The Impact of Technology Insertion on Organisations

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HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 5.3.1.2.2 <strong>Technology</strong>, Usefulness and Predicted Behaviour <strong>The</strong> <strong>Technology</strong> Acceptance Model (TAM) describes how users come to accept and use a technology. <strong>The</strong> model suggests that when users are presented with a new technology, a number <strong>of</strong> factors influence their decisions about how and when they will use it. TAM is an adaptation <strong>of</strong> Ajzen and Fishbein’s [81] theory <strong>of</strong> reasoned action (TRA), which proposes that perceived usefulness and perceived ease <strong>of</strong> use determine an individual's intention to use a system [82]. TRA is an important model from social psychology that focuses on the drivers <strong>of</strong> consciously intended behaviours. According to TRA, behavioural intention and attitude drives an individual’s performance. TAM replaces many <strong>of</strong> the TRA attitude measures with the technology acceptance measures, ease <strong>of</strong> use, and usefulness. See Figure 2 below. Figure 2: Venkatesh et al’s <strong>Technology</strong> Acceptance Model (TAM) Some researchers have found TAM to account for around 40% <strong>of</strong> user acceptance [83]. TAM2 extended the original model to explain perceived usefulness and usage intentions in terms <strong>of</strong> social influence and cognitive instrumental processes. <strong>The</strong> extended model was tested in both voluntary and mandatory settings. <strong>The</strong> results strongly supported TAM2 and the authors were able to explain 60% <strong>of</strong> their adoption model using this updated version <strong>of</strong> TAM [84]. TRA and TAM assume that when someone forms an intention to act, that they will be free to act without limitation. In reality however, constraints such as limited ability, time, environmental or organisational factors, and unconscious habits limit people’s freedom to act [85]. Yet the existing research on TAM presents inconsistencies and <strong>of</strong>fers relatively low explanatory powers. Researchers have started to question the generalisability <strong>of</strong> TAM [83], [86], [87]. [88]. Moderating factors such as age, gender, experience, characteristics <strong>of</strong> technology [87], among other factors [89], [90] have been shown to account for the inconsistent relationships. In an attempt to integrate the main competing user acceptance models, Venkatesh et al. formulated the Unified <strong>The</strong>ory <strong>of</strong> Acceptance and Use <strong>of</strong> <strong>Technology</strong> (UTAUT). This model was found to outperform each <strong>of</strong> the individual models [84]. An alternative diffusion <strong>of</strong> innovation theory, the ‘Unified <strong>The</strong>ory <strong>of</strong> Acceptance and Use <strong>of</strong> <strong>Technology</strong>’ (UTAUT), aims to explain user intentions to use technology as well as 23
HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 explain subsequent usage behaviour. <strong>The</strong> theory holds that four key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) are direct determinants <strong>of</strong> usage intention and behaviour [82]. Gender, age, experience, and ‘voluntariness’ <strong>of</strong> use are posited to mediate the impact <strong>of</strong> the four key constructs on usage intention and behaviour. See Figure 3, below [84]. Figure 3: Unified <strong>The</strong>ory <strong>of</strong> Acceptance and Use <strong>of</strong> <strong>Technology</strong> (UTAUT). Despite the explanatory appeal <strong>of</strong> these models, there are a number <strong>of</strong> limitations <strong>of</strong> DoI <strong>The</strong>ory. Clarke [91] argues that ‘at its best (DoI) is a descriptive tool, (it is) less strong in its explanatory power, and less useful still in predicting outcomes, and providing guidance as to how to accelerate the rate <strong>of</strong> adoption’. <strong>The</strong>re is also some doubt about the extent to which DoI theory can give rise to readily refutable hypotheses. On top <strong>of</strong> this, diffusion <strong>of</strong> innovation theory has been criticised for the fact that ‘many <strong>of</strong> its elements may be specific to the culture in which it was derived (such as North America in the 60s)’ and that it is ‘less relevant in, for example, East Asian and African countries’ [91]. 5.3.1.3 Organisational Norms and Cultures Organisational culture and climate are found to impact attitudes toward innovation adoption both for technology [77] and mode <strong>of</strong> practice [92]. Organisational culture and climate are related to organisational processes, service quality, client outcomes, and worker attitudes, perceptions, and behaviours [93], [94], [95]. In the US, some authors [96], [97] have argued that the military has so far ‘failed to match the rhetoric <strong>of</strong> transformation with action’. While each (US military service) claims to embrace new ways <strong>of</strong> war, ‘none has yet demonstrated a sustained commitment to fundamental change’. Nothing shows this more clearly than military acquisition budgets. Service funding is still dominated by incremental improvements to traditional systems; radically new technology, doctrine, and organisations have received smaller resources. <strong>The</strong> authors argue that ‘none <strong>of</strong> this should be surprising. Large bureaucracies such as the US armed forces are designed to minimise uncertainty, including that brought 24
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