Operationalizing and Validating Contextual Financial Judgment: Scale Development and Structural Modeling for AI-Driven Personal Finance

With Artificial Intelligence (AI) and automated choice architectures remodelling consumer finance, traditional measures of financial literacy are ineffective in measuring how individuals network with, assess and supersede algorithmic stimuli. For bridging this theoretical and empirical gap, this paper operationalises a new construct - Contextual Financial Judgment (CFJ) – defined as a unique cognitive-attitudinal competence to appraise programmed financial recommendations vis-a-vis non-quantifiable, qualitative life realities.
Building upon Dual-Process Theory, Human Agency Theory and the Industry 5.0 human-centric AI paradigm, this study introduces a comprehensive psychometric scale architecture and Structural Equation Modeling (SEM) framework to measure human agency in personal finance. Firstly, the study establishes a theoretically refined 12-item scale consisting of three reflective first-order dimensions: Contextual Financial Awareness (CFA), In-Depth Systemic Knowledge (ISK) and Evaluative and Overriding Attitude (EOA). Secondly, Contextual Financial Judgment (CFJ) is integrated as a second-order latent construct into a structural model that links exogenous antecedents like Algorithmic Exposure and Perceived Financial Importance, to vital downstream outcomes like Decision Autonomy, Mitigation of Impulse Financial Behaviour and Long-Term Value Alignment, while Explainable AI (XAI) is modelled as a moderating mechanism strengthening the translation of systemic knowledge into overriding attitude. This is further supported by six testable propositions (P1 to P6) to guide future empirical testing.
Finally, the study creates a conceptual structural architecture of AI-mediated financial governance and presents a comprehensive, five-step methodological roadmap for psychometric validation (EFA, CFA and PLS-SEM/CB-SEM), thereby offering a rigorous foundation for evaluating consumer agency in AI-driven financial ecosystems.

Financial Literacy, Contextual Financial Judgment (CFJ), Personal Finance, Scale Development, Structural Equation Modeling (SEM), Choice Architecture

1. Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500. https://doi.org/10.2307/1879431 [Google Scholar] [Crossref]

2. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012 [Google Scholar] [Crossref]

3. Balaskas, S. (2026). Transparency by design: A narrative synthesis of AI disclosure, explainability, and trust in consumer-facing FinTech. FinTech, 5(2), 41. https://doi.org/10.3390/fintech5020041 [Google Scholar] [Crossref]

4. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. https://doi.org/10.1146/annurev.psych.52.1.1 [Google Scholar] [Crossref]

5. Breque, M., De Nul, L., & Petridis, A. (2021). Industry 5.0 – Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union. [Google Scholar] [Crossref]

6. https://doi.org/10.2777/308407 [Google Scholar] [Crossref]

7. Brenner, L., & Meyll, T. (2020). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25, 100275. https://doi.org/10.1016/j.jbef.2020.100275 [Google Scholar] [Crossref]

8. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020). Explainable AI in Fintech risk management. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00026 [Google Scholar] [Crossref]

9. Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.2307/3150876 [Google Scholar] [Crossref]

10. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. [Google Scholar] [Crossref]

11. Cummings, M. L. (2004). Automation bias in intelligent time critical decision support systems. In Proceedings of the AIAA 1st Intelligent Systems Technical Conference. https://doi.org/10.2514/6.2004-6313 [Google Scholar] [Crossref]

12. D'Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. The Review of Financial Studies, 32(5), 1983–2020. https://doi.org/10.1093/rfs/hhz014 [Google Scholar] [Crossref]

13. 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 [Google Scholar] [Crossref]

14. De Brito Duarte, R., Correia, F., Arriaga, P., & Paiva, A. (2023). AI trust: Can explainable AI enhance warranted trust? Human Behavior and Emerging Technologies, 2023, Article 4637678. https://doi.org/10.1155/2023/4637678 [Google Scholar] [Crossref]

15. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. [Google Scholar] [Crossref]

16. https://doi.org/10.1207/S15327965PLI1104_01 [Google Scholar] [Crossref]

17. Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P. (2021). The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. Proceedings of the 52nd Hawaii International Conference on System Sciences. [Google Scholar] [Crossref]

18. DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications. [Google Scholar] [Crossref]

19. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033 [Google Scholar] [Crossref]

20. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643 [Google Scholar] [Crossref]

21. Dreyfus, S. E., & Dreyfus, H. L. (1980). A five-stage model of the mental activities involved in directed skill acquisition (ORC-80-2). University of California, Berkeley, Operations Research Center. [Google Scholar] [Crossref]

22. Emirbayer, M., & Mische, A. (1998). What is agency? American Journal of Sociology, 103(4), 962–1023. https://doi.org/10.1086/231294 [Google Scholar] [Crossref]

23. Evans, J. St. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241. [Google Scholar] [Crossref]

24. https://doi.org/10.1177/1745691612460685 [Google Scholar] [Crossref]

25. Financial Conduct Authority. (2022). Gaming trading: How trading apps could be engaging consumers for the worse. FCA. [Google Scholar] [Crossref]

26. Gomber, P., Koch, J.-A., & Siering, M. (2017). Digital finance and FinTech: Current research and future research directions. Journal of Business Economics, 87(5), 537–580. [Google Scholar] [Crossref]

27. https://doi.org/10.1007/s11573-017-0852-x [Google Scholar] [Crossref]

28. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. [Google Scholar] [Crossref]

29. 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. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]

30. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. [Google Scholar] [Crossref]

31. https://doi.org/10.1080/10705519909540118 [Google Scholar] [Crossref]

32. Huang, M.-H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266 [Google Scholar] [Crossref]

33. Hung, A. A., & Yoong, J. K. (2010). Asking for help: Survey and experimental evidence on financial advice and behavior change (Working Paper WR-714-1). RAND Corporation. [Google Scholar] [Crossref]

34. https://doi.org/10.7249/WR714-1 [Google Scholar] [Crossref]

35. Huston, S. J. (2010). Measuring financial literacy. Journal of Consumer Affairs, 44(2), 296–316. [Google Scholar] [Crossref]

36. https://doi.org/10.1111/j.1745-6606.2010.01170.x [Google Scholar] [Crossref]

37. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. [Google Scholar] [Crossref]

38. https://doi.org/10.1016/j.bushor.2018.03.007 [Google Scholar] [Crossref]

39. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux. [Google Scholar] [Crossref]

40. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. [Google Scholar] [Crossref]

41. https://doi.org/10.1007/BF02291575 [Google Scholar] [Crossref]

42. Kinder, G., & Rowland, M. (2014). Life planning for you: How to design & deliver the life of your dreams. Serenity Point Press. [Google Scholar] [Crossref]

43. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press. [Google Scholar] [Crossref]

44. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50.30392 [Google Scholar] [Crossref]

45. Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 31–57. [Google Scholar] [Crossref]

46. https://doi.org/10.1145/3236386.3241340 [Google Scholar] [Crossref]

47. Liu, Y., Wang, R., & Siau, K. (2025). FinTech digital transformation: Generative AI, humanoid robots, Metaverse, human-AI collaboration, and Industry 5.0. In K.-W. Huang, Q. Cao, & R. Su (Eds.), Financial Technology: 5th International Conference, ICFT 2024, Proceedings (CCIS, Vol. 2437, pp. 122–136). Springer. https://doi.org/10.1007/978-981-96-3811-6_12 [Google Scholar] [Crossref]

48. Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5 [Google Scholar] [Crossref]

49. Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–386. https://doi.org/10.1097/00006199-198611000-00017 [Google Scholar] [Crossref]

50. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. [Google Scholar] [Crossref]

51. https://doi.org/10.1037/1082-989X.1.2.130 [Google Scholar] [Crossref]

52. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334. https://doi.org/10.2307/23044045 [Google Scholar] [Crossref]

53. Mathur, A., Acar, G., Friedman, M. J., Lucherini, E., Mayer, J., Chetty, M., & Narayanan, A. (2019). Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), Article 81. https://doi.org/10.1145/3359183 [Google Scholar] [Crossref]

54. Mehdiabadi, A., Shahabi, V., Shamsinejad, S., Amiri, M., Spulbar, C., & Birau, R. (2022). Investigating Industry 5.0 and its impact on the banking industry. Applied Sciences, 12(10), 5126. https://doi.org/10.3390/app12105126 [Google Scholar] [Crossref]

55. Mills, S. (2026, January 28). The FCA's long term review into AI and retail financial services: Designing for the unknown [Speech transcript]. Financial Conduct Authority. [Google Scholar] [Crossref]

56. https://www.fca.org.uk/news/speeches/fca-long-term-review-ai-retail-financial-services-designing-unknown [Google Scholar] [Crossref]

57. Narayanan, A., Mathur, A., Chetty, M., & Kshirsagar, M. (2020). Dark patterns: Past, present, and future. Queue, 18(2), 67–92. https://doi.org/10.1145/3400899.3400901 [Google Scholar] [Crossref]

58. OECD. (2020). OECD/INFE 2020 international survey of adult financial literacy. OECD Publishing. [Google Scholar] [Crossref]

59. Overton, R. (2008). Theoretical rationale for financial life planning. In D. B. Yeske & E. K. Anthes (Eds.), Financial planning theory in practice. Financial Planning Association. [Google Scholar] [Crossref]

60. Panos, G. A., & Wilson, J. O. S. (2020). Financial literacy and responsible finance in the FinTech era: Capabilities and challenges. The European Journal of Finance, 26(4–5), 297–301. [Google Scholar] [Crossref]

61. https://doi.org/10.1080/1351847X.2020.1717569 [Google Scholar] [Crossref]

62. Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055 [Google Scholar] [Crossref]

63. Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. [Google Scholar] [Crossref]

64. https://doi.org/10.1002/nur.20147 [Google Scholar] [Crossref]

65. Remund, D. L. (2010). Financial literacy explicated: The case for a clearer definition in an increasingly complex economy. Journal of Consumer Affairs, 44(2), 276–295. [Google Scholar] [Crossref]

66. https://doi.org/10.1111/j.1745-6606.2010.01169.x [Google Scholar] [Crossref]

67. Sironi, P. (2016). FinTech innovation: From robo-advisors to goal based investing and gamification. Wiley. [Google Scholar] [Crossref]

68. Skitka, L. J., Mosier, K. L., & Burdick, M. (2000). Accountability and automation bias. International Journal of Human-Computer Studies, 52(4), 701–717. https://doi.org/10.1006/ijhc.1999.0349 [Google Scholar] [Crossref]

69. Soomro, Z. A., Ali, Q., & Parveen, S. (2022, September). Diffusion of Industry 5.0 in the financial sector: A developmental study [Conference presentation]. British Academy of Management Conference. [Google Scholar] [Crossref]

70. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. [Google Scholar] [Crossref]

71. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

72. Willis, G. B. (2005). Cognitive interviewing: A tool for improving questionnaire design. SAGE Publications. [Google Scholar] [Crossref]

73. Xiao, J. J., & Porto, N. (2017). Financial education and financial satisfaction: Financial literacy, behavior, and capability as mediators. International Journal of Bank Marketing, 35(5), 805–817. [Google Scholar] [Crossref]

74. https://doi.org/10.1108/IJBM-01-2016-0009 [Google Scholar] [Crossref]

75. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535. [Google Scholar] [Crossref]

76. https://doi.org/10.1016/j.jmsy.2021.10.006 [Google Scholar] [Crossref]

• AWS Security Architecture and Machine Learning for APT Detection in Cloud Environments