Adoption of Artificial Intelligence Tools in Small Enterprises in the EU and the Czech Republic: Barriers, Opportunities and Implications for Marketing Strategies
| dc.contributor.author | Nunvarova, Jana | |
| dc.contributor.other | Ekonomická fakulta | cs |
| dc.date.accessioned | 2025-10-02T10:24:24Z | |
| dc.date.available | 2025-10-02T10:24:24Z | |
| dc.description.abstract | This paper focuses on analyzing the adoption rate of artificial intelligence (AI) tools in small enterprises in the Czech Republic and the European Union. It utilizes secondary quantitative data from Eurostat databases, the Czech Statistical Office, and other relevant sources. The aim is to identify current trends, cross-country differences, and key factors influencing small businesses' decisions to implement AI technologies, with an emphasis on their application in marketing. The findings show that despite growing interest in AI technologies, their adoption among small firms remains low. While 41% of large enterprises in the EU implemented at least one AI tool in 2024, only 12% of small enterprises did so, and in the Czech Republic, the figure was just 9%. This confirms the persistent digital divide between companies of different sizes and highlights the potential decline in small firms’ competitiveness. The main barriers to the adoption of AI technologies include high costs and a lack of expertise. The analysis includes correlation and regression analysis, which demonstrated a statistically significant relationship between the level of employee training in information and communication technologies and the level of adoption of AI technologies in small enterprises. Small firms most often implement more accessible tools focused on natural language generation, text analysis and automated customer support. The paper also provides an overview of possible support measures, which can contribute to more effective implementation of AI technologies in marketing and other business processes of small enterprises. | en |
| dc.format | text | |
| dc.identifier.doi | 10.15240/tul/009/lef-2025-15 | |
| dc.identifier.isbn | 978-80-7494-747-6 | |
| dc.identifier.uri | https://dspace.tul.cz/handle/15240/178037 | |
| dc.language.iso | en | |
| dc.publisher | Technická Univerzita v Liberci | cs |
| dc.publisher | Technical university of Liberec, Czech Republic | en |
| dc.publisher.abbreviation | TUL | |
| dc.relation.isbasedon | BASRI, W. (2020). Examining the Impact of Artificial Intelligence (AI)-Assisted Social Media Marketing on the Performance of Small and Medium Enterprises: Toward Effective Business Management in the Saudi Arabian Context: International Journal of Computational Intelligence Systems, 13(1), 142. https://doi.org/10.2991/ijcis.d.200127.002 BORGES, A. F. S., LAURINDO, F. J. B., SPÍNOLA, M. M., GONÇALVES, R. F., & MATTOS, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225 CHATTERJEE, S., CHAUDHURI, R., VRONTIS, D., & KADIĆ-MAGLAJLIĆ, S. (2023). Adoption of AI integrated partner relationship management (AI-PRM) in B2B sales channels: Exploratory study. Industrial Marketing Management, 109, 164–173. https://doi.org/10.1016/j.indmarman.2022.12.014 ČSU. (2024). Využívání informačních a komunikačních technologií v podnikatelském sektoru—2024: Technologie umělé inteligence (06200524a11). chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://csu.gov.cz/docs/107508/800fb934-0e20-a125-0639-3677ec12e677/06200524a11.pdf?version=1.0 CUBRIC, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257. https://doi.org/10.1016/j.techsoc.2020.101257 ELOUNDOU, T., MANNING, S., MISHKIN, P., & ROCK, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (arXiv:2303.10130). arXiv. https://doi.org/10.48550/arXiv.2303.10130 ERDIL, E., & BESIROGLU, T. (2024). Explosive growth from AI automation: A review of the arguments (arXiv:2309.11690). arXiv. https://doi.org/10.48550/arXiv.2309.11690 EUROPEAN COMMISSION. (2003, May). Commission Recommendation of 6 May 2003 concerning the definition of micro, small and medium-sized enterprises. https://eur-lex.europa.eu/eli/reco/2003/361/oj/eng EUROSTAT. (2025a). Enterprise statistics by size class and NACE Rev. 2 activity (from 2021 onwards). https://ec.europa.eu/eurostat/databrowser/view/sbs_sc_ovw/default/table?lang=en&category=bsd.sbs.sbs_ovw EUROSTAT. (2025b). Use of artificial intelligence in enterprises. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises&utm_source=chatgpt.com#Context KÜHL, N., MÜHLTHALER, M., & GOUTIER, M. (2020). Supporting customer-oriented marketing with artificial intelligence: Automatically quantifying customer needs from social media. Electronic Markets, 30(2), 351–367. https://doi.org/10.1007/s12525-019-00351-0 LEUNG, X. Y., BAI, B., & STAHURA, K. A. (2015). The Marketing Effectiveness of Social Media in the Hotel Industry: A Comparison of Facebook and Twitter. Journal of Hospitality & Tourism Research, 39(2), 147–169. https://doi.org/10.1177/1096348012471381 MAROUFKHANI, P., TSENG, M.-L., IRANMANESH, M., ISMAIL, W. K. W., & KHALID, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190 OECD. (2024). OECD Digital Economy Outlook 2024 (Volume 1): Embracing the Technology Frontier. OECD. https://doi.org/10.1787/a1689dc5-en PARTEKA, A., & KORDALSKA, A. (2023). Artificial intelligence and productivity: Global evidence from AI patent and bibliometric data. Technovation, 125, 102764. https://doi.org/10.1016/j.technovation.2023.102764 SINGLA, A., SUKHAREVSKY, A., HALL B., & YEE, L. (2023, 8). The state of AI in 2023: Generative AI’s breakout year. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year TANG, L., NI, Z., XIONG, H., & ZHU, H. (2015). Locating targets through mention in Twitter. World Wide Web, 18(4), 1019–1049. https://doi.org/10.1007/s11280-014-0299-8 THEODORIDIS, P. K., & GKIKAS, D. C. (2019). How Artificial Intelligence Affects Digital Marketing. In A. Kavoura, E. Kefallonitis, & A. Giovanis (Eds.), Strategic Innovative Marketing and Tourism (pp. 1319–1327). Springer International Publishing. https://doi.org/10.1007/978-3-030-12453-3_151 WAMBA-TAGUIMDJE, S.-L., FOSSO W. S., KALA KAMDJOUG, J. R., & TCHATCHOUANG W. C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411 | |
| dc.relation.ispartof | Liberecké ekonomické fórum 2025 | cs |
| dc.relation.ispartof | Liberec Economic Forum 2025 | en |
| dc.subject | Small Enterprises | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Marketing | en |
| dc.subject | Digital Transformation | en |
| dc.subject | AI Barriers | en |
| dc.subject.classification | C21 | |
| dc.subject.classification | R13 | |
| dc.title | Adoption of Artificial Intelligence Tools in Small Enterprises in the EU and the Czech Republic: Barriers, Opportunities and Implications for Marketing Strategies | en |
| dc.type | proceeding paper | en |
| local.access | open | |
| local.citation.epage | 206 | |
| local.citation.spage | 195 | |
| local.faculty | Faculty of Economics | |
| local.fulltext | yes | |
| local.relation.abbreviation | LEF | cs |
| local.relation.abbreviation | LEF | en |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Nunvarova.pdf
- Size:
- 605.16 KB
- Format:
- Adobe Portable Document Format
- Description:
- article