Business Strategy and Development最新文献

Barriers to women's participation in family businesses, both within and beyond the family, may hinder their performance. This study focuses on exploring and assessing the significant barriers that women experience in their family-owned businesses and examining the impact of these barriers on the Sustainable Development Goals (SDGs) in the pursuit of the circular economy. The study employed the convenience sampling method, followed by snowball sampling, to collect data from regions of North India in order to obtain direct, personalized, and valuable information. Semi-structured interview questions were developed, with input from experts, to explore the barriers faced by women entrepreneurs and to understand how these barriers impact the SDGs in the pursuit of the circular economy. After validation of the findings with experts, respondents, and previous studies, the barriers identified include primogeniture, invisibility, role incongruity or role conflict, lack of family support, low self-confidence, the “old boys' network,” lack of role models, social attitudes, lack of prior experience, work–life balance, and limited managerial, leadership, and financial skills. These barriers significantly affect women owners during their entrepreneurial endeavors. Moreover, they limit women's contributions toward gender equality as well as personal, social, and national growth. Academic institutions and governments should address the barriers identified in this study that affect women's participation in family entrepreneurial activities. Women are vital members of both family and society; their personal, financial, and intellectual development contributes to overall societal progress and positively impacts the SDGs—particularly SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth), and SDG 10 (Reduced Inequalities) in the pursuit of the circular economy. This is one of the foremost studies aligning the barriers to women's participation in family businesses with the SDGs in the pursuit of the circular economy.

This paper examines how sovereign green bonds, artificial intelligence (AI) adoption, and child-labor policies relate to corporate environmental, social, and governance (ESG) performance. The analysis uses a panel of 2239 firm-year observations from 51 countries during 2019–2023 to explore these relationships. Sovereign green bonds are positively associated with governance and show a small positive link to social performance, while the environmental association is weaker, reflecting the slower pace of project execution and verification. During the COVID-19 period, the interaction between sovereign programs and the pandemic is positive for environmental and governance pillars, indicating a buffering effect. AI adoption is positively related to all three pillars. Child-labor policies are associated with higher scores, especially in the social and governance dimensions, with an additional environmental gain. These results highlight pillar-specific pathways through which public green finance, digital capabilities, and labor standards shape firm-level sustainability.

As environmental urgency and digital transformation converge, understanding how emerging technologies create sustainable value has become a strategic imperative for businesses. This study investigates how artificial intelligence (AI) enables circular economy (CE) practices and strategic green marketing orientation (SGMO) to improve environmental performance (EP). Anchored in the resource orchestration view (ROV) and supported by the dynamic capability view (DCV) and complementarity theory, the research highlights AI not only as a technological asset but as an orchestrated capability that complements circular and green strategic initiatives. A sequential mixed-method design was employed: a model developed from an extensive literature review was tested using survey data from 310 participants in 115 firms, and a qualitative content analysis of integrated corporate reports provided contextual validation. The model captures the relationships among AI usage, CE capacity, SGMO, and EP, and PLS-SEM was used to assess mediation (SGMO) and moderation (AI) effects. Findings reveal that CE capacity enhances EP directly and through SGMO, while strategically orchestrated AI strengthens these effects. The study contributes to theory by integrating ROV, DCV, and complementarity theory to explain how digital transformation and environmental strategies jointly create long-term environmental values.

Sustainable corporate governance, which integrates environmental, social, and governance principles and sustainable development goals into corporate decision-making processes, has increasingly been recognized as a vital component of long-term business success. Although sustainable corporate governance (SCG) practices are widely believed to be positively associated with higher firm value, often accompanied by lower risk exposure, enhanced innovation capability, and stronger stakeholder relationships, existing empirical evidence remains mixed, particularly in emerging market contexts. This study examines the relationship between sustainable corporate governance and firm value using data from Chinese listed companies from 2014 to 2023. Results reveal a significant positive relationship between sustainable corporate governance and firm value. This study provides empirical evidence on the relationship between sustainable corporate governance and firm value within China's unique institutional context, thereby enriching the corporate governance literature. The findings also offer important implications for policymakers and practitioners, suggesting that companies pursuing economic performance while balancing environmental and social responsibilities are more likely to achieve superior long-term outcomes.

This study examines whether green innovation (GIN) helps newly established small and medium-sized enterprises (SMEs) ease credit constraints and strengthen their debt repayment capacity. Drawing on signaling and stakeholder theories, it conceptualizes GIN as a credible signal that mitigates information asymmetries and aligns firms with the rising expectations of sustainability-oriented financial stakeholders. The analysis uses a longitudinal sample of 7,027 Italian entrepreneurial ventures founded in 2013. GIN is measured through patent-based indicators obtained by matching International Patent Classification (IPC) codes with the WIPO Green Inventory. Panel regression models assess the effects of GIN on short- and long-term bank debt and the Interest Coverage Ratio (ICR), accounting for firm lifecycle heterogeneity and the pre and post-EU Taxonomy periods. Green-innovative firms show greater access to bank financing and stronger debt-servicing capacity. The positive impact on credit access is strongest for early-stage ventures but weakens as firms mature. Conversely, the association between GIN and ICR strengthens over time. However, the signaling power of GIN appears to be reduced following the EU Taxonomy. The study provides novel evidence on the signaling role of GIN in facilitating bank finance for young ventures – an underexplored area in sustainable entrepreneurial finance research. Financial institutions are encouraged to incorporate GIN into credit assessment frameworks while considering firm lifecycle and evolving regulatory contexts. The study shows that the signaling relevance of GIN varies across the corporate life cycle and institutional developments, extending signaling theory to sustainable entrepreneurship.

In the wake of pressing global environmental challenges and the urgent need for sustainable development (SD), green entrepreneurship (GE) has emerged as a promising avenue for supporting economic growth while addressing environmental and social concerns in developing economies. This research paper explores the critical success factors (CSFs) of GE that can potentially impact the digitalization of circular supply chain management (CSCM) toward a net-zero economy in the Indian textile industry. The CSFs were identified through a comprehensive literature review and validated using expert feedback. To prioritize these factors, the “Best-Worst Method” (BWM) was applied, and “Total Interpretive Structural Modelling” (ISM) and “Matrice d'Impacts Croisés Multiplication Appliquée à un Classement” (MICMAC) techniques were used to classify them into clusters. The results indicate that institutional CSFs and financing for entrepreneurs received the highest priority and were identified as the key driving factors based on expert evaluations. These insights will aid entrepreneurs, managers, and policymakers in formulating effective policies and strategies for achieving SDGs.