Is There Conceptual Convergence in Entrepreneurship Research? A Co-Citation Analysis of Frontiers of Entrepreneurship Research, 1981-2004
Bibliographic record
Abstract
Scholars have often called for entrepreneurship research to achieve conceptual convergence, since convergence is seen as a sign of a discipline's maturity. This study examines the actual extent and nature of convergence in entrepreneurial research using co-citation analysis of conference papers from the Frontiers of Entrepreneurship Research conference from 1981 to 2004. The study provides empirical evidence for thenature and levels of conceptual convergence, discusses the scholarly conversations over time, and shows how a method from the sociology of science can be adapted to follow the evolution of a field of study. The authors present three arguments (institutional arrangements, novelty-driven research, and competition-reduction growth) to suggest why a field like entrepreneurship may be unlikely to support high levels of convergence. Of greater interest may be the kinds of convergence that characterize the field. The study is based on co-citation relationships in the 20,184 references in the 960 articles in the Frontiers of Entrepreneurship Research series published by the Babson College Entrepreneurship Research Conference. The research investigates convergence in four successive periods. For each period, co-citation networks are presented to show the degree that most-cited references are cited with one another. The concepts of the networks are also identified to trace conceptual forces that have shaped the field. Findings indicate that there has been convergence in entrepreneurship research. Levels of convergence have been relatively low, and convergence has not been stable. In addition, it was found that the field has passed through cycles of convergence and divergence and draws from a wide array ofdisciplines. Absence of high convergence is not necessarily due to the field's level of maturity. There is little reason to expect high levels of convergence in a field such an entrepreneurship. (TNM)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".