Earticle

초록

영어

This study examines the potential of the 7C framework as a common organizing structure for review-based UX monitoring in mobile vertical commerce apps. We develop and evaluate the model-development phase of a staged pipeline that identifies UX-related reviews, extracts topics, maps them to 7C categories, and trains a Korean pretrained language model (KcBERT) to reproduce the resulting label structure at the individual-review level. Among 51,505 Google Play reviews from ten apps, manual labeling of 5,100 reviews followed by Naïve Bayes classification yielded 8,260 UX-related reviews (16.0%). LDA produced 11 interpretable topics, including login errors, server speed, delivery/customer support, and product search/filtering. These topics were mapped to 7C categories to generate rule-based training labels. Against these labels, KcBERT achieved 66.16% accuracy and a macro F1-score of 62.81%, outperforming a TF-IDF logistic regression baseline. In a separate human validation of 420 UX-related reviews, the rule-based labels, KcBERT, and TF-IDF achieved accuracy/macro F1 values of 0.6762/0.6683, 0.6333/0.6240, and 0.5405/0.5068, respectively. These findings provide proof-of-concept evidence that the 7C framework can serve as a basis for organizing large-scale app-review data, while application of the trained classifier in the proposed operational monitoring phase remains to be empirically evaluated.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Theoretical Background
2.1. Usability and User Experience
2.2. User Experience Evaluation for Mobile Apps
2.3. The 7C Framework
Ⅲ. Research Method
3.1. Overview of Analytical Methods
3.2. Data Collection
3.3. Data Preprocessing
3.4. Manual Labeling of UX Reviews
3.5. UX Review Monitoring Pipeline
Ⅳ. Analysis and Findings
4.1. Predicting UX-related Reviews via Machine Learning
4.2. Topic Modeling Results
4.3. Sentiment Analysis
4.4. Review Type Classification Using BERT
4.5. Human Validation of Topic-Derived and Model-Predicted 7C Labels
Ⅴ. Discussion
5.1. Validity of Model Selection forUX-related Review Filtering
5.2. Interpretation of the Topic–7C Mapping and 7C Classification Performance
Ⅵ. Conclusion
6.1. Summary of Findings
6.2. Theoretical Contributions and Practical Implications
6.3. Limitations and Directions for Future Research

키워드

저자

• Juhyun Lee [ M.S., School of Business Administration, Sookmyung Women’s University, Korea ]
• Joongsan Oh [ Professor, School of Business Administration, Sookmyung Women’s University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658