Modelling for Mobile: Developing the mUTAUT Model: An Abstract

M-commerce has been defined in a variety of ways over the years and essentially encompasses all online-based transactional activities conducted through wireless handheld mobile devices (e.g., Hillman and Neustaedter 2017). As such, it has been considered an umbrella term for more specific types of commerce activities; Marriott et al. (2017) suggest that m-commerce can be divided into three main subcategories: mobile banking (m-banking), mobile payments (m-payments), and mobile shopping (m-shopping). M-shopping is defined as the online searching, browsing, comparing, and purchasing of goods and services by consumers through wireless handheld mobile devices, in particular, smartphones and tablets (Marriott et al. 2017).

Three fundamental observations arise within the m-shopping literature. First, the role of m-shopping within the global marketplace is apparent with the surge of literature since 2015 (Marriott et al. 2017) and academic response to emerging retailing trends. Second, the incorporation of risk and trust within m-shopping literature remains in its infancy; although discussions surrounding risk and trust have increased in the recent years, more research is required to examine their specific roles within m-shopping adoption. Finally, most m-shopping literature adopt a technology-based acceptance model as a theoretical grounding; although this is commonplace and has given rise to an interesting array of findings, it becomes questionable as to validity of using a technology-based model to predict a service-based activity. Therefore, this research aims to develop understanding into consumer’s m-shopping adoption intention through incorporating risk and trust into a contemporary theoretically grounded technology adoption model to explore their future roles within this area. The theoretical foundation for this study was UTAUT2 (Venkatesh et al. 2012) due to its contemporary nature and suitability to a voluntary or service setting.

In the form of online and face-to-face questionnaires, data were gathered from 435 consumers who are UK residents over the age of 18 and who have had at least some experience in the m-shopping process. Covariance-based Structural Equation Modelling, using AMOS software, was used to test the hypothesized relationships to identity the influence of utilitarian, hedonic, trust, and risk factors on consumer m-shopping adoption intention.