Abstract
Information behavior research is an interdisciplinary field in essence due to the investigation of interdisciplinary in previous work. To track the changes in interdisciplinarity of this field, more efforts should be put on basis of previous work. Based on publications searched from Web of Science from 2000 to 2018, we explored the interdisciplinarity of this field drawing on network analysis and diversity measure. Findings showed that although variety of disciplines in this field augmented significantly, the distribution of disciplines is unbalanced and concentrated on some dominant disciplines such as computer science, engineering, psychology, social science and medicine, etc. Relationships among disciplines have evolved over time and mainly focused on neighboring disciplines instead of distinct disciplines. Computer science, engineering, psychology, health science and social science function as intermediate disciplines connecting distinct disciplinary groups. Besides, the measurement using diversity measure shows that interdisciplinary degree of this field appears to decrease. This study contributes to the evolution and measurement of interdisciplinarity of information behavior research, which has implications for researchers and practitioners in this field.
Similar content being viewed by others
References
Abramo, G., D’Angelo, C. A., & Zhang, L. (2018). A comparison of two approaches for measuring interdisciplinary research output: The disciplinary diversity of authors vs the disciplinary diversity of the reference list. Journal of Informetrics,12(4), 1182–1193.
Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. Journal of the American Society for Information Science and Technology,54(6), 550–560.
Anderson, K. E. (2018). Getting acquainted with social networks and apps: Combating fake news on social media. Library Hi Tech News,35(3), 1–6.
Beauchamp, M. A. (1965). An Improved Index of Centrality. Behavioral Science,10(2), 161–163.
Bellanca, L. (2009). Measuring interdisciplinary research: Analysis of co-authorship for research staff at the University of York. Bioscience Horizons,2(2), 99–112.
Besselaar, P. V. D., & Heimeriks, G. (2001). Disciplinary, multidisciplinary, interdisciplinary concepts and indicators. Community Development Journal,38(4), 344–357.
Boyack, K. W. (2004). Mapping knowledge domains: Characterizing PNAS. Proceedings of the National Academy of Sciences of the United States of America,101(2), 5192–5199.
Buente, W., & Robbin, A. (2008). Trends in Internet information behavior, 2000–2004. Journal of the Association for Information Science and Technology,59(11), 1743–1760.
Burnett, G., & Buerkle, H. (2006). Information exchange in virtual communities: A comparative study. Journal of Computer-Mediated Communication,9(2), 123–141.
Burnett, G., & Erdelez, S. (2010). Forecasting the next 10 years in information behavior research: A fish bowl dialogue. Bulletin of the American Society for Information Science and Technology,36(3), 44–48.
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics,22(1), 155–205.
Cambrosio, A., Limoges, C., Courtial, J. P., & Laville, F. (1993). Historical scientometrics? Mapping over 70 years of biological safety research with coword analysis. Scientometrics,27(2), 119–143.
Case, D. O. (2006). Information behavior. Annual Review of Information Science & Technology,40(1), 293–327.
Cassi, L., Mescheba, W., & de Turckheim, É. (2014). How to evaluate the degree of interdisciplinarity of an institution. Scientometrics,101(3), 1871–1895.
Champeimont, R. L., Mescheba, W., & De Turckheim, É. (2017). Analysing institutions interdisciplinarity by extensive use of Rao-stirling diversity index Lorenzo Cassi. PLoS ONE,12(1), e0170296. https://doi.org/10.1371/journal.pone.0170296.
Chen, S., Arsenault, C., Gingras, Y., & Larivière, V. (2015). Exploring the interdisciplinary evolution of a discipline: The case of biochemistry and molecular biology. Scientometrics,102(2), 1307–1323.
Chen, C., Li, Q., Deng, Z., Chiu, K., & Wang, P. (2018). The preferences of Chinese LIS journal articles in citing works outside the discipline. Journal of Documentation,74(1), 99–118.
Commisso, C. (2017). The post-truth archive: Considerations for archiving context in fake news repositories. Preservation, Digital Technology and Culture,46(3), 99–102.
Cooke, N. A. (2017). Posttruth, truthiness, and alternative facts: Information Behavior and critical information consumption for a new age. The Library Quarterly,87(3), 211–221.
Cummings, J. N., & Kraut, R. E. (2002). Domesticating computers and the internet. The Information Society,18(3), 221–231.
Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management,37(6), 817–842.
Dork, M., Carpendale, S., & Williamson, C. (2011). The information flaneur: A fresh look at information seeking. In Proceedings of the 2011 annual conference on human factors in computing systems. https://doi.org/10.1145/1978942.1979124.
Eungi, K. (2017). The trends in information behavior research, 2000–2016: The emergence of new topical area. Journal of the Korean BIBLIA Society for Library and Information Science,28(2), 119–135.
Feng, Y., & Agosto, D. (2019). From health to performance: Amateur runners’ personal health information management with activity tracking technology. Aslib Journal of Information Management,71(2), 217–240.
Fisher, K. E., & Julien, H. (2009). Information behavior. Annual Review of Information Science and Technology,43(1), 1–73.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry,40(1), 35–41.
Ghaddar, S., Valerio, M. A., Garcia, C. M., & Hansen, L. (2012). Adolescent health literacy: the importance of credible sources for online health information. Journal of School Health,82(1), 28–36.
Gibson, C., & Jacobson, T. E. (2018). Habits of mind in an uncertain information world. Reference and User Services Quarterly,57(3), 183–192.
Given, L. M., Julien, H., & Case, D. (2012). The evolution of information behavior research: Looking back to see the future. Proceedings of the ASIST Annual Meeting,49(1), 1–2.
González-Teruel, A., Gonzale-Zalcaide, G., Barrios, M., & Abadgarcia, M. (2015). Mapping recent information behavior research: An analysis of co-authorship and co-citation networks. Scientometrics,103(2), 687–705.
Grauwin, S., & Jensen, P. (2011). Mapping scientific institutions. Scientometrics,89(3), 943–954.
Greifeneder, E. (2014). Trends in information behaviour research. Information Research,19(4), 159–170.
Haunschild, R. (2015). Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact. Journal of Scientometric Research,4(1), 40–49.
He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends,48(1), 133–159.
Hernandez, C. (2017). Fake news and information literacy: Creating resources to develop source evaluation skills at the University of Oregon Libraries. OLA Quarterly,23(1), 13–15.
Hu, J., Huang, R., & Wang, Y. (2018). Geographical visualization of research collaborations of library science in China. Electronic Library,36(3), 414–429.
Hu, J., & Zhang, Y. (2018). Measuring the interdisciplinarity of Big Data research: A longitudinal study. Online Information Review,42(5), 681–696.
Huang, M. H., & Chang, Y. W. (2011). A study of interdisciplinarity in information science: Using direct citation and co-authorship analysis. Journal of Information Science,37(4), 369–378.
Ioannidis, J. P. A., Boyack, K. W., & Klavans, R. (2014). Estimates of the continuously publishing core in the scientific workforce. PLoS ONE,9(7), e101698. https://doi.org/10.1371/journal.pone.0101698.
Jost, L. (2006). Entropy and diversity. Oikos,113(2), 363–375.
Julien, H., & O’Brien, M. (2014). Information behaviour research: Where have we been, where are we going? Canadian Journal of Information and Library Science,38(4), 239–250.
Julien, H., Pecoskie, J. J. L., & Reed, K. (2011). Trends in information behavior research, 1999–2008: A content analysis. Library and Information Science Research,33(1), 19–24.
Karlovčec, M., & Mladenić, D. (2015). Interdisciplinarity of scientific fields and its evolution based on graph of project collaboration and co-authoring. Scientometrics,102(1), 433–454.
Kim, S. U., & Syn, S. Y. (2014). Research trends in teens’ health information behavior: A review of the literature. Health Information and Libraries Journal,31(1), 4–19.
Klein, J. T. (2008). Evaluation of interdisciplinary and transdisciplinary research: A literature review. American Journal of Preventive Medicine,35(2), 116–123.
Levitt, J. M., & Thelwall, M. (2008). Is multidisciplinary research more highly cited? A macro level study. Journal of the American Society for Information Science and Technology,59(12), 1973–1984.
Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinary of scientific journals. Journal of the American Society for Information Science and Technology,58(9), 1303–1319.
Leydesdorff, L., & Probst, C. (2009). The delineation of an interdisciplinary specialty in terms of a journal set: The case of communication studies. Journal of the American Society for Information Science and Technology,60(8), 1709–1718.
Leydesdorff, L., & Rafols, I. (2011). Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. Journal of Informetrics,5(1), 87–100.
Liu, Y., Li, H., Goncalves, J., Kostakos, V., & Xiao, B. (2016). Fragmentation or cohesion? Visualizing the process and consequences of information system diversity, 1993–2012. European Journal of Information Systems,25(6), 509–533.
Luzar, B., Levnajic, Z., Povh, J., & Perc, M. (2014). Community structure and the evolution of interdisciplinarity in Slovenia's scientific collaboration network. PLoS ONE,9(4), e94429. https://doi.org/10.1371/journal.pone.0094429.
Middleton, L., Hall, H., & Raeside, R. (2019). Applications and applicability of Social Cognitive Theory in information science research. Journal of Librarianship and Information Science,51(4), 927–937.
Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinary in science: A tentative typology of disciplines and research areas. Journal of the American Society for Information Science and Technology,54(13), 1237–1249.
Mugabushaka, A. M., Kyriakou, A., & Papazoglou, T. (2016). Bibliometric indicators of interdisciplinarity: The potential of the Leinster-Cobbold diversity indices to study disciplinary diversity. Scientometrics,107(2), 593–607.
Muñoz-Leiva, M. I., Viedma-del-Jesús, J.-F., & López-Herrera, A. G. (2012). An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective. Quality and Quantity,46(4), 1077–1095.
Porter, A. L., Cohen, A. S., Roessner, J. D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics,72(1), 117–147.
Porter, A. L., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics,81(3), 719–745.
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics,82(2), 263–287.
Rogers, D. L. (1974). Sociometric analysis of interorganizational relations: Application of theory and measurement. Rural Sociology,39(4), 487–503.
Small, H., & Griffith, B. C. (1974). The structure of scientific literatures I: Identifying and graphing specialties. Science Studies,4(1), 17–40.
Shen, L., Xiong, B., & Hu, J. (2017). Research status, hotspots and trends for information behavior in China using bibliometric and co-word analysis. Journal of Documentation,73(4), 618–633.
Sosulski, N. W., & Tyckoson, D. A. (2018). Reference in the age of disinformation. Reference and User Services Quarterly,57(3), 178–182.
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface,4(15), 707–719.
Sullivan, M. C. (2019). Libraries and fake news: What’s the problem? what’s the plan? Communications in Information Literacy,13(1), 91–113.
Torabi Asr, F., & Taboada, M. (2019). Big Data and quality data for fake news and misinformation detection. Big Data and Society,6(1), 1–14.
Viedmadeljesus, M. I., Perakakis, P., Munoz, M. A., Lopezherrera, A. G., & Vila, J. (2011). Sketching the first 45 years of the journal Psychophysiology (1964–2008): A co-word-based analysis. Psychophysiology,48(8), 1029–1036.
Van Rijnsoever, F. J., & Hessels, L. K. (2011). Factors associated with disciplinary and interdisciplinary research collaboration. Research Policy,40(3), 463–472.
Vakkari, P. (2008). Trends and approaches in information behavior research. Information Research,13(4), 361–374.
Vishwanath, A., Xu, W., & Ngoh, Z. (2018). How people protect their privacy on facebook: A cost-benefit view. Journal of the Association for Information Science and Technology,69(5), 700–709.
Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics,5(1), 14–26.
Waltman, L., & Van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology,63(12), 2378–2392.
Wang, J., Bart, T., Wolfgang, G., & Smalheiser, N. R. (2015). Interdisciplinarity and impact: Distinct effects of variety, balance, and disparity. PLoS ONE,10(5), e0127298. https://doi.org/10.1371/journal.pone.0127298.
Wettler, M., & Rapp, R. (1993). Computation of word associations based on co-occurrences of words in large corpora. In Proc. of the 1st workshop on very large corpora: academic and industrial perspectives. https://en.scientificcommons.org/42994748.
Wilson, T. D. (1994). Information needs and uses: fifty years of progress. In Fifty years of information progress: a Journal of Documentation review. Retrieved from https://informationr.net/tdw/publ/papers/1994FiftyYears.
Wilson, T. D. (1997). Information behaviour: An interdisciplinary perspective. Information Processing & Management,33(4), 551–572.
Wilson, T. D. (2000). Human information behavior. Informing Science,3(2), 49–56.
Xu, H., Guo, T., Yue, Z., Ru, L., & Fang, S. (2016). Interdisciplinary topics of information science: A study based on the terms interdisciplinarity index series. Scientometrics,106(2), 583–601.
Yang, C. H., Park, H. W., & Heo, J. (2010). A network analysis of interdisciplinary research relationships: The Korean government’s R&D grant program. Scientometrics,83(1), 77–92.
Ying, H., Lin, Z., Bei-Bei, S., et al. (2019). Interdisciplinarity measurement: External knowledge integration, internal information convergence and research activity pattern. Studies in Science of Science,37(1), 25–35.
Zhang, L., Rousseau, R., & GlNzel, W. (2016). Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account. Journal of the Association for Information Science and Technology,67(5), 1257–1265.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Deng, S., Xia, S. Mapping the interdisciplinarity in information behavior research: a quantitative study using diversity measure and co-occurrence analysis. Scientometrics 124, 489–513 (2020). https://doi.org/10.1007/s11192-020-03465-x
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-020-03465-x