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<p>As online shopping becomes increasingly integral to consumers’ lives, driven by the convenience of digital devices and evolving customer expectations, understanding and optimizing mobile application design has emerged as a critical challenge. Designing mobile applications that effectively meet user expectations requires a robust understanding of user preferences and priorities. This study proposes an integrated methodology combining the Kano model, Bayesian Best–Worst Method (BBWM), and Conjoint analysis to address the complexities of mobile shopping application design. By classifying features through the Kano model and employing conjoint analysis for structured evaluation, the approach reduces design alternatives and prioritizes customer-centric features. In this study, a systematic four-stage methodology was employed: (1) refining 55 mobile app features to 33 using machine learning, (2) categorizing features with the Kano model, (3) weighting features through BBWM, and (4) validating satisfaction levels via focus group testing and conjoint analysis. Results provide actionable insights for optimizing mobile shopping platforms and adapting to evolving consumer demands, ensuring long-term success in the competitive e-commerce landscape. This research fills a critical gap in experimental design for e-commerce applications and offers practical solutions for creating platforms that align with customer expectations and business objectives.</p>

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A new model proposal for measuring interface effectiveness of mobile applications

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Selcuk Cebi,
  • Necip Fazıl Karakurt,
  • Gülşah Şahin,
  • İsmail Buğra Bölükbaşı,
  • Esra İlbahar

摘要

As online shopping becomes increasingly integral to consumers’ lives, driven by the convenience of digital devices and evolving customer expectations, understanding and optimizing mobile application design has emerged as a critical challenge. Designing mobile applications that effectively meet user expectations requires a robust understanding of user preferences and priorities. This study proposes an integrated methodology combining the Kano model, Bayesian Best–Worst Method (BBWM), and Conjoint analysis to address the complexities of mobile shopping application design. By classifying features through the Kano model and employing conjoint analysis for structured evaluation, the approach reduces design alternatives and prioritizes customer-centric features. In this study, a systematic four-stage methodology was employed: (1) refining 55 mobile app features to 33 using machine learning, (2) categorizing features with the Kano model, (3) weighting features through BBWM, and (4) validating satisfaction levels via focus group testing and conjoint analysis. Results provide actionable insights for optimizing mobile shopping platforms and adapting to evolving consumer demands, ensuring long-term success in the competitive e-commerce landscape. This research fills a critical gap in experimental design for e-commerce applications and offers practical solutions for creating platforms that align with customer expectations and business objectives.