Evaluating the linkages between technological strategies and competitive strategies of business units in different technological environments : a U.S./Canada contrast.
Bibliographic record
Abstract
The links between technological and competitive strategies of business units (BUs) trying to achieve a competitive edge in the market have recently drawn a considerable amount of attention. Current research recognizes the strategic nature of technology itself and suggests that business managers have to understand their technological environments before they can gain any substantial competitive advantage. This study provides a structural framework for empirical research into the relationship between a business unit's technological strategy and its competitive strategy, in the context of its technological environment. Using the Profit Impact of Market Strategy (PIMS) Data Base, a sample of 3,336 business units in the U.S. and Canada are cross-classified into stable, fertile and turbulent technological environments and by the three stages (growth, mature and decline) of their product life-cycle. Analysis of variance is applied to a set of variables in an exploratory attempt to determine response patterns of five Technological Strategy variables (dependent variables) in each of six Strategic Configurations (independent variables). The research attempts to examine the links that emerge between Technological Strategy and Competitive Strategy variables, in the context of BUs' technological environment and stage of product life-cycle. The sample is divided into U.S. and Canadian business units to explore any significant differences in competitive positioning between the two countries.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".