## 1 Introduction  
Artificial intelligence (AI), particularly machine learning, has brought a revolutionary transformation in decision-making (DM) processes within organizations, such as the gradual assumption of numerous DM responsibilities that were previously carried out by humans [1]. An important driving factor that has contributed to the recent increase in interest in human-AI DM is the expanding capacity of AI models to support decision processes. Within this domain, the three most used AI models are deep models, shallow models, and the _Wizard of Oz_[2]. As a result of these AI models, AI-based systems can autonomously learn and uncover hidden insights from data, enabling individuals to make decisions that demonstrate rational superiority [3, 4] Indeed, algorithms, defined by [PERSON] et al. [5] as \"_super-carriers of formal rationality_\", offer several advantages over human decision-makers, including the ability to process large volumes of data, rapidly analyze data, and replicate logical and mathematical processes. For these reasons, AI-based DM is considered more efficient, accurate, and flexible [6, 7, 8]. AI applied to DM processes has already been implemented in many fields, including finance, banking, healthcare, justice, and human resource management [9]. Indeed, within organizations, the employment of AI technologies has increased [10; 11], offering powerful resources for organizational DM [12]. In this context, decision-support systems (DSSs) have emerged as crucial tools in aiding management across various activities, including planning and operational execution [13]. More specifically, AI can be beneficial for performance management, employee evaluation, and overall assessment within organizations [14]. In recent decades, the crucial key in selecting an AI approach in DM has been the nature of the task itself [15; 16; 17]. Routine and well-structured tasks have typically been automated, while more intricate and ambiguous tasks have been addressed through augmentation, which emphasizes a supportive approach rather than outright substitution. As DSSs integrated with AI technology, they have demonstrated their ability to adapt and organize even within uncertain and dynamic environments [18]. Originally conceived as individual tools, these systems have undergone a significant evolution to incorporate sophisticated properties that facilitate group DM and tackle unstructured problems [19; 20]. Nevertheless, managers' acceptance of AI-based solutions in DM remains an ongoing concern, as managers' attitudes toward artificial agents are still unbalanced toward human primary. This inclination towards human control in DM has been highlighted in the study conducted by [PERSON] et al. [21]. He found out that a significant majority of managers prefer a partnership where humans hold the majority of DM power, and a considerable percentage of managers desire complete control of human agents. The literature offers various explanations to clarify this resistance. One explanation for this imbalance is humans' fear of being replaced in their strategic DM roles and losing their jobs [22]. In addition, the \"_black box_\" nature of AI and the lack of transparency in algorithmic DM processes pose significant challenges and greatly affect user trust, which is a crucial factor in the human acceptance of AI [23]. Specifically, [PERSON] explored factors that influence the trust and acceptance of AI, showing that the perception of algorithmic features such as fairness, accountability, and transparency (FAT) have a direct effect on trust and that causability, or the quality of explanations, plays an antecedent role to explainability in building trust. Therefore, recent studies have emphasized the need to provide practical guidelines for the development of user-centered algorithms in order to establish justified trust in AI-based systems [23].