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Exploring barriers affecting eLearning usage intentions: an NLP-based multi-method approach

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  • Arghya Ray
  • Pradip Kumar Bala
  • Yogesh K. Dwivedi

Abstract

With online-learning becoming the new mode of learning, providers need to understand the barriers that learners face. The objective of this study is to utilise a multi-method approach to examine the barriers that affect learner’s intention to use e-Learning services. The multi-method approach consists of qualitative semi-structured interviews of 8 participants, topic-modelling on 3227 reviews from Coursera dataset and 463 responses from an online survey for quantitative analysis. The interviews revealed themes like ‘rigid-course-structure’, ‘complexity’, ‘quality-of-facilitator’, and ‘value-addition’. The topic-modelling approach extracted themes like, ‘technique-of-teaching’, ‘language-of-speaker’, ‘course-content’, ‘privacy’, ‘payment-issues’, etc. The empirical study revealed that value [course-content (‘course-content’, ‘value-addition’) and facilitator-issues (‘quality-of-facilitator’, ‘handling-of-queries’)], tradition [trust (‘privacy concerns’, ‘authenticity’, ‘reliability’)] and risk [payment issues (‘payment-failures’, ‘refund issues’)] barriers have a notable negative impact on usage-intention. The originality of this works lies in the fact that it explores payment-failure, facilitator-quality, and course-value affecting the acceptance of e-Learning services from the innovation-resistance-theory stance utilising data from various sources (qualitative data from interviews and online reviews and quantitative survey-based data). This work has also discussed different limitations in this study and scope for future research.

Suggested Citation

  • Arghya Ray & Pradip Kumar Bala & Yogesh K. Dwivedi, 2022. "Exploring barriers affecting eLearning usage intentions: an NLP-based multi-method approach," Behaviour and Information Technology, Taylor & Francis Journals, vol. 41(5), pages 1002-1018, April.
    • Handle: RePEc:taf:tbitxx:v:41:y:2022:i:5:p:1002-1018
    DOI: 10.1080/0144929X.2020.1849403
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    Cited by:

    1. Amit Shankar & Abhishek Behl & Vijay Pereira & Meena Chavan & Francesco Chirico, 2024. "Exploring enablers and inhibitors of AI‐enabled drones for manufacturing process audits: A mixed‐method approach," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 3749-3768, July.

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