The simultaneous identification of strategic/performance groups and underlying dimensions for assessing an industry's competitive structure
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to integrate aspects of the literature on strategic and performance groups and explicitly derive strategic/performance groups which exhibit differences with respect to both strategy and performance, as well as display associations and potential interrelationships between the two sets of variables. Design/methodology/approach A two‐way clusterwise bilinear spatial model was formulated (e.g. a scalar products or vector multidimensional scaling model (MDS)) for the analysis of two‐way strategic and performance data which simultaneously performs MDS and cluster analysis. An efficient alternating least‐squares procedure was devised that estimates conditionally globally optimum estimates of the model parameters within each iterate in analytic, closed‐form expressions. Findings This bilinear MDS methodology was deployed in the context of strategic/performance group estimation using archival data for public banks in the NY‐NJ‐PA tri‐state area. For this illustration, four strategic/performance groups and two underlying dimensions were found. Practical implications Consideration of both strategy and performance data should be employed in describing the heterogeneity amongst firms competing in the same industry. Originality/value The paper provides a new spatial methodology to derive strategic/performance groups in any given industry to more completely summarize intra‐industry heterogeneity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it