An efficient resource allocation in strategic management using a novel hybrid method
Bibliographic record
Abstract
Purpose – The purpose of this paper is to suggest a novel hybrid method by integrating a decision sciences approach with balanced scorecard (BSC) in order to scientifically enable the efficient strategic management of an organization under limited resources. The proposed research model endeavors to improve critical basis deficiencies of the original BSC as well as formerly improved forms of BSC by appropriately integrating three disparate methods: BSC, analytic network process (ANP), and zero-one goal programming (ZOGP). Design/methodology/approach – The designed approach is separated into three major parts. At first, the traditional BSC, concentrating on both financial and intellectual capital, was adopted as the strategic management framework, and then priorities as well as the importance of tactical drivers derived from BSC application were consecutively identified by the application of ANP. Finally, the study further applied the obtained results of integrated BSC and ANP to ZOGP in order to scientifically identify the optimal strategic investment under simulated constraints of the considered organization. Findings – An application of BSC, ANP, and ZOGP with a case study of an academic institution provided an improved strategic management approach for optimally and scientifically utilizing the limited resources of the organization. The suggested results indicated that only 11 of the 23 strategic projects should be executed. Moreover, the selected tactical tasks would efficiently use less than 36 percent of the strategic expenses of the traditional management approach. Originality/value – Based on the intensive literature reviews, the proposed method could be determined as a novel hybrid approach. It newly conveyed the practical management approach by innovatively including the proper decision sciences method to BSC. This improvement scientifically considered on the resource allocation process that has never been studied before in formerly improved BSC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".