Skip to main content
SpringerLink
Log in

Measuring destabilization and consolidation in scientific knowledge evolution

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The dynamic process of knowledge evolution can be divided into two routes: (1) new knowledge is cumulatively built upon past scientific achievements, and (2) the new replaces the old in a non-cumulative fashion. While existing measures such as citation counts are central to assessing the impact of articles and the viability of research streams, they do not quantify the two routes of knowledge evolution. In this research we develop two indexes, destabilization (D) and consolidation (C), that measure the effects that an article may have on the subsequent use of its predecessors—whether it consolidates or destabilizes the existing literature in terms of the influences on its predecessors’ future usage. Using a dataset of 45,616 papers from 24 premium business journals, this study shows that the D and C indexes are complementary to the citation count to measure the impact of a scientific article and capture the two directions of knowledge evolution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly: Management Information Systems, 25(1), 107–136.

    Article  Google Scholar 

  • Arthur, W.B. (2009), The nature of technology: What it is and how it evolves, Free Presee: https://philpapers.org/rec/ARTTNO.

  • Arts, S., & Fleming, L. (2019). Paradise of novelty-or loss of human capital? Exploring new fields and inventive output. Organization Science, 29(6), 1074–1092.

    Article  Google Scholar 

  • Balachandran, S., & Hernandez, E. (2018). Networks and innovation: Accounting for structural and institutional sources of recombination in brokerage triads. Organization Science, 29(1), 80–99.

    Article  Google Scholar 

  • Barkema, H. G., Chen, X. P., George, G., Luo, Y., & Tsui, A. S. (2015). West meets East: New concepts and theories. Academy of Management Journal, 58(2), 460–479.

    Article  Google Scholar 

  • Binnig, G., & Rohrer, H. (1987). Scanning tunneling microscopy from birth to adolescence. Reviews of Modern Physics, 59(3), 615–625.

    Article  Google Scholar 

  • Caplan, S. (2014), “Why we are not ready for radical changes in science publishing | Steve Caplan | Science | theguardian.com”, The Gaurdian: (Retrieved 12 September 2021) http://www.theguardian.com/science/occams-corner/2014/jan/06/radical-changes-science-publishing-randy-schekman

  • Carter, C. (2013). Nobel prize winner accuses scientific journals of ’ tyranny ’ (pp. 1–2). The Telegraph.

    Google Scholar 

  • Chen, J., Shao, D., & Fan, S. (2021). Destabilization and consolidation: Conceptualizing, measuring, and validating the dual characteristics of technology. Research Policy, 50(1), 104115.

    Article  Google Scholar 

  • Conroy, G. (2020), “What’s wrong with the h-index, according to its inventor”, Nature Index, available at: (Retrieved 12 September 2021).https://www.natureindex.com/news-blog/whats-wrong-with-the-h-index-according-to-its-inventor?utm_source=NI-newsletter&utm_medium=email&utm_campaign=6-May-2021

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319.

    Article  Google Scholar 

  • Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132.

    Article  Google Scholar 

  • Foster, J. G., Rzhetsky, A., & Evans, J. A. (2015). Tradition and innovation in scientists’ research strategies. American Sociological Review, 80(5), 875–908.

    Article  Google Scholar 

  • Funk, R. J., & Owen-Smith, J. (2017). A dynamic network measure of technological change. Management Science, 63(3), 791–817.

    Article  Google Scholar 

  • Gavetti, G., & Levinthal, D. (2000). Looking forward and looking backward: Cognitive and experiential search. Administrative Science Quarterly, 45(1), 113–137.

    Article  Google Scholar 

  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and tam in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.

    Article  Google Scholar 

  • Hargadon, A., & Sutton, R. (1997). Technology brokering and innovation in a product design firm. Administrative Science Quarterly, 42(4), 716–749.

    Article  Google Scholar 

  • Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.

    Article  Google Scholar 

  • Jaffe, A. B. (1986). Technological opportunity and spillovers of R & D: evidence from firms’ patents, profits, and market value. The American Economic Review, 76, 984–1001.

    Google Scholar 

  • Judge, T. A., Cable, D. M., Colbert, A. E., & Rynes, S. L. (2007). What causes a management article to be cited-article author, or journal? Academy of Management Journal, 50(3), 491–506.

    Article  Google Scholar 

  • Jung, H. J., & Lee, J. J. (2016). The quest for originality: A new typology of knowledge search and breakthrough inventions. Academy of Management Journal, 59(5), 1725–1753.

    Article  Google Scholar 

  • Kaplan, S., & Vakili, K. (2015). The double-edged sword of recombination in breakthrough innovation. Strategic Management Journal, 36(10), 1435–1457.

    Article  Google Scholar 

  • Katila, R., & Ahuja, G. (2002). Something old, something new: A longitudinal study of search behavior and new product introduction. Academy of Management Journal, 45(6), 1183–1194.

    Google Scholar 

  • Kim, S. K., Arthurs, J. D., Sahaym, A., & Cullen, J. B. (2013). Search behavior of the diversified firm: The impact of fit on innovation. Strategic Management Journal, 34(8), 999–1009.

    Article  Google Scholar 

  • Kuhn, T. S. (1996). The structure of scientific revolutions (3rd ed.). University of Chicago Press.

    Book  Google Scholar 

  • Leahey, E., Beckman, C. M., & Stanko, T. L. (2017). Prominent but less productive: The impact of interdisciplinarity on scientists’ research. Administrative Science Quarterly, 62(1), 105–139.

    Article  Google Scholar 

  • Leung, K. (2007). The glory and tyranny of citation impact: An east Asian perspective. Academy of Management Journal, 50(3), 510.

    Article  Google Scholar 

  • Li, J. and Chen, J. (2017), “A Dynamic Network Measure of Knowledge Evolution: A Case Study of MIS Quarterly”, Proceedings of the 50th Hawaii International Conference on System Sciences.

  • March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

    Article  Google Scholar 

  • Monastersky, R. (2005), “The number that’s devouring science”, Chronicle of Higher Education, Vol. 52 No. 8, (Retrieved 12 September 2021).https://www.chronicle.com/article/the-number-thats-devouring-science/

  • Nelson, R. R., & Winter, S. G. (2009). An evolutionary theory of economic change. Administrative Science Quarterly. https://doi.org/10.2307/2393143

    Article  Google Scholar 

  • Oswick, C., Fleming, P., & Hanlon, G. (2011). From borrowing to blending: Rethinking the processes of organizational theory building. Academy of Management Review, 36(2), 318–337.

    Google Scholar 

  • Porter, A. L., Cohen, A. S., David Roessner, J., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117–147.

    Article  Google Scholar 

  • Schoenmakers, W., & Duysters, G. (2010). The technological origins of radical inventions. Research Policy North-Holland, 39(8), 1051–1059.

    Article  Google Scholar 

  • Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits capital, credit, interest, and the business cycle. Harvard Economic Studies, 34, 46.

    Google Scholar 

  • Spisak, B. R., O’Brien, M. J., Nicholson, N., & Van Vugt, M. (2015). Niche construction and the evolution of leadership. Academy of Management Review, 40(2), 291–306.

    Article  Google Scholar 

  • Taylor, A., & Greve, H. R. (2006). Superman or the fantastic four? Knowledge combination and experience in innovative teams. Academy of Management Journal, 49(4), 723–740.

    Article  Google Scholar 

  • Trajtenberg, M., Henderson, R., & Jaffe, A. (1997). “University versus corporate patents: A window on the basicness of invention.” Economics of Innovation and New Technology, 5(1), 19–50.

    Article  Google Scholar 

  • Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative Science Quarterly, 31(3), 439.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  Google Scholar 

  • Wang, D., Song, C., & Barabási, A. L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132.

    Article  Google Scholar 

  • Weisberg, R. W. (2014). Creativity and knowledge: A challenge to theories (pp. 226–250). Handbook of Creativity.

    Google Scholar 

  • Wu, L., Wang, D., & Evans, J. A. (2019). Large teams develop and small teams disrupt science and technology nature. Nature Publishing Group, 566(7744), 378–382.

    Google Scholar 

  • Zhu, J., & Liu, W. (2020). “A tale of two databases: The use of web of science and scopus in academic papers.” Scientometrics, 123(1), 321–335.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Decision Sciences, College of Business & Economics, Western Washington University, Bellingham, WA, 98225, USA

    Jiexun Li

  2. Department of Strategy & Entrepreneurship, College of Business, Oregon State University, Corvallis, OR, 97331, USA

    Jiyao Chen

Authors
  1. Jiexun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiyao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiexun Li.

Ethics declarations

Conflict of interest

The authors received no funding, financial or non-financial interets for this study.

Appendices

Appendix 1

Papers published in UTD-24 top-tier journals in WoS

Journals

Years

Total count

Article, review & reprint

Academy of Management Journal

1970–2018

3138

2420

Academy of Management Review

1983–2018

2316

1268

Accounting Review

1970–2018

4 449

2161

Administrative Science Quarterly

1970–2018

2999

1194

Information Systems Research

1990–2018

922

848

Journal of Accounting & Economics

1982–2018

996

951

Journal of Accounting Research

1970–2018

1797

1435

Journal of Consumer Research

1974–2018

2323

2106

Journal of Finance

1970–2018

5991

3717

Journal of Financial Economics

1976–2018

2663

2604

Journal of International Business Studies

1976–2018

2180

1658

Journal of Marketing

1970–2018

2926

1897

Journal of Marketing Research

1970–2018

3542

2286

Journal of Operations Management

1999–2018

787

724

Journal on Computing

1999–2018

859

819

MIS Quarterly

1979–2018

1407

1223

Management Science

1975–2018

5934

5470

Manufacturing and Service Operations Management

2006–2018

491

474

Marketing Science

1987–2018

1410

1195

Operations Research

1970–2018

5882

4142

Organization Science

1990–2018

1651

1564

Production and Operations Management

1999–2018

1338

1249

Review of Financial Studies

1988–2018

1818

1753

Strategic Management Journal

1980–2018

2701

2458

Total

–

60,520

45,616

Appendix 2

Regression model for predicting D5 and C5

 

D5

A5

Independent variables

 Search diversity

0.022 (0.030)***

0.001 (0.026)***

 Search depth

0.011 (0.116)***

0.000 (0.102)***

Control variables

 Ref_count

0.001 (0.199)***

− 0.000 (− 0.056)***

 Top_ref_count

0.000 (0.015)*

− 0.000 (− 0.050)***

 Auth_count

0.004 (0.026)***

0.000 (0.002)

 Top_auth_count

0.007 (0.226)***

0.002 (0.119)***

 Impact_Factor

0.005 (0.053)***

− 0.000 (− 0.004)

R 2

0.137

0.034

p value

0.000

0.000

  1. Standard errors reported in parentheses
  2. ***Indicates statistically significant at 0.1% level, ** at 1% level, * at 5% level, and+ at 10% level

Appendix 3

Regression model for predicting D5 and C5 Percentile

 

D5p

A5p

Independent variables

 IDR

17.302 (0.108)***

11.723 (0.073)***

 Search depth

2.389 (0.142)***

2.807 (0.167)***

Control variables

 Ref_count

0.121 (0.135)***

− 0.022 (− 0.025)**

 Top_ref_count

0.334 (0.144)***

0.447 (0.193)***

 Auth_count

1.242 (0.044)***

1.073 (0.038)***

 Top_auth_count

1.183 (0.225)***

1.003 (0.191)***

 Impact_Factor

0.840 (0.056)***

0.097 (0.007)

R 2

0.206

0.135

p value

0.000

0.000

  1. Standard errors reported in parentheses
  2. ***Indicates statistically significant at 0.1% level, ** at 1% level, * at 5% level, and+ at 10% level

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Chen, J. Measuring destabilization and consolidation in scientific knowledge evolution. Scientometrics 127, 5819–5839 (2022). https://doi.org/10.1007/s11192-022-04479-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-022-04479-3

Keywords

Search

Navigation