Three experiments were conducted to identify the impact of factors associated with the environment (predictability of the environment) and factors associated with an available expert (the accuracy of the expert and the cost of the expert's advice) on the optimal utilization of the expert. Across all three experiments, participants overutilized the expert (requesting advice when advice was suboptimal) when the expert accuracy was higher and when the environment was less predictable. Conversely, participants underutilized the expert (not requesting advice when doing so was optimal) more when the environment was more predictable and the expert was less accurate. Participants showed little sensitivity to the cost of advice, further compounding the errors. Requiring participants to request advice on every training trial increased reliance on the later optional expert and led to properties of the expert primarily influencing the decision to request advice. Requiring participants to rely only on the environmental cue during training decreased overall reliance on the later optional expert and led to properties of the environment primarily influencing the decision to request advice. Requiring participants to interact with both the environment and the expert during training led to better overall decisions and to the integration of environmental cues and properties of the expert to inform the decision to request advice.

Yes / With the rapid evolution of data over the last few years, many new technologies have arisen with artificial intelligent (AI) technologies at the top. Artificial intelligence (AI), with its infinite power, holds the potential to transform patient healthcare. Given the gaps revealed by the 2020 COVID-19 pandemic in healthcare systems, this research investigates the effects of using an artificial intelligence-driven public healthcare framework to enhance the decision-making process using an extended model of Shaft and Vessey (2006) cognitive fit model in healthcare organizations in Saudi Arabia. The model was validated based on empirical data collected using an online questionnaire distributed to healthcare organizations in Saudi Arabia. The main sample participants were healthcare CEOs, senior managers/managers, doctors, nurses, and other relevant healthcare practitioners under the MoH involved in the decision-making process relating to COVID-19. The measurement model was validated using SEM analyses. Empirical results largely supported the conceptual model proposed as all research hypotheses are significantly approved. This study makes several theoretical contributions. For example, it expands the theoretical horizon of Shaft and Vessey's (2006) CFT by considering new mechanisms, such as the inclusion of G2G Knowledge-based Exchange in addition to the moderation effect of Experience-based decision-making (EDBM) for enhancing the decision-making process related to the COVID-19 pandemic. More discussion regarding research limitations and future research directions are provided as well at the end of this study.