The Behavioral Model of Health Services Use was initially developed over 25 years ago. In the interim it has been subject to considerable application, reprobation, and alteration. I review its development and assess its continued relevance.

Revisiting the behavioral model and access to medical care: does it matter?

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

Course Description In this course, we will explore the question of the actual and potential connections between democracy and education. Our focus of attention will be placed on a critical examination of democratic theory and its implications for the civic education roles and contributions of teachers, adult educators, community development practitioners, and community organizers. We will survey and deal critically with a range of competing conceptions of democracy, variously described as classical, republican, liberal, radical, marxist, neomarxist, pragmatist, feminist, populist, pluralist, postmodern, and/or participatory. Using narrative inquiry as a means for illuminating and interpreting contemporary practice, we will analyze the implications of different conceptions of democracy for the practical work of civic education.

Democracy and education.

HEALTH-related quality of life (HRQL) is increasingly used as an outcome in clinical trials, effectiveness research, and research on quality of care. Factors that have facilitated this increased usage include the accumulating evidence that measures of HRQL are valid and "reliable,"1the publication of several large clinical trials showing that these outcome measures are responsive to important clinical changes,2-5and the successful development and testing of shorter instruments that are easier to understand and administer.6-13Because these measures describe or characterize what the patient has experienced as the result of medical care, they are useful and important supplements to traditional physiological or biological measures of health status. Given this improved ability to assess patients' health status, how can physicians and health care systems intervene to improve HRQL? Implicit in the use of measures of HRQL in clinical trials and in effectiveness research is the concept that clinical

Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes

Principles and Practice of Structural Equation Modeling (2nd ed.)

In this chapter, we consider building structural equation models with latent (unobservable) variables with measurement errors.

Structural Equation Models with Latent Variables

Objective:To test a causal model of the impact of (a) nursing unit context on professional nursing practice; (b) professional practice on selected organizational (nurses' work satisfaction, nursing turnover, average length of patient stay) and patient outcomes (patient satisfaction, rate of reported

Professional nursing practice: Impact on organizational and patient outcomes

List of Tables. List of Figures. About the Author. Foreword. Acknowledgments. 1. An Introduction to Evidence-Based Management of Health Care. 2. Causal Inference: Foundations for Health Care Managerial Decision-Making. 3. Research on Health Services Management: The Search for Structure. 4. Exploratory Analytical Modeling Methods. 5. Introduction to Structural Equation Modeling. 6. Confirmatory Factor Analysis. 7. Structural Equations for Directly Observed Variables: Recursive and Non-Recursive Models. 8. Structural Equation Models with Latent Variables. 9. Covariance Structure Models. 10. Multiple Group Comparison with Panel Data. 11. Multilevel Covariance Modeling. 12. Growth Curve Modeling with Longitudinal Data. 13. Epilogue. Index.

Evidence-Based Health Care Management: Multivariate Modeling Approaches

This study explores the adoption of information technology (IT) and the association between organizational and market factors, and IT adoption in hospitals. Results suggest that a wide range of amounts and types of IT are adopted. Hospitals with higher overall IT adoption adopt strategic IT most often. Hospitals with lower IT adoption adopt administrative IT most often. Results also show hospital IT adoption to be positively associated with hospital size, location, system membership, ownership, and market competition.

Exploring Hospitals' Adoption of Information Technology

Objectives To study the number of health information systems (HISs), applicable to administrative, clinical, and executive decision support functionalities, adopted by acute care hospitals and to examine how hospital market, organizational, and financial factors influence HIS adoption. Methods A cross-sectional analysis was performed with 1441 hospitals selected from metropolitan statistical areas in the United States. Multiple data sources were merged. Six hypotheses were empirically tested by multiple regression analysis. Results HIS adoption was influenced by the hospital market, organizational, and financial factors. Larger, system-affiliated, and for-profit hospitals with more preferred provider organization contracts are more likely to adopt managerial information systems than their counterparts. Operating revenue is positively associated with HIS adoption. Conclusion The study concludes that hospital organizational and financial factors influence on hospitals' strategic adoption of clinical, administrative, and managerial information systems.

Thomas T. H. Wan

Papers

Structural Equation Models with Latent Variables

Professional nursing practice: Impact on organizational and patient outcomes

Evidence-Based Health Care Management: Multivariate Modeling Approaches

Exploring Hospitals' Adoption of Information Technology

Factors influencing health information system adoption in American hospitals.