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Cited by 115Technology Adoption and User BehaviourEducational Technology and E-LearningOnline and Blended Learning

Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic

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Osei, Hannah Vivian, Kwateng, Kwame Owusu, Boateng, Kofi Agyenim·2022·Education and Information Technologies
DOI 10.1007/s10639-022-11047-yPMC PMC9019292
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Abstract

The adoption of e-learning in response to COVID-19 is to ensure the continuous development of human capital through alternative means. Nevertheless, the success of e-learning systems depends much on the attitude of the users. This study developed and empirically tested a model to predict antecedents of students' actual usage of e-learning during the COVID-19 period. A synthesis of UTAUT 2, Self Determination Theory and Core Self-Evaluation Theory were employed to examine the behaviour of students using a sample of 1024. PLS-SEM was used to analysed the hypothesised paths in the model. The results revealed that (1) Personality is positively related to behavioural intention (2) Actual usage is positively influenced by motivational factors (3) Behavioural Intention positively mediates the relationship between motivational factors and actual use (4) motivational factors positively mediate the relationship between UTAUT 2 constructs and behavioural intention. The results will guide stakeholders in education, especially e-learning system designers to incorporate personality and motivational factors in the designing of e-learning systems in order to increase the acceptability of the system by students. This study is among the first few attempts to incorporate personality, motivation and UTAUT2 to examine e-learning users' behaviour, especially in Sub-Saharan Africa during the COVID-19 pandemic. This work presents a contemporary perspective of e-learning users' behaviour during the COVID-19 pandemic.

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  • Perceived skill development has been identified as a critical factor in technology adoption, particularly in the Tunisian context.

    “In this study, we extend the UTAUT2 model by introducing the variable of perceived skill development, which has been identified as a critical factor in technology adoption, particularly in the Tunisian context.”

    From Unraveling the Determinants of Platform Economy Adoption in Developing Countries: An Extended Application of the UTAUT2 Model with a Privacy Calculus Perspective (2023) · cites this paper as [58]

    The Research model and hypotheses development section links perceived competence and students’ skills to e-learning adoption, which supports the general idea that skills-related perceptions matter. It does not identify perceived skill development as a distinct critical factor, and the Introduction places the study in Ghana, not Tunisia. Page numbers were not available in our copy. (LLM, conf=0.78)

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  1. Abay KA Blalock G Berhane G Locus of control and technology adoption in developing country agriculture: Evidence from Ethiopia Journal of Economic Behaviour & Organization 2017 143 98 115 10.1016/j.jebo.2017.09.012 · DOI
  2. Ain N Kaur K Waheed M The influence of learning value on learning management system use: An extension of UTAUT2 Information Development 2016 32 5 1306 1321 10.1177/0266666915597546 · DOI
  3. Aldholay AH Abdullah Z Ramayah T Isaac O Mutahar AM Online learning usage and performance among students within public universities in Yemen International Journal of Services and Standards 2018 12 2 163 179 10.1504/IJSS.2018.091842 · DOI
  4. Almaiah MA Alismaiel OA Examination of factors influencing the use of mobile learning system: An empirical study Education and Information Technologies 2019 24 1 885 909 10.1007/s10639-018-9810-7 · DOI
  5. Alrajawy, I., Daud, N. M., Isaac, O., and Mutahar, A. M. (2016). Factors influence intention to use mobile-learning within public universities students in Yemen. In The 7th international conference postgraduate education (ICPE7) (pp. 1050e1064). Malaysia: Shah Alam.
  6. Ameri A Khajouei R Ameri A Jahani Y Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model Education and Information Technologies 2020 25 1 419 435 10.1007/s10639-019-09965-5 · DOI
  7. Buchanan T Smith JL Using the Internet for psychological research: Personality testing on the World Wide Web British Journal of Psychology 1999 90 1 125 144 10.1348/000712699161189 10085550 · DOI
  8. Byrne BM Structural equation modeling with Mplus: Basic concepts, applications, and programming 2013 Routledge
  9. Chavoshi A Hamidi H Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran Telematics and Informatics 2019 38 133 165 10.1016/j.tele.2018.09.007 · DOI
  10. Chawinga WD Zozie PA Increasing access to higher education through open and distance learning: Empirical findings from Mzuzu University, Malawi International Review of Research in Open and Distributed Learning 2016 17 4 1 20 10.19173/irrodl.v17i4.2409 · DOI
  11. Chen G Evaluating the core: Critical assessment of core self-evaluations theory Journal of Organizational Behaviour 2012 33 2 153 160 10.1002/job.761 · DOI
  12. Choudhury S Pattnaik S Emerging themes in e-learning: A review from the stakeholders' perspective Computers & Education 2020 144 103657 10.1016/j.compedu.2019.103657 · DOI
  13. Deci EL Ryan RM The General Causality Ori-entations Scale: Self-determination in personality Journal of Research in Personality 1985 19 2 109 134 10.1016/0092-6566(85)90023-6 · DOI
  14. Deci, E. L., and Ryan, R. M. (2016). Optimizing students’ motivation in the era of testing and pressure: A self-determination theory perspective. In Building autonomous learners (pp. 9–29). Springer, Singapore.
  15. Deng L Tavares NJ From Moodle to Facebook: Exploring students' motivation and experiences in online communities Computers & Education 2013 68 167 176 10.1016/j.compedu.2013.04.028 · DOI
  16. Di Fabio, A., and Palazzeschi, L. (2020). Core Self‐Evaluation. The Wiley Encyclopedia of Personality and Individual Differences: Personality Processes and Individual Differences, 83–87.
  17. Fornell C Larcker DF Evaluating structural equation models with unobservable variables and measurement error Journal of Marketing Research 1981 18 1 39 50 10.1177/002224378101800104 · DOI · Paper page
  18. Gold AH Malhotra A Segars AH Knowledge management: An organizational capabilities perspective Journal of Management Information Systems 2001 18 1 185 214 10.1080/07421222.2001.11045669 · DOI · Paper page
  19. Hagger MS Chatzisarantis NL Integrating the theory of planned behaviour and self-determination theory in health behaviour: A meta-analysis British Journal of Health Psychology 2009 14 2 275 302 10.1348/135910708X373959 18926008 · DOI
  20. He, D. and Lu, Y. (2007), “Consumers perceptions and acceptances towards mobile advertising: an empirical study in China”, International Conference on Wireless Communications, Networking and Mobile Computing, WiCom, pp. 3775–3778.
  21. Henseler J Ringle CM Sinkovics RR The use of partial least squares path modeling in international marketing 2009 Emerald Group Publishing Limited
  22. Hew J-J Lee V-H Ooi K-B Wei J What catalyses mobile apps usage intention: An empirical analysis Industrial Management & Data Systems 2015 115 7 1291 1269 10.1108/IMDS-01-2015-0028 · DOI · Paper page
  23. Ho, C. K., Ke, W., Liu, H., and Chau, P. Y. (2020). Separate Versus Joint Evaluation: The Roles of Evaluation Mode and Construal Level In Technology Adoption. MIS Quarterly, 44(2).
  24. Hossain MA Dwivedi YK Chan C Standing C Olanrewaju A-S Sharing political content in online social media: A planned and unplanned behaviour approach Information Systems Frontiers 2018 20 3 485 501 10.1007/s10796-017-9820-9 · DOI
  25. Hsiao CH Chang J Tang KY Exploring the influential factors in continuance usage of mobile social apps: Satisfaction, habit, and customer value perspectives Telematics and Informatics 2016 33 2 342 355 10.1016/j.tele.2015.08.014 · DOI
  26. Johnson RE Rosen CC Levy PE Getting to the core of core self-evaluation: A review and recommendations Journal of Organizational Behaviour: The International Journal of Industrial, Occupational and Organizational Psychology and Behaviour 2008 29 3 391 413 10.1002/job.514 · DOI
  27. Judge TA Erez A Bono JE Thoresen CJ The core self-evaluations scale: Development of a measure Personnel Psychology 2003 56 2 303 331 10.1111/j.1744-6570.2003.tb00152.x · DOI · Paper page
  28. Judge TA Locke EA Durham CC Kluger AN Dispositional effects on job and life satisfaction: The role of core evaluations Journal of Applied Psychology 1998 83 1 17 10.1037/0021-9010.83.1.17 9494439 · DOI
  29. Khan IU Hameed Z Yu Y Islam T Sheikh Z Khan SU Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory Telematics and Informatics 2018 35 4 964 978 10.1016/j.tele.2017.09.009 · DOI
  30. Kizgin H Jamal A Dey BL Rana NP The impact of social media on consumers’ acculturation and purchase intentions Information Systems Frontiers 2018 20 3 503 514 10.1007/s10796-017-9817-4 · DOI
  31. Lawson-Body, A., Willoughby, L., Lawson-Body, L., and Tamandja, E. M. (2018). Students’ acceptance of E-books: An application of UTAUT. Journal of Computer Information Systems.
  32. Lee Y Lee J Hwang Y Relating motivation to information and communication technology acceptance: Self-determination theory perspective Computers in Human Behavior 2015 51 418 428 10.1016/j.chb.2015.05.021 · DOI
  33. Lin C “Analysis of the e-learning innovation process in higher education”, doctoral dissertation 2010 University of Nottingham
  34. Lin HF An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust International Journal of Information Management 2011 31 3 252 260 10.1016/j.ijinfomgt.2010.07.006 · DOI · Paper page
  35. Lin JCC Online stickiness: Its antecedents and effect on purchasing intention Behaviour & Information Technology 2007 26 6 507 516 10.1080/01449290600740843 · DOI
  36. Macedo IM Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2 Computers in Human Behavior 2017 75 935 948 10.1016/j.chb.2017.06.013 · DOI · Paper page
  37. Marjanovic Z Greenglass ER Coffey S The relevance of psychosocial variables and working conditions in predicting nurses’ coping strategies during the SARS crisis: An online questionnaire survey International Journal of Nursing Studies 2007 44 6 991 998 10.1016/j.ijnurstu.2006.02.012 16618485 PMC7094220 · DOI · Paper page
  38. Martins R Oliveira T Thomas MA An empirical analysis to assess the determinants of SaaS diffusion in firms Computers in Human Behavior 2016 62 19 33 10.1016/j.chb.2016.03.049 · DOI
  39. McAuley E Duncan T Tammen VV Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: A confirmatory factor analysis Research quarterly for exercise and sport 1989 60 1 48 58 10.1080/02701367.1989.10607413 2489825 · DOI · Paper page
  40. Mohammadi H Investigating users’ perspectives on e-learning: An integration of TAM and IS success model Computers in Human Behavior 2015 45 359 374 10.1016/j.chb.2014.07.044 · DOI · Paper page
  41. Ngai EW Poon J Chan Y Empirical examination of the adoption of WebCT using TAM Computers and Education 2007 48 2 250 267 10.1016/j.compedu.2004.11.007 · DOI · Paper page
  42. Nikou SA Economides AA Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance Computers in Human Behaviour 2017 68 83 95 10.1016/j.chb.2016.11.020 · DOI
  43. Preacher KJ Hayes AF Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models Behavior Research Methods 2008 40 3 879 891 10.3758/BRM.40.3.879 18697684 · DOI · Paper page
  44. Rai A Editor’s Comments: The COVID-19 Pandemic: Building Resilience with IS Research Management Information Systems Quarterly 2020 44 2 iii vii
  45. Shukor, N. B. A., Tasir, Z., & van der Meijden, H. A. T. (2015). An examination of online learning effectiveness using data mining. Procedia—Social and Behavioural Sciences, 172(1):555–562, 10.1016/j.sbspro.2015.01.402 · DOI
  46. Skinner E Edge K Deci EL Ryan RM Self-determination, coping, and development Handbook of self-determination research 2002 University of Rochester Press 297 337
  47. Slade EL Williams MD Dwivedi YK Devising a research model to examine adoption of mobile payments: An extension of UTAUT2 The Marketing Review 2014 14 3 310 335 10.1362/146934714X14024779062036 · DOI
  48. Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2020). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers,1–19,. 10.1007/s10796-020-10007-6 · DOI
  49. Tarhini A Mohammed AB Maqableh M Modeling Factors Affecting Student's Usage Behaviour of E-Learning Systems in Lebanon International Journal of Business and Management 2016 11 2 299
  50. Tennakoon KUS Da Silveira GJ Taras DG Drivers of context-specific ICT use across work and nonwork domains: A boundary theory perspective Information and Organization 2013 23 2 107 128 10.1016/j.infoandorg.2013.03.002 · DOI
  51. Venkatesh V Morris MG Davis GB Davis FD User acceptance of information technology: Toward a unified view MIS Quarterly 2003 27 3 425 478 10.2307/30036540 · DOI · Paper page
  52. Venkatesh V Zhang X Unified theory of acceptance and use of technology: US vs China Journal of Global Information Technology Management 2010 13 1 5 27 10.1080/1097198X.2010.10856507 · DOI
  53. Venkatesh V Thong JY Xu X Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology Management Information System Quarterly 2012 36 1 157 178 10.2307/41410412 · DOI · Paper page
  54. Voorhees CM Brady MK Calantone R Ramirez E Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies Journal of the Academy of Marketing Science 2016 44 1 119 134 10.1007/s11747-015-0455-4 · DOI · Paper page
  55. Wang HI Yang HL The role of personality traits in UTAUT model under online stocking Contemporary Management Research 2005 1 1 69 82 10.7903/cmr.73 · DOI
  56. Weerakkody V Irani Z Kapoor K Sivarajah U Dwivedi YK Open data and its usability: An empirical view from the Citizen’s perspective Information Systems Frontiers 2017 19 2 285 300 10.1007/s10796-016-9679-1 · DOI
  57. Wood, D.R., 2016. The impact of students’ perceived relatedness and competence upon their motivated engagement with learning activities: a self-determination theory perspective (Doctoral dissertation, University of Birmingham).
  58. Yoo Y Henfridsson O Lyytinen K Research commentary—the new organizing logic of digital innovation: An agenda for information systems research Information Systems Research 2010 21 4 724 735 10.1287/isre.1100.0322 · DOI · Paper page
  59. Zengin B Arikan A Dogan D Opinions of English major students about their departments' websites Contemporary Educational Technology 2011 2 4 294 307 10.30935/cedtech/6060 · DOI
  60. Zhou T Lu Y Wang B Integrating TTF and UTAUT to explain mobile banking user adoption Computers in Human Behaviour 2010 26 4 760 767 10.1016/j.chb.2010.01.013 · DOI · Paper page

Cited by

Sorted by citations

Authors
Osei, H. V., Kwateng, K. O., & Boateng, K. A.
Affiliations
Kwame Nkrumah University of Science and Technology (GH)
Corresponding author
Osei, Hannah Vivian
ORCID
Venue
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Published
2022-04-20
Volume
27
Issue
8
Pages
10705-10730
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
DOI
10.1007/s10639-022-11047-y
PMC
PMC9019292
OpenAlex
W4224280616
Cited by
115
References
9
Citations by year
2026: 19 · 2025: 29 · 2024: 32 · 2023: 29 · 2022: 6
FWCI
20.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Education and Information Technologies (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
  • Social Sciences › Education › Online and Blended Learning
Keywords
e-learning adoption, COVID-19 pandemic, UTAUT2, personality traits, motivation, self-determination theory, behavioral intention, actual use, student behavior, e-learning system design, sub-Saharan Africa

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