Understanding determinants of cloud computing adoption using an integrated TAM-TOE model

Purpose – The purpose of this paper is to integrate TAM model and TOE framework for cloud computing adoption at organizational level. Design/methodology/approach – A conceptual framework was developed using technological and organizational variables of TOE framework as external variables of TAM mode...

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Veröffentlicht in:Journal of enterprise information management 2015-02, Vol.28 (1), p.107-130
Hauptverfasser: Gangwar, Hemlata, Date, Hema, Ramaswamy, R
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Sprache:eng
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Zusammenfassung:Purpose – The purpose of this paper is to integrate TAM model and TOE framework for cloud computing adoption at organizational level. Design/methodology/approach – A conceptual framework was developed using technological and organizational variables of TOE framework as external variables of TAM model while environmental variables were proposed to have direct impact on cloud computing adoption. A questionnaire was used to collect the data from 280 companies in IT, manufacturing and finance sectors in India. The data were analyzed using exploratory and confirmatory factor analyses. Further, structural equation modeling was used to test the proposed model. Findings – The study identified relative advantage, compatibility, complexity, organizational readiness, top management commitment, and training and education as important variables for affecting cloud computing adoption using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Also, competitive pressure and trading partner support were found directly affecting cloud computing adoption intentions. The model explained 62 percent of cloud computing adoption. Practical implications – The model can be used as a guideline to ensure a positive outcome of the cloud computing adoption in organizations. It also provides relevant recommendations to achieve conducive implementation environment for cloud computing adoption. Originality/value – This study integrates two of the information technology adoption models to improve predictive power of resulting model.
ISSN:1741-0398
1758-7409
DOI:10.1108/JEIM-08-2013-0065