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Article type: Research Article
Authors: Qendraj, Daniela Halidinia; * | Xhafaj, Evgjenib | Thanasi, Teutaa
Affiliations: [a] Department of Mathematics, Faculty of Information Technology, University “Aleksander Moisiu” Durres, Albania | [b] Department of Mathematics, Faculty of Mathematics and Physics Engineering, Polytechnic University of Tirana, Albania
Correspondence: [*] Corresponding author. Daniela Halidini Qendraj, Department of Mathematics, Faculty of Information Technology, University “Aleksander Moisiu” Durres, 1003 Albania. E-mail: daniela_qendraj@hotmail.com.
Abstract: Learning Management Systems is a challenge of implementing information technology (IT) in the higher educational field. This paper introduces a framework for assessing an LMS by integrating partial last squares-structural equation modeling (PLS-SEM) and fuzzy analytic hierarchic process with Z-numbers (Fuzzy Z-AHP). The objective is to propose the combination of the two approaches via results of PLS-SEM for the construction of the decision matrix for Fuzzy Z-AHP. The PLS-SEM method was used firstly to evaluate the conceptual model Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and extracting the significant connections between the independent constructs and the behavioral intention to use an LMS. Secondly is adapted the Fuzzy Z-AHP method to rank the independent significant constructs initializing from the PLS-SEM results. Using a questionnaire survey, the study sampled 530 users of LMS in 4 Albanian universities as respondents. To the best of our knowledge this paper is among the first that combines PLS-SEM with Fuzzy Z-AHP for the UTAUT2 model while using an LMS. This combination showed that the most important construct of UTAUT2 affecting behavioral intention to use an LMS was habit. This study assist the decision makers and policy makers to provide the means to obtain better managerial conclusions for the improvement and progress of an LMS.
Keywords: Google classroom, UTAUT2, PLS-SEM, Fuzzy Z-AHP, behavioral intention
DOI: 10.3233/JIFS-212396
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 657-669, 2022
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