Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.

User acceptance of information technology: toward a unified view

The present research develops and tests a theoretical extension of the Technology Acceptance Model (TAM) that explains perceived usefulness and usage intentions in terms of social influence and cognitive instrumental processes. The extended model, referred to as TAM2, was tested using longitudinal data collected regarding four different systems at four organizations ( N = 156), two involving voluntary usage and two involving mandatory usage. Model constructs were measured at three points in time at each organization: preimplementation, one month postimplementation, and three months postimplementation. The extended model was strongly supported for all four organizations at all three points of measurement, accounting for 40%--60% of the variance in usefulness perceptions and 34%--52% of the variance in usage intentions. Both social influence processes (subjective norm, voluntariness, and image) and cognitive instrumental processes (job relevance, output quality, result demonstrability, and perceived ease of use) significantly influenced user acceptance. These findings advance theory and contribute to the foundation for future research aimed at improving our understanding of user adoption behavior.

https://www.jstor.org/stable/pdfplus/2634758.pdf

A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies

Ten years ago, we presented the DeLone and McLean Information Systems (IS) Success Model as a framework and model for measuring the complex-dependent variable in IS research. In this paper, we discuss many of the important IS success research contributions of the last decade, focusing especially on research efforts that apply, validate, challenge, and propose enhancements to our original model. Based on our evaluation of those contributions, we propose minor refinements to the model and propose an updated DeLone and McLean IS Success Model. We discuss the utility of the updated model for measuring e-commerce system success. Finally, we make a series of recommendations regarding current and future measurement of IS success.

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The DeLone and McLean Model of Information Systems Success: A Ten-Year Update

The Theory of Planned Behaviour (TPB) has received considerable attention in the literature. The present study is a quantitative integration and review of that research. From a database of 185 independent studies published up to the end of 1997, the TPB accounted for 27% and 39% of the variance in behaviour and intention, respectively. The perceived behavioural control (PBC) construct accounted for significant amounts of variance in intention and behaviour, independent of theory of reasoned action variables. When behaviour measures were self-reports, the TPB accounted for 11% more of the variance in behaviour than when behaviour measures were objective or observed (R2s = .31 and .21, respectively). Attitude, subjective norm and PBC account for significantly more of the variance in individuals' desires than intentions or self-predictions, but intentions and self-predictions were better predictors of behaviour. The subjective norm construct is generally found to be a weak predictor of intentions. This is partly attributable to a combination of poor measurement and the need for expansion of the normative component. The discussion focuses on ways in which current TPB research can be taken forward in the light of the present review.

/pdf/efficacy-of-the-theory-of-planned-behaviour-a-meta-analytic-32gm69pazu.pdf

Efficacy of the Theory of Planned Behaviour: a meta-analytic review.

The Technology Acceptance Model and two variations of the Theory of Planned Behavior were compared to assess which model best helps to understand usage of information technology. The models were compared using student data collected from 786 potential users of a computer resource center. Behavior data was based on monitoring 3,780 visits to the resource center over a 12-week period. Weighted least squares estimation revealed that all three models performed well in terms of fit and were roughly equivalent in terms of their ability to explain behavior. Decomposing the belief structures in the Theory of Planned Behavior provided a moderate increase in the explanation of behavioral intention. Overall, the results indicate that the decomposed Theory of Planned Behavior provides a fuller understanding of behavioral intention by focusing on the factors that are likely to influence systems use through the application of both design and implementation strategies.

Understanding Information Technology Usage: A Test of Competing Models

A variety of models that incorporate attitudinal, social, and control factor have been advanced to explain IT usage, of which the Technology Acceptance Model (TAM) is the most well know. One goal of such models is to develop diagnostic tools to predict information systems acceptance and facilitate design changes before users have experience with a system. However, empirical tests of these models have generally focused on either systems that were already in use by the study participants, or systems that the participants were familiar with, such as word processing packages and spreadsheets. Given this, it is unclear (1) whether models such as TAM are predictive of behavior for inexperienced users and, more importantly, (2) whether the determinants of IT usage are the same for experienced and inexperiened users of a system.

https://www.jstor.org/stable/pdfplus/249633.pdf

Assessing IT usage: the role of prior experience

Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions

Delays in service are becoming increasingly common; yet their effects on service evaluations are relatively unknown. The author presents a model of the wait experience, which assesses the effects o...

Waiting for service: The relationship between delays and evaluations of service

Although research into the determinants of service provider switching has grown in recent years, the focus has been predominantly on transactional, not relational, variables. In this research, the authors address the role of consumer commitment on consumers’ intentions to switch. Drawing from the organizational behavior literature, they build on previous service switching research by developing a switching model that includes a three-component conceptualization of customer commitment. Structural equation modeling is used to test the model based on data from a survey of 356 auto repair service customers. The authors’ results support the notion that customer commitment affects intentions to switch service providers and that the psychological states underlying that commitment may differ. As such, future marketing research should consider these different forms of commitment in understanding customer retention. The implications of this model for theory and practice are discussed.

Shirley Taylor

Papers

Understanding Information Technology Usage: A Test of Competing Models

Assessing IT usage: the role of prior experience

Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions

Waiting for service: The relationship between delays and evaluations of service

A Three-Component Model of Customer to Service Providers