In an effort to understand how academic scientists seek information relevant to their research in today's environment of ubiquitous electronic access, a correlation framework is built and regression analysis is applied to the survey results from 2,063 academic researchers in natural science, engineering, and medical science at five research universities in the United States. Previous work has reported descriptive statistics about these scientists' information-seeking behavior.This study extends that work to examine relationships between scientists' information-seeking behaviors and their personal and environmental factors. Several regression models, including the Poisson model, the logit model, and the ordered logit model, are built to interpret the correlation among scientists' behaviors. In addition, exploratory factor analysis is used for data reduction. Overall, many factors were found to affect the specific informationseeking behaviors of scientists, including demographic, psychological, role-related, and environmental factors. Of the factors having an effect, academic position was the most important determinant of information behavior. The arrival of various electronic resources, including e-journals, online databases, and digital libraries, has significantly changed the way scholarly communication takes place. Nowadays, academic researchers have convenient and easy access to an unprecedented amount of electronic scholarly resources while also facing a dizzying array of different types of content delivery, including books, journals, web pages, and online databases. In an effort to understand how scientists are adapting to the increased use of electronic scholarly communication and what effect this has on their information-seeking behaviors, the NeoRef research group (http://neoref.ils.unc.edu/) at the University of North Carolina at Chapel Hill has conducted a national survey of 2,063 academic scientists from five research universities The focus of this article is to understand, for academic scientists engaged in scholarly information searching, the interactions between attributes of the user, their environmental context, and their information-searching behavior. An exploratory analysis using regression techniques was performed to identify the correlations among informationseeking behaviors. Poisson regression, logit regression, and ordered logit regression analyses are applied to identify the specific linkages between contextual factors and informationsearching and use behaviors. A framework is proposed for understanding the interactions. The goal is to discover correlations between factors and behaviors and to be able to predict specific behaviors when subject and environmental context are known. Background work related to information-seeking behavior for academics and the survey methodology are described in a previous publication detailing the national study (Niu et al., 2010) . This section describes prior research results and methods that have examined correlating factors for explanations of information-seeking behaviors, which are relevant to the analyses performed in this article. In addition, references also are made in the results section to draw comparisons between the results of this study and prior experiments. A significant amount of research during the past 10 years has focused on how academics have responded to the transition from print to electronic scholarly communications. This research has shown a trend in which researchers are becoming more accepting of electronic resources and increasingly utilizing them as their primary sources (e.g., see the excellent studies involving multiple universities spanning large time periods by Tenopir and King, including King, Tenopir,
In a follow-up study, Niu and Hemminger REF reported several factors affecting the information-seeking behavior of researchers, including demographics, psychological aspects, academic position, and discipline.
A study of factors that affect the information-seeking behavior of academic scientists
J. Assoc. Inf. Sci. Technol.
J. Assoc. Inf. Sci. Technol.
Computer Science