Adding the dimension of knowledge trading to source impact assessment: Approaches, indicators, and implications

Journal of the Association for Information Science and Technology

Volumn 68, Issue 5, 2017, Pages 1090-1104

  Yan, Erjia ^a   Zhu, Yongjun ^a  

^a Drexel University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOCHEMISTRY;

AMERICAN CHEMICAL SOCIETY; BETWEENNESS CENTRALITY; BIOLOGICAL CHEMISTRY; CITATION NETWORKS; IMPACT ASSESSMENTS; KNOWLEDGE DIFFUSION; NATIONAL ACADEMY OF SCIENCE; TRADING PARTNERS;

COMMERCE;

EID: 85014886968     PISSN: 23301635     EISSN: 23301643     Source Type: Journal    
DOI: 10.1002/asi.23670     Document Type: Article

References (56)

1. Alvarez, P., & Pulgarín, A. (1997). The diffusion of scientific journals analyzed through citations. Journal of the American Society for Information Science, 48(10), 953–958.
2. Bergstrom, C.T., & West, J.D. (2008). Assessing citations with the Eigenfactor™ Metrics. Neurology, 71(23), 1850–1851.
3. Bollen, J., Rodriguez, M.A., & Van de Sompel, H. (2006). Journal status. Scientometrics, 69(3), 669–687.
4. Bollen, J., Van de Sompel, H., Hagberg, A., & Chute, R. (2009). A principal component analysis of 39 scientific impact measures. PLoS One, 4(6), e6022.
5. Borgatti, S.P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.
6. Borgatti, S.P., & Everett, M.G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28(4), 466–484.
7. Borgman, C.L. (1990). Scholarly communication and bibliometrics. Newbury Park, CA: Sage.
8. Börner, K., Klavans, R., Patek, M., Zoss, A.M., Biberstine, J.R., Light, R.P., …, & Boyack, K.W. (2012). Design and update of a classification system: The UCSD map of science. PLoS One, 7(7), e39464.
9. Bornmann, L., & Daniel, H.D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.
10. Brandes, U. (2001). A faster algorithm for betweenness centrality*. Journal of Mathematical Sociology, 25(2), 163–177.
11. Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2), 136–145.
12. Breschi, S., & Lissoni, F. (2009). Mobility of skilled workers and co-invention networks: An anatomy of localized knowledge flow. Journal of Economic Geography, 9(4), 439–468.
13. Clauset, A., Shalizi, C.R., & Newman, M.E. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661–703.
14. Cronin, B., & Meho, L.I. (2008). The shifting balance of intellectual trade in information studies. Journal of the American Society for Information Science & Technology, 59(4), 551–564.
15. Cronin, B., & Pearson, S. (1990). The export of ideas from information science. Journal of Information Science, 16(6), 381–391.
16. David, P.A., & Foray, D. (2002). An introduction to the economy of the knowledge society. International Social Science Journal, 54(171), 9–23.
17. Frandsen, T.F., Rousseau, R., & Rowlands, I. (2006). Diffusion factors. Journal of Documentation, 62(1), 58–72.
18. Freeman, L.C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.
19. García, J.A., Rodríguez-Sánchez, R., Fdez-Valdivia, J., Robinson-García, N., & Torres-Salinas, D. (2012). Mapping academic institutions according to their journal publication profile: Spanish universities as a case study. Journal of the American Society for Information Science and Technology, 63(11), 2328–2340.
20. Garfield, E., & Merton, R.K. (1979). Citation indexing: Its theory and application in science, technology, and humanities (Vol. 8). New York: Wiley.
21. Guerrero-Bote, V.P., & Moya-Anegón, F. (2012). A further step forward in measuring journals' scientific prestige: The SJR2 indicator. Journal of Informetrics, 6(4), 674–688.
22. Guerrero-Bote, V., Zapico-Alonso, F., Espinosa-Calvo, M., Gómez-Crisóstomo, R., & de Moya-Anegón, F. (2007). Import-export of knowledge between scientific subject categories: The iceberg hypothesis. Scientometrics, 71(3), 423–441.
23. Haustein, S. (2012). Multidimensional journal evaluation: Analyzing scientific periodicals beyond the impact factor. München, Germany: De Gruyter Saur.
24. Hessey, R., & Willett, P. (2013). Quantifying the value of knowledge exports from librarianship and information science research. Journal of Information Science, 39(1), 141–150.
25. Hirst, G. (1978). Discipline impact factors: Method for determining core journal lists. Journal of the American Society for Information Science, 29(4), 171–172.
26. Jaffe, A.B., Trajtenberg, M., & Fogarty, M.S. (2000). Knowledge spillovers and patent citations: Evidence from a survey of inventors. American Economic Review, 90(2), 215–218.
27. Larivière, V., Sugimoto, C.R., & Cronin, B. (2012). A bibliometric chronicling of library and information science's first hundred years. Journal of the American Society for Information Science and Technology, 63(5), 997–1016.
28. Leydesdorff, L. (1998). Theories of citation? Scientometrics, 43(1), 5–25.
29. Leydesdorff, L. (2009). How are new citation-based journal indicators adding to the bibliometric toolbox? Journal of the American Society for Information Science and Technology, 60(7), 1327–1336.
30. Leydesdorff, L., & Rafols, I. (2011). Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. Journal of Informetrics, 5(1), 87–100.
31. Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145–151.
32. Lockett, A., & McWilliams, A. (2005). The balance of trade between disciplines: Do we effectively manage knowledge? Journal of Management Inquiry, 14(2), 139–150.
33. Moed, H.F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4(3), 265–277.
34. Newman, M.E.J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(suppl. 1), 5200–5205.
35. Newman, M.E. (2005). Power laws, Pareto distributions and Zipf's law. Contemporary Physics, 46(5), 323–351.
36. Ni, C., Shaw, D., Lind, S.M., & Ding, Y. (2013). Journal impact and proximity: An assessment using bibliographic features. Journal of the American Society for Information Science and Technology, 64(4), 802–817.
37. Nicolaisen, J. (2007). Citation analysis. Annual Review of Information Science and Technology, 41(1), 609–641.
38. Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
39. Pinski, G., & Narin, F. (1976). Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing & Management, 12(5), 297–312.
40. Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.
41. Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), 1823–1835.
42. Ruiz-Castillo, J., & Waltman, L. (2015). Field-normalized citation impact indicators using algorithmically constructed classification systems of science. Journal of Informetrics, 9(1), 102–117.
43. Schubert, A., & Glänzel, W. (1986). Mean response time: A new indicator of journal citation speed with application to physics journals. Czechoslovak Journal of Physics, 36(1), 121–125.
44. Sugimoto, C.R., Larivière, V., Ni, C., & Cronin, B. (2013). Journal acceptance rates: A cross-disciplinary analysis of variability and relationships with journal measures. Journal of Informetrics, 7(4), 897–906.
45. Tang, X., Wang, J., Zhong, J., & Pan, Y. (2014). Predicting essential proteins based on weighted degree centrality. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(2), 407–418.
46. Van Leeuwen, T., & Tijssen, R. (2000). Interdisciplinary dynamics of modern science: Analysis of cross-disciplinary citation flows. Research Evaluation, 9(3), 183–187.
47. Veugelers, R. (2010). Towards a multipolar science world: Trends and impact. Scientometrics, 82(2), 439–456.
48. 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.
49. Waltman, L., Yan, E., & van Eck, N.J. (2011). A recursive field-normalized bibliometric performance indicator: An application to the field of library and information science. Scientometrics, 89(1), 301–314.
50. Yan, E. (2014). Finding knowledge paths among scientific disciplines. Journal of the Association for Information Science & Technology, 65(11), 2331–2347.
51. Yan, E. (2016). Disciplinary knowledge production and diffusion in science. Journal of the Association for Information Science & Technology, 67(9), 2223–2245.
52. Yan, E., & Yu, Q. (2016). Using path-based approaches to examine the dynamic structure of discipline-level citation networks: 1997-2011. Journal of the Association for Information Science & Technology, 67(8), 1943–1955.
53. Yan, E., Ding, Y., Cronin, B., & Leydesdorff, L. (2013). A bird's-eye view of scientific trading: Dependency relations among fields of science. Journal of Informetrics, 7(2), 249–264.
54. Zhang, G., Ding, Y., & Milojević, S. (2013). Citation content analysis (cca): A framework for syntactic and semantic analysis of citation content. Journal of the American Society for Information Science and Technology, 64(7), 1490–1503.
55. Zhang, L., Liu, X., Janssens, F., Liang, L., & Glänzel, W. (2010). Subject clustering analysis based on ISI category classification. Journal of Informetrics, 4(2), 185–193.
56. Zhao, S.X., Rousseau, R., & Ye, F. (2011). H-degree as a basic measure in weighted networks. Journal of Informetrics, 5(4), 668–677.