Aaltodoc publication archive (Aalto University institutional repository)
School of Business | Department of Marketing | Marketing | 2013
Thesis number: 14477
Factors affecting entertainment mobile application adoption
|Title:||Factors affecting entertainment mobile application adoption|
|Year:||2013 Language: eng|
|Department:||Department of Marketing|
|Index terms:||markkinointi; marketing; palvelut; service; mobiilitekniikka; mobile technology; internet; internet; viihde; entertainment; kuluttajakäyttäytyminen; consumer behaviour|
» hse_ethesis_14477.pdf size:2 MB (1237067)
|Key terms:||Mobile services, mobile internet, TAM, technology acceptance model, UTAUT, UTAUT2, technology adoption, mobile application, entertainment|
Today consumers are requiring more and more services that can be used regardless of time and location; the high adoption of next-generation mobile handsets is creating huge opportunities for new and innovative mobile services. However, research of mobile communications has not been able to keep up with this development.
The purpose of this thesis is to answer the need to understand the factors that drive entertainment mobile services´ acceptance and adoption. Simultaneously the thesis further strengthens the Unified Theory of Acceptance and Use 2 (UTAUT2) by expanding it to cover the context of entertainment mobile applications.
Overview of the past research is provided and UTAUT2 is chosen as a framework for this thesis and is tested with survey data of 150 respondents. Factor analyses is used to test the 7 factors that influence consumer adoption of entertainment mobile applications: (1)performance expectancy, (2) effort expectancy, (3) social influence, (4) facilitating conditions, (5) hedonic motivation, (6) price value, and (7) habit. Also cluster analysis and cross-tabulation are provided.
The results indicate that altogether six factors (performance expectancy, effort expectancy, social influence, hedonic motivation, price value, and habit) affect adoption of an entertainment mobile application. Cluster analysis and cross-tabulation found five different user segments that are (1) average app users seeking enjoyment and usefulness, (2) unlikely users, (3) super users, (4) users under social pressure, and (5) paying users seeking usefulness.
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