Search Engine or Content Website? A Local Information Seeking Classification Model Based on Consumer Characteristics and Website Perceptions

Abstract

Decisions concerning everyday life activities such as patronizing restaurants require obtaining information about them. Some consumers go directly to content websites when they need such information; others go directly to search engines. How do search engine users differ from content website users for a given type of local information? This local information-seeking classification model posits that they differ in their prior experiences with their “go-to” websites, their perceived search skills, their habit of using search engines, their involvement with the activity for which information is sought, their tendency to conduct extensive information search, and their beliefs about their “go-to” website types. Empirical results support the model. By integrating everyday life information seeking (ELIS), technology acceptance model (TAM), and consumer behavior literatures, the model in this study fills a theoretical gap in the literature and opens new lines of inquiries for both ELIS and TAM research.

Additional information

Notes on contributors

Li-ling Hsu

Li-ling Hsu is an Analyst at the Office of Institutional Effectiveness at Pikes Peak Community College. She received her Ph.D. from University of Colorado Denver. Her research interests include end user behavior, online information seeking, information privacy, information security, and Web-based information providers.

Zhiping Walter

Zhiping Walter is an Associate Professor of MIS at University of Colorado Denver. She received her Ph.D. from University of Rochester. Her research interests include electronic commerce, IT user acceptance, and online information seeking behavior. Her work has appeared in International Journal of Electronic Commerce, Decision Support Systems, and other publications.