often think of the things I buy and do in terms of how I can use them to shape a more unusual personal image. CCM2: I am often on the lookout for new products or brands that will add to my personal uniqueness. CCM3: I actively seek to develop my personal uniqueness by buying special products or brands. CCM4: Buying and using products that are interesting and unusual assists me in establishing a distinctive image. Trust in the Internet banking TRIB1: The internet has enough safeguards to make me feel comfortable using IB. TRIB2: I feel assured that legal and technological structures adequately protect me from problems on the Internet. TRIB3: In general, the Internet is a robust and safe environment in which IB can be used. TRIB4: I feel confident that encryption and other technological advances on the Internet make it safe for me to use IB. Trust in the physical bank TRPB1: My bank has the ability to meet its promises. TRPB2: My bank would not do anything against my interests. TRPB3: My bank always treats me with goodwill. Intention to adopt Internet banking ITAIB1: I intend to use IB in the next few months. ITAIB2: I predict that I would use IB in the next few months ITAIB3: I plan to use IB in the next few months

Source Adapted from Oliveira et al. (2014)

Adopted from Martins et al. (2014)

Adapted from Venkatesh et al. (2012)

Adopted from Arbore et al. (2014)

Adapted from Carter et al. (2011)

Adopted from Lee et al. (2011)

Adopted from AbuShanab and Pearson (2007)

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Table 2 Descriptive statistics, psychometric characteristics and correlation results. Construct

CMM

SI

TRPB

TRIB

PE

EE

ITAIB

nn

Items

CCM1 CCM2 CCM3 CCM4 SI1 SI2 SI3 TRPB1 TRPB2 TRPB3 TRIB1 TRIB2 TRIB3 PE1 PE2 PE3 PE4 EE1 EE2 EE3 EE4 ITAIB1 ITAIB2 ITAIB3

Loadings

0.97 0.89 0.96 0.87 0.92 0.94 0.94 0.96 0.97 0.95 0.95 0.87 0.95 0.94 0.94 0.96 0.93 0.93 0.95 0.96 0.95 0.89 0.95 0.83

t-Statistics

151.57 25.04 104.40 21.21 58.86 53.04 24.18 66.03 135.99 59.96 71.14 27.93 86.10 49.12 34.45 68.92 42.95 36.63 64.82 53.81 52.29 49.78 112.03 19.91

Mean

SD

Correlations

2.81

1.86

4.47

1.02

5.66

1.30

3.59

1.30

4.08

1.23

1.97

1.45

3.12

1.17

EE

CAs

CRs

AVE

SI

TRPB

TRIB

PE

ITAIB

0.629nn

 0.382nn

0.177nn

0.397nn

0.683nn

0.410nn

0.95

0.96

0.86

 0.106

0.246nn

0.583nn

0.481nn

0.411nn

0.93

0.95

0.87

0.216nn

0.179nn

 0.458nn

0.420nn

0.95

0.97

0.92

0.401nn

0.423nn

0.522nn

0.95

0.97

0.92

0.381nn

0.662nn

0.96

0.97

0.90

0.365nn

0.96

0.97

0.90

0.87

0.92

0.80

Correlation is significant at 0.01 level.

0.340** R2.150 -0.479***

CCM

0.275*

R2.523

TRPB

0.351***

PE

0.311**

0.723***

ITAIB -0.166

0.193 SI

R2.567

TRIB

0.317**

0.468***

R2.316

EE R2.242

0.468*** -0.114 CCM: Counter-conformity motivation; SI: Social influence; TRPB: Trust in physical bank; TRIB: Trust in Internet banking; PE: Performance expectancy; EE: Effort expectancy; ITAIB: Intention to adopt Internet banking. (*): P <.05 (**): p <.01 (***): P < .001 Fig. 2. Structural model with path coefficients and R2.

assumption, and model complexity (Gefen et al., 2000, 2011; Henseler et al., 2009; Marcoulides et al., 2009; Reinartz et al., 2009; Chin, 2010; Hair et al., 2012; Rigdon, 2012; Ringle et al., 2012; Hair et al., 2014; Lowry and Gaskin, 2014). The analysis follows two steps; the first step evaluates the measurement model, while the second assesses the structural model. Statistical significances of item loadings and path coefficients are generated

using a bootstrapping technique (Hinkley, 1988; Efron and Tibshirani, 1993; Davison and Hinkley, 1997; Lowry and Gaskin, 2014). 4.1. The measurement model In order to test the quality of the measurement model, individual item reliability, internal consistency (or construct

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reliability), average variance extracted analysis, and discriminant validity are examined (Barroso et al., 2010). First, as shown in Table 2, all indicator loadings range from 0.83 to 0.97, with the exception of TRIB4 which is removed from the analysis because of its low loading (0.63). They are all higher than 0.70, as recommended by Carmines and Zeller (1979). Furthermore, they are all statistically significant (all t-statistics 41.96). Second, all Cronbach's Alphas (CAs) range from 0.87 to 0.96. They are higher than the threshold of 0.7 suggested by Nunnally (1978). In addition, composite reliabilities (CRs) are above the threshold of 0.7 (Fornell and Larcker, 1981) and range from 0.92 to 0.97. Both CAs and CRs reflect high internal consistency. Furthermore, the average variance extracted (AVE) of each construct is higher than 0.5 (Bagozzi and Yi, 1988). Therefore, convergent validity is assessed for all constructs. Finally, the condition of discriminant validity is satisfied since the square root of the AVE of each latent variable is greater than its correlations with the other constructs (Chin, 1998). Hence, the validity and reliability of the model are confirmed; the second step is to evaluate the structural model (Sosik et al., 2009). 4.2. The structural model As shown in Fig. 2, the model explains 56.7% of the variance in ITAIB, 52.3% of the variance in PE, 24.2% of the variance in EE, 31.6% in the variance in TRIB and 15% of the variance in TRPB. Regarding path coefficients, SI is found to have a significant and positive impact on TRIB (β ¼0.317; t¼ 2.666), but no significant impact on TRPB (β ¼0.193; t¼1.455) and ITAIB (β ¼  0.114; t¼1.163). Thus, H2 is accepted while H1 and H3 are rejected. On the other hand, CCM is found to have a significant and negative impact on TRPB (β ¼  0.479; t¼3.864), but a significant and positive impact on both TRIB (β ¼.275; t¼2.239)and ITAIB (β ¼0.340; t¼2.953). Therefore, H4, H5 and H6 are retained. Results show that TRPB has a significant and positive impact on TRIB (β ¼0.351; t¼3.868), which in turn has a significant and positive impact on PE (β ¼0.723; t¼19.427), EE (β ¼0.468; t¼8.272) and ITAIB (β ¼0.468; t¼5.270). Thus, H7, H10, H11 and H12 are all accepted. Finally, ITAIB is found to be positively and significantly influenced by PE (β ¼ 0.311; t¼ 2.699), but not by EE (β ¼ 0.166; t¼ 1.405). Consequently, H8 is accepted and H9 is rejected.

5. Discussion and research implications 5.1. Discussion This research provides an in-depth understanding of the salient factors that influence ITAIB by integrating CCM, SI, TRIB, and TRPB into the UTAUT model. First, the results provide strong evidence of a positive and direct influence of TRIB, CCM and PE on ITAIB. TRIB has the strongest impact on ITAIB, followed by CCM and PE. Though Carter et al. (2011) report that TRIB has no significant effect on ITAIB, the present study finds that the former has both a direct and indirect impact (via PE) on the latter. Thus, the higher customers’ trust in the Internet banking services, the higher their performance expectancy and the higher their intention to adopt Internet banking. Consistent with previous research (Hong and Tam, 2006; Arbore et al., 2014), CCM is found to have both direct and indirect effect on ITAIB. According to Bellezza et al. (2014), customers are driven by the need to affirm and show their nonconformity with others. However, in the context of the present study (i.e., an emerging country), respondents were found to be less inclined to seek differentness (M¼ 2.81). Despite their low CCM, this latter is found to have a negative impact on TRPB but a positive impact on TRIB. The study concludes that CCM has both direct and indirect impacts (via TRPB, TRIB and PE) on ITAIB. Indeed, the

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higher customers' counter-conformity motivation, the higher their intention to adopt Internet banking. Also, the higher customers’ counter-conformity motivation, the lower their trust in the physical bank, the higher their trust in Internet banking and PE. As for PE, respondents believe in the usefulness (i.e., PE) of Internet banking (M¼ 4.08), an underpinning factor in explaining their intention to adopt Internet banking. This is consistent with prior studies (Venkatesh et al., 2003; Im et al., 2011; Riffai et al., 2012). Thus, when offline bank customers perceive the advantages and benefits of IB, they are more likely to adopt it. However, the results show that, in contrast to PE, EE has no significant impact on the intention to adopt Internet banking. This result is consistent with Arbore et al. (2014). Respondents do not believe that EE is a key factor in influencing their intentions to use Internet banking. A look at the mean scores shows that they have low effort expectancy (M ¼1.97). Surprisingly, despite quite a familiarity with online activities (i.e., heavy Internet users), respondents do not expect IB to be easy. This might be explained by the importance of the task and that Internet banking in emerging countries is perceived as a complex task (i.e., perceived ease of use is low) and much riskier than other online activities, such as gaming. Regarding SI, results show that it has no direct impact on ITAIB but an indirect one either through TRIB alone or through TRIB and then PE. This is in line with past studies’ results (Lu et al., 2005) stipulating that SI does not directly affect intention. This finding further stresses the importance of internalization as a social influence process (Venkatesh and Bala, 2008). Though past studies (e.g., Watjatrakul, 2013) report that SI has a direct impact on PE (or perceived usefulness) and EE (or perceived ease of use), the present study contends that the effect of SI on PE and EE is mediated by TRIB. We argue that before developing certain expectancy about the usefulness or the ease of use of the online service, customers should first trust the online system. An increase in customers' trust will result in an increase in their PE and EE, and ultimately in their adoption of Internet banking. As for the role of TRPB, it is found that it has a significant effect on ITAIB either through TRIB or through TRIB and PE. Consistent with Lee et al. (2007), TRPB emerges as a focal concept that generates initial TRIB. In other words, when offline bank customers have not yet experienced Internet banking, their trust in the offline channel is likely to be transferred to the online one. As is the case with the physical channel, customers will think that the bank is able to insure the success of online transactions by adopting draconian security rules, stringent online safeguards, and increased safety standards. 5.2. Theoretical implications The integration of the UTAUT, CMM, and two typologies of trust provides an enhanced comprehension of customers’ intention to adopt Internet banking. First, results empirically validate the relevance of TRIB and PE in shaping ITAIB. Thus, customers are driven by their trust in Internet banking and their perceptions of positive consequences of IB adoption. Furthermore, the direct and indirect impact of CCM on ITAIB implies that counter-conformity motivation plays a key role in influencing customers’ adoption of Internet banking. Additionally, counter-conformity motivation is able to significantly affect customers’ trust, both in the physical bank and in the Internet banking services. Thus, an increase in customers' nonconformity with others will encourage them to lessen their trust in the offline channel while increasing their trust in the online one. In other words, individuals seeking uniqueness and driven by the motivation to stand out from the crowd tend to trust the online channel at the expense of the offline one. Prior research did not simultaneously consider the typology of offline and online trust (Yap et al., 2010; Bock et al., 2012).

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However, the present study emphasizes the role of TRPB (as a marketplace-related factor) and TRIB (as a marketspace-related factor) in determining ITAIB. Considering the positive relationship between TRPB and TRIB, customers are likely to use their trust in the physical banking services as a heuristic cue to form initial trust in the online system, as they do not have prior experience with Internet banking. Additionally, an increase in trust in Internet banking will certainly have a positive impact on their performance expectancy, and ultimately on their intention to adopt Internet banking. When customers think that the technology is prone to errors or breakdowns, they will think that it will not provide the expected utility for which it was created (Lee and Wan, 2010; Lu et al., 2008). However, when they perceive the online channel as safe and secure, they are more inclined to adopt (Lee et al., 2007). As for SI, its indirect impact on ITAIB (through TRIB) implies that more accents should be put on internalization as a social influence mechanism. Customers integrate important others’ perceptions about the consequences and user-friendliness of IB into their set of beliefs.

bank customers. Also, comparing between those who use Internet banking and those who do not use it might shed further light on the reasons behind the adoption of Internet banking. Second, it is a cross-sectional study and future research should adopt a longitudinal approach in order to get a richer picture of the ITAIB phenomenon in emerging countries. Third, this study considers trust in the physical bank to explain customers’ trust in the Internet banking services. Future studies need to integrate other factors such as offline satisfaction (Chiou and Shen, 2012), offline loyalty (Lee et al.,2011), and offline service quality (Yap et al., 2010).

Acknowledgment The authors gratefully thank the reviewers and the editor for their insightful comments and valuable feedback.

References 5.3. Managerial implications The banking industry should use these findings as guidelines in order to design “corrective actions” fora successful IB implementation and “enhance business impact resulting from the large investments in time and money” related to the provision of IB (Davis et al., 1989, p. 999). PE is found to have a strong effect on intention. Thus, when designing bank websites, functions such as quick payments, optimization of financial operations, and convenience should be considered. PE itself is found to be influenced by TRIB and indirectly by TRPB. The fact that customers still rely on TRPB to use IB indicates that offline bank-related services are used as a heuristic cue to perform online banking. Thus, the present study empirically validates the “halo effect” of customers’ trust in offline banking (Doong et al., 2011, p. 215).This implies that in emerging countries where the Internet banking services are still in the early stage of their life cycle, managers need to foster trust in their physical presence in order to persuade customers and encourage them to use online banking services. Also, it seems that a multi-channel banking service is the best way to approach customers in emerging countries. As stated by Toufaily et al. (2013), retailers can take advantage of their physical presence (i.e., physical channel) to enhance online trust. In such a case, offline customers have the possibility to combine online and offline banking services. The authors add that multichannel retailers (i.e., concomitant use of offline and online channels) need to develop cross-channel customer services in order to increase customers’ online trust. Additionally, and in order to increase customers’ intention to adopt Internet banking, bank managers in emerging countries need to find the right balance between stressing nonconformity and/or emphasizing social influence. For instance, in their communication strategies, they may emphasize the symbolic dimension of adopting IB. This is in line with previous studies (e.g., Kulviwat et al., 2009; Choi and Totten, 2012) stipulating that advertising campaigns using celebrities and members of reference groups can endorse IB.

6. Limitations and future research avenues Despite the scientific value of the present study, it has limitations. First, it uses a sample composed of students. Future studies should involve a more representative sample by enrolling real

AbuShanab, E., Pearson, J.M., 2007. Internet banking in Jordan: the unified theory of acceptance and use of technology (UTAUT) perspective. J. Syst. Inf. Technol. 9 (1), 78–97. Agarwal, R., Rastogi, S., Mehrotra, A., 2009. Customers' perspectives regarding e-banking in an emerging economy. J. Retail. Consum. Serv. 16 (5), 340–351. Ajzen, I., 1991. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Processes 50, 179–211. Aladwani, A.M., 2001. Online banking: a field study of drivers, development challenges, and expectations. Int. J. Inf. Manag. 21 (3), 213–225. Anderson, J.E., Schwager, P.H., Kerns, R.L., 2006. The drivers for acceptance of tablet PCs by faculty in a college of business. J. Inf. Syst. Educ. 17 (4), 429–440. Arbore, A., Soscia, I., Bagozzi, R.P., 2014. The role of signaling identity in the adoption of personal technologies. J. Assoc. Inf. Syst. 15 (2), 86–110. Bagozzi, R.P., Yi, Y., 1988. On the evaluation of structural equation models. J. Acad. Mark. Sci. 16 (1), 74–94. Barney, J.B., Hansen, M.H., 1994. Trustworthiness as a source of competitive advantage. Strat. Manag. J. 15 (1), 175–190. Barone, J.M., Jewell, R.D., 2012. How category advertising norms and consumer counter-conformity influence comparative advertising effectiveness. J. Consum. Psychol. 22 (4), 496–506. Barroso, C., Carrion, G.C., Roldan, J.L., 2010. Applying maximum likelihood and PLS on different sample size: studies on SERVQUAL model and exployee behavior model. In: Vinzi, V.E., Chin, W.W., Henseler, J., Wang, H. (Eds.), Handbook of Partial Least Squares. Springer-Verlag, Berlin, pp. 427–447. Bellezza, S., Gino, F., Keinan, A., 2014. The red sneakers effect: inferring status and competence from signals of nonconformity. J. Consum. Res. 41 (1), 35–54. Bock, G.-W., Lee, J., Kuan, H.-H., Kim, J.-H., 2012. The progression of online trust in the multi-channel retailer context and the role of product uncertainty. Decis. Support. Syst. 53 (1), 97–107. Boush, D.M., Kim, C.-H., Kahle, L.R., Batra, R., 1994. Cynicism and conformity as correlates of trust in product information sources. J. Curr. Issues Res. Advert. 15 (Fall), 1–9. Carmines, E.G., Zeller, R.A., 1979. Relability and Validity Assessment, N. 07-017, Sage University Papers Series on Quantitative Applications in the Social Sciences,. Sage, Beverly Hills, CA. Carter, L., Schaupp, L.C., McBride, M.E., 2011. The U.S. e-file initiative: an investigation of the antecedents to adoption from the individual taxpayers' perspective. e-Serv. J. 7 (3), 2–19. Casey, T., Wilson-Evered, E., 2012. Predicting uptake of technology innovations in online family dispute resolution services: an application and extension of the UTAUT. Comput. Hum. Behav. 28 (6), 2034–2045. Çelik, H., 2008. What determines Turkish customers' acceptance of internet banking? Int. J. Bank. Mark. 26 (5), 353–370. Cheema, A., Kaikati, A.M., 2010. The effect of need for uniqueness on word of mouth. J. Mark. Res. 47 (3), 553–563. Cheng, T.C.E., Lam, D.Y.C., Yeung, A.C.L., 2006. Adoption of internet banking: An empirical study in Hong Kong. Decis. Support. Syst. 26 (5), 1558–1572. Chin, W.W., 1998. Issues and opinion on structural equation modeling. MIS Q. 22 (1), 7–15. Chin, W.W., 2010. How to write up and report PLS analyses. In: Vinzi, V.E., Chin, W. W., Henseler, J., Wang, H. (Eds.), Handbook of Partial Least Squares. SpringerVerlag, Berlin, pp. 655–690. Chiou, J.-S., Shen, C.-C., 2012. The antecedents of online financial service adoption: the impact of physical banking services on Internet banking acceptance. Behav. Inf. Technol. 31 (9), 859–871.

W. Chaouali et al. / Journal of Retailing and Consumer Services 28 (2016) 209–218

Choi, Y.K., Totten, J.W., 2012. Self-construal's role in mobile TV acceptance: extension of TAM across cultures. J. Bus. Res. 65 (11), 1525–1533. Davis, D.F., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319–340. Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1989. User acceptance of computer technology: A comparaison of two theoretical models. Manag. Sci. 35 (8), 982–1003. Davison, A.C., Hinkley, D.V., 1997. Bootstrap Methods and their Application, Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University press, Cambridge. Dholakia, U.M., Talukdar, D., 2004. How social influence affects consumption trends in emerging markets: an empirical investigation of the consumption convergence hypothesis. Psychol. Mark. 21 (10), 775–797. Doong, H.-S., Wang, H.-C., Foxall, G.R., 2011. An investigation of consumers’ webstore shopping: a view of click-and-mortar company. Int. J. Inf. Manag. 31 (3), 210–216. Dwivedi Y., Rana N., Chen H., Williams M, 2011. A meta-analysis of the unified theory of acceptance and use of technology (UTAUT). In: Proceedings of the Governance and Sustainability in IS, vol. 366. pp. 155–170. Efron, B., Tibshirani, R.J., 1993. An Introduction to the Bootstrap – Monographs on Statistics and Applied Probability. Chapman and Hall, New York, NY. Fishbein, M., Ajzen, I., 1975. Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Addison-Wesley, Reading, MA. Fiske, A., Kitayama, S., Markus, H.R., Nisbett, R.E., 1998. The cultural matrix of social psychology. In: Gilbert, D.T., Fiske, S.T., Lindzey, G. (Eds.), . The Handbook of Social Psychology. McGraw-Hill, New York, pp. 915–981. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (3), 39–50. Gefen, D., Benbasat, I., Pavlou, P.A., 2008. A research agenda for trust in online environments. J. Manag. Inf. Syst. 24 (4), 275–286. Gefen, D., Karahanna, E., Straub, D.W., 2003. Trust and TAM in online shopping: an integrated model. MIS Q. 27 (1), 51–90. Gefen, D., Rigdon, E.E., Straub, D., 2011. An update and extension to SEM guidelines for administrative and social science research. MIS Q. 35, 2, iii-A7. Gefen, D., Straub, D., Boudreau, M.-C., 2000. Structural equation modeling and regression: guidelines for research practice. Commun. Assoc. Inf. Syst., vol. 4, Article 7. Grabner-Kraeuter, S., 2002. The role of consumers’ trust in online-shopping. J. Bus. Ethics 39, 43–50 1/2. Grabner-Kräuter, S., Breitenecker, R.J., 2011. Factors influencing online banking adoption: evidence from the Austrian market. Int. J. Internet Mark. Aadvert. 6 (4), 333–351. Guo, Y., Barnes, S., 2007. Why people buy virtual items in virtual worlds with real money. ACM SIGMIS Database. 38 (4), 69–76. Hair, J.F., Sarstedt, M., Hopkins, L., Kuppelwieser, V.G., 2014. Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research. Eur. Bus. Rev. 26 (2), 106–121. Hair, J.F., Sarstedt, M., Pieper, T.M., Ringle, C.M., 2012. The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Plan. 45, 320–340. Henseler, J., Ringle, C.M., Sinkovics, R.R., 2009. The use of partial least squares path modeling in international marketing. In: Sinkovics, R.R., Ghauri, P.N. (Eds.), Advances in International Marketing. 20; 2009, pp. 277–320. Hinkley, D.V., 1988. Bootstrapping methods. J. R. Stat. Soc. Ser. B 50 (3), 321–337. Hong, S.-J., Tam, K.Y., 2006. understanding the adoption of multipurpose information appliances: the case of mobile data services. Inf. Syst. Res. 17 (2), 162–179. Im, I., Hong, S., Kang, M.S., 2011. An international comparison of technology adoption: testing the UTAUT model. Inf. Manag. 48 (1), 1–8. Irmak, C., Vallen, B., Sen, S., 2010. You like what I like, but I don't like what you like: uniqueness motivations in product preferences. J. Consum. Res. 37 (3), 443–455. Jalal, A., Marzooq, J., Nabi, H.A., 2011. Evaluating the impacts of online banking factors on motivating the process of E-banking. J. Manag. Sustain. 1 (1), 32–42. Jarvenpaa, S.L., Tractinsky, N., Vitale, M., 2000. Consumer trust in an Internet store. Inf. Technol. Manag. 1 (1/2), 45–71. Kaman, L., 2008. Opportunities for green marketing: young consumers. Mark. Intell. Plan. 26 (6), 573–586. Kelman, H.C., 1958. Compliance, Identification, and Internalization: three processes of attitude change. J. Confl. Resolut. 2 (1), 51–60. Kijsanayotin, B., Pannarunothai, S., Speedie, S.M., 2009. Factors influencing health information technology adoption in Thailand's community health centers: applying the UTAUT model. Int. J. Med. Inf. 78 (6), 404–416. Kim, H., Markus, R., 1999. Deviance or uniqueness, harmony or conformity? A cultural analysis. J. Pers. Soc. Psychol. 77 (4), 785–800. Kuan, H.-H., Bock, G.-W., 2007. Trust transference in brick and click retailers: an investigation of the before-online-visit phase. Inf. Manag. 44 (2), 175–187. Kulviwat, S., Bruner, G.C., Al-Shuridah, O., 2009. The role of social influence on adoption of high tech innovations: the moderating effect of public/private consumption. J. Bus. Res. 62, 706–712. Lassar, W.M., Manolis, C., Lassar, S.S., 2005. The relationship between consumer innovativeness, personal characteristics, and online banking adoption. Int. J. Bank. Mark. 23 (2), 176–199. Laukkanen, T., 2007. Internet vs mobile banking: Comparing customer value perceptions. Bus. Process. Manag. J. 13 (6), 788–797. Lee, C.B.P., Wan, G., 2010. Including Subjective Norm and technology trust in the Technology Acceptance Model: A case of e-ticketing in China. DATA BASE Adv.

217

Inf. Syst. 41 (4), 40–51. Lee, J.-H., Song, C.-H., 2013. Effects of trust and perceived risk on user acceptance of a new technology service. Soc. Behav. pers. 41 (4), 587–598. Lee, K.C., Kang, I., McKnight, D.H., 2007. Transfer from offline trust to key online perceptions: An empirical study. IEEE Trans. Eng. Manag. 54 (4), 729–741. Lee, K.-W., Tsai, M.-T., Lanting, M.C.L., 2011. From marketplace to marketspace: Investigating the consumer switch to online banking. Electron. Commer. Res. Appl. 10 (1), 115–125. Liang, B., He, Y., 2012. The effect of culture on consumer choice: the need for conformity vs. the need for uniqueness. Int. J. Consum. Stud. 36 (3), 352–359. Liao, S., Shao, Y.P., Wang, H., Chen, A., 1999. The adoption of virtual banking: An empirical study. Int. J. Inf. Manag. 19 (1), 63–74. Lin, H.-F., 2011. An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. Int. J. Inf. Manag. 31 (3), 252–260. Lin, J., Lu, Y., Wang,B., K.K., 2011. The role of inter-channel trust transfer in establishing mobile commerce trust. Electron. Commer. Res. Appl. 10 (6), 615–625. Lowry, P.B., Gaskin, J., 2014. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Trans. Prof. Commun. 57 (2), 123–146. Lu, J., Liu, C., Yu, C.-S., Wang, K., 2008. Determinants of accepting wireless mobile data services in China. Inf. Manag. 45 (1), 52–64. Lu, J., Yao, J.E., Yu, C.-S., 2005. Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. J. Strat. Inf. Syst. 14 (3), 245–268. Luo, X., Li, H., Zhang, J., Shim, J.P., 2010. Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: an empirical study of mobile banking services. Decis. Support. Syst. 49 (2), 222–234. Marcoulides, G.A., Chin, W.W., Saunders, C., 2009. A critical look at partial least squares modeling. MIS Q. 33 (1), 171–175. Martins, C., Oliveira, T., Popovič, A., 2014. Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. Int. J. Inf. Manag. 34 (1), 1–13. Mayer, R.C., Davis, J.H., Schoorman, F.D., 1995. An integrative model of organizational trust. Acad. Manag. Rev. 20 (3), 709–734. McKnight, D.H., Choudhury, V., Kacmar, C., 2002. The impact of initial consumer trust on intentions to transact with a web site: a trust building model. J. Strateg. Inf. Syst. 11 (3), 297–323. McKnight, D.H., Cummings, L.L., Chervany, N.L., 1998. Initial trust formation in new organizational relationships. Acad. Manag. Rev. 23 (3), 473–490. Moore, G.C., Benbasat, I., 1991. Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2 (3), 192–222. Nasri, W., Charfeddine, L., 2012. Factors affecting the adoption of Internet banking in Tunisia: an integration theory of acceptance model and theory of planned behavior. J. High Technol. Manag. Res. 23 (1), 1–14. Nunnally, J., 1978. Psychometric Theory, 2nd ed.. McGraw-Hill, New York, NY. Oliveira, T., Faria, M., Thomas, M.A., Popovič, A., 2014. Extending the understanding of mobile banking adoption: when UTAUT meets TTF and ITM. Int. J. Inf. Manag. 34 (5), 689–703. Oye, N.D., Iahad, N.A., Rahim, N.A., 2014. The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Educ. Inf. Technol. 19, 251–270. http://dx.doi.org/10.1007/s10639-012-9189-9 1. Parashar, S., Puranik, R., Rai, R., Sarogi, A., 2008. Consumer need for uniqueness and product innovation in selection of mobile phone. In: Dhar, U., Nath, V.V., Nair, S. K., Yadav, P.K. (Eds.), New Age Marketing: Emerging Realities. Institute of Management, Nirma University of Science and Technology-Ahmedabad, New Delhi, pp. 177–188. Parsons, A.G., Ballantine, P.W., Ali, A., Grey, H., 2014. Deal is on! Why people buy from daily deal websites. J. retail. Consum. Serv. 21 (1), 37–42. Pavlou, P.A., 2003. Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 7 (3), 101–134. Pikkarainen, K., Pikkarainen, T., Karjaluoto, H., Pahnila, S., 2006. The measurement of end-user computing satisfaction of online banking services: empirical evidence from Finland. Int. J. Bank. Mark. 24 (3), 158–172. Powell, A., Williams, C.K., Bock, D.B., Doellman, T., Allen, J., 2012. E-voting intent: A comparison of young and elderly voters. Gov. Inf. Q. 29 (3), 361–372. Reinartz, W., Haenlein, M., Henseler, J., 2009. An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int. J. Res. Mark. 26 (4), 332–344. Riffai, M.M.M.A., Grant, K., Edgar, D., 2012. Big TAM in Oman: Exploring the promise of on-line banking, its adoption by customers and the challenges of banking in Oman. Int. J. Inf. Manag. 32 (3), 239–250. Rigdon, E.E., 2012. Rethinking partial least squares path modeling: In praise of simple methods. Long Range Plan. 45 (5/6), 341–358. Ringle, C.M., Sarstedt, M., Straub, D.W. 2012. Editor's comments: a critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly. 36, 1, iii-xiv. Ringle, C.M., Wende, S., Will, A. 2005. SmartPLS2.0 M3. Available at: 〈www. smartpls.de〉(accessed 12.11.12.). Rogers, E.M., 2003. Diffusion of Innovations, 5th ed.. Free Press, New York, NY. Rotchanakitumnuai, S., Speece, M., 2009. Modeling electronic service acceptance of an e-securities trading system. Ind. Manag. Data Syst. 109 (8), 1069–1084. Ryan, R.M., Lynch, M.F., Vansteenkiste, M., Deci, E.L., 2011. Motivation and autonomy in counseling, psychotherapy, and behavior change: a look at theory and practice. Couns. Psychol. 39 (2), 193–260.

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Safeena, R., Date, H., Hundewale, N., Kammani, A., 2013. Combination of TAM and TPB in Internet Banking adoption. Int. J. Comput. Theory Eng. 5 (1), 146–150. Schaupp, L.C., Carter, L., 2010. The impact of trust, risk and optimism bias on E-file adoption. Inf. Syst. Front. 12 (3), 299–309. Schoorman, F.D., Mayer, R.C., Davis, J.H., 2007. An integrative model of organizational trust: past, present, and future. Acad. Manag. Rev. 32 (2), 344–354. Shapiro, S.P., 1987. The social control of impersonal trust. Am. J. Sociol. 93 (3), 623–658. Schiffman, L.G., Kanuk, L.L., 2004. Consumer Behavior. 9th ed. Prentice Hall, Englewood Cliffs, NJ. Shukla, P., 2011. Impact of interpersonal influences, brand origin and brand image on luxury purchase intentions: measuring interfunctional interactions and a cross-national comparison. J. World Bus. 46 (2), 242–252. Sosik, J.J., Kahai, S.S., Piovoso, M.J., 2009. Silver bullet or voodoo statistics?: A primer for using the partial least squares data analytic technique in group and organization research. Group Organ. Manag. 34 (1), 5–36. Souiden, N., M’Saad, B., Pons, F., 2011a. A cross-cultural analysis of consumers' conspicuous consumption of branded fashion accessories. J. Int. Consum. Mark. 23 (5), 329–343. Souiden, N., Pons, F., Mayrand, M.E., 2011b. Marketing high-tech products in emerging markets: the differential impacts of country image and country-oforigin's image. J. Prod. Brand Manag. 20 (5), 356–367. Sundarraj, R.P., Wu, J., 2005. Using information-systems constructs to study onlineand telephone-banking technologies. Electron. Commer. Res. Appl. 4 (4), 427–443. Tan, Y.H., Thoen, W., 2001. Toward a generic model of trust for electronic commerce. Int. J. Electron. Commer. 5 (2), 61–74. Taylor, S., Todd, P., 1995a. Understanding information technology usage: a test of competing models. Inf. Syst. Res. 19 (4), 144–176. Teo, T.S.H., Srivastava, S.C., Li, J., 2009. Trust and e-government success: an empirical study. J. Manag. Inf. Syst. 25 (3), 99–131. Tian, K.T., Bearden, W.O., Hunter, G.L., 2001. Consumers' need for uniqueness: scale development and validation. J. Consum. Res. 28 (1), 50–66. Toufaily, E., Souiden, N., Ladhari, R., 2013. Consumer trust toward retail websites: comparison between pure click and click-and-brick retailers. J. Retail. Consum. Serv. 20, 538–548. Tsai, H.T., Bagozzi, R.P., 2014. Contribution behavior in virtual communities: cognitive, emotional, and social influences. MIS Q. 38 (1), 143–163. Venkatesh, V., Bala, H., 2008. Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39 (2), 273–315. Venkatesh, V., Davis, F.D., 2000. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46 (2), 186–204. Venkatesh, V., Morris, M.G., Davis, F.D., Davis, G.B., 2003. User acceptance of information technology: towards a unified view. MIS Q. 27 (3), 425–478. Venkatesh, V., Thong, J.Y.L., Xu, X., 2012. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36 (1), 157–178. Wang, Y.-S., Shih, Y.-W., 2009. Why do people use information kiosks? A validation of the unified theory of acceptance and use of technology. Gov. Inf. Q. 26 (1), 158–165. Watjatrakul, B., 2013. Intention to use a free voluntary service: the effects of social influence, knowledge and perceptions. J. Syst. Inf. Technol. 15 (2), 202–220. Wu, I.-L., Chen, J.-L., 2005. An extension of Trust and TAM model with TPB in the

initial adoption of on-line tax: an empirical study. Int. J. Hum. Comput. Stud. 62 (6), 784–808. Yap, K.B., Wong, D.H., Loh, C., Bak, R., 2010. Offline and online banking – where to draw the line when building trust in e-banking? Int. J. Bank. Mark. 28 (1), 27–46. Yousafzai, S., Yani-De-Soriano, M., 2012. Understanding customer-specific factors underpinning internet banking adoption. Int. J. Bank. Mark. 30 (1), 60–81. Yousafzai, S., Pallister, J.G., Foxall, G.R., 2005. Strategies for building and communicating trust in electronic banking: a field experiment. Psychol. Mark. 22 (2), 181–201. Yuen, Y., Yeow, P., Lim, N., Saylani, N., 2010. Internet banking adoption: Comparing developed and developing countries. J. Comput. Inf. Syst. 51 (1), 52–61. Zhao, A.L., Koenig-Lewis, N., Hanmer-Lloyd, S., Ward, P., 2010. Adoption of internet banking services in China: is it all about trust? Int. J. Bank. Mark. 28 (1), 7–26. Zhou, T., 2011. An empirical examination of initial trust in mobile banking. Internet Res. 21, 527–540. Zhou, T., Lu, Y., Wang, B., 2010. Integrating TTF and UTAUT to explain mobile banking user adoption. Comput. Hum. Behav. 26 (4), 760–767.

Mr. Chaouali is a Ph.D. student at the Faculty of Economics and Management of Sfax. He is a member of the Research Laboratory ARBRE (Applied Research in Business Relationships and Economics) at the Higher Institute of Management in Tunis. His research includes pre-adoption and post-adoption usage of information technology, online consumer behavior, and services marketing.

Dr. Ben Yahia held a Ph.D. in Marketing from Paris Dauphine University. Actually, she is an Assistant Professor at the High Institute of Finance and Taxation in Sousse, Tunisia. Her research interests include social media, knowledge management, and consumer empowerment. She is a member of the Research Laboratory ARBRE (Applied Research in Business Relationships and Economics) at the High Institute of Management in Tunis.

Dr. Souiden is a Professor of Marketing at Laval University, Canada. His research interests include global marketing strategies, international market segmentation, cross-cultural consumer behavior, brand image, corporate branding, country branding, and services marketing. His articles have appeared in well-known international journals such as Psychology and Marketing, International Marketing Review, European Journal of Marketing, Journal of Brand Management, Journal of Retailing and Consumer Services, International Journal of Bank Marketing, Journal of Consumer Marketing, Journal of Product and Brand Management, and Journal of Financial Services Marketing.