Social Commerce: The Good, the Bad, and the Ugly

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    Introduction to the Minitrack on Social Commerce: The Good, the Bad, and the Ugly
    ( 2023-01-03) Sundaram, David ; Sadovykh , Valeria ; Peko, Gabrielle
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    Exploring Consumers Risk Mitigation Strategies in E-Commerce: A Qualitative Study of High-Risk Transactions
    ( 2023-01-03) Kollmer, Tim ; Durani, Khalid ; Peterhänsel, Florian ; Eckhardt, Andreas ; Augustin, Nils
    The recent Covid-19 pandemic has led to a sharp increase in online shopping. While the promises of shopping on e-commerce platforms are vast, there are simultaneously novel and exacerbated risks compared to traditional brick-and-mortar retail purchases. Existing research outlines numerous risk dimensions associated with online shopping. In addition, scholars examine the underlying reasons for consumers' risk perceptions, such as the inability of physical quality checks. However, there is a lack of research investigating how consumers attempt to navigate and mitigate risk perceptions when confronted with a high-risk online transaction. To address this research gap, we conducted 18 semi-structured interviews with consumers who had recently performed an online transaction associated with high-risk perceptions. Our study contributes to the existing literature by identifying an affective and cognitive risk mitigation strategy and respective underlying mechanisms. Notably, we find that online social networks play a central role in shaping consumers' risk perceptions.
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    Detecting Fake Reviews: Just a Matter of Data
    ( 2023-01-03) Theuerkauf, René ; Peters, Ralf
    Along with the ever-increasing portfolio of products online, the incentive for market participants to write fake reviews to gain a competitive edge has increased as well. This article demonstrates the effectiveness of using different combinations of spam detection features to detect fake reviews other than the review-based features typically used. Using a spectrum of feature sets offers greater accuracy in identifying fake reviews than using review-based features only, and using a machine learning algorithm for classification and different amounts of feature sets further elucidates the difference in performance. Results compared by benchmarking show that applying a technique prioritizing feature importance benefits from prioritizing features from multiple feature sets and that creating feature sets based on reviews, reviewers and product data can achieve the greatest accuracy.
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    Social Group Buying as a Marketing Strategy
    ( 2023-01-03) Chen, Huiyan ; Peng, Jing ; Guan, Mengcheng ; Li, Jianbin
    Social group buying (SGB) is a novel form of group buying that encourages customers to purchase deeply discounted products together with friends. Over the past few years, SGB has become a popular marketing strategy for online sellers to acquire new customers. Using a dataset from an e-commerce platform, we investigate whether and how SGB affects the sales of sellers. We find that enrolling a few products into SGB has a positive spillover effect on the sales of the sellers’ other products, and the effect varies substantially across different types of sellers. Specifically, the positive spillover effect is larger for smaller sellers and more diversified sellers. Moreover, we find that the spillover effect exhibits similar heterogeneity at the brand level, except that it can be negative for large brands and non-diversified brands. This finding suggests that sellers may gain from SGB at the expense of large or non-diversified brands.
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    An Avatar a Day Keeps the Stress Away: The Implementation of Avatars as Technostress Relievers on Online Shopping Websites
    ( 2023-01-03) Feste, Jasmin ; Peters, Leonore ; Schirmer, Michael
    In contrast to stationary retail, online shopping websites are characterized by the fact that no human salesperson is available to buyers as a reference and supporting function for the purchase. With increasing technological penetration and thus online shoppers facing the challenges of technologically induced stress (technostress), it becomes crucial for e-commerce operators to reduce this impersonality of online stores to avoid negative consequences of technostress. Our study proposes the means of an avatar as a technostress reliever. We empirically assess the indirect effect of technostress on purchase intention mediated by online store quality and moderated by avatar presence on the online store website. Our 2x2-factorial between-subjects experimental study reveals a negative indirect effect of technostress on purchase intention of respondents. Further, our findings show that independently of perceived online store quality the total level of the purchase intention is higher for the presence of an avatar than for the absence of an avatar.
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    Mobile Commerce - Analysis and Investigation of the Online Safety, Privacy, and Data Forensics of Amazon and Etsy Apps
    ( 2023-01-03) Dorai, Gokila ; Hutchinson, Shinelle ; Rodriguez, Beatriz ; Karabiyik, Umit
    The COVID19 pandemic has led to the proliferation of the usage of online shopping applications among millions of customers worldwide. The enormous potential in technological advancements, particularly mobile technology, has directly impacted mobile commerce, where the shopping process has become so convenient. While the benefits of mobile commerce are multi-fold, the current privacy practices and the extent of user data residue in shopping apps have been less explored. In this paper, we have conducted an in-depth, systematic analysis of two of the most popular mobile shopping apps - Amazon and Etsy. Our analysis led to the recovery of user data and shopping activity artifacts from Amazon’s and Etsy’s buyer and seller apps on Android/iOS devices. Based on the user data and artifacts found, we have also discussed the implications of default privacy settings, the importance of online safety policies prior to product listings, and implications for research and practice.