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Cited by 25Family Business Performance and SuccessionEnvironmental Sustainability in BusinessSustainability and Innovation in Business

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Key drivers of green innovation in family firms: a machine learning approach

Veiga, Pedro Mota·2025·Journal of Family Business Management
DOI 10.1108/jfbm-08-2024-0191
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Abstract

Purpose: This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses. Design/methodology/approach: Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes. Findings: The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions. Research limitations/implications: The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies. Practical implications: Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy. Originality/value: This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.

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Metadata

Authors
Veiga, P. M.
Affiliations
University of Beira Interior (PT)
Corresponding author
Veiga, Pedro Mota
ORCID
Veiga, Pedro Mota
Venue
Journal of Family Business Management
Publisher
Emerald
Published
2024-09-24
Volume
15
Issue
2
Pages
346-372
ISSN
2043-6238 (ISSN-L)
DOI
10.1108/jfbm-08-2024-0191
OpenAlex
W4402679615
Cited by
25
References
3
Citations by year
2026: 7 · 2025: 18
FWCI
12.56
Type
journal article
Language
en
Open access
closed
Licence
www.emerald.com/insight/site-policies (tdm)
Locations
Journal of Family Business Management (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Family Business Performance and Succession
  • Social Sciences › Business, Management and Accounting › Marketing › Environmental Sustainability in Business
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Sustainability and Innovation in Business
Keywords
green innovation, family business, resource-based view, location theory, firm size, digital capabilities, firm age, geographic location, socioemotional wealth, legacy preservation, sustainable practices, regulatory environment

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Veiga, P. M. (2025). Key drivers of green innovation in family firms: a machine learning approach. Journal of Family Business Management. https://doi.org/10.1108/jfbm-08-2024-0191