## 5 Results  
H1 suggested that a transparent RA together with low cognitive effort (T2) will have a greater positive impact on participants' perception regarding source credibility (H1a), control (H1b), decision quality (H1c), and satisfaction (H1d) than other conditions (T1 and T3). A linear regression with a random intercept model was performed to test the difference between the means of participants' perceptions for each combination of tasks (see Table 1). The results provided strong support for H1b (T2 is greater than T1 and T3, respectively 0.3500, \(p=0.0185\); 0.3000, \(p=0.0365\)). However, as illustrated in Table 1, H1a, H1c, and H1d were only partially confirmed. The results showed that exposing the logical reasoning behind the RA (i.e., a transparent RA) had a significant effect on the assortment planners' perception towards the RA regarding source credibility (H1a), decision quality (H1c), and satisfaction (H1d) (T2 greater than T1, respectively 0.7055, \(p\leq 0.0001\); 0.4706, \(p=0.0474\); 0.6068, \(p=0.0027\)). Furthermore, the increased cognitive effort necessary to access and process the explanations of a transparent RA had no impact on the assortment planners' perception towards the RA regarding source credibility (H1a), decision quality (H1c), and satisfaction (H1d) (i.e., the difference between transparent RA and low cognitive effort (T2) and transparent RA and high cognitive effort (T3) was not statistically significant). H2 stipulated that a transparent RA together with low cognitive effort (T2) will have a greater positive impact on the assortment planners' objective decision quality than other conditions (T1 and T3). A linear regression with a random intercept model was performed to test the difference between the means of participants' objective decision quality for each combination of conditions. As illustrated in Table 2, the results provided strong support for H2 (T2 is greater than T1 and T3, respectively 1.0500, \(p=0.0001\) and 0.2330, \(p=0.0001\)). H3 suggested that assortment planners will consult throughout their decision-making process the recommendations of a transparent RA coupled with high cognitive effort (T3) more frequently than when exposed to other conditions (T1 and T2). A Poisson regression with a random intercept model \begin{table}
\begin{tabular}{c c c c c} \hline  & **Hypothesis** & **Result** & **Estimate** & \(p\)**-Value** \\ \hline Source credibility & H1a (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T1 & 0.7055 & \textless{}0.0001 \\  & H1b (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T3 & 0.0916 & 0.2554 \\ Control & H1b (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T1 & 0.3500 & 0.0185 \\ Decision quality & H1c (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T3 & 0.3000 & 0.0365 \\  & H1c (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T1 & 0.4706 & 0.0474 \\ Satisfaction & H1d (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T3 & 0.2991 & 0.0678 \\  & H1d (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T1 & 0.6068 & 0.0027 \\  & H2 \textgreater{} T3 & T2 \textgreater{} T3 & 0.0964 & 0.3052 \\ \hline \end{tabular} Note: Linear regression with random intercept model. One-tailed \(p\)-value adjusted for multiple comparison by the method of Holm.  
\end{table} 
Table 1: Participants’ perception results. \begin{table}
\begin{tabular}{c c c c} \hline  & **Result** & **Estimate** & \(p\)**-Value** \\ \hline Decision quality & H2 (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T1 & 1.0500 & 0.0001 \\  & H2 (T2 \textgreater{} T1 and T2 \textgreater{} T3) & T2 \textgreater{} T3 & 0.2330 & 0.0001 \\ \hline \end{tabular} Note: Linear regression with random intercept model. One-tailed \(p\)-value adjusted for multiple comparison by the method of Holm.  
\end{table} 
Table 2: Participants’ decision quality results.