Enterprise Survey Data on Artificial Intelligence Adoption and Implementation Conditions in Slovakia
This repository provides enterprise-level survey datasets on artificial intelligence (AI) adoption and implementation conditions among companies operating in Slovakia. The data were collected in March and April 2026 using a two-phase mixed-mode survey design combining Computer-Assisted Web Interviewing (CAWI) and Computer-Assisted Telephone Interviewing (CATI).
The datasets provide micro-level survey data on the current state of AI adoption, organizational readiness, perceived barriers, implementation conditions, expected AI application areas, and public policy preferences among Slovak enterprises. The target population comprised private enterprises operating in Slovakia with at least 10 employees, at least five years of operation, non-negative equity, and no specialization in AI development.
The repository contains two analytical datasets:
- The CAWI dataset provides a detailed survey instrument focused on organizational AI maturity, business sentiment towards AI, AI adoption and use, Technology–Organisation–Environment implementation factors, public policy preferences, and barriers to AI adoption.
- The CATI dataset provides a shorter diagnostic survey instrument designed for telephone interviewing and covers company profile, respondent position, AI use, AI awareness, management engagement, digital readiness, AI-related attitudes, barriers, support preferences, public programme awareness, expected application areas, and information sources.
The released datasets are anonymized. Direct identifiers such as company registration numbers and company names are not included in the public analytical files. The datasets are accompanied by structured documentation, including data dictionaries/codebooks and questionnaire files in Slovak and English versions. The documentation enables transparent interpretation of variable names, question wording, value coding, skip logic, and analytical applicability.
10.5281/zenodo.20760725 / https://doi.org/10.5281/zenodo.20760725
