Drawing on the rational addiction framework, this study explores the digital vulnerabilities driven by dependence on mobile social apps (e.g., social network sites and social games). Rational addicts anticipate the future consequences of their current behaviors and attempt to maximize utility from their intertemporal consumption choices. Conversely, myopic addicts tend toward immediate gratification and fail to fully recognize the future consequences of their current consumption. In lieu of conducting self-report surveys or aggregate-level demand estimation, this research examines addictive behaviors on the basis of consumption quantity at an individual level. To empirically validate rational addiction in the context of social app consumption, we collect and analyze 13-month, individual-level panel data on the weekly app usage of thousands of smartphone users. Results indicate that the average social app user conducts herself in a forward-looking manner and rationally adjusts consumption over time to derive optimal utility. The subgroup analysis, however, indicates that substantial variations in addictiveness and forward-looking propensities exist across demographically diverse groups. For example, addictive behaviors toward social network sites are more myopic in nature among older, less-educated, high-income groups. Additionally, the type of social app moderates the effects of demographic characteristics on the nature of addictive behaviors. We provide implications that policymakers can use to effectively manage mobile addiction problems, with the recommendations focusing on asymmetric social policies (e.g., information- and capacity-enhancing measures).
We explore how Internet browsing behavior varies between mobile phones and personal computers. Smaller screen sizes on mobile phones increase the cost to the user of browsing for information. In addition, a wider range of offline locations for mobile Internet usage suggests that local activities are particularly important. Using data on user behavior at a (Twitter-like) microblogging service, we exploit exogenous variation in the ranking mechanism of posts to identify the ranking effects. We show that (1) ranking effects are higher on mobile phones suggesting higher search costs: links that appear at the top of the screen are especially likely to be clicked on mobile phones and (2) the benefit of browsing for geographically close matches is higher on mobile phones: stores located in close proximity to a user's home are much more likely to be clicked on mobile phones. Thus, the mobile Internet is somewhat less "Internet-like": search costs are higher and distance matters more. We speculate on how these changes may affect the future direction of Internet commerce.
Drawing from the social and relational perspectives, this study offers an innovative conceptualization and operational approach regarding the validation of self-reported customer demographic data, which has become an essential corporate asset for harnessing business intelligence. Specifically, based on social network and homophily paradigms in which individuals have a natural tendency to associate and interact frequently with others with similar characteristics, we constructed a relational inference model to determine the accuracy of self-administered consumer profiles. In addition, to further enhance the reliability of our model's prediction capability, we employed the entropy mechanism that minimizes potential biases that may arise from a simple probabilistic approach. To empirically validate the accuracy of our inference framework, we obtained and analyzed over 20 million actual call transactions supplied by one of the largest global telecommunication service providers. The results suggest that our social network-based inference model consistently outperforms other competing mechanisms (e.g., weighted average and simple relational classifier) regardless of the criteria choice (e.g., number of call receivers, call duration, and call frequency), with an accuracy rate of approximately 93 percent. Finally, to confirm the generalizability of our findings, we conducted simulation experiments to validate the robustness of the results in response to variations in parameter values and increases in potential noise in the data. We discuss several implications related to business intelligence for both research and practice, and offer new directions for future studies.