An Exploration of the Drivers of Non- Adoption Behavior

Abstract

While there has been a substantial amount of attention within the information systems research community towards understanding the phenomenon of adoption, much less is known about non-adoption. This study examines the factors surrounding the decision to not adopt a technology and whether certain factors exert differing effects on individuals in particular ways such that concurrent factors could be identified to develop a classification of the specific types of non-adoption behavior. Utilizing inhibitor theory and the symbolic adoption model as a foundational framework for the different types of non-adoption, we posit that different types of non-adoption exist which is demonstrated by determining the perceptions towards technology that coalesce around different types of non-adoption. We conducted a two-phase investigation into nonadoption with two goals in mind: 1) identify and explore specific factors of the IT that are associated with the rejection decision and are distinct from the adoption decision; and 2) determine the extent to which these factors (along with traditional enablers) differentiate between different types of non-adoption. The results from a discriminant function analysis (DFA) indicate the coalescence of specific perceptual variables according to the types of non-adoption behavior, specifically, the discriminatory power of differing perceptions of IT between trial rejecters, symbolic rejecters, trial accepters, symbolic adopters, and adopters. The implications for research and implications for practice are discussed.

Why College Students Prefer Typing Over Speech Input: The Dual Perspective

“…This result also indicates that users should have a single state of acceptance or rejection. This conclusion is similar to the concept of symbolic adoption proposed by Wolverton and Cenfetelli [102]. Symbolic adopters are users who accept the idea of the technology but have not considered using it.…”

Can Alexa serve customers better? AI-driven voice assistant service interactions

“…DFT, meanwhile, is valuable for capturing the impact of both enablers and inhibitors. Scholars have adopted the fairly well-established DFT in the context of social media (Sullivan and Koh, 2019), mobile wallets (Talwar et al , 2021) and consumers’ non-adoption of innovative technology products (Wolverton and Cenfetelli, 2019). The current study, thus, adopts the theoretical lens of DFT to examine the ways in which enablers and inhibitors influence consumers’ trust in VA as well as the association between consumers’ trust and their intentions to use VAs for service interactions for transactional purposes.…”

Individual Information Technology (IT) creativity: a conceptual and operational definition

“…Moreover, creativity has been linked to job performance (Gong et al, 2009;Wang and Netemeyer, 2004) and job satisfaction (Wang and Netemeyer, 2004;Shalley et al, 2000). These types of outcomes differ from traditional outcomes such as IT use and include outcomes such as routinization and infusion (Sundaram et al, 2007), non-adoption (Wolverton and Cenfetelli, 2020), and examining the impact of IITC on the various IT acceptance stages (Schwarz et al, 2014a, b). Thus, we posit that focusing on creativity provides an opportunity for future studies that examine impacts of IT that are different than those traditionally utilized in extant adoption work and other domains of research in IT.…”