Technological Progress and Workforce Development: The New Work Organizational Processes as Challenges and Opportunities for Micro-Enterprises
Bibliographic record
Abstract
(1) Background/Purpose: Our research focuses on stakeholders’ attitudes in the Slovak SME sector, which we assessed through a questionnaire. The ability to evolve and adapt continuously is critical for micro-enterprises in competitive markets. (2) Study Design/Methodology/Approach: The primary objective of our research is to gain a comprehensive understanding of the new work organization processes in SMEs operating in a rapidly changing economic environment. Our research employed a single-sample cross-sectional sampling method, wherein respondents completed a questionnaire within a defined time frame. The primary data collection was carried out using a questionnaire containing closed questions. The research assesses the opinions of the respondents regarding the problem under study over a given period of time. The survey was conducted online. (3) Findings: 31% of the companies surveyed considered that introducing new work organization processes would make the company more adaptable to the changing economic environment. Only one-third considered involving employees in the processes the most important means to achieve this. Higher employee satisfaction is reported as a positive by 24% of companies. However, only a quarter of them believe the best way to achieve this is to involve employees in processes. For micro-enterprises, limited resources and smaller staff prioritize effective communication, and gaps can cause significant difficulties. Regarding the sample examined, among the hypotheses based on the literature background, we were only able to accept hypothesis H3 with modifications, which states that the biggest challenge for micro-companies in the industrial sector in a competitive market is understanding new technologies when improving employee skills. (4) Originality/Value: The research shows that micro-enterprises are particularly sensitive to the challenges associated with new technologies, which can be important information for designing training programs and developing support strategies for enterprises.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".