Coauthorship Dynamics and Knowledge Capital: The Patterns of Cross-Disciplinary Collaboration in Information Systems Research
Bibliographic record
Abstract
From the social network perspective, this study explores the ontological structure of knowledge sharing activities engaged in by researchers in the field of information systems (IS) over the past three decades. We construct a knowledge network based on coauthorship patterns extracted from four major journals in the IS field in order to analyze the distinctive characteristics of each subfield and to assess the amount of internal and external knowledge exchange that has taken place among IS researchers. This study also tests the role of different types of social capital that influence the academic impact of researchers. Our results indicate that the proportion of coauthored IS articles in the four journals has doubled over the past 25 years, from merely 40 percent in 1978 to over 80 percent in 2002. However, a significant variation exists in terms of the shape, density, and centralization of knowledge exchange networks across the four subfields of IS--namely, behavioral science, organizational science, computer science, and economic science. For example, the behavioral science subgroup, in terms of internal cohesion among researchers, tends to develop the most dense collaborative relationships, whereas the computer science subgroup is the most fragmented. Moreover, external collaboration across these subfields appears to be limited and severely unbalanced. Across the four subfields, on average, less than 20 percent of the research collaboration ties involved researchers from different subdisciplines. Finally, the regression analysis reveals that knowledge capital derived from a network rich in structural holes has a positive influence on an individual researcher's academic performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".