https://orcid.org/0000-0003-0151-230X
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Abstract
The instant access to gambling anytime, anywhere, has made online gambling highly habitual for some people. As a result, some online gamblers choose to volitionally enable the website-provided disruptive information technology (IT) features to control their gambling routines. The objective of this study is to examine the role of these features in regulating online gambling behavior. It is worthwhile to note that in this research, we do not make the assumption that habitual or regular online gambling is a bad thing; nor does the development of our conceptual framework — which focuses on the effect of disruptive IT features and moderating roles of individual regularity and game type in modifying gambling routines — depend on such an assumption.
Drawing on theories of habitual automaticity and habit disruption, the conceptual framework theorizes the efficacy and mechanism of disruptive features while taking into account heterogeneity in individual regularity and game type. We tested the model using data collected over 10 years from 3,526 users of a gambling website. First, we found that individuals’ repetitive gambling patterns weakened as the duration of exposure to disruptive features increased. Second, the behavior of more regular gamblers was more resistant to the disruptive features, because more regular gamblers exhibited a stronger habitual pattern. Third, disruptive features were less effective on sports games compared with casino games, because sports gamblers tended to exhibit stronger gambling routines. Overall, the present study contributes to the information systems (IS) literature by clarifying how simple IT features may disrupt unwanted and difficult-to-break online gambling habits as judged by the gamblers. Our findings are likeliest to apply to broader areas of online services in which the application in question is integrated into everyday life and the system can offer a disruptive mechanism.
Acknowledgement
The authors deeply appreciate the constructive comments and guidance offered by the Editor-in-Chief Dr. Vladimir Zwass and the anonymous reviewers. They would like to thank the Harvard Medical School for their generous sharing of access into a large-scale de-identified dataset regarding online gambling activities. All errors and mistakes are the authors’ responsibilities.
Notes
1. In terms of why some gamblers wanted to change or better control their gambling routines, we have no way to determine. While using the term “unwanted,” we neither assume nor imply that habitual online gambling is a bad habit. Our focus is on whether disruptive features can change what these gamblers would themselves want to change.
2. A typical disruptive IT feature is called “modified routines,” for example, placing a partial or temporary restriction on certain gaming or payment option in order to make it inconvenient (but not impossible) for an online gambler to execute an established gambling routine. More examples and details on the applications of disruptive IT features will be discussed later in the Study Context and Method sections.
5. Among the original sample of 4,113 users, 587 users had unusable data. Specifically, 263 users had records of gambling amounts that the dataset source advised us not to use (i.e., missing or zero-valued betting amounts); 96 users did not bet anything on online sports or online casinos; and 228 users stopped playing shortly after opening an account. These and more details regarding the final sample are reported in Appendix A.
6. Because of the large sample size (both N and T are large in our data), running System GMM on the entire dataset was not feasible. Therefore, we took numerous random subsets of data (N’ = 500 and T’ = 10) and ran the model on each subset. We found a consistent pattern across all of these results.
7. Although autocorrelation seemed to be properly specified with time dummies, we still used the robust standard errors to ensure that they are robust for any remaining serial correlation as well as for possible heteroscedasticity in the error across time [Citation94].
8. We also used alternative threshold values for separating these two groups to ensure our results are robust to different specifications.
9. The difference of the first-differenced gambling amount between the Study and the Comparison groups became smaller after we used PSM. Thus, PSM effectively corrected for the self-selection bias.
Additional information
Notes on contributors
Jinghui (Jove) Hou
Jinghui (Jove) Hou ([email protected]) is affiliated with the Department of Decision and Information Sciences in the C. T. Bauer College of Business at the University of Houston. Her research focuses on social and psychological effects and uses of information technologies and management systems in the context of e-commerce, social media, and online health communities. She holds a Ph.D. in Communication from the University of Southern California, concentrating on information and communication technologies.
Keehyung Kim
Keehyung Kim ([email protected]) is an Assistant Professor in the Department of Decision Sciences and Managerial Economics at the CUHK Business School, Chinese University of Hong Kong. He received his Ph.D. in Operations and Information Management from the Wisconsin School of Business, University of Wisconsin-Madison. His research draws from disciplines of economics, psychology, and machine learning to shed light on decision-making behaviors in online and mobile platforms as well as on consumer marketplaces. His research has appeared in Management Science and Journal of Management Information Systems.
Sung S. Kim
Sung S. Kim ([email protected]) is the Peter T. Allen Professor of Operations and Information Management in the Wisconsin School of Business at the University of Wisconsin-Madison. He holds a Ph. D. in Information Technology Management from the Georgia Institute of Technology. His primary research focuses on automaticity in IT use, online consumer behavior, information privacy, and philosophical and methodological issues. His research has appeared in Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Journal of the Association for Information Systems, and Decision Sciences.
Xiao Ma
Xiao Ma ([email protected]; corresponding author) is an Assistant Professor of Business Analytics in the C. T. Bauer College of Business at the University of Houston. He graduated with a Ph.D. in Business from the University of Wisconsin-Madison, concentrating on information systems and management. His research focuses on online gambling behavior and proper interventions, participation behavior in online labor and knowledge communities, healthcare analytics, and methodological issues in management research. His work has appeared in premier Information Systems journals, including Information Systems Research, Journal of Management Information Systems, and Journal of the Association for Information Systems.