Drawing on the self-regulation theory, the current paper explores the impacts of two types of fitness app feature sets (i.e., personal-oriented and social-oriented features) on users' health behavior and well-being. The results from fitness app users show that both personal-oriented features and social-oriented features of fitness apps can significantly improve exercise adherence and social engagement of users. Users' exercise proficiency level negatively moderates the relationship between social-oriented features and (a) exercise adherence and (b) social engagement. High levels of social engagement promote users' physical adherence to exercises. Exercise adher-ence and social engagement both enhance users' subjective well-being, but their impacts on different dimensions of well-being vary. Furthermore, regardless of specific features, sufficient use of fitness apps, in general, can significantly help users lead more positive and healthier lives by maintaining exercise adherence, reducing emotional exhaustion, and improving their satisfaction with the overall quality of life. Our findings offer important insights into the underlying mechanisms that help explain fitness app features on users' well-being, and on a practical level, provide suggestions for mobile app developers in designing better fitness app prod-ucts and for exercisers in optimizing the benefits of fitness technology adoption.

Accommodation-sharing services are gaining great popularity via online community platforms in recent years. Meanwhile, users’ privacy concerns over social interactions and online transactions on these platforms are escalating. This study investigates whether and how privacy policy can properly mitigate hosts’ privacy concerns, enhance perceived benefits, and subsequently encourage their information disclosure on the accommodation sharing platforms (ASPs). Through a scenario-based survey and a controlled experiment, we find that the hosts are more concerned about the other users’ misappropriating the private information that the hosts disclose on the platform than the platforms’ privacy invasion behaviors. However, this major concern is not significantly mitigated by the current privacy policy. Moreover, privacy policy engenders two types of perceived benefits, among which the perceived social benefit has a stronger effect than economic benefit on the hosts’ intentions to disclose information on ASPs.

Basketball is known for the vast amount of data collected for each player, team, game, and season. As a result, basketball is an ideal domain to work on different data analysis techniques to gain useful insights. In this study, we continued our previous study published in 2020 Computational Collective Intelligence (12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings) reviewing some important factors to predict players’ future performance and being selected in an All-Star game, one of the most prestigious events, of National Basket Association league. Besides traditional Machine Learning, Deep Learning is also applied in this study for prediction purpose. However, compared to traditional Machine Learning, Deep Learning’s performance is not as good for our dataset. It is understandable when our data are relatively small and structured with a few predictor variables which limited Deep Learning’s ability to deal with a vast amount of Big Data. Our final results, through both Regression and Classification Analysis, indicated that scoring is the most important factor from the primary players for any team and also basketball fan’s favourable style.

Basketball is known for the vast amounts of statistics that are collected for each player, team, game, and season. As a result, basketball is an ideal domain to work on different data analysis techniques to gain useful insights. In this study, we reviewed some important factors to predict players’ future performance and being selected in an All-Star game, one of most prestigious events, of National Basket Association league. Our result showed gradient boosting machine is more qualified to predict NBA players’ future performance, and balanced under-sampling random forest has better predictive ability compared to other algorithms for All-Star prediction. Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is chosen as backbone of the project to tackle this data mining problem and provide a systematic process.

Many of the phenomena of interest in information systems (IS) research are nonlinear, and it has consequently been recognized that by applying linear statistical models (e.g., linear regression), we may ignore important aspects of these phenomena. To address this issue, IS researchers are increasingly applying nonlinear models to their datasets. One popular analytical technique for the modeling and analysis of nonlinear relationships is polynomial regression, which in its simplest form fits a "U-shaped" curve to the data. However, the use of polynomial regression can be problematic when the independent variables are contaminated with measurement error, and the implications of error can be more severe than in linear models. In this research, we discuss a number of techniques that can be used for modeling polynomial relationships while simultaneously taking measurement error into account and examine their performance by using a simulation study. In addition, we discuss the use of marginal and response surface plots as interpretational aides when evaluating the results of polynomial models and showcase their use through a practical example using a well-known dataset. Our results clearly indicate that the use of a linear regression analysis for this kind of model is problematic, and we provide a set of recommendations for future IS research practice.

The proliferation of the Internet and platform economy has given rise to the sharing economy as a popular business model. While much research has focused on the economic and social benefits aspect, privacy issue in sharing economy is often overlooked. Drawing on the privacy calculus theory and the literature of information-as-a-commodity perspective, this study focuses on accommodation sharing platform Airbnb and aims to investigate the critical role of hosts’ information commercialization in leading to their private information disclosure behavior on the platform. This study fills in the research gap by theorizing the concept of information commercialization in accommodation sharing platforms and addressing its formation mechanisms and behavioral outcome.

The proliferation of the smartphones and wearable devices has given rise to the fitness app as one of the major app categories in current mobile app market. Drawing on the social cognitive theory, this study explores the impacts of fitness app functionalities on users' behavioral and psychological outcomes, in terms of exercise adherence and social engagement. Data from 267 college students at a public university in the United States indicated that personal-oriented functionalities of fitness apps can significantly improve both exercise adherence and social engagement of users; however, social-oriented functionalities can only help with perceived social engagement. Users' exercise proficiency level negatively moderates the relationship between personal-oriented functionalities and social engagement. Moreover, our findings show that perceived social engagement can encourage users' physical adherence to exercises. Implications and limitations of this study are discussed.

Lack of careful consideration of common method effects in empirical research can lead to several negative consequences for the interpretation of research outcomes, such as biased estimates of the validity and reliability of the measures employed as well as bias in the estimates of the relationships between constructs of interest, which in turn can affect hypothesis testing. Taken together, these make it very difficult to make any interpretations of the results when those are affected by substantive common method effects. In the literature, there are several preventive, detective, and corrective techniques that can be employed to assuage concerns about the possibility of common method effects underlying observed results. Among these, the most popular has been Harman's Single-Factor Test. Though researchers have argued against its effectiveness in the past, the technique has continued to be very popular in the discipline. Moreover, there is a dearth of empirical evidence on the actual effectiveness of the technique, which we sought to remedy with this research. Our results, based on extensive Monte Carlo simulations, indicate that the approach shows limited effectiveness in detecting the presence of common method effects and may thus be providing a false sense of security to researchers. We therefore argue against the use of the technique moving forward and provide evidence to support our position.