UTAUT2 Survey on Online Fitness Adoption in India (2026)
Description
This dataset accompanies a study examining whether constructs of the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) predict behavioral intention (BI) to use online fitness services among Indian respondents. The data were collected in 2026 via a Google Forms survey and include two files: the raw responses and the cleaned dataset with pre-computed construct scores. Each of the eight UTAUT2 constructs (Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), Habit (HAB), and Behavioral Intention (BI)) is measured by three Likert-scale items (1–5). The dataset also includes demographic variables: age, gender, state of residence, education level, monthly income, and prior experience with online fitness. Multiple linear regression (R² = 0.53, F = 25.1, p < 0.001) identified four significant predictors of BI: PU (β* = 0.415), HAB (β* = 0.307), HM (β* = 0.191), and PEOU (β* = 0.129). These results are broadly consistent with prior UTAUT2 research on fitness app adoption. The data can be used to replicate the analysis, test alternative models or compare findings across populations. The attached PDF contains screenshots of the interactive dashboard built in Yandex DataLens to visualize the survey results. The dashboard can be accessed via the Related links. This dataset was produced as part of a coursework project at the Faculty of Computer Science, HSE University (Moscow).
Files
Steps to reproduce
The questionnaire was distributed via Google Forms in 2026, targeting adult residents of India through social media. Responses were collected anonymously. An analytical subsample (N = 164) was formed from the raw responses (N = 186) by excluding respondents who do not exercise and do not plan to start. Construct scores were computed as the mean of three items per construct. Statistical analysis was performed in Python (pandas, numpy, scipy, statsmodels). Visualizations were built with plotly and Yandex DataLens. Analysis scripts and custom Yandex DataLens visualization code are available in the project repository (see Related links). The full pipeline can be executed via run.py in the project repository.