مدلسازی اثر استفاده از هوش مصنوعی بر قصد خرید در نسل زد

چکیده مقاله :

پژوهش حاضر با رویکرد مدل سازی به بررسی تأثیر استفاده از فناوری ‌های هوش مصنوعی بر رفتار خرید مصرف‌ کنندگان نسل زد می ‌پردازد. با توجه به تحول دیجیتال و رشد سریع فناوری ‌های هوش مصنوعی، نسل زد به‌عنوان اولین نسلی که با این فناوری ‌ها بزرگ شده ، واکنش ‌های متفاوتی نسبت به آن‌ ها دارد و ازاینرو، پژوهش حاضر به شناسایی و مدلسازی مؤلفه‌ های مختلف هوش مصنوعی وتأثیر آن ‌ها بر قصد خرید نسل زد می‌ پردازد.  برای این منظور پس از بررسی گسترده پیشینه پژوهش، توسعه فرضیه ها و استخراج مدل، داده ها با استفاده از ابزار پرسشنامه گردآوری شد و با استفاده از روش نمونه گیری هدفمند،384 نفر مورد بررسی قرار گرفتند و در نهایت برای آزمون مدل و فرضیه‌های پیشنهادی، از مدلسازی معادلات ساختاری استفاده شد.پژوهش حاضر حاکی از آن است که لذت ادراک‌ شده و سهولت استفاده بیشترین تأثیر را بر استفاده از هوش مصنوعی در انجام خرید در میان نسل زد دارند، در حالی که مفید بودن ادراک‌ شده اثر معناداری در این رابطه نداشت. همچنین، استفاده از هوش مصنوعی تأثیر بالایی بر بهینه سازی نرخ تبدیل و درگیری ذهنی با رسانه‌ های اجتماعی داشت. تجربه مشتری نیز نقش واسطه‌ ای قدرتمندی بین استفاده از هوش مصنوعی و قصد خرید و به دنبال آن بازاریابی دهان ‌به‌ دهان الکترونکی ایفا کرد. این نتایج نشان ‌دهنده نقش پررنگ عوامل احساسی و اجتماعی در پذیرش فناوری میان نسل زد است.

چکیده انگلیسی :

The purchasing behavior of Generation Z consumers. Given the ongoing digital transformation and the rapid advancement of AI, Generation Z—recognized as the first cohort to grow up alongside these technologies—demonstrates distinct reactions toward their use. Accordingly, this research seeks to identify and model the key dimensions of AI and their influence on Gen Z’s purchase intentions. To achieve this, an extensive review of prior studies was conducted, hypotheses were developed, and a conceptual model was formulated. Data were collected through a structured questionnaire, and 384 respondents were surveyed using purposive sampling. Structural equation modeling was then applied to test the proposed model and hypotheses. The findings reveal that perceived enjoyment and ease of use exert the strongest effects on Gen Z’s adoption of AI in purchasing, whereas perceived usefulness did not show a significant impact. Moreover, AI utilization was found to strongly enhance conversion rate optimization and cognitive engagement with social media platforms. Customer experience also played a powerful mediating role between AI adoption and purchase intention, subsequently fostering electronic word-of-mouth (eWOM). These results underscore the critical importance of emotional and social factors in shaping technology acceptance among Generation Z

منابع و مأخذ:


[1] Dwi Santy, R., & Iffan, M. (2023). The effect of artificial intelligence and gamification on online purchase intention mediated by customer experience: study on Indonesian marketplace users. Mix: Jurnal Ilmiah Manajemen, 13(1).
[2] Ahmadi Sharif, M., & Saadani, J.(2023): The effect of digital marketing on purchase intention: Moderating effect of brand equity)Case study: Clients of Farsad Machine Company "manufacturer of wood industry machinery"(.Journal of Business Management and Information Science, Vol 2, No 1, PP: 37-50.
[3]Wang, W., & Lei, S. (2018). Research on the influence of AI on consumption and shopping experience in the new retail environment—Based on the perspective of commercial retail reform and reconstruction of people-goods-yard system. J. Commer. Econ, 17, 5-8.‏
[4]Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 113838.‏
[5]Korteling, J. E., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human- versus Artificial Intelligence. Frontiers in Artificial Intelligence, 4, 622364.
[6]Devagiri, J. S., Paheding, S., Niyaz, Q., Yang, X., & Smith, S. (2022). Augmented Reality and Artificial Intelligence in industry: Trends, tools, and future challenges. Expert Systems with Applications, 207, 118002.
[7]Saputra, F. H., & Sutarso, Y. (2024). Factors influencing satisfaction and intention to use Chatbot on purchase intention on E-Commerce Shopee. At-Tadbir: jurnal ilmiah manajemen, 8(1), 1-14.
[8]Saadah, A., Suliyanto, S., & Rahab, R. (2023). The role of artificial intelligence (AI) in digital marketing: How personalization of content has implications for purchase intention in ecommerce. In Proceeding of International Conference Sustainable Competitive Advantage (Vol. 4, No. 1).‏
[9] Hajibabaei, H., Noorshargh, F., & Ghobadi Lamuki, T. (2024). Modeling of Z Gen purchase intention, Iranian Rubber Magazine, 28(112), 89-107. (In Persian)
[10]Kim, W. G., & Park, H. (2020). The effect of image search, social influence characteristics and anthropomorphism on purchase intention in mobile shopping. The Journal of Industrial Distribution & Business, 11(6), 41-53.
[11]Zhang, D., Pee, L. G., & Cui, L. (2021). Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse. International journal of information management, 57, 102304.
[12]Mogaji, E., Soetan, T. O., & Kieu, T. A. (2020). WITHDRAWN: The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers.
[13]Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of management review, 46(1), 192-210.
[14]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.
[15]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, 101807.
[16]Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19.
[17]LaMothe, R. W. (2010). Types of faith and emotional intelligence. Pastoral Psychology, 59(3), 331-344.
[18]Cook, J. (2016). Young adults’ hopes for the long-term future: From re-enchantment with technology to faith in humanity. Journal of Youth Studies, 19(4), 517-532.
[19]Bilal, M., Zhang, Y., Cai, S., Akram, U., & Halibas, A. (2024). Artificial intelligence is the magic wand making customer-centric a reality! An investigation into the relationship between consumer purchase intention and consumer engagement through affective attachment. Journal of Retailing and Consumer Services, 77, 103674.
[20]Lai, C. K., & Cheng, E. W. (2016). Green purchase behavior of undergraduate students in Hong Kong. The Social Science Journal, 53(1), 67-76.
[21]Fauzi, M. A., Tan, C. N. L., Thurasamy, R., & Ojo, A. O. (2019). Evaluating academics’ knowledge sharing intentions in Malaysian public universities. Malaysian Journal of Library and Information Science, 24(1), 123-143.
[22]Ye, T., Xue, J., He, M., Gu, J., Lin, H., Xu, B., & Cheng, Y. (2019). Psychosocial factors affecting artificial intelligence adoption in health care in China: cross-sectional study. Journal of medical Internet research, 21(10), e14316.
[23]Shi, S., Gong, Y., & Gursoy, D. (2021). Antecedents of trust and adoption intention toward artificially intelligent recommendation systems in travel planning: a heuristic–systematic model. Journal of Travel Research, 60(8), 1714-1734.
[24]Yin, J., & Qiu, X. (2021). AI technology and online purchase intention: Structural equation model based on perceived value. Sustainability, 13(10), 5671.
[25]Gündüzyeli, B. (2025). Cyber Resilience in Digital Marketing Within the Framework of Sustainable Management. Sustainability, 17(5), 2080.
[26]Wibowo, A., Chen, S.-C., Wiangin, U., Ma, Y., & Ruangkanjanases, A. (2021). Customer Behavior as an Outcome of Social Media Marketing: The Role of Social Media Marketing Activity and Customer Experience. Sustainability, 13(1), 189.
[27] Nadeem, W., Tan, T. M., Tajvidi, M., & Hajli, N. (2021). How do experiences enhance brand relationship performance and value co-creation in social commerce? The role of consumer engagement and self brand-connection. Technological Forecasting and Social Change, 171, 120952.
[28] Loureiro, S. M. C., Gorgus, T., & Kaufmann, H. R. (2017). Antecedents and outcomes of online brand engagement: The role of brand love on enhancing electronic-word-of-mouth. Online Information Review, 41(7), 985-1005.
[29] Rasoolimanesh, S. M., Md Noor, S., Schuberth, F., & Jaafar, M. (2019). Investigating the effects of tourist engagement on satisfaction and loyalty. The Service Industries Journal, 39(7-8), 559-574.
[30] Majeed, M., Asare, C., Fatawu, A., & Abubakari, A. (2022). An analysis of the effects of customer satisfaction and engagement on social media on repurchase intention in the hospitality industry. Cogent Business & Management, 9(1), 2028331.
[31] Bag, S., Srivastava, G., Bashir, M. M. A., Kumari, S., Giannakis, M., & Chowdhury, A. H. (2022). Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion. Benchmarking: An International Journal, 29(7), 2074-2098.
[32] Di Fatta, D., Patton, D., & Viglia, G. (2018). The determinants of conversion rates in SME e-commerce websites. Journal of Retailing and Consumer Services, 41, 161-168.
[33] McDowell, W. C., Wilson, R. C., & Kile Jr, C. O. (2016). An examination of retail website design and conversion rate. Journal of Business Research, 69(11), 4837-4842.
[34] Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.
[35] Garrido-Moreno, A., García-Morales, V. J., Lockett, N., & King, S. (2018). The missing link: Creating value with social media use in hotels. International Journal of Hospitality Management, 75, 94-104.
[36] 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.
[37] Gu, Jiatao, Wang, Yong, Chen, Yun, Li, Victor O. K., & Cho, Kyunghyun. (2018). Meta-Learning for Low-Resource Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3622-3631). Association for Computational Linguistics.
[38] Filieri, R. (2015). What makes online reviews helpful? A diagnosticity–adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261–1270.
[39] Kautish, P., & Khare, A. (2022). Antecedents of sustainable fashion apparel purchase behavior. Journal of Consumer Marketing, 39(5), 475–487.
[40] Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., ... & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659.
[41] Perez-Vega, R., Kaartemo, V., Lages, C. R., Borghei Razavi, N., & Männistö, J. (2021). Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework. Journal of Business Research, 129, 902-910.
[42] Eggert, A., & Ulaga, W. (2002). Customer perceived value: a substitute for satisfaction in business markets?. The Journal of Business and Industrial Marketing, 17(2-3), 107-118.
[43] Izuagbe, R. , Ibrahim, N. A. , Ogiamien, L. O. , Olawoyin, O. R. , Nwokeoma, N. M. , Ilo, P. I. , Osayande, O. (2019). Effect of perceived ease of use on librarians' e-skills: Basis for library technology acceptance intention. Library and Information Science Research. 100969. https://doi. org/10. 1016/j. lisr. 2019. 100969.
[44] shhirmohammadi, Y., & Bostan manesh, A. (2022). Designing a model for customers to buy from smart stores in the days of Corona with an emphasis on artificial intelligence.Journal of Intelligent Business Management Studies. (In Persian)
[45] Li, X. , Zhao, X. , Xu, W. , Pu, W. (2020). Measuring ease of use of mobile applications in e-commerce retailing from the perspective of consumer online shopping behavior patterns. Journal of Retailing and Consumer Services 55-102093. https://doi. org/10. 1016/j. jretconser. 2020. 102093.
[46]Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
[47]Tiutiu, M., & Dabija, D. C. (2023). Improving customer experience using artificial intelligence in online retail. In Proceedings of the International Conference on Business Excellence (Vol. 17, No. 1, pp. 1139-1147). Sciendo.
[48]Nazir, S., Khadim, S., Asadullah, M. A., & Syed, N. (2023). Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach. Technology in Society, 72, 102190.‏
[49] Cheragh Sahar, R., Sasanpour, M., & Bahrani, M. H. (2024). Investigating the impact of artificial intelligence applications on organizational performance through the mediation of business-tobusiness marketing capabilities (case study: Canbo chain stores). Journal of New Research Approaches in Management and Accounting, 8(28), 1252–1283.
[50] Rony M, Kayesh I, Bala Sh, Akter F, Parvin R. Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study. Heliyon 2024 February; 10.
[51] Sadeh, M., Gorjizadeh, D., & Fatehpoor, M. (2023). The relationship between convenience, perceived value, and repurchase intention in online shopping of cosmetics. Journal of New Research Approaches in Management and Accounting, 7(26), 2040–2054. (In Persian)
‏ [52] Duarte, P., e Silva, S. C., & Ferreira, M. B. (2018). How convenient is it? Delivering online shopping convenience to enhance customer satisfaction and encourage e-WOM. Journal of Retailing and Consumer Services, 44, 161-169.
[53]Chen, J. S., Tran-Thien-Y, L., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512-1531.
[54] Bambauer-Sachse, S., & Mangold, S. (2011). Brand equity dilution through negative online word-of-mouth communication. Journal of retailing and consumer services, 18(1), 38-45.
[55] Riemenschneider CK, McKinney VR. (2001-2002), “Assessing belief differences in small business adopters and non-adopters of web-based ecommerce”, Journal of Computer Information Systems, pp 101-107.
[56]Vlašić, G., Keleminić, K., & Šubić, R. (2022). Understanding drivers of consumer loyalty in the banking
[57] Thach, L., Riewe, S., & Camillo, A. (2021). Generational cohort theory and wine: analyzing how gen Z differs from other American wine consuming generations. International Journal of Wine Business Research, 33(1), 1-27.
[58]Hajibabaei, H. (2024). Anthropomorphism in artificial intelligence and its impact on purchase intention. Intelligent Marketing Management, 5(4), 24–45. (In Persian)
[59] Sharma, V., et al. (2025). Psychological impacts of AI-induced job displacement among Indian IT professionals.
[60]Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in artificial intelligence, 7, 1323512.
[61]Ruiz-Viñals, C., Pretel-Jiménez, M., Del Olmo Arriaga, J. L., & Miró Pérez, A. (2024). The Influence of Artificial Intelligence on Generation Z’s Online Fashion Purchase Intention. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 2813-2827.