The Who, Where, What, How and When of Market Entry
Bibliographic record
Abstract
Abstract This introductory, along with the eight articles contained within this Special Issue, highlights and brings greater clarity to entrant‐incumbent interactions and to firm movement – when entrants traverse market territories for the creation and/or delivery of offerings, where ‘markets’ include service or product categories, technology or resource spaces, industries, sectors and/or geographies. Collectively, this Special Issues explains that firm movement across market boundaries is highly consequential, influencing resource‐capability mixes inside firms, interfirm relations, market logic and industry value chains, and of course, people, communities and even nations. Specifically, we develop a field‐wide perspective of market entry by expanding on the framework of market entry that Zachary and his colleagues developed (Zachary et al., 2015) – i.e., the who (players such as incumbents, entrants, suppliers, etc.), when (the timing and sequence of entry), how (the strategy, resources, capabilities, etc.), where (the space of entry) and what (product, service, business model, etc.) – to include two additional categories: complements (networks, platforms, ecosystems) and non‐market elements (government, political, social and cultural arrangements). We also summarize the eight highly diverse and insightful articles that make this Special Issue, and conclude with a discussion to highlight foundational questions that point to new directions in future research in this field. In sum, we hope to inspire scholars to go beyond counting outcomes (e.g., entry/exit rates, or profiling successful versus unsuccessful entrants), to consider contexts, processes and contingencies (e.g., cost, time, collaboration, competition, interfirm relations, etc.) and to discover boundary conditions that inform a theory of market entry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".