Determinants of Business Resilience Framework for Small Businesses: Moderating Effects of Financial Literacy
Bibliographic record
Abstract
Covid-19 has significantly disrupted and devastated the world's economy.Data from Malaysia shows that more than 30,000 companies have closed their operations since the movement control order (MCO) implementation due to Covid-19 that began in March 2020.However, the effects on small businesses are especially severe, mainly due to the higher vulnerability levels and lower resilience related to their size.This study provides an empirical analysis of the key drivers leading to the business resilience of small businesses in Malaysia that have survived the Covid-19 pandemic.Data from 215 small businesses were collected physically and online across Malaysia from May 2021 to December 2021.Structural Equation Modeling (SEM) using Smart PLS 3.2.4 was used to analyse the data, whereby nine hypotheses were tested in the current study.The results showed that technology acceptance, government support, and financial literacy significantly influence business resilience among small businesses in Malaysia.The results also indicated that financial literacy moderates the relationship between compliance cost and government support with business resilience.Thus, the findings revealed three important determinants of small businesses' resilience framework, namely technology acceptance, government support, and financial literacy.The study recommends a dynamic, resilient framework to adopt in the "new normal" situation for the successful navigation of small businesses in the future.Moreover, the study provides insight into the key drivers for business resilience factors that small businesses must be concerned with, as the framework can be used to deal with not only the global pandemic but also uncertain conditions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".