Green entrepreneurial orientation and business model innovation in start-ups: The mediating role of boundary-spanning search
Yueting Shao et al.
What the paper says
Purpose: This study examines when and how green entrepreneurial orientation (GEO) influences business model innovation (BMI) in start-ups, focusing on boundary-spanning search (BSS) as a conversion mechanism and big data capability (BDC) as a boundary condition. Design/methodology/approach: Grounded in resource-based theory and organisational search theory, the research employs an empirical approach using survey data collected from 307 start-ups. The study examines the mediating effect of BSS and the moderating role of BDC through quantitative analysis. Findings/results: The analysis reveals three key findings: (1) GEO has a positive impact on BMI. (2) Boundary-spanning search mediates the relationship between GEO and BMI. (3) Big data capability positively moderates the link between BSS and BMI. Practical implications: For start-ups, the results imply that ‘going green’ is more likely to lead to BMI when firms design a focused external-search portfolio and build minimum viable data capabilities (e.g. data governance, cross-functional information sharing and decision-linked analytics) to reduce information overload and accelerate experimentation. Originality/value: The study advances an orientation–conversion perspective by explaining heterogeneous BMI outcomes amongst green-oriented ventures and highlighting the contingent value of BSS. The findings are particularly informative for start-ups in emerging-market contexts (including South Africa and many African economies), where resource constraints and uneven digital infrastructure can make the conversion of sustainability intent into a scalable business model change highly contingent.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.