User attitude to online shopping can be explained by consumer behaviour theories, and in particular the influential Theory of Reasoned Action (Fishbein and Ajzen 1975), the Theory of Planned Behaviour (Ajzen 1991) and Innovation Diffusion Theory (Rogers 1962). These became significant in understanding and theorising about technology adoption and its uses, as demonstrated in the Technology Acceptance Model (Davis 1989), the Unified Theory of Acceptance and Use of Technology (Venkatesh et al. 2003) and the Unified Theory of Acceptance and Use of Technology 2 (Venkatesh et al. 2012).
The Theory of Reasoned Action underpins many later theories commonly found in this field (Fishbein and Ajzen 1975). Its central factor is intention, the motivation to perform a behaviour, for example how hard individuals try to perform a given behaviour. The core constructs of attitude toward behaviour and subjective norm determine behavioural intention. Individuals’ positive or negative evaluations about performing the behaviour with technology will determine their attitude towards it. The subjective norm is the individual’s perception of significant other people’s or individual’s approval or disapproval about performing the behaviour. If the individual believes these people think they should perform the behaviour, then they are more likely to do so. In the context of social media, online groups and tribes, the theory can help explain why individuals use mobile communications and their behaviour in adopting new technologies, from the behaviour of friends and other social groups. The Theory of Planned Behaviour (TPB) extends the Theory of Reasoned Action with the addition of perceived behavioural control, which concerns the individual’s perception of the ease or difficulty in performing a behaviour, for example learning to use a new technology. It can also vary in different situations and activities. In the TPB perceived behavioural control along with behavioural intention can be used to predict behavioural achievement (Ajzen 1991). With the aim of explaining behaviour, the theory deals with antecedents of behaviour of which the most salient are behavioural, normative (underlying beliefs) and control beliefs. These three beliefs influence behavioural intention, which affects actual behaviour and can determine the extent to which an individual perceives the performance of the behaviour to be easy or difficult. Such perceived external and internal factors act as constraints on behaviour where an underlying foundation of beliefs determines attitudes, subjective norms and perceived behavioural control. The acceptance of information technologies amongst consumers can be supplemented by an understanding of their diffusion, through the Diffusion of Innovations Theory (Rogers 1962). It provides a consumer-centric explanation of when and how innovations, for example new ideas or technologies, are adopted. Rogers (1962, p. 5) defined diffusion as “the process by which an innovation is communicated through certain channels over time among the members of a social system”. An innovation can be an idea, object or practice, and its characteristics explain different rates of adoption by members of a social system:
The theory explains communication as part of a two-way process between individuals with time as the third element, from an individual’s first knowledge of the innovation, to decision-making and adoption. Finally the social system involves interrelationships in “joint problem solving to accomplish a common goal” (Rogers 1962, p. 23). Thus innovations are not adopted outside a social system of leaders, networks and structures, but are defined by and expedited by it. Adopter categories classify members of the social system on the basis of how early they adopt new ideas, and are defined as innovators, early adopters, early majority, late majority and laggards. The expansion of online retailing through a multiplicity of technologically-enabled channels may disrupt existing business models since technological changes in disruptive innovations present a different set of performance attributes that are either valued by existing customers or that attract new customers. The theory of Disruptive Innovation (Christensen and Tedlow 2000) provides a framework for explaining how technology is often the source of disruptive innovation in service industries, as evidenced by the rise of computers, mobile phones, iKiosks and other electronic devices, which have replaced traditional means of interaction between firms and customers (Padgett and Mulvey 2007). It follows that the expansion of e-channels and e-channel touchpoints have the power to disrupt by fragmenting prevailing modes of online retailing and thus demanding a change of perspective by retailers (Wagner et al. 2015). The Theory of Reasoned Action (Fishbein and Ajzen 1975) subsequently informed the development of a model to more specifically explain the use of technologies: the Technology Acceptance Model (Davis 1989; Davis et al. 1989). The Technology Acceptance Model (TAM) was originally applied to workplace tasks, rather than consumer behaviour, and in its simplest form shows that a user’s perceptions of usefulness and ease of use of a technology determines their attitudes and hence their likelihood to adopt the technology as a way of life (Davis 1989). The user’s intention to adopt is determined by their attitude toward using the technology, focusing on general technology perceptions, the direct and indirect adoption drivers. The direct adoption drivers are perceived usefulness (PU) and perceived ease of use (PEOU) whilst the indirect adoption drivers refer to external variables (Li et al. 2012). The model has been used widely to predict individuals’ behavioural intention to buy and use a particular piece of technology (Ko et al. 2009), and has been applied to the acceptance of online retailing (O’Cass and Fenech 2003) and m-commerce (Wu and Wang 2005). TAM has been continually developmental goals and new factors added to reflect the increasing adoption of the internet and continuing evolution of online retailing. Thus, factors such as trust, enjoyment, intrinsic and extrinsic motivation, and human and social change process variables have been applied in more recent iterations of the model, namely the TAM2 (Venkatesh and Davis 2000) and the TAM3 (Venkatesh and Bala 2008). These new factors reflect the combination of hedonic as well as utilitarian motivations, which influence consumer behaviour.
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