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Small- and medium-sized enterprises (SMEs) are key players in economic activities. Yet little research focuses on their management innovation’s effect on innovation performance, this paper explores the impact of three sub-dimensions of management innovation (new organizational structure, new processes and new practices) on innovation performance (innovation outcome performance and innovation process performance) of SMEs in the manufacturing industry. It elucidates the mediating role of external knowledge search (search depth and search breadth) and identifies the moderating effect of digital capability.

By collecting primary data through a questionnaire survey, this paper employs Mplus and SPSS to analyze data.

This paper finds that management innovation positively affects innovation performance, with a greater impact on innovation outcome performance. Search breadth partially mediates the relationship between new organizational structure and innovation performance. Digital capability positively moderates the positive relationship between management innovation and external knowledge search.

The theoretical contribution of this study is to supplement the research on the consequences of management innovation and to improve the theoretical relationship between management innovation and external knowledge search based on firm behavior theory.

The relationship between cooperative R&D network embeddedness and firm innovation resilience is understudied. This paper seeks to answer the questions of whether and how embedding in cooperative R&D networks improve digital firms’ innovation resilience.

Based upon social capital theory, this paper proposes a conceptual model with several hypotheses. The empirical analysis is based on a sample of 2,908 observations from 2005 to 2022. We measure firm innovation resilience by drawing on economic resilience and use LSM tests to assess the effect of cooperative R&D network position on innovation resilience.

The results indicate that cooperative R&D network centrality has a positive impact on firm innovation resilience and that the structural holes of the cooperative R&D network have an inverted U-shaped relationship with firm innovation resilience. Moreover, technological turbulence negatively moderates the relationship between centrality and firm innovation resilience while also steepening the inverted U-shaped relationship between structural holes and firm innovation resilience. Market turbulence positively moderates the relationship between centrality and firm innovation resilience. However, the moderating effect of market turbulence on the inverted U-shaped relationship between structural holes and firm innovation resilience is not significant.

Innovators' knowledge needs, bounded rationality, interests, and even organizational environments change over time, thus prompting them to constantly seek new opportunities to exchange and integrate knowledge, meet new beneficial partners, maintain beneficial cooperation, or terminate unhelpful cooperation. The utility of the network structure has dynamic characteristics. Only by considering the dynamics of the network can research on the mechanism of network structure be more complete, accurate and convincing. Therefore, future research can pay more attention to the relationship between dynamic networks and firm innovation resilience.

Firms should actively embed themselves in the cooperative R&D network and occupy a beneficial network position. By joining the cooperative R&D network, firms can gain resource advantages and enhance their ability to resist external shocks and improve innovation resilience.

This research advances our understanding of the antecedents of firm innovation resilience through the lens of organizational cooperation and uncovers the boundary conditions under which network embeddedness influences innovation resilience, thereby further enriching the theoretical framework of innovation resilience.

This study aims to reduce carbon emissions and minimize waste in the event of disruptions in a short and fast-food perishable such as fruits, vegetables, packaged food items, etc. supply chain through optimal investment in green and preservation technologies.

This study utilized a Hessian matrix approach to optimize decision variables with an objective to maximize the profit function.

The study demonstrates that investing in both green and preservation technology within a short and fast-food supply chain is highly beneficial for decarbonization and waste reduction and it leads to profit maximization. It has been shown with the help of a numerical experiments with investment in both green and preservation technology that total profit is 3.09% higher than without investment made in either technology.

This study aids the industry in achieving food sustainability by minimizing waste of perishables and also minimizes carbon emissions which is essential for environmental protection. It assists industries in determining the optimal investment in preservation technology to minimize waste and in green technology to reduce emissions, thereby maximizing profits.

The current study formulates an inventory model that helps in decarbonization and waste reduction in food supply chain with the consideration of machine learning, demand disruption, preservation technology investment, screening of purchased items, waste disposal, a double triangular distribution deterioration rate, green technology investment, carbon emissions from various supply chain activities, carbon tax policy and fuel price variation over time for perishable food products in a two-warehouse system.