Collaborating with Competitors: How Do Small Firm Accounting Associations and Networks Successfully Manage Coopetitive Tensions?*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it