Can advanced society 5.0 technology create economic and social value for millennial and generation Z MSMEs in Surabaya, Indonesia? An economic resilience perspective
Bibliographic record
Abstract
This study aims to analyze the critical influence of advanced technology on the future capabilities of millennial and Gen Z digital micro, small, and medium-sized enterprises (MSMEs) and their contribution to creating economic and social value through entrepreneurial orientation. This study adopts a quantitative approach, focusing on 268 MSME business owners in the millennial generation and Gen Z in Surabaya, which is considered a hub for millennial and Gen Z Indonesians. Respondent data were collected via an online survey and analyzed using partial least square-structural equation modeling. The results show that social media, big data, and the Internet of Things influence the advanced technology business capabilities of millennial and Gen Z MSMEs. Artificial intelligence and blockchain have not yet played significant roles, as these trends are still emerging. Furthermore, the advanced technology business capabilities of millennials and Gen Z MSMEs enhance their entrepreneurial orientation. These MSMEs have become more courageous and better able to identify future opportunities, ultimately creating economic and social value.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".