Are the First Gray’s Shreds of Light Still Bright?: An Almost Forty Years of Bibliometric and Systematic Literature Review on Hofstede-Gray’s Cultural Model in Accounting
Bibliographic record
Abstract
The cultural values of a nation influence every aspect of its environment. As a subculture, accounting is not exempt from this influence. From this perspective, Gray proposed a model based on Hofstede’s cultural dimensions, identifying four accounting cultural values that have served as the theoretical foundation for subsequent research. This study presents a systematic and bibliometric literature review on the application of Hofstede-Gray’s model in accounting, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 framework. It examines the model’s impact and scope as well as the key findings of various studies. A total of seventy articles, published between 1988 and 2024 in journals indexed in the Scopus and Web of Science databases, were identified and analyzed. The collected data was assessed using the tools available in VOSviewer and Bibliometrix. The findings indicate that Hofstede-Gray’s model has been widely used as a theoretical framework for numerous studies, has been progressively adapted to various contexts, and has been applied to explain several accounting-related phenomena. The model remains a subject of critique and proposals for revision. This study aims to contribute to academia by providing a state-of-the-art review of a model proposed nearly forty years ago, from which future research can build upon the identified gaps, potentially leading to both social and practical implications arising from new investigations.
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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 | 0.055 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.060 | 0.083 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".