# Improving User Experience with Recommender Systems by
## 5 Discussion and Conclusions
### Contributions to Practice  
The results of this study have improved our understanding of the impact of message design on users' intention to accept an algorithmically generated recommendation proposed to them and their intention to use the RS that provided the recommendation. This advances our contemporary understanding of RS designs more broadly by looking at ways to optimize the information presentation and/or interaction layers of the user-RS interactions. For example, e-tailers such as Amazon rely on algorithms to observe patterns and identify the optimal candidates to recommend for subsequent purchases to each consumer/user; however, the way that product recommendation is presented will also impact the likelihood of the consumer/user clicking through the recommended product link and potentially proceeding with the purchase of an algorithmically generated recommendation. Similarly, in a decision-support context, a supply chain management (SCM) AI-enabled system presents exception events to its operators/users; how the emergent problem and the recommended solution are presented to a user will impact their ability to process the situational information and to make a decision, thus impacting the efficacy and use of the system. The findings of this study can inform RS designers in how to construct (design) system-generated recommendation messages in order to either minimize the time needed by the user to make a decision and/or to accept the recommendation or the system at large, for example, if the RS is used during a demo or trial period. Our results showed that information sequence helped to reduce decision-making time. Indeed, when the problem was presented before the solution, i.e., in a situational frame, users experienced less cognitive load and spent less time making a decision than when they were shown recommendations consisting of the solution followed by the problem. Hence, a best practice emerges for RS designers to structure the content of recommendation messages in a manner progressing from framing the situation (problem) to presenting the supporting information and concluding with the recommended action (solution). However, extending from the scenario and focus of this study on new RS users, it is plausible that as user trust in the system-generated recommendations increases over time (i.e., during the continued use of the RS), users may eventually prefer to quickly review the recommendation and approve it without processing the underlying problem and supporting information. In this special use case, the reverse sequence (i.e., solution-to-problem) may be preferred by the user; if so, a second recommendation might allow for the user to specify the recommendation message's construction. This would be feasible if at the system level, such recommendations are generated not as event-to-outcome rules, where recommendations are designed in full a priori for each exception event alert specifically, but are instead generated by combining message elements that are marked-up or tagged according to a library identifying each content element by its property (e.g., product name, product quantity, exception event, delivery mode, etc.) and synthesized according to the exception event. However, before formally putting forth such a recommendation, additional research is required to obtain support for this anticipated utility. Another recommendation emerging from this study's findings is for RS designers to focus on explainability by embedding sufficient detail regarding both the problem and the solution, thereby boosting perceptions of information transparency and information sufficiency. Such perceptions contribute to a significantly more frequent acceptance of system-generated recommendations, thereby saving users time as they would not need to delve deeper (e.g., by clicking on 'details') before making a decision. The above recommendations when implemented can serve as catalysts for both the adoption and continued use of systems that generate recommendations algorithmically for users to consider in their context.