Determinants of AI adoption intention in SMEs. Romanian case study
Abstract
The paper investigates the drivers and barriers that encourage or hinder the adoption of artificial intelligence (AI) technologies within Romanian SMEs. By using the Technology-Organisation-Environment (TOE) framework, we examined the role of several factors from each TOE dimension in predicting the AI adoption behaviour. The factors were constructed through factor analysis followed by the estimation of a linear regression model. Partial least squares structural equation modelling was then used in order to further explore the relationships and to check the robustness of the linear regression model. Our findings highlight the significant role played by leadership, organizational readiness, as well as the “push-and-pull” effect of competitors and customers in encouraging SMEs to adopt AI technologies. However, in the case of Romania, specific challenges related to a lack of digital skills among employees, a limited understanding of the relative advantage that digitalisation can offer, as well as a lack of marketing efforts from the side of vendors make it difficult for SMEs to consider the implementation of AI technologies. This exploratory study seeks to understand the underlying trends of the phenomenon and serves as a stepping stone for vendors, managers, as well as researchers to better understand the market for AI tools and solutions among Romanian SMEs.
Keyword : artificial intelligence, small and medium sized enterprises, TOE framework, AI adoption behaviour, Romania, structural equation modelling
How to Cite
Apostoaie, C.-M., Roman, T., Maxim, A., & Jijie, D.-T. (2025). Determinants of AI adoption intention in SMEs. Romanian case study. Journal of Business Economics and Management, 26(2), 277–296. https://doi.org/10.3846/jbem.2025.23650
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2021). Identifying key factors for adopting artificial intelligence-enabled auditing techniques by joint utilization of fuzzy-rough set theory and MRDM technique. Technological and Economic Development of Economy, 27(2), 459–492. https://doi.org/10.3846/tede.2020.13181
Indaco. (2017). Anexa – Lista domeniilor de specializare inteligentă | Ordin 7021/2017. https://lege5.ro/gratuit/gi3deobzge2a/lista-domeniilor-de-specializare-inteligenta-ordin-7021-2017?dp=gi2dsmzqgu4dmoa
Ingalagi, S. S., Mutkekar, R. R., & Kulkarni, P. M. (2021). Artificial Intelligence (AI) adaptation: Analysis of determinants among Small to Medium-sized Enterprises (SME’s). IOP Conference Series: Materials Science and Engineering, 1049(1), Article 012017. https://doi.org/10.1088/1757-899X/1049/1/012017
Jaumotte, F., Li, L., Medici, A., Oikonomou, M., Pizzinelli, C., Shibata, I., Soh, J., & Tavares, M. M. (2023). Digitalization during the COVID-19 crisis: Implications for productivity and labor markets in advanced economies. (IMF Staff Discussion Note, SDN2023/003). International Monetary Fund. https://doi.org/10.5089/9798400232596.006
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Khaliq, A., Waqas, A., Nisar, Q. A., Haider, S., & Asghar, Z. (2022). Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective. Technology in Society, 68, Article 101807. https://doi.org/10.1016/j.techsoc.2021.101807
Khanzode, A. G., Sarma, P. R. S., Mangla, S. K., & Yuan, H. (2021). Modeling the Industry 4.0 adoption for sustainable production in Micro, Small & Medium Enterprises. Journal of Cleaner Production, 279, Article 123489. https://doi.org/10.1016/j.jclepro.2020.123489
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Lada, S., Chekima, B., Karim, M. R. A., Fabeil, N. F., Ayub, M. S., Amirul, S. M., Ansar, R., Bouteraa, M., Fook, L. M., & Zaki, H. O. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), Article 100144. https://doi.org/10.1016/j.joitmc.2023.100144
Lee, C. S., & Tajudeen, F. P. (2020). Usage and impact of artificial intelligence on accounting: evidence from Malaysian organisations. Asian Journal of Business and Accounting, 13(1), 213–240. https://doi.org/10.22452/ajba.vol13no1.8
Mangla, S. K., Raut, R., Narwane, V. S., Zhang, Z., & priyadarshinee, P. (2021). Mediating effect of big data analytics on project performance of small and medium enterprises. Journal of Enterprise Information Management, 34(1), 168–198. https://doi.org/10.1108/JEIM-12-2019-0394
Marikyan, D., & Papagiannidis, S. (2023). Technology acceptance model: A review. In S. Papagiannidis (Ed.), TheoryHub book. https://open.ncl.ac.uk/theory-library/TheoryHubBook.pdf
Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems, 123(1), 278–301. https://doi.org/10.1108/IMDS-11-2021-0695
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, Article 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190
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Morikawa, M. (2016). The effects of artificial intelligence and robotics on business and employment: Evidence from a survey on Japanese firms (Discussion paper, 16066). Research Institute of Economy, Trade and Industry.
Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal Information Systems Evaluation, 14(1), 110–121.
Phuoc, N. V. (2022). The critical factors impacting Artificial Intelligence applications adoption in Vietnam: A Structural equation modeling analysis. Economies, 10(6), Article 129. https://doi.org/10.3390/economies10060129
Popa, I., Cioc, M. M., Breazu, A., & Popa, C. F. (2024). Identifying sufficient and necessary competencies in the effective use of Artificial Intelligence technologies. Amfiteatru Economic, 26(65), 33–52. https://doi.org/10.24818/EA/2022/59/46
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with Artificial Intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1). https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/
Rawashdeh, A., Bakhit, M., & Abaalkhail, L. (2023). Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting automation. International Journal of Data and Network Science, 7, 25–34. https://doi.org/10.5267/j.ijdns.2022.12.010
Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial Intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98–117. https://doi.org/10.3846/jbem.2020.13641
Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2024). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management. https://doi.org/10.1080/00472778.2024.2379999
Sharma, S., Singh, G., Islam, N., & Dhir, A. (2024). Why do SMEs adopt Artificial Intelligence-based chatbots? IEEE Transactions on Engineering Management, 71, 1773–1786. https://doi.org/10.1109/TEM.2022.3203469
Soni, N., Sharma, E. K., Singh, N., & Kapoor, A. (2020). Artificial Intelligence in business: From research and innovation to market deployment. Procedia Computer Science, 167, 2200–2210. https://doi.org/10.1016/j.procs.2020.03.272
Sun, S., Hall, D. J., & Cegielski, C. G. (2020). Organizational intention to adopt big data in the B2B context: An integrated view. Industrial Marketing Management, 86, 109–121. https://doi.org/10.1016/j.indmarman.2019.09.003
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The process of technological innovation. Lexington Books.
Treiblmaier, H. (2018). The impact of the blockchain on the supply chain: A theory-based research framework and a call for action. Supply Chain Management: An International Journal, 23(6), 545–559. https://doi.org/10.1108/SCM-01-2018-0029
Wang, M., & Pan, X. (2022). Drivers of Artificial Intelligence and their effects on supply chain resilience and performance: An empirical analysis on an emerging market. Sustainability, 14(24), Article 16836. https://doi.org/10.3390/su142416836
Wong, L.-W., Leong, L.-Y., Hew, J.-J., Tan, G. W.-H., & Ooi, K.-B. (2020). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, Article 101997. https://doi.org/10.1016/j.ijinfomgt.2019.08.005
Wu, W., Chin, W., & Liu, Y. (2022). Technostress and the smart hospitality employee. Journal of Hospitality and Tourism Technology, 13(3), 404–426. https://doi.org/10.1108/JHTT-01-2021-0032
Xu, W., Ou, P., & Fan, W. (2017). Antecedents of ERP assimilation and its impact on ERP value: A TOE-based model and empirical test. Information Systems Frontiers, 19(1), 13–30. https://doi.org/10.1007/s10796-015-9583-0
Almashawreh, R., Talukder, M., Charath, S. K., & Khan, M. I. (2024). AI adoption in Jordanian SMEs: The influence of technological and organizational orientations. Global Business Review. https://doi.org/10.1177/09721509241250273
AlSheibani, S., Cheung, Y., & Messom, C. (2018). Artificial Intelligence adoption: AI-readiness at Firm-Level. In PACIS 2018 Proceedings (Article 37). https://aisel.aisnet.org/pacis2018/37
Apostoaie, C.-M., & Bilan, I. (2020). Macro determinants of shadow banking in Central and Eastern European countries. Economic Research-Ekonomska Istraživanja, 33(1), 1146–1171. https://doi.org/10.1080/1331677X.2019.1633943
Baker, J. (2012). The technology–organization–environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information systems theory: Explaining and predicting our digital society (Vol. 1, pp. 231–245). Springer New York. https://doi.org/10.1007/978-1-4419-6108-2_12
Beck, T., Demirgüç-Kunt, A., & Maksimovic, V. (2005). Financial and legal constraints to growth: Does firm size matter? The Journal of Finance, 60(1), 137–177. https://doi.org/10.1111/j.1540-6261.2005.00727.x
Chaudhuri, R., Chatterjee, S., Vrontis, D., & Chaudhuri, S. (2022). Innovation in SMEs, AI dynamism, and sustainability: The current situation and way forward. Sustainability, 14(19), Article 12760. https://doi.org/10.3390/su141912760
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
Chen, Y., Hu, Y., Zhou, S., & Yang, S. (2023). Investigating the determinants of performance of artificial intelligence adoption in hospitality industry during COVID-19. International Journal of Contemporary Hospitality Management, 35(8), 2868–2889. https://doi.org/10.1108/IJCHM-04-2022-0433
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology, Cambridge, MA, USA.
Dong, J. Q., & Yang, C.-H. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), Article 103124. https://doi.org/10.1016/j.im.2018.11.001
European Commission. (2024). Entrepreneurship and small and medium-sized enterprises (SMEs). https://single-market-economy.ec.europa.eu/smes_en
European Investment Bank. (2023a). Digitalisation in Europe 2022–2023. Evidence from the EIB Investment Survey. https://www.eib.org/en/publications/20230112-digitalisation-in-europe-2022-2023
European Investment Bank. (2023b). Digitalisation of SMEs in Romania. An assessment of the level of digitalisation of SMEs in Romania and recommendations to increase their level of digitalisation. https://www.eib.org/attachments/lucalli/20230198_digitalisation_of_smes_in_romania_en.pdf
European Investment Bank. (2023c). Investment Report 2022/23: Resilience and renewal in Europe.
El-Haddadeh, R. (2020). Digital innovation dynamics influence on organisational adoption: The case of cloud computing services. Information Systems Frontiers, 22(4), 985–999. https://doi.org/10.1007/s10796-019-09912-2
Eurostat. (2023, December). Use of artificial intelligence in enterprises. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises
Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press. https://doi.org/10.4324/9780203838020
Hansen, E. B., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, 362–372. https://doi.org/10.1016/j.jmsy.2020.08.009
Hoffmann, M., & Nurski, L. (2021). What is holding back artificial intelligence adoption in Europe? Policy Contribution, 24. https://www.bruegel.org/policy-brief/what-holding-back-artificial-intelligence-adoption-europe
Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2021). Identifying key factors for adopting artificial intelligence-enabled auditing techniques by joint utilization of fuzzy-rough set theory and MRDM technique. Technological and Economic Development of Economy, 27(2), 459–492. https://doi.org/10.3846/tede.2020.13181
Indaco. (2017). Anexa – Lista domeniilor de specializare inteligentă | Ordin 7021/2017. https://lege5.ro/gratuit/gi3deobzge2a/lista-domeniilor-de-specializare-inteligenta-ordin-7021-2017?dp=gi2dsmzqgu4dmoa
Ingalagi, S. S., Mutkekar, R. R., & Kulkarni, P. M. (2021). Artificial Intelligence (AI) adaptation: Analysis of determinants among Small to Medium-sized Enterprises (SME’s). IOP Conference Series: Materials Science and Engineering, 1049(1), Article 012017. https://doi.org/10.1088/1757-899X/1049/1/012017
Jaumotte, F., Li, L., Medici, A., Oikonomou, M., Pizzinelli, C., Shibata, I., Soh, J., & Tavares, M. M. (2023). Digitalization during the COVID-19 crisis: Implications for productivity and labor markets in advanced economies. (IMF Staff Discussion Note, SDN2023/003). International Monetary Fund. https://doi.org/10.5089/9798400232596.006
Joiner, I. A. (2018). Chapter 1 – Artificial Intelligence: AI is nearby. In I. A. Joiner (Ed.), Emerging library technologies (pp. 1–22). Chandos Publishing. https://doi.org/10.1016/B978-0-08-102253-5.00002-2
Khaliq, A., Waqas, A., Nisar, Q. A., Haider, S., & Asghar, Z. (2022). Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective. Technology in Society, 68, Article 101807. https://doi.org/10.1016/j.techsoc.2021.101807
Khanzode, A. G., Sarma, P. R. S., Mangla, S. K., & Yuan, H. (2021). Modeling the Industry 4.0 adoption for sustainable production in Micro, Small & Medium Enterprises. Journal of Cleaner Production, 279, Article 123489. https://doi.org/10.1016/j.jclepro.2020.123489
Kock, N. (2021). WarpPLS© User Manual: Version 7.0. https://www.scriptwarp.com/warppls/UserManual_v_7_0.pdf
Lada, S., Chekima, B., Karim, M. R. A., Fabeil, N. F., Ayub, M. S., Amirul, S. M., Ansar, R., Bouteraa, M., Fook, L. M., & Zaki, H. O. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), Article 100144. https://doi.org/10.1016/j.joitmc.2023.100144
Lee, C. S., & Tajudeen, F. P. (2020). Usage and impact of artificial intelligence on accounting: evidence from Malaysian organisations. Asian Journal of Business and Accounting, 13(1), 213–240. https://doi.org/10.22452/ajba.vol13no1.8
Mangla, S. K., Raut, R., Narwane, V. S., Zhang, Z., & priyadarshinee, P. (2021). Mediating effect of big data analytics on project performance of small and medium enterprises. Journal of Enterprise Information Management, 34(1), 168–198. https://doi.org/10.1108/JEIM-12-2019-0394
Marikyan, D., & Papagiannidis, S. (2023). Technology acceptance model: A review. In S. Papagiannidis (Ed.), TheoryHub book. https://open.ncl.ac.uk/theory-library/TheoryHubBook.pdf
Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems, 123(1), 278–301. https://doi.org/10.1108/IMDS-11-2021-0695
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, Article 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190
Mittal, N., Saif, I., & Ammanath, B. (2022). Fueling the AI transformation: Four key actions powering widespread value from AI, right now. Deloitte’s state of AI in the enterprise (5th edition report). Deloite. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-ai-institute-state-of-ai-fifth-edition.pdf
Morikawa, M. (2016). The effects of artificial intelligence and robotics on business and employment: Evidence from a survey on Japanese firms (Discussion paper, 16066). Research Institute of Economy, Trade and Industry.
Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal Information Systems Evaluation, 14(1), 110–121.
Phuoc, N. V. (2022). The critical factors impacting Artificial Intelligence applications adoption in Vietnam: A Structural equation modeling analysis. Economies, 10(6), Article 129. https://doi.org/10.3390/economies10060129
Popa, I., Cioc, M. M., Breazu, A., & Popa, C. F. (2024). Identifying sufficient and necessary competencies in the effective use of Artificial Intelligence technologies. Amfiteatru Economic, 26(65), 33–52. https://doi.org/10.24818/EA/2022/59/46
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with Artificial Intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1). https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/
Rawashdeh, A., Bakhit, M., & Abaalkhail, L. (2023). Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting automation. International Journal of Data and Network Science, 7, 25–34. https://doi.org/10.5267/j.ijdns.2022.12.010
Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial Intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98–117. https://doi.org/10.3846/jbem.2020.13641
Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2024). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management. https://doi.org/10.1080/00472778.2024.2379999
Sharma, S., Singh, G., Islam, N., & Dhir, A. (2024). Why do SMEs adopt Artificial Intelligence-based chatbots? IEEE Transactions on Engineering Management, 71, 1773–1786. https://doi.org/10.1109/TEM.2022.3203469
Soni, N., Sharma, E. K., Singh, N., & Kapoor, A. (2020). Artificial Intelligence in business: From research and innovation to market deployment. Procedia Computer Science, 167, 2200–2210. https://doi.org/10.1016/j.procs.2020.03.272
Sun, S., Hall, D. J., & Cegielski, C. G. (2020). Organizational intention to adopt big data in the B2B context: An integrated view. Industrial Marketing Management, 86, 109–121. https://doi.org/10.1016/j.indmarman.2019.09.003
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The process of technological innovation. Lexington Books.
Treiblmaier, H. (2018). The impact of the blockchain on the supply chain: A theory-based research framework and a call for action. Supply Chain Management: An International Journal, 23(6), 545–559. https://doi.org/10.1108/SCM-01-2018-0029
Wang, M., & Pan, X. (2022). Drivers of Artificial Intelligence and their effects on supply chain resilience and performance: An empirical analysis on an emerging market. Sustainability, 14(24), Article 16836. https://doi.org/10.3390/su142416836
Wong, L.-W., Leong, L.-Y., Hew, J.-J., Tan, G. W.-H., & Ooi, K.-B. (2020). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, Article 101997. https://doi.org/10.1016/j.ijinfomgt.2019.08.005
Wu, W., Chin, W., & Liu, Y. (2022). Technostress and the smart hospitality employee. Journal of Hospitality and Tourism Technology, 13(3), 404–426. https://doi.org/10.1108/JHTT-01-2021-0032
Xu, W., Ou, P., & Fan, W. (2017). Antecedents of ERP assimilation and its impact on ERP value: A TOE-based model and empirical test. Information Systems Frontiers, 19(1), 13–30. https://doi.org/10.1007/s10796-015-9583-0