The Synergy between Engineering Business Strategies and Management Practices: An Analytical Review
Bibliographic record
Abstract
This study explores the synergy between engineering business strategies and management practices, analyzing their integration and impact on organizational performance. Through a systematic literature review of 147 peer-reviewed articles published between 2000 and 2023, the research identifies key trends, correlations, and challenges in aligning technical and managerial disciplines. Findings reveal that innovation and R&D (62.6%) and sustainable engineering (53.1%) are the most prevalent engineering strategies, while agile project management (57.8%) and lean management (49.0%) dominate management practices. Strong correlations were observed between innovation and agile methodologies (r = 0.72) and digital transformation and change management (r = 0.75), highlighting the interdependence of these domains. The integration of engineering and management significantly enhances organizational performance, particularly in innovation output (mean = 4.5) and customer satisfaction (mean = 4.3). However, challenges such as resistance to change (47.6%), lack of cross-functional skills (44.2%), and misalignment of goals (40.8%) hinder effective integration. The study underscores the importance of adopting agile methodologies, fostering cross-functional collaboration, and aligning strategic goals to overcome these barriers. Practical implications include investing in training programs, implementing robust change management practices, and leveraging emerging technologies like artificial intelligence and blockchain. Future research should focus on longitudinal studies, cross-industry comparisons, and the role of emerging technologies in enhancing this synergy. This study contributes to the growing body of knowledge on engineering management by providing actionable insights for practitioners and researchers, emphasizing the critical role of integrating engineering and management practices for sustainable organizational success
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".