Open Universiteit

Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/7275
Title: Social motivations to use gamification: an empirical study of gamifying in relation to generational differences
Authors: Teensma, DWI
Keywords: Gamification
Social Network
Run-app
Social Influence
Generational Differences
Issue Date: 1-Aug-2016
Publisher: Open Universiteit Nederland
Abstract: Today people use a variety of social and gameful (mobile) applications in order to motivate themselves and others to maintain difficult habits such as exercise, sustainable consumption and healthy eating. However, we have yet lacked understanding of how social influence affects willingness to maintain these difficult habits with the help of gamification services. In order to investigate this phenomenon, this study measures how social influence predicts attitudes, use and further exercise in the context of gamification exercise in relation to generational differences. The specific objective of this research was; how do generational differences moderate the relationship between attitude and intention to continue to use gamification. The research model draws from the theory of planned behaviour and is extended with recognition and perceived reciprocal benefits. Both are hypothesised to be relevant social factors that predict attitudes and user behaviour in gamification services. The social factors used in this study can be categorised as follows: Attitude, Continued Use, Continued Exercise, Reciprocal Benefits, Recognition, Subjective Norms, and Word-of-Mouth intentions. Generational difference is used to incorporate the demographic effect. For this study four generations were defined: (1) Veterans; (2) Baby Boomers; (3) Generation Xers; and (4) Nexters. The data was gathered via a questionnaire filled in by users of a running app ( an app that tracks running that people do for fitness), a mobile service that gamifies running. Each variable included four items except for Continued Exercise, which included three items, and were measured with 7-point Likert scales. The findings of this thesis do not support all of the hypothesized relationships proposed in the theoretical model. Specifically, the results revealed that subjective norms have a significant effect on recognition, and recognition also has a significant effect on reciprocal benefits. The final relationship that shows a significant effect is that users who continued to use the app continued to exercise. The other relationships hypothesized in this study appear to have no significant effect. After analysing the results, it seems there is no moderation effect of generational differences on the relationship between a user’s attitude and continued use of gamification. The relationship between the elements could be described as more of a ‘dissatisfiers’ type because only the method of linear regression is used. Based on the outcome of this research, there is no need to reorganize the practical approach. In-depth research on the relationship between ATT and CU is recommended by the researchers because it is too early to claim that ATT has no role in predicting CU intentions. It begs for further theoretical and empirical investigations. As Liao et al. (2006) states, CU will increase job performance and organizational rewards. It would be interesting to investigate the through motivator for CU. This would contribute by interpreting this research mode and by creating a stepping stone for further research and alternative models. There is no need to focus on a specific generation (e.g., generational differences), because the outcome seems to show there is no direct relationship between generational differences and the need to continue using a running app. . Furthermore, it is wise to develop a community around a gamification service so that users can receive feedback on their exercise results. This creates the possibility of influencing the subjective norms of the users. Also the sample method (i.e., convenience sampling) has limitations because there is no sound basis for estimating statistical confidence intervals around the sample statistics of interest (Green, 1988). In further research it would be interesting to see on what gameful aspects a run app have the best social influence on user’s attitudes.
URI: http://hdl.handle.net/1820/7275
Appears in Collections:MSc Management Science

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