Ripples in the Pond: Product Portfolio Reconfiguration and Dynamism in the Competitive Environment
Bibliographic record
Abstract
Abstract The management literature often overlooks how firms can alter the competitive landscape without introducing groundbreaking changes or innovations. Applying the awareness, motivation, and capability framework from competitive dynamics, we posit that as a firm intensifies its product portfolio reconfiguration, its rivals become increasingly aware and motivated to respond, creating a ripple effect that accelerates change in the competitive environment. However, extreme reconfigurations may decrease rivals' motivation due to the costly and disruptive nature of keeping pace. We also argue that rivals will be more motivated to respond to reconfigurations from a firm with high customer orientation and when they possess financial slack. Using a longitudinal dataset of 6382 firms’ product portfolios and those of their rivals, we find support for these arguments. This study contributes to competitive dynamics research by expanding the focus to a firm's entire set of rivals and highlighting how a firm's competitive moves can result in ripple effects throughout its rival network. We also demonstrate how moderate organizational changes can quickly lead to increased environmental dynamism, an overlooked avenue in research of dynamism. Finally, we suggest that a broadened competitive dynamics perspective rooted in Austrian economics can enhance our understanding of firm adaptation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".