I'll be there for you: coopetition and competitor-oriented activities among South Asian restaurants in two UK regional clusters
Bibliographic record
Abstract
Purpose This study investigates how a competitor orientation (knowledge of and acting on competitors' strengths and weaknesses) facilitates coopetition activities (collaboration with competitors), within networks of competing micro-sized, independent, family restaurants, owned by entrepreneurs from ethnic minority backgrounds. Design/methodology/approach An instrumental case study features data collected from interviews with 30 owners (as key informants) of micro-sized, independent, family-owned restaurants, in two urban clusters within the Midlands (UK). Specifically, the context involves restaurants offering South Asian cuisine and where the owner originated from the Indian sub-continent (Bangladesh, India or Pakistan). Secondary data were collected wherever possible. These two clusters (not named for ethics reasons) are highly populated by members of these respective ethnic communities; also, they contain a relatively large number of restaurants offering South Asian cuisine. Findings A competitor orientation facilitated strong coopetition-oriented partnerships comprised of extended family and intra-community members that helped enhance individual firms' performance, maintained family employment and sustained their cluster. It also helped owners develop subtle counter strategies where weak ties existed, such as via inter-community networks. For example, strategies attracted customers that were not loyal to a particular restaurant, or indeed, sub-ethnic cuisine (within Bangladesh, India or Pakistan, like the Punjab region). Subtle as opposed to outright counter strategies minimised retaliation, since restaurant owners wanted to avoid price wars, or spreading misinformation where the reputation of a cluster may suffer alongside the likely survival of individual businesses within that regional cluster. Originality/value Mixed evidence exists in earlier studies regarding the competitive rivalry in certain sectors where ethnic minority ownership is prominent; not least, restaurants located in regional clusters. However, this investigation considers the notion – what if some of these earlier studies are wrong? More specifically, does certain prior research under-represent the extent that rival entrepreneurs of an ethnic minority origin collaborate rather than compete for mutually beneficial purposes? New evidence emerges regarding ways in which a competitor orientation can influence the performance-enhancing nature of coopetition activities among business owners originating from both intra and inter-ethnic communities.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".