A Small Step from Price Competition to Price War: Understanding Causes, Effects and Possible Countermeasures
Why this work is in the frame
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Bibliographic record
Abstract
<p>The first part of this paper describes the characteristics of price wars, pointing to recent examples that have caused a stir among the public as well as in the respective industries. A new, concise definition of the term price war is suggested. In the second part drivers for price wars are discussed and explained based on behavioral economics (understanding the competitor’s strategy as well as a company’s own cost situation). Particularly in industries that are characterized by a high proportion of costs that are unchangeable in the medium-term and low variable costs there is a substantial risk for unintended price competition possibly ending in a price war. Even slight price reductions can have fatal consequences when decision makers mistakenly estimate the price elasticities too high. In the third part a case study of a price war is presented by focusing on the market of long-distance bus journeys in Germany. Since the market for intercity bus connections was liberalized in 2013, the newly created market segment faces a very strong growth and intensive competition. Using a multi-source-multi-method-approach it is shown how the market entry of UK-based company Megabus affected price levels for bus journeys und initiated competitive reactions of the German railway operator Deutsche Bahn. The interaction of various parameters (low barriers to enter the market; high similarity of products/services; fixation on market share and capacity utilization) leads to a ruinous price competition and leaves few chances for a sustainable profitability. Measures to avoid an impending or to terminate an ongoing price war are presented.</p>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it