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Cited by 943

The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.

Revealing the theoretical basis of gamification: A systematic review and analysis of theory in research on gamification, serious games and game-based learning

Open access
Krath, Jeanine, Schürmann, Linda, von Korflesch, Harald F.O.·2021·Computers in Human Behavior
DOI 10.1016/j.chb.2021.106963
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Abstract

Despite increasing scientific interest in explaining how gamification supports positive affect and motivation, behavior change and learning, there is still a lack of an overview of the current theoretical understanding of the psychological mechanisms of gamification. Previous research has adopted several different angles and remains fragmented. Taking both an observational and explanatory perspective, we examined the theoretical foundations used in research on gamification, serious games and game-based learning through a systematic literature review and then discussed the commonalities of their core assumptions. The overview shows that scientists have used a variety of 118 different theories. Most of them share explicitly formulated or conceptual connections. From their interrelations, we derived basic principles that help explain how gamification works: Gamification can illustrate goals and their relevance, nudge users through guided paths, give users immediate feedback, reinforce good performance and simplify content to manageable tasks. Gamification mechanics can allow users to pursue individual goals and choose between different progress paths, while the system can adapt complexity to the user's abilities. Social gamification elements may enable social comparison and connect users to support each other and work towards a common goal.

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Metadata

Authors
Krath, J., Schürmann, L., & von Korflesch, H. F. O.
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2021-12
Volume
125
Pages
106963
ISSN
0747-5632 (ISSN-L)
DOI
10.1016/j.chb.2021.106963
OpenAlex
W3191036075
Cited by
943
References
69
FWCI
28.88
Type
journal article
Language
en
Open access
hybrid

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.