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In this study, we integrate the signal institutional theory and stakeholder theory to examine partnership restructure as a critical mechanism linking environmental corporate social responsibility (ECSR) to corporate financial performance. Keeping in line with most prior studies, we first argue that a positive relationship exists between ECSR and firm performance. Then we propose that partnership restructure mediates the nexus between ECSR and firm performance because ECSR may motivate firms to change their partners in the better interests of the firms. In addition, we propose that the firms' industry power will exaggerate while dysfunctional competition will weaken the positive nexus between ECSR and partnership restructure. Evidence based on a survey covering 206 manufacturing firms in China offers good support for our predictions. This last section offers research contributions and implications for the managers based on the findings., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The installation of Distributed Generation (DG) units in the Radial Distribution Networks (RDNs) has significant potential to minimize active power losses in distribution networks. However, inaccurate size(s) and location(s) of DG units increase power losses and associated Annual Financial Losses (AFL). A comprehensive review of the literature reveals that existing analytical, metaheuristic and hybrid algorithms employed on DG allocation problems trap in local or global optima resulting in higher power losses. To address these limitations, this article develops a parallel hybrid Arithmetic Optimization Algorithm and Salp Swarm Algorithm (AOASSA) for the optimal sizing and placement of DGs in the RDNs. The proposed parallel hybrid AOASSA enables the mutual benefit of both algorithms, i.e., the exploration capability of the SSA and the exploitation capability of the AOA. The performance of the proposed algorithm has been analyzed against the hybrid Arithmetic Optimization Algorithm Particle Swarm Optimization (AOAPSO), Salp Swarm Algorithm Particle Swarm Optimization (SSAPSO), standard AOA, SSA, and Particle Swarm Optimization (PSO) algorithms. The results obtained reveals that the proposed algorithm produces quality solutions and minimum power losses in RDNs. The Power Loss Reduction (PLR) obtained with the proposed algorithm has also been validated against recent analytical, metaheuristic and hybrid optimization algorithms with the help of three cases based on the number of DG units allocated. Using the proposed algorithm, the PLR and associated AFL reduction of the 33-bus and 69-bus RDNs improved to 65.51% and 69.14%, respectively. This study will help the local distribution companies to minimize power losses and associated AFL in the long-term planning paradigm., Competing Interests: The authors have declared that no competing interests exist.