Factors for and against resistance to smart services: role of consumer lifestyle and ecosystem related variables

Abstract

Purpose Consumers are increasingly connected to, and make use of, a multitude of technologies in their daily lives. The exponential growth in the use of Internet of Things (IoT)-based services is ushering in a new era of e-services, in which the service experience is becoming autonomous (intelligence), devices are intercommunicating (connectivity) and consumers can access the service anytime, anywhere and using any device (ubiquity). However, a number of challenges have arisen. The purpose of this paper is to investigate factors that reduce consumer resistance to smart services (factors against resistance) and factors that promote this resistance (factors for resistance), by means of a dual-factor approach. Design/methodology/approach To test this theoretical model, the authors developed a Web-based survey and used structural equation modeling. Findings Results show that consumer-lifestyle factors (individual “mobiquity” and self-image congruence) reduce consumer resistance to smart services (factors against resistance). Conversely, innovation-related factors (perceived security, perceived complexity) and ecosystem-related factors (perceived government surveillance and general skepticism toward IoT) promote consumer resistance to smart services (factors for resistance). In addition, general skepticism toward IoT has a significant positive effect on perceived complexity, perceived security risk and perceived government surveillance. Originality/value This research investigates consumer resistance to smart services using a dual-factor perspective (Cenfetelli, 2004; Claudy et al., 2015): factors reducing resistance versus factors promoting resistance. This paper provides evidence for the importance of consumer lifestyle-related factors, innovation-related factors and ecosystem-related factors in explaining consumer resistance to smart services. This work enriches previous studies of consumer resistance to innovation (Ram and Sheth, 1989; Ram, 1987) by studying original variables (individual mobiquity, technological innovativeness, government surveillance).