Investigating the relationships between MOOC consumers' perceived quality, emotional experiences, and intention to recommend: an NLP-based approach

Abstract

Purpose

The paper aims to explore and examine the factors that influence the post-consumption behavioral intentions of education consumers with the help of online reviews from a Massive Open Online Course (MOOC) platform in the knowledge payment context.

Design/methodology/approach

The paper adopted a novel mixed-method approach based on natural language processing (NLP) techniques. Variables were identified using topic modeling drawing upon 14,585 online reviews from a global commercial MOOC platform (Udemy.com). The relationships among identified factors, such as perceived quality dimensions, consumption emotions, and intention to recommend, were then tested from a cognition-affect-behavior (CAB) perspective using partial least squares structural equation modeling (PLS-SEM).

Findings

Results indicate that course content quality, instructor quality, and platform quality are strong predictors of consumers' emotions and intention to recommend. Interestingly, course content quality displays a positive effect on invoking negative emotions in the MOOC context. Additionally, positive emotions mediate the relationships between three perceived qualities and the intention to recommend.

Originality/value

Limited research has been conducted regarding MOOC consumers' post-consumption intentions in the knowledge payment context. Findings of this study address the limited literature on MOOC qualities and consumer post-consumption behaviors, which contribute to a comprehensive understanding of MOOC learners' experiences at a meso-level for future paid-MOOC creators.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2021-0482/

Citation

Ma, L., Pahlevan Sharif, S., Ray, A. and Khong, K.W. (2023), "Investigating the relationships between MOOC consumers' perceived quality, emotional experiences, and intention to recommend: an NLP-based approach", Online Information Review, Vol. 47 No. 3, pp. 582-603. https://doi.org/10.1108/OIR-09-2021-0482