How to cite this paper
Mathani, B., Ajrash, H., Dalaeen, A., Alshboul, K., Almahameed, H., Alibraheem, M., Khalifeh, A., Alzoubi, M & Ahmad, A. (2024). Identifying variables influencing the adoption of artificial intelligence big data analytics among SMEs in Jordan.International Journal of Data and Network Science, 8(4), 2615-2626.
Refrences
Akter, S., Wamba, S., Mariani, I., & Hanu, I. (2021). How to build an AI climate-driven service analytics capability for in-novation and performance in industrial markets? Industrial Marketing Management, 97, 258–273.
Alzoubi, S., & Zoubi, M. (2023). Exploring the relationship between robot employees' perceptions and robot-induced un-employment under COVID-19 in the Jordanian hospitality sector. International Journal of Data and Network Science, 7(4), 1563-1572.
Alzoubi, S. I., & Azloubi, S. (2020). Determinants of E-Learning Based on Cloud Computing adoption: Evidence from a Students’ Perspective in Jordan. International Journal of Advanced Science and Technology, 29(4), 1361-1370.
Badi, S., Ochieng, E., Nasaj, M., & Papadaki, M. (2021). Technological, organizational and environmental determinants of smart contracts adoption: UK construction sector viewpoint. Construction Management and Economics, 39(1), 36–54.
Bag, S., Gupta, S., & Kumar, S. (2021). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844.
Baker, J. (2011). The technology-organization-environment framework. In Y. K.Dwivedi, M. R.Wade, & S. L. Schne-berger (Eds.), Information systems theory: Explaining and predicting our digital society (Vol. 1, pp. 231–245). Spring-er Science & Business Media.
Battistoni, E., Gitto, S., Murgia, G., & Campisi, D. (2023). Adoption paths of digital transformation in manufacturing SME. International Journal of Production Economic, 255 (October 2022), 108675 https://doi.org/10.1016/j.ijpe.2022.108675.
Chaveesuk, S., & Horkondee, S. (2015). An integrated model of business intelligence adoption in Thailand logistics ser-vice firms. Proceedings - 2015 7th International Conference on Information Technology and Electrical Engineering: Envisioning the Trend of Computer, Information and Engineering.
Christiansen, V., Haddara, M., & Langseth, M. (2022). Factors Affecting Cloud ERP Adoption Decisions in Organizations. Procedia Computer Science, 196, 255–262.
Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553.
Daoud, L., Marei, A., Al-Jabaly, S., & Aldaas, A. (2021). Moderating the role of top management commitment in usage of computer-assisted auditing techniques. Accounting, 7(2), 457–468. https://doi.org/10.5267/j.ac.2020.11.005.
David, A., & Jos´e, A. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale. In: Pro-ceedings of the Spanish STATA Meeting 2015, 1–39. https://www.stata.com/meeting/spain15/abstracts/materials/spain15_ alarcon.pdf.
Deepu, T., & Ravi, V. (2021). Exploring critical success factors influencing adoption of digital twin and physical internet in electronics industry using grey-DEMATEL approach. Digital Business, 1(2), 100009 https://doi.org/10.1016/j.digbus.2021.100009.
Drydakis, N. (2022). Artificial intelligence and reduced SMEs’ business risks. A dynamic capabilities analysis during the COVID-19 pandemic. Information and System Frontier, 24(4), 1223–1247. https://doi.org/10.1007/s10796-022 10249-6.
Dubey, R., Gunasekaran, A., Childe, S., Papadopoulos, T., Hazen, B., & Roubaud, D. (2018). Examining top management commitment to TQM diffusion using institutional and upper echelon theories. International Journal of Production Re-search, 56(8), 2988–3006. https://doi.org/ 10.1080.
Faliagka, E., Iliadis, L., Karydis, I., & Rigou, M. (2014). On-line consistent ranking on e-recruitment: Seeking the truth behind a well-formed CV. Artificial Intelligence Review, 42(3), 515–528. https://doi.org/10.
Fan, Y., & Lin, T. (2023). Identifying university students’ online academic help-seeking patterns and their role in Internet self-efficacy. The Internet and Higher Education, 56, 100893.
Fountaine, T., Mccarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harv. Bus. Rev. 97 (4), 62–73.
Ganlin, P., Qamruzzaman, M., Mehta, A., Naqvi, W., & Karim, S. (2021). Innovative finance, technological adaptation and SMEs sustainability: the mediating role of government support during covid-19 pandemic. Sustainability, 13(16). https://doi. org/10.
Gonçalves, R., Dias, A., Costa, R., & Da, Q. (2022). Gaining competitive advantage through artificial intelligence adop-tion. International Journal of Electronic Business, 1(1), 1. https://doi.org/10.1504/ijeb.2022.10044363.
Guenole, N., & Feinzig, S. (2018). The business case for AI in HR: With insights and tips on getting started. IBM Smarter Workforce Institute, IBM. 2018.
Gutierrez, A., Boukrami, E., & Lumsden, R. (2015). Technological, organizational and environmental factors influencing managers’ decision to adopt cloud computing in the UK. Journal of Enterprise Information Management, 28(6), 788-807.
Hair. (2017). A primer on partial least squares structural equation modeling (PLS-SEM).
Hansen, E., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: a sur-vey. Journal of Manufacturing System, 58(October 2019), 362–372. https://doi.org/10.1016/j.jmsy.2020.08.009.
Hashim, N., Samsuri, A., & Idris, N. (2021). Assessing organizations’ readiness for technological changes in construction industry. International Journal of Sustainable Construction Engineering Technology, 12(1), 130–139. https://doi.org/10.30880/ijscet.2021.12.01.013.
Hmoud, B., & Várallyai, L. (2020). Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants. International Journal of Engineering and Management Sciences, 5(1), 749–765.
Hoang, T., & Nguyen, H. (2022). Towards an economic recovery after the COVID-19 pandemic: empirical study on elec-tronic commerce adoption by small and medium-sized enterprises in Vietnam. Management & Marketing. Challenges for the Knowledge Socie.
Hoyle, R. (2011). Structural equation modeling for social and personality psychology. Sage, London.
Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65(February), 102497 https://doi.org/10.1016/j.ijinfomgt.2022.102497.
Huang, C., Wang, H., Yang, C., & Shiau, S. (2020). A derivation of factors influencing the diffusion and adoption of an open-source learning platform. Sustainability, 12(18), 7532.
Huang, M., & Rust, R. (2022). A framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98 (2), 209–223. https://doi.org/10.1016/j. jretai.2021.03.001.
Jalagat, R. (2016). The impact of change and change management in achieving corporate goals and objectives: organiza-tional perspective. International Journal of Science and Research, 5(November), 1233–1239. https://doi.org/10.21275/ART20163105.
Jayashree, S., Reza, M., Malarvizhi, C., & Mohiuddin, M. (2021). Industry 4.0 implementation and triple bottom line sus-tainability: an empirical study on small and medium manufacturing firms. Heliyon, 7(8), e07753. https://doi.org/10.1016/ j.heliyon.
Kline, R. (2010). Principles and practice of structural equation modeling. Guilford Press, New York.
Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of new-age technologies on marketing: a research agenda. Journal of Business Research, 125(January 2020), 864–877. https:// doi.org/10.1016/j.jbusres.2020.01.007.
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM).
Lemos, S., Ferreira, F., Zopounidis, S., Galariotis, E., & Ferreira, N. (2022). Artificial intelligence and change manage-ment in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives. Annals of Operera-tions Research. https://doi.org/10.1007/s10479-022-05159-4.
Lu, X., Wijayaratna, K., Huang, Y., & Qiu, A. (2022). AI-enabled opportunities and transformation challenges for SMEs in the post-pandemic era: a review and research agenda. Frontier in Public Health, 10(April), 1–11. https://doi.org/10.3389/ fpubh.2022.885067.
Maroufkhani, P., Tseng, M., & Iranmanesh, M. (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.
McDougall, N., Wagner, B., & MacBryde, J. (2022). Competitive benefits & incentivisation at internal, supply chain & societal level circular operations in UK agri-food SMEs. Journal of Business Research, 144(November 2020), 1149–1162. https://doi.org/10.1016/j. jbusres.2022.
Mohammad, N., & Muhammad, T. (2023). The effects of the internal and the external factors affecting artificial intelli-gence (AI) adoption in e-innovation technology projects in the UAE? Applying both innovation and technology ac-ceptance theories. International Journal of Data and Network Science, 7, 1321–1332.
Mohammed, A., Algerafi, M., Yueliang, Z., Hind, A., & Tommy, T. (2023). Understanding the Factors Influencing Higher Education Students’ Intention to Adopt Artificial Intelligence-Based Robots. IEEE Acess, 11(5), 99752-99764.
Murphy, S. (2016). 2016. Individual adaptability as a predictor of job performance. Dissertation Abstracts International Section A: Humanities and Social Sciences, 77(4-A(E), No-Specified.〈http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference &D=psyc13&NEWS=psyc13&NEWS=N&A.
Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125–1147.
Perifanis, N., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: a literature review. Information, 14(2). https://doi.org/10.3390/info14020085.
Pillai, R., Sivathanu, B., Mariani, M., Rana, N., Yang, B., & Dwivedi, Y. (2022). Adoption of AI-empowered industrial ro-bots in auto component manufacturing companies. Production Planning & Control, 33(16), 1517-1533.
Ragazou, K., Passas, I., Garefalakis, A., & Zopounidis, C. (2023). Business intelligence model empowering SMEs to make better decisions and enhance their competitive advantage. Discovery Analysis, 1(1) https://doi.org/10.1007/s44257-022-00002-3.
Rameshwar, D., J, D., Bryde, B., & Yogesh, K. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Eco-nomics ,25(9), 1-15.
Rao, D., & Verweij, G. (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PwC Publication.
Rehman, M., & Rajkumar, M. (2022). Overcoming the Complexities in Decision-Making for Enterprise Software Prod-ucts: Influence of Technological Factors. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) (pp. 393–410). Spri.
Rogers, E. (1995). The diffusion of innovations (5th ed.). The Free Press.
Rosa, I., Liliawati, W., Efendi, R., Ingalagi, S., Mutkekar, R., & Kulkarni, P. (2021). Artificial intelligence (AI) adapta-tion: analysis of determinants among small to medium-sized enterprises (SME’s). IOP Conf. Ser.: Mater. Sci. Eng. 1049 (1), 0120.
Suddin, L., Brahim, C., Mohd, R., & Noor, F. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Com-plexity, 24(11), 1-10.
Tajeddini, K., Gamage, T., Tajeddini, O., & Kallmuenzer, A. (2023). How entrepreneurial bricolage drives sustained com-petitive advantage of tourism and hospitality SMEs: The mediating role of differentiation and risk management. Inter-national Journal of Hospitality Management, 111.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. Lexington Books.
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future re-search direction. International Journal of Information Management Data Insights, 1(1), 100002.
Wang, Y., Wang, Y., & Yang, Y. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77(5), 803–815. https://doi.org/10.1016/j.techfore.2010.03.006.
Wu, Q., Yan, D., & Umair, M. (2023). Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Economic Analysis and Policy, 77, 1103-1114. https://doi.org/10.1016/j.eap.2022.11.024.
Yuan, P., Fabian, F., Ni, L., Yunyang, H., & Maolin, Y. (2021). The adoption of artificial intelligence in employee re-cruitment: The influence of contextual factors. The International Journal of Human Resource Management, 19(15), 1-24.
Alzoubi, S., & Zoubi, M. (2023). Exploring the relationship between robot employees' perceptions and robot-induced un-employment under COVID-19 in the Jordanian hospitality sector. International Journal of Data and Network Science, 7(4), 1563-1572.
Alzoubi, S. I., & Azloubi, S. (2020). Determinants of E-Learning Based on Cloud Computing adoption: Evidence from a Students’ Perspective in Jordan. International Journal of Advanced Science and Technology, 29(4), 1361-1370.
Badi, S., Ochieng, E., Nasaj, M., & Papadaki, M. (2021). Technological, organizational and environmental determinants of smart contracts adoption: UK construction sector viewpoint. Construction Management and Economics, 39(1), 36–54.
Bag, S., Gupta, S., & Kumar, S. (2021). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844.
Baker, J. (2011). The technology-organization-environment framework. In Y. K.Dwivedi, M. R.Wade, & S. L. Schne-berger (Eds.), Information systems theory: Explaining and predicting our digital society (Vol. 1, pp. 231–245). Spring-er Science & Business Media.
Battistoni, E., Gitto, S., Murgia, G., & Campisi, D. (2023). Adoption paths of digital transformation in manufacturing SME. International Journal of Production Economic, 255 (October 2022), 108675 https://doi.org/10.1016/j.ijpe.2022.108675.
Chaveesuk, S., & Horkondee, S. (2015). An integrated model of business intelligence adoption in Thailand logistics ser-vice firms. Proceedings - 2015 7th International Conference on Information Technology and Electrical Engineering: Envisioning the Trend of Computer, Information and Engineering.
Christiansen, V., Haddara, M., & Langseth, M. (2022). Factors Affecting Cloud ERP Adoption Decisions in Organizations. Procedia Computer Science, 196, 255–262.
Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553.
Daoud, L., Marei, A., Al-Jabaly, S., & Aldaas, A. (2021). Moderating the role of top management commitment in usage of computer-assisted auditing techniques. Accounting, 7(2), 457–468. https://doi.org/10.5267/j.ac.2020.11.005.
David, A., & Jos´e, A. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale. In: Pro-ceedings of the Spanish STATA Meeting 2015, 1–39. https://www.stata.com/meeting/spain15/abstracts/materials/spain15_ alarcon.pdf.
Deepu, T., & Ravi, V. (2021). Exploring critical success factors influencing adoption of digital twin and physical internet in electronics industry using grey-DEMATEL approach. Digital Business, 1(2), 100009 https://doi.org/10.1016/j.digbus.2021.100009.
Drydakis, N. (2022). Artificial intelligence and reduced SMEs’ business risks. A dynamic capabilities analysis during the COVID-19 pandemic. Information and System Frontier, 24(4), 1223–1247. https://doi.org/10.1007/s10796-022 10249-6.
Dubey, R., Gunasekaran, A., Childe, S., Papadopoulos, T., Hazen, B., & Roubaud, D. (2018). Examining top management commitment to TQM diffusion using institutional and upper echelon theories. International Journal of Production Re-search, 56(8), 2988–3006. https://doi.org/ 10.1080.
Faliagka, E., Iliadis, L., Karydis, I., & Rigou, M. (2014). On-line consistent ranking on e-recruitment: Seeking the truth behind a well-formed CV. Artificial Intelligence Review, 42(3), 515–528. https://doi.org/10.
Fan, Y., & Lin, T. (2023). Identifying university students’ online academic help-seeking patterns and their role in Internet self-efficacy. The Internet and Higher Education, 56, 100893.
Fountaine, T., Mccarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harv. Bus. Rev. 97 (4), 62–73.
Ganlin, P., Qamruzzaman, M., Mehta, A., Naqvi, W., & Karim, S. (2021). Innovative finance, technological adaptation and SMEs sustainability: the mediating role of government support during covid-19 pandemic. Sustainability, 13(16). https://doi. org/10.
Gonçalves, R., Dias, A., Costa, R., & Da, Q. (2022). Gaining competitive advantage through artificial intelligence adop-tion. International Journal of Electronic Business, 1(1), 1. https://doi.org/10.1504/ijeb.2022.10044363.
Guenole, N., & Feinzig, S. (2018). The business case for AI in HR: With insights and tips on getting started. IBM Smarter Workforce Institute, IBM. 2018.
Gutierrez, A., Boukrami, E., & Lumsden, R. (2015). Technological, organizational and environmental factors influencing managers’ decision to adopt cloud computing in the UK. Journal of Enterprise Information Management, 28(6), 788-807.
Hair. (2017). A primer on partial least squares structural equation modeling (PLS-SEM).
Hansen, E., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: a sur-vey. Journal of Manufacturing System, 58(October 2019), 362–372. https://doi.org/10.1016/j.jmsy.2020.08.009.
Hashim, N., Samsuri, A., & Idris, N. (2021). Assessing organizations’ readiness for technological changes in construction industry. International Journal of Sustainable Construction Engineering Technology, 12(1), 130–139. https://doi.org/10.30880/ijscet.2021.12.01.013.
Hmoud, B., & Várallyai, L. (2020). Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants. International Journal of Engineering and Management Sciences, 5(1), 749–765.
Hoang, T., & Nguyen, H. (2022). Towards an economic recovery after the COVID-19 pandemic: empirical study on elec-tronic commerce adoption by small and medium-sized enterprises in Vietnam. Management & Marketing. Challenges for the Knowledge Socie.
Hoyle, R. (2011). Structural equation modeling for social and personality psychology. Sage, London.
Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65(February), 102497 https://doi.org/10.1016/j.ijinfomgt.2022.102497.
Huang, C., Wang, H., Yang, C., & Shiau, S. (2020). A derivation of factors influencing the diffusion and adoption of an open-source learning platform. Sustainability, 12(18), 7532.
Huang, M., & Rust, R. (2022). A framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98 (2), 209–223. https://doi.org/10.1016/j. jretai.2021.03.001.
Jalagat, R. (2016). The impact of change and change management in achieving corporate goals and objectives: organiza-tional perspective. International Journal of Science and Research, 5(November), 1233–1239. https://doi.org/10.21275/ART20163105.
Jayashree, S., Reza, M., Malarvizhi, C., & Mohiuddin, M. (2021). Industry 4.0 implementation and triple bottom line sus-tainability: an empirical study on small and medium manufacturing firms. Heliyon, 7(8), e07753. https://doi.org/10.1016/ j.heliyon.
Kline, R. (2010). Principles and practice of structural equation modeling. Guilford Press, New York.
Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of new-age technologies on marketing: a research agenda. Journal of Business Research, 125(January 2020), 864–877. https:// doi.org/10.1016/j.jbusres.2020.01.007.
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM).
Lemos, S., Ferreira, F., Zopounidis, S., Galariotis, E., & Ferreira, N. (2022). Artificial intelligence and change manage-ment in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives. Annals of Operera-tions Research. https://doi.org/10.1007/s10479-022-05159-4.
Lu, X., Wijayaratna, K., Huang, Y., & Qiu, A. (2022). AI-enabled opportunities and transformation challenges for SMEs in the post-pandemic era: a review and research agenda. Frontier in Public Health, 10(April), 1–11. https://doi.org/10.3389/ fpubh.2022.885067.
Maroufkhani, P., Tseng, M., & Iranmanesh, M. (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.
McDougall, N., Wagner, B., & MacBryde, J. (2022). Competitive benefits & incentivisation at internal, supply chain & societal level circular operations in UK agri-food SMEs. Journal of Business Research, 144(November 2020), 1149–1162. https://doi.org/10.1016/j. jbusres.2022.
Mohammad, N., & Muhammad, T. (2023). The effects of the internal and the external factors affecting artificial intelli-gence (AI) adoption in e-innovation technology projects in the UAE? Applying both innovation and technology ac-ceptance theories. International Journal of Data and Network Science, 7, 1321–1332.
Mohammed, A., Algerafi, M., Yueliang, Z., Hind, A., & Tommy, T. (2023). Understanding the Factors Influencing Higher Education Students’ Intention to Adopt Artificial Intelligence-Based Robots. IEEE Acess, 11(5), 99752-99764.
Murphy, S. (2016). 2016. Individual adaptability as a predictor of job performance. Dissertation Abstracts International Section A: Humanities and Social Sciences, 77(4-A(E), No-Specified.〈http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference &D=psyc13&NEWS=psyc13&NEWS=N&A.
Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125–1147.
Perifanis, N., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: a literature review. Information, 14(2). https://doi.org/10.3390/info14020085.
Pillai, R., Sivathanu, B., Mariani, M., Rana, N., Yang, B., & Dwivedi, Y. (2022). Adoption of AI-empowered industrial ro-bots in auto component manufacturing companies. Production Planning & Control, 33(16), 1517-1533.
Ragazou, K., Passas, I., Garefalakis, A., & Zopounidis, C. (2023). Business intelligence model empowering SMEs to make better decisions and enhance their competitive advantage. Discovery Analysis, 1(1) https://doi.org/10.1007/s44257-022-00002-3.
Rameshwar, D., J, D., Bryde, B., & Yogesh, K. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Eco-nomics ,25(9), 1-15.
Rao, D., & Verweij, G. (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PwC Publication.
Rehman, M., & Rajkumar, M. (2022). Overcoming the Complexities in Decision-Making for Enterprise Software Prod-ucts: Influence of Technological Factors. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) (pp. 393–410). Spri.
Rogers, E. (1995). The diffusion of innovations (5th ed.). The Free Press.
Rosa, I., Liliawati, W., Efendi, R., Ingalagi, S., Mutkekar, R., & Kulkarni, P. (2021). Artificial intelligence (AI) adapta-tion: analysis of determinants among small to medium-sized enterprises (SME’s). IOP Conf. Ser.: Mater. Sci. Eng. 1049 (1), 0120.
Suddin, L., Brahim, C., Mohd, R., & Noor, F. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Com-plexity, 24(11), 1-10.
Tajeddini, K., Gamage, T., Tajeddini, O., & Kallmuenzer, A. (2023). How entrepreneurial bricolage drives sustained com-petitive advantage of tourism and hospitality SMEs: The mediating role of differentiation and risk management. Inter-national Journal of Hospitality Management, 111.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. Lexington Books.
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future re-search direction. International Journal of Information Management Data Insights, 1(1), 100002.
Wang, Y., Wang, Y., & Yang, Y. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77(5), 803–815. https://doi.org/10.1016/j.techfore.2010.03.006.
Wu, Q., Yan, D., & Umair, M. (2023). Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Economic Analysis and Policy, 77, 1103-1114. https://doi.org/10.1016/j.eap.2022.11.024.
Yuan, P., Fabian, F., Ni, L., Yunyang, H., & Maolin, Y. (2021). The adoption of artificial intelligence in employee re-cruitment: The influence of contextual factors. The International Journal of Human Resource Management, 19(15), 1-24.