The Internet of Things (IoT) is expected to be the next phase of the Internet revolution and to transform consumers’ service experience. It nevertheless raises challenges on innovation in services. Based on Ram and Sheth's () theoretical framework, this work attempts to provide a better understanding of the barriers that lead to consumers’ resistance to smart services as an innovation. To this end, our research adopts an integrative framework that combines functional barriers, psychological barriers, and individual barriers to explain consumer resistance to smart services. Structural equation modeling was used to test this theoretical framework. Our research enriches the existing literature by (1) adapting Ram and Sheth's () theoretical framework to the evolution of digital technologies (technological vulnerability barriers), (2) taking into account the ideological aspect of resistance (ideological barriers), and (3) considering dispositional variables (individual barriers). Moreover, it highlights the key role of skepticism toward IoT devices as a mediator between technological vulnerability barriers and individual barriers on the one hand and consumer resistance to smart services on the other.

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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).

The aim of this study is to provide a better understanding of the factors that explain consumer resistance to smart services from a privacy perspective. To this end, an exploratory qualitative study and a quantitative study were carried out. 653 French consumers answered an online questionnaire regarding smart services in the banking sector. Structural equation modeling was used to test the conceptual model. The findings show that information privacy, the unauthorized secondary use of personal information and perceived intrusion have an impact on consumer resistance to smart services. Moreover, our research highlights the major role of the "Big Brother effect" as an antecedent to these various privacy concerns.

Cette recherche se focalise sur la résistance aux objets connectés exprimée par le consommateur sur le web. Elle apporte un éclairage sur les freins à l’utilisation des objets connectés et aux facteurs susceptibles d’entraver leur développement. L’étude netnographique réalisée a permis d’identifier trois types de facteurs de résistance : des facteurs liés au système, des facteurs liés aux objets connectés et des facteurs liés au consommateur. En outre, elle a mis en évidence deux formes d’expression de la résistance : le discours « complotiste » et le discours « rationnel ».