User-adapted Gamification: developing a user-centred design feature preference model to inform engaging design
Lally, Harkeeret (2023) User-adapted Gamification: developing a user-centred design feature preference model to inform engaging design. Doctoral thesis, Birmingham City University.
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Harkeeret Lally PhD Thesis published_Final version_Submitted Oct 2022_Final Award Aug 2023 .pdf - Accepted Version Download (3MB) |
Abstract
Gamification is a behavioural intervention that applies game-like elements to non-game contexts (Deterding et al., 2011) for the purpose of increasing performance of a target behaviour within a non-game context (or task therein). Existing literature highlights a substantial number of instances wherein Gamification is unsuccessful, such that applied design features elicit little to no impact on a target behaviour. The field of Adaptive Gamification seeks to improve the effectiveness of Gamification, by adopting a user-centred design approach wherein the design features used to increase the performance of a target behaviour are tailored or “adapted” to meet an often-unique set of user needs.
Existing methodologies which support the Adaptive Gamification approach are, however, limited. Principally, there exists no model which can effectively measure the level of preference an end user possesses towards a given design feature. In the context of how research can inform Adaptive Gamification design, understanding the level of preference a user possesses towards a given design feature is of critical importance, given that this relationship can directly inform design of a user-centred and tailored Gamification experience. This doctoral research project sought to develop a design feature preference model which could be used to accurately capture the design feature preference of users and provide insight into which design features users are likely to be more receptive to. To this end, the doctoral research project aimed to fulfil three research aims.
The first research aim was to develop a model which could measure user design feature preference, the fulfilment of was achieved across Study one, Study two, and Study three. Combined across all three studies, data from 2322 players was analysed. The first of these studies (Study one) operationalised a total of 37 design features (later increased to 47) using vignette methodology to describe the functionality and purpose of each feature. The second of these studies (Study two) subjected the 47 design features to an Exploratory Factor Analysis (EFA)that returned a nine-factor solution. The third of these studies (Study three) furthered model development, by subjecting the nine-factor solution to a Confirmatory Factor Analysis (CFA), which confirmed the nine-factor solution (though some amendments and reallocation of items were made).
The second research aim was to identify how user characteristics could predict design feature preference, the fulfilment of which was achieved across Study two and Study three. Combined across both studies, data from 2011 players was analysed. The first of these studies (Study two) measured user design feature preference, user motivation, and user personality. The results of this study revealed which design features were most likely to predict variance in user engagement, as well as which motivations and personality traits were associated with predicting variance in preference for these design features. Using the same methodology, the second of these studies (Study three) expanded the range of user characteristics measured in relation to design feature preference, by measuring Gamefulness (a concept stemming from the area of Gamification that refers to what aspects of a gaming experience the end user values). Study three also revealed how Gamefulness could impact design feature preference.
The third research aim was to test whether any relationships between user
characteristics and design feature preference would correspond to tangible difference in user engagement, the fulfilment of which was achieved across Study four and Study five. Combined across both studies, data from 96 players was analysed. The first of these studies (Study four) sought to test the relationships between design feature preference and user engagement, when measuring user engagement via an online task-performance experiment, wherein participants were asked to play a series of online browser games (selected due to the design features they comprised of) while their engagement was measured. The second of these studies (Study five) sought to improve on the measurement of user engagement using in-game behavioural metrics, which is argued as a more representative account of user engagement. Both studies returned non-significant results, which were not consistent with relationships identified in Study two and Study three, though the role of methodological limitations in these findings are extensively discussed at the end of each study chapter.
| Item Type: | Thesis (Doctoral) | ||||||
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| Uncontrolled Keywords: | Gamification, Game mechanics, Engagement, Design feature, Preference, User Research, Personality, Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism, Emotionality, Honesty Humility, Motivation, Trojan, Bartle, Typology, Yee, Psychology, Regression, World of Warcraft, League of Legends, Reddit. | ||||||
| Subjects: | CAH04 - psychology > CAH04-01 - psychology > CAH04-01-01 - psychology (non-specific) | ||||||
| Divisions: | Doctoral Research College > Doctoral Theses Collection Faculty of Business, Law and Social Sciences > School of Social Sciences > Dept. Psychology |
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| Depositing User: | Jaycie Carter | ||||||
| Date Deposited: | 01 Sep 2023 12:25 | ||||||
| Last Modified: | 01 Sep 2023 12:25 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14723 |
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