The adoption of management accounting innovations in emerging economies: exploring market, institutional and organizational factors
Bibliographic record
Abstract
Purpose This paper aims to investigate how market and institutional pressures, mediated by organizational support, impact the adoption of management accounting innovations (MAIs) in Egypt, one of the emerging economies. Design/methodology/approach The authors collected data using a questionnaire sent to 93 joint venture manufacturing firms in Egypt’s public business sector. To test the theoretical model, partial least squares structural equation modeling (SEM) was performed using the SEMinR package in R. Findings The findings reveal that market pressures significantly drive the adoption of MAIs, whereas institutional pressures influence adoption indirectly by shaping organizational strategies and cultural frameworks. Organizational support plays a crucial role, both as a direct factor in adopting MAIs and as a transformative channel that aligns external pressures with organizational capabilities. The external pressures serve as triggers for change, whereas the successful adoption of MAIs depends on robust internal organizational support structures. Research limitations/implications The focus on Egypt may limit the applicability of the findings to other emerging economies or developed markets. Future research should conduct comparative studies across different countries or regions to understand context-specific differences in MAI adoption. In addition, this study mainly considers market and institutional pressures along with organizational support, potentially overlooking other influential factors such as industry-specific dynamics, cultural dimensions and leadership styles. Exploring these factors could provide a more comprehensive understanding of the adoption of MAIs. Practical implications This study provides actionable insights for organizations aiming to improve their MAS. By aligning management accounting practices with external market dynamics and strong internal capabilities, organizations can better handle rapid market and regulatory changes. Policymakers can use these insights to create supportive frameworks that encourage innovation adoption and sustainable economic development. Originality/value This study makes a significant contribution by examining the various pressures and factors that influence the adoption of MAIs in emerging economies, particularly within contexts shaped by unique market-driven and regulatory forces, such as public–private ownership structures. It highlights the dual role of organizational support as both a direct enabler and mediator, transforming external pressures into practical procedures and improvements that drive the adoption of MAIs. Furthermore, it challenges the notion of pressures as mere constraints, illustrating how firms actively absorb and leverage them to drive the adoption of MAIs.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".