| Abstrakt: |
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). [ABSTRACT FROM AUTHOR] |
