Data and Code for "Gamified online surveys: Assessing experience with self-determination theory"

Aubert, A.H., Scheidegger, A., Schmid, S., Gamified online surveys: Assessing experience with self-determination theory

Abstract

We developed four online interfaces supporting citizen participation in decision-making. We included (1) learning loops (LLs), good practice in decision analysis, and (2) gamification, to enliven an otherwise long and tedious survey. We investigated the effects of these features on drop-out rate, perceived experience, and basic psychological needs (BPNs): autonomy, competence, and relatedness, all from self-determination theory. We also investigated how BPNs and individual causality orientation influence experience of the four interfaces. Answers from 785 respondents, representative of the Swiss German-speaking population in age and gender, provided insightful results. LLs and gamification increased drop-out rate. Experience was better explained by the BPN satisfaction than by the interface, and this was moderated by respondents’ causality orientations. LLs increased the challenge, and gamification enhanced the social experience and playfulness. LLs frustrated all three needs, and gamification satisfied relatedness. Autonomy and relatedness both positively influenced the social experience, but competence was negatively correlated with challenge. All observed effects were small. Hence, using gamification for decision-making is questionable, and understanding individual variability is a prerequisite; this study has helped disentangle the diversity of responses to survey design options.

Data

The directory data contains:

- `rq2_df_compl.csv`: Anonymized data of participants that completed the whole survey. This is the basis data analyzed in the script.

- `rq2_df_compl_start_data.csv`:  Anonymized data of participants that completed at least the GCOS questionnaire. This data file is to carry out complementary analysis (shown in Supplementary Information).

These data files are the result of the preprocessing pipeline contained and described in the data package https://doi.org/10.25678/0008WS (still to come, at the time of publishing the current data package).

Analysis

All models and figures in the paper were produced with R. The code is contained in Analysis_and plots.R.

The plots for the investigation of the drop-out rates (see SI 7.7) are in Drop_out_analysis.R

Funding

This research was supported by a Swiss National Science Foundation Ambizione grant (project 173973, Environmental Decision Analysis with Games -- Edanaga) to Aubert, A.H.