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

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.