What Drives M-Shoppers to Continue Using Mobile Devices to Buy?

Page 1 What drives m-shoppers to continue using mobile devices to buy? Paula Rodriguez-Torrico, PhD candidate, University of Burgos, C/Parralillos, s/n, 09001, Burgos (Spain). [email protected], +34 947 25 89 73. Sonia San-Martín, PhD, Associate professor, University of Burgos, C/Parralillos, s/n, 09001, Burgos (Spain). [email protected], +34 947 25 90 34. Rebeca San José-Cabezudo, PhD, Associate professor, University of Valladolid, Av. Valle de Esgueva, 47011, Valladolid (Spain). rebe[email protected], +34 983 42 39 51. Paula Rodríguez-Torrico (Master’s degree, University of Salamanca), Ph.D. candidate of Marketing, Universidad de Burgos, Burgos, Spain, [email protected] Sonia San-Martín (Ph.D., University of Valladolid), Associate professor of Marketing, Universidad de Burgos, Burgos, Spain, sanmarg[email protected]s Rebeca San José-Cabezudo (Ph.D., University of Valladolid), Associate professor of Marketing, Universidad de Valladolid, Valladolid, Spain, [email protected] Acknowledgments: First author would like to thank the FPU training program of the University of Burgos. The authors wish to thank all the editors and the three anonymous reviewers of the Journal of Marketing Theory and Practice who have contributed to this study. Funding: The authors are grateful for the financial support received from the Ministry of Education and Science, the Junta de Castilla y León (Spain) and the European Regional Development Fund (ERDF) (Research Projects ECO20178217-R, VA001B10-1 and VA112P17). This is the accepted version of the manuscript: Rodríguez-Torrico, P. San-Martín, S. y San JoséCabezudo, R. (2019). What Drives M-Shoppers to Continue Using Mobile Devices to Buy? Journal of Marketing Theory and Practice 27 (1), 83-102. https://doi.org/10.1080/10696679.2018.1534211 Page 2 What drives m-shoppers to continue using mobile devices to buy? The aim of this work is to offer a better understanding of consumer continued intentions to use mobile devices to shop. An integrated model is developed to identify the drivers that lead mshoppers to repurchase. Specifically, navigation, safety and ubiquity are posited as stimuli guiding consumers’ affective (satisfaction) and cognitive (trust and convenience) reactions that will, in turn, increase repurchase intention. Results show the impact of ubiquity on mconvenience and safety m-experience on both affective and cognitive reactions. Finally, repurchase intention is explained directly by m-satisfaction and m-convenience and indirectly by m-trust. This paper involves to the field of mobile marketing literature and practitioner management. Page 3 Mobile commerce, or m-commerce, refers to purchasing goods and services through wireless handheld devices (Chong 2013). These devices are usually small, light and portable and may be used on the move. In line with this definition, the present research considers both smartphones and tablets as mobile devices. Figures show the number of mobile device shoppers to be growing fast. In 2016, 34% of online sales were made with mobiles or tablets, a figure that will increase to 47% by 2022 (Forrester 2017). In addition, the expenditure of mobile shoppers (m-shoppers) was $7.8 billion in 2012, $60.2 billion in 2016 and Forrester anticipates it will reach $93.5 billion in 2018 and $175.4 billion in 2022 (Forrester 2018). But this situation has been the consequence of a gradual development. In the last three decades, consumer behaviour has experienced a strong change because of technology evolution. Prior to m-commerce, the e-commerce –the computermediated electronic commerce– attracted the attention of academics and practitioners. Although both are considered variations of electronic transactions, literature has acknowledged mcommerce as a separate e-commerce channel (Rodríguez-Torrico, San-Jose Cabezudo and SanMartín 2017; Shin et al. 2017). M-commerce has many distinct characteristics from existing, well-known e-commerce. The main differentiating characteristic of m-commerce is its flexibility in terms of time and location (Yun et al. 2011). The nature of mobile devices allows consumers to use them ubiquitously, that means consumers can use them anywhere and at any time. In addition, mobile devices are considered a personal tool which enables mobile vendors to contact with consumers in real-time, but also to send them personalised information (Gao, Waechter and Bai 2015). Nevertheless, these distinguishing characteristics pose new challenges for mobile vendors. As a result of the clear differences of mobile devices (screen size, capacity, portability, personal nature …), m-shoppers face restrictions and uncertainty when using their mobile device to shop (Nilashi et al. 2015). In this sense, prior m-commerce experiences (m-experiences) become critical for consumers when Page 4 deciding to continue using the mobile devices to make purchases (Gao, Waechter and Bai 2015; Okazaki and Mendez 2013; Ozok and Wei 2010). Concretely, literature has acknowledged that interface and safety issues are important indicators of firms´ efficiency used by m-shoppers in order to reduce their uncertainty (Davis, Sajtos and Chaudhri 2011; Köster, Matt and Hess 2016). In consequence, these aspects associated with the m-commerce environment, including navigation and safety, become challenges for mobile retailers´ success (Nilashi et al. 2015; Wang, Ngamsiriudom and Hsieh 2015). Given such a context, overcoming these challenges will require m-vendors to have a complete understanding of consumers’ motivations to engage in m-commerce and their behavioural patterns. Moreover, in a crowded market, those who know how consumers use their mobile devices to shop will retain clients. In this regard, there is a consensus among the Information Systems (IS) literature concerning continuance intentions as a key to success (Jasperson, Carter and Zmud 2005), sometimes considering it central to the survival of many business-to-consumer e-commerce relationships (Bhattacherjee 2001). Many authors have expanded the importance of continuance intentions to different contexts, namely networking services (Gwebu, Wang and Guo 2014; Ku, Chen and Zhang 2013), games (Chang 2013a; Jolley, Mizerski and Olaru 2006) or other internet services (Lin et al. 2012). Nevertheless, the focus in academic mobile research has usually been on initial adoption and acceptance of mobile devices vis-à-vis making purchases (Hahn and Kim 2013). Although initial adoption is crucial for diffusion of an innovation, adoption does not guarantee its continued usage (Kim and Malhotra 2005; Ozturk et al. 2016). Therefore, the aim of this paper is to fill this gap by identifying key drivers of m-commerce and understand their relative influence on consumer continued intentions to use mobile devices for purchasing. Page 5 This work contributes to m-commerce literature in two ways. Firstly, this paper develops an integrative model which includes key drivers of m-commerce continuance. This model allows us to move forward on the m-commerce research by identifying the drivers that impact the mconsumers’ reactions and finally their intention to repurchase through mobile devices. Secondly, in contrast to previous mobile research focusing on m-commerce adoption, this work explores mshopper repurchase intentions, furthering the study of m-commerce continuance as suggested by the latest research into m-commerce (Al-Ghazali et al. 2015; Gao, Waechter and Bai 2015; Shin et al. 2017; Susanto, Chang and Ha 2016). In the following section, the theoretical framework and hypotheses development of the study are presented. In the third section, the methodology is explained and, in section four, data analysis and results are shown and discussed. The last section includes the research conclusions as well as limitations and future research. THEORETICAL FRAMEWORK AND HYPOTHESES DEVELOPMENT Consumers are changing the way they purchase. In the past, they used to go to brick-and-mortar stores to shop because that was the only possibility. With the emergence of the internet, the retailing context changed dramatically, making the e-commerce and m-commerce central in the scene. The internet has not only increased the number of channels to purchase but has also transformed the way consumers shop. With the proliferation of mobile devices, consumers have incorporated them into their shopping processes. In contrast to e-shoppers, m-shoppers use these devices to shop anytime and anywhere, that is ubiquitously, but also, they are willing to receive personalized services (Ozok and Wei 2010). However, mobile environment presents more uncertainty and risk than offline and online environments (Zhou 2013). Despite ubiquity presents a big benefit for consumers, their confidence is still lower in m-commerce compared to e- Page 6 commerce, and screen size and functionalities of mobile devices constrain consumers when making transactions (Nilashi et al. 2015). In this sense, although one of the aspects that influence this uncertainty and constriction is the website interface (Lee and Benbasat 2004), literature has acknowledged that these limitations can be addressed through offering an interface with robust navigation (Nilashi et al. 2015). Mobile website navigability was confirmed as a key aspect of mobile websites (Venkatesh and Ramesh 2006), solving the major problem for mobile users that is to navigate on a small screen. Besides navigation issues, to increase the confidence in the m-commerce context, several factors such as reputation and guarantees are considered key by m-shoppers (Köster, Matt and Hess 2016). These issues, together with security and privacy concerns, ensure a safe m-experience and can determine the future behaviour of m-shoppers (Gao, Waechter and Bai 2015). For these reasons, in order to retain m-shoppers, the mobile vendor´s actions in terms of website interface and navigation, but also security and protection are crucial (Nilashi et al. 2015). As a result, retailers are urged to adapt their strategies to the mobile context and respond to mshoppers´ demands (Ozok and Wei 2010). In this sense, despite the huge growth of m-commerce, marketers have yet limited understanding of consumer motivations to engage in m-commerce and of the factors that contribute to the development of m-commerce. For this reason, there are calls for more research into m-commerce, and concretely regarding m-shopper future intentions (Roy and Moorthi 2017; Shin et al. 2017). In an effort to address this issue, this paper proposes an integrated model based on the Stimulus-Organism-Response (S-O-R) model. This model improves the marketing discipline understanding of m-shopper continued intentions by identifying the key drivers and their impact on m-shoppers’ reactions and responses. Figure 1 summarizes the proposed model and, below, the theoretical foundations and the hypotheses are explained. Page 7 PLACE FIGURE 1 ABOUT HERE Stimulus-Organism-Response (S-O-R) model In recent years, academics have used several theoretical models to explain m-shoppers’ acceptance (e.g. Theory Acceptance Model, Theory of Planed Behaviour…). However, these approaches display certain limitations when seeking to understand consumer continuance behaviours since they are based on primary adoption variables. Therefore, other approaches are required to explore the determinants of m-commerce behaviour and repurchase intention. In this point, the S-O-R model has served as a theoretical foundation for several studies aimed at gaining an insight into consumer intentions and behaviours (Hsu, Chang and Chen 2012; Kim and Lennon 2013). In this sense, S-O-R has been employed in different contexts but is generally limited to understanding how atmospheric cues impact on emotions and behaviours and has scant application in m-commerce. However, this approach can be substantially developed by considering a large number of stimuli and organisms (Mehrabian and Russell 1974) and can serve as a baseline model for the m-commerce context. Consequently, this study proposes an integrated model of m-commerce key drivers –navigation m-experience, safety m-experience and ubiquity– to predict affective and cognitive reactions, as well as m-repurchase intention, applied to the clothing sector. The S-O-R model is grounded on environmental psychology and has been used extensively to understand consumers’ psychological and behavioural responses toward contextual stimuli. Specifically, it suggests that certain environmental cues act as stimuli that may influence an individual’s cognitive and affective reactions (organism) which, in turn, affect behavioural intentions or consumer responses (Mehrabian and Russell 1974). The SOR model was initially developed from the SR model, where the Stimuli-Response relation was studied to explain the effects of the environment on human behaviour. Furthermore, the fact that individuals’ impact on Page 8 its response was overlooked has been criticized (Lazarus 1998). Thus, a new variable -organismwas included. The SOR model was therefore established where S is related to the shopping environment, O to consumers themselves, and R is associated with the organism function as a consequence of stimuli activity. Stimuli (S) affect the individual’s internal states and can be defined as an environmental influence that motivates the individual (Eroglu, Machleit and Davis 2001). In this specific context, the environmental influences can be determined by the nature of the device. As it has been explained, mobile devices are ubiquitous, which allows consumers to use them anywhere and at any time; but they have also restrictions because of their physical characteristics, which increase the m-shoppers’ uncertainty. In this point, literature has recognized that website navigation is vital to reduce the small screen limitations (Nelson, Todd and Wixom 2005; Siau and Shen 2003), and that when m-shoppers perceive a safe environment their level of uncertainty is minimized (Wang, Ngamsiriudom and Hsieh 2015). Therefore, in order to capture the environmental aspects that influence the m-commerce continuance, this study considers as stimuli ubiquity –which is the main characteristic that differences m-commerce from ecommerce–; visual appeal, interactivity, enjoyment and personalization –which form the navigation m-experience–; and reputation, guarantees and security and privacy –that are variables related with the safety m-experience. Both of these and their definitions are specified in Table 1. PLACE TABLE 1 ABOUT HERE Organism (O) refers to two sets of internal processes and structures intervening between external stimuli on the person and the final actions or responses emitted. According to the S-O-R model, the organism comprises emotional/affective and non-emotional/cognitive reactions that are influenced by stimuli and that impact on responses (Mehrabian and Russell 1974). In this line, Page 9 the literature has focused mainly on affective reactions such as the organism and has scarcely considered cognitive states. However, both affective and cognitive internal states are essential if a full and adequate understanding of consumer behaviour is to be achieved (Eroglu, Machleit and Davis 2001). In an effort to understand consumer behaviour better, we will thus focus on the O characterised as consumers’ cognitive and affective states and processes (Chang and Chen 2008). In order to observe the O in depth and to consider a wider perspective of m-shopper reactions, we will consider m-satisfaction (as an affective O) and m-trust and m-convenience (as cognitive O) (see Table 2). PLACE TABLE 2 ABOUT HERE Effects of stimuli on organism Effects of navigation m-experience on satisfaction and trust As it has been exposed, the m-shoppers’ experiences during the navigation can be crucial to overcome the mobile devices limitations (Venkatesh and Ramesh 2006). In this sense, visual appeal, interactivity, enjoyment, and personalization have been considered aspects that can improve this shopping experience (Choi et al. 2008; Kang, Mun and Johnson 2015; Xu, Peak and Prybutok 2015). Further, in particular, these features related with the navigation experience are determining factors in consumer website use (Wang and Li 2012), being websites generally recognized in the literature as predictors of m-shopper satisfaction with and trust in the retailer (Lee et al. 2015; Xu, Peak and Prybutok 2015). Specifically, visual design has proved to be an important determinant of m-commerce adoption (Lee and Benbasat 2004). Visual appeal affects user appraisal of mobile websites (Cyr, Head and Ivanov 2006) and sparks positive emotions that generate positive shopping attitudes (Wang, Hernandez and Minor 2010). In addition, when consumers perceive a good mobile visual appearance, their satisfaction (Xu, Peak and Prybutok 2015) and trust in the mobile website Page 16 The clothing industry was selected to carry out this study for several reasons. On the one hand, bearing in mind that mobile commerce has not developed equally in all industries, clothing has been one of the fastest growing sectors (Criteo 2015). On the other hand, clothing was the leading category purchased amongst mobile device owners in Europe in 2016 (Yelken 2017). Finally, it has been one of the main topics in marketing literature in digital contexts (Kim and Kim 2004; Magrath and Mccormick 2013). Measures Variables were taken from the literature and were measured using a 5-point Likert scale adapted from previous research. Visual appeal was measured using three items taken from Mathwick, Malhotra and Rigdon (2001); enjoyment was measured by three items previously used by Lu and Yu-Jen Su (2009); interactivity was measured with five items adapted from Ballantine and Fortin (2009); personalization was measured based on a study by Swaid and Wigand (2012). In order to measure guarantees as well as security and privacy policies, three and six items, respectively, were adapted from Harris and Goode (2004), Ramus and Nielsen (2005) and Ranaweera, McDougall and Bansal (2005); reputation was measured using three measurement items adapted from Kim, Shin and Lee (2009). Ubiquity was adapted from Lee (2005). Trust was measured using three items adapted from Ganesan (1994) and Doney and Cannon (1997), and satisfaction was measured using three items based on the research of Bennett, Härtel and McColl-Kennedy (2005). Convenience was measured through three items taken from Li, Dong and Chen (2012). Finally, the three items used to measure repurchase intention were obtained from Lin (2011). As control variables, we included consumer experience as a mobile shopper and client experience so as to dismiss alternative effects. DATA ANALYSES AND RESULTS Page 17 Structural equation modelling (SEM) analysis was used to test the hypotheses. Specifically, we used the Partial Least Squares (PLS) approach since it is robust for small samples and allows both types of measurement scales, reflective and formative (Hair, Ringle and Sarstedt 2011) to be worked with. In our case, navigation and safety m-experiences are reflective-formative secondorder constructs. The remaining variables were measured with reflective scales. Power analysis allowed us to demonstrate the adequacy of our sample size. Using G*Power software (Faul et al. 2007), to a standard level of significance α = 0.05, an effect size (f2 = 0.15) and five predictors, the power achieved for our sample size (N = 122) was 91.8%, exceeding the recommended 80% power level (Cohen 1988). Therefore, it can be said that our estimates by PLS prove adequate. Measurement model In order to validate the scales and rule out problems of common method bias, Harman’s onefactor test (Podsakoff et al. 2003) was conducted. All the indicators of the variables were entered into an exploratory factor analysis and all the items loaded in one factor. The analysis showed that the unique unrotated factor explains 45.1% of data variance. We then conducted exploratory factorial analysis without forcing one factor and the cumulative variance explained by all the factors in the proposed model was 87.9%, ruling out common variance problems. Navigation and safety m-experiences are considered as formative second-order constructs created by three and four dimensions, respectively. Following Bagozzi and Yi (2012), including a higherorder construct implies that measurement assessment needs to be undertaken at two levels. The two-stage approach was thus used as it offers the advantage of estimating a more parsimonious model on the higher-level analysis (Parveen et al. 2016). First of all, at the first-order level, the validity and reliability of the dimensions were assessed (Hair, Ringle and Sarstedt 2011) (Table 3). Cronbach α and composite reliability (CR) Page 18 coefficients were above .7 and .6, respectively (Bagozzi and Yi 1988), confirming the reliability and internal consistency of the scales. In addition, convergent validity is confirmed, since the average variance extracted (AVE) presented values above .5 in all cases (Bagozzi and Yi 1988). Further, the direct relations between the dimensions and their indicators were confirmed, showing in all cases significant loading values (t>1.96, at a confidence level of 95%). PLACE TABLE 3 ABOUT HERE Thus, following literature recommendations (Iacobucci 2010), the previously validated first-order constructs (navigation and safety m-experiences) could then be incorporated into the secondorder measurement model. The results of the second-order measurement model are presented in Table 4. As regards reflective latent variables, Cronbach α, CR, and AVE values confirmed the scales’ reliability and validity, with values above .7, .6 and .5, respectively. As can be seen, all loading coefficients shew significant values at a confidence level of 95% (t>1.96). For the formative constructs (navigation and safety m-experiences), multicollinearity was ruled out, because the variance inflation factor (VIF) values were below 5 and tolerance value (IT) above .10 as recommended (Hair et al. 2017). For formative indicators, weights represent the relative contribution in the formation of the latent variable. In order to analyse construct validity, item weights were examined (Petter, Straub and Rai 2007). Although several items’ weights were not significant, following Hair et al. (2017) we checked the loadings of these items and confirmed that they had values above .5 and were significant. PLACE TABLE 4 ABOUT HERE Discriminant validity was confirmed by calculating whether the square root of the AVE from each reflective construct was in every case greater than the correlations (Table 5) of the other reflective constructs, using the Fornell-Larcker Criterion (Fornell and Larcker 1981). Page 19 PLACE TABLE 5 ABOUT HERE Structural Model After validating the measurement model, we tested the hypotheses by estimating the overall model. Before accepting the proposed hypotheses, we analysed R2 in order to assess the model’s explanatory power. Results show that R2 is satisfactory since it exceeded .1 for repurchase intention (.258), convenience (.563), trust (.476) and satisfaction (.494) (Falk and Miller 1992). Considering past research on consumer behaviour (Berry, Seiders and Grewal 2002; Chen and Dhillon 2003; Gupta and Kim 2007; Homburg, Koschate and Hoyer 2006; Khalifa and Liu 2007; Venkatesh, Thong and Xu 2012) and in order to ensure the validity of our results, mobile shopper experience and client experience were included in the model as control variables. Although some of these studies conjecture that habit is an important explanatory variable of consumer behaviour, it is linked to a certain amount of repetition or practice (Aarts, Verplanken and Knippenberg 1998) and requires a stable environment (Venkatesh, Thong and Xu 2012). In this sense, considering that mobile devices and mobile users are changing and growing (Forrester 2017), this variable might be considered in future, when the environment is more stable and consumers have gained some practice. Table 6 shows the results of the estimation of the structural model after controlling for these variables. As shown, four of our six hypotheses were fully supported. Regarding the stimuli-organism relationships, results show the impact of navigation m-experience on m-satisfaction (β = .264; p< .1), confirming H1a. In contrast, the relationship between navigation m-experience and m-trust is not significant, therefore H1b cannot be confirmed. In addition, safety m-experience is confirmed as determinant of m-satisfaction (β = .453; p< .001) and m-trust (β = .640; p< .001), fully Page 20 supporting H2. H3, which posited a positive relationship between ubiquity and m-convenience, is also confirmed (β = .740; p< .001). Finally, for the organism-response relationship, three hypotheses were suggested. First, the results show that m-satisfaction, as an affective reaction, increases m-repurchase intention (β = .334; p< .05), confirming H4. As regards cognitive reactions, the m-repurchase intention is not explained by m-trust, such that H5 is not confirmed. Finally, m-convenience is significantly related with m-repurchase intention (β = .217; p< .05), thus confirming H6. PLACE TABLE 6 ABOUT HERE Considering that trust has no significant effect on repurchase intention, a mediation effect was tested following previous literature findings (Lin and Wang 2006; Susanto, Chang and Ha 2016). An increasingly popular method of testing mediation and indirect effects is bootstrapping (Montoya and Hayes 2017). The macro PROCESS developed by Hayes (2013) for SPSS was employed. This program calculates 95 percent bias-corrected bootstrap confidence intervals of mediation on the basis of 5,000 bootstrap samples. The results of the mediation effect of satisfaction in the trust-repurchase intention relationship show that trust is not significantly related to repurchase intention after controlling for the mediating variable (satisfaction) (β =.095; p>.05); and the indirect conditional effect of the confidence interval (CI) bias-corrected bootstrap at 95% is above zero (CI = .081 - .717). Thus, the full mediation role of satisfaction between trust and repurchase intention is supported. The Sobel test also confirms this finding (t-test = 2.407, p< .05). In addition, we carried out the test of mediation of organism variables between stimuli and response. Only for the ubiquity-repurchase intention relationship the indirect conditional effect of the CI bias-corrected bootstrap at 95% is above zero (CI = .099 - .455), confirming the mediation effect. Satisfaction and trust mediation effects are not found, and indirect effects are no different Page 21 from zero in any case, where bias-corrected bootstrap CI includes zero. Sobel tests also support these findings. Only convenience shows a significant mediation effect for ubiquity and the repurchase intention relationship (t-test = 3.105, p< .05). DISCUSSION AND IMPLICATIONS This study addresses the key drivers of psychological affective and cognitive consumer reactions to finally explain m-continuance intention. First, our findings demonstrate that both navigation and safety aspects can influence female consumer reactions and that these reactions impact on consumer response. Specifically, as an affective variable, satisfaction towards the m-vendor is explained by both the navigation and safety m-experiences. These results also confirm that the safety m-experience has a higher impact on trust than the navigation m-experience. These findings demonstrate that the more guarantees, reputation as well as security and privacy are perceived, the greater is the m-satisfaction developed in the case of women’s clothes, supporting the results of previous research (Gao, Waechter and Bai 2015). In addition, navigation mexperience features have also been confirmed as satisfaction predictors. Therefore, when consumers perceive the mobile shopping experience as attractive, fun, interactive and personalized, their satisfaction is improved. Second, our results show that shopper m-trust can be increased by the safety m-experience, confirming the findings of previous m-commerce research (Davis, Sajtos and Chaudhri 2011). In contrast, our results show that the navigation m-experience, related to visual appeal, interactivity, enjoyment, and personalization, does not have any impact on trust. Therefore, if clothing mvendors wish to enhance consumer trust, they must highlight the safety related aspects, such as reputation, guarantees and security and privacy. Page 22 Summarizing, our results confirm a higher importance of safety aspects on consumer satisfaction and trust development. This finding can be explained due to the fact that sample is composed by women. Prior literature has established that women perceive higher levels of risk in the online context than men (Garbarino and Strahilevitz 2004). In consequence, women can be more worried about safety issues than men, specially taking into account that m-commerce is in the early stage of its growth, where safety issues are crucial for consumers (Nilashi et al. 2015). The relation between ubiquity and m-convenience is also confirmed. This relation has not been tested previously, although various authors have claimed that the two variables are related (Li, Dong and Chen 2012; Okazaki and Mendez 2013). The results of the present work confirm these claims and show that the unique characteristic of m-commerce determines consumer convenience therein. In sum, consumers who see the mobile phone as an anywhere and anytime shopping device perceive greater convenience in this kind of shopping. The results also demonstrate an indirect effect of ubiquity on repurchase intention, mediated by convenience. Finally, mobile repurchase intention is explained by m-satisfaction, as an affective reaction, and m-convenience, as a cognitive reaction. Therefore, in order to increase consumers’ m-repurchase intention it is necessary that they feel satisfied and perceive m-shopping as convenient. These results are in line with previous m-commerce research (Liu, Guo and Lee 2011; van Riel and Pura 2005) and confirm that both affective and cognitive reactions are important in consumer intentions. For its part, m-trust is not confirmed as a direct m-repurchase intention predictor. This means that even if consumers trust a firm, this will not directly affect their intention to repurchase through mobile devices, a result reported in other studies (Susanto, Chang and Ha 2016). However, as Susanto, Chang and Ha (2016) have also revealed, trust can have an indirect effect on repurchase intentions. After detecting a mediating effect, our findings show the effect of mtrust on repurchase intentions mediated by m-satisfaction. This means consumers may feel that Page 23 trust is necessary in m-commerce, and is a basic element in mobile purchases. However, it does not seem to be enough to make consumers repeat the purchase if they are not satisfied with the mobile vendor, which is again in line with previous research findings (Lin and Wang 2006). Theoretical Implications Our research contributes to m-commerce research by providing a comprehensive understanding of m-shopper continuance intentions. Firstly, the main theoretical contribution lies in studying the key drivers of m-shoppers repurchase intention. As highlighted earlier, there is still limited knowledge about how firms must face the m-commerce scene to maintain customers. In this sense, continuance intentions are crucial to firms’ success and adoption does not guarantee this (Kim and Malhotra 2005). To date, most mobile research has emphasized technology adoption for buying (Hahn and Kim 2013) whilst research into continuance behaviours remains scarce and it has been called for research (Roy and Moorthi 2017; Shin et al. 2017). Therefore, considering the mobile devices and m-shopper differences, this research identifies the key dimensions of mcommerce success. The findings move forward on the understanding of m-shoppers’ drivers of satisfaction, trust, and convenience which are decisive to securing the success of m-commerce. Secondly, this work is built up from S-O-R model considering its full scope. Our research is an integrative work that has included the key drivers of m-shopper continuance intentions as well as affective and cognitive consumer reactions. Thus far, m-commerce literature has lacked any studies which explore consumer reaction and response, and which focus on the identified key drivers. Therefore, this paper offers better insight into both consumers external stimuli and internal reactions that impact the intention to continue shopping through mobile devices. Finally, this study focuses on female clothing repurchase intentions. Considering that clothing has been deemed a more feminine area and one seen to occupy a more central position in women’s lives (O'Cass 2004), our sample increases the strength of our findings and contributes to Page 24 the literature by providing a more accurate image of reality. Additionally, women have been assumed to be more involved in buying clothes than men (O'Cass 2004; O’Cass 2000) and the most attractive segment for firms due to the volume of purchases they make and their important role as opinion leaders (Google 2017). Therefore, our sample can be considered to display a high degree of involvement with the industry studied. Managerial Implications M-commerce is a hot topic in marketing today. Our findings offer several important contributions for mobile commerce practitioners, particularly in the female clothing sector. This work proposes in an integrative way the keys to developing an m-commerce strategy. First, safety features of the m-experience are vital to ensure satisfaction and trust. In this sense, security and privacy policies must be clear and transparent and must explain how consumers’ personal and financial information will be used. Policies must also be accessible to shoppers on the mobile website. It is important to make available to consumers the firm’s personal data protection policy such that consumers may easily access, consult, amend and even erase any personal details the firm may be privy to. Firms should also use security certification agencies and make certificates (such as SSL certificates) visible on the mobile website and the Secure M-Commerce System (SMCS) so as to avoid payment fraud. Guarantees should allow changes to be made if the product is defective or if the consumer is dissatisfied and must also be clear and transparent as well as accessible on the mobile website in order to give the consumer the opportunity to consult them easily during the shopping process. It is important that guarantees, together with security and privacy policies, be visible and understandable for the m-shopper, otherwise, consumers may abandon the purchase. Although safety m-experience features are more relevant for women when buying clothes, navigation aspects are also important for mobile satisfaction development. Therefore, a mobile website should be both suitable and pleasing to the eye and its layout and design should enhance Page 25 the shopping experience. In order to boost the visual appeal of a mobile website, it is necessary to consistently combine colours, text, images, and sound, etc. Additionally, m-shoppers demand personal attention and the mobile can enable firms to offer their customers exactly what they need, immediately. What is more, according to Google (2018), from 2015 to 2017, mobile searches within “for me” and “near me” have grown over 60% and 150%, respectively. Therefore, to satisfy m-shoppers, firms must first know and understand their personal needs. The very nature and use which users make of mobile devices, particularly smartphones, makes them a “personal object” (it should be noted that several different people may use a PC, whereas a smartphone is highly unlikely to be shared). This is why personalized services channelled through them should be “made to measure”. Finally, and in line with the previous idea, mobile ubiquity is perceived as convenient for saving consumers time and effort, which in turn also affects repurchase intentions. It is, therefore, necessary to develop strategies that allow anywhere and anytime purchases, taking advantage of the uniqueness that mobile devices offer. The mobile website must be designed in a way that allows purchases to be made easily on the move without the need to use a larger device. Firms should also remind purchasers of the advantages of buying through this medium such that the idea of convenience remains fresh in buyers’ minds and impacts on their repurchase intention. To achieve this, firms can develop communication campaigns containing such messages as (“enjoy the advantages of buying anytime, anywhere and instantaneously”), or sales campaigns exclusive to mobiles that make the consumer aware of the benefits this new distribution channel has to offer when it comes to convenient shopping (e.g. “special offer this weekend only! Purchase through your mobile and get a .....!”). Research Limitations and Future Research Page 32 Harris, Lloyd C, and Mark MH Goode. (2004), "The four levels of loyalty and the pivotal role of trust: a study of online service dynamics," Journal of Retailing, 80(2), 139-158. Hayes, Andrew F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach: Guilford Press. Homburg, Christian, Nicole Koschate, and Wayne D Hoyer. (2006), "The role of cognition and affect in the formation of customer satisfaction: a dynamic perspective," Journal of Marketing, 70(3), 21-31. Hsu, Chia-Lin, Kuo-Chien Chang, and Mu-Chen Chen. (2012), "The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators," Information Systems and e-Business Management, 10(4), 549-570. Hung, Ming-Chien, Shih-Ting Yang, and Ting-Chu Hsieh. (2012), "An examination of the determinants of mobile shopping continuance," International Journal of Electronic Business Management, 10(1), 29. Iacobucci, Dawn. (2010), "Structural equations modeling: Fit indices, sample size, and advanced topics," Journal of Consumer Psychology, 20(1), 90-98. Islam, Md. Aminul, Mohammad Aktaruzzaman Khan, T. Ramayah, and Muhammad Muazzem Hossain. (2011), "The Adoption of Mobile Commerce Service among Employed Mobile Phone Users in Bangladesh: Self-efficacy as a Moderator," International Business Research, 4, 80-90. Jasperson, Jon Sean, Pamela E Carter, and Robert W Zmud. (2005), "A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems," MIS quarterly, 29(3), 525-557. Page 33 Johnsen, Rhona E., and David Ford. (2008), "Exploring the concept of asymmetry: A typology for analysing customer–supplier relationships," Industrial Marketing Management, 37, 471-483. Jolley, Bill, Richard Mizerski, and Doina Olaru. (2006), "How habit and satisfaction affects player retention for online gambling," Journal of Business Research, 59(6), 770-777. Joseph-Mathews, Sacha, Mark A Bonn, and David Snepenger. (2009), "Atmospherics and consumers' symbolic interpretations of hedonic services," International Journal of Culture, Tourism and Hospitality Research, 3(3), 193-210. Kang, Ju-Young M, Jung Mee Mun, and Kim KP Johnson. (2015), "In-store mobile usage: Downloading and usage intention toward mobile location-based retail apps," Computers in Human Behavior, 46, 210-217. Khalifa, Mohamed, and Vanessa Liu. (2007), "Online consumer retention: contingent effects of online shopping habit and online shopping experience," European Journal of Information Systems, 16(6), 780-792. Kim, Changsu, Mirsobit Mirusmonov, and In Lee. (2010), "An empirical examination of factors influencing the intention to use mobile payment," Computers in Human Behavior, 26(3), 310-322. Kim, Eun Young, and Youn-Kyung Kim. (2004), "Predicting online purchase intentions for clothing products," European Journal of Marketing, 38, 883-897. Kim, Gimun, BongSik Shin, and Ho Geun Lee. (2009), "Understanding dynamics between initial trust and usage intentions of mobile banking," Information Systems Journal, 19, 283-311. Kim, Hyejeong, and Linda S. Niehm. (2009), "The Impact of Website Quality on Information Quality, Value, and Loyalty Intentions in Apparel Retailing," Journal of Interactive Marketing, 23, 221-233. Page 34 Kim, Jiyoung, and Sharron J Lennon. (2013), "Effects of reputation and website quality on online consumers´emotion, perceived risk and purchase intention.," Journal of Research in Interactive Marketing, 7, 33-56. Kim, Sung S, and Naresh K Malhotra. (2005), "A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena," Management Science, 51(5), 741-755. Ko, Eunju, Eun Young Kim, and Eun Kyung Lee. (2009), "Modeling Consumer Adoption of Mobile Shopping for," Psychology & Marketing, 26, 669-687. Komiak, Sherrie YX, and Izak Benbasat. (2006), "The effects of personalization and familiarity on trust and adoption of recommendation agents," MIS quarterly, 941-960. Köster, Antonia, Christian Matt, and Thomas Hess. (2016), "Carefully choose your (payment) partner: How payment provider reputation influences m-commerce transactions," Electronic Commerce Research and Applications, 15, 26-37. Kourouthanassis, Panos, Costas Boletsis, Cleopatra Bardaki, and Dimitra Chasanidou. (2015), "Tourists responses to mobile augmented reality travel guides: The role of emotions on adoption behavior," Pervasive and Mobile Computing, 18, 71-87. Ku, Yi-Cheng, Rui Chen, and Han Zhang. (2013), "Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan," Information & Management, 50(7), 571-581. Lazarus, RS. (1998), "Fifty Years of the Research and Theory of RS Lazarus: An Analysis of Historical and Perennial Issues/RS Lasarus," Mahwah, NJ Publication.–1998. Lee, Dongwon, Junghoon Moon, Yong Jin Kim, and Y Yi Mun. (2015), "Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty," Information & Management, 52(3), 295-304. Page 35 Lee, Grace Yuna, and Youjae Yi. (2008), "The Effect of Shopping Emotions and Perceived Risk on Impulsive Buying : The Moderating Role of Buying Impulsiveness Trait," Seoul Journal of Business, 14, 67-92. Lee, Thaemin. (2005), "The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce," Journal of Electronic Commerce Research, 6(3), 165-181. Lee, Young Eun, and Izak Benbasat. (2004), "A Framework for the Study of Customer Interface Design for Mobile Commerce," International Journal of Electronic Commerce, 8, 79-102. Li, Min, Z.Y. Dong, and Xi Chen. (2012), "Factors influencing consumption experience of mobile commerce: A study from experiential view," Internet Research, 22(2), 120-141. Li, Yung-Ming, and Yung-Shao Yeh. (2010), "Increasing trust in mobile commerce through design aesthetics," Computers in Human Behavior, 26, 673-684. Lin, Chinho, and Watcharee Lekhawipat. (2016), "How Customer Expectations Become Adjusted After Purchase," International Journal of Electronic Commerce, 20(4), 443-469. Lin, Hsin-Hui, and Yi-Shun Wang. (2006), "An examination of the determinants of customer loyalty in mobile commerce contexts," Information & Management, 43, 271-282. Lin, Hsiu-Fen. (2011), "An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust," International Journal of Information Management, 31, 252-260. Lin, Hui, Weiguo Fan, and Patrick YK Chau. (2014), "Determinants of users’ continuance of social networking sites: A self-regulation perspective," Information & Management, 51(5), 595-603. Page 36 Lin, Tung-Ching, Sheng Wu, Jack Shih-Chieh Hsu, and Yi-Ching Chou. (2012), "The integration of value-based adoption and expectation–confirmation models: An example of IPTV continuance intention," Decision Support Systems, 54(1), 63-75. Liu, Chung-Tzer, Yi Maggie Guo, and Chia-Hui Lee. (2011), "The effects of relationship quality and switching barriers on customer loyalty," International Journal of Information Management, 31(1), 71-79. Liu, Yuping, and L.J. Shrum. (2002), "What is interactivity and is it always such a good thing ? Implications of definition, person, and situation for the influence of interactivity on advertising effectiveness," Journal of Advertising, 31, 53-64. Lu, Hsi-Peng, and Philip Yu-Jen Su. (2009), "Factors affecting purchase intention on mobile shopping web sites," Internet Research, 19(4), 442-458. Lu, June. (2014), "Are personal innovativeness and social influence critical to continue with mobile commerce?," Internet Research, 24(2), 134-159. Luqman, Azyanee, Razli Che Razak, Mohammad Ismail, and Mohd Afifie Mohd Alwi. (2016). Predicting Continuance Intention in Mobile Commerce Usage Activities: The Effects of Innovation Attributes. Paper presented at the 8th International Conference on Humanities and Social Sciences. Magrath, Victoria, and Helen Mccormick. (2013), "Marketing design elements of mobile fashion retail apps," Journal of Fashion Marketing and Management, 17, 115-134. Mathwick, Charla, Naresh Malhotra, and Edward Rigdon. (2001), "Experiential value : conceptualization , measurement and application in the catalog and Internet shopping environment," Journal of Retailing, 77, 39-56. Page 37 Mccormick, Helen, and Charlotte Livett. (2012), "Analysing the influence of the presentation of fashion garments on young consumers ’ online behaviour," Journal of Fashion Marketing and Management, 16, 21-41. Mehrabian, Albert, and James A Russell. (1974). An approach to environmental psychology. Cambridge, MA: MIT Press. Montgomery, Alan L, and Michael D Smith. (2009), "Prospects for Personalization on the Internet," Journal of Interactive Marketing, 23(2), 130-137. Montoya, Amanda K, and Andrew F Hayes. (2017), "Two-condition within-participant statistical mediation analysis: A path-analytic framework," Psychological Methods, 22(1), 6. Nelson, R Ryan, Peter A Todd, and Barbara H Wixom. (2005), "Antecedents of information and system quality: an empirical examination within the context of data warehousing," Journal of Management Information Systems, 21(4), 199-235. Nilashi, Mehrbakhsh, Othman Ibrahim, Vahid Reza Mirabi, Leili Ebrahimi, and Mojtaba Zare. (2015), "The role of Security, Design and Content factors on customer trust in mobile commerce," Journal of Retailing and Consumer Services, 26, 57-69. O'Cass, Aron. (2004), "Fashion clothing consumption: antecedents and consequences of fashion clothing involvement," European Journal of Marketing, 38(7), 869-882. O’Cass, Aron. (2000), "An assessment of consumers product, purchase decision, advertising and consumption involvement in fashion clothing," Journal of Economic Psychology, 21(5), 545-576. Okazaki, Shintaro, Hairong Li, and Morikazu Hirose. (2009), "Consumer privacy concerns and preference for degree of regulatory control," Journal of Advertising, 38(4), 63-77. Okazaki, Shintaro, and Felipe Mendez. (2013), "Exploring convenience in mobile commerce: Moderating effects of gender," Computers in Human Behavior, 29, 1234-1242. Page 38 Ozok, A Ant, and June Wei. (2010), "An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices : results from a college student population," Electronic Commerce Research, 10, 111-137. Ozturk, Ahmet Bulent, Khaldoon Nusair, Fevzi Okumus, and Nan Hua. (2016), "The role of utilitarian and hedonic values on users’ continued usage intention in a mobile hotel booking environment," International Journal of Hospitality Management, 57, 106-115. Parveen, Farzana, Noor Ismawati Jaafar, Sulaiman Ainin, Andy Adcroft, and Franz Lohrke. (2016), "Social media's impact on organizational performance and entrepreneurial orientation in organizations," Management Decision, 54(9), 2208-2234. Petter, Stacie, Detmar Straub, and Arun Rai. (2007), "Specifying formative constructs in information systems research," MIS quarterly, 31(4), 623-656. Podsakoff, Philip M, Scott B MacKenzie, Jeong-Yeon Lee, and Nathan P Podsakoff. (2003), "Common method biases in behavioral research: a critical review of the literature and recommended remedies," Journal of Applied Psychology, 88(5), 879-903. Ramus, Kim, and Niels Asger Nielsen. (2005), "Online grocery retailing: what do consumers think?," Internet Research, 15(3), 335-352. Ranaweera, Chatura, Gordon McDougall, and Harvir Bansal. (2005), "A model of online customer behavior during the initial transaction: Moderating effects of customer characteristics," Marketing Theory, 5(1), 51-74. Rodríguez-Torrico, Paula, Rebeca San-Jose Cabezudo, and Sonia San-Martín. (2017), "Tell me what they are like and I will tell you where they buy. An analysis of omnichannel consumer behavior," Computers in Human Behavior, 68, 465-471. Page 39 Roy, Subhadip, and Y.L.R. Moorthi. (2017), "Technology readiness, perceived ubiquity and Mcommerce adoption: The moderating role of privacy," Journal of Research in Interactive Marketing, 11(3), 268-295. Rust, Roland T, and Ming-Hui Huang. (2014), "The service revolution and the transformation of marketing science," Marketing Science, 33(2), 206-221. Sha, Wei. (2009), "Types of structural assurance and their relationships with trusting intentions in business-to-consumer e-commerce," Electronic Markets, 19, 43-54. Shin, Nina, Donghui Kim, Sangwook Park, and Jungsuk Oh. (2017), "The moderation effects of mobile technology advancement and system barrier on m-commerce channel preference behavior," Information Systems and e-Business Management, 16(1), 125-154. Siau, Keng, and Zixing Shen. (2003), "Building customer trust in mobile commerce," Communications of the ACM, 46, 91-94. Susanto, Aries, Younghoon Chang, and Youngwook Ha. (2016), "Determinants of continuance intention to use the smartphone banking services: An extension to the expectationconfirmation model," Industrial Management & Data Systems, 116(3), 508-525. Swaid, Samar I., and Rolf T Wigand. (2012), "The Effect of Perceived Site-to-Store Service Quality on Perceived Value and Loyalty Intentions in Multichannel Retailing," International Journal of Management, 29, 301-314. van Riel, Allard CR, and Minna Pura. (2005), "Linking perceived value and loyalty in locationbased mobile services," Managing Service Quality: An International Journal, 15(6), 509538. Venkatesh, Viswanath, and Venkataraman Ramesh. (2006), "Web and wireless site usability: Understanding differences and modeling use," MIS quarterly, 181-206. Page 40 Venkatesh, Viswanath, James YL Thong, and Xin Xu. (2012), "Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology," MIS quarterly, 36(1), 157-178. Wang, Rebecca Jen-Hui, Edward C Malthouse, and Lakshman Krishnamurthi. (2015), "On the go: How mobile shopping affects customer purchase behavior," Journal of Retailing, 91(2), 217-234. Wang, Stephen W, Waros Ngamsiriudom, and Chia-Hung Hsieh. (2015), "Trust disposition, trust antecedents, trust, and behavioral intention," The Service Industries Journal, 35(10), 555572. Wang, Wei-Tsong, and Hui-Min Li. (2012), "Factors influencing mobile services adoption: a brand-equity perspective," Internet Research, 22, 142-179. Wang, Yong Jian, Monica D Hernandez, and Michael S Minor. (2010), "Web aesthetics effects on perceived online service quality and satisfaction in an e-tail environment: The moderating role of purchase task," Journal of Business Research, 63(9), 935-942. Wu, Cedric Hsi-Jui, Hsiao-Chun Liao, Kuang-Peng Hung, and Yi-Hsuan Ho. (2012), "Service guarantees in the hotel industry: Their effects on consumer risk and service quality perceptions," International Journal of Hospitality Management, 31, 757-763. Xu, Chenyan, Daniel Peak, and Victor Prybutok. (2015), "A customer value, satisfaction, and loyalty perspective of mobile application recommendations," Decision Support Systems, 79, 171-183. Xu, Hongjiang. (2013), "The Effect of Perceived Security on Consumers’ Intent to Use: Satisfaction and Loyalty to M-Commerce in China," Journal of Electronic Commerce in Organizations (JECO), 11(4), 37-51. Page 41 Yelken, Yuecel. (2017). "EUROPE M-COMMERCE 2017". Retrieved 25/09/2017 (available at https://www.ystats.com/market-reports/europe-m-commerce-2017/). Yun, Haejung, Choong C Lee, Byung Geun Kim, and William J Kettinger. (2011), "What determines actual use of mobile web browsing services? A contextual study in Korea," Communications of the Association for Information Systems, 28(21), 313-328. Zhou, Tao. (2013), "An empirical examination of continuance intention of mobile payment services," Decision Support Systems, 54, 1085-1091. Page 48 Path Coefficient t-test (p-value) Results Navigation m-experience → M-satisfaction .264┼ 1.882 (.060) Supported Navigation m-experience → M-trust .055 ns .529 (.597) Not Supported Safety m-experience → M-satisfaction .453*** 3.220 (.001) Supported Safety m-experience → M-trust .640*** 5.938 (.000) Supported Ubiquity → M-convenience .740*** 13.513 (.000) Supported M-satisfaction → M-repurchase intention .334* 2.253 (.024) Supported M-trust → M-repurchase intention .015 ns .086 (.931) Not supported M-convenience → M-repurchase intention .217* 2.168 (.030) Supported Control variables Mobile shopper experience → M-satisfaction .107 ns 1.639 (.101) - Mobile shopper experience → M-trust .074ns 1.165 (.244) - Mobile shopper experience → M-convenience .058 ns .946 (344) - Mobile shopper experience → M-repurchase intention .097 ns 1.141 (.254) - Client experience → M-satisfaction -.157** 2.661 (.008) - Client experience → M-trust -.061 ns 1.060 (.289) - Client experience → M-repurchase intention .176* 2.521 (.012) - Note. Significant coefficients are in boldface. ***p<0.001; **p<0.01; *p<0.05; ┼p< .1; ns, not significant.