Barriers and AI-based Technologies Adoption: A Configurational View from a European-Wide Survey
Bibliographic record
Abstract
Artificial intelligence (AI) is one of the most important, ground-breaking, and debated technologies of our age that has gained in popularity thanks to the availability of huge amount of data (Big Data), the advances made in algorithmics, and the improvement witnessed in both computing power and storage capability of machines. Although there is a consensus on the disruptive nature of AI, some firms have already adopted this technology while others are still lagging. Conventional wisdom in innovation management posits that perceived barriers prevent organizations from adopting an innovation such as AI-based technologies. However, previous research has yielded mixed results with respect to the relationship between perceived barriers to the adoption of an innovation and its actual adoption. Analyzing data obtained from the European Commission on 7549 firms with a configurational approach based on a combination of hierarchical and non-hierarchical cluster analysis followed by post-hoc analysis, this exploratory study seeks to expand our understanding of AI-based technologies perceived barriers-adoption link. Using 15 variables to measure perceived barriers to the adoption of AI-based technologies, we highlight the complex nature of this relationship that echoes the inconsistent findings of previous studies. We identify three clusters of firms that face different configurations of perceived barriers while exhibiting orthogonality on all 15 perceived barriers to AI-based technologies adoption (cluster I – High Level perceived barriers, n=2249, 32.4%; Cluster II- Low Level perceived barriers, n=1879, 24.9%; Cluster III -Moderate Level barriers, n=3221, 42.7%). Among the three configurational solutions, two exhibit asymmetrical relationships between the causal conditions (configuration of perceived barriers) and the outcome (AI-based technologies adoption intensity).
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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 | 0.001 | 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".