The Drivers of Showrooming Behavior : A Meta-Analysis

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ The Drivers of Showrooming Behavior : A Meta-Analysis © 2024 Unverza v Mariboru, Univerzitetna založba Accepted version (Final draft) Holkkola, Matilda; Tyrväinen, Olli; Makkonen, Markus; Karjaluoto, Heikki; Kemppainen, Tiina; Paananen, Tiina; Frank, Lauri Holkkola, M., Tyrväinen, O., Makkonen, M., Karjaluoto, H., Kemppainen, T., Paananen, T., & Frank, L. (2024). The Drivers of Showrooming Behavior : A Meta-Analysis. In A. Pucihar, M. Kljajić Borštnar, S. Blatnik, R. W. H. Bons, K. Smit, & M. Heikkilä (Eds.), 37th Bled eConference : Resilience Through Digital Innovation : Enabling the Twin Transition (pp. 597-614). University of Maribor Press. https://doi.org/10.18690/um.fov.4.2024.35 2024 THE DRIVERS OF SHOWROOMING BEHAVIOR: A META-ANALYSIS Keywords: showrooming behavior, meta-analysis, omnichannel, consumer behavior, cross-channel behavior. MATILDA HOLKKOLA1, OLLI TYRVÄINEN1, MARKUS MAKKONEN2,1, HEIKKI KARJALUOTO1, TIINA KEMPPAINEN3, TIINA PAANANEN1 & LAURI FRANK1 1 University of Jyvaskyla, Faculty of Information Technology, Jyvaskyla, Finland; e-mail: matilda.i.holkk[email protected], [email protected], [email protected], heikki.karja[email protected], [email protected], [email protected] 2 Tampere University, Faculty of Management and Business, Tampere, Finland; e-mail: [email protected] 3 University of Jyvaskyla, School of Business and Economics, Jyvaskyla, Finland; e-mail: tiina.j.kemppa[email protected] Abstract Showrooming behavior refers to consumer behavior where consumers first physically evaluate products in offline channels and then compare the potential purchases in online channels. Although the drivers of showrooming behavior have gained interest from many quantitative researchers and resulted in multiple conflicting results, there is no established framework for these drivers. Therefore, we made a meta-analysis of the drivers of showrooming behavior. To analyze prior results, we conducted a systematic literature review resulting in 24 independent study samples that fit our criteria. Of these samples, 18 drivers were meta-analytically analyzed, resulting in 13 drivers being found to have a statistically significant association and five drivers being found to have no statistically significant association with showrooming behavior. As a theoretical contribution, we provide an established framework and solve prior conflicting findings. As a managerial contribution, we provide advice to decrease customers’ competitive showrooming behavior according to the identified main drivers. 1 Introduction In the retail context, new means and technologies to diversify consumers’ options in their decision-making process have multiplied. Thanks to advancements in information and communication technologies (ICTs), today’s smart consumers can weigh their options based on online information, also simultaneously when shopping in offline stores (Verhoef et al., 2015; Holkkola et al., 2023a). These possibilities to seamlessly utilize both offline and online channels of the same retailer are referred to as omnichannel retailing, which is considered the next step of multichannel retailing (Lin et al., 2023; Makkonen et al., 2023; Rigby, 2011). However, also comparing multiple retailers’ products is easy for smart consumers in the digital age. The phenomenon of consumers physically evaluating products in offline channels and comparing or buying the product in online channels is referred to as showrooming behavior (Fiestas & Tuzovic, 2021; Grewal et al., 2016). The verb “to showroom” originates from physical showrooms, where instead of buying the product directly, consumers can gain knowledge and consultancy of the displayed products and leave an order or buy it in other channels (Rapp et al., 2015; Fan et al., 2021). Thus, today’s showroomers can be perceived as using offline stores as showrooms for products purchased online (Mehra et al., 2018; Brynjolfsson et al., 2013). According to statistics, showrooming behavior is very popular – it is estimated that 84% of consumers are doing it (Retail Touch Points, 2018). Although showrooming can happen in the same retailer’s channels and, thus, be so-called loyal showrooming (Schneider & Zielke, 2020), showroomers have shown a tendency to ultimately buy the product via competing retailer’s online channels (Spaid et al., 2019). This kind of competitive showrooming makes it a particularly challenging dilemma for brick-and-mortar (B&M) store retailers (Rapp et al., 2015). Indeed, showroomers are often attracted by the possibility of physically touching and feeling the product and still utilizing lower prices offered by online retailers, but the reasons and motives behind this crosschannel behavior are suggested to be more diverse than that (Gensler et al., 2017; Frasquet & Miquel-Romero, 2021). Therefore, identifying the drivers of showrooming behavior becomes important (Arora et al., 2022). However, there is a research gap in systematically and statistically combining the existing quantitative results of the drivers of showrooming behavior. Also, our literature review shows that up to seven drivers have resulted in conflicting findings: gender, age, income, brand loyalty, online trust, offline service, and exploratory shopping, which need further research. In the past decades of Information Systems (IS) research, meta-analysis has been proven as an efficient way to synthesize prior results and tackle contradictory findings and, thus, provide more reliable knowledge (Jeyaraj & Dwivedi, 2020). Meta-analysis consists of a Systematic Literature Review (SLR) and a statistical analysis where the data consists of samples from existing studies. Synthesizing the data from prior showrooming studies is vital for retail practitioners who want to retain existing or find new customers in the digital age (Mehra et al., 2018). Arora et al. (2017, 2022) also called for more research on the factors behind showrooming behavior. In addition, Holkkola et al. (2022a) call for research on showrooming drivers that have resulted in contradictory study results, such as gender. The goal of this paper is to fill this gap in the literature. Thus, we statistically synthesize the existing quantitative results concerning the drivers of showrooming behavior by identifying (1) what the main drivers of showrooming behavior are and (2) whether the drivers that seem contradictory in prior literature actually drive showrooming behavior. Despite the researchers’ growing interest and multiple quantitative studies on showrooming behavior, no meta-analytical framework for the drivers of showrooming behavior has been proposed. Sahu et al. (2021) have made a descriptive SLR on showrooming and webrooming. Webrooming refers to behavior where the information search and actual purchase happen in the opposite channels compared to showrooming (Konuş et al., 2008). The findings of Sahu et al. (2021) bring together various drivers of showrooming behavior but do not provide a statistical synthesis of drivers’ average associations, statistical significance, and the correctness of conflicting prior results. Nor do they consider publication bias, which arises when statistically significant rather than not significant findings are more typically submitted to and accepted by peer-reviewed publications (Jeyaraj & Dwivedi, 2020). Therefore, in this paper, we statistically synthesize the existing quantitative results concerning the drivers of showrooming behavior. To find all the drivers studied, we carried out an SLR on existing showrooming literature. Then, we integrated the existing constructs and executed a meta-analysis to find out the mean associations of the existing samples. In the next section, prior findings on showrooming behavior are presented. In the third section, the meta-analysis method is presented. The fourth section presents the findings of this study and, finally, the fifth section provides a discussion and conclusion. 2 Showrooming behaviour The causes and consequences of showrooming behavior have gained interest from researchers. The consequences of showrooming behavior have included, for example, an increase in consumers’ innovative purchase tendencies (Sahu et al., 2021), a negative impact on offline store staff’s performance (Rapp et al., 2015; Park & Hur, 2023), and a positive effect on revisit intention (Holkkola et al., 2023b). Thus, although the showrooming phenomenon could be perceived as a challenge for offline retailers, the findings in prior literature seem multifaceted. Also, the drivers of showrooming have been studied with a great variety of variables. Sahu et al.’s (2021) SLR found 42 drivers of showrooming and webrooming behavior from prior studies. They classified these drivers into three categories: customer-led, company-led, and situational drivers. According to Sahu et al. (2021), customer-led showrooming drivers include, for instance, consumers’ capabilities and normative beliefs. Also, consumers’ sociodemographic characteristics behind showrooming behavior have been studied (Holkkola et al., 2022a). Some studies report that younger age increases showrooming behavior (Kolehmainen, 2018; Holkkola et al., 2022a) whereas other studies propose that age has no effect on the matter (Dahana et al., 2018; Li et al., 2018; Fang et al., 2021). This raises the question of which result is correct. Also in terms of gender, contradictory results have been found. For instance, Dahana et al. (2018) found that gender has no effect on showrooming behavior while Holkkola et al. (2022a) found women to showroom more than men. Regarding consumers’ income, higher income has been associated with more active showrooming behavior (Fang et al., 2021; Holkkola et al., 2022a). However, Jo et al. (2020) found no association between income and multichannel shopping behavior. In prior literature, consumers’ online trust and the lack of perceived online risks have also resulted in conflicting findings. Arora and Sahney (2018) found that consumers’ online trust increases their showrooming behavior. However, Quach et al. (2022) found that privacy risk has no effect on showrooming behavior, although, based on Arora and Sahney’s (2018) findings, the perceived privacy risk could be hypothesized to decrease showrooming behavior and the perceived lack of privacy risk to increase showrooming behavior. Similarly, Kolehmainen (2018) found no association between security risk and showrooming behavior. The company-led showrooming drivers, in turn, consist of the things that are under a retailer’s control, such as price, customer service, and channel integration (Sahu et al., 2021). In prior quantitative studies, many of these company-led showrooming drivers have resulted in effects with the same direction: either positive or negative. For instance, Li et al. (2018), Fang et al. (2021), and Goraya et al. (2022) all found a positive effect of channel integration on showrooming behavior, although the strength of these effects varied. In line with this positive effect, utilizing a retailer’s online channels is suggested to enhance consumers’ perceptions of the same retailer’s channel integration and available services (Fang et al., 2021). However, some associations between showrooming behavior and company-led drivers have even resulted in opposite results. For instance, the effects of customer service in an offline store on showrooming behavior have been found both positive (Arora & Sahney, 2018; Shankar et al., 2021) and negative (Burns et al., 2018), whereas other studies (Kang, 2018) have found no association between them, thus underlining the need for this meta-analytical review. Regarding situational showrooming drivers, brand loyalty and exploratory shopping have resulted in contradictory results. Brand loyalty has been associated both positively (Quach et al., 2022) and negatively (Borges, 2018) with showrooming behavior. In addition, Burns et al. (2018) found no association between these (Burns et al., 2018). In exploratory shopping, consumers are involved and immersed in products (Christodoulides & Michaelidou, 2010; Quach et al., 2022) and may experience flow, which consists of immersion, enthusiasm, and losing track of time (Rose et al., 2012). Exploratory shopping has resulted in positive (Quach et al., 2022) and statistically not significant (Herrero-Crespo et al., 2022) associations with showrooming behavior. Banerjee and Longstreet (2016) conceptualized showroomers as having high consciousness in both physical and virtual dimensions, which is related to the immersion aspect of exploratory shopping. Also, shopping enjoyment, which is a component of customers’ flow, is more typical for multi-channel shoppers than for single-channel or low-commitment shoppers (Konuş et al., 2008). However, shopping enjoyment did not affect customers’ showrooming intention (Kolehmainen, 2018). Thus, exploratory shopping and its related components have resulted in both positive and statistically not significant effects on showrooming and multichannel behaviors in general. Based on the above, multiple conflicting drivers need further analysis. 3 Methodology 3.1 Data Collection and Coding The literature search for the meta-analysis was performed using various search terms, such as “showrooming”, “research shopping”, “omnichannel retailing”, “multichannel retailing”, and “cross-channel retailing” in several databases (ABI/INFORM, Scopus, ProQuest Central, Emerald, EBSCO Business Source Premier, ProQuest Dissertations and Theses, and Google Scholar). In addition, several proceedings of IS conferences (AMCIS, Bled eConference, ECIS, HICCS, ICIS, MCIS, PACIS, WHICEB, and Wirtschaftsinformatik) were searched or manually screened. In our inclusion criteria, studies had to 1) address showrooming behavior; 2) provide quantitative empirical results based on independent samples; 3) provide the required information for effect size integration; and 4) be written in English. The search resulted in 24 independent samples with a total of 12,129 respondents. These samples were from studies that were published between 2017 and 2024 (see Appendix 1). The resulting data was coded according to the guidelines of Rust and Cooil (1994). More specifically, information representing effect sizes, sample sizes, and reliability of measurements was extracted. Correlation coefficients were selected to represent effect sizes. If the studies did not report correlation coefficients, we converted other statistics to correlations using the procedures by Lipsey and Wilson (2001) as well as Peterson and Brown (2005). Also, if studies reported multiple correlations for the same relationship, average correlations were calculated. 3.2 Effect-Size Integration and Construct Integration Effect size integration followed the random-effect approach by Hunter and Schmidt (2004). First, we corrected effect sizes in terms of reliability: effect sizes were divided by the square root of the product of reliabilities of independent and dependent variables. If this information was missing, the average correlation of the construct was used. Next, effect sizes were corrected in terms of sample sizes. Average correlations were calculated using the random-effect approach (Hunter & Schmidt, 2004). Regarding constructs, we found 86 constructs that were studied as drivers of showrooming behavior. Some of them had only been used in a single study and some in several studies. Some constructs measured the same thing as other constructs in other studies, such as the constructs of online risk and privacy risk. When analyzing the data, we integrated these overlapping constructs which are presented in Table 1. Table 1: Results of construct integration Construct Definition Aliases Showrooming self-efficacy Consumers’ judgments of their capabilities and resources to showroom (Makkonen et al., 2022) Perceived behavioral control Consumer innovativeness Consumers’ perceived innovativeness and power to seek information in the channels of their choice (Huh et al., 2022) Smart shopper feelings, consumer empowerment Online trust Trust in online vendors (Tan & Sutherland, 2004) and data protection (Mahrous & Hassan, 2017) Security risk (reversed), privacy risk (reversed) Attitude toward showrooming Customers’ attitudes toward and positive evaluations of showrooming (Arora et al., 2020) – Social influence The extent to which consumers’ showrooming behavior is influenced by other people and social norms (Rejón-Guardia & Luna-Nevarez, 2017) Socialization, subjective norm Offline search value The extent how much offline evaluation helps consumers (Rajkumar et al., 2021; Kim, 2004). In-store search value, perceived search benefits, feel of product Offline service The desire for offline assistance (Kim & Stoel, 2005) and social encounters (Haytko & Baker, 2004) as well as satisfaction with the store staff (Reynolds & Beatty, 1999) Desire for customer service, sales staff assistance, desire for social interaction, attentiveness convenience Channel integration The extent to which consumer perceives all information systems and their management successfully integrated across channels (Shi et al., 2020) Cross-channel integration, information integration, perceived integration Ease of use of online purchase The degree to which customers believe that switching to online purchasing would be effortless (Davis, 1989; Arora & Sahney, 2018) Effort expectancy Monetary savings The expected monetary saving benefits of showrooming (Atkins & Kim, 2012) Deals and discounts, cost savings, price comparison Better assortment The access to assortments with a wide range of products, brands, prices, and qualities (Eastlick & Feinberg, 1999; Kahn & Wansink, 2004; Emrich et al., 2015) Assortment seeking, perceived assortment, better product assortment Perceived usefulness of showrooming The expected usefulness and functionality of showrooming to achieve desired outcomes (Davis, 1989; Venkatesh et al., 2003; Chimborazo-Azogue et al., 2021) Performance expectancy Brand loyalty Customers’ attitudinal and behavioral loyalty to a brand (Baldinger & Rubinson, 1996) – Product involvement The level of importance and relevance of the purchase to a consumer (Zaichkowsky, 1986) Purchase involvement Exploratory shopping Shopping by being involved (Christodoulides & Michaelidou, 2010) and immersed (Quach et al., 2022) in products Exploratory information seeking, exploratory acquisition, flow After having integrated parallel constructs, we excluded the remaining constructs that had been used in less than three studies (Tyrväinen et al., 2023). After this, 18 constructs remained in the final model. We wanted to include every construct that had been studied in a sufficient number of samples, because, as Dahana et al. (2018) reasoned, “any factor associated with these [offline and online] behaviors is expected to eventually influence the extent to which consumers engage in showrooming”. 4 Results The results of effect-size integration for each integrated construct in terms of the number of analyzed samples (k), the total N of these samples, the reliabilityadjusted, sample size weighted average correlation (RC), the lower (CIlow) and upper limits (CIhigh) of its 95% confidence intervals, the Q-statistic, I2 statistic, and fail-safe N (FSN) to address the file-drawer problem are shown in Table 2. Table 2: Results of effect-size integration k N RC CIlow CIhigh Q I2 FSN Customer-led drivers Age 7 3721 -0.031 -0.254 0.200 279.388*** 97.852 – Gender 6 3225 -0.002 -0.093 0.089 29.818*** 83.230 – Income 5 2725 0.109*** 0.072 0.146 3.011 0.000 34 Showrooming selfefficacy 8 3693 0.385*** 0.276 0.485 91.622*** 92.360 1130 Consumer innovativeness 4 1287 0.291*** 0.165 0.408 17.674** 83.026 117 Online trust 3 1365 0.201 -0.183 0.531 92.099*** 97.828 – Attitude toward showrooming 5 1862 0.557*** 0.456 0.637 28.288*** 85.860 883 Social influence 4 1676 0.375** 0.157 0.559 61.107*** 95.091 193 Company-led drivers Offline search value 4 1230 0.419*** 0.225 0.581 44.814*** 93.305 242 Offline service 4 1513 0.243 -0.071 0.513 125.893*** 97.617 – Channel integration 3 2148 0.327*** 0.221 0.426 8.147* 75.450 107 Ease of use of online purchase 4 2097 0.357*** 0.163 0.524 65.312*** 95.410 325 Monetary savings 9 3544 0.361*** 0.175 0.523 293.238*** 97.270 978 Better assortment 3 1275 0.221** 0.063 0.386 17.639*** 88.660 42 Kang, J. Y. M. (2018). Showrooming, webrooming, and user-generated content creation in the omnichannel era. Journal of Internet Commerce, 17(2), 145–169. Kolehmainen, T. (2018). The drivers of showrooming: The role of channel benefits, consumer attributes and products. Aalto University (Master's thesis). Konuş, U., Verhoef, P.C., Neslin, S.A. (2008). Multichannel shopper segments and their covariates. Journal of Retailing, 84(4), 398–413. Li, Y., Liu, H., Lim, E. T., Goh, J. M., Yang, F., Lee, M. K. (2018). Customer’s reaction to crosschannel integration in omnichannel retailing: the mediating roles of retailer uncertainty, identity attractiveness, and switching costs. Decision Support Systems, 109, 50–60. Lin, S.-W., Huang, E. Y., Cheng, K.-T. (2023). A binding tie: why do customers stick to omnichannel retailers? Information Technology & People, 36(3), 1126–1159. Lipsey, M. W., Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks: Sage. Liu, T., Liu, M. (2024). Does cross-channel consistency always create brand loyalty in omnichannel retailing? International journal of retail & distribution management, 52(1), 125– 145. Makkonen, M., Frank, L., Paananen, T., Holkkola, M., Kemppainen, T. (2023). The cross-channel effects of in-store customer experience in the case of omnichannel fashion retailing in Finland. In: 36th Bled eConference: Digital Economy and Society: The Balancing Act for Digital Innovation in Times of Instability. Ed. by A. Pucihar, M. Kljajić Borštnar, R. Bons, G. Ongena, M. Heikkilä, D. Vidmar. University of Maribor, 609–625. Makkonen, M., Nyrhinen, J., Frank, L., Karjaluoto, H. (2022). The effects of general and mobile online shopping skilfulness and multichannel self-efficacy on consumer showrooming behaviour. In: 35th Bled eConference: Digital Restructuring and Human (Re)action. Ed. by A. Pucihar, M. Kljajić Borštnar, R. Bons, A. Sheombar, G. Ongena, and D. Vidmar. University of Maribor, 113–128. Mehra, A., Kumar, S., Raju, J. (2018). Showrooming and the competition between store and online retailers. Management Science, 64(7), 3076–3090. Paananen, T., Holkkola, M., Makkonen, M., Frank, L., Kemppainen, T. (2023). Customers’ QR Code Usage Barriers in a Brick-and-Mortar Store: A Qualitative Study. In: 36th Bled eConference: Digital Economy and Society: The Balancing Act for Digital Innovation in Times of Instability. Ed. by A. Pucihar, M. Kljajić Borštnar, R. Bons, G. Ongena, M. Heikkilä, & D. Vidmar. University of Maribor, 171–188. Park, H., Hur, W. M. (2023). Customer showrooming behavior, customer orientation, and emotional labor: Sales control as a moderator. Journal of Retailing and Consumer Services, 72, 103268. Peterson, R., Brown, S. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90(1), 175–181. Quach, S., Barari, M., Moudrý, D. V., Quach, K. (2022). Service integration in omnichannel retailing and its impact on customer experience. Journal of Retailing and Consumer Services, 65, 102267. Rajkumar, N., Vishwakarma, P., Gangwani, K. K. (2021). Investigating consumers’ path to showrooming: A perceived value-based perspective. International journal of retail & distribution management, 49(2), 299–316. Rapp, A., Baker, T. L., Bachrach, D. G., Ogilvie, J., Beitelspacher, L. S. (2015). Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance. Journal of Retailing, 91(2), 358–369. Retail Touch Points (2018). Shoppers prefer self-service kiosks, mobile-armed associates in-store. https://retailtouchpoints.com/topics/store-operations/study-reveals-why-96-ofshoppers-leave-stores-empty-handed Rose, S., Clark, M., Samouel, P., Hair, N. (2012). Online customer experience in e-retailing: an empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308–322. Rigby, D. (2011). The Future of Shopping. Harvard Business Review, 89(12), 64–75. Rust, R. T., Cooil, B. (1994). Reliability measures for qualitative data: Theory and implications. Journal of Marketing Research, 31(1), 1–14. Sahu, K. C., Naved Khan, M., Gupta, K. D. (2021). Determinants of webrooming and showrooming behavior: A systematic literature review. Journal of Internet Commerce, 20(2), 137–166. Schneider, P. J., Zielke, S. (2020). Searching Offline and Buying Online: An Analysis of Showrooming Forms and Segments. Journal of Retailing and Consumer Services, 52. Shankar, A., Gupta, M., Tiwari, A. K., Behl, A. (2021). How does convenience impact showrooming intention? Omnichannel retail strategies to manage global retail apocalypse. Journal of Strategic Marketing, 1–22. Sharma, N., Sharma, A., Dutta, N. Priya, P. (2023). Showrooming: a retrospective and prospective review using the SPAR-4-SLR methodological framework. International Journal of Retail & Distribution Management, 51(11), 1588–1613. Spaid, B. I., O’Neill, B. S., Ow, T. T. (2019). The upside of showrooming: How online information creates positive spill-over for the brick-and-mortar retailer. Journal of Organizational Computing and Electronic Commerce, 29(4), 294–315. Tyrväinen, O., Karjaluoto, H. Ukpabi, D. (2023). Understanding the role of social media content in brand loyalty: A meta-analysis of user-generated content versus firm-generated content. Journal of Interactive Marketing, 58(4). Verhoef, P. C., Kannan, P. K., Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing. Journal of Retailing, 91(2), 174–181. Wilska, T.-A., Holkkola, M., Tuominen, J. (2023). The role of social media in the creation of young people’s consumer identities. Sage Open, 13(2). Appendix 1: Selected samples and constructs for the meta-analysis Paper Selected constructs 1 Holkkola et al. (2023b) self-efficacy, age, gender, income 2 Arora & Sahney (2018) sales staff assistance (offline service), feel of the product (offline search value), socialization (social influence), subjective norm (social influence), online trust, perceived behavioral control (showrooming self-efficacy), deals and discounts (monetary savings), cost savings (monetary savings), better product assortment (better assortment), ease of use of online purchase, perceived usefulness of showrooming, attitude toward showrooming, perceived integration (channel integration) 3 Fang et al. (2021) information integration (channel integration), age, gender 4 Li et al. (2018) cross-channel integration (channel integration), age, gender, income 5 Liu & Liu (2024) brand loyalty 6 Shankar et al. (2021) attentiveness convenience (offline service), product involvement 7 Dahana et al. (2018) product involvement, age, gender 8 Rajkumar et al. (2021) smart shopper feelings (consumer innovativeness), enhanced product evaluation (offline search value), monetary savings 9 Chimborazo-Azogue et al. (2022) attitude toward showrooming 10 Quach et al. (2022) flow (exploratory shopping), reversed privacy risk (online trust), brand loyalty 11 Huh & Kim (2022) consumer innovativeness 12 Kang (2018) desire for social interaction (offline service), price comparison (monetary savings), assortment seeking (better assortment) 13 Borges (2018) brand loyalty, product involvement, age, gender, income 14 Burns et al. (2018) desire for customer service (offline service), brand loyalty 15 Kolehmainen (2018) reversed security risk (online trust), perceived behavioral control (showrooming self-efficacy), attitude toward showrooming 16 Chokkannan et al. (2023) product involvement, age 17 Goraya et al. (2022), sample 1 consumer empowerment (consumer innovativeness), perceived assortment (better assortment), channel integration 18 Goraya et al. (2022), sample 2 consumer empowerment (consumer innovativeness), perceived assortment (better assortment), channel integration 19 Arora et al. (2020) in-store search value (offline search value), showrooming selfefficacy, attitude toward showrooming, product involvement 20 Arora et al. (2017) perceived search benefits (offline search value), subjective norm (social influence), showrooming self-efficacy, perceived behavioral control (showrooming self-efficacy), attitude toward showrooming 21 Chimborazo-Azogue et al. (2021) subjective norm (social influence), ease of use of online purchase, perceived usefulness of showrooming, product involvement 22 Herrero-Crespo et al. (2022) exploratory information search (exploratory shopping), exploratory acquisition (exploratory shopping), ease of use of online purchase, perceived usefulness of showrooming 23 Holkkola et al. (2022a) age, gender, income 24 Makkonen et al. (2022) self-efficacy