Technology Acceptance Model 3 and a Research Agenda on Interventions
TL;DR: This work draws from the vast body of research on the technology acceptance model (TAM) to develop a comprehensive nomological network of the determinants of individual level IT adoption and use and present a research agenda focused on potential pre- and postimplementation interventions that can enhance employees' adopted and use of IT.
Abstract: Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre- and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.
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TL;DR: An alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations was formalized and the empirical model was empirically examined using a combination of meta-analysis and structural equation modelling techniques.
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.
830 citations
Cites background from "Technology Acceptance Model 3 and a..."
...…quality User training Sabherwal et al. (2006) TAM3 Behavioural intention, Behaviour NONE Perceived usefulness Perceived ease of use Subjective norm Image Job relevance Output quality Result demonstrability Venkatesh & Bala (2008) between these types of beliefs and intention (Taylor and Todd 1995b)....
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TL;DR: Overall, the TAM explains technology acceptance well; yet, the role of certain key constructs and the importance of external variables contrast some existing beliefs about the TAM.
Abstract: The extent to which teachers adopt technology in their teaching practice has long been in the focus of research. Indeed, a plethora of models exist explaining influential factors and mechanisms of technology use in classrooms, one of which—the Technology Acceptance Model (TAM) and versions thereof—has dominated the field. Although consensus exists about which factors in the TAM might predict teachers’ technology adoption, the current field abounds in some controversies and inconsistent findings. This meta-analysis seeks to clarify some of these issues by combining meta-analysis with structural equation modeling approaches. Specifically, we synthesized 124 correlation matrices from 114 empirical TAM studies (N = 34,357 teachers) and tested the fit of the TAM and its versions. Overall, the TAM explains technology acceptance well; yet, the role of certain key constructs and the importance of external variables contrast some existing beliefs about the TAM. Implications for research and practice are discussed.
676 citations
Journal Article•
TL;DR: Greller, W., & Drachsler, H. (2012).
Abstract: Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics.
Educational Technology & Society, 15(3), 42–57.
664 citations
Cites background from "Technology Acceptance Model 3 and a..."
...…get a better grasp on this issue, current scientific debate, therefore, should focus on empirical evaluation methods of learning analytics tools (Ali et al., 2012) and on advanced technology acceptance models (cf. Venkatesh & Bala 2008), inspired by the early work in this area (Davis, 1989, 1993)....
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TL;DR: The results support the expanded model that provides a rich understanding of the changes in the pre‐usage beliefs and attitudes through the emergent constructs of disconfirmation and satisfaction, ultimately influencing IS continuance intention.
Abstract: This study presents two extensions to the two-stage expectation-confirmation theory of information systems (IS) continuance. First, we expand the belief set from perceived usefulness in the original IS continuance model to include three additional predictors identified in the unified theory of acceptance and use of technology, namely effort expectancy, social influence and facilitating conditions. Second, we ground the IS continuance model in the context of transactional systems that involve transmission of personal and sensitive information and include trust as a key contextual belief in the model. To test the expanded IS continuance model, we conducted a longitudinal field study of 3159 Hong Kong citizens across two electronic government (e-government) technologies that enable citizens' access to government services. In general, the results support the expanded model that provides a rich understanding of the changes in the pre-usage beliefs and attitudes through the emergent constructs of disconfirmation and satisfaction, ultimately influencing IS continuance intention. Finally, we discuss the theoretical and practical implications of the expanded model.
632 citations
TL;DR: A model based on a slightly-altered version of the classical unified theory of acceptance and use of technology (UTAUT) is developed, which revealed the existence of distinct adoption behaviors between India-based and USA-based professionals.
543 citations
References
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TL;DR: The extent to which method biases influence behavioral research results is examined, potential sources of method biases are identified, the cognitive processes through which method bias influence responses to measures are discussed, the many different procedural and statistical techniques that can be used to control method biases is evaluated, and recommendations for how to select appropriate procedural and Statistical remedies are provided.
Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
52,531 citations
01 Jan 1989
TL;DR: Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage.
40,975 citations
"Technology Acceptance Model 3 and a..." refers background or methods or result in this paper
...TAM constructs—that is, perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI)—were operationalized using items adapted from Davis (1989) and Davis et al....
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...Venkatesh and Davis (2000) proposed an extension of TAM—TAM2—by identifying and theorizing about the general determinants of perceived usefulness—that is, subjective norm, image, job relevance, output quality, result demonstrability, and...
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...As of December 2007, the Social Science Citation Index listed over 1,700 citations and Google Scholars listed over 5,000 citations to the two journal articles that introduced TAM (Davis, 1989; Davis et al., 1989)....
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...Such strong psychometric properties were consistent with much prior research employing these constructs and measures (Davis, 1989; Davis et al., 1989; Mathieson, 1991; Taylor & Todd, 1995; Agarwal & Karahanna, 2000; Karahanna et al., 2006)....
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...TAM constructs—that is, perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI)—were operationalized using items adapted from Davis (1989) and Davis et al. (1989). Consistent with Davis (1989), use (USE) was operationalized by asking the respondents, “On average, how much time to you spend on the system every day? hours and minutes....
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TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.
Abstract: Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.
40,720 citations
Book•
01 Jun 1975
36,032 citations
"Technology Acceptance Model 3 and a..." refers background in this paper
...Subjective Norm The degree to which an individual perceives that most people who are important to him think he should or should not use the system (Fishbein & Ajzen, 1975; Venkatesh & Davis, 2000)....
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Book•
01 Jan 1991TL;DR: In this article, the effects of predictor scaling on the coefficients of regression equations are investigated. But, they focus mainly on the effect of predictors scaling on coefficients of regressions.
Abstract: Introduction Interactions between Continuous Predictors in Multiple Regression The Effects of Predictor Scaling on Coefficients of Regression Equations Testing and Probing Three-Way Interactions Structuring Regression Equations to Reflect Higher Order Relationships Model and Effect Testing with Higher Order Terms Interactions between Categorical and Continuous Variables Reliability and Statistical Power Conclusion Some Contrasts Between ANOVA and MR in Practice
27,897 citations