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 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

Much previous research has established that perceived ease of use is an important factor influencing user acceptance and usage behavior of information technologies. However, very little research has been conducted to understand how that perception forms and changes over time. The current work presents and tests an anchoring and adjustment-based theoretical model of the determinants of system-specific perceived ease of use. The model proposes control (internal and external--conceptualized as computer self-efficacy and facilitating conditions, respectively), intrinsic motivation (conceptualized as computer playfulness), and emotion (conceptualized as computer anxiety) as anchors that determine early perceptions about the ease of use of a new system. With increasing experience, it is expected that system-specific perceived ease of use, while still anchored to the general beliefs regarding computers and computer use, will adjust to reflect objective usability, perceptions of external control specific to the new system environment, and system-specific perceived enjoyment. The proposed model was tested in three different organizations among 246 employees using three measurements taken over a three-month period. The proposed model was strongly supported at all points of measurement, and explained up to 60% of the variance in system-specific perceived ease of use, which is twice as much as our current understanding. Important theoretical and practical implications of these findings are discussed.

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Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model

This paper discusses the role of individuals' beliefs about their abilities to competently use computers (computer self-efficacy) in the determination of computer use. A survey of Canadian managers and professionals was conducted to develop and validate a measure of computer self-efficacy and to assess both its impacts and antecedents. Computer self- efficacy was found to exert a significant influence on individuals' expectations of the outcomes of using computers, their emotional reactions to computers (affect and anxiety), as well as their actual computer use. An individual's self-efficacy and outcome expecta- tions were found to be positively influenced by the encouragement of others in their work group, as well as others' use of computers. Thus, self-efficacy represents an important individual trait, which moderates organizational influences (such as encouragement and support) on an individual's decision to use computers. Understanding self-efficacy, then, is important to the successful implementation of systems in organizations. The existence of a reliable and valid measure of self-efficacy makes assessment possible and should have implications for organizational support, training, and implementation.

https://www.jstor.org/stable/pdfplus/249688.pdf

Computer self-efficacy: development of a measure and initial test

The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a significant percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory (cf. McKeen et al. 1994, Weill and Olson 1989). In our survey of such studies, the majority failed to either detect or provide an estimate of the effect size. In cases where effect sizes are estimated, the numbers are generally small. These results have led some researchers to question both the usefulness of contingency theory and the need to detect interaction effects (e.g., Weill and Olson 1989). This paper addresses this issue by providing a new latent variable modeling approach that can give more accurate estimates of interaction effects by accounting for the measurement error that attenuates the estimated relationships. The capacity of this approach at recovering true effects in comparison to summated regression is demonstrated in a Monte Carlo study that creates a simulated data set in which the underlying true effects are known. Analysis of a second, empirical data set is included to demonstrate the technique's use within IS theory. In this second analysis, substantial direct and interaction effects of enjoyment on electronic-mail adoption are shown to exist.

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A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study

A model, based on Bandura's Social Cognitive Theory, was developed to test the influence of computer self-efficacy, outcome expectations, affect, and anxiety on computer usage The model was tested using longitudinal data gathered from 394 end users over a one-year interval Significant relationships were found between computer self-efficacy and outcome expectations, and between self-efficacy and affect and anxiety and use Performance outcomes were found to influence affect and use, while affect was significantly related to use Overall, the findings provide strong confirmation that both self-efficacy and outcome expectations impact on an individual's affective and behavioral reactions to information technology

Social cognitive theory and individual reactions to computing technology: a longitudinal study

While computer training is widely recognized as an essential contributor to the productive use of computers in organizations, very little research has focused on identifying the processes through which training operates, and the relative effectiveness of different methods for such training. This research examined the training process, and compared a behavior modeling training program, based on Social Cognitive Theory Bandura [Bandura, A. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psych. Rev.842 191--215; Bandura, A. 1978. Reflections on self-efficacy. Adv. Behavioral Res. Therapy1 237--269; Bandura, A. 1982. Self-efficacy mechanism in human agency. Amer. Psychologist372 122--147; Bandura, A. 1986. Social Foundations of Thought and Action. Prentice-Hall, Englewood Cliffs, NJ.], to a more traditional, lecture-based program. According to Social Cognitive Theory, watching others performing a behavior, in this case interacting with a computer system, influences the observers' perceptions of their own ability to perform the behavior, or self-efficacy, and the expected outcomes that they perceive, as well as providing strategies for effective performance. The findings provide only partial support for the research model. Self-efficacy exerted a strong influence on performance in both models. In addition, behavior modeling was found to be more effective than the traditional method for training in Lotus 1-2-3, resulting in higher self-efficacy and higher performance. For WordPerfect, however, modeling did not significantly influence performance. This finding was unexpected, and several possible explanations are explored in the discussion. Of particular surprise were the negative relationships found between outcome expectations and performance. Outcome expectations were expected to positively influence performance, but the results indicated a strong negative effect. Measurement limitations are presented as the most plausible explanation for this result, but further research is necessary to provide conclusive explanations.

Application of Social Cognitive Theory to Training for Computer Skills

Multigroup or between-group analyses are common in the information systems literature. The ability to detect the presence or absence of between-group differences and accurately estimate the strength of moderating effects is important in studies that attempt to show contingent effects. In the past, IS scholars have used a variety of approaches to examine these effects, with the partial least squares (PLS) pooled significance test for multigroup becoming the most common (e.g., Ahuja and Thatcher 2005; Enns et al. 2003; Zhu et al. 2006). In other areas of social sciences (Epitropaki and Martin 2005) and management (Mayer and Gavin 2005; Song et al. 2005) research, however, there is greater emphasis on the use of covariance-based structural equation modeling multigroup analysis. This paper compares these two methods through Monte Carlo simulation. Our findings demonstrate the conditions under which covariance-based multigroup analysis is more appropriate as well as those under which there either is no difference or the component-based approach is preferable. In particular, we find that when data are normally distributed, with a small sample size and correlated exogenous variables, the component-based approach is more likely to detect differences between-group than is the covariance-based approach. Both approaches will consistently detect differences under conditions of normality with large sample sizes. With non-normally distributed data, neither technique could consistently detect differences across the groups in two of the paths, suggesting that both techniques struggle with the prediction of a highly skewed and kurtotic dependent variable. Both techniques detected the differences in the other paths consistently under conditions of non-normality, with the component-based approach preferable at moderate effect sizes, particularly for smaller samples.

Assessing between-group differences in information systems research: a comparison of covariance-and component-based SEM

Organizations today face great pressure to maximize the bene its from their investments in information technology (IT). They are challenged not just to use IT, but to use it as effectively as possible. Understanding how to assess the competence of users is critical in maximizing the effectiveness of IT use. Yet theuser competence construct is largely absent from prominent technology acceptance and it models, poorly conceptualized, and inconsistently measured. We begin by presenting a conceptual model of the assessment of user competence to organize and clarify the diverse literature regarding what user competence means and the problems of assessment. As an illustrative study, we then report the findings from an experiment involving 66 participants. The experiment was conducted to compare empirically two methods (paper and pencil tests versus self-report questionnaire), across two different types of software, or domains of knowledge (word processing versus spreadsheet packages), and two different conceptualizations of competence (software knowledge versus self-efficacy). The analysis shows statistical significance in all three main effects. How user competence is measured, what is measured, what measurement context is employed:all influence the measurement outcome. Furthermore, significant interaction effects indicate that different combinations of measurement methods, conceptualization, and knowledge domains produce different results. The concept of frame of reference, and its anchoring effect on subjects' responses, explains a number of these findings. The study demonstrates the need for clarity in both defining what type of competence is being assessed and in drawing conclusions regarding competence, based upon the types of measures used. Since the results suggest that definition and measurement of the user competence construct can change the ability score being captured, the existing information system (IS) models of usage must contain the concept of an ability rating. We conclude by discussing how user competence can be incorporated into the Task-Technology Fit model, as well as additional theoretical and practical implications of our research.

Deborah Compeau

Papers

Computer self-efficacy: development of a measure and initial test

Social cognitive theory and individual reactions to computing technology: a longitudinal study

Application of Social Cognitive Theory to Training for Computer Skills

Assessing between-group differences in information systems research: a comparison of covariance-and component-based SEM

Assessing User Competence: Conceptualization and Measurement