Datasets:

itseffi
/

epfl-enterprise-osai-adoption-research-data

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

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