Assessing Gen Z Consumers’ Perceptions, Ethical Concerns, and Behavioral Intention Towards AI-Driven Marketing: Case of Anadolu and Woldia University Students.

Tsadiku Setegne Dessie

Abstract


Nowadays, Artificial Intelligence (AI) has profoundly reshaped business and marketing practices, bringing both opportunities and challenges, particularly concerning ethics and privacy. The main objective of the study is to investigate  Generation Z consumers’ perception of Ethical concern and behavioural intention towards AI-driven marketing. Particularly, it strives to measure Gen Z awareness level, trust level, perception, attitude, and ethical concern towards AI-driven marketing and its influence on the intention to engage with it.   The data was collected from 275 students in Anadolu University, Turkey, and Woldia University, Ethiopia, who are aged between 17 to 30 years, using a convenience sampling technique, using a structured 5-point Likert scale questionnaire mainly. The collected data were analyzed using descriptive and inferential statistics(Multiple regression) on JASP software. The major findings showed that all variables except demographic profile had a statistically significant influence on Gen Z behavioral intention towards AI-driven marketing. Specifically,  awareness level, trust level, perception towards AI personalization, and attitude had a positive influence; on the contrary, ethical concern had a weak negative effect. Furthermore, the descriptive statistics revealed that Gen Z has high awareness of AI-driven marketing, a neutral view on its ethics, cautious trust, positive perceptions of personalization, and a generally positive yet careful intent to engage with AI-driven marketing efforts. Finally, the study recommends,  to engage Gen Z effectively, organizations should ensure data transparency, use personalized AI marketing, build trustworthy AI systems, and raise awareness about AI benefits to reduce uncertainty.


DOI: https://doi.org/10.54663/2182-9306.2025.v.13.n.281-317


 


Keywords


AI-driven marketing, Awareness level, Trust level, Intention, Attitude, personalization, Ethical concern, and Gen Z

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References


Ajzen, I. (1980). Understanding attitudes and predictiing social behavior. Englewood cliffs.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Akram, U., Hui, P., Khan, M. K., Hashim, M., & Rasheed, S. (2016). Impact of store atmosphere on impulse buying behaviour: Moderating effect of demographic variables. International Journal of u-and e-Service, Science and Technology, 9(7), 43-60.

Alaeddin, O., & Altounjy, R. (2018). Trust, technology awareness and satisfaction effect into the intention to use cryptocurrency among generation Z in Malaysia. International Journal of Engineering & Technology, 7(4.29), 8-10.

Alhitmi, H. K., Mardiah, A., Al-Sulaiti, K. I., & Abbas, J. (2024). Data security and privacy concerns of AI-driven marketing in the context of economics and business field: an exploration into possible solutions. Cogent Business & Management, 11(1), 2393743.

Amil, Y. (2024). The Impact of AI-Driven Personalization Tools on Privacy Concerns and Consumer Trust in E-commerce.

Amoroso, D., & Lim, R. (2017). The mediating effects of habit on continuance intention. International Journal of Information Management, 37(6), 693-702.

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta‐analytic review. British journal of social psychology, 40(4), 471-499.

Benanav, A. (2020). Automation and the Future of Work: Verso.

Blauth, T. F., Gstrein, O. J., & Zwitter, A. (2022). Artificial intelligence crime: An overview of malicious use and abuse of AI. Ieee Access, 10, 77110-77122.

Bowden, J., & Mirzaei, A. (2021). Consumer engagement within retail communication channels: an examination of online brand communities and digital content marketing initiatives. European Journal of Marketing, 55(5), 1411-1439.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies: WW Norton & company.

Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534.

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society.

Cai, R., Cain, L. N., & Jeon, H. (2022). Customers’ perceptions of hotel AI-enabled voice assistants: does brand matter? International Journal of Contemporary Hospitality Management, 34(8), 2807-2831.

Capatina, A., Kachour, M., Lichy, J., Micu, A., Micu, A.-E., & Codignola, F. (2020). Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations. Technological Forecasting and Social Change, 151, 119794.

Chawla, D., & Joshi, H. (2018). The moderating effect of demographic variables on mobile banking adoption: An empirical investigation. Global Business Review, 19(3_suppl), S90-S113.

Chen, H., Chan-Olmsted, S., Kim, J., & Sanabria, I. M. (2021). Consumers’ perception on artificial intelligence applications in marketing communication. Qualitative Market Research: An International Journal, 25(1), 125-142.

Choi, T.-M., Guo, S., & Luo, S. (2020). When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management? Transportation Research Part E: Logistics and Transportation Review, 135, 101860.

Chu, S.-C., Deng, T., & Cheng, H. (2020). The role of social media advertising in hospitality, tourism and travel: a literature review and research agenda. International Journal of Contemporary Hospitality Management.

Conner, M. (2020). Theory of planned behavior. Handbook of sport psychology, 1-18.

Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. BCL Rev., 55, 93.

Cukier, K. (2019). Ready for robots: how to think about the future of AI. Foreign Aff., 98, 192.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the academy of marketing science, 48, 24-42.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205, 219.

DeVellis, R. (2003). Scale Development: Theory and applications. Thousand Oaks, California: Sage.

Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: an Indian perspective. International journal of medical informatics, 141, 104164.

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.

Eagly, A. H. (1993). The psychology of attitudes. Thomson Wadsworth.

Faqih, K. M., & Jaradat, M.-I. R. M. (2015). Mobile healthcare adoption among patients in a developing country environment: Exploring the influence of age and gender differences. International Business Research, 8(9), 142.

Farbod, S. (2024). Exploring the Dark Side of AI-enabled Services: Impacts on Customer Experience and Well-being. University of Twente.

Feng, C. M., Park, A., Pitt, L., Kietzmann, J., & Northey, G. (2021). Artificial intelligence in marketing: A bibliographic perspective. Australasian Marketing Journal, 29(3), 252-263.

Forgas, J. P., Cooper, J., & Crano, W. D. (2011). The psychology of attitudes and attitude change: Psychology Press.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.

Godinić, D., & Obrenovic, B. (2020). Effects of economic uncertainty on mental health in the COVID-19 pandemic context: social identity disturbance, job uncertainty and psychological well-being model.

Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective (Vol. 48, pp. 1-8): Springer.

Gurlitt, J., & Renkl, A. (2010). Prior knowledge activation: How different concept mapping tasks lead to substantial differences in cognitive processes, learning outcomes, and perceived self-efficacy. Instructional Science, 38, 417-433.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24.

Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119-132.

Han, R., Lam, H. K., Zhan, Y., Wang, Y., Dwivedi, Y. K., & Tan, K. H. (2021). Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems, 121(12), 2467-2497.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135.

Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3), 407-434.

Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive marketing, 51(1), 57-71.

Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.

Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective. Education and Information Technologies, 25, 4615-4635.

Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.

Ismagiloiva, E., Dwivedi, Y., & Rana, N. (2020). Visualising the knowledge domain of artificial intelligence in marketing: A bibliometric analysis. Paper presented at the Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18–19, 2020, Proceedings, Part I.

Kan, M. P., & Fabrigar, L. R. (2017). Theory of planned behavior Encyclopedia of personality and individual differences (pp. 1-8): Springer.

Kara, D., Kim, H., Lee, G., & Uysal, M. (2018). The moderating effects of gender and income between leadership and quality of work life (QWL). International Journal of Contemporary Hospitality Management, 30(3), 1419-1435.

Kesharwani, A., & Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International journal of bank marketing, 30(4), 303-322.

Kim, H.-J., Lee, J.-M., & Rha, J.-Y. (2017). Understanding the role of user resistance on mobile learning usage among university students. Computers & Education, 113, 108-118.

Kirchbuchner, F., Grosse-Puppendahl, T., Hastall, M. R., Distler, M., & Kuijper, A. (2015). Ambient intelligence from senior citizens’ perspectives: Understanding privacy concerns, technology acceptance, and expectations. Paper presented at the Ambient Intelligence: 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings 12.

Kline, R. B. (2023). Principles and practice of structural equation modeling: Guilford publications.

Kotler, P., & Keller, K. L. (2016). A framework for marketing management: Pearson Boston, MA.

Kumar, D., & Suthar, N. (2024). Ethical and legal challenges of AI in marketing: an exploration of solutions. Journal of Information, Communication and Ethics in Society, 22(1), 124-144.

Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, 102783.

Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 125, 864-877.

Kunz, W. H., & Wirtz, J. (2024). Corporate digital responsibility (CDR) in the age of AI: implications for interactive marketing. Journal of Research in Interactive Marketing, 18(1), 31-37.

Kuronen, J. (2023). FINNISH CONSUMER'S ATTITUDES TOWARDS AI-GENERATED PERSONALIZED RECOMMENDATIONS.

Lai, Z., & Yu, L. (2021). Research on digital marketing communication talent cultivation in the era of artificial intelligence. Paper presented at the Journal of Physics: Conference Series.

Loh, X.-M., Lee, V.-H., Tan, G. W.-H., Ooi, K.-B., & Dwivedi, Y. K. (2021). Switching from cash to mobile payment: what's the hold-up? Internet Research, 31(1), 376-399.

Londono, J. C., Davies, K., & Elms, J. (2017). Extending the Theory of Planned Behavior to examine the role of anticipated negative emotions on channel intention: The case of an embarrassing product. Journal of Retailing and Consumer Services, 36, 8-20.

Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51.

Malaquias, R. F., & Hwang, Y. (2019). Mobile banking use: A comparative study with Brazilian and US participants. International Journal of Information Management, 44, 132-140.

Miah, M. S. (2024). Navigating Consumer Behavior in the Digital Age: The Role of Emerging Technologies and Ethical Considerations. Asian Journal of Economics, Business and Accounting, 24(9), 463-471.

Narteh, B., Mahmoud, M. A., & Amoh, S. (2017). Customer behavioural intentions towards mobile money services adoption in Ghana. The Service Industries Journal, 37(7-8), 426-447.

Nunally, J., & Bernstein, L. (1994). Psychometric Theory. New York: MacGrow-Hill Higher: INC.

Pandey, A., & Kumar, S. (2024). AI's Effect on Employment Displacement and the Future of Work. Knowledgeable Research: A Multidisciplinary Peer-Reviewd Refereed Journal, 3(03), 43-55.

Papa, A., Mital, M., Pisano, P., & Del Giudice, M. (2020). E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change, 153, 119226.

Park, J., Hong, E., & Le, H. T. (2021). Adopting autonomous vehicles: The moderating effects of demographic variables. Journal of Retailing and Consumer Services, 63, 102687.

Paschen, J., Kietzmann, J., & Kietzmann, T. C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing, 34(7), 1410-1419.

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134.

Rajagopal, P. (2022). Agile Marketing Strategies: Springer.

Rosário, A. T., & Dias, J. C. (2023). How has data-driven marketing evolved: Challenges and opportunities with emerging technologies. International Journal of Information Management Data Insights, 3(2), 100203.

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach: Pearson.

Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of retailing, 94(4), vi-xi.

Sharma, K. K., Tomar, M., & Tadimarri, A. (2023). AI-driven marketing: Transforming sales processes for success in the digital age. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(2), 250-260.

Shin, D. (2020). User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64(4), 541-565.

SIHAM, B., BOUMADIENE, B., & ABDELHAMID, M. (2024). Symbiosis or Surveillance? Exploring the Relationship between AI, Big Data, and Consumer Privacy in Digital Marketing. Symbiosis, 9(04), 192-208.

Sipos, D. (2025). The Effects of AI-Powered Personalization on Consumer Trust, Satisfaction, and Purchase Intent. European Journal of Applied Science, Engineering and Technology, 3(2), 14-24.

Smith, J. R., Terry, D. J., Manstead, A. S., Louis, W. R., Kotterman, D., & Wolfs, J. (2008). The attitude–behavior relationship in consumer conduct: The role of norms, past behavior, and self-identity. The Journal of social psychology, 148(3), 311-334.

Spiekermann, S. (2015). Ethical IT innovation: A value-based system design approach: CRC Press.

Stoecklin, M. P., Jang, J., & Kirat, D. (2018). Deeplocker: How AI can power a stealthy new breed of malware. Security Intelligence, 8(2018).

Suh, W., & Ahn, S. (2022). Development and validation of a scale measuring student attitudes toward artificial intelligence. SAGE Open, 12(2), 21582440221100463.

Swaminathan, V. (2003). The impact of recommendation agents on consumer evaluation and choice: the moderating role of category risk, product complexity, and consumer knowledge. Journal of Consumer Psychology, 13(1-2), 93-101.

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135-146.

Trieu, K., & Yang, Y. (2018). Artificial intelligence-based password brute force attacks.

Umamaheswari, D. D. (2024). Role of Artificial Intelligence in Marketing Strategies and Performance. Migration Letters, 21(S4), 1589-1599.

Vallabhaneni, A. S., Perla, A., Regalla, R. R., & Kumari, N. (2024). The power of personalization: AI-driven recommendations Minds Unveiled (pp. 111-127): Productivity Press.

Vashishth, T. K., Sharma, K. K., Kumar, B., Chaudhary, S., & Panwar, R. (2024). Enhancing customer experience through AI-enabled content personalization in e-commerce marketing. Advances in digital marketing in the era of artificial intelligence, 7-32.

Vashishth, T. K., Sharma, K. K., Kumar, B., Chaudhary, S., & Panwar, R. (2025). Enhancing Customer Experience through AI-Enabled Content Personalization in E-Commerce Marketing. Advances in Digital Marketing in the Era of Artificial Intelligence, 7-32.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.

Wang, H. J., Yue, X. L., Ansari, A. R., Tang, G. Q., Ding, J. Y., & Jiang, Y. Q. (2022). Research on the influence mechanism of consumers’ perceived risk on the advertising avoidance behavior of online targeted advertising. Frontiers in Psychology, 13, 878629.

Wang, Q., & Sun, X. (2016). Investigating gameplay intention of the elderly using an Extended Technology Acceptance Model (ETAM). Technological Forecasting and Social Change, 107, 59-68.

Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology innovation management review, 9(11).

Wirtz, J., Kunz, W. H., Hartley, N., & Tarbit, J. (2023). Corporate digital responsibility in service firms and their ecosystems. Journal of Service Research, 26(2), 173-190.

Wirtz, J., Tarbit, J., Hartley, N., & Kunz, W. (2022). Corporate digital responsibility: Dealing with ethical, privacy and fairness challenges of AI. Journal of AI, Robotics & Workplace Automation, 1(4), 325-328.

Xu, H., Dinev, T., Smith, H. J., & Hart, P. (2008). Examining the formation of individual's privacy concerns: Toward an integrative view.

Yi, Y., Wu, Z., & Tung, L. L. (2005). How individual differences influence technology usage behavior? Toward an integrated framework. Journal of Computer Information Systems, 46(2), 52-63.

Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, edn. PublicAffairs, New York.


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International Journal of Marketing, Communication and New Media

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DOI: 10.54663/2182-9306

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