PurposeThis study aims to examine how servitization affects the environmental and social performance of manufacturing firms.Design/methodology/approachThe hypotheses are tested using fixed-effect panel models based on secondary data of 1,413 manufacturing firms publicly listed in the USA.FindingsResults show that servitization is positively related to the social performance of manufacturing firms; this positive relationship is more prominent under high levels of human resource slack. However, the impact of servitization on environmental performance depends on the level of absorptive capacity and human resource slack. Servitization improves environmental performance under high levels of absorptive capacity and human resource slack, while this positive impact is insignificant under low levels of absorptive capacity and human resource slack.Research limitations/implicationsThe study focuses on the degree (depth) of servitization but ignores the scope of services provided by manufacturing firms (breadth of servitization).Practical implicationsThis research suggests that servitization is an effective way of achieving simultaneous improvements in environmental and social performance. However, high levels of absorptive capacity and human resource slack are needed to achieve this goal.Originality/valueThis study contributes to the servitization literature by demonstrating the environmental and social sustainability benefits of servitization. The findings also highlight the crucial role of absorptive capacity and human resource slack on improving environmental and social performance through servitization.

With the rapid development of information technology, the sharing economy based on “Internet plus” cloud platforms has become a new collaborative innovation mode and a hot topic in recent years. Considering that government regulation restricts green innovation cooperation among cloud manufacturing enterprises, an evolutionary game model involving the government and cloud manufacturing enterprises A and B with potential differences in their technology knowledge is established using evolutionary game theory. A replication dynamic equation is established, the evolutionarily stable equilibrium strategy of the three parties is analysed, and the key factors affecting the cooperative selection strategy of the government and cloud manufacturing enterprises are discussed through a MATLAB-based numerical simulation. This research shows that when governmental incentives and punishments, the platform load capacity, the trust between enterprises, the technology loss coefficient, and the informatization degree are increased, the government will tend to choose supervision, and cloud manufacturing enterprises A and B will tend to choose the “collaborative innovation” strategy. These results provide a scientific basis suggesting that the government should not only formulate rules and regulations for cloud manufacturing enterprises but also promote green collaborative innovation among such enterprises and enhance their core competitiveness.

Recycling and gradient utilization (GU) of new energy vehicle (NEV) power batteries plays a significant role in promoting the sustainable development of the economy, society and environment in the context of China’s NEV power battery retirement tide. In this paper, the battery recycling subjects and GU subjects were regarded as members in an alliance, and an evolutionary game model of competition and cooperation between the two types of subjects was established. Evolution conditions and paths of the stable cooperation modes between these two were explored. Suggestions were proposed to avoid entering a state of deadlock and promote the alliance to achieve the “win-win” cooperation mode of effective resource recovery and environmental sustainability. The results revealed four types of certain situations, two types of uncertain situations, and one type of deadlock situation for the evolution of alliance cooperation. The factors of the market environment are evident in not only changing the evolution paths and steady-states of the alliance but also in breaking the evolution deadlock. However, the sensitivity of the members in the alliance to different types of parameters varies greatly. It is difficult for the government to guide the formation of an ideal steady-state of cooperation or break the deadlock of evolution by a single strategy, such as subsidies or supervision. The combination of subsidy-and-supervision or phased regulation should be adopted. Only increasing subsidies is likely to weaken the function of the market and have a counterproductive effect.

Collaborative innovation networks have the basic attributes of complex networks. The interaction of innovation network members has promoted the development of collaborative innovation networks. Using the game-based theory in the B-A scale-free network context, this paper builds an evolutionary game model of network members and explores the emergence mechanism from collaborative innovation behavior to the macroevolution of networks. The results show that revenue distribution, compensation of the betrayer, government subsidies, and supervision have positively contributed to the continued stability of collaborative innovation networks. However, the effect mechanisms are dissimilar for networks of different scales. In small networks, the rationality of the revenue distribution among members that have similar strengths should receive more attention, and the government should implement medium-intensity supervision measures. In large networks, however, compensation of the betrayer should be attached greater importance to, and financial support from the government can promote stable evolution more effectively.Collaborative innovation networks are the main topic in this paper. After reviewing some recent research, we found that the main fields of related work focused on the characteristics of the collaborative networks members (micro), the evolution processes of the networks (macro), and the networks' collaborative performance. We report some of our findings below.In terms of the works on members of in collaborative innovation networks, social network analysis is the main method to study the characteristics of network nodes and the relationships between them. Landart et al. found that innovation networks that are well managed can providemany benefits to enterprises [9]. Dhanaraj studied the activity and openness of network nodes and found that the core members of innovation networks can coordinate overall network actions to achieve the effects of collaborative innovation [10]. Tsai pointed out that if enterprises occupy the center of the networks, they can generate more innovations. Tseng analyzed centrality and density of firms in the innovation networks, and found that the higher centrality and higher density, the stronger innovation capability [11].The research on the evolution of innovative networks mainly focuses on network topology and its influencing factors. Woo found that the connection mechanism has a great impact on network evolution using social network analysis to study the dynamic evolution of high-tech innovation networks [12]. Fleming analyzed the impacts of core firms on the evolution of innovation networks and thought that core innovation network members are important factors that drive the network to centralize [13]. Lazzeretti investigated the impact of neighborhood on innovation network dynamics [14]. Liang explored the evolution of collaboration network within government sponsored and found that network structure and composition should be involved into the specific policies [15].In terms of collabora...

Digitalization has reshaped the way of value co‐creation among innovation subjects, expanded the existing innovation ecosystem theories, and triggered the thinking about the digital innovation ecosystem. How to continuously promote value co‐creation between focal companies and non‐focal subjects within the digital innovation ecosystem to elevate the sustainable development of the system is an urgent issue to be solved. In this paper, we built a model of value co‐creation behavior evolution of focal companies and non‐focal subjects in the digital innovation ecosystem based on the complex network evolutionary game theory. The dynamic decision‐making process and critical factors of value co‐creation behavior of focal companies and non‐focal subjects were explored, and the emergence mechanism from micro‐behavior of value co‐creation to macro‐evolution was studied. The results showed that (1) increasing the variability of digital resources shared by focal companies and non‐focal subjects and the level of digital innovation benefits could promote value co‐creation in the system, but digital innovation ecosystems of different scales have various sensitivities to the variability of digital resources and the level of digital innovation benefits; (2) during the initial construction period of digital innovation ecosystems, the distribution of digital innovation benefits should be dominated by focal companies. With the expansion of the ecosystem, the focus of benefit distribution should gradually shift to non‐focal subjects. (3) In the evolution of the digital innovation ecosystem, focal companies should bear relatively more coordination costs of value co‐creation to promote the stable development of the system. (4) It is necessary to establish a punishment mechanism for opportunistic behavior, and the punishment should be gradually increased as the scale of digital innovation ecosystem expands. This study characterizes the digital innovation ecosystem with scale‐free networks in complex networks and constructs a complex network evolutionary game model to study the dynamic decision‐making process of value co‐creation behavior in the system, which makes up for the limitations of traditional evolutionary game research in which game subjects interact in a uniformly mixed manner and highlights the macroscopic phenomena emerging from the dynamic decision‐making of value co‐creation behavior of micro subjects. The research findings have important implications for the co‐creation of value by focal companies and non‐focal subjects in the digital innovation ecosystem and the sustainable development of the system.