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.

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Structural equation modeling and regression: guidelines for research practice

While the technology acceptance model (TAM), introduced in 1986, continues to be the most widely applied theoretical model in the IS field, few previous efforts examined its accomplishments and limitations. This study traces TAM’s history, investigates its findings, and cautiously predicts its future trajectory. One hundred and one articles published by leading IS journals and conferences in the past eighteen years are examined and summarized. An openended survey of thirty-two leading IS researchers assisted in critically examining TAM and specifying future directions.

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The Technology Acceptance Model: Past, Present, and Future

The technology acceptance model (Davis 1989) is one of the most widely used models of IT adoption. According to TAM, IT adoption is influenced by two perceptions: usefulness and ease-of-use. Research has shown that perceived usefulness (PU) affects intended adoption of IT, but has mostly failed to do so regarding perceived ease of use (PEOU). The basic proposition of this study is that this varying importance of PEOU may be related to the nature of the task. PEOU relates to assessments of the intrinsic characteristics of IT, such as the ease of use, ease of learning, flexibility, and clarity of its interface. PU, on the other hand, is a response to user assessment of its extrinsic, i.e., task-oriented, outcomes: how IT

The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption

The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research raises the need to compare and contrast the different types of SEM so that research designs can be selected on the basis of appropriateness. After discussing a running example which analyzes the same dataset via three very different statistical techniques, the paper assesses the extent to which these techniques are currently being used in IS research. It then compares two classes of SEM, covariance-based SEM and partial-least-squares-based SEM. Finally, it discusses linear regression models and suggests guidelines as to when both SEM and regression techniques should be used. The paper concludes with heuristics and rule of thumb thresholds to guide practice, and a description of the extent to which practice is in accord with these guidelines. The paper is written in such a way that readers with basic knowledge of multivariate statistics can follow the logic and examples. It does not assume the reader is already conversant with LISREL, PLS, or other SEM tools. To address this audience, the paper contains straightforward examples to explain more complex topics, a glossary with clickable links, and a rudimentary structural model applying the Technology Acceptance Model (TAM) to e-Commerce which is analyzed via PLS, LISREL, and linear regression techniques.

Structural Equation Modeling Techniques and Regression: Guidelines for Research Practice

The value added by an organization’s IT assets is a critical concern to both research and practice. Not surprisingly, a large number of IS effectiveness measures can be found in the IS literature. What is not clear in the literature is what measures are appropriate in a particular context. In this paper we propose a two-dimensional matrix for classifying IS Effectiveness measures. The first dimension is the type of system studied. The second dimension is the stakeholder in whose interests the system is being evaluated. The matrix was tested by using it to classify IS effectiveness measures from 186 empirical papers in three major IS journals for the last nine years. The results indicate that the classifications are meaningful. Hence, the IS Effectiveness Matrix provides a useful guide for conceptualizing effectiveness measurement in IS research, and for choosing appropriate measures, both for research and practice.

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Dimensions of information systems success

Evidence suggests that information centers (ICs) have significantly more interest in evaluating software packages and assisting in the selection of software packages than end users have. However, the selection of software packages by the information center product specialists (ICPSs) can compromise their usage. Ease of use and usefulness are believed to be fundamental predictors of usage. The question of whether ICPSs are able to correctly evaluate ease of use and usefulness of software packages for end users is posed. An insight into this issue could enhance end-user computing (EUC) policy and lead to a more effective partnership between end users and information systems (IS) professionals. A search for this insight provided the motivation for our empirical investigation of the perception of ICPSs and end users in assessing the ease of use and usefulness of thirty different software packages. Our investigation was performed in an organization with an IC that had evolved to the formalization stage. The findings show that the perceptions of the end users and ICPSs were similar in terms of assessing the ease of use of software packages. However, end users found the software packages less useful than did IC product specialists. Therefore, in sophisticated environments, end users should be empowered to develop their own user groups and suggest to IC personnel which useful software packages to acquire. Otherwise, selection of software packages without end-user participation could have an adverse effect on their usage.

David A. Cameron

Papers

An empirical study of factors affecting software package selection