The DOI matches its Crossref record; 45 of the 49 references it cites were found in a registry or an index.
A study of factors that affect the information‐seeking behavior of academic scientists
Abstract
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 information‐seeking 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.
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“In fact, it was due to its great ease of use and performance in informational and exploratory searches78 that Google Scholar emerged as the number one go‐to academic search engine for most academic users.79, 80, 81, 82”
Citations
Cites
Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344614 · Cited by 7,703
Introduction to the Practice of Statistics
(2004) · Technometrics · doi:10.1198/tech.2004.s753 · Cited by 2,275
Second-Level Digital Divide: Differences in People's Online Skills
Hargittai, E. (2002) · First Monday · doi:10.5210/fm.v7i4.942 · Cited by 1,951
Information behaviour: An interdisciplinary perspective
Wilson, T. D. (1997) · Information Processing & Management · doi:10.1016/s0306-4573(97)00028-9 · Cited by 1,133
Meho, L. I., & Yang, K. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20677 · Cited by 974
Second-Level Digital Divide: Differences in People's Online Skills
Hargittai, E. (2007) · Defence Science Journal · Cited by 841
A comparison of reading paper and on-line documents
O'Hara, K. P., & Sellen, A. J. (1997) · doi:10.1145/258549.258787 · Cited by 582
Use and Users of Electronic Library Resources: An Overview and Analysis of Recent Research Studies
Tenopir, C. (2003) · Zenodo · doi:10.5281/zenodo.7779277 · Cited by 361
Introduction to the Practice of Statistics
Huberty, C. J., Moore, D. S., & McCabe, G. P. (1991) · Journal of Educational Statistics · doi:10.2307/1165100 · Cited by 254
The Information Behaviour of the Researcher of the Future
ROWLANDS, I. H., Nicholas, D. M., & Huntington, P. A. (2007) · UCL Discovery (University College London) · Cited by 181
Research works printed but not linked: 4
- Honey P., 1986, The manual of learning styles
- Kennedy T., 2003, Gendering the digital divide, IT & Society, 1, 72
- 10.1002/asi.20883 · DOI
- Tenopir C., 2003, Report of the Council on Library and Information Resources
Cited by
Sorted by citations
Gusenbauer, M., & Haddaway, N. R. (2020) · Research Synthesis Methods · doi:10.1002/jrsm.1378 · Cited by 2,379
“79-82 Students in particular utilize Google as a main source in information seeking.”
BackgroundLooking for Information: A Survey of Research on Information Seeking, Needs, and Behavior
(2016) · Studies in information · doi:10.1108/s2055-53772016 · Cited by 1,537
Who reads research articles? An altmetrics analysis of M endeley user categories
Mohammadi, E., Thelwall, M. A., Haustein, S., & Larivière, V. (2015) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23286 · Cited by 196
Information overload in healthcare: too much of a good thing?
Klerings, I., Weinhandl, A. S., & Thaler, K. J. (2015) · Zeitschrift für Evidenz Fortbildung und Qualität im Gesundheitswesen · doi:10.1016/j.zefq.2015.06.005 · Cited by 162
Where and how early career researchers find scholarly information
Nicholas, D., Boukacem‐Zeghmouri, C., Rodríguez‐Bravo, B., Xu, J., Watkinson, A., Abrizah, A., Herman, E., & Świgoń, M. (2017) · Learned Publishing · doi:10.1002/leap.1087 · Cited by 119
Metadata
- Authors
- Niu, X., & Hemminger, B. M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- Journal of the American Society for Information Science and Technology
- Publisher
- Wiley
- Published
- 2011-10-31
- Volume
- 63
- Issue
- 2
- Pages
- 336-353
- ISSN
- 1532-2882 (ISSN-L)
- 1532-2890
- OpenAlex
- W1991725352
- Semantic Scholar
- 9bb20d5e66a29daad644a936f9f9d9f907616f45
- Cited by
- 129
- References
- 49
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 4 · 2023: 10 · 2022: 9 · 2021: 10 · 2020: 8 · 2019: 14 · 2018: 12 · 2017: 8 · 2016: 21 · 2015: 12 · 2014: 10 · 2013: 3 · 2012: 1
- FWCI
- 31.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Society for Information Science and Technology (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Colorado State University
- Topics
- Social Sciences › Library and Information Sciences › Library Science and Information Literacy
- Social Sciences › Library and Information Sciences › Library Science and Information
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- information seeking, academic scientists, academic rank, demographic factors, psychological factors, environmental factors, research universities, medical sciences
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