Bibliographic record
Abstract
Abstract Several well-established research streams examine how incumbent firm behavior affects the entry decisions of later entrants, e.g., in terms of herding or differentiation. While it makes sense for a new entrant to take into account an incumbent’s behavior to inform its entry decisions, it would be risky to base such a decision solely on that information. In particular, the potential entrant may also want to conduct its own market research. Naturally, the market research should account for incumbent behavior. Yet, little is known about how a second mover decides where it should conduct market research. Is the information gained from observing the incumbent a substitute or a complement to market research? The information a second mover gathers through observation includes the incumbent’s choice of market. Even more important is the signal generated by an incumbent’s decision to exit or stay in a market. This decision signals to a second mover whether a market is viable, at least for one firm. A second mover that considers entry between an existing market (with an operating incumbent) and a new market (that has no incumbents) chooses between different types of uncertainty. Our paper addresses how this uncertainty affects the second mover’s market research decision. Should a second mover do market research in the competitor’s backyard or should it boldly go where no firm has gone before and research a new market? How is this decision affected by factors such as expected demand conditions and competition? Intuition suggests that information about a virgin market is always more valuable because the first mover already provides information about the existing market. Our research shows that this intuition is not always correct. It is correct when market research generates perfect information. However, market research is rarely perfect. When market research provides estimates subject to an error, a second mover may gain by conducting market research in a market with an existing competitor. Here, the complementarity of the competitor’s performance coupled with market research amplifies the value of the research.
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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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.020 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".