Integrated Approach for Mobile Sales App Feature Classification: Kano Model and BBWM Perspective

This research focuses on the examination of mobile sales applications, aiming to enhance the understanding of user preferences by employing a comprehensive methodology. Through an extensive review of the literature, a feature set comprising 33 distinct elements was identified. Subsequently, an in-depth classification study was conducted using the Kano model, where surveys were administered to assess user satisfaction with each feature in the identified set. The outcomes of the Kano model classification were integrated into a feature weighting analysis through the application of Multi-Criteria Decision Making. The classification information derived from the Kano model was incorporated into a hierarchical grouping process using the Bayesian Best Worst Method (BBWM). A comprehensive online survey involving 628 participants was conducted within the scope of BBWM, leading to the determination of feature weights for each of the 33 features. Additionally, binary importance rankings and reliability values, also known as credal rankings, were obtained to provide designers with insights into the pairwise importance levels among features and their associated reliability. The study proposes an innovative approach that combines the Kano model and BBWM, demonstrating the synergistic application of feature classification methods and group decision-making techniques. The results highlight the potential for designers to employ methodologies such as BBWM alongside feature classification models like the Kano model to effectively predict and prioritize product features, offering valuable insights for future product design endeavors.