Toward a better understanding of behavioral intention and system usage constructs

Abstract

To understand user behavior, researchers have examined intention to use, and system usage through some common conceptualizations such as actual usage, reported usage, and assessed usage. Although this entire body of research has produced important findings, it has yet to appreciably advance our theoretical understanding of behavioral intention (BI) and usage constructs. To fill this gap, this paper critically examines and compares these core variables as well as their relationships with key technology acceptance determinants. We find that (1) BI has a much higher correlation with the determinants than does usage, and thus more variance in BI than in usage can be explained; (2) BI is not a good surrogate for usage; (3) among the three usage constructs, assessed usage is the most and actual usage is the least highly correlated with BI; and (4) researchers should examine both actual usage and assessed usage in their every single study to bring to light the true relationships between system usage and its antecedents. This study thus helps IS scholars expand their baseline knowledge of these core variables, interpret the important messages conveyed by the extant literature, and conduct more fruitful and illuminating future research on user behavior.

Keywords:

• actual usage
• behavioral intention
• meta-analysis
• perceived ease of use
• perceived usefulness
• system usage

Additional information

Notes on contributors

Jiming Wu

About the authors

Jiming Wu is an assistant professor in the Department of Management at California State University, East Bay. He received his Ph.D. from the University of Kentucky. His research interests include knowledge management, IT adoption and acceptance, and computer and network security. His work has appeared in MIS Quarterly, Decision Support Systems, and elsewhere.

Hongwei Du

Hongwei Du is a full professor of Information Technology Management in the College of Business and Economics at California State University, East Bay. He holds a Ph.D. in Operations Research from Florida Institute of Technology and an MS in Computer Science from Bowling Green State University. His current research interests are artificial intelligence, database, electronic commerce, and decision support systems. His works have been published in the International Journal of Innovation and Learning, the International Journal of Electronic Healthcare, the Journal of Economic Studies, and the Journal of Communications of the IIMA.