Bibliographic record
Abstract
Purpose The purpose of this paper, applying concepts in the plant-based food sector, is a focus on the competitive rival trap for startup firms, with their initial advantage for under-served market segments, only to be overtaken by scale, speed, and brands of incumbent brand firms. As a case study of industry transformation, the food production sector illustrates how organizational innovation brings new forms of rivalry, from the farm gate to the kitchen plate. As a result, startups face a rivalry trap, if unable to scale quickly, as incumbents reframe their strategic response with startup acquisitions, corporate incubators or alliance partnerships consumer demand. Design/methodology/approach This paper outlines the features of precision agriculture, a new paradigm for agriculture and food production, requiring new competences and skillsets in the protein revolution, including issues like virus, bacteria and the molecular structure of food groups, animal breeding and veterinary medicine. Plant-based foods is used as a case study for startups and the rivalry trap. Findings The emergence of plant-based foods is a case study of market opportunity and creative destruction, where the potential market varies from $25bn to $72bn, and growing faster in the dairy sector. However, food incumbents bring new strategic responses and a rivalry trap, where startups must gain scale quickly in capabilities, talent and marketing prowess, often exploiting demand in market niches unimpeded by incumbent rivals. Research limitations/implications Startups in biological sciences face massive challenges to increase scale and scope, even with unique intellectual property. Practical implications Startup firms need multidisciplinary management teams with a global outlook. Social implications Plant-based foods form part of the protein revolution but face challenges of scale, cost competitiveness and taste, despite advantages for climate mitigation. Originality/value The impact of technological and science applications has blurred the traditional concept of industry boundary, with huge variations in the intangible knowledge component in their core activities and capabilities. Underlying variations imply that not all industries have similar supply demand conditions, with variations in input costs, capital intensity and innovation needs, with strategic implications for the rivalry trap.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".