---
license: cc-by-4.0
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- open-source-ai
- ai-adoption
- research-data
- qualitative-research
- quantitative-research
size_categories:
- 1K<n<10K
configs:
- config_name: survey_data
  data_files:
    - "EPFL_Enterprise_OSAI_Adoption_Survey_Data.csv"
- config_name: interview_data
  data_files:
    - "EPFL_Enterprise_OSAI_Adoption_Interview_Data.csv"
- config_name: coding_data
  data_files:
    - "EPFL_Coding_Matrix.csv"
    - "EPFL_Survey_Qualitative_Coding.csv"
    - "EPFL_Qualitative_Coding_Analysis.csv"
    - "EPFL_Code_Frequencies_with_RQ.csv"
- config_name: supporting_data
  data_files:
    - "EPFL_Quantitative_Summary_Statistics.csv"
    - "EPFL_Method_Appendix_Instrument_RQ_Mapping.csv"
---

# EPFL Enterprise Open-Source AI Adoption Research Dataset

## Dataset Summary

This dataset contains mixed-methods research data from 100 organizations regarding their strategic adoption of open-source AI through the Hugging Face ecosystem. The research was conducted at EPFL (École Polytechnique Fédérale de Lausanne) and supports the development of the Gate-Lever framework for enterprise open-source AI adoption.

## Dataset Description

- **Repository**: `epfl-enterprise-osai-adoption-research-data`
- **Institution**: EPFL (École Polytechnique Fédérale de Lausanne)
- **Research Type**: Mixed-methods (quantitative survey + qualitative interviews)
- **Sample Size**: 100 organizations + 6 in-depth interviews
- **Industries**: 12 sectors (Technology, Finance, Healthcare, Energy, etc.)
- **Collection Period**: 2025
- **Anonymization**: All identifying information removed

## Dataset Structure

This dataset is organized into **4 configurations** to handle different data schemas:

### Configuration 1: Survey Data (`survey_data`)
- **`EPFL_Enterprise_OSAI_Adoption_Survey_Data.csv`**: Main survey responses from 100 organizations

### Configuration 2: Interview Data (`interview_data`)
- **`EPFL_Enterprise_OSAI_Adoption_Interview_Data.csv`**: Interview transcripts and analysis from 6 organizations

### Configuration 3: Coding Data (`coding_data`)
- **`EPFL_Coding_Matrix.csv`**: Gate-Lever coding framework structure
- **`EPFL_Survey_Qualitative_Coding.csv`**: Complete survey coding with adoption stages
- **`EPFL_Qualitative_Coding_Analysis.csv`**: Coding frequency analysis
- **`EPFL_Code_Frequencies_with_RQ.csv`**: Coding frequencies mapped to research questions

### Configuration 4: Supporting Data (`supporting_data`)
- **`EPFL_Quantitative_Summary_Statistics.csv`**: Statistical summary tables
- **`EPFL_Method_Appendix_Instrument_RQ_Mapping.csv`**: Survey questions mapped to research questions

## Research Framework

This dataset supports the **Gate-Lever Framework** for strategic open-source AI adoption:

- **Governance Gates**: Compliance, data privacy, security, documentation, licensing clarity
- **Execution Levers**: Performance, cost efficiency, customization, support, time-to-value
- **Adoption Stages**: Pre-adoption, Adopting, Adopted
- **Organizational Clusters**: Performance-Driven Adopters, Governance-Locked Organizations, Balanced Transition Organizations

## Key Variables

### Survey Data Variables
- **Respondent_ID**: Anonymous respondent identifier
- **Org_Size**: Organization size category
- **Industry**: Industry sector (12 categories)
- **HF_Familiarity**: Hugging Face familiarity (1-5 scale)
- **AI_Solution_Type**: Current AI solution type (Proprietary/Both/Open-source)
- **Decision_Factors**: Key decision factors (open text)
- **Integration_Difficulty**: Integration challenge rating (1-5 scale)
- **Adoption_Intention**: Plan to increase Hugging Face use (Yes/No/Not sure)
- **AI_Stage_Overall**: AI maturity stage (Early/Intermediate/Advanced)
- **OpenSource_Stage**: Open-Source AI Adoption stage (Pre-adoption/Adopting/Adopted)
- **Gate_Index**: Governance/compliance factors index (0-1)
- **Lever_Index**: Performance/optimization factors index (0-1)
- **Adoption_Intention_Binary**: Binary adoption intention (0/1)
- **Cluster**: Organizational profile cluster (0-2)

### Interview Data Variables
- **ID**: Interview identifier (I1-I6)
- **Industry**: Participant industry sector
- **Role**: Participant role/title
- **Stage**: Adoption stage classification
- **Theme 1-6**: Coded themes from thematic analysis
- **Framework_Link**: Connection to Gate-Lever framework
- **Main_Takeaway**: Key insights from each interview

## Methodology

### Data Collection
- **Survey**: Online survey with structured and open-text questions
- **Interviews**: Semi-structured interviews (30-45 minutes each)
- **Coding**: Systematic thematic analysis using Gate-Lever framework
- **Validation**: Inter-coder reliability testing (85% agreement)

### Statistical Analysis
- **Descriptive Statistics**: Sample characteristics and variable distributions
- **Correlation Analysis**: Gate-Lever relationships and adoption predictors
- **Cluster Analysis**: K-means clustering for organizational segmentation
- **Regression Analysis**: Logistic and linear regression for adoption prediction
- **ANOVA**: Integration difficulty differences across adoption stages

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load specific configurations
survey_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "survey_data")
interview_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "interview_data")
coding_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "coding_data")
supporting_data = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data", "supporting_data")

# Or load all configurations
dataset = load_dataset("itseffi/epfl-enterprise-osai-adoption-research-data")
```

### Suitable For
- Open-source AI adoption frameworks
- Strategic AI adoption research
- Enterprise technology adoption studies
- Mixed-methods research validation
- Technology adoption theory development

### Limitations
- Sample limited to European organizations
- Self-reported data may introduce bias
- Focus on Hugging Face ecosystem specifically

## License

This dataset is released under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**.

## Contact

For questions about this dataset, please use the Hugging Face repository discussions.

## Repository Information

- **Repository**: https://huggingface.co/datasets/itseffi/epfl-enterprise-osai-adoption-research-data
- **Institution**: EPFL (École Polytechnique Fédérale de Lausanne)
- **Research Project**: Strategic Enterprise Adoption of Hugging Face
- **Framework**: Enterprise Open-Source AI Adoption Framework