Corporate governance practices and firm performance: a configurational analysis across corporate life cycles
Bibliographic record
Abstract
Purpose This study aims to explore corporate governance (CG) practices that can lead to firms’ better performance in different organizational life cycles. The authors propose a configurational approach to explore how a set of CG practices combine in bundles to achieve high performance outcomes for firms across their corporate life cycles. Design/methodology/approach Fuzzy-set qualitative comparative analysis was used to analyze a sample of data of 21 countries and 9 industries. Data referred to the period of 9 years extending from the year 2005 to the year 2013. Findings This study reveals that there are multiple CG practices that exist through firms that can achieve high firm performance. Moreover, CG practices combine in different ways for firms in their growth, maturity and declining stages. Research limitations/implications This study demonstrates the value of using a configurational analytical approach to explore both the firm and country-specific CG practices (together) that engage firms to achieve the desired level of performance across the corporate life cycles. Practical implications The current study draws attention to the policymakers’ need to assess the current level of regulatory and competitive development of their countries and form policy accordingly. The approach used in the current research study not only offers the linkages between CG and performance to managers as incentives to comply with regulation but also to view CG-related activity as a strategic move. Social implications The approach used in the current research study not only offers the linkages between CG and performance to managers as incentives to comply with regulation but also to view CG-related activity as a strategic move. Originality/value This study broadening the focus of CG studies to include a rigorous explanation of the global CG phenomena and to provide effective solutions for the practitioners. Contribution to Impact This study demonstrates the value of using a configurational analytical approach to explore both the firm and country-specific CG practices (together) that engage firms to achieve the desired level of performance across the corporate life cycles.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".