Razlike med moškimi in ženskami pri zaznavanju umetne inteligence v trženju: zaupanje, koristnost in nakupne odločitve
DOI:
https://doi.org/10.55707/eb.v13i1.156Ključne besede:
umetna inteligenca, trženje, zaupanje, zaznana koristnost, pripravljenost za nakup, razlike med spolomaPovzetek
Cilj raziskave je bil preučiti razlike med moškimi in ženskami pri zaupanju v umetno inteligenco (UI), zaznani koristnosti in pripravljenosti za nakup izdelkov, ki jih priporoča UI. V raziskavi je sodelovalo 212 udeležencev, podatki so bili zbrani s strukturiranim vprašalnikom. Eksploratorna faktorska analiza (EFA) je potrdila veljavnost konstruktov in visoko zanesljivost lestvic (Cronbach α > 0,8). Mann-Whitneyjev U-test je pokazal, da moški izražajo višje zaupanje v UI pri postavkah, ki ocenjujejo zanesljivost, nepristranskost in delovanje v interesu uporabnika, ter zaznavajo večjo koristnost pri hitrejšem odločanju in prihranku časa, medtem ko razlike pri postavkah pripravljenosti za nakup niso bile značilne. To kaže, da kljub večjemu zaupanju in zaznani uporabnosti moški in ženske v podobni meri sledijo priporočilom UI. Ugotovitve prispevajo k razumevanju vedenja potrošnikov in usmerjanju ciljno prilagojenih trženjskih strategij.
Literatura
1. 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
2. Chowdhury, S., Basu, S. in Singh, P. K. (2024). Influence of AI driven digital marketing on consumer purchase intention: An empirical study. Journal of Informatics Education and Research, 4(2), 575–582. https://doi.org/10.52783/jier.v4i2.811
3. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
4. Draxler, F., Buschek, D., Tavast, M., Hämäläinen, P., Schmidt, A., Kulshrestha, J. in Welsch, R. (2023). Gender, age, and technology education influence the adoption and appropriation of large language models (LLMs). ArXiv. https://arxiv.org/abs/2310.06556
5. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4. izd.). Sage Publications.
6. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5. izd.). Sage.
7. Gantumur, K. (2025). The impact of artificial intelligence on online shopping. European Scientific Journal, ESJ, 21(4). https://doi.org/10.19044/esj.2025.v21n4p1
8. Gefen, D. in Straub, D. W. (2004). Consumer trust in B2C e-commerce and the importance of social presence: experiments in e-products and e-services. Omega, 32(6), 407–424. https://doi.org/10.1016/j.omega.2004.01.006
9. Gefen, D., Karahanna, E. in Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
10. Hair, J. F., Black, W. C., Babin, B. J. in Anderson, R. E. (2019). Multivariate data analysis (8 izd.). Cengage Learning.
11. Hussain, N., Shoaib, M., Hanif, M. E. in Shahzad, M. (2024). AI is the key to customer centricity: Analysis of consumer purchase intention and involvement through affective connection. Advance Social Science Archive Journal, 2(4), 568–584.
12. Kolar, N., Milfelner, B. in Pišnik, A. (2024). Factors for customers’ AI use readiness in physical retail stores: The interplay of consumer attitudes and gender differences. Information, 15(6), Članek 346. https://doi.org/10.3390/info15060346
13. Kotzé, T. G., Anderson, O. in Summerfield, K. (2016). Technophobia: Gender differences in the adoption of high-technology consumer products. South African Journal of Business Management, 47(1), 21–34. https://doi.org/10.4102/sajbm.v47i1.49
14. Nunnally, J. C. (1978). Psychometric theory. McGraw-Hill.
15. 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. https://doi.org/10.1080/10864415.2003.11044275
16. Shaouf, A. in Altaqqi, O. (2018). The impact of gender differences on adoption of information technology and related responses: A review. International Journal of Management and Applied Research, 5(1), 22–41. https://doi.org/10.18646/2056.51.18-003
17. Slyke, C. V., Bélanger, F., Johnson, R. D. in Hightower, R. (2010). Gender-based differences in consumer e-commerce adoption. Communications of the Association for Information Systems, 26(1), članek 2. https://doi.org/10.17705/1CAIS.02602
18. Sohaib, O., Kang, K. in Nurunnabi, M. (2019). Gender-based iTrust in e-commerce: The moderating role of cognitive innovativeness. Sustainability, 11(1), članek 175. https://doi.org/10.3390/su11010175
19. Sugiarto, A. (2024). Gender differences in the adoption of mobile banking: Review of using TAM on banking customers. Jurnal Dinamika Akuntansi, 2(1), 1–13.
20. Tabachnick, B. G. in Fidell, L. S. (2019). Using multivariate statistics (7 izd.). Pearson.
21. Teodorescu, D., Aivaz, K.-A., Vancea, D. P. C., Condrea, E., Dragan, C. in Olteanu, A. C. (2023). Consumer trust in AI algorithms used in e-commerce: A case study of college students at a Romanian public university. Sustainability, 15(15), članek 11925. https://doi.org/10.3390/su151511925
22. Venkatesh, V., Morris, M. G., Davis, G. B. in Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
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