Aaltodoc publication archive (Aalto University institutional repository)
School of Business | Department of Marketing | Marketing | 2011
Thesis number: 12656
Factors affecting mobile gaming adoption - A study of Chinese users and contexts
|Title:||Factors affecting mobile gaming adoption - A study of Chinese users and contexts|
|Year:||2011 Language: eng|
|Department:||Department of Marketing|
|Index terms:||markkinointi; marketing; markkinat; markets; Kiina; China; pelit; games; viihde; entertainment; internet; internet; mobiilitekniikka; mobile technology; palvelut; service; matkapuhelimet; cellular phones|
» hse_ethesis_12656.pdf size:3 MB (2655501)
|Key terms:||3G; China; mobile gaming; mobile internet; mobile services; segmentation; smartphones; TAM; Technology Acceptance Model; technology adoption|
Objective of the Study
The objective of the study was to discover the main factors affecting mobile gaming in the Chinese context. The theoretical base of the study was built on the Technology Acceptance Model (TAM) and its later revisions. Special attention was paid on recent TAM studies on mobile gaming in China or other East Asian areas. In addition, to bring the TAM theory closer to marketing applications, the study produced clusters from the respondent data in order to identify different consumer segments and divide the users into them.
The data for the study was collected in the spring of 2011 through a quantitative online survey, which was distributed on a Finnish game company’s Chinese microblog sites and other relevant Chinese online channels. A total of 492 usable responses were obtained for further analysis. Four main analytical methods were used in the study: factor analysis, regression analysis, cluster analysis and cross-tabulation.
Findings and Conclusions
The study identified four key factors that affect mobile gaming adoption in China: Perceived Ease of Use, Perceived Enjoyment, Social Influence and Flow. Of these factors, perceived enjoyment had the largest effect on adoption, followed by social influence, flow and lastly, ease of use. The factors also seemed to interact with each other. For example, social influence has a strong effect on perceived enjoyment, and the link between perceived enjoyment and flow is also very strong. Marketing-wise, the cluster analysis identified five user or potential user segments. Their profiles were interpreted based on the clustering solutions and additional cross-tabulation with background variables age, gender, experience and use context.
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