Hamburg School of Business Administration

ABSTRACT Analyzing a sample of Chinese firms spanning from 2007 to 2022, we yield the following findings: (1) Local environmental regulations have a negative impact on short‐term firm financial performance, and this impact can be weakened by corporate bribery. However, local environmental regulations enhance firm long‐term financial performance after initiatives by the central government to rectify issues within the “top‐down” management system; (2) Further analysis indicates that the effects of bribery on firm financial performance depend on the focus of the central government. Additionally, bribery undermines firm's research and development investment, though this investment is beneficial for long‐term development. Our research not only provides suggestions for firms in emerging economies on how to respond to environmental regulations but also provides insights for governments about how to achieve a “win‐win” situation through proper environmental regulations.

ABSTRACT With net zero CO 2 emissions as the benchmark for mitigating the worst impacts of climate change by mid‐century, businesses are urged to deploy robust reduction measures. However, in light of increasing emissions globally, the effectiveness of current corporate decarbonization strategies remains unclear. To identify underlying drivers of and potential solutions to this problem, this study explores the aspects that accelerate or limit the effectiveness of corporate decarbonization strategies. Through a qualitative approach based on semi‐structured expert interviews, we assessed and synthesized advantages, disadvantages, synergies and trade‐offs associated with various mitigation options. The results yield four dimensions for enhanced decarbonization effectiveness: redefining perceived impact boundaries, closing the mitigation readiness gap, integrating climate and corporate strategies and leveraging the value creation mechanisms emanating from climate action. These insights shed light on the dynamics of effective decarbonization and on how corporations may approach the design, implementation and evaluation of related strategies.

Abstract Employment is crucial to the integration of refugees in host countries. Using Bourdieu's theory of practice and data from interviews with refugees, employment agency representatives, and managers, we examine these issues through two complementary studies conducted in Germany. Study I explores how refugees experience the recognition of their academic and professional credentials while seeking employment, Study II examines the question of how various actors, including employment agencies, employers, and colleagues, contribute to the development of relevant cultural capital. Our finding show that refugees' pre‐ and in‐employment experiences are largely shaped by their foreignness and by national institutions' protectionism. Even where foreign qualifications are formally recognized, employers invoke criteria such as the lack of local professional experience. While this research echoes the theory of practice by showing the limited capacity of refugees to access and accumulate capital, our analysis shows that while the collaborative efforts of different actors can provide information and guidance‐related benefits, they offer limited support to refugees to access or utilize forms of capital to advance employment effectively.

Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

ABSTRACT The article compares family small and medium-sized enterprises (SMEs) from Germany and Sweden with a large family company in their work on corporate social responsibility and sustainability. This study aims to understand how and why (small) family firms work with sustainability. In total, 14 interviews were conducted with family firms in different industries. Large family companies are a benchmark, as they have worked with these questions since 2000. However, family SMEs also work with these issues. It is the family owner-managers who actively work with these questions. Accordingly, findings are of relevance for family firms and their managers, working with sustainability.

The constraint (26a) has to be replaced by In the original definition of the constraint, the brackets were missing, which could inadmissibly restrict the selection probability of the no-choice alternative. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. (26a) X i,j,m ≥ p i,j,m