The Effects of Past Experiences, Trust, and Perception on Decisions to Adopt New AI Technology
DOI:
https://doi.org/10.47611/jsrhs.v13i4.7977Keywords:
Artifical Intelligence, Decision Making, Neurofinance, Past Experience, AI Risks, Trust, PerceptionAbstract
Artificial Intelligence (AI) offers numerous benefits across various fields, including education, finance, and workplace productivity. However, the factors that prompt the use of new AI technology have yet to be fully explored. This paper explores the neuroscientific origins of decision-making as well as the factors influencing the acceptance and adoption of AI, focusing on the role of past experiences, trust, and perception in decision-making. This study employs a mixed-method approach, combining quantitative surveys and qualitative interviews to provide a comprehensive understanding of the attitudes of participants towards AI. The results reveal that positive past experiences generally enhance trust and willingness to engage with AI, whereas negative ones tend to give rise to skepticism and avoidance. However, while past experiences with AI can influence future decisions, trust and perception play more critical roles in the decision-making process. The findings highlight the importance of trust-building and addressing common misconceptions about AI, suggesting that educational programs and simulations that provide positive initial experiences with AI can help recalibrate risk perceptions and build confidence among potential users. By promoting balanced views of AI's capabilities and limitations, this study contributes to societal progress by using robust evidence to support the integration of AI into various sectors of life.
Downloads
References or Bibliography
References
Ahmad, S. F., Alam, M. M., Rahmat, M. K., Mubarik, M. S., & Hyder, S. I. (2022). Academic and administrative role of artificial intelligence in education. Sustainability, 14(3), 1101. https://doi.org/10.3390/su14031101
Al-Baity, H. H. (2023). The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed framework. Sustainability, 15(18), 13725. https://doi.org/10.3390/su151813725
Barber, B. M., & Odean, T. (2001). Boys will be Boys: Gender, Overconfidence, and Common Stock Investment. the Quarterly Journal of Economics, 116(1), 261–292. https://doi.org/10.1162/003355301556400
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214–1221. https://doi.org/10.1038/nn1954
Berridge, K. C., & Kringelbach, M. L. (2015). Pleasure systems in the brain. Neuron, 86(3), 646–664. https://doi.org/10.1016/j.neuron.2015.02.018
Brown, J. W., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science, 307(5712), 1118–1121. https://doi.org/10.1126/science.1105783
Camarinha-Matos, L. M., Boucher, X., & Ortiz, A. (2023). Collaborative Networks in Digitalization and Society 5.0. In IFIP advances in information and communication technology. https://doi.org/10.1007/978-3-031-42622-3
Camerer, C. (2005). Three Cheers—Psychological, theoretical, empirical—for loss aversion. Journal of Marketing Research, 42(2), 129–133. https://doi.org/10.1509/jmkr.42.2.129.62286
Camerer, C., & Weber, M. (1992). Recent developments in modeling preferences: Uncertainty and ambiguity. Journal of Risk and Uncertainty, 5(4), 325–370. https://doi.org/10.1007/bf00122575
Collaborative Networks in Digitalization and Society 5.0. (2023). In IFIP advances in information and communication technology. https://doi.org/10.1007/978-3-031-42622-3
Diederen, K. M. J., & Schultz, W. (2015). Scaling prediction errors to reward variability benefits error-driven learning in humans. Journal of Neurophysiology, 114(3), 1628–1640. https://doi.org/10.1152/jn.00483.2015
Grassini, S. (2023). Shaping the Future of Education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692
Hirshleifer, D. (2001). Investor psychology and asset pricing. the Journal of Finance/the Journal of Finance, 56(4), 1533–1597. https://doi.org/10.1111/0022-1082.00379
Horowitz, M. C., Kahn, L., Macdonald, J., & Schneider, J. (2023). Adopting AI: how familiarity breeds both trust and contempt. AI & Society. https://doi.org/10.1007/s00146-023-01666-5
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263. https://doi.org/10.2307/1914185
Kolemba, M., & Maciuszek, J. (2013). Perception of risk and risk-taking among the lucky and the luckless. Polish Psychological Bulletin, 44(3), 358–370. https://doi.org/10.2478/ppb-2013-0039
Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47(5), 763–770. https://doi.org/10.1016/j.neuron.2005.08.008
Lewis, D. A., Fish, K. N., Arion, D., & Gonzalez-Burgos, G. (2011). Perisomatic inhibition and cortical circuit dysfunction in schizophrenia. Current Opinion in Neurobiology, 21(6), 866–872. https://doi.org/10.1016/j.conb.2011.05.013
Li, Z., Wu, Q., Hong, P., & Tian, R. (2023). Effects of Investment experience on the stock Investment Task: The Mediating role of risk perception. Behavioral Sciences, 13(2), 115. https://doi.org/10.3390/bs13020115
Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267–286. https://doi.org/10.1037/0033-2909.127.2.267
Miendlarzewska, E. A., Kometer, M., & Preuschoff, K. (2017). Neurofinance. Organizational Research Methods, 22(1), 196–222. https://doi.org/10.1177/1094428117730891
Payzan-LeNestour, E., Dunne, S., Bossaerts, P., & O’Doherty, J. P. (2013). The Neural Representation of Unexpected Uncertainty during Value-Based Decision Making. Neuron, 79(1), 191–201. https://doi.org/10.1016/j.neuron.2013.04.037
Pelrine, K., Reksoprodjo, M., Gupta, C., Christoph, J., & Rabbany, R. (2023). Towards reliable misinformation Mitigation: generalization, uncertainty, and GPT-4. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2305.14928
Pölzlbauer, G., Dittenbach, M., & Rauber, A. (2006). Advanced visualization of Self-Organizing Maps with vector fields. Neural Networks, 19(6–7), 911–922. https://doi.org/10.1016/j.neunet.2006.05.013
Porcelli, A. J., & Delgado, M. R. (2009). Acute stress modulates risk taking in financial decision making. Psychological Science, 20(3), 278–283. https://doi.org/10.1111/j.1467-9280.2009.02288.x
Preuschoff, K., Quartz, S. R., & Bossaerts, P. (2008). Human insula activation reflects risk prediction errors as well as risk. the Journal of Neuroscience/the Journal of Neuroscience, 28(11), 2745–2752. https://doi.org/10.1523/jneurosci.4286-07.2008
Pulford, B. D., & Gill, P. (2014). Good luck, bad luck, and ambiguity aversion. Judgment and Decision Making, 9(2), 159–166. https://doi.org/10.1017/s1930297500005520
Rangel, A., & Hare, T. (2010). Neural computations associated with goal-directed choice. Current Opinion in Neurobiology, 20(2), 262–270. https://doi.org/10.1016/j.conb.2010.03.001
Rushworth, M. F., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E. (2011). Frontal cortex and Reward-Guided learning and Decision-Making. Neuron, 70(6), 1054–1069. https://doi.org/10.1016/j.neuron.2011.05.014
Sanusi, I. T., Ayanwale, M. A., & Chiu, T. K. F. (2023). Investigating the moderating effects of social good and confidence on teachers’ intention to prepare school students for artificial intelligence education. Education and Information Technologies, 29(1), 273–295. https://doi.org/10.1007/s10639-023-12250-1
Schultz, W. (2016). Dopamine reward prediction-error signalling: a two-component response. Nature Reviews. Neuroscience, 17(3), 183–195. https://doi.org/10.1038/nrn.2015.26
Shiller, R. J. (2003). From efficient markets theory to behavioral finance. the Journal of Economic Perspectives/the Journal of Economic Perspectives, 17(1), 83–104. https://doi.org/10.1257/089533003321164967
Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic Advances from the Brain Disease Model of Addiction. New England Journal of Medicine/the New England Journal of Medicine, 374(4), 363–371. https://doi.org/10.1056/nejmra1511480
Wallis, J. D. (2007). Orbitofrontal cortex and its contribution to Decision-Making. Annual Review of Neuroscience, 30(1), 31–56. https://doi.org/10.1146/annurev.neuro.30.051606.094334
Published
How to Cite
Issue
Section
Copyright (c) 2024 Andy Ziqi Lei; Cory Cultrera
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
