Assessing the Early Impact of Industry 4.0 Technologies on the Activity, Efficiency, and Profitability of Croatian Micro-, Small-, and Medium-Sized Enterprises
Bibliographic record
Abstract
This study examines the early impact of Industry 4.0 (I4.0) implementation on the financial performance of Croatian companies, focusing on indicators of profitability, efficiency, and activity. The research investigates whether firms adopting I4.0 technologies achieve superior results compared to traditional companies. A unique feature of this study is its integration of primary data—collected via an online survey of Croatian enterprises—with secondary data from publicly available financial reports. Statistical methods, including Analysis of Variance (ANOVA) and linear regression, were employed to test the hypotheses. The results show that I4.0 adopters perform significantly better in terms of net profit margin, return on assets, business efficiency, and supplier bonding days, while no significant difference was found in days sales outstanding. This paper contributes to the literature by offering one of the first empirical analyses of early-stage I4.0 adoption in the context of a transition economy, using firm-level financial data. The findings provide valuable insights for managers, policymakers, and investors aiming to understand the tangible business benefits of digital transformation. The results also highlight the importance of supporting I4.0 adoption strategies to enhance competitiveness and recovery in post-pandemic economic conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".