Purpose -The purpose of this paper is to explore the relationship between green supply chain integration (GSCI) and green innovation and to analyze the moderating effects of environmental uncertainty. Design/methodology/approach -A multi-dimensional approach was employed to develop a comprehensive theoretical framework which encompasses various aspects of GSCI, green innovation and environmental uncertainty. To identify the effects of GSCI and environmental uncertainty on green innovation, hierarchical moderated regression analysis was applied using questionnaire data from 211 Taiwanese, information technology (IT) manufacturers. Findings -Supplier, customer and internal integration enhance both green product and process innovations. Demand uncertainty positively moderates each GSCI-green innovation link. However, the moderating effect of technological uncertainty is insignificant.Research limitations/implications -This study conducted a cross-sectional investigation of the Taiwanese IT industry. Therefore, it lacks the advantages of a longitudinal case study and its findings cannot be generalized to other industries. Practical implications -This study argues that, to improve green innovation performance, managers should endeavor to integrate resources and capabilities within their organizations, suppliers and customers. It also suggests that managers should constantly follow demand trends in the market and maintain tight technological networks among supply chain partners. Originality/value -This study provides new insights into green supply chain management (GSCM) knowledge: theoretically creating GSCI-green innovation links with the natural-resource-based view, and empirically demonstrating that effective green innovation requires multi-dimensional GSCI. Furthermore, it develops a valid and reliable scale of environmental uncertainty for future research in GSCM, and presents the characteristics of implementing GSCI in Taiwan's IT industry.

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Recently, the trend toward sustainable development has led to different categories of sustainability-oriented innovations (SOIs). Even though each SOI category could play an important role in enhancing sustainability performance (SP), which covers economic, environmental, and social dimensions, developing SOIs is a difficult task for small and medium enterprises (SMEs) as they have limited resources and expertise. From the sustainable supply chain management perspective, the practices of socially responsible supplier development (SRSD) by buying firms may help strengthen the SOIs in which their SME suppliers engage, thus contributing to improving SMEs' SP. Therefore, this study created a multidimensional research framework that links buying firms' SRSD with SME suppliers' SOIs and investigated the influence of SRSD and SOIs on SP. The empirical results show that SRSD practices significantly and positively affect SOIs, with SOIs helping to improve SP and fully mediating the relationship between SRSD and SP.

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This research proposes a dynamic decision-making framework for a hybrid production system that incorporates manufacturing and remanufacturing procedures into a closed-loop supply chain network with merchandise substitution and shortages within traditional markets (TM) and electronic markets (EM). In particular, we develop models of profit maximization and equilibrium analysis by using calculus with dynamic programming under four business schemes, including a manufacturing-only model within TM/EM and a hybrid remanufacturing model within TM/EM. Dynamic decision-making planning was taken for brand-new and like-new decayed merchandise in hybrid production systems. The results demonstrate that solutions generated within EMs surpass those within TMs in terms of maximizing profits. Further, the hybrid remanufacturing model did not surpass the manufacturing-only model under a general setting, but had better performance under certain conditions, including intense competition, a smaller remanufacturing cost, a larger brand-new merchandise market size, and a smaller like-new merchandise market size.