Dataset on Mobile Video Game Engagement, Gamer Experience, and Behavioral Intentions among Indonesian Gamers in a Transient Educational Hub using PLS-SEM

Description

This dataset explores video game engagement and gamer experience among Indonesian Millennial and Generation Z players in a Transient Educational Hub. Data were collected via Google Forms from 202 respondents who actively played video games. The study investigates the antecedents of Gamer Experience (Telepresence, Focused Attention, Role Projection, Fantasy Fulfillment, Emotional Involvement, Enjoyment, and Arousal) and their influence on Video Game Engagement and Behavioral Intentions (Intention to Continue Playing, Intention to Purchase Game Items, Intention to Engage in Word-of-Mouth, and Intention to Recruit New Players). Analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4. This dataset includes the raw and cleaned survey datasets (N = 202), PLS-SEM algorithm and bootstrapping outputs (5,000 subsamples), IPMA and PLSpredict results, questionnaire items and measurement scales, English and Indonesian questionnaires with informed consent forms, and a statement regarding participant eligibility criteria.

Steps to reproduce

1. Start by reviewing the questionnaire and measurement scales in 07_Questionnaire_Items_and_Measurement_Scale.xlsx, together with the English and Indonesian versions of the questionnaire and informed consent forms (08 and 09). 2. Check 10_Statement_Regarding_Participant_Eligibility.pdf for information on the criteria used to determine participant eligibility. 3. Open 01_Raw_Dataset_202_Respondents.xlsx to access the original survey responses. 4. Use 02_SmartPLS_Ready_Dataset_202_Respondents.xlsx as the input file for SmartPLS 4. 5. Build the PLS-SEM model in SmartPLS 4 using the following constructs: Telepresence (TP), Focused Attention (FA), Role Projection (RP), Fantasy Fulfillment (FF), Emotional Involvement (EI), Enjoyment (EJ), Arousal (AR), Gamer Experience (GE), Video Game Engagement (VGE), Intention to Continue Playing (ICP), Intention to Purchase In-Game Items (IPI), Intention to Engage in In-Game Word-of-Mouth (IWOM), and Intention to Recruit New Players (IRN). 6. Run the PLS Algorithm to obtain the measurement and structural model results. The output is available in 03_PLS_SEM_Algorithm_Output.xlsx. 7. Run Bootstrapping with 5,000 subsamples to assess the significance of the relationships. The results are provided in 04_Bootstrapping_Results.xlsx. 8. Conduct Importance-Performance Map Analysis (IPMA) using Video Game Engagement or the behavioral intention constructs as the target variable. The output is available in 05_IPMA_Results.xlsx. 9. Run PLSpredict to evaluate the model's predictive performance. The results are provided in 06_PLSpredict_Results.xlsx.

Institutions

• Pelita Harapan University

  Banten, Tangerang

Categories

Licence