Examining the causal factors influencing organizational survival of retail food businesses during the COVID-19 outbreak
Bibliographic record
Abstract
Retail food businesses are important contributors to the economic stability of nations, contributing to the economy in different ways, including providing jobs in the supply chain pipeline. Global events like the pandemic arising from the spread of COVID-19 threatened the aptitude of retail food outlets to operate due to the restrictions put in place to mitigate the spread of the virus. The objective of this research is to investigate the determinants that impact the longevity of organizations in the food retail industry and construct a survival model that accounts for these characteristics considering the COVID-19 pandemic, and examining the roles of variables such as organizational survival, external general environment, corporate governance policy, transformational change management, economic condition, and marketing innovation. The study utilized quantitative methodology, employing a survey questionnaire to gather data from a sample of 360 entrepreneurs. Subsequently, the data was analyzed using statistical analysis and the Structural Equation Model (SEM). The research findings reveal that the external general environment, transformational change management, economic conditions, corporate governance policies, and marketing innovation have statistically significant positive impacts on organizational survival. Consequently, food retail businesses must adapt within the COVID-19 context, under evolving external conditions, to strategize for enhanced competitive capabilities. This includes managing change and economic conditions for survival in a COVID-19 pandemic characterized by rapid economic shifts, human behavioral changes, and adaptive governance policies that align with situations. These findings can be applied to organizational structures, addressing weaknesses and further enhancing organizational strengths to create innovative marketing approaches that align appropriately with current circumstances for organizational survival. An important limitation of the study is that data were collected from only 360 food retailers in Thailand, representing a partial population of the food retail business in Thailand, hence limiting the generalizability of the results beyond the context of the study and other countries.
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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 | 0.003 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".