Survey Data on the Effect of Reward Features on Impulse Buying in TikTok Shop and Shopee

Description

This dataset contains survey responses collected to investigate the effect of reward features on impulse buying behavior among users of TikTok Shop and Shopee in Indonesia. The data were gathered through an online questionnaire distributed to active users of both e-commerce platforms. The dataset consists of 60 respondents, including 30 Shopee users and 30 TikTok Shop users, enabling comparative analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) in SmartPLS 4. The questionnaire measures five latent constructs: 1. Reward Features (REW) 2. Hedonic Enjoyment (HED) 3. Utilitarian Value (UTIL) 4. Parasocial Interaction (PSI) 5. Impulse Buying (IMP) Each construct is measured using multiple indicators on a 5-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). This dataset is intended for evaluating the measurement model (validity and reliability), testing structural relationships among constructs, and comparing the influence of reward features on impulse buying behavior between TikTok Shop and Shopee users through PLS-MGA.

Steps to reproduce

1. Download the combined_data_final.csv dataset. 2. Import the dataset into SmartPLS 4. 3. Build the PLS-SEM model using the five constructs: - Reward Features (REW) - Hedonic Enjoyment (HED) - Utilitarian Value (UTIL) - Parasocial Interaction (PSI) - Impulse Buying (IMP) 4. Assign the Group variable (1 = Shopee, 2 = TikTok Shop) for Multi-Group Analysis. 5. Run the PLS Algorithm to evaluate the measurement model (Outer Loadings, Cronbach's Alpha, Composite Reliability, AVE, and HTMT). 6. Perform Bootstrapping to obtain path coefficients, t-statistics, and p-values for hypothesis testing. 7. Conduct MICOM followed by PLS-MGA to compare the structural relationships between Shopee and TikTok Shop users.

Institutions

• Binus University

  Jakarta, Jakarta

Categories

Licence