Microenterprise performance and microenterprise zones (MEZOs) in China
Bibliographic record
Abstract
Purpose The purpose of this exploratory study is to examine whether performance of microenterprises located in a microenterprise zone (MEZO) in China is positively related to key management practices, entrepreneurial orientation, marketing capability, and technology capability. The paper aims to introduce the concept of MEZOs as a supplemental tool for governments to strengthen microenterprise activity. Design/methodology/approach A total of 150 randomly selected microenterprises located in a MEZO in Changchun, an industrial city in Northeast China, completed a survey consisting of a three‐part measure of microenterprise performance developed by Zingeret al.and entrepreneurial orientation. Factor analysis was performed on 11 management issues and correlation analysis was performed. Findings The study found that key management practices, marketing capability, and technology capability of microenterprises in MEZOs do have a positive impact on performance sales, net profit, and growth. Research limitations/implications The study's results are limited by a sample in one city in one province of China collected in one month. The data are cross‐sectional rather than longitudinal. Sample bias may exist. Practical implications This study is valuable to government officials, policy makers, non‐government organizations and consulting firms as they examine new measures to unleash the economic power of microenterprises. Social implications MEZOs need to be explored as one solution to aide microenterprise development to improve the poverty level, create jobs, and stimulate economic activity. Originality/value To the authors' knowledge, this is the first exploratory study of the performance of microenterprises located in a MEZO in China. Through research, the factors that contribute to microenterprise performance and success can be better understood and the impact that the MEZO model has on microenterprises can be better delineated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".