Technology and Strategic Management Decision-Making as a Constrained Shortest Path Problem
Bibliographic record
Abstract
A constrained shortest path algorithm is developed and implemented in Matlab to optimize the management decision-making process, which is a potential tool for managers. An empirical analysis is performed using Statistics Canada’s Workplace and Employee Survey (WES), which consists of variables relating to employers and their employees, conducted from years 1999 through 2004, inclusively. Specifically, the research explores the relationships among variables such as innovation, technology use, training and human resource management and its effect on the success of the firm in terms of profit and labor productivity. The results are compared to the current literature in technology and organizational management. In general, it is discovered that optimal management strategies are highly dependent upon the performance in which the firm operates. Additionally, the constrained shortest path algorithm developed for the thesis is tested against other leading methods in the literature and is found to be quite competitive. The tests are run on randomly generated constrained shortest path problems of varying degrees of complexity with the algorithm performing well on all levels.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".