DEGREE OF CONFORMITY ACROSS INSTITUTIONAL FIELDS AND ITS MODERATING EFFECT ON THE RELATIONSHIP BETWEEN ORGANIZATIONAL DIVERSITY AND FIRM PERFORMANCE
Bibliographic record
Abstract
Decades of empirical work have produced mixed results and no proven direct link between diversity and performance at the group, business, or organizational level (Jehn & Bezrukova, 2004). Prior research has not yet explored the impact of a firm’s institutional context as an important influence on the relationship between diversity and firm performance. In this\nthesis, I develop theoretical arguments on how an organizational field can weaken or amplify this relationship. I integrate conceptual arguments and empirical findings from the literatures on organizational diversity, institutional theory, and the Resource-Based View to offer a multilevel framework of organizational diversity and performance. My analysis is based on Employment Equity data for the time period 1997 to 2007 on the representation of Canada’s four designated groups that is published by the Government of Canada. I test my hypotheses using Hierarchical\nLinear Modeling (HLM) to analyze Employment Equity data of 550 federally regulated private and public Canadian firms. These firms are grouped into four broad categories: banking, telecommunications, transportation, and other. Results show that categories differ significantly in their levels of firm diversity and the degree of conformity around these levels. My analysis supports arguments for a curvilinear relationship between organizational diversity and firm performance that is moderated by a field’s degree of conformity. My thesis highlights the importance of the interface between firm and field, and contributes to the strategy literature the\ninsight that a firm’s performance outcomes from organizational diversity depend on broader institutional factors.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".