## 2 Background
### Cognitive Effort and RA Transparency
Therefore, in order to reduce cognitive effort, decision makers will be willing to settle for a non-optimal choice [30]. Hence, users will adapt their decision-making strategies to their environment [31]. For instance, in an online context, shoppers could first begin their decision-making process by screening the available products (i.e., initial screening stage) [32]. They could then select the most relevant items and compare them in depth [32]. This could finally lead to the purchase of a product [32]. By including a decision aid (e.g., RA) throughout this decision-making process, users could be able to make more accurate decisions more easily [33]. When used in a decision-making process, RAs have been shown to increase decision quality and reduce cognitive effort [34; 35]. Previous research on decision aids confirmed that decision makers can increase the depth of their evaluation, between the available alternatives, by consulting the recommendations of a decision aid, thus positively affecting decision quality [36]. With a transparent RA, users are able to perceive the usefulness of its recommendations through their decision-making process [13]. By considering these recommendations, both subjective and objective decision quality (i.e., the user's perceived and actual performance) increases [35]. However, the cognitive effort associated with understanding the logical reasoning behind these recommendations (i.e., the increase in cognitive load) will negatively impact the user's subjective and objective decision quality [35]. **Satisfaction. ** User satisfaction with a decision-making process is perceived when expectations are confirmed [37]. Therefore, the users' actual performance with an RA must be in line with or exceed their expectations [23]. Prior research on decision support systems has shown the importance of information quality on users' decision-making satisfaction [38; 39]. Information quality is composed of multiple dimensions (e.g., information accuracy, information completeness, information relevance) [40]. Thus, understanding the logical reasoning behind the recommendations of an AI-based RA, through knowledgeable explanations, is essential for users to perceive the quality of these suggestions. Furthermore, these recommendations, when perceived as useful, have been demonstrated to positively influence user satisfaction with the RA and the decision-making process with the RA [23]. However, a transparent RA (i.e., increase in cognitive load) will decrease user satisfaction [18; 23; 35; 39]. **RA adoption and usage. ** To consider the recommendations of an AI-based RA in an online context, shoppers must perceive its suggestions as accurate, easy to use, satisfactory, trustworthy, and useful [8; 23; 35; 41]. Thus, the first interaction of a user with an RA (i.e., pre-adoption) is crucial in influencing its adoption and usage [8; 42]. A transparent RA is then essential for users to accept these recommendations throughout their decision-making process [24; 43]. However, in an organizational context, employees have no choice in adopting their employer's information system [44]. Nevertheless, the continued usage of that system mainly depends on its perceived usefulness and ease of use [45]. In the context of a transparent RA, a user experiencing information overload while consulting its knowledgeable explanations is expected to accept the RA's recommendations [46]. Therefore, as a result of cognitive fatigue or confusion, a user would accept the RA's recommendations without understanding the logical reasoning behind them [17]. The RA's recommendations would then be consulted more frequently by the user due to their increased importance in the user's decision-making process [47; 48].