The Determinants of Risk-Sharing Strategies of Food-Retailers: A Study on Chittagong, Bangladesh
Bibliographic record
Abstract
Food-retailers have access to various risk management strategies to manage the risks in food processing and trading. Risk-sharing is a powerful instrument amongst risk management strategies. It comprises a negotiation of risk allocation between at least two agents to reduce risk and to increase expected utility. The objective of the study is to identify the factors that affect the selection of risk-sharing strategies of food-retailers. In this regard, the study explains risk-sharing instruments from three perspectives: risk reduction; risk mitigation; and risk coping strategies. Food-retailers choose these risk-sharing strategies according to their preference. We link a theoretical understanding of the existing risk-sharing strategies with an empirical model. For quantitative analysis, primary data encompassing 24 variables is sampled from 109 randomly selected food-retailers from and around the city of Chittagong, Bangladesh. This study uses a multiple regression model to identify the significant factors in selecting risk-sharing strategies. The results infer that Family Employment, Hired Employment, Value Chain Challenges, Institutional Challenges, Societal Challenges, Risk Attitudes on Marketing and Promotion, Risk Attitudes on Innovation, Risk Attitudes on Business-in-General, Gender and Expectation for Succession, have a significant effect on the selection of risk-sharing strategies. The analysis is performed on SPSS (version-26). This study covers only off-business risk-sharing instruments of food-retailing. Consequently, this result is irrespective of on-business risk management strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".