Dataset on Video Game Engagement and Gamer Experience among Indonesian Video Game Players using Partial Least Squares Structural Equation Model (PLS-SEM)

Published: 11 June 2026| Version 2 | DOI: 10.17632/46np2n5cc6.2
Contributor:
Fyona Chelindiva

Description

This dataset explores video game engagement and gamer experience among Indonesian Millennial (born 1981–1996) and Generation Z (born 1997–2012) players. 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. The dataset includes raw survey data, cleaned PLS input data, SmartPLS algorithm results, bootstrapping results (5,000 subsamples), Importance-Performance Map Analysis (IPMA) output, and PLSpredict results.

Files

Steps to reproduce

Open the raw survey data file (Gform_complete_data__202_responden_.xlsx) to view demographics and Likert-scale responses. Use the cleaned PLS input file (2__Input_PLS_Fyona_23_01_2026_drop_responden_sempls_format.xlsx) as direct input into SmartPLS 4. In SmartPLS 4, create the structural model with 13 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), ICP, IPI, IWOM, and IRN. Run the PLS Algorithm to obtain outer loadings, path coefficients, R-squared, and model fit (SRMR). Results are provided in file (c). Run Bootstrapping with 5,000 subsamples to obtain T-statistics and p-values. Results are in file (d). Run IPMA analysis with VGE or behavioral intentions as target construct. Results are in file (e). Run PLSpredict to obtain Q² predict, RMSE, and MAE. Results are in file (f).

Institutions

Categories

Information Systems Management, International Business and Management

Licence