Understanding Gender Dynamics on the Intentions of Small-Scale Traders to Trade in Edible Insects in Kenya
Bibliographic record
Abstract
Grounded in their status as a delicacy in East Africa, the trade of edible insects is increasingly acknowledged as a viable solution to food security and sustainable livelihoods. However, gender differences in trading intentions remain underexplored, particularly in informal and emerging markets. Existing research emphasizes economic and structural barriers but often overlooks the psychological and social determinants. This study addresses this gap by examining gender-specific factors influencing the intention to engage in edible insect trade, integrating demographic, socio-economic, and psychological perspectives. Using structural equation modeling (SEM), multiple regression, and an ordered probit model, we analyzed survey data from 550 traders across key food markets in Kenya, the analysis distinguished how gender moderates the effects of participation intentions. Results revealed that psychological determinants exerted a more significant influence than socio-economic variables on trading intentions. For females, perceived behavioral control (β = 0.775, p < 0.001) and descriptive norms (β = 0.536, p < 0.001) were the strongest predictors, underscoring the importance of self-efficacy and social influence. Conversely, for male traders, attitude (β = 0.331, p < 0.001) and descriptive norms (β = 0.580, p < 0.001) emerged as dominant factors, suggesting a more individualistic decision-making process. These findings highlight the necessity to enhance women’s self-efficacy through training, financing, and market access while leveraging attitudinal and social reinforcement strategies to encourage male participation. The study contributes to the behavioral economics literature on emerging markets and offers practical insights for policymakers, development agencies, and entrepreneurs seeking to promote sustainable insect-based trade.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".