Understanding the role of Ownership Structure in the Board Strategic Decision-Making Process: A Multiple Case Study of State-owned, Pseudo-Private and Private Banks in Iran
Bibliographic record
Abstract
Objective:This study aims to explain the role of ownership structure in the board strategic decision-making process in the Iranian institutional context as a developing country. The Agency Theory does not direct this research as the dominant theory in the corporate governance field; instead, this study is guided through the behavioral approach. Methods: The strategy of this research is a multiple case study with an abductive theory-building approach. By examining the ownership structure of Iranian banks, four banks were selected. Thirty-four semi-structured interviews are conducted with the CEOs, board members, CEO deputies, and officers relevant to strategy and corporate governance. Data analysis and theorizing were followed by systematic coding of interviews through eight steps, including within-case analysis, cross-case analysis, initial hypothesis building, and enfolding the literature. As a result, six propositions are suggested and conceptualized. Results: The results reveal that ownership concentration harms the balance of power, and thus the effectiveness of the decision-making process. At the same time, mixed ownership hurts strategic decision-making with a negative effect on the balance of trust and emotional climate and balance of the cognitive conflict. Regulatory bodies can moderate the negative effect of ownership concentration on the balance of power. Conclusion: Contrary to existing literature, results show that both "ownership concentration" and "mixed ownership" have negative impacts on the "effectiveness of strategic decision-making". Three primary processes, the balances of "power", "trust and emotional climate" and "cognitive conflict", appear to explain the findings. Regulatory bodies, as critical institutional forces, moderate these adverse effects. We explain our conflicting findings with the differences between institutional contexts. Due to the importance of banking in the Iranian economy and considering the moderating role of supervisory agencies on the mentioned negative impacts, improving regulatory institutions and strengthening supervisory mechanisms to limit major shareholders' interference is suggested.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".