BIG FIVE PERSONALITY TRAITS, TECHNOLOGY ACCEPTANCE, AND ACADEMIC ENGAGEMENT IN ONLINE HIGHER EDUCATION: THE ROLE OF CONSCIENTIOUSNESS AND DIGITAL LITERACY

Emmanouil Choustoulakis, Aggeliki Strati, Ilias Baralis, Dimitris Nikoloudakis

Abstract


Online and blended delivery is now a permanent feature of higher education, yet how personality, technology acceptance, and digital competence jointly shape engagement remains under-tested with primary data. This paper reports an exploratory, cross-sectional survey (N = 95) of predominantly Greek undergraduates testing an integrated model linking the Big Five traits, the Technology Acceptance Model (TAM), digital literacy, and three-dimensional academic engagement (behavioural, cognitive, emotional), focused on Conscientiousness as a personality-level predictor and digital literacy as a moderator of the personality-to-technology-acceptance pathway. Personality was measured with a Greek-language Ten-Item Personality Inventory, technology acceptance with TAM/UTAUT items, digital literacy with DigComp-aligned items, and engagement with items grounded in Fredricks, Blumenfeld and Paris's framework. Conscientiousness robustly and directly predicted all three engagement dimensions, most strongly behavioural engagement (b = 0.41, p < .001), independent of technology-acceptance beliefs. Perceived ease of use strongly predicted perceived usefulness (b = 0.67–0.68, p < .001), consistent with TAM sequencing. However, Big Five traits did not significantly predict PU/PEOU, technology-acceptance beliefs did not significantly predict engagement, and the hypothesised digital-literacy moderation was unsupported. An unanticipated positive Agreeableness–cognitive/emotional engagement association emerged. These patterns are discussed alongside instrument-reliability limitations and a key sampling feature — 70.5% of respondents had no prior online-course experience — with implications for testing this model in larger, more online-experienced samples.

Keywords


Big Five personality traits, Technology Acceptance Model, conscientiousness, digital literacy, academic engagement

Full Text:

PDF

References


Aiken LS, West SG, 1991. Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA, USA: Sage. Retrieved from https://books.google.ro/books/about/Multiple_Regression.html?id=LcWLUyXcmnkC&redir_esc=y

Bandura A, 1997. Self-Efficacy: The Exercise of Control. New York, USA: W.H. Freeman. Retrieved from https://psycnet.apa.org/record/1997-08589-000

Coenen J, Borghans L, Diris R, 2021. Personality traits, preferences and educational choices: a focus on STEM. Journal of Economic Psychology 84: 102361. https://doi.org/10.1016/j.joep.2021.102361

Costa PT, McCrae RR, 1992. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) Professional Manual. Odessa, FL, USA: Psychological Assessment Resources. Retrieved from https://www.researchgate.net/publication/240133762_Neo_PI-R_professional_manual

Cronbach LJ, 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16(3): 297-334. Retrieved from https://doi.org/10.1007/BF02310555

Davis FD, 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3): 319-340. https://doi.org/10.2307/249008

Devaraj S, Easley RF, Crant JM, 2008. How does personality matter? Relating the five-factor model to technology acceptance and use. Information Systems Research 19(1): 93-105. https://doi.org/10.1287/isre.1070.0153

Eckhaus E, Davidovitch N, 2021. Driving value creation in the new economy following the COVID-19 crisis. Data-mining students' satisfaction from online teaching in the virtual academic climate. Electronic Journal of e-Learning 19(5): 452-468. https://files.eric.ed.gov/fulltext/EJ1328929.pdf

Fredricks JA, Blumenfeld PC, Paris AH, 2004. School engagement: Potential of the concept, state of the evidence. Review of Educational Research 74(1): 59-109. https://doi.org/10.3102/00346543074001059

Gosling SD, Rentfrow PJ, Swann WB, 2003. A very brief measure of the Big-Five personality domains. Journal of Research in Personality 37(6): 504-528. https://doi.org/10.1016/S0092-6566(03)00046-1

Hair JF, Black WC, Babin BJ, Anderson RE, 2019. Multivariate Data Analysis, 8th edn. Andover, UK: Cengage. Retrieved from https://books.google.ro/books/about/Multivariate_Data_Analysis.html?id=0R9ZswEACAAJ&redir_esc=y

Hayes AF, 2018. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, 2nd edn. New York, USA: Guilford Press. Retrieved from https://books.google.ro/books/about/Introduction_to_Mediation_Moderation_and.html?id=MglQEAAAQBAJ&redir_esc=y

John OP, Srivastava S, 1999. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In: Pervin LA, John OP (eds.) Handbook of Personality: Theory and Research, 2nd edn. New York, USA: Guilford Press, pp 102-138. Retrieved from https://psycnet.apa.org/record/1999-04371-004

Khechine H, Lakhal S, Ndjambou P, 2016. A meta-analysis of the UTAUT model: Eleven years later. Canadian Journal of Administrative Sciences 33(2): 138-152. Retrieved from https://doi.org/10.1002/cjas.1381

Khechine H, Lakhal S, Pascot D, Bytha A, 2014. UTAUT model for blended learning: The role of gender and age in the intention to use webinars. Interdisciplinary Journal of E-Learning and Learning Objects 10: 33-52. https://doi.org/10.28945/1994

Lakhal S, Khechine H, 2017. Relating personality (Big Five) to the core constructs of the Unified Theory of Acceptance and Use of Technology. Journal of Computers in Education 4(3): 251-282. https://doi.org/10.1007/s40692-017-0086-5

Lakhal S, Frenette E, Sevigny S, Khechine H, 2012. Relationship between choice of a business major type (thing-oriented versus person-oriented) and Big Five personality traits. International Journal of Management Education 10(2): 88-100. Retrieved from https://doi.org/10.1016/j.ijme.2012.03.003

Mustafa S, Qiao Y, Yan X, Anwar A, Hao T, Rana S, 2022. Digital students' satisfaction with and intention to use online teaching modes, role of Big Five personality traits. Frontiers in Psychology 13: 956281. Retrieved from https://doi.org/10.3389/fpsyg.2022.956281?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle

Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP, 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology 88(5): 879-903. Retrieved from https://doi.org/10.1037/0021-9010.88.5.879

Soto CJ, John OP, 2017. The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology 113(1): 117-143. https://doi.org/10.1037/pspp0000096

Svendsen GB, Johnsen JAK, Almas-Sorensen L, Vitterso J, 2013. Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour & Information Technology 32(4): 323-334. https://doi.org/10.1080/0144929X.2011.553740

Tice DM, Baumeister RF, 1997. Longitudinal study of procrastination, performance, stress, and health: The costs and benefits of dawdling. Psychological Science 8(6): 454-458. https://doi.org/10.1111/j.1467-9280.1997.tb00460.x

Venkatesh V, Morris MG, Davis GB, Davis FD, 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3): 425-478. https://doi.org/10.2307/30036540

Vuorikari R, Kluzer S, Punie Y, 2022. DigComp 2.2: The Digital Competence Framework for Citizens. Luxembourg: Publications Office of the European Union. Retrieved from https://publications.jrc.ec.europa.eu/repository/handle/JRC128415




DOI: http://dx.doi.org/10.46827/ejoe.v11i2.7012

Refbacks

  • There are currently no refbacks.


Copyright © 2016-2026. European Journal of Open Education and E-learning Studies (ISSN 2501-9120) is a registered trademark of Open Access Publishing Group. All rights reserved.

This journal is a serial publication uniquely identified by an International Standard Serial Number (ISSN) serial number certificate issued by Romanian National Library (Biblioteca Nationala a Romaniei). All the research works are uniquely identified by a CrossRef DOI digital object identifier supplied by indexing and repository platforms.

All the research works published on this journal are meeting the Open Access Publishing requirements and can be freely accessed, shared, modified, distributed and used in educational, commercial and non-commercial purposes under a Creative Commons Attribution 4.0 International License (CC BY 4.0).