Data-Driven Analysis of Palm Oil Derivative Innovation Acceleration through Business Incubation: Evidence from Pasaman Barat
Description
The dataset presented in this research provides comprehensive information related to the development of a sustainable business incubation model for palm oil derivative small and medium enterprises (SMEs) in West Pasaman, Indonesia. It consists of both qualitative and quantitative data collected through expert interviews, structured questionnaires, and secondary document analysis. The qualitative data include detailed interview transcripts from experts, government representatives, and SME actors involved in palm oil derivative production. These transcripts capture valuable insights into the challenges, opportunities, and key success factors influencing incubation performance. The quantitative component comprises Analytical Hierarchy Process (AHP) pairwise comparison matrices that evaluate the relative importance of various criteria and sub-criteria across incubation stages—pre-incubation, incubation, and post-incubation. Each comparison matrix was processed to obtain weighted priority values and consistency ratios (CR), ensuring that all judgments met the required reliability threshold (CR ≤ 0.1). The dataset also contains summary tables displaying aggregated weights from multiple respondents, which provide a clear representation of how experts perceive the most critical elements of successful incubation. Supporting secondary data were gathered from national and regional sources, including the Central Bureau of Statistics (BPS), the Palm Oil Fund Management Agency (BPDPKS), and government reports that describe the socio-economic conditions of the West Pasaman region. These additional sources help contextualize the primary data and strengthen the validity of the model. All data files are organized in spreadsheet format (.xlsx), accompanied by metadata describing variable definitions, criteria hierarchy, and processing steps. Sensitive or identifying information from respondents has been anonymized. This dataset can be reused for comparative studies on business incubation, SME empowerment, or regional innovation systems, and may serve as a foundation for further development of decision-support tools for sustainable agribusiness management.