Human-AI collaboration in digital innovation decision making: a case-based reasoning model

Human-AI collaboration in digital innovation decision-making faces challenges, particularly the risk of over-reliance on AI systems, which can diminish human judgment and creativity. While Case-Based Reasoning (CBR) excels in analyzing data and identifying patterns, it can sometimes overlook the subtle, creative insights that human decisionmakers offer. This study explores the effectiveness of CBR in addressing these challenges compared to traditional decision-making models such as the Delphi Method, Bounded Rationality, Socratic Method, and Cynefin Framework. The research employs various analytical methods, including observed frequencies, analysis of variance (ANOVA), and eta-squared values, to evaluate the performance of these models across criteria like integration of human judgment, adaptability to complexity, and efficiency in decision-making. CBR consistently outperforms other models. It received the highest number of “Excellent” ratings in “Integration of Human Judgment” (151), “Adaptability to Complexity” (139), and “Efficiency in Decision-Making” (145). Weighted mean results demonstrate that CBR performs better than other methods in all the criteria. ANOVA results show CBR has the highest F-values and significant <i>p</i>-values, while etasquared values indicate it explains the most variability (0.45, 0.38, and 0.38). These findings underscore CBR’s ability to integrate human judgment effectively, adapt to complex scenarios, and enhance decision-making efficiency, advocating for its broader adoption in digital innovation strategies to foster more balanced and innovative solutions.