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Service networks are inherently complex as they comprise of many interrelated actors, often driven by divergent interests. This can result in imbalance, which refers to a situation where the interests of at least one actor in a network are not secured. Drawing on the “balanced centricity” perspective, the purpose of this paper is to explore the causes of imbalance in complex service networks.

Adopting a qualitative case-based approach, this paper examines a public health service network that experienced imbalance that was detrimental to the lives of its users: the Mid-Staffordshire National Health Service (NHS) Trust, UK. Drawing on service-dominant logic and stakeholder theory, case evidence provides insight into the origin and drivers of imbalance in complex public service networks.

The origin of imbalance stems from competing institutional logics of various actors (patients/public, employees, managers, regulatory bodies, etc.), but the degree to which these competing institutional logics lead to imbalance is moderated by accountability, communication, engagement, and responsiveness within the service network.

By uncovering causes of imbalance in complex public service networks, this paper pinpoints important research avenues for developing the balanced centricity perspective.

The inherent existence of multiple parallel institutional arrangements makes networks imbalanced, but value creation can be achieved when the appropriate mechanisms are fostered to manage balance between divergent logics.

By examining imbalance as the underlying cause of network dysfunction, this research contributes to understanding of the dynamics in, and performance of, complex public service networks.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

The purpose of this paper is to explore the challenges facing companies that operate within complex supply networks and the strategies they use to manage such complex supply networks.

The paper uses mixed methods by combining in-depth case studies with an executive forum with those of senior industry stakeholders. The two in-depth supply network case studies were carried out through multiple interviews with focal (or ‘developer’) firms that supply energy through offshore wind power and key suppliers such as wind turbine manufacturers.

The findings show the challenges the offshore wind power industry faces because of complex supply networks, including attempts by several actors to exert their power and control. Despite the networks facing similar complexities and challenges, two distinctly different strategies for orchestrating and governing supply networks are uncovered: one strategy resembles an interventionist strategy, while the other is based on delegation.

Based on the findings, the authors identify and develop a classification of complex supply network divided into intervention and delegation strategies, thereby adding to existing research on ways to manage complex supply networks.

The authors identify strategies for focal firms for managing in complex supply networks, based on control and intervention or coordination and delegation.

Existing research on supply network strategies has largely focused on non-complex contexts. This paper draws from complex adaptive systems and organisational behaviour perspectives to contribute original insights into supply network strategies in complex supply networks.

In recent years, with the increase in computing power, artificial intelligence can gradually be regarded as intelligent agents and interact with humans, this interactive network has become increasingly complex. Therefore, it is necessary to model and analyze this complex interactive network. This paper aims to model and demonstrate the evolution of crowd intelligence using visual complex networks.

This paper uses the complex network to model and observe the collaborative evolution behavior and self-organizing system of crowd intelligence.

The authors use the complex network to construct the cooperative behavior and self-organizing system in crowd intelligence. Determine the evolution mode of the node by constructing the interactive relationship between nodes and observe the global evolution state through the force layout.

The simulation results show that the state evolution map can effectively simulate the distribution, interaction and evolution of crowd intelligence through force layout and the intelligent agents’ link mode the authors proposed.

Based on the complex network, this paper constructs the interactive behavior and organization system in crowd intelligence and visualizes the evolution process.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

The purpose of this paper is to advance supply chain network theory by applying theoretical and empirical developments in complex network literature to the context of supply chains as complex adaptive systems. The authors synthesize these advancements to gain an understanding of the network properties underlying efficient supply chains. To develop a suitable theory of supply chain networks, the authors look to mirror the properties of complex network models with real‐world supply chains.

The authors review complex network literature drawn from multiple disciplines in top scientific journals. From this interdisciplinary review a series of propositions are developed around supply chain complexity and adaptive phenomena.

This paper proposes that the structure of efficient supply chains follows a “scale‐free” network. This proposal emerges from arguments that the key properties of efficient supply chains are a short characteristic path length, a high clustering coefficient and a power law connectivity distribution.

The authors' discussion centres on applying advances found in recent complex network literature. Hence, the need is noted to empirically validate the series of propositions developed in this paper in a supply chain context.

If efficient supply chains resemble a scale‐free network, then managers can derive a number of implications. For example, supply chain resilience is derived by the presence of hub firms. To reduce the vulnerability of supply chains to cascading failures, it is recognized that managers could build in redundancy, undertake a multi‐sourcing strategy or intermediation between hub firms.

This paper advances supply chain network theory. It offers a novel understanding of supply chains as complex adaptive systems and, in particular, that efficient and resilient supply chain systems resemble a scale‐free network. In addition, it provides a series of propositions that allow modelling and empirical research to proceed.

The purpose of this paper is to seek to develop a theoretical framework for understanding the structure of effective knowledge sharing networks in professional organizations.

A literature review is performed to achieve the purpose. This article integrates two streams of literature: related to knowledge network structure and related to professional complex systems, to gain insight into the structure of effective knowledge sharing networks in professional complex systems. This preliminary theoretical framework is then used to put forth strategies for knowledge management and collective learning in professional organizations.

An analysis of knowledge networks and complex systems literatures suggests that effective knowledge sharing networks in complex systems may be richer in density compared to brokerage. However, integrating this analysis with the literature on professional organizations, including “subgoals” theory, suggests that the reverse may be true in professional complex systems, i.e. that effective knowledge sharing networks in professional complex systems may be richer in brokerage and hierarchy, rather than in density.

The paper provides a foundation for future research avenues in the professional organizational context. For instance the framework could be used to explore effective knowledge sharing structures across professional subgroups and hierarchical levels in a hospital context; and across faculty, staff, and administrators in a college/university context.

A key implication is that, in order to enable collective learning in professional organizations, senior executives must make proactive and unceasing efforts to: coordinate knowledge exchange across professional subgroups; create cognitive linkages between subgroup actions and organizational outcomes; and connect professional subgroups with the changing external environment.

The theoretical framework lays a foundation for addressing the gap in the literature related to knowledge creation and collective learning in professional organizations.

The engineering education accreditation (EEA) is a principal quality assurance mechanism. However, at many education institutions, the most labor-intensive work of EEA process is accomplished manually. Without the support of computer and information technology, the EEA process leads to high labor intensity, low work efficiency and poor management level. The purpose of this paper is to build a complex network model and realize an information management system of talent training program for supporting the EEA process.

Based on polychromatic graph (PG), this paper builds a network model of talent training program for engineering specialty. The related information and data are organized and processed in this network model. From the bidirections of top-down and bottom-up, the user requirements are retrieved automatically in logic layer. Together with the specialty of mechanical engineering, the proposed PG-based network modeling method is applied and the corresponding information management system is realized.

The study results show that the PG-based network modeling method takes full advantages of the strong simulation ability of PG to model the complex network system and has some unique merits in formal expression of problem, efficient processing of information and lightweight realization of system. Further, the information management system of talent training program can reduce the tedious human labor and improve the management level of EEA process dramatically.

This paper proposes a PG-based network modeling method, in which the nodes and the edges can be painted by some unified colors to describe the different kinds of activities and the various types of interactions. Theoretically, this modeling method does not distinguish the activities, the interactions and their properties in graphic symbol and the problem size is diminished about a half. Furthermore, this paper provides an effective experience and idea to the education institutions for implementing the engineering education accreditation, increasing the education management efficiency and promoting the talent training quality.

The purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.

Applications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland^® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.

This paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.

The learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.

The models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.

The method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.

Many firms engage in co-opetitive projects during which they have simultaneously competitive and collaborative relationships with many rivals in a complex network. A co-opetitive network offers them access to a large volume of resources and knowledge, for example, to support new markets and/or territories. So, does the network grow with the scope of the co-opetition project? The paper aims to discuss this issue.

An empirical study of 106 French boating intermediate-sized enterprises (ETIs) and small and medium enterprises provides a descriptive and explanatory analysis of co-opetitive networks.

The results support this definition of a complex co-opetitive network only when the objectives of a firm are part of the geographical expansion of its activities. In contrast, these relations remain simple (dyadic) when a firm favours a strategy of diversifying its activities while maintaining its unique local geographical market.

First, the work is based on a quantitative methodology, so is static. It would be interesting to analyze the process of the building of co-opetitive relationships and opportunism between rival firms, for example, through a qualitative study. Second, this work focusses on boating companies in France. It may be appropriate to consider the sanctions placed on the opportunism of foreign firms in co-opetition. Third, future work could increase understanding, not only of the nature of reprisals inflicted on individualistic co-opetitors, but also on the structure, objectives and results of these reprisals.

The study deepens our knowledge of the definition, composition and determinants of co-opetitive networks.