A Small Step from Price Competition to Price War: Understanding Causes, Effects and Possible Countermeasures
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".