Understanding the relationship of self-service technology quality with user adoption using SSTQUAL

Abstract

Purpose

The study aimed to examine the antecedents to self-service technology (SST) adoption behavior and the relationships between the constructs using empirical research.

Design/methodology/approach

Based on synthesis of the extant literature, a model was hypothesized, hypotheses were framed. Field data collected were analyzed using structural equation modeling.

Findings

Few interesting findings were noted in this research. First, SST service quality had a direct positive linkage with perceived value, but no linkage with e-satisfaction. Second, strong positive linkage existed between perceived value and e-satisfaction. Therefore, the connection between SST service quality and satisfaction was completely mediated by perceived value. Third, no relationship existed between perceived value and behavioral intentions, but a direct positive relationship existed between e-satisfaction and behavioral intentions. Thus, the relationship of perceived value with behavioral intentions was fully mediated by e-satisfaction. Fourth, no direct connection was found between SST service quality and behavioral intentions. Rather, the connection was fully mediated by perceived value and e-satisfaction. Fifth, direct positive association was found between behavioral intentions and actual adoption of SST.

Research limitations/implications

This empirical research was conducted primarily on the young population.

Practical implications

The study will benefit managers in making better decisions on how to make SST work successfully for their organizations.

Originality/value

First, this research further refined the SST adoption process of a customer, thus making a meaningful contribution to the literature on SST. Second, the research validated SSTQUAL scale in a different geographical setting.

Citation

Ghosh, M. (2021), "Understanding the relationship of self-service technology quality with user adoption using SSTQUAL", The TQM Journal, Vol. 33 No. 2, pp. 293-314. https://doi.org/10.1108/TQM-12-2019-0291