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

With applications using SmartPLS (www.smartpls.com)—the primary software used in partial least squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.

A primer on partial least squares structural equation modeling (PLS-SEM)

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

/pdf/a-new-criterion-for-assessing-discriminant-validity-in-4xs4c94ene.pdf

A new criterion for assessing discriminant validity in variance-based structural equation modeling

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

A separate and distinct interaction with both the actual e-vendor and with its IT Web site interface is at the heart of online shopping Previous research has established, accordingly, that online purchase intentions are the product of both consumer assessments of the IT itself-specifically its perceived usefulness and ease-of-use (TAM)-and trust in the e-vendor But these perspectives have been examined independently by IS researchers Integrating these two perspectives and examining the factors that build online trust in an environment that lacks the typical human interaction that often leads to trust in other circumstances advances our understanding of these constructs and their linkages to behavior

Our research on experienced repeat online shoppers shows that consumer trust is as important to online commerce as the widely accepted TAM use-antecedents, perceived usefulness and perceived ease of use Together these variable sets explain a considerable proportion of variance in intended behavior The study also provides evidence that online trust is built through (1) a belief that the vendor has nothing to gain by cheating, (2) a belief that there are safety mechanisms built into the Web site, and (3) by having a typical interface, (4) one that is, moreover, easy to use

Trust and TAM in online shopping: an integrated model

The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be selected appropriately. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM. Finally, the article discusses linear regression models and offers guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines.

/pdf/structural-equation-modeling-and-regression-guidelines-for-2y96bdeejv.pdf

Structural equation modeling and regression: guidelines for research practice

The process of information technology adoption and use is critical to deriving the benefits of information technology. Yet from a conceptual standpoint, few empirical studies have made a distinction between individuals' pre-adoption and post-adoption (continued use) beliefs and attitudes. This distinction is crucial in understanding and managing this process over time. The current study combines innovation diffusion and attitude theories in a theoretical framework to examine differences in pre-adoption and post-adoption beliefs and attitudes. The examination of Windows technology in a single organization indicates that users and potential adopters of information technology differ on their determinants of behavioral intention, attitude, and subjective norm. Potential adopter intention to adopt is solely determined by normative pressures, whereas user intention is solely determined by attitude. In addition, potential adopters base their attitude on a richer set of innovation characteristics than users. Whereas pre-adoption attitude is based on perceptions of usefulness, ease-of-use, result demonstrability, visibility, and trialability, post-adoption attitude is only based on instrumentality beliefs of usefulness and perceptions of image enhancements.

https://www.jstor.org/stable/pdfplus/249751.pdf

Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs

This tutorial explains in detail what factorial validity is and how to run its various aspects in PLS. The tutorial is written as a teaching aid for doctoral seminars that may cover PLS and for researchers interested in learning PLS. An annotated example with data is provided as an additional tool to assist the reader in reconstructing the detailed example.

/pdf/a-practical-guide-to-factorial-validity-using-pls-graph-3ygb3hy2fv.pdf

A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example

While researchers go to great lengths to justify and prove theoretical links between constructs, the relationship between measurement items and constructs is often ignored. By default, the relationship between construct and item is assumed to be reflective, meaning that the measurement items are a reflection of the construct. Many times, though, the nature of the construct is not reflective, but rather formative. Formative constructs occur when the items describe and define the construct rather than vice versa.

In this research, we examine whether formative constructs are indeed being mistaken for reflective constructs by information systems researchers. By examining complete volumes of MIS Quarterly and Information Systems Research over the last 3 years, we discovered that a significant number of articles have indeed misspecified formative constructs. For scientific results to be valid, we argue that researchers must properly specify formative constructs. This paper discusses the implications of different patterns of common misspecifications of formative constructs on both Type I and Type II errors. To avoid these errors, the paper provides a roadmap to researchers to properly specify formative constructs. We also discuss how to address formative constructs within a research model after they are specified.

/pdf/specifying-formative-constructs-in-information-systems-1bdpzwab2g.pdf

Detmar W. Straub

Papers

Trust and TAM in online shopping: an integrated model

Structural equation modeling and regression: guidelines for research practice

Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs

A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example

Specifying formative constructs in information systems research