The entrepreneurship of accounting work and its role in reducing information asymmetry: Evidence from insurance companies
Bibliographic record
Abstract
The primary aim of this research is to provide a comprehensive understanding of the role played by entrepreneurial accounting practices in mitigating information disparities, as perceived by employees within the realm of Jordanian financial intermediaries. This role is encapsulated in various components, including accounting systems and policies, international financial reporting standards, international external auditing standards, international internal auditing standards, as well as professional and ethical conduct guidelines. In pursuit of our research objectives, we adopted two distinct methodological approaches. Firstly, a descriptive analytical approach was employed, involving the development of a structured questionnaire to gather primary data pertaining to the assessment of independent variables associated with entrepreneurial accounting practices. The second approach encompassed an applied perspective, seeking to gauge the impact of entrepreneurial accounting practices on reducing information asymmetry. This was achieved by examining the financial statements of insurance companies listed on the Amman Stock Exchange for the period spanning 2016 to 2022, thereby constituting our dependent variable. Rigorous statistical techniques were employed to meticulously analyze the collected data, with the validation of our hypotheses achieved through a comprehensive multiple regression analysis. The findings of this study distinctly underscore the pivotal role played by entrepreneurial accounting practices in ameliorating information asymmetry issues within the corporate economic landscape. A robust and statistically significant correlation emerged between our dependent variable, namely the reduction of information asymmetry, and both the independent variables associated with entrepreneurial accounting practices and their inherent characteristics. This study's significant recommendation extends beyond the mere technical dimensions of accounting, encompassing a call for a holistic reevaluation of the accounting profession. Addressing the enduring phenomenon of information asymmetry among stakeholders is not merely a concern confined to the technical aspects of accounting. Rather, it represents a profound professional and ethical crisis that permeates the entire accounting ecosystem. This crisis emanates from the very foundations and legal frameworks of the profession, extends through accounting standards, and encompasses professional conduct and ethics. Ultimately, it concludes with the supervision and oversight of the quality of professional performance, an obligation shared by accountants, certified auditors, and management alike.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".