IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p204-d1307440.html
   My bibliography  Save this article

Exploring the Acceptance and User Satisfaction of AI-Driven e-Learning Platforms (Blackboard, Moodle, Edmodo, Coursera and edX): An Integrated Technology Model

Author

Listed:
  • Raneem Rashad Saqr

    (Management Information System Department, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    The Management of Digital Transformation and Innovation Systems in Organization Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Sabah Abdullah Al-Somali

    (Management Information System Department, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    The Management of Digital Transformation and Innovation Systems in Organization Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mohammad Y. Sarhan

    (Management Information System Department, Faculty of Economics and Administration, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

As e-learning platforms gain traction globally, understanding students’ perceptions and intentions towards these platforms is paramount, especially within the context of Saudi universities, where e-learning is rapidly emerging as a transformative educational tool for sustainable development. This study examined the influence of different AI-based social learning networks, personal learning portfolios, and personal learning environments on Saudi university students’ perceived usefulness and ease of use regarding AI-driven platforms (Blackboard, Moodle, Edmodo, Coursera and edX). Furthermore, the study explored the direct effects of these perceptions on students’ satisfaction and intentions to use e-learning. The study also delved into the moderating effects of individual characteristics like readiness for self-directed e-learning, self-efficacy, and personal innovativeness on students’ e-learning intentions. A cross-sectional design was employed, collecting self-reported data from a strong sample of Saudi university students using stratified random sampling. The study targeted 500 students from different universities in Saudi Arabia. Results underscored the significant influence of AI-based social learning networks, personal learning portfolios, and personal learning environments on perceived usefulness and ease of use. Both perceived usefulness and ease of use also significantly and positively influenced satisfaction, influencing students’ attitudes toward e-learning but not their intention to use it. Student characteristics, especially self-efficacy, showed notable impacts on e-learning intentions. However, their interaction with satisfaction yielded insignificant effects on intentions.

Suggested Citation

  • Raneem Rashad Saqr & Sabah Abdullah Al-Somali & Mohammad Y. Sarhan, 2023. "Exploring the Acceptance and User Satisfaction of AI-Driven e-Learning Platforms (Blackboard, Moodle, Edmodo, Coursera and edX): An Integrated Technology Model," Sustainability, MDPI, vol. 16(1), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:204-:d:1307440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/204/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Natarajan, Thamaraiselvan & Balasubramanian, Senthil Arasu & Kasilingam, Dharun Lingam, 2017. "Understanding the intention to use mobile shopping applications and its influence on price sensitivity," Journal of Retailing and Consumer Services, Elsevier, vol. 37(C), pages 8-22.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sohn, Stefanie, 2017. "A contextual perspective on consumers' perceived usefulness: The case of mobile online shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 22-33.
    2. Zhang, Wenqing & Liu, Liangliang, 2022. "Exploring non-users' intention to adopt ride-sharing services: Taking into account increased risks due to the COVID-19 pandemic among other factors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 180-195.
    3. Canova, Luciano & Nicolini, Marcella, 2019. "Online price search across desktop and mobile devices: Evidence on cyberslacking and weather effects," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 32-39.
    4. Su-Chen(Cecilia) Lin & Mei-Chen Chuang & Chen-Yuan Huang & Chia-En Liu, 2023. "Nursing Staff’s Behavior Intention to Use Mobile Technology: An Exploratory Study Employing the UTAUT 2 Model," SAGE Open, , vol. 13(4), pages 21582440231, November.
    5. Christino, Juliana Maria Magalhães & Silva, Thaís Santos & Cardozo, Erico Aurélio Abreu & de Pádua Carrieri, Alexandre & de Paiva Nunes, Patricia, 2019. "Understanding affiliation to cashback programs: An emerging technique in an emerging country," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 78-86.
    6. Matemba, Elizabeth D. & Li, Guoxin, 2018. "Consumers' willingness to adopt and use WeChat wallet: An empirical study in South Africa," Technology in Society, Elsevier, vol. 53(C), pages 55-68.
    7. Bailey, Ainsworth Anthony & Bonifield, Carolyn M. & Arias, Alejandro, 2018. "Social media use by young Latin American consumers: An exploration," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 10-19.
    8. Singh, Nidhi & Sinha, Neena, 2020. "How perceived trust mediates merchant's intention to use a mobile wallet technology," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    9. Kala Kamdjoug, Jean Robert & Wamba-Taguimdje, Serge-Lopez & Wamba, Samuel Fosso & Kake, Ingrid Bive'e, 2021. "Determining factors and impacts of the intention to adopt mobile banking app in Cameroon: Case of SARA by afriland First Bank," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    10. Pillai, Rajasshrie & Sivathanu, Brijesh & Dwivedi, Yogesh K., 2020. "Shopping intention at AI-powered automated retail stores (AIPARS)," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    11. Lova Rajaobelina & Isabelle Brun & Sandrine Prom Tep & Manon Arcand, 2018. "Towards a better understanding of mobile banking: the impact of customer experience on trust and commitment," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 23(3), pages 141-152, December.
    12. Hongmin Ahn, 2023. "Unrevealing Voice Search Behaviors: Technology Acceptance Model Meets Anthropomorphism in Understanding Consumer Psychology in the U.S. Market," Sustainability, MDPI, vol. 15(23), pages 1-12, November.
    13. Kareem M. Selem & Muhammad Haroon Shoukat & Syed Asim Shah & Marianny Jessica Brito Silva, 2023. "The dual effect of digital communication reinforcement drivers on purchase intention in the social commerce environment," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    14. Federico E. Contiggiani & Fernando Delbianco & Fernando Tohm'e, 2021. "A Graph-based Similarity Function for CBDT: Acquiring and Using New Information," Papers 2104.14268, arXiv.org.
    15. Singh, Sonika & Swait, Joffre, 2017. "Channels for search and purchase: Does mobile Internet matter?," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 123-134.
    16. Hoffmann, Stefan & Lasarov, Wassili & Reimers, Hanna, 2022. "Carbon footprint tracking apps. What drives consumers' adoption intention?," Technology in Society, Elsevier, vol. 69(C).
    17. Luceri, Beatrice & (Tammo) Bijmolt, T.H.A. & Bellini, Silvia & Aiolfi, Simone, 2022. "What drives consumers to shop on mobile devices? Insights from a Meta-Analysis," Journal of Retailing, Elsevier, vol. 98(1), pages 178-196.
    18. Jacek Woźniak & Alexandra Zbuchea, 2021. "Give or buy contexts and internet experience as a factor differentiating of readiness to provide different types of personal data in m-commerce," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(4), pages 53-67, June.
    19. Sohn, Stefanie & Groß, Michael, 2020. "Understanding the inhibitors to consumer mobile purchasing intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    20. Pop, Rebeka-Anna & Hlédik, Erika & Dabija, Dan-Cristian, 2023. "Predicting consumers' purchase intention through fast fashion mobile apps: The mediating role of attitude and the moderating role of COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:204-:d:1307440. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.