Accounting Policies, Institutional Factors, and Firm Performance: Qualitative Insights in a Developing Country
Bibliographic record
Abstract
This study aims to uncover the determinants for the formulation of accounting practices and their impact on firm performance in Pakistan through the lens of institutional theory. Based on a pragmatic approach, this study has collected data from 455 participants and 21 semi-structured interviews have been conducted. Firstly, it is noted that accounting practices can be traced back to the Mughal regime, and subsequently underwent a major development in the British colonial system. Secondly, our results indicate that institutional factors, namely, accounting regulatory framework, political factors, economic factors, cultural factors, and country-specific factors have also played a major role in the development of accounting practices after the creation of Pakistan as a separate state. Finally, this study suggests that the development of accounting practices have a novel contribution towards the performance of firms. This research thus provides a pathway for policymakers in this county to closing the gaps between accounting practices and the policies of the International Accounting Standard Board (IASB). Furthermore, firms can enhance their performance by implementing international accounting standards. This paper helps Pakistan’s regulatory institutions such as the SECP (Securities and Exchange Commission of Pakistan) and SBP (State Bank of Pakistan) in the process of developing new policies. Such decisions are related, but not limited to: attracting foreign investments, economic expansion, and international trade. Furthermore, it provides a pathway for firms to improve their performance. Ultimately, this research fills the gap as concerns international accounting standards by assessing, both empirically and theoretically, the role of various determinants for the formulation of accounting practices and their impact on the performance of firms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".