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Positioning knowledge: schools of thought and new knowledge creation

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Abstract

Cohesive intellectual communities called “schools of thought” can provide powerful benefits to those developing new knowledge, but can also constrain them. We examine how developers of new knowledge position themselves within and between schools of thought, and how this affects their impact. Looking at the micro and macro fields of management publications from 1956 to 2002 with an extensive dataset of 113,000+ articles from 41 top journals, we explore the dynamics of knowledge positioning for management scholars. We find that it is significantly beneficial for new knowledge to be a part of a school of thought, and that within a school of thought new knowledge has more impact if it is in the intellectual semi-periphery of the school.

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Notes

  1. Reclusterings using data for 5 years after publication t (0) to t (5) and for the 10 years around the publication t (−5) to t (5) yielded comparable similar distance measures, implying that cluster centrality changes gradually.

  2. We anticipate that numerous papers will not be cited for structural reasons, including article type, journal, and bibliography characteristics. Other papers in our database received no citations for the time period covered simply due to chance. There are also papers that are cited frequently, leading to over-dispersion in our dataset.

  3. 15.03 is the slope of the line plotted in Fig. 6. It is the difference between predicted citation counts for in-cluster papers and non-cluster papers after removing extreme outliers.

  4. 2.88 is the difference in Model II between predicted citations at distance = 0.54 and ±1 SD (distance ≈ .36 or distance ≈ 0.68).

  5. Given the mechanism of our clustering algorithm there could be different reasons for a paper to be on the semi-periphery of a cluster. Papers with high impact could be on the semi-periphery because they cite unusual papers, either inside or outside their own cluster, or because they cite a mix of papers both within and outside their cluster. To explore the drivers of semi-peripheral placement for high-impact papers we constructed two additional variables to capture the average distance of each paper’s citations within and outside its cluster and added both these variables into our model for Hypothesis 2. We found that the coefficients for both these variables were positive and highly significant, implying that successful papers in the semi-periphery tended to cite a mix of central papers both within cluster and in central papers within other clusters.

    We hypothesized that a paper which combined knowledge from its own cluster with a few, perhaps one or two, outside clusters rather than many outside clusters would be the most successful. This would allow it to act as a bridge between a few audiences or research communities. To test this we constructed for each paper a Herfindahl Index of cluster concentration for citations made to a paper within another cluster. We did this by summing, for each paper with more than four outside citations, the percentage of citations to papers in each outside cluster. A higher Herfindahl cluster concentration score would imply that the paper made a high percentage of outside citations to one cluster; a lower Herfindahl score would imply that a paper scattered its outside citations to many papers. We added this variable for concentration into the regression for Model II, and it was positive and highly significant. This confirms the intuition that papers which bring together knowledge from a few schools tend to be well cited.

  6. For two of these four models there were too few papers with zero citations to justify a zero-inflated model and the standard NB model produced a sufficiently accurate fit for this analysis. Thus for these two models we used a negative binomial regression (see Fig. 9).

  7. In this case the percentage change in citations is equal to exp(β*δ) where δ is the incremental change in which you are interested and β represents the respective coefficient in the model (Long 1997).

  8. We wish to confirm that our clustering algorithm is identifying meaningful schools of thought. While it compares favorably to other algorithms of its kind, we wish to separately test whether the specific tests we claim remain significant if we randomize the cluster assignment for all papers in the same proportions that exist in our dataset and monitor our results for any changes. Our first hypothesis claims that being in a cluster is advantageous in garnering citations; once we randomize citation assignments (and therefore cluster membership) this effect should disappear if the clusters are in fact meaningful. This would add confidence that our results are not an artifact of clustering or statistical methodology. We find that indeed after randomizing we do lose significance for our binary cluster variable as expected. The cluster assignments were successfully randomized (\( \chi_{{\left( {324} \right)}}^{2} = 2 7 6.0 2 30 \), p = 0.975). The coefficients for our new binary cluster membership variable in our model are now insignificant (NB portion: p = 0.403; zero-inflation portion: p = 0.361). These results support the validity of our clustering methodology.

  9. Lastly, we want to further rule out the possibility that the excess zero counts in our data obscured the true trends, despite our use of the ZINBR formulation. To accomplish this we excluded all zero-citation papers and proceeded to refit our data with a standard Negative Binomial model. The direction and significance of our coefficients in the NB portion of our previous models remain unchanged. We do not believe that the zero-inflation portion of our model was incorrectly identifying and modeling the excess zero counts or obscuring the trend among those papers that received citations.

  10. Patents will differ from papers in many respects. The purpose of patents is to establish a proprietary claim on a method, idea or technology while the purpose of a paper is to advance knowledge and share information. Nevertheless, we believe both will exhibit interesting and useful clusters of patterns.

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Acknowledgements

Our special thanks to Henry Small and Thomson ISI for their generous use of data in this project.

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Authors and Affiliations

  1. The Wharton School, University of Pennsylvania, Philadelphia, PA, 19104-6370, USA

    S. Phineas Upham & Lori Rosenkopf

  2. CIS Department, University of Pennsylvania, Philadelphia, PA, 19104-6309, USA

    Lyle H. Ungar

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  1. S. Phineas Upham
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Correspondence to S. Phineas Upham.

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Upham, S.P., Rosenkopf, L. & Ungar, L.H. Positioning knowledge: schools of thought and new knowledge creation. Scientometrics 83, 555–581 (2010). https://doi.org/10.1007/s11192-009-0097-8

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