Exploring the impact of affordances on consumers' adoption

The rapid advancement of technology has resulted in the proliferation of a wide array of digital products and services, which differ from traditional artefacts by incorporating digital components. It becomes then crucial to consider the implications of such evolution of the nature of an artefact on its associated affordances. The term "affordance," introduced in the 1970s by Gibson, has generated ambiguity and confusion in academic discourse over the years. This term denotes the potential actions that an artefact enables or facilitates for its users. Examining how users accept digital products and services is essential for achieving success in a competitive market. Acceptance models are theoretical frameworks designed to understand and explain the key factors influencing the adoption and use of new technologies. Over the years, various models have been proposed, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), the Value-based Adoption Model (VAM), and the Innovation Diffusion Theory (IDT). These models aid researchers in better comprehending users’ needs and behaviours, leading to the successful implementation and widespread acceptance and usage of new technologies. This thesis begins with a literature review of the two key concepts of "artefact" and "affordance." It then examines technology diffusion and adoption theories to identify the influential factors in the adoption decision and explores how affordances impact the acceptance of new technologies. Based on previous studies, five major affordances are proposed to significantly influence users' acceptance: cognitive affordance, physical affordance, functional affordance, and sensory affordance. Drawing from the Technology Acceptance Model (TAM) and prior research, a comprehensive affordance model is then developed, along with a questionnaire based on the case study of the digital evolution of vacuum cleaners into robotic ones. This research can serve as a foundation for future studies aimed at improving smart products by enhancing the users’ experience and gaining a deeper understanding of customers’ needs.