Urban entrepreneurship and sustainable businesses in smart cities: Exploring the role of digital technologies
Bibliographic record
Abstract
The entrance of sustainable and digital technologies into urban entrepreneurship is a new approach that provides a fertile ground for innovation in businesses. Hence, businesses use new models and methods of entrepreneurship in the context of smart cities to increase their capability and become more sustainable, which leads to their development and expansion. This research aims to investigate the effects of urban entrepreneurship on sustainable businesses in smart cities considering the role of digital technologies. The statistical population of this study is all active technology-based firms located in Tehran in 2022. Then, according to Cochran's formula, 315 firms were selected randomly as the sample. This research is an applied and descriptive-survey research and is quantitative in terms of the type of collected data. The data were analysed using Smart PLS 3 software, structural equation modelling (SEM), and the partial least squares methods. As a result, research findings show that urban entrepreneurship creates and develops the studied firms in both quantitative and qualitative aspects by using and benefiting from digital technologies considering the new needs of cities and achieving business sustainability in smart cities.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".