No. 43
Research articles

Willingness to pay and dependence on Generative Artificial Intelligence. Scale validation and structural model

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Carlos Arturo Torres Gastelú
https://orcid.org/0000-0003-2527-9602 University of Veracruz image/svg+xml

Bio
Carlos Torres Real
https://orcid.org/0000-0003-3500-7803 University of Veracruz image/svg+xml

Bio

Published 2026-08-17

How to Cite

Torres Gastelú, C. A., & Torres Real, C. (2026). Willingness to pay and dependence on Generative Artificial Intelligence. Scale validation and structural model. Revista Panamericana De Pedagogía, 43. https://doi.org/10.21555/rpp.3920

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Abstract

The proliferation of monetization models in Generative Artificial Intelligence (GenAI) raises critical concerns regarding equity and intellectual autonomy among university students. The objective was to validate the Intention to Pay and Dependence on Generative Artificial Intelligence Among University Students (INPADE) scale and determine the predictive value of payment intention on dependence toward generative artificial intelligence in the university context. A quantitative cross-sectional study was conducted with a sample of 1,047 undergraduate students in Basic Education from the Universidad Pedagógica Veracruzana in Mexico. The sample distribution by biological sex was 78.6% female and 21.4% male. Data processing included Exploratory and Confirmatory Factor Analysis (EFA/CFA), measurement invariance by sex, and Structural Equation Modeling (SEM) using SPSS, AMOS, and JASP software. The results confirmed a two-factor structure comprising 10 items, with acceptable internal consistency (α > .852) and scalar invariance between males and females. The primary finding through SEM demonstrated that payment intention positively and significantly predicts technological dependence (β = .297; p < .001). It is concluded that the INPADE scale possesses robust psychometric properties, and it is empirically demonstrated that payment intention significantly predicts greater dependence toward artificial intelligence.

Keywords

  • Educational technology,
  • Higher education,
  • Internet,
  • Learning,
  • University student
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References

  1. Alotaibi, H., & Alayed, A. (2025). Adoption of generative AI in higher education: Understanding usage through a technology acceptance model. International Journal of Advanced and Applied Sciences, 12(12), 31-43. https://doi.org/10.21833/ijaas.2025.12.004
  2. Arce, C., Gavilanes, J., Arce, E., Haro, E., & Bonilla-Jurado, D. (2025). Artificial intelligence in higher education: Predictive analysis of attitudes and dependency among Ecuadorian university students. Sustainability, 17(17), Artículo 7741. https://doi.org/10.3390/su17177741
  3. 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, Artículo 103674. https://doi.org/10.1016/j.jretconser.2023.103674
  4. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2.ª ed.). The Guilford Press.
  5. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2.ª ed.). Routledge.
  6. Cea-D’Ancona, M. Á. (2002). Análisis multivariable: Teoría y práctica en la investigación social. Síntesis.
  7. Chakraborty, D., & Subramani, D. (2025). Development and validation of the Academic AI Usage Scale (AAIUS) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7605472/v1
  8. Chen, J., He, M., & Sun, J. (2025). AI anxiety and knowledge payment: The roles of perceived value and self-efficacy. BMC Psychology, 13(1), Artículo 208. https://doi.org/10.1186/s40359-025-02510-9
  9. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233-255. https://doi.org/10.1207/S15328007SEM0902_5
  10. DeVellis, R. F. (2012). Scale development: Theory and applications (3.ª ed.). SAGE.
  11. Ding, N., Chen, M., & Hu, L. (2025). Examining the impact of Big Five personality traits on Generation Z designers’ subscription to paid AI drawing tools using SEM and fsQCA. Scientific Reports, 15(1), Artículo 17587. https://doi.org/10.1038/s41598-025-02926-8
  12. García-Castro, R. A., Bartesaghi-Aste, W. M., Morales-Quezada, J. L., & Arocutipa-Huanacuni, L. E. (2025). Artificial intelligence dependence in academic tasks: Design and validation of the SAID questionnaire. Online Journal of Communication and Media Technologies, 15(4), Artículo e202529. https://doi.org/10.30935/ojcmt/17303
  13. Goli, A., Chintagunta, P. K., & Sriram, S. (2022). Effects of payment on user engagement in online courses. Journal of Marketing Research, 59(1), 11-34. https://doi.org/10.1177/00222437211016360
  14. Griffin, M. M., & Steinbrecher, T. D. (2013). Large-scale datasets in special education research. En R. C. Urbano (Ed.), International review of research in developmental disabilities (vol. 45, pp. 155-183). Elsevier. https://doi.org/10.1016/B978-0-12-407760-7.00004-9
  15. Guevara-Reyes, R. J., Saltos-Verdezoto, Y. M., Montero-Garofalo, M. F., & Ordóñez-Vargas, A. E. (2025). Impacto de la inteligencia artificial en el aprendizaje autónomo de estudiantes universitarios. MQRInvestigar, 9(3), Artículo e985. https://doi.org/10.56048/MQR20225.9.3.2025.e985
  16. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60. https://doi.org/10.21427/D7CF7R
  17. 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. https://doi.org/10.1080/10705519909540118
  18. Jo, H. (2025). Uncovering the reasons behind willingness to pay for ChatGPT-4 Premium. International Journal of Human–Computer Interaction, 41(2), 994-1009. https://doi.org/10.1080/10447318.2024.2307692
  19. Karahan-Adalı, G., & Bilgili, A. (2025). Generative AI in higher education: Students’ perspectives on adoption, ethical concerns, and academic impact. Acta Infologica, 9(1), 147-166. https://doi.org/10.26650/acin.1670197
  20. Kline, R. B. (2016). Principles and practice of structural equation modeling (4.ª ed.). The Guilford Press.
  21. Kuberska, D., & Ruszczyk, K. (2025). To pay or not to pay? Investigating students’ willingness to pay for ChatGPT. Olsztyn Economic Journal, 20(2), 169-183. https://doi.org/10.31648/oej.11625
  22. Liu, Y., Park, Y., & Wang, H. (2025). The mediating effect of user satisfaction and the moderated mediating effect of AI anxiety on the relationship between perceived usefulness and subscription payment intention. Journal of Retailing and Consumer Services, 84, Artículo 104176. https://doi.org/10.1016/j.jretconser.2024.104176
  23. Lupa-Wojcik, I. (2024). Students’ willingness to pay for access to ChatGPT. European Research Studies Journal, 27(3), 730-745. https://doi.org/10.35808/ersj/3462
  24. Morales-García, W. C., Sairitupa-Sánchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Frontiers in Education, 9, Artículo 1323898. https://doi.org/10.3389/feduc.2024.1323898
  25. Namatovu, A., & Kyambade, M. (2025). Leveraging AI in academia: University students’ adoption of ChatGPT for writing coursework (take home) assignments through the lens of UTAUT2. Cogent Education, 12(1), Artículo 2485522. https://doi.org/10.1080/2331186X.2025.2485522
  26. Robayo-Pinzón, O., Rojas-Berrio, S., Camargo, J., & Foxall, G. (2025). Generative artificial intelligence (GenAI) use and dependence: An approach from behavioral economics. Frontiers in Public Health, 13, Artículo 1634121. https://doi.org/10.3389/fpubh.2025.1634121
  27. Rojas-Marín, F. Á., Espinoza-Padilla, J. G., & Mendoza-Pacheco, M. F. (2024). Inteligencia artificial: Dependencia y la afección del pensamiento crítico. Ciencia Latina Revista Científica Multidisciplinar, 8(4), 12590-12608. https://doi.org/10.37811/cl_rcm.v8i4.13462
  28. Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025). Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), Artículo ep571. https://doi.org/10.30935/cedtech/16039
  29. Torres-Gastelú, C. A., & Torres-Real, C. (2025). Validación de una escala sobre la percepción de la inteligencia artificial en la educación superior. Ciencia Latina Revista Científica Multidisciplinar, 9(2), 5706-5725. https://doi.org/10.37811/cl_rcm.v9i2.17324