How can topic-modelling of user-reviews reshape market surveys? Exploring factors influencing usage intention of e-learning services through a novel multi-method approach

Arghya Ray, Pradip Kumar Bala and Rashmi Jain

International Journal of Business Information Systems, 2022, vol. 40, issue 2, 259-284

Abstract: Online user-generated-content is not only a critical performance parameter for service-providers but also serve as a vital information source for prospective customers. For understanding the factors influencing the adoption or continuance of an e-service, traditional approaches required a qualitative or quantitative-based analysis. In this study, we propose a novel approach to generate and analyse path model in real-time by combining the qualitative, quantitative and natural language processing (NLP)-based approaches. We undertook an emic approach using semi-structured interview schedule (ten participants) and an etic approach using topic modelling on extant literature (3,570 articles) for exploring factors influencing motives behind use of e-learning services. We tested the path-model using traditional quantitative-based (542 respondents) and the proposed NLP-based approaches (3,227 online reviews) through structural equation modelling (SEM). Results of this study revealed content gratification as the most important predictor of usage intention. This study concludes with the implications, limitations and future research directions.

Keywords: e-learning; consumer perspectives; latent Dirichlet allocation; LDA; multi-method approach; NLP-based approach; path analysis; qualitative analysis; quantitative analysis; sentiment analysis; text mining. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=123646 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:40:y:2022:i:2:p:259-284

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().