Examining the Factors Affecting Marketers Willingness to Adopt AI Technologies for Marketing Campaigns in industrial Automation industry in Egypt
Author(s)
Download Full PDF Pages: 32-60 | Views: 38 | Downloads: 15 | DOI: 10.5281/zenodo.16994668
Abstract
The rapid advancement of Artificial Intelligence (AI) has opened new avenues in marketing, particularly in content creation and personalization. Despite global trends, the adoption of AI technologies in Egypt’s industrial automation sector remains limited and underexplored. This study aims to investigate the factors influencing marketers’ intention to adopt and use AI tools within this context. Drawing on an extended version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the research incorporates Perceived Behavioral Control (PBC) as a mediating variable and Perceived Risk (PR) as a moderator to improve the model's contextual fit. A mixed-method approach was adopted, combining expert interviews and a structured survey of 400 professionals working in industrial marketing. Quantitative analysis using factor analysis, regression, mediation, and moderation techniques revealed that Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions significantly influence Behavioral Intention (BI). Furthermore, PBC was found to mediate the relationship between these predictors and intention, while PR significantly moderated their effect. Notably, Behavioral Intention was also a strong predictor of actual AI usage. These findings offer valuable theoretical insights by validating an extended UTAUT2 model in an emerging B2B market and provide actionable recommendations for marketers and policymakers to support responsible and strategic AI integration
Keywords
AI adoption; Marketing campaign; perceived behavioral control; perceived risk; industrial marketing; Egypt
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Alice, S. I., & Ebuka, O. D. (2024). The Potential and Challenges of AI Adoption in Marketing Across Africa: Opportunities for Digital Transformation. https://lgdpublishing.org/index.php/birev/article/view/140
Alshurideh, M., Kurdi, B. A., & Salloum, S. A. (2023). Factors influencing the behavioral intention to use AI-driven marketing tools in MENA: An extended UTAUT2 approach. Journal of Theoretical and Applied Electronic Commerce Research.
Barnes, S. J. (2021). Artificial intelligence and marketing: Emerging research and opportunities. Journal of Business Research, 124, 353–362. https://doi.org/10.1016/j.jbusres.2020.11.048
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Brown, T., Alharthi, M., & Williams, M. D. (2022). Perceived risk and AI adoption in organizational settings: A moderated model. Journal of Enterprise Information Management, 35(4), 987–1004. https://doi.org/10.1108/JEIM-09-2021-0381
Chatterjee, S., Rana, N. P., Tamilmani, K., Sharma, A., & Dwivedi, Y. K. (2023). Artificial intelligence in marketing: Systematic review and future research agenda. Journal of Business Research, 156, 113447. https://doi.org/10.1016/j.jbusres.2022.113447
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.
Dwivedi, Y. K., Rana, N. P., Tamilmani, K., & Sharma, S. K. (2021). A meta-analytic structural equation modeling approach to understanding the determinants of e-Government services adoption. International Journal of Information Management, 55, 102139.
Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2021). A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 38(1), 101474. https://doi.org/10.1016/j.giq.2020.101474
Egypt Vision 2030. (n.d.). Sustainable Development Strategy: Egypt Vision 2030. Ministry of Planning and Economic Development. Retrieved from
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications Ltd.
Fishbein, M., & Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Prentice Hall.
Fouad, A., Mansour, F., & Ramadan, H. (2023). Economic Volatility and Its Impact on Industrial Automation Investments in Egypt. Economic Analysis and Policy, 72, 88-102.
Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51(1), 115–134. https://doi.org/10.1037/0022-0167.51.1.115
Gad, A., Shalaby, S., & Youssef, M. (2023). Government Policies and Their Impact on Industrial Automation in Egypt. Economic Development Quarterly, 37(2), 187-200.
Gamal, A., Abdelrahman, A., & Abdelkader, M. (2023). Addressing the Skills Gap in Egypt's Industrial Automation Sector. Human Resource Management Review, 33(1), 112-125. https://doi.org/10.1016/j.hrmr.2023.06.002
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672-2680.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hassan, A., El-Masry, K., & Zaki, M. (2023). Challenges and Barriers to Industrial Automation in Egypt: An Empirical Study. Journal of Manufacturing Systems, 58, 239-250. https://doi.org/10.1016/j.jmsy.2023.03.005
Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104–121.
Hinton, P. R., McMurray, I., & Brownlow, C. (2004). SPSS explained. Routledge.
Islam, M. A., Fakir, S. I., & Masud, S. B. (2024). Artificial Intelligence in Digital Marketing: Enhancing Personalization and Ethical Integration. https://www.researchgate.net/publication/386286863
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.
Kapoor, K. K., Dwivedi, Y. K., Piercy, N. F., & Slade, E. L. (2022). Innovation in digital marketing: A review and research agenda. Journal of Business Research, 144, 1150–1168. https://doi.org/10.1016/j.jbusres.2022.02.056
Kapoor, K., & Dwivedi, Y. K. (2023). AI in marketing: Current trends and future directions. Journal of Marketing Management, 39(7), 663-689. https://doi.org/10.1080/0267257X.2023.2159387
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105. https://doi.org/10.1145/3065386
Kumar, V., & Rajan, K. (2023). Personalized Recommendations and Their Impact on User Retention and Revenue in Streaming Services. Journal of Digital Content, 15(1), 85-102.
Kumar, V., Rajan, C., & Benassi, S. (2022). AI-driven content creation and marketing: Trends, challenges, and opportunities. International Journal of Information Management, 62, 102465. https://doi.org/10.1016/j.ijinfomgt.2021.102465
OECD. (2021). Digital Egypt: Towards a Digital Transformation Strategy. OECD Digital Economy Papers.
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414.
Parker, S., Williams, G., & Lee, S. (2023). AI-driven content creation for industrial automation: Case studies of ABB and Schneider Electric. Journal of Marketing Innovation, 31(5), 223-242.
Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115–143. https://doi.org/10.2307/25148720
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
Roberts, P., Anderson, H., & Kumar, V. (2023). AI-driven content marketing in industrial automation: Case studies of General Electric and Siemens. Journal of Marketing Analytics, 12(2), 112-127.
S. Kumar, Talukder, M. B., & Tyagi, P. K. (2024). The impact of artificial intelligence on improving efficiency in service and implementing best practices in service marketing. IGI Global.
Sekaran, U. (2003). Research methods for business: A skill-building approach (4th ed.). John Wiley & Sons.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Wiley.
Sharma, A., & Sinha, S. (2023). Data Privacy in AI-Powered Marketing: Challenges and Solutions. Journal of Digital Privacy and Security, 12(1), 67-83. https://doi.org/10.1016/j.dps.2023.01.003
Sharma, A., Dwivedi, Y. K., Arya, V., & Siddiqui, M. Q. (2022). The role of artificial intelligence (AI) in social media marketing: A review and research agenda. Journal of Business Research, 142, 1018–1033. https://doi.org/10.1016/j.jbusres.2021.12.063
Sharma, R., Sahay, S., & Singh, A. (2022). Exploring AI adoption in marketing: A review and research agenda. Journal of Business Research, 144, 602–615.
Sharma, S. K., Al-Badi, A. H., Rana, N. P., & Al-Azizi, L. (2022). Exploring the role of contextual factors in e-government adoption: A UTAUT2 perspective. Information Technology & People, 35(4), 1246–1272. https://doi.org/10.1108/ITP-12-2020-0863
Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., ... & Summers, R. M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298. https://doi.org/10.1109/TMI.2016.2528162
Singh, R., & Ahmed, S. (2021). Bridging the gap: The role of perceived behavioral control in AI adoption in marketing. Technological Forecasting and Social Change, 169, 120795.
Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics (6th ed.). Pearson Education.
Venkatesh, V., Davis, F. D., & Morris, M. G. (2007). Dead or alive? The development, trajectory and future of technology adoption research. Journal of the Association for Information Systems, 8(4), 267–286. https://aisel.aisnet.org/jais/vol8/iss4/5
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2016). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Venkatesh, V., Sykes, T. A., & Zhang, X. (2022). 'ICT for Development in the Context of Developing Countries: An Extended UTAUT Perspective,' MIS Quarterly, 46(1), 555–582. https://doi.org/10.25300/MISQ/2022/16202
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
Williams, G., Lee, S., & Parker, R. (2023). AI and the future of marketing: Insights from ABB and Schneider Electric. Digital Marketing Journal, 29(4), 201-219.
Williams, J., Thompson, B., & Green, H. (2023). AI-Enhanced Advertising and Its Impact on Revenue: Insights from Snap Inc. Digital Marketing Review, 16(1), 67-85.
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443-488.
Williams, R., Johnson, A., & Smith, D. (2023). Evaluating the ROI of AI-Powered Advertising Campaigns. Marketing Science, 42(3), 432-448.
Zhang, J., Liu, X., Xu, X., & Wang, Y. (2023). Advanced Techniques in GAN-Based Image and Video Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 1157-1172. https://doi.org/10.1109/TPAMI.2022.3180372
Zhang, X., Zhao, K., & Xu, X. (2022). Application of NLP in business intelligence: A review and case analysis. Decision Support Systems, 156, 113758. https://doi.org/10.1016/j.dss.2021.113758
Cite this Article: