Investigating the resilience of micro, small and medium enterprises in entering the digital market us-ing social media: Evidence from Aceh province, Indonesia
Bibliographic record
Abstract
Technological developments are increasingly sophisticated, so micro, small and medium enterprises (MSMEs) must maintain their business through digital markets. The main problem faced by MSMEs in Aceh province is the lack of use of social media as a medium for promoting or selling products online. Thus, this study analyzes the MSMEs' product marketing model, determines the factors that influence MSME labor productivity, and determines MSMEs' resilience strategies in entering the digital market. The research location is Aceh Province which consists of 23 districts/cities. The population in this study were all MSME actors in Aceh Province who were spread across districts/cities, using a purposive random sampling technique. The samples in the study were related agencies and MSMEs actors in Aceh Province, which are spread across 13 regencies/cities, namely Banda Aceh, Sabang, Lhokseumawe, Subulussalam, Langsa, Aceh Tamiang, East Aceh, North Aceh, Central Aceh, West Aceh, Aceh Singkil, Aceh Besar and Aceh Jaya. The results of the study show that (1) the marketing model that is used effectively is the marketing mix, namely the marketing mix, (2) the productivity of MSME workers is influenced by the level of education, age, work experience, gender and expertise or skills possessed by the workforce, (3) The MSMEs resilience strategy is dealing with the digital market can be pursued through government policies by providing training or assistance to business actors to increase product innovation and increase promotion or product sales online through various types of social media, such as Instagram, Facebook, WhatsApp, and market places other.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".