- Original authors / creators
- Hyo Young Kim; Young Soo Park
- Original publication
- Behavioral Sciences · Multidisciplinary Digital Publishing Institute (MDPI) · 2024-10-17
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- CC BY 4.0
© 2024 by the authors.
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- Evidence limits
- Adjacent research, not a directly sampled family-office study. Applicability to a family office requires separate assessment. Despite its novel contributions, this study has several limitations that open up several interesting avenues for future research. First, the scope of this research is limited to specific types of structural breaks, namely Additive Outliers and Level Shifts, and focuses solely on the immediate trust response to these disruptions. However, individual heterogeneity—such as gender, education level, work experience, and risk preferences (e.g., risk aversion)—could play a significant role in shaping trust dynamics in AI versus human experts. Investigating how these individual differences affect trust responses would provide deeper insights into the nuanced psychological factors influencing decision making. Second, while this study only focused on the stock price forecasting scenario, there is significant potential to expand the scope to other forecasting domains. Areas such as economic trends, climate change predictions, or healthcare diagnostics present opportunities for examining whether similar trust dynamics hold in diverse and highly impactful contexts. Broadening the application of this research would not only enhance the generalizability of the findings but also provide practical relevance for decision makers across various industries. Third, conducting experiments in real-world settings would offer deeper insights and lead to more actionable conclusions. In real-world decision making, a multitude of additional factors and complexities come into play that were beyond the scope of this study [43,44]. For example, financial forecasting in practice often deals with heightened uncertainty, relies on probabilistic estimates, and incorporates richer, more detailed contextual information [9,45,46,47]. These real-world conditions could have a significant impact on how trust in AI versus human experts is formed, maintained, and potentially eroded. By integrating these practical complexities, future research could better reflect the intricate nature of trust dynamics, thus providing more precise and relevant conclusions for real-world applications. Finally, future studies could also investigate how AI transparency and explainability impact the maintenance and recovery of trust following structural breaks. Recent research showed that providing clear explanations for errors can enhance trust in decision-making systems [48,49]. As suggested by Arrieta et al. [48], explainable artificial intelligence (XAI) plays a crucial role in fostering trust by offering transparency and clarity on how AI systems make decisions. Studies could explore whether providing more detailed information about AI’s decision-making processes or offering explanations for predictions could mitigate the erosion of trust during periods of market instability. For example, transparent AI models that explain their reasoning could help users understand why certain predictions are made, especially during periods of volatility such as Level Shifts. This enhanced understanding may enable users to feel more confident in AI-driven advice and potentially reduce their reliance on human experts when trust in AI is maintained through clear and interpretable explanations. By examining how explainability influences trust in AI under dynamic market conditions, future research can provide valuable insights into designing AI systems that are both effective and trusted by users in high-stake environments like financial forecasting. By integrating explainable AI, users could gain a better understanding of the decision-making processes behind AI-generated advice, potentially reducing trust erosion and promoting faster trust recovery after disruptions.
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