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. Chopdar, Prasanta Kr & Paul, Justin & Prodanova, Jana, 2022. "Mobile shoppers’ response to Covid-19 phobia, pessimism and smartphone addiction: Does social influence matter?," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Garima Malik & A. Sajeevan Rao, 2019. "Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy," Information Technology & Tourism, Springer, vol. 21(4), pages 461-482, December.
    3. 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.
    4. Rodríguez-López, María Eugenia & Higueras-Castillo, Elena & Rojas-Lamorena, Álvaro J. & Alcántara-Pilar, Juan Miguel, 2024. "The future of TV-shopping: predicting user purchase intention through an extended technology acceptance model," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. 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.
    6. Gabriel JIPA, 2018. "Mobile Applications Buying Opinions Exploration using Topic Modeling," Expert Journal of Economics, Sprint Investify, vol. 6(2), pages 44-55.
    7. 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.
    8. 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.
    9. Iranmanesh, Mohammad & Min, Connie Low & Senali, Madugoda Gunaratnege & Nikbin, Davoud & Foroughi, Behzad, 2022. "Determinants of switching intention from web-based stores to retail apps: Habit as a moderator," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    10. 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.
    11. Sha Zhang & Xiting Wu & Yonggui Wang & Peiqi Jiang, 2024. "Does time of day affect consumers’ price sensitivity?," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    12. 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.
    13. Thipnapa Huansuriya & Kris Ariyabuddhiphongs, 2023. "Predicting Residential Photovoltaic Adoption Intention of Potential Prosumers in Thailand: A Theory of Planned Behavior Model," Energies, MDPI, vol. 16(17), pages 1-20, August.
    14. Acquila-Natale, Emiliano & Iglesias-Pradas, Santiago, 2021. "A matter of value? Predicting channel preference and multichannel behaviors in retail," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    15. Hasan Al-Banna & Izra Berakon, 2024. "From bank to P2P lending: switching intention of SME’s: evidence from Indonesia," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(2), pages 568-581, June.
    16. Brusch, Ines & Rappel, Nina, 2020. "Exploring the acceptance of instant shopping – An empirical analysis of the determinants of user intention," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    17. 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.
    18. Kasilingam, Dharun Lingam, 2020. "Understanding the attitude and intention to use smartphone chatbots for shopping," Technology in Society, Elsevier, vol. 62(C).
    19. 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).
    20. 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).

    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.