Application of SEM and fsQCA to predict m-health adoption in the BoP market

Abstract

Purpose

Drawing on the adapted unified theory of acceptance and use of technology (UTAUT2) framework in the bottom of pyramid (BoP) context, this paper examines the number of causal recipes that foster m-health adoption in a developing country (Bangladesh). This paper aims to propose an extended UTAUT2 model along with identifying the necessary and sufficient factors affecting the m-health adoption intention in the BoP market.

Study design/methodology/approach

The research model was empirically tested, combining two approaches: structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Data were collected from 221 housemaids and female security guards who earn around US$6 per day.

Findings

The SEM results revealed that while performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions, perceived cost (PC) and technology anxiety (TA) significantly influence the behavioural intention of BoP markets, hedonic motivation is the non-significant predictor. The fsQCA revealed that the two necessary conditions, PC and SI, can be combined with TA to increase the possibility of the success of m-health adoption in the BoP market.

Practical implications

For practitioners concerned with fostering the m-health adoption intention in BoP markets, the present study, which points out equifinality, recommends integrating the PC and SI in several combinations with PE, EE and TA.

Originality/value

To the best of the authors’ knowledge, no previous studies using the UTAUT2 theory examined the m-health services in the BoP market. This study contributes empirical data to the predominantly theoretical literature by offering a deeper understanding of the inclusion of TA and PC in several combinations with other UTUAT2 factors as predictors for explaining the m-health adoption intention of BoP markets.

Citation

Hussain, M., Begum, Q., Rahman, M.S. and Imran, A. (2023), "Application of SEM and fsQCA to predict m-health adoption in the BoP market", Digital Policy, Regulation and Governance, Vol. 25 No. 6, pp. 545-567. https://doi.org/10.1108/DPRG-02-2023-0024