Social Media and e-Business Transformation
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ItemThe Pursuit of Transparency and Control: A Classification of Ad Explanations in Social Media( 2021-01-05)Online advertising on social media platforms has been at the center of recent controversies over growing concerns regarding users' privacy, dishonest data collection, and a lack of transparency and control. Facing public pressure, some social media platforms have opted to implement explanatory tools in an effort to empower consumers and shed light on marketing practices. Yet, to date research shows significant inconsistencies around how ads should be explained. To address this issue, we conduct a systematic literature review on ad explanations, covering existing research on how they are generated, presented, and perceived by users. Based on this review, we present a classification scheme of ad explanations that offers insights into the reasoning behind the ad recommendation, the objective of the explanation, the content of the explanation, and how this content should be presented. Moreover, we identify challenges that are unaddressed by either current research or explanatory tools deployed in practice, and we discuss avenues for future research to address these challenges. This paper calls attention to and helps to solidify an agenda for interdisciplinary communities to collaboratively approach the design and implementation of explanations for online ads in social media.
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ItemModerating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach( 2021-01-05)Given the importance of online reviews, as shown by extensive research, we address the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling and social influence theories. We use review order and time interval to incorporate the moderating effects of the time-related variable on the reviewer’s valuation of products and the related details they provide. Applying deep learning techniques in text processing and model building on a dataset of 239297 reviews, the empirical findings represent strong support of the proposed approach and show its superior performance in predicting review helpfulness compared to current approaches. This research contributes to theory by analyzing online reviews from the points of two well-known information processing theories and contributes to practice by developing a model to sort the newly posted reviews.
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ItemLandlords of the Digital World: How Territoriality and Social Identity Predict Playing Intensity in Location-based Games( 2021-01-05)Popular location-based games (LBGs) such as Pokémon GO have been downloaded hundreds of millions of times and have been shown to have a positive impact on mild exercise and social well-being of their players. Several currently popular LBGs introduce a gamified implementation of territorial conflict, where players are divided into teams that battle for the ownership of geographically distributed points of interest. We investigate how social factors and territoriality influence playing intensity in the context of Pokémon GO. Using reasoning from social identity theory, we propose a structural model connecting territoriality, sociality and playing intensity. To test the model, we analyze data collected from a global sample of Pokémon GO players (N=515). Our results show social self-efficacy, territorial self-efficacy and altruism to influence players' identification with their team. Team identification in turn predicts both in-game territorial control behavior and playing intensity.
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ItemHow does Doctors’ Information Sharing Behavior Influence Reputation in Online Health Consultation Platform?( 2021-01-05)The online health consultation platforms provide a unique context for doctors to share health information privately and publicly. However, how doctors’ reputation is shaped in the context of online information sharing has been largely neglected in the current literature. This study explores the relationship between information sharing and reputation by distinguishing private and public information sharing behaviours and investigating the contingent roles of doctors’ professional and online seniority. Data from a leading online consultation platform in China was obtained to test the research model and associated hypotheses. The results reveal that both private and public sharing can contribute to doctors’ online reputation and the effects of the two information sharing behaviours are different about doctors within different professional and online seniority. This study contributes to the literature on health information sharing and online reputation development.
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ItemEnterprise Social Network Applications: Enhancing and Driving Innovation Culture and Productivity Through Digital Technologies( 2021-01-05)Enterprise Social Network (ESN) applications offer new opportunities for organizations to mobilize employees, promoting innovation beyond traditional R&D functions. Despite the popularity and success of these applications, current research has yet to fully explore the potential of ESN applications as both drivers of productive innovation and innovation culture, specifically. This paper proposes a theoretical framework that explains the role of ESN applications in facilitating organizational-wide ideation, collaboration, and socialization, thereby promoting innovation culture and innovation productivity. This study reveals that the dimensions of innovation culture, namely knowledge sharing, transparency, and risk tolerance, mediate the effects of ESN applications on the measures of firm innovation productivity— product/service innovation, process innovation, and social innovation. The findings presented here have implications for theory and practice, namely concerning building an organizational culture that promotes open innovative behavior using social technologies.