## 4 Methodology  
### Experimental Setting  
A within-subject laboratory experiment was performed with subjects who had to make assortment decisions by using an experimental RA prototype for assortment planning developed by JDA (Scottsdale, AZ, USA). This prototype was developed with Axure RP 8 and was made available, to the participants, through a \(1680\times 1050\) resolution monitor. A total of twenty professionals (11 men and 9 women) participated in the study and all participants had experience in using application software to perform the product assortment task involved in this experiment. The average age of this sample was 26 years old with a standard deviation of 3.9 years. Each participant completed a consent form and received a $30 gift card as a compensation. This project was approved by the Institutional Review Board (IRB) of our institution. As illustrated in Figure 1, participants had to make assortment decisions for two similar fictitious scenarios that were counterbalanced. The context of these scenarios was the retail clothing industry (i.e., dresses (Scenario 1) and male upper body clothing (Scenario 2). Each scenario began with a practice task that did not include an RA to familiarize participants with the assortment planning software. This practice task was followed for each scenario by three distinct conditions in a counterbalanced order. Each condition displayed a particular recommendation representation that was designed based on the two experimental factors (i.e., transparency and cognitive effort). Task 1 reflected a non-transparent RA and low cognitive effort condition (T1), Task 2 represented a transparent RA and low cognitive effort condition (T2), and Task 3 was a transparent RA and high cognitive effort condition (T3). The non-transparent RA and high cognitive effort condition was not part of the experiment since it is a type of RA that neither organizations nor employees would realistically use. This experiment lasted on average two hours and no time constraint was imposed for each task (about 5 min per task). For each task (3 tasks) of each scenario (2 scenarios), 24 different products were displayed (see Figure 2). From these 24 distinctive products, participants needed to select a fixed number of products (ranging from 6 to 7) to create an optimal assortment of products. The total number of products that needed to be selected by the participants for each condition was stated in both scenarios. Furthermore, the products of each task were similarly presented to the participants with an image, its name including its brand, and its product score (i.e., RA's recommendation). The RA's recommendation of each product was generated using AI and varied between 0 and 100. In order to ensure the authenticity of the recommendations, product scores were generated from previous data from an analogous industry using JDA Luminate (Scottsdale, AZ), an artificial intelligence platform to predict market demand. Depending on its score number, each RA recommendation was surrounded by a specific color (i.e., green \(>\) 66, 66 \(\geq\) orange \(>\) 33, and red \(\leq\) 33). Figure 1: Experimental protocol. Figure 2: Twenty-four products displayed per task. For T1 (non-transparent RA and low cognitive effort), the RA's recommendations were the only information made available to the participants. For T2 (transparent RA and low cognitive effort) and T3 (transparent RA and high cognitive effort), the RA's recommendations were also made available to the participants, however, participants could also have access to the partially exposed details of the algorithm responsible for the RA's recommendations (i.e., explanations). By clicking on each product, participants could consult further product information (e.g., attributes, past sales, margin, and comparative products), which was included in the algorithm responsible for each RA recommendation. The cognitive effort necessary  
At the end of the experiment, a semi-structured interview was conducted (about 15 min).