https://orcid.org/0000-0001-6275-9387
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ABSTRACT
Social gamification, which allows technology users to interact with each other in gamified tasks, has drawn increasing interest due to its effectiveness in facilitating users’ game engagement and task efforts. In social gamification, users can compete or cooperate with other users or teams to complete game tasks and achieve game goals. However, it remains unclear how various social interaction mechanisms (SIMs), such as cooperation, interpersonal competition, and intergroup competition, influence gamification outcomes when they are separately or jointly implemented. In addition, the effects of SIMs on experiential and instrumental gamification outcomes have not been well differentiated. In this study, we systematically investigate the influences of these fundamental SIMs, as well as the possible interaction effects among them, on fitness app users’ game engagement and fitness behavior. Using a fitness app custom-developed for the Chinese market, Fitness Castle, we conducted a longitudinal field experiment to test our proposed model and hypotheses. The results indicate that when separately implemented, cooperation and interpersonal competition can lead to differential instrumental gamification outcomes in the fitness context. We also systematically compare the differential gamification outcomes when cooperation, interpersonal competition, and intergroup competition are combined in various coopetition settings. Our study offers a theory-based framework and design principles for social gamification. Our findings help practitioners better design SIMs in their gamified technologies with the purpose of achieving optimal experiential and instrumental gamification outcomes simultaneously.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplementary Material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2023.2196776
Notes
1 Task interdependence refers to the degree to which individuals must coordinate with others to complete a certain task more effectively [Citation18], or the amount of interaction between individuals required to complete a certain task [Citation26].
2 Such design messaging (or artifacts) emphasizes the individual user without reference to others. Examples of this messaging could include: “You earned a new badge,” “You have completed three challenges in total,” and “You are doing an excellent job and are now at the silver level.”
3 Such design messaging only emphasizes individual users with respect to the overall team’s accomplishments or performance. Examples of this include “Your team earned a new badge,” “Your team has completed three challenges in total,” and “Your team is doing well and has achieved the bronze level.”
4 Such design messaging only emphasizes individual users with respect to the performances of other users. Examples would include “You have three badges in total, which is more than 60 percent of users” and “You are in the top 25 percent of app users for daily exercise achievement.”
5 Such design messaging only emphasizes the group with respect to the performances of other groups. Examples could include “Your group has earned 20 points and is ranked second of 10 groups” and “Your group is consistently in the bottom 25 percent of all groups for daily exercise achievement.”
6 The following is an example of individual-based competitive game goals: “you need to perform better than other users in the game,” while “you need to help your team perform better and win against other teams in the game” is an example of group-based cooperative and competitive game goals.
7 Such people to whom one compares oneself are referred to as one’s comparative reference group [Citation100].
8 By pure cooperation, we refer to the situation where cooperative mechanisms are provided without any competitive mechanisms (including both interpersonal and intergroup competition).
9 For instance, Domínguez et al. [Citation21] suggested that students’ offline learning activities decreased along with the use of gamified e-learning systems, which can be to some extent attributed to poorly designed competitive mechanisms in the system. Hanus and Fox [34] also found that user satisfaction and learning performance diminished with the use of an individual leaderboard in an e-learning program.
10 Before the experiment, we conducted an a priori statistical power analysis for repeated-measures ANOVA, which assumes a reasonable effect size (f = 0.2) for our treatment effects with a correlation of 0.5 among the longitudinally observed dependent variables. This result suggested that a total sample size of approximately 180 (e.g., 30*6) was needed for all six experimental groups to ensure a statistical power of 0.8 to detect such a small effect size. Hence, we stopped participant recruitment when we had approximately three times the required sample size of participants who completed the registration and fitApp installation.
11 These respectively correspond to the perceived competitive climate among individual users and the perceived competitive climate among groups.
12 As shown in Table C.4, users in the treatment groups with cooperation (Groups 3-6) had a significantly higher degree of perceived positive interdependence (perceived cooperative climate; mean = 5.006, SD = 1.301) than those in the groups without cooperation (Groups 1, 2) (mean = 4.309, SD = 1.664), with t = 6.37 and p < 0.001. Similarly, users in the treatment groups with interpersonal competition (Groups 2, 4, and 6) reported a significantly higher degree of perceived negative interpersonal interdependence (perceived competitive climate at the individual level; mean = 4.632, SD = 1.363) than those in the groups (Groups 1, 3, and 5) without interpersonal competition (mean = 4.018, SD = 1.649), with t = 4.28 and p < 0.001. Lastly, users in the treatment groups with intergroup competition (Groups 5, 6) reported a significantly higher degree of perceived negative intergroup interdependence (perceived competitive climate at the group level; mean = 5.052, SD = 1.175) than those in the groups without intergroup competition (Groups 1–4; mean = 3.465, SD = 1.791), with t = 11.14 and p < 0.001.
13 The only exceptional case is the interaction term in Model D6-5. Despite insignificance, the p-value is 0.112, which is very close to the level of marginal significance. This may result from relatively low variance in the original value, which cannot be precisely estimated in negative binomial models. We leave this issue for open discussion.
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Notes on contributors
Jun Zhang
Jun Zhang ([email protected]) is an Associate Professor at the School of Information Management, Wuhan University, China. He received his PhD in Information Systems from the City University of Hong Kong. Dr. Zhang’s research interests include deviant online behavior, information privacy and security, e-health, and human-computer interaction. His research has been published in such academic journals and conference proceedings as Journal of Management Information Systems, Information Systems Research, Information & Management, Information Technology & People, Computers in Human Behavior, ICIS, and PACIS. He serves as an associate editor for the Communications of the Association for Information Systems.
Qiqi Jiang
Qiqi Jiang ([email protected]) is an Associate Professor in the Department of Digitalization at Copenhagen Business School, Denmark. He received his PhD in Information Systems from City University of Hong Kong. Dr. Jiang is interested in examining the business value of emerging technology, collective intelligence, and management and economy of information and data security. His work has been published or forthcoming in Journal of Management Information Systems, MIS Quarterly, Journal of the Association for Information Systems, Journal of Strategic Information Systems, and other journals.
Wenping Zhang
Wenping Zhang ([email protected]; corresponding author) is an Associate Professor in School of Information, Renmin University of China. He received his PhD in Information Systems from City University of Hong Kong. Dr. Zhang’s research interests include machine learning, deep learning, interpretable AI, and business analytics. His work has been published in Journal of Management Information Systems, INFORMS Journal on Computing, Production and Operations Management, Decision Support Systems, and other journals.
Lele Kang
Lele Kang ([email protected]) is an Associate Professor of Information Management and Information Systems in School of Information Management, Nanjing University, China. He is also a member at Laboratory of Data Intelligence and Interdisciplinary Innovation at Nanjing University. Dr. Kang received his PhD in Information Systems from City University of Hong Kong. His current research interests include patent analysis, digital transformation, mobile commerce, and IT-enabled innovation. His research has been published in Information Systems Research, Journal of the Association for Information Systems, Information & Management, Journal of the Association for Information Science and Technology, and other journals.
Paul Benjamin Lowry
Paul Benjamin Lowry ([email protected]) is an Eminent Scholar and the Suzanne Parker Thornhill Chair Professor in Business Information Technology at the Pamplin College of Business at Virginia Tech where he serves as Graduate Programs Director. He received his PhD in Management Information Systems from the University of Arizona and an MBA from the Marriott School of Business. He has to his credit over 270 publications, including over 150 journal papers in the Journal of Management Information Systems, Information Systems Research, MIS Quarterly, and many others. Dr. Lowry is a member of the Editorial Board of JMIS. He is also a senior or associate editor of other journals. His research interests include organizational and behavioral security and privacy; online deviance, online harassment, and computer ethics; human-computer interface, social media, and gamification; and business analytics, decision sciences, innovation, and supply chains.
Xiong Zhang
Xiong Zhang ([email protected]) is an Associate Professor in the Department of Information Management at Beijing Jiao Tong University, China. He earned his Ph.D. in Information Systems from City University of Hong Kong. Dr. Zhang has published in the Journal of Management Information Systems, Journal of the Association for Information Systems, Information Systems Frontiers, Information Technology and People, and other journals and conference proceedings.