Venkatesh et al., 2003

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. JSTOR. https://doi.org/10/gc8zn2

This article is distributed under the terms of the Creative Commons At tribution 4.0 International License (http:/ / creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Extracted Annotations (2020-04-20, 7:33:39 a.m.)

Abstract Based on a critical review of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study first formalized an alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations. The revised theoretical model was then empirically examined using a combination of meta-analysis and structural equation modelling (MASEM) techniques. The meta-analysis was based on 1600 observations on 21 relationships coded from 162 prior studies on IS/ IT acceptance and use. The SEM analysis showed that attitude: was central to behavioural intentions and usage behaviours, partially mediated the effects of exogenous constructs on behavioural intentions, and had a direct influence on usage behaviours. A number of implications for theory and practice are derived based on the findings. (p. 719)

Keywords UTAUT Meta-analysis Structural equation . . . modelling MASEM Behavioural intention . Attitude Usage (p. 719)

In order to advance theory and identifying future research directions, we have attempted to critically review and refine the original UTAUT model. Specifically, we argue that the moderators specified in the original UTAUT model may not be applicable in all contexts, the path from facilitating conditions to behavioural intention missing in the original UTAUT model should be included, and individual characteristics such as attitude not theorized in the original UTAUT model should be introduced. We empirically examine our revised model using a combination of meta-analysis and structural equation modelling techniques. (p. 720)

2.2 Unified Theory of Acceptance and Use of Technology (UTAUT, Venkatesh et al. 2003) (p. 720)

One major difference between UTAUT and its precursors was that UTAUT proposed four moderators (i.e., gender, age, experience, and voluntariness) to further enhance the predictive power of the model. (p. 721)

While it has been tested and modified in various ways, studies that utilised UTAUT have illustrated (explicitly or implicitly) certain limitations (p. 721)

First, the moderators proposed in the original UTAUT model may be reconsidered. Prior studies have generally not applied the complete UTAUT model as found in Venkatesh et al. (2003). A similar observation was made by Venkatesh et al. (2012), who noted that most studies employed only a subset of the model and that moderators were typically dropped. (p. 721)

Second, the relationships proposed in the original UTAUT model may be reconsidered for completeness. In formulating the UTAUT model, Venkatesh et al. (2003) argued that one would expect facilitating conditions to predict behavioural intention only if effort expectancy was not included in the model. (p. 721)

Despite the evidence that these four constructs explain a significant proportion of variance in the adoption and usage behaviours, a key element missing from the UTAUT model is theBindividual^ engaging in the behaviour—i.e., individual characteristics that describe the dispositions of the users may be influential in explaining their behaviours. (p. 721)

Further, we include user attitude as a mediating construct in the basic UTAUT model. (p. 721)

Finally, the original UTAUT model may be reconsidered from the light of other constructs that may explain adoption and usage behaviours of individuals. The four exogenous constructs in the UTAUT model may be viewed as representing technology attributes (i.e., performance expectancy and effort expectancy) and contextual factors (i.e., facilitating conditions and social influence) even when they may be viewed as perceptions held by individuals regarding the technology and the context. (p. 721)

2.3 Proposed Model of IS/IT Acceptance and Use (p. 721)

Based on the foregoing, we first exclude the four moderators from the original UTAUT model and identify the remaining relationships in the original UTAUT model as the basic UTAUT model. We next include the relationship between facilitating conditions and behavioural intention to the basic UTAUT model. (p. 721)

performance expectations and is easy to use can influence the individual's attitude leading to intention. We position attitude as a mediator between performance expectancy and behavioural intention and between effort expectancy and behavioural intention. This is because the extent to which the IS/IT is useful and consistent with (p. 723)

3 Research methods (p. 724)
3.1 Meta-Analysis (p. 724)

Performance Expectancy (PE) Performance expectancy is defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance (Venkatesh et al. 2003). (p. 724)

Effort Expectancy (EE) Effort expectancy is defined as the degree of ease associated with the use of the system (Venkatesh et al. 2003). (p. 724)

Social Influence (SI) Social influence is defined as the degree to which an individual perceives that important others believe he or she should use the new system (Venkatesh et al. 2003). (p. 724)

Facilitating Conditions (FC) Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system (Venkatesh et al. 2003). (p. 724)

Attitude (AT) An individual's positive or negative feelings about performing the target behaviour (Davis et al. 1989; Fishbein and Ajzen 1975; Taylor and Todd 1995a, b). (p. 724)

Behavioural Intention (BI) Behavioural intention is defined as a measure of the strength of one's intention to perform a specific behaviour (Fishbein and Ajzen 1975). (p. 724)

3.2 Meta-Analytic Structural Equation Modelling (MASEM) (p. 725)
5 Discussion (p. 727)
5.1 Findings (p. 727)

We found that attitude played a central role in acceptance and use of IS/IT innovations. More specifically: a) attitude was also influenced by facilitating conditions and social influence, b) attitude had a direct effect on behavioural intention, which implies that attitude partially mediated the effects of performance expectancy, effort expectancy, facilitating conditions, and social influence, and c) by attitude exerted a direct influence on usage behaviour. These findings are crucial since they underscore the importance of explicitly modelling individual characteristics in theories of IS/IT acceptance and use. (p. 727)

However, we found that attitude may be influenced by facilitating conditions and social influence, which are the contextual factors in our model. This is perhaps not completely surprising—facilitating conditions such as training programs and help desks may be instrumental in enabling individuals to form positive attitudes about the technology (e.g., Chiu et al. 2012; Pynoo et al. 2007; Ravishankar 2008; Sahu and Gupta 2007; Sandeep and Ravishankar 2014) whereas individuals may also refine their attitudes based on information or stories shared by others who have already adopted the technology (e.g., Abubakre et al. 2015; Chiu et al. 2012; Pynoo et al. 2007; Sumak et al. 2010). (p. 728)

We had expected attitude to have a direct effect on behavioural intention and to partially mediate the effects of performance expectancy and effort expectancy on behavioural intention (Dwivedi et al. 2017; Rana et al. 2016). However, we found that attitude partially mediated the effects of facilitating conditions and social influence on behavioural intention as well. (p. 728)

However, explicit modelling of attitude significantly improves the explanatory power of the theoretical model—i.e., 38% to 45% without and with attitude respectively for behavioural intention. (p. 728)

Finally, we had expected behavioural intention to fully mediate the effect of attitude on usage behaviour but we found that attitude had a direct effect on usage behaviour as well. (p. 728)

5.3 Implications for Theory (p. 729)

In other words, moderators may not be universally applicable to all contexts and hence run the danger of being non-relevant in certain settings. Perhaps, this is one reason why a majority of the studies we included in our meta-analysis did not consider these moderators in their research models. (p. 729)

5.4 Implications for Practice (p. 729)

Our findings show that attitude played a central role in an individual's intention to use and usage of IS/IT innovations. (p. 729)

We found that the technology attributes (i.e., performance expectancy and effort expectancy) had direct effects on attitude and behavioural intention (Rana et al. 2017; Weerakkody et al. 2017). This implies that the individuals attribute considerable importance to the extent to which the technology in question may be useful and easy to use. (p. 729)

We also found that contextual factors (i.e., facilitating conditions and social influence) had direct effects on attitude and behavioural intention. (p. 730)

Hence, organizations should consider providing adequate infrastructural facilities and proper training to users so that they can be positively inclined to use new technologies. (p. 730)