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008			191112t20192019my a|||fram|| 000 0 eng d
020			_aTHE0008426(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFSKKP .A44 2019 r Thesis
100	1		_aAl-Emran, Mostafa Nadhir Hassan, _eauthor.
245	1	0	_aExtending the technology acceptance model with knowledge management factors to examine the acceptance of mobile learning / _cMostafa Nadhir Hassan Al-Emran
264		1	_aKuantan, Pahang : _bUMP, _c2019
264		4	_c© 2019
300			_axvi, 221 pages : _billustrations ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Computer Systems and Software Engineering
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2019
504			_aIncludes bibliographical references
520	3		_aIn today’s technological era, Mobile learning (M-learning) has become an essential tool that enables the students to access the learning materials on anytime anywhere settings. Determining the factors that affect the acceptance of M-learning is still one of the ongoing and critical issues by Information System (IS) scholars. The Technology Acceptance Model (TAM) has witnessed a lot of modifications and enhancements, which in turn contribute to the identification of the factors that affect the M-learning acceptance. Extending the TAM with other factors is still an open door for IS scholars to further examine the M-learning acceptance. Additionally, Knowledge Management (KM) is regarded as an essential component for developing M-learning systems. Besides, it is crucial for enhancing the students’ learning abilities that KM factors should be incorporated in M-learning systems. Research shows that KM factors (knowledge acquisition, knowledge sharing, knowledge application, and knowledge protection) have a significant effect on the adoption and success of many ISs. However, research has overlooked the impact of KM factors on M-learning acceptance. In line with this issue, the research objectives of this study are threefold. First, to analyze the students’ perceptions towards the integration of KM factors in M-learning systems through a preliminary study. Our research problem was motivated by the analysis of the preliminary study results, in which 93% of the students indicated that they would use the M-learning system in their studies if KM factors would be taken into consideration. Second, to develop a new model by extending the TAM with the KM factors as external variables. In that, it is suggested that the two main constructs of TAM (i.e., perceived usefulness and perceived ease of use) are affected by the four KM factors. Besides, the behavioral intention to use is suggested to be influenced by the two main constructs of TAM, whereas the behavioral intention itself is assumed to affect the actual system use. Third, to validate the proposed model through the development of M-learning application and the use of statistical analyses methods. This study employs the Partial Least Squares-Structural Equation Modeling (PLS-SEM) to validate the developed model. Data were collected through a questionnaire survey from 735 IT undergraduate students in two different universities in two different countries, namely Universiti Malaysia Pahang (UMP) in Malaysia and Al Buraimi University College (BUC) in Oman. The selection of these two samples is attributed to the intention to validate the developed model in a cross-cultural setting. The results suggest that knowledge acquisition, application, and protection have a positive effect on perceived ease of use and perceived usefulness of M-learning systems in both samples. However, knowledge sharing was found to be partially supported in both samples. Furthermore, perceived usefulness and perceived ease of use were found to be significant determinants of the behavioral intention to use M-learning systems. More interesting, the developed model explains a substantial variance (50%) in the actual use of M-learning systems in both samples, which clearly shows that the developed structural model is sound and valid, and hence, it could provide a plentiful explanation of the actual use of M-learning systems. The results of this study contribute to the existing literature by validating and extending the TAM with the KM factors in two different contexts (i.e., UMP and BUC) and provide various implications to the theory, research, and practice.
610	2	0	_aFaculty of Computer Systems and Software Engineering _xDissertations
650		0	_aUniversities and colleges _xDisertations
650		0	_aTheses
942			_2lcc _cTHESIS