Middle-up-down and top-down approaches: strategy implementation, uncertainty, structure, and foodservice segment.
Bibliographic record
Abstract
Middle-up-down and top-down approaches: Strategy implementation, uncertainty, structure, and foodservice segmentThis study explores the relationship between various profiles of the strategy implementation process and managers' perception of the task environment, complexity and dynamism.This study addresses the following research questions: Do differences exist between levels of perceived environmental change/uncertainty and users of middle-up-down and top-down strategy implementation approaches?And, does this relationship become more meaningful when ownership, firm structure and foodservice segment characteristics are considered?There has been very little research on the food service industry that assesses the relationship between eleven task environment measures of complexity and dynamism and the use of a predominately top-down or middle-up-down approach to the implementation of strategies.Using a sample of food industry managers, multiple discriminate analysis (MDA) was used to predict the use of implementation strategies.Substantive differences appear to exist between levels of perceived environmental change/uncertainty and users of middle-up-down and top-down strategy implementation approaches for foodservice firms.The ability to correctly classify users of middle-up-down and top-down approaches using a multivariate combination of environmental variables is improved radically when ownership, firm structure, and market segment classifications were are considered.Taken as a whole, the findings are most convincing and support the basic hypotheses.Study findings indicate that a broad brushstroke approach to determining whether a middle-updown or top-down is used or appropriate based on the perceived task environment may not be valuable.The results support previous findings in other industries in that the prediction is better for market segments served and the public versus private nature of the firms involved.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".