Public raw data for FOMO AI Accounting is a 390.6 KB CSV file by Gunawan Wibisono, last updated on 2026-04-28. The dataset supports a study examining heterogeneous user responses to AI-generated accounting information in digital investment environments. The research distinguishes between cognitive and affective fear of missing out (FoMO) to explain these responses.
Use Cases
- Analyzing relationships between cognitive FoMO and information-seeking behavior based on the described psychological constructs.
- Modeling affective FoMO as a predictor of social comparison in investment decisions based on the study's theoretical framework.
- Testing hypotheses from Self-Determination Theory regarding user motivation and AI-generated accounting data.
- Investigating the usefulness-trust logic in user acceptance of automated financial information.
Strengths
- Dataset is openly licensed under CC-BY-4.0, permitting reuse with attribution.
- File size is 390.6 KB, indicating a manageable download and processing footprint.
- Update timestamp of 2026-04-28 is provided for version tracking.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- Gunawan Wibisono via figshare
- Collection Method
- Likely collected via survey or experiment for the described study on user responses to AI-generated accounting information.
- Time Range
- null
- Freshness
- Last updated 2026-04-28 03:21:22; freshness should be verified.
- Geography
- null