Online social networks greatly facilitate social exchange among friends. At times, for amusement, individuals may be targeted by friends' playful teases, which often involve exposing individuals' private embarrassing information, such as information that reveals their past indecent behavior, mischief, or clumsiness. Although individuals sometimes do enjoy the humor, they might also be offended by the involuntary exposure. Drawing on social exchange theory, this paper elucidates the consequences of an embarrassing exposure in online social networks. Specifically, this study examines the effects of information dissemination and network commonality on individuals' exchange assessment as well as how this assessment shapes their behavioral responses. The results of our experiment provide strong evidence that information dissemination and network commonality jointly influence individuals' perceived privacy invasion and perceived relationship bonding. In addition, whereas perceived privacy invasion increases transactional avoidance, it reduces approach behavior. Furthermore, whereas perceived relationship bonding impedes both transactional avoidance and interpersonal avoidance, it leads to approach behavior. The theoretical and practical implications of the findings are discussed.
The increasing adoption of product recommendation agents (PRAs) by e-commerce merchants makes it an important area of study for information systems researchers. PRAs are a type of Web personalization technology that provides individual consumers with product recommendations based on their product-related needs and preferences expressed explicitly or implicitly. Whereas extant research mainly assumes that such recommendation technologies are designed to benefit consumers and focuses on the positive impact of PRAs on consumers' decision quality and decision effort, this study represents an early effort to examine PRAs that are designed to produce their recommendations on the basis of benefiting e-commerce merchants (rather than benefiting consumers) and to investigate how the availability and the design of warning messages (a potential detection support mechanism) can enhance consumers' performance in detecting such biased PRAs. Drawing on signal detection theory, the literature on warning messages, and the literature on message framing, we identified two content design characteristics of warning messagesÑthe inclusion of risk-handling advice and the framing of risk-handling adviceÑand investigated how they influence consumers' detection performance. The results of an online experiment reveal that a simple warning message without accompanying advice on how to detect bias is a double-edged sword, because it increases correct detection of biased PRAs ( hits ) at the cost of increased incorrect detection ( false alarms ). By contrast, including in warning messages risk-handling advice about how to check for bias (particularly when the advice is framed to emphasize the loss from not following the advice) increases correct detection and, more importantly, also decreases incorrect detection. The patterns of findings are in line with the predictions of signal detection theory. With an enriched understanding of how the availability and the content design of warning messages can assist consumers in the context of PRA-assisted online shopping, the results of this study serve as a basis for future theoretical development and yield valuable insights that can guide practice and the design of effective warning messages.
E-governments have become an increasingly integral part of the virtual economic landscape. However, e-government systems have been plagued by an unsatisfactory, or even a decreasing, level of trust among citizen users. The political exclusivity and longstanding bureaucracy of governmental institutions have amplified the level of difficulty in gaining citizens' acceptance of e-government systems. Through the synthesis of trust-building processes with trust relational forms, we construct a multidimensional, integrated analytical framework to guide our investigation of how e-government systems can be structured to restore trust in citizen-government relationships. Specifically, the analytical framework identifies trust-building strategies (calculative-based, prediction-based, intentionality-based, capability-based, and transference-based trust) to be enacted for restoring public trust via e-government systems. Applying the analytical framework to the case of Singapore's Electronic Tax-Filing (E-Filing) system, we advance an e-government developmental model that yields both developmental prescriptions and technological specifications for the realization of these trust-building strategies. Further, we highlight the impact of sociopolitical climates on the speed of e-government maturity.
With the advent of e-commerce, the potential of new Internet technologies to mislead or deceive consumers has increased considerably. This paper extends prior classifications of deception and presents a typology of product-related deceptive information practices that illustrates the various ways in which online merchants can deceive consumers via e-commerce product websites. The typology can be readily used as educational material to promote consumer awareness of deception in e-commerce and as input to establish benchmarks for good business practices for online companies. In addition, the paper develops an integrative model and a set of theory-based propositions addressing why consumers are deceived by the various types of deceptive information practices and what factors contribute to consumer success (or failure) in detecting such deceptions. The model not only enhances our conceptual understanding of the phenomenon of product-based deception and its outcomes in e-commerce but also serves as a foundation for further theoretical and empirical investigations. Moreover, a better understanding of the factors contributing to or inhibiting deception detection can also help government agencies and consumer organizations design more effective solutions to fight online deception.
Recommendation agents (RAs) are software agents that elicit the interests or preferences of individual consumers for products, either explicitly or implicitly, and make recommendations accordingly. RAs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products online. They can reduce the information overload facing consumers, as well as the complexity of online searches. Prior research on RAs has focused mostly on developing and evaluating different underlying algorithms that generate recommendations. This paper instead identifies other important aspects of RAs, namely RA use, RA characteristics, provider credibility, and factors related to product, user, and user--RA interaction, which influence users' decision-making processes and outcomes, as well as their evaluation of RAs. It goes beyond generalized models, such as TAM, and identifies the RA-specific features, such as RA input, process, and output design characteristics, that affect users' evaluations, including their assessments of the usefulness and ease-of-use of RA applications. Based on a review of existing literature on e-commerce RAs, this paper develops a conceptual model with 28 propositions derived from five theoretical perspectives. The propositions help answer the two research questions: (1) How do RA use, RA characteristics, and other factors influence consumer decision making processes and outcomes? (2) How do RA use, RA characteristics, and other factors influence users' evaluations of RAs? By identifying the critical gaps between what we know and what we need to know, this paper identifies potential areas of future research for scholars. It also provides advice to information systems practitioners concerning the effective design and development of RAs.