An Empirical Study on User Experience in Short-Video Recommendation Systems: The Role of Personalization and Engagement
Description
This dataset contains the results of an empirical study investigating user experience in short-video recommendation systems, specifically examining the influence of personalization and engagement variables. The data consists of 37 structured questionnaire responses collected from users, featuring a 5-point Likert scale across three primary dimensions: personalization (X1), engagement (X2), and the resulting user experience (Y), covering 15 survey items in total. The repository also includes supplemental PLS-SEM algorithmic outputs and bootstrapping results derived from this sample, providing a comprehensive foundation for structural equation modeling analysis.
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Steps to reproduce
This research utilized a quantitative survey method to examine the impact of personalization and engagement on user experience in short-video recommendation systems. Data were collected via an online questionnaire using convenience sampling, targeting active users of TikTok, YouTube Shorts, and Instagram Reels. The instrument consisted of 15 items measured on a five-point Likert scale (1: Strongly Disagree to 5: Strongly Agree), covering three constructs: Personalization (5 items), Engagement (5 items), and User Experience (5 items), adapted from established literature and the User Experience Questionnaire (UEQ) framework. The collected raw data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS software, comprising two stages: (1) Measurement model evaluation (convergent validity, discriminant validity, and reliability) and (2) Structural model evaluation (coefficient of determination (R²), path coefficients, and hypothesis testing). The provided dataset includes the processed questionnaire responses (CSV format) and the resulting PLS-SEM algorithmic and bootstrapping outputs (Excel format).
Institutions
- Binus UniversityJakarta, Jakarta