Collaborating with Competitors: How Do Small Firm Accounting Associations and Networks Successfully Manage Coopetitive Tensions?*
Bibliographic record
Abstract
ABSTRACT The “coopetition” paradox exists when two or more organizations are simultaneously involved in cooperative and competitive interactions. In the accounting industry, small firms encounter coopetition when they align themselves with other independent firms to form accounting associations and networks (AANs). AANs are a type of interorganizational relationship (IOR) that provide opportunities for member firms to collaborate by sharing important resources such as expertise, best practices, and manpower. However, member firms also compete in the marketplace for clients and human capital, which incentivizes uncooperative and opportunistic behavior. If managed inadequately, coopetitive tensions can significantly hamper AAN benefits and may lead to IOR failure. Given the considerable longevity of AANs, we interview 42 high‐level accounting professionals to understand AANs' apparent successful management of these tensions. Leveraging coopetition and IOR theory, our analysis suggests that transactional mechanisms (contractual agreements, organizational structure, selection/monitoring processes) and relational mechanisms (trust, social ties, reciprocity) play key roles in encouraging healthy cooperation and competition among member firms. One of our main conclusions is that these mechanisms contribute to AAN success because they are leveraged comprehensively across each IOR life cycle phase, and they are mutually reinforcing, with transactional mechanisms providing the foundation to inspire confidence and encourage the development of relational mechanisms. Our research enriches existing accounting and coopetition literature, provides a new perspective for AANs, and responds to calls to understand key factors of IOR success.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".