Inventory Management Practices among Small and Micro Businesses during COVID-19 Pandemic
Bibliographic record
Abstract
Developing micro, small, and medium businesses (SMEs) encourages Malaysia's economic growth. The Covid-19 pandemic has greatly affected SMEs. During the Covid-19 pandemic, with material shortages and shipping delays, the ability to track the inventory is critical. Adopting digital technology in the inventory tracking process will assist the company in managing its inventory effectively. SMEs need to have relevant knowledge and technical skills to adopt digital technology. Specifically, this study explores SMEs' current inventory management practices to gauge their knowledge of inventory management. Data was collected using semi-structured interviews using a questionnaire with ten SMEs in Selangor. This study documents that SME organisation value is positively associated with best inventory management practices. In addition, results show that divergence can intensify the negative relationship between low inventory management practices and SMEs' value. Findings support the need for more scrutiny by SME owners, regulators, policymakers, and standard setters to monitor the conflict, which is crucial for SMEs to overcome the issues and challenges they face. This study will contribute to the sustainability of SMEs, consistent with the national agenda to strengthen the SMEs as highlighted in the government’s new Economic Transformation Program (ETP) through Bumiputera’s Economic Transformation Agenda.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".