Evaluating Beyond Meat’s Success in the Consumer Market for Plant-Based Meat
Bibliographic record
Abstract
Behavioral economics theories were used to evaluate Beyond Meat's success in changing consumer tastes and preferences for plant-based protein in North America-mainly through its retail presence and partnerships with fastfood restaurants.Conclusions were drawn through the analysis of Beyond Meat's corporate decisions and various secondary sources, such as research conducted by Cattlemen's Beef Promotion and Research Board, Kansas University, and Purdue.The study revealed that biases, including status quo, distinction, familiarity, anchoring, and confirmation, are extensive in consumer perceptions of plant-based meats, often requiring immense efforts in choice architecture and nudging to create changes in preestablished habits such as meat consumption.However, by leveraging theories such as advertised responsibility, framing, nudging, herd behavior, and FOMO, Beyond Meat has significantly impacted the industry and the acceptance of plant-based meats.Biases pose problems in our day-to-day lives as change is required for improvement.Through exploring the factors in decision-making that influence human stagnance, we could alter the environment and context in which choices are presented through choice architecture, "nudging" consumers in the right direction.This can lead to low-cost and wide-scale change, such as more sustainable diets and reducing the impacts of imminent issues such as climate change.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".