EVALUACIÓN DE LA PROPUESTA

The theory of planned behaviour (TPB), as developed by Ajzen (1991) was modified from TRA, and assumes that behavioural action is a function of two determinants: individual attitudes towards behaviour and subjective norms within the social environment. In contrast, the decomposed theory of planned behaviour (DTPB) introduced by Taylor and Todd (1995), suggests that behavioural intention is a primary direct determinant of behaviour, although the three main constructs still exist.

These theories are not efficient to study technology diffusion in hospitals, as the core construct of behavioural intention may not offer meaningful value. Individual attitudes towards RFID may reflect acceptance, and the subjective norm within the hospitals is to perhaps not disrupt the social environment. Behavioural intention cannot be considered primary determinant for technology adoption in such environments.

2.2.2.1Social Cognitive Theory

Social cognitive theory (SCT) (Bandura 1986) suggests that human functioning should be viewed as the product of a dynamic interplay amongst personal, behavioural and environmental influences. This theory emphasises that cognition plays a critical role in people’s capacity to construct reality, self regulate, encode information and perform behaviours (Kripanont & Tatnall 2009).

To a certain extent, SCT offers value to study technology acceptance or diffusion in hospitals. However, levels of cognition within the environment may be disparate. This theory can be used where a group of clinicians or nurses (equals in their roles) are being studied for accepting RFID. In a hospital, where different cognition levels interact, this theory may not be useful for studying technology diffusion.

2.2.2.2Technology Acceptance Model

Equally, the technology acceptance model (TAM) (Davis 1986: 7) strives to evaluate user acceptance of computer-based information systems. Davis (1986) posits that people adopt a technology because they perceive it as beneficial and also due to perceived ease of use. Specifically, it is based on the notion of user perceptions. There are many augmented models of TAM, including combining TAM and TPB, TAM2 and the unified theory of acceptance and use of technology (UTAUT), where Venkatesh and Davis (2003) provide a refined view of how the determinants of intention and behaviour evolve over time.

In an attempt to refine and extend TAM, Chitungo and Munongo (2013) investigated factors that influenced mobile banking adoption in rural communities of Zimbabwe. The proposed TAM3 model (Venkatesh & Bala 2013) aims to advance TAM with a focus on mobile interventions. To a significant extent, these theories can be used to study user acceptance of RFID in general. However, when acceptance is tested on multiple users with disparate yet significant roles, these models do not offer significant value to already existing literature.

Equally, Murthi and Mani (2013) argue that while technology adoption (acceptance) is well appreciated in the literature, the phenomenon of technology rejection is not yet understood. According to them, rejection does not imply in its totality, but varies in terms of its kind and/or intensiveness. Their exhaustive literature study focused on users (individuals), initially discussing the technology-society nexus and providing a preliminary technology-user interface model, leading to a detailed discussion of the determinants of technology rejection.

All these approaches are classified as ‘essentialist approaches’ (Tatnall 2011), in that some essential capacity or ‘essence’ is largely responsible for determining the rate of adoption (Tatnall 2011: 28). However, the difficulty is that people often see different essential attributes in specific technologies or human entities. What may be perceived as the best use of a technology by one user may not be the same for another. In this case, the use of RFID by an orderly (or patient care assistant) may be different to a nurse or a clinician. In hospitals, where all actors need to work together, and also accept the technology intervention, the above essentialist approaches fall short of interpretive analysis in the context.

As derived from the literature, the gap in RFID technology acceptance in hospitals is technological, economic and social. The social factors are yet to be explored in detail, particularly in the Australian context. An understanding of the way in which a technology is embedded in its social context helps clarify the interaction between that technology and society (Cerezo & Verdadero 2003). Murthi and Mani (2013) argue that society and technology intermingle and co-evolve, and culture and social structures shape the design and use of technology; in turn, technology influences cultural and social experience (Murthi & Mani 2013:1). In the process of merging a technology into daily routines, an individual offers the technology a physical, symbolic and social place (Silverstone, Hirsch & Morley 1992), thus assigning a position for that technology in agreement with a set of existing rules, routines and processes (Selwyn 2003). Therefore, Murthi and Mani (2013) suggest that understanding the complex relationship between user and technology is important, from social and technical viewpoints. The authors identify technical complexity, technology fatigue, level of flexibility, altering user-base, switching cost and loss aversion as factors for technology rejection.

Earlier in this chapter (Part I), I highlighted that factors (or actors) in hospitals are disparate regarding their roles and perceived advantages of a technology; yet, they need to work together within the health sector. It is evident that technologies have to be ‘translated’ into the context, rather than just being adopted in the form offered.

In the 1980s, Bruno Latour developed ANT, which argues that humans and artefacts (or things) have equal effect in the process of translating an innovation. The concept of human and non-human actors thus emerged from ANT and in turn, inspired the theory of innovation translation (Callon 1986).

The concept of ‘translation’ proposed by Callon et al., (1983) and Callon (1986) describes it as the strategies that an actor implements, to identify other actors and to arrange them in relation to each other. Unlike diffusion of innovations concepts, this translation model acknowledges that the original idea and the innovation are not bestowed with sovereign power. Rather, the innovation only translates if it interests the actors (Tatnall 2011). Hence, a model that studies innovation translation is necessary.

Another issue with technology adoption is that not all innovations are adapted in the form in which they are proposed, nor without changing some essential attributes (Tatnall 2011). Innovation translation (Latour 1986) uses a model of technological innovation that involves the concept of heterogeneity in a world with hybrid entities (Latour 1993), containing both human and non-human elements. It also notes that innovations are neither adopted entirely, nor in their original form. Rather, they are translated into a form more appropriate for users (Tatnall 2011).

There are still other approaches studying the creation of an innovation itself. For example, Mumford (2000) argues that organisations should consider multiple interventions that take into account the individual, the group, the organisation, and the strategic environment when selecting interventions intended to enhance creativity. Equally, Checkland (2000) offers soft systems methodology. This is a seven-step approach to uncovering problems with the diffusion of innovation and for taking action to rectify problems.

Against these ‘essentialist approaches’ to adopting technology, innovation translation approaches argue for identifying key players (actors or factors) and the processes they are engaged in, to better understand the translation process. In the next section, I develop a conceptual framework that will include key socio-technical facts, and stages (or moments) in the innovation translation process.