Validation of the UTAUT Model: Re-Considering Non-Linear Relationships of Exogeneous Variables in Higher Education Technology Acceptance Research
Brandford Bervell 1 * , Irfan Naufal Umar 2
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1 College of Distance Education, University of Cape Coast, Cape Coast, GHANA2 Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, MALAYSIA* Corresponding Author


Over the years, The Unified Theory of Acceptance and Use of Technology (UTAUT) has served many researchers in unravelling technology acceptance intentions. What has become a chasm in the literature has been the seeming exclusion of non-linear relationships of UTAUT exogeneous variables (Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions) in model formation and the overall determination of construct predictive relationships. Secondly, there is a dearth in technology acceptance research in distance-based higher education settings. In an attempt to bridge these gaps, this study adopted the UTAUT model and utilized the Partial Least Squares approach to evaluate a combined linear and non-linear relationships based UTAUT model. The survey design was employed in which a questionnaire was used to obtain data from a sample of 267 respondents (tutors) from a distance-based higher education milieu with a country-wide distribution. Results obtained indicated that non-linear relationships exist between exogeneous factors to better explain constructs’ behaviour in the model. A new relationship between facilitating condition and social influence was also discovered. The study thus concluded that in technology acceptance research, there is the need to include non-linear relationships in the UTAUT model to augment the predictive effects and explanations of the constructs’ relationships. It further recommended a comparative analysis between a proposed comprehensive UTAUT model with non-linear relationships and moderators to the original UTAUT model for further empirical analysis. This is to compare results in terms of coefficient of determination (R2) and predictive relevance (Q2).


This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 13, Issue 10, October 2017, 6471-6490

Publication date: 29 Sep 2017

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