Beyond Triggering Curiosity

This dataset supports the study "Beyond Triggering Curiosity: Value Orientations, Future-Reward Expectations, and Sustained Engagement in Games," which develops the Future Rewards Maximise Curiosity (FRMC) framework. FRMC proposes that players' stable Value Orientations (VOs) toward Efficiency, Exploration, Story, and Challenge act as an evaluative filter shaping which future rewards are anticipated as valuable, and that these Future Reward Expectations (FREV) help regulate sustained engagement through adversity. Data were collected via an online questionnaire from 550 adult players of digital games, recruited through online gaming communities and offline outreach, across three scenario-family branches (role-playing, strategy/card, exploration/building). Each participant completed the 12-item VO scale, the FREV and negative-experience-influence measures, an established motivation taxonomy (Achievement, Discovery, Immersion), curiosity-facet items, and two genre-specific decision scenarios. A pre-specified quality-control rule flags 32 participants; the primary analysis sample is N = 518, with the full N = 550 used as a sensitivity check. Files: participant_measures.csv: one row per participant (N = 550); raw items, derived scale means, QC flags. scenario_responses_long.csv: one row per participant-scenario (N = 1100); forced-choice decision, VO alignment flags, follow-up items. scenario_motives_long.csv: one row per offered motive (N = 5127). qc_exclusion_details.csv: participants flagged by the primary QC rule (N = 32). CODEBOOK.md: full variable dictionary, design/confounding notes, the authoritative Q1 choice-alignment key, scale-construction details. README.md: package overview and reproduction instructions. analysis_script.py: Python (pandas, numpy, scipy, statsmodels; semopy optional) reproducing every reported statistic, including reliabilities, HTMT discriminant validity, correlations, incremental validity, bootstrapped mediation, genre-stratified analyses, and confirmatory factor analysis. Interpretation notes: Condition_Code is a deterministic scenario-family branch, not an independent covariate; it is confounded with scenario content and fixed presentation order and should not be read as a pure "game type" effect. The four Q1_Aligned columns are the authoritative alignment variables. FRMC_Exp_Influence items index negative-experience interference, so negative associations with future-reward value are expected. FRMC_Curiosity_* items are facet indicators, not a single reflective scale. The value "none" is a documented "not applicable" category, not a missing code. The data contain no personally identifying information; demographics were not collected to protect anonymity. Use Exclude_Primary == 0 for the primary sample. All relationships in the article are cross-sectional and associative, not causal.