Artificial intelligence adoption and revenue growth in European SMEs: synergies with IoT and big data analytics
Bibliographic record
Abstract
Purpose The conventional notion that adopting Artificial Intelligence (AI) positively affects firm performance is often confronted with various examples of failures. In this context, large-scale empirical evidence of the economic performance implications of adopting AI is poor, especially in the context of Small and Medium Sized Enterprises (SMEs). Drawing upon the Resource-Based View and the Digital Complementary Asset literature, we assessed whether the adoption of AI affects SMEs’ revenue growth. Design/methodology/approach First, we examine the relationship between the adoption of AI and SMEs’ revenue growth. Second, we assess whether AI complements the Internet of Things (IoT) and Big Data Analytics (BDA). We use firm-level data from the European Commission in 2020 on 11,429 European SMEs (Flash Eurobarometer 486). Findings Among the key findings, we found that ceteris paribus, the adoption of AI positively affects SMEs’ revenue growth and, in conjunction with IoT and BDA, appears to be even more beneficial. Originality/value Our results suggest that AI fosters SME growth, especially in combination with IoT and BDA. Thus, SME managers should be aware of the positive impacts of investments in AI and make decisions accordingly. Likewise, policymakers are aware of the positive effects of SMEs’ reliance on AI, so they may design policies and funding schemes to push this digitalization of SMEs further.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".