Online customers’ habit-inertia nexus as a conditional effect of mobile-service experience: A moderated-mediation and moderated serial-mediation investigation of mobile-service use resistance

Journal of Retailing and Consumer Services 47 (2019) 282–292

Contents lists available at ScienceDirect

Journal of Retailing and Consumer Services journal homepage: www.elsevier.com/locate/jretconser

Online customers’ habit-inertia nexus as a conditional effect of mobileservice experience: A moderated-mediation and moderated serial-mediation investigation of mobile-service use resistance

T

Jacques Nela, , Christo Boshoffb ⁎

a b

Department of Business Management, University of the Free State, Bloemfontein, South Africa Department of Business Management, Stellenbosch University, Stellenbosch, South Africa

ABSTRACT

Retail managers are grappling with the problem to convert online shoppers to mobile shoppers. Therefore, this study investigates how mobile shopping-service experience can lower mobile-service use resistance by disrupting online shoppers’ habit-inertia behavior. The moderation results showed that mobile-service experience (1) using the retailer’s mobile service for product information; (2) buying products using other retailers’ mobile service decreases the positive (but undesirable) influence of habit on online-service inertia. Moderated-mediation and moderated serial-mediation results suggest that the positive indirect effects of online-service use habit on mobile-service use resistance through online-service inertia and mobile-service relative-advantage perceptions decrease as the two moderators increase.

1. Introduction With smartphones in their hands, consumers can now shop anywhere, anytime, using the mobile shopping service (from here on, ‘mobile service’) of multichannel retailers. However, in the fourth quarter of 2016, desktop retail e-commerce spending (excluding travel) by American consumers was still four times higher than mobile retailing (comScore, 2017). Moreover, the 2016 Digital Insights Mobile Retail report confirms that consumers still prefer retailers’ desktop service over the mobile service to purchase products. In this report it was revealed that 26% of shopping carts on desktops turn into an order, compared with a completion rate of 16% for smartphone carts, and that desktops drive 4.7 times more revenue than smartphones (Adobe, 2016). It thus appears that many customers of multichannel retailers still prefer to use the desktop service to purchase products, even though mobile services are available that could be more convenient to use, due to the ubiquity benefit smartphones offer. Research investigating consumers’ intention to use mobile services has attracted some attention in recent years, mainly due to the hype that surrounds mobile shopping. However, mobile-service use resistance behavior, and such resistance behavior in a multichannel context, has received considerably less attention from scholars. This contention is supported by Groß (2015) what states that current mobile shopping research suffers from a pro-innovation bias, focusing more on investigating favorable factors contributing to adoption, whilst ignoring investigating the factors that lead to mobile shopping resistance. ⁎

Moreover, Groß (2015) further argues that it is equally important to understand the factors leading to non-acceptance of mobile shopping, as to understand the factors leading to mobile shopping acceptance. Evidence of the absence of mobile shopping resistance research, in contrast to mobile shopping adoption research, can be observed in the recent publications on mobile shopping in the Journal of Retailing and Consumer Services. For example, since 2012 studies such as Chen et al. (2018), Groß (2018), Sohn (2017), Agrebi and Jallais (2015) and Yang (2012) reported results on the factors positively influencing mobile shopping adoption. On the other hand, in the same period only Gupta and Arora (2017) and Marriott and Williams (2018) included in their research factors negatively influencing mobile shopping adoption, but again in both instances the focus was not on investigating resistance. In the extant body of literature on mobile shopping, only Yang et al. (2015) investigated the relationship between using the online shopping service of the retailer and use extension to the mobile shopping service based on trust transfer. Similar to the other studies reported, the study of Yang et al. (2015) reported on a cross-channel synergy leading to adoption, evidence of a pro-innovation bias also in online-mobile multichannel research. Thus, as stated, there is a dearth of research on mobile shopping resistance in general, as well an overlooking by scholars of mobile shopping-service resistance based on online shopping-service use in a multichannel retail context. Online-only customers of retailers sometimes consciously decide not to use the mobile service. This behavior manifests due to online-service inertia; and inertia is reinforced by habit (Polites and Karahanna,

Corresponding author. E-mail address: [email protected] (J. Nel).

https://doi.org/10.1016/j.jretconser.2018.12.003 Received 13 June 2018; Received in revised form 5 December 2018; Accepted 6 December 2018 0969-6989/ © 2018 Elsevier Ltd. All rights reserved.

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

2012). One potential way to disrupt the habit-inertia relationship is to exploit online customers’ earlier experiences with mobile services. The role of experience in consumer decision-making has been noted and examined in several research studies (Dodd et al., 2005). Bettman and Park (1980) have singled out experience as one of two primary aspects influencing how consumers choose which product to buy. Experience with a product determines how consumers process information (Maclnnis and Jaworski, 1989), and this processing influences how purchase decisions are made. Similar to studies investigating the moderating effect of experience in purchase decisions, several other studies also confirmed the moderating effect of experience in the formation of electronic-service adoption behavior (see for example Rodgers et al., 2005; Elliott and Speck, 2005; Park and Yang, 2006). In this study, we propose that online customers’ ‘mobile-service experience’ – which includes the experience of using the retailer's mobile service for product information, and the experience of buying products using the mobile service of other retailers – cultivates knowledge of mobile services, and can disrupt the influence of habit on online-service inertia. Against this background, the primary objective of the study is to investigate online customers’ habit-inertia behavior, as a conditional effect of their mobile-service shopping experience, in mobile-service use resistance behavior. The novelty of the study is that it investigates mobile shoppingservice use resistance behavior in a multichannel context, addressing the highlighted lack of research on the topic. Secondly, it is the first study in the domain of retailing and services marketing to investigate the influence of domain experience with an alternative service as a moderator of the incumbent's service habit-inertia relationship, and to report the moderated-mediation and moderated serial-mediation effects of such domain experience. Overall, the exploration of moderatedmediation and moderated serial-mediation results in online-mobile services research in a multichannel context is scant. The testing of moderated-mediation and moderated serial-mediation effects provide insights into whether the indirect effect (mediation effect) is a conditional effect based on a moderation effect forming part of the indirect effect. Therefore, the testing and reporting these conditional indirect effects further add to the novelty of the study. The moderation results and the conditional indirect effect results offer unique theoretical contributions to the retailing and services-marketing literature, and specifically to the literature on online-mobile retailing. Next, the role of experience in consumer behavior is discussed. After that, the conceptual model that was empirically tested to realize the objective of the study is presented. This is followed by a review of the research methodology employed and the results of the data analysis. The main findings are then discussed, theoretical and managerial implications are presented, and the paper concludes with the limitations of the study.

resulting in the consumer being more knowledgeable about a greater number of attributes of a product. Moreover, familiarity leads to increased expertise (Alba and Hutchinson, 1987), which is more stable, often difficult to change, and provides order, structure, and predictability (Sheinin, 2000). Increased expertise also results in enhanced task performance, a reduction in the cognitive effort in conducting the task, and the automaticity of performing the task (Alba and Hutchinson, 1987). Another important outcome of familiarity is that it contributes to trust development. Gefen (2000) argues that familiarity, according to the ‘trust and power’ theory of Luhmann (1979), is a prerequisite for trust, as familiarity creates an understanding of the environment and the trusted party to explain the trust expectation. Gefen (2000) believes that trust is based on one's favorable expectations of another in a specific context where familiarity is required to understand the prevailing context. Thus, in the absence of familiarity with the prevailing context, trust cannot be anchored to specific favorable behaviors, and this vacuum constrains the development of trust. Due to the recognized importance of experience in consumer decision-making, several scholars investigated the role of experience in consumers’ use of electronic services. Although experience can be modeled as a direct determinant of the intention to use or the actual usage of self-service channels (see for example Klopping and McKinney, 2006), the focus has been on the influence of experience in electronicservices research as a moderator of consumers’ adoption behavior of self-service channels. The role that experience plays as a moderator in decision-making can be explained by three dominant theories. Koo (2016) suggested the accessibility-diagnosticity framework developed by Feldman and Lynch (1988) as one such an explanation. Mangleburg et al. (1998) advanced the elaboration-likelihood-model (ELM) of Petty and Cacioppo (1986), while the heuristic-systematic model (HSM) was proposed by Chaiken (1980). From a review of the extant literature on experience as a moderator in online or mobile consumer research, it is clear that the ELM emerged as the favored theory to explain the moderation effect of experience. The use of the ELM in this study is also based on the contention that dual-processing theories can be useful in investigating the moderating role of experience on the influence of habit on attitudes and behavior (Lankton et al., 2012). The ELM postulates that individuals follow two distinct routes of information processing: the central route, and the peripheral route (Petty and Cacioppo, 1986). Central-route information processing entails the processing of persuasive arguments that affect the extent and direction of attitude change. Under the central route, attitude change is determined by issue-related arguments. On the other hand, peripheralroute information processing entails effecting attitude change in the absence of argument processing (Petty and Cacioppo, 1986). Peripheral-route information processing relies more on cues than on persuasive arguments in forming attitudes. In the ELM, ‘elaboration likelihood’ refers to the likelihood that an individual would think about issue-relevant information, and that it is determined by ‘ability’ and ‘motivation’ to elaborate. ‘Ability’ refers to prior knowledge that can be gained by means of issue-related prior experience. The strongest source of motivation to process information is ‘personal relevance’, which is linked to personal involvement. Petty and Cacioppo (1986) regard personal relevance as an issue that has significant consequences for the life of a person. Thus the ELM further posits that those in a high state of elaboration likelihood are more likely to engage in scrutinizing or in the thoughtful processing of issue-related information, resulting in information processing via the central route. This issue-related elaboration is likely to result in new arguments, or an individual's personal translation of the arguments, being integrated into the underlying belief schema for the attitude object. On the other hand, when motivation and/or ability are low (a low state of elaboration), information processing is likely to take place via the peripheral route. Several studies used the ELM to investigate experience as a moderator in electronic-service adoption behavior. Rodgers et al. (2005),

2. Theoretical framework 2.1. Experience Experience can be described as a proxy for consumer knowledge (Pillai and Hofacker, 2007; Rodgers et al., 2005), which is stored as information in a person's memory (Ratchford, 2001). This information is a body of facts, principles, and procedures accumulated by people and social groups about a domain or area of interest (Page et al., 2012). Knowledge can be classified as subjective knowledge, objective knowledge, or usage experience (Raju et al., 1995). In this study the focus is on knowledge emanating from usage experience. Consumer knowledge consists of two elements: familiarity and expertise. Familiarity can be described as the “number of product-related experiences that have been accumulated by the consumer”, while expertise is defined as “the ability to perform product-related tasks successfully” (Alba and Hutchinson, 1987: 141). Rao and Monroe (1988) assert that, as familiarity increases, prior knowledge increases as well, 283

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

Fig. 1. Conceptual model.

for instance, proposed that online experience moderates the influence of online satisfaction on online loyalty, so that the influence of satisfaction on loyalty will be stronger for consumers with more online experience than for consumers with less online experience. Elliott and Speck (2005) investigated whether online-shopping experience moderates the influence of website factors such as ease of use, product information, entertainment, trust, customer support, and currency on attitude towards the retail web site. They found this to be true for ease of use, product information, and customer support. Using the ELM, Park and Yang (2006) investigated mobile device attitude formation based on internet experience. Their results showed that, consistent with the ELM, when internet experience is high, the influence of utilitarian value is high, while the influence of hedonic value is low. Conversely, the results of Park and Yang (2006) confirmed, in consonance with the ELM, that when internet experience is low, the influence of hedonic value was stronger than the influence of utilitarian value.

3.1. Moderating effect of mobile-service experience (H1 and H2) Status quo bias behavior manifests itself externally as inertia – “user attachment to, and persistence in, using an incumbent system (i.e., the status quo), even if there are better alternatives or incentives to change” (Polites and Karahanna, 2012: 24). Thus in this study online-service inertia is defined as an online customer's attachment to, and persistence in, using the online service, even if the customer can also use the mobile service to buy products. According to Polites and Karahanna (2012), inertia consists of three components: affective-based inertia, behavioral-based inertia, and cognitive-based inertia. Affective-based inertia means that users continue to use a system (in this instance, the online service of the retailer) because it would be stressful to change, because they enjoy using it, because they feel comfortable doing so, or because they have developed a strong emotional attachment to the current way of doing things. Behavioral-based inertia implies that the use of the system (the online service of the retailer) will continue simply because it is what the user has always done. Cognitive-based inertia occurs when a user consciously continues to use a system (the online service of the retailer), even though they are aware that it might not necessarily be the best, most efficient, or most effective way of doing things. Due to online-service inertia, it is anticipated that online customers would be resistant to using the mobile service of the retailer. Onlineservice inertia symbolizes an attachment to, and persistence in, using the online service, thereby promoting mobile-service use resistance. In the study of Ram and Sheth (1989: 6), innovation resistance was defined as “the resistance offered by consumers to an innovation, either because it poses potential changes from a satisfactory status quo or because of conflict with their belief structure”. Affective-based inertia is based on satisfaction with the status quo, while behavioral-based inertia and cognitive-based inertia can be linked to the belief structure of the online customer. Habit is a subconscious source of inertia (Polites and Karahanna, 2012). Habit can be defined as a “learned sequence of acts that have

3. Model development Fig. 1 shows the conceptual model developed based on the ELM, status quo bias theory, cognitive dissonance theory and innovation diffusion theory, to address the objective of the study. As seen in Fig. 1, we proposed that the influence of online-service use habit on mobileservice use resistance is mediated by online-service inertia (reflectiveformative higher-order construct) and mobile-service perceived relative advantage. Moreover, we hypothesized that mobile-service experience moderates the online-service habit-inertia relationship. In Fig. 1 the proposed conditional effect of online-service use habit on inertia forms part of two mediation effects highlighted in panel A and panel B in Fig. 2. In the rest of this section hypotheses are developed for these conditional indirect effects, resulting in the proposing of two moderated-mediation hypotheses and two moderated serial-mediation hypotheses that were tested empirically.

284

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

Fig. 2. Nested mediation models.

become automatic responses to specific cues, and are functional in obtaining certain goals or end states” (Aarts et al., 1998: 104). Habitual behavior is influenced by satisfaction with earlier behavior (Jolley et al., 2006). Therefore, we regard habit in this study as the automatic use of the retailer's online service to purchase products based on satisfaction with the use of the online service. From a theoretical perspective the positive influence of habit on inertia can be explained as follows. First, habit enhances affective-based inertia. Habitual behavior is based on earlier behavior that has led to satisfactory outcomes. Thus habitual behavior holds low uncertainty, reinforcing the ‘it's too stressful to change, because they enjoy using it, because they feel comfortable doing so’ justifications underpinning affective-based inertia. Habit also leads to an emotional attachment stemming from earlier satisfactory experiences with the product (Dowling and Uncles, 1997), thereby reinforcing the ‘emotional attachment’ aspect that underpins affective-based inertia. The automaticity of behavior denoting habitual behavior also reinforces behavioral-based inertia that entails the continuing of behavior without giving it much thought. And lastly, habitual behavior is associated with satisfactory outcomes and requires little cognitive effort (Verplanken and Aarts, 1999). Garbarino and Edell (1997) assert that humans will expend only the minimum effort needed to make a satisfactory decision, rather than making an optimal decision. Thus habitual behavior would re-inforce cognitive-based inertia – online customers continuing using the online service ‘even though they are aware that it might not necessarily be the best, most efficient, or most effective way of doing things’. In their seminal article, Burnham et al. (2003) argued that experience with an alternative provider weakens the relational bond with the current service provider. Based on this proposition, we advance the premise that experience from using the mobile service of the retailer (direct experience) and experience from buying products from other retailers using their mobile services (indirect experience) would

weaken the influence of online-service use habit on online-service inertia. This premise is consistent with the ELM, in that online-service use habit is a peripheral cue, as habitual behavior entails the automaticity of behavior, and results in the absence of argument-processing in conducting the subsequent behavior. Experience with the use of the retailer's mobile service to find product information, and experience from purchasing products using other retailers’ mobile services, support elaboration likelihood in the following ways. Firstly, the two types of experience lead to the accumulation of issue-related knowledge that enhances the ‘ability’ component of elaboration likelihood. The two types of experience would also strengthen the accessibility – the speed of retrieving affect from memory – of satisfaction judgments based on the two types of experience (Khalifa and Liu, 2007), supporting the motivation to elaborate. Secondly, both types of prior experience can enhance mobile-service self-efficacy, which can also serve as an additional source of motivation to elaborate. Thus, as the two types of experience increase, online customers are likely to engage in a centralroute information process, resulting in habit as a peripheral cue exerting a weaker positive influence on online-service inertia. Based on the discussion to this point, the following moderation hypotheses were developed. H1. Experience from using the retailer's mobile service for product information moderates the positive influence of online-service use habit on online-service inertia, such that the influence of habit on inertia is less pronounced (smaller) at higher values of the moderator H2. Experience from using other retailers’ mobile service to buy products moderates the positive influence of online-service use habit on online-service inertia, such that the influence of habit on inertia is less pronounced (smaller) at higher values of the moderator

285

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

3.2. Panel A – moderated-mediation hypothesis (H3 and H4)

conditional effect of the two types of mobile-service experience, and (2) the proposed negative relationship between online-service inertia and relative-advantage perceptions of the mobile service, with (3) the negative influence of mobile-service relative-advantage perceptions on use resistance of the mobile service, leads to the conclusion that the influence of online-service use habit on mobile-service use resistance is mediated by online-service inertia and mobile-service relative-advantage perceptions in serial, and this indirect positive effect is a conditional effect of mobile-service experience. The following moderated serial-mediation hypotheses were therefore developed.

In panel A (Fig. 2) it is shown that the influence of online-service use habit on mobile-service use resistance is mediated by online-service inertia, and this indirect effect is a conditional effect based on online customers’ mobile-service experience. Habit drives status quo bias behavior – decision-makers’ bias towards maintaining the status quo (Polites and Karahanna, 2012). Consequently, habit would promote the continued use of the online service (the status quo), thereby leading to mobile service use resistance. Already explained, online-service inertia also drives mobileservice use resistance. Allowing for the direct effect of online-service use habit on mobile-service use resistance, and joining (1) the conditional effect of online-service use habit on inertia (see H1 and H2) and (2) the influence of online-service inertia on mobile-service use resistance, leads to following moderated-mediation hypotheses.

H5. Experience from using the retailer's mobile service for product information moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia and mobileservice relative-advantage perceptions in serial, such that the indirect positive effect will be less pronounced (smaller) at higher values of the moderator

H3. Experience from using the retailer's mobile service for product information moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia, such that the positive indirect effect will be less pronounced (smaller) at higher values of the moderator

H6. Experience from using other retailers’ mobile service to buy products moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia and mobile-service relative-advantage perceptions in serial, such that the indirect positive effect will be less pronounced (smaller) at higher values of the moderator

H4. Experience from using other retailers’ mobile service to buy products moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia, such that the positive indirect effect will be less pronounced (smaller) at higher values of the moderator

3.4. Influence of online-service use habit on mobile-service perceived relative advantage In Fig. 1 it is demonstrated that online-service use habit also influences mobile-service relative-advantage perceptions negatively. Similar to the argued negative influence of online-service inertia on relative-advantage perceptions of the mobile service, online-service use habit could lead to cognitive dissonance with the perceptions of relative advantage of the mobile-service. Online-service use habit, based on satisfaction with the online service, provides a motivation to continue with the use of the online service, while relative-advantage perceptions of the mobile service present a motivation to use the mobile service. To realize consonance between online-service use habit and mobile-service relative-advantage perceptions, the online customer would revert to biased assimilation as a dissonance reduction technique by interpreting relative advantage in a biased way – that is, biasing relative-advantage perceptions downwards to rationalize online-service inertia. No formal hypothesis was developed for this relationship as the focus of the study is on investigating the online-service habit-inertia relationship in mobile-service use resistance. However, the inclusion of the relationship is essential to formalize the nomological network on which the analysis is based.

3.3. Panel B – moderated serial-mediation hypotheses (H5 and H6) In panel B (Fig. 2) it is shown that the influence of online-service habit on mobile-service use resistance is mediated by online-service inertia and mobile-service relative-advantage perceptions in serial, and this indirect effect is a conditional effect based on the moderation effect of mobile-service experience. Innovation diffusion theory proposes that five attributes of an innovation influence the rate of the adoption: relative advantage, compatibility, complexity, trialability, and observability (Rogers, 2003). Of these five factors, perceived relative advantage is the most important predictor of the rate of the adoption of innovations (Rogers, 2002). Perceived relative advantage can be defined as “the degree to which an innovation is perceived as being better than the idea it supersedes” (Rogers, 2003: 229). Consistent with this definition, the perceived relative advantage of a mobile service is defined in this study as the degree to which the mobile service is perceived as being better than using the online service of the retailer to purchase products. According to Ram (1987), lower relative advantage perceptions lead to more resistance to the adoption of a new technology. Therefore, higher perceived relative advantage would reduce online shoppers’ resistance to using the mobile service. Polites and Karahanna (2012) hypothesized and empirically proved that incumbent system inertia negatively influences new system relative-advantage perceptions. This negative relationship can be explained by cognitive dissonance theory. According to cognitive dissonance theory (CDT), individuals in a state of cognitive dissonance would strive for cognitive consistency within themselves (Festinger, 1957). Depending on the prevailing situation, a person can use biased assimilation, impact minimization, or trivialization to reduce the dissonance (Kwon and Lennon, 2009). The relative advantage online shoppers perceive in the use of the mobile service would lead to cognitive dissonance with their online-service inertia. Using biased assimilation as a dissonance reduction technique, the online customer would bias the perceived relative advantage of the mobile service downwards to achieve consonance with their inertia. Merging (1) the proposition that the relationship of habit on online-service inertia is a

4. Methodology 4.1. Study population and sampling The study population was defined as users, aged 18 years or older, of the online service of an online retailer selling household furnishings that also offers a mobile service in the format of a mobile version of the website that customers can access on mobile phones. A convenience sample of 466 online customers completed the online questionnaire. All the respondents affirmed in the survey that they had not previously used the mobile service to buy products from the online retailer. The mean age of the respondents was 39.3 years old. On average the respondents had been customers of the retailer for 3.6 years. The median number of products bought from the online retailer is 10 products. Of the respondents, 63.3% had not used the mobile service to find product information, while 44.4 per cent of the respondents had bought products from other retailers using their mobile services. To address situations in which the respondents might not know 286

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

287

Online-service use-habit Source: Limayem and Cheung (2008)

Source: Polites and Karahanna (2012)

Online-service cognitive-based inertia

Results of the assessment of the measurement model are presented

Source: Polites and Karahanna (2012)

5.1. Assessment of the measurement model

Online-service behavioral-based inertia

5. Results

Online-service affective-based inertia Source: Polites and Karahanna (2012)

Table 1 Questionnaire items.

As shown in Fig. 1, online-service inertia is an endogenous construct, and it is measured as a reflective-formative higher-order construct. Therefore, as recommended in Hair et al. (2011), SmartPLS 3.2.7 was used to evaluate the measurement model and to test the main effects. According to Jarvis et al. (2003) the characteristics that differentiate a formative model from a reflective model are: (1) the direction of causality is from the measure to the construct, (2) there is no reason to expect that the measures are correlated, (3) the omission of one of the indicators would alter the meaning of the construct and (4) measurement error is taken into account at the construct level. Polites and Karahanna (2012) argue that the dimensions of inertia they identified would not necessarily covary, are not interchangeable, do not necessarily have the same predictors, and together cause the construct. Thus, drawing on the work of Jarvis et al. (2003), they argued that it would be methodically correct to specify inertia as a reflective-formative higher-order construct. In conducting the analysis, online-service inertia was specified using the repeated-indicator, two-stage approach and Mode B measurement, as recommended by Becker et al. (2012). Construct validity of the measurement model was assessed by investigating its convergent and discriminant validity (Hair et al., 2017). The criteria for convergent validity were the following: all standardized loadings (in SmartPLS, outer loadings) are statically significant and 0.7 or higher, the average variance extracted (AVE) of each construct is 0.5 or higher, and the composite reliability (CR) value is 0.7 or higher. To assess discriminant validity, the heterotrait-monotrait ratio of correlations (HTMT) was evaluated (Henseler et al., 2015). Evidence of discriminant validity is a ratio of less than 0.85. To test the moderation hypotheses, and the moderated-mediation and moderated serial-mediation hypotheses, the latent-variable scores (M = 0, SD = 1) generated in SmartPLS were analyzed using the SPSS macro, Process version 3.0. Using the Process macro generates the same results for the moderation test as using SmartPLS, but with the additional advantage of providing simple-slopes results (slope, standard-error, t-value, and the p-value of each slope) and the testing of moderated-mediation and moderated serial-mediation effects. Additionally, the Process macro estimates an index of moderated mediation that provides statistical evidence that the conditional indirect effect is statistically significant, and provides simple slopes results for each conditional indirect effect. SmartPLS 3.2.7 does not provide these results. The statistical significance of the index of moderated mediation, and the moderated-mediation and moderated serial-mediation effects, were assessed by interpreting the 95% bias-corrected confidence interval (5000 samples).

Source: Lu et al. (2011)

4.3. Analysis procedure

Mobile-service perceived relative advantage

Nineteen items, adapted from previous studies, were used to measure the six first-order factors. The items used to measure each construct are listed in Table 1. The two experience factors were measured by the self-reported number of times the retailer's mobile shopping service had been previously used to collect product information, and the number of products bought using the mobile service of other retailers on a smartphone.

Source: Schierz et al. (2010)

4.2. Measurement

Mobile-service use resistance

When making my next purchase from the online retailer, … … I am not likely to use the mobile version of the online service on a mobile phone to purchase the product(s) (UR1) … I do not intend to use the mobile version of the online service on a mobile phone to make the purchase (UR2) … I will not use the mobile version of the online service on a mobile phone for the purchasing task (UR3) Using the mobile version of the online service on a mobile phone to purchase products… … is more convenient than using the desktop version of the online service on a desktop computer/laptop to purchase products (RA1) … is more efficient than using the desktop version of the online service on a desktop computer/laptop to purchase products (RA2) … is more effective than using the desktop version of the online service on a desktop computer/laptop to purchase products (RA3) I [will] continue using the desktop version of the online service on a desktop computer/laptop to purchase products … … because I am comfortable doing so (IAB1) … because I enjoy doing so (IAB2) … because it would be stressful to change to using the mobile version of the online service on a mobile phone to purchase products (IAB3) I [will] continue using the desktop version of the online service on a desktop computer/laptop to purchase products … … simply because that is what I have always done when purchasing from the online retailer (IBB1) … simply because it is part of my normal routine when purchasing from the online retailer (IBB2) … simply because I have done so regularly in the past when purchasing from the online retailer (IBB3) I [will] continue using the desktop version of the online service on a desktop computer/laptop to purchase products … … even if I know it is not the best way to purchase from the online retailer (ICB1) … even if I know it is not the most efficient way to purchase from the online retailer (ICB2) … even if I know that it is not the most effective way to purchase from the online retailer (ICB3) Using the desktop version of the online service on a desktop computer/ laptop to purchase products has become automatic to me (H1) When faced with the task of purchasing from the online retailer, using the desktop version of the online service on a desktop computer/laptop is an obvious choice for me (H2) I have a habit of using the desktop version of the online service on a desktop computer/laptop to purchase products (H3) Using the desktop version of the online service on a desktop computer/ laptop to purchase products has become natural to me (H4)

about the mobile shopping service of the retailer, they were shown screen grabs of the mobile service in the online questionnaire. Each screen grab was supplemented by information to provide more insights into the shopping process using the mobile shopping service.

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

Table 2 Measurement model results. Constructs

Items

Weight

Outer loadings

Weight Reflective measurement MS use resistance (MSUR)

Sig.

UR1 UR2 UR3 RA1 RA2 RA3 H1 H2 H3 H4

MS relative advantage (MSRA) OS habit (OSH)

Formative measurement Online-service inertia (second-order construct, repeated items) Affective-based inertia (IAB) (reflective) IAB1 IAB2 IAB3 Behavioral-based inertia (IBB) (reflective) IBB1 IBB2 IBB3 Cognitive-based inertia (ICB) (reflective) ICB1 ICB2 ICB3

0.628

0.000

0.264

0.001

0.290

0.000

ICB

MSRA

0.740 0.628 0.434 0.425 0.421

0.773 0.547 0.517 0.593

0.489 0.335 0.403

0.278 0.475

0.604

0.970

0.828

0.935

0.817

0.947

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.669

0.854

1.797

0.922

0.973

1.845

0.967

0.989

1.355

The statistical analysis confirmed that earlier experience from using the retailer's mobile service for product information moderates the online-service habit-inertia relationship (the interaction effect was statistically significant, B = −0.091, p = 0.001). Using the latent variable scores, simple slopes analysis revealed that, when the value of the moderator is low (1 SD below the mean or −1 SD), the influence of online-service use habit on inertia is 0.700 (p = 0.000). When the value of the moderator increases to medium (the mean), the influence decreases to 0.656 (p = 0.000). Then, as the value of the moderator increases to high (1 SD above the mean or +1 SD), the influence of habit further decreases to 0.565 (p = 0.000). Thus, as the value of the moderator increases, the positive influence of online-service use habit on online-service inertia decreases. It can be concluded, therefore, that the use of the retailer's mobile service for product information negatively moderates the positive influence of online-service use habit on online-service inertia. Thus H1 was accepted. Statistical analysis also showed that experience from using other retailer's mobile service to purchase products moderates the onlineservice habit-inertia relationship (the interaction effect was statistically significant, B = −0.083, p = 0.002). Simple slopes analysis revealed that, when the value of the moderator is low (-1 SD) the influence of use habit on inertia is 0.665 (p = 0.000). When the value of the moderator increases to medium (the mean), the influence decreases to 0.621 (p = 0.000). Then, as the value of the moderator increases to high (+1 SD), the influence of online-service use habit further decreases to 0.537 (p = 0.000). The conclusion is that mobile-service experience from buying products using other retailers’ mobile service negatively moderates the positive influence of online-service use habit on onlineservice inertia. Thus, H2 was accepted.

Table 3 HTMT results. IBB

0.914

5.3. Testing of the moderation hypotheses (H1 and H2)

In this section the results of the main effects are briefly reported. Please see Annexure 1 for the model results, including the first-order constructs for online-service inertia, in the SmartPLS interface. Overall, the predictive accuracy of the model is adequate as it explains 42.6% of

IAB

VIF

the variance in mobile-service use resistance. Also seen in Fig. 3 is that all structural paths are statically significant, except for the direct influence of online-service use habit on mobile-service use resistance resistance.

5.2. Assessment of the structural model

H

0.921 0.902 0.589 0.946 0.966 0.968 0.975 0.986 0.989

CR

Sig.

0.947 0.975 0.946 0.885 0.923 0.921 0.855 0.894 0.929 0.935

in Table 2, where it can be seen that all outer loadings are above 0.7 and statistically significant, except for the loading of item IAB3 (0.589, p = 0.000). Considering that the loading does not threaten the validity and reliability of the construct, the item was retained in the analysis. Also shown in Table 2 is that the AVE and CR for each first-order construct are above the recommended cut-off values (AVE > 0.5 and CR > 0.7). The variance inflation factors (VIFs) and weights for the three components of online-service inertia are also presented in Table 2. The VIF should not exceed 5.0 (Diamantopoulos et al., 2008). The highest VIF of the three causal components of inertia is 1.845 (see Table 2). As the highest VIF is well below 5.0, it is concluded that collinearity is not a threat to the measurement of the construct. Of the three causal components of inertia, only two are statistically significant. The weight of affective-based inertia is 0.628 and statistically significant, while the weight of cognitive-based inertia is 0.290 and statistically significant. The weight of behavioral-based inertia is 0.264, and is not statistically significant. The HTMT ratios of correlations are presented in Table 3 as evidence of discriminant validity in the measurement model. As can be seen in Table 3, no ratio exceeds the 0.85 benchmark. Thus it can also be concluded that the measurement model exhibits adequate discriminant validity to continue with the testing of the hypotheses.

IAB IBB ICB MSRA MSUR

Loading

AVE

5.4. Testing of the total effect and the direct effect The total and direct effect of online-service use habit on mobile288

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

Fig. 3. Main effects. Table 4 Total effect and direct effect.

Table 6 Moderated serial-mediation results. B

95% bias-corrected confidence interval (BCCI) Lower limit

OS use habit → MS use resistance (total effect) OS use habit → MS use resistance (direct effect)

H

0.403

0.319

0.486

− 0.005

− 0.095

0.084

Use of retailer's mobile service for product information

4

Products bought using other retailers’ mobile service

Low (−1 SD) Medium (the mean) High (+1 SD) Low (−1 SD) Medium (the mean) High (+1 SD)

Use of retailer's mobile service for product information

6

Products bought using other retailers’ mobile service

Low (−1 SD) Medium (the mean) High (+1 SD) Low (−1 SD) Medium (the mean) High (+1 SD)

Lower limit

Upper limit

0.080 0.075

0.046 0.043

0.122 0.115

0.065 0.076 0.071

0.037 0.044 0.041

0.101 0.114 0.107

0.062

0.035

0.095

The index of moderated mediation for the conditional indirect effect of online-service use habit on mobile-service use resistance, through online-service inertia and mobile-service perceived relative advantage in serial, based on experience from using the retailer's online service for product information was statistically significant (-0.010, 95% BCCI [-0.019; −0.002]). Likewise, the index of moderated mediation for the conditional indirect effect of online-service use habit on mobile-service use resistance, through online-service inertia and mobile-service perceived relative advantage in serial, based on experience from buying products using other retailers’ mobile service was also statistically significant (-0.010, 95% BCCI [-0.019; −0.001]). The conditional indirect effect results reported in Table 6 show that as the moderator increases, the conditional indirect effect decreases. Thus, as hypothesized for both H5 and H6, the indirect effect is smaller at higher values of the moderator. Thus, H5 and H6 were accepted. 5.7. Summary of the hypotheses testing results In Table 7 the results of the hypotheses testing are summarized. As seen from the results table, all six hypotheses were accepted.

Table 5 Moderated-mediation results.

3

5

95% bias-corrected confidence interval (BCCI)

5.6. Assessment of the moderated serial-mediation hypotheses (H5 and H6)

The index of moderated mediation for the conditional indirect effect of online-service use habit on mobile-service use resistance, through online-service inertia, based on experience from using the retailer's online service for product information was statistically significant (-0.036, 95% BCCI [-0.059; −0.006]). Likewise, the index of moderated mediation for the conditional indirect effect of online-service use habit on mobile-service use resistance, through online-service inertia, based on experience from buying products using other retailers’ mobile service was also statistically significant (-0.033, 95% BCCI [-0.062; −0.003]). The conditional indirect effect results reported in Table 5 show that as the moderator increases, the conditional indirect effect decreases. Thus, as hypothesized for both H3 and H4, the indirect effect is smaller

Values of the moderator

Effect

at higher values of the moderator. Thus, H3 and H4 were accepted.

5.5. Assessment of the moderated-mediation hypotheses (H3 and H4)

Moderator

Values of the moderator

Upper limit

service use resistance are reported in Table 4. The total effect was 0.403 and statistically significant (95% BCCI [0.319; 0.486]). The direct effect was not statistically significant (B = −0.005, 95% BCCI [-0.095; 0.084]). Thus it can be concluded that the online-service inertia and mobile-service perceived relative advantage, including the moderation effects of mobile-service experience, in the different configurations in Fig. 1 overall fully mediate the relationship between online-service use habit and mobile-service use resistance.

H

Moderator

Effect

95% bias-corrected confidence interval (BCCI)

6. Discussion

Lower limit

Upper limit

6.1. Main findings

0.280 0.262

0.205 0.195

0.361 0.337

0.226 0.266 0.248

0.169 0.197 0.185

0.297 0.344 0.321

0.215

0.155

0.286

The purpose of this study was to investigate in a multichannel context the moderation effect of mobile-service experience on the influence of online-service use habit on online-service inertia in mobileservice use resistance behavior. The acceptance of the two moderation hypotheses confirmed that both types of mobile-service experience – experience from using the mobile service of the retailer for product information, and experience from buying products using the mobile 289

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

Table 7 Results of the hypotheses testing. Hypotheses

Accepted or rejected

H1

Accepted

H2 H3 H4 H5 H6

Experience from using the retailer's mobile service for product information moderates the positive influence of online-service use habit on onlineservice inertia, such that the influence of habit on inertia is less pronounced (smaller) at higher values of the moderator Experience from using other retailers’ mobile service to buy products moderates the positive influence of online-service use habit on online-service inertia, such that the influence of habit on inertia is less pronounced (smaller) at higher values of the moderator Experience from using the retailer's mobile service for product information moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia, such that the positive indirect effect will be less pronounced (smaller) at higher values of the moderator Experience from using other retailers’ mobile service to buy products moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia, such that the positive indirect effect will be less pronounced (smaller) at higher values of the moderator Experience from using the retailer's mobile service for product information moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia and mobile-service relative-advantage perceptions in serial, such that the indirect positive effect will be less pronounced (smaller) at higher values of the moderator Experience from using other retailers’ mobile service to buy products moderates the influence of online-service use habit on mobile-service use resistance through online-service inertia and mobile-service relative-advantage perceptions in serial, such that the indirect positive effect will be less pronounced (smaller) at higher values of the moderator

Accepted Accepted Accepted Accepted Accepted

advance the use of their mobile service among online shoppers in finding product information. As shown by the empirical results, the influence of online-service use habit on online-service inertia weakens as experience with the retailer's mobile service grows, due to the influence of the use of the service in finding product information. To cultivate this type of user experience, retailers can, for example, highlight the pervasiveness benefit of using the mobile service to develop customers’ motivation to use the service to find product information. To do so, retailers can use advertisements in different media such as print or television to demonstrate these benefits. Additionally, videos embedded in the retailer's online shopping service can also be used to demonstrate the benefits. Retailers could also encourage online shoppers to use the mobile service by making promotional codes available on the mobile service that can be used when shopping online. Retailers should also leverage experience using other retailers’ mobile service to promote the use of their own mobile service, by making use of marketing communications. Advertisements in print media and on television, in conjunction with e-mail and social-media campaigns, can be used to remind current online customers of their familiarity with mobile shopping through previous mobile purchases and therefore they have the knowledge and expertise to successfully use the mobile service of the retailer to purchase products.

service of other retailers – lower the positive influence of habit on inertia. The acceptance of the two moderated-mediation hypotheses led to the findings that both types of mobile-service experience also weaken this positive indirect effect of online-service use habit on mobile-service use resistance through online-service inertia. Likewise, the acceptance of the two moderated serial-mediation hypotheses showed that both types of mobile-service experience also weaken the indirect positive effect through online-service inertia and relative-advantage perceptions of the mobile service in serial. These findings suggest that greater mobile-service experience can loosen the ‘shackle’ of online-service usehabit on online-service inertia, thereby leading to online-service use habit having a smaller positive influence on mobile-service use resistance through mechanisms such as online-service inertia and mobileservice relative-advantage perceptions. Overall, the findings discussed in this section confirm that disrupting the influence of online-service use-habit on online-service inertia is vital in reducing online shoppers’ use resistance of the mobile service emanating from the online-service use habit and inertia relationship. 6.2. Theoretical and managerial implications The foundation of the theoretical contributions flowing from this study is allied to the investigation of mobile-service use resistance in an online-mobile retail context. Venkatesh and Brown (2001) assert that, in the non-adoption of a new technology, divergent factors are more important than those factors that are influential in the adoption decision. Considering that most mobile service studies focus on adoption rather than on resistance behavior, the study contributes to the dearth of knowledge on mobile-service use resistance in an online-mobile retail context. The primary theoretical contributions emerge from the statistically significant moderation effects of the two types of mobileservice experience. Specifically, the statistically significant moderation effects show that the two types of mobile-service experience alleviate the influence of online-service use habit on online-service inertia, and ultimately contribute to a weaker indirect effect of online-service use habit on mobile-service use resistance, thanks to online-service inertia and relative-advantage perceptions. These results have not been previously reported in the retailing and services-marketing literature. Studies using the ELM as a theory to explain the moderation effect of experience are also limited. Therefore, an additional theoretical contribution is the validation of the ELM as a theory to highlight the influence of online-service use-habit on online-service inertia as a conditional effect of the two types of mobile-service experience. The results of the study showed how mobile-service experience can disrupt the influence of online-service use habit on inertia, thereby reducing mobile-service use resistance. The following managerial recommendations are forwarded in relation to mobile-service experience and the online-service habit-inertia relationship. Retailers must

6.3. Limitations and directions for future research A limitation of the study is that the mobile service that was the focus of the study is the one that can be accessed by means of the internet browser application on a smartphone. With the rise of mobile-shopping applications that could offer additional features compared with browser-based mobile services, it would be opportune to investigate whether the results of the study hold true in the case of the mobileshopping application. Also, while this study focused on a retailer selling a physical product, replication of the study in the context of the purchasing of services would also be informative about whether or not the findings are product-specific, thereby contributing to future theory building. 7. Conclusion For online-and-mobile retailers, the use of the mobile service by online customers may have several benefits. However, conscious decision-making manifested by inertia inhibits online customers’ use of the mobile service. Moreover, this inertia is fueled by the habit of using the online service. The results of the study provide managers with an understanding of how to disrupt online shoppers’ mobile-service resistance based on their online-service habit-inertia. Simply by encouraging the use of mobile service to collect product information retailers can lessen the formation of online-service inertia based on 290

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff

habit, thereby ultimately reducing the contributing effect of onlineservice use habit to mobile-service use resistance. Additionally, the experience with mobile shopping of online customers who have bought products using other retailer's mobile service can also be used to create the same result. In conclusion, leveraging online customers’ mobile shopping-service experience can be an effective strategy to disrupt their online shopping status quo bias behavior, thereby increasing mobile

purchases in the future. Acknowledgement This work is based on the research supported in part by the "National Research Foundation" of South Africa for the grant, Unique Grant No. 87881.

Annexure 1. Main model estimated using latent variable scores see Fig. A1

Fig. A 1. PLS results of the main effects.

comscore.com/Insights/Press-Releases/2017/2/comScore-Reports-109-Billion-inQ4-2016-Total-Digital-US-Retail-E-Commerce-Spending-Up-18-Percent-vs-Year-Ago〉 (Accessed 25 October 2017). Diamantopoulos, A., Riefler, P., Roth, K.P., 2008. Advancing formative measurement models. J. Bus. Res. 61 (12), 1203–1218. https://doi.org/10.1016/j.jbusres.2008.01. 009. Dodd, T.H., Laverie, D.A., Wilcox, J.F., Duhan, D.F., 2005. Differential effects of experience, subjective knowledge, and objective knowledge on sources of information used in consumer wine purchasing. J. Hosp. Tour. Res. 29 (1), 3–19. Dowling, G.R., Uncles, M., 1997. Do customer loyalty programs really work? Sloan Manage Rev. 38 (4), 71–82. Elliott, M.T., Speck, P.S., 2005. Factors that affect attitude toward a retail web site. J. Mark. Theory Pract. 13 (1), 40–51. https://doi.org/10.1080/10696679.2005. 11658537. Feldman, J.M., Lynch, J.G., 1988. Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. J. Appl. Psy. 73 (3), 421–435. Festinger, L., 1957. A Theory of Cognitive Dissonance. Row, Peterson, Evanston. Garbarino, E.C., Edell, J.A., 1997. Cognitive effort, affect, and choice. J. Consum. Res. 24 (2), 147–158. Gefen, D., 2000. E-commerce: the role of familiarity and trust. Omega 28 (6), 725–737. Groß, M., 2015. Mobile shopping: a classification framework and literature review. Int. J. Retail. Distrib. 43 (3), 221–241. https://doi.org/10.1108/IJRDM-06-2013-0119. (Retrieved from 〈https://www.emeraldinsight.com/doi/abs/10.1108/IJRDM-062013-0119〉). Groß, M., 2018. Heterogeneity in consumers' mobile shopping acceptance: a finite mixture partial least squares modelling approach for exploring and characterising different shopper segments. J. Retail Cons. Serv. 40, 8–18. https://doi.org/10.1016/j.

References Aarts, H., Verplanken, B., Knippenberg, A., 1998. Predicting behavior from actions in the past: repeated decision making or a matter of habit? J. Appl. Soc. Psychol. 28 (15), 1355–1374. Adobe, 2016. Adobe data shows retailers fail to maximize mobile revenue. Retrieved from 〈http://news.adobe.com/press-release/digital-marketing/adobe-data-showsretailers-fail-maximize-mobile-revenue〉 (Accessed 28 March 2018). Agrebi, S., Jallais, J., 2015. Explain the intention to use smartphones for mobile shopping. J. Retail. Cons. Serv. 22, 16–23. https://doi.org/10.1016/j.jretconser.2014.09.003. Alba, J.W., Hutchinson, J.W., 1987. Dimensions of consumer expertise. J. Consum. Res. 13 (4), 411–454(Retrieved from 〈http://www.jstor.org/stable/2489367〉). Becker, J.-M., Klein, K., Wetzels, M., 2012. Hierarchical latent variable models in PLSsem: guidelines for using reflective-formative type models. Long Range Plan. 45 (5–6), 359–394. https://doi.org/10.1016/j.lrp.2012.10.001. Bettman, J.R., Park, C.W., 1980. Effects of prior knowledge and experience and phase of the choice process on consumer decision processes: a protocol analysis. J. Consum. Res. 7 (3), 234–248. Burnham, T.A., Frels, J.K., Mahajan, V., 2003. Consumer switching costs: a typology, antecedents, and consequences. J. Acad. Mark. Sci. 31 (2), 109–126. Chaiken, S., 1980. Heuristic versus systematic information processing and the use of source versus message cues in persuasion. J. Pers. Soc. Psychol. 39 (5), 752–766. Chen, Y.-M., Hsu, T.-H., Lu, Y.-J., 2018. Impact of flow on mobile shopping intention. J. Retail. Cons. Serv. 41, 281–287. https://doi.org/10.1016/j.jretconser.2017.04.004. comScore, 2017. comScore reports $109 billion in Q4 2016 total digital U.S. retail ecommerce spending, up 18 percent vs. year ago. Retrieved from 〈https://www.

291

Journal of Retailing and Consumer Services 47 (2019) 282–292

J. Nel, C. Boshoff jretconser.2017.09.002. Gupta, A., Arora, N., 2017. Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory. J. Retail Cons. Serv. 36, 1–7. https://doi. org/10.1016/j.jretconser.2016.12.012. Hair, J.F., Ringle, C.M., Sarstedt, M., 2011. PLS-SEM: indeed a silver bullet. J. Mark. Theor. Pract. 19 (2), 139–152. https://doi.org/10.2753/MTP1069-6679190202. Hair Jr, J.F., Hult, T.G.M., Ringle, C.M., Sarstedt, M., 2017. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE, Los Angeles. Henseler, J., Ringle, C.M., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43 (1), 115–135. https://doi.org/10.1007/s11747-014-0403-8. Jarvis, C.B., MacKenzie, S.B., Podsakoff, P.M., 2003. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Cons. Res. 30 (2), 199–218(Retrieved from 〈http://www.jstor.org/stable/ 10.1086/376806〉). Jolley, B., Mizerski, R., Olaru, D., 2006. How habit and satisfaction affects player retention for online gambling. J. Bus. Res. 59 (6), 770–777. https://doi.org/10.1016/j. jbusres.2006.01.017. Khalifa, M., Liu, V., 2007. Online consumer retention: contingent effects of online shopping habit and online shopping experience. Eur. J. Inf. Syst. 16 (6), 780–792. https://doi.org/10.1057/palgrave.ejis.3000711. Klopping, I.M., McKinney Jr, E., 2006. Practice makes a difference: experience and ecommerce. Inf. Technol. Learn. Perform. J. 24 (1), 25–37. Koo, D.-M., 2016. Impact of tie strength and experience on the effectiveness of online service recommendations. Electron. Commer. R. A. 15, 38–51. https://doi.org/10. 1016/j.elerap.2015.12.002. Kwon, W.-S., Lennon, S.J., 2009. Reciprocal effects between multichannel retailers' offline and online brand images. J. Retail. 85 (3), 376–390. https://doi.org/10.1016/j.jretai. 2009.05.011. Lankton, N.K., McKnight, D.H., Thatcher, J.B., 2012. The moderating effects of privacy restrictiveness and experience on trusting beliefs and habit: an empirical test of intention to continue using a social networking website. IEEE Trans. Eng. Manag. 59 (4), 654–665. https://doi.org/10.1109/TEM.2011.2179048. Limayem, M., Cheung, C.M.K., 2008. Understanding information systems continuance: the case of internet-based learning technologies. Inf. Manage. 45 (4), 227–232. https://doi.org/10.1016/j.im.2008.02.005. Lu, T., Tu, R., Jen, W., 2011. The role of service value and switching barriers in an integrated model of behavioural intentions. Total Qual. Manag. Bus. 22 (10), 1071–1089. https://doi.org/10.1080/14783363.2011.614867. Luhmann, N., 1979. Trust and Power. John Wiley and Sons, Inc., Chichester. Maclnnis, D.J., Jaworski, B.J., 1989. Information processing from advertisements: toward an integrative framework. J. Mark. 53 (4), 1–23. https://doi.org/10.2307/1251376. Mangleburg, T.F., Sirgy, M.J., Grewal, D., Axsom, D., Hatzios, M., Claiborne, C.B., Bogle, T., 1998. The moderating effect of prior experience in consumers' use of user-image based versus utilitarian cues in brand attitude. J. Bus. Psychol. 13 (1), 101–113(Retrieved from 〈http://www.jstor.org/stable/25092625〉). Marriott, H.R., Williams, M.D., 2018. Exploring consumers perceived risk and trust for mobile shopping: a theoretical framework and empirical study. J. Retail. Cons. Serv. 42, 133–146. https://doi.org/10.1016/j.jretconser.2018.01.017. Page, K.L., Robson, M.J., Uncles, M.D., 2012. Perceptions of web knowledge and usability: when sex and experience matter. Int. J. Hum.-Comput. St. 70 (12), 907–919. https://doi.org/10.1016/j.ijhcs.2012.07.006.

Park, J., Yang, S.J., 2006. The moderating role of consumer trust and experiences: value driven usage of mobile technology I. J. Mob. Mark. 1 (2), 24–32. Petty, R.E., Cacioppo, J.T., 1986. The elaboration likelihood model of persuasion. In: Berkowitz, L. (Ed.), Advance in Experimental Social Psychology. Academic Press, New York USA, pp. 123–205. Pillai, K.G., Hofacker, C., 2007. Calibration of consumer knowledge of the web. Int. J. Res. Mark. 24 (3), 254–267. https://doi.org/10.1016/j.ijresmar.2007.02.001. Polites, G.L., Karahanna, E., 2012. Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quart. 36 (1), 21–42(Retrieved from 〈https://www.jstor.org/stable/41410404〉). Raju, P.S., Lonial, S.C., Mangold, W.G., 1995. Differential effects of subjective knowledge, objective knowledge, and usage experience on decision making: an exploratory investigation. J. Consum. Psychol. 4 (2), 153–180. Ram, S., 1987. A model of innovation resistance. ACR N. Am. Adv. 14, 208–212(Accessed 20 March 2018). (Retrieved from 〈http://acrwebsite.org/volumes/6688/volumes/ v14/NA-14〉). Ram, S., Sheth, J.N., 1989. Consumer resistance to innovations: the marketing problem and its solutions. J. Consum. Mar. 6 (2), 5–14. https://doi.org/10.1108/ EUM0000000002542. Rao, A.R., Monroe, K.B., 1988. The moderating effect of prior knowledge on cue utilization in product evaluations. J. Consum. Res. 15 (2), 253–264. Ratchford, B.T., 2001. The economics of consumer knowledge. J. Consum. Res. 27 (4), 397–411. https://doi.org/10.1086/319617. Rodgers, W., Negash, S., Suk, K., 2005. The moderating effect of on‐line experience on the antecedents and consequences of on‐line satisfaction. Psychol. Mark. 22 (4), 313–331. https://doi.org/10.1002/mar.20061. Rogers, E.M., 2002. Diffusion of preventive innovations. Addict. Behv. 27 (6), 989–993. https://doi.org/10.1016/S0306-4603(02)00300-3. Rogers, E.M., 2003. Diffusion of Innovations. Free Press, New York, NY. Schierz, P.G., Schilke, O., Wirtz, B.W., 2010. Understanding consumer acceptance of mobile payment services: an empirical analysis. Electron. Commer. R. A 9 (3), 209–216. https://doi.org/10.1016/j.elerap.2009.07.005. Sheinin, D.A., 2000. The effects of experience with brand extensions on parent brand knowledge. J. Bus. Res. 49 (1), 47–55. https://doi.org/10.1016/S0148-2963(98) 00116-7. Sohn, S., 2017. A contextual perspective on consumers' perceived usefulness: the case of mobile online shopping. J. Retail. Cons. Serv. 38, 22–33. https://doi.org/10.1016/j. jretconser.2017.05.002. Venkatesh, V., Brown, S.A., 2001. A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Quart. 25 (1), 71–102. https://doi.org/10.2307/3250959.(Retrieved from 〈http://www.jstor.org/stable/ 3250959〉). Verplanken, B., Aarts, H., 1999. Habit, attitude, and planned behaviour: is habit an empty construct or an interesting case of goal-directed automaticity? Eur. Rev. Soc. Psychol. 10 (1), 101–134. Yang, K., 2012. Consumer technology traits in determining mobile shopping adoption: an application of the extended theory of planned behavior. J. Retail. Cons. Serv. 19, 484–491. https://doi.org/10.1016/j.jretconser.2012.06.003. Yang, S., Chen, Y., Wei, J., 2015. Understanding consumers' web-mobile shopping extension behavior: a trust transfer perspective. J. Comput. Inf. Syst. 55 (2), 78–87. https://doi.org/10.1080/08874417.2015.11645759.

292