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A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The purpose of this research study was to examine the role of Social Networking Technologies (SNT) in developing Public Services Supply Chain (PSSC) during COVID-19 pandemic. Due to lack of sufficient support from corporations and non-government groups, citizens in developing democracies were dependent upon their governments for the delivery of vital public services. During the COVID-19 epidemic, a number of organisations attempted to assist governments in managing the supply of public services. However, organisations often lacked a thorough grasp of how to cultivate social ties for the delivery of efficient and effective public services for citizens. In this research work, to study the delivery of efficient and effective public services to citizens, the authors have proposed a social network viewpoint. This was at a ‘meso-level’ lens so as to examine the intersection between organisational SNT and PSSC. According to the qualitative study conducted in this research work, it became evident that organisational social ties could play a significant role in facilitating homophilic and heterophilic ties. This was specially so for the distribution of public services in pandemic situation like COVID-19. However, the research study findings also found out that these network forces were highly dynamic and dependent upon a set of factors. These factors included tie frequency, the sequencing of interaction with social ties and the prevailing norms. This research study enriched the understanding regarding the role of SNT. This was in the context of developing PSSC during critical crisis situations such as prevalent during COVID-19 pandemic. This research study offered a better understanding of social ties and motivational factors in the social networking environment. The social ties were analysed based on the contact incidents of six participants focusing on the PSSC during the COVID-19. Future research studies could consider a diverse set of participants in complex global PSSC consisting of multiple constraints. Furthermore, this research study had practical implications for managers. The study revealed that homophilic social ties were more supportive as compared to the sequence of interactions starting with heterophilic ties. Therefore, managers needed to develop better PSSC especially as compared to unrelated institutions.

This paper aims to explore how a shared service centre (SSC) is mobilised in a power-dominant context of a Chinese state-owned enterprise (SOE). Specifically, it examines the mobilisation of SSC within this multi-divisional SOE, the role and dynamics of actors involved and the influence of changes in the integrated information system (IIS) during the mobilisation process.

The study follows a qualitative case study methodology. The authors draw on actor-network theory to examine the network and translation processes constructed in mobilising SSC in the chosen SOE. The data sources of this study were collected through semi-structured interviews, observations and documentary reviews.

The mobilisation of SSC is not a linear process but rather a “spiral” interplay through continuous interactions and compromises between human and non-human actors. Power gave the core actor as an orchestrator legitimacy and formality to reduce resistance and obstruction in translation for the mobilisation of SSC. The changes in IIS appear to facilitate the interaction between the heterogeneous actors.

This case study contributes towards understanding the mobilisation of SSC in a power-dominant context by highlighting the impact of changes in IIS and the details of the mobilisation of SSC in terms of the role played by both the individual actors and the technology.

This study provides a broader understanding of the interactions of the heterogeneous actors for mobilising SSC in a power-dominant context. More importantly, the study inspires future research into examining how SSC practices unfold and how the changes in IIS influence the mobilisation of SSC.

This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.

Sustainable management requires companies to build up new knowledge to acquire the competencies needed for action. This chapter aims to deliver knowledge about sustainability and knowledge for sustainability. Firstly, we systematically analyse the sustainability literature in the social sciences through a bibliographic analysis and topic modelling using VOSviewer and Mallet software. We outline research directions, themes and critical contributions for each research cluster identified. Additionally, we categorise over 30 definitions of sustainability identified by Meuer, Koelbel, and Hoffmann (2020). Secondly, we enumerate knowledge types needed for effective sustainability transitions of organisations. We trace typologies of sustainable business models and their distinct evaluations of sustainability. In this chapter, we argue that integrating the triad of social, ecological and economic goals is central for sustainability attempts as well as long-term thinking. Therefore, our research offers a comprehensive overview of sustainability in the social sciences supporting researchers and practitioners to navigate this miscellaneous and scattered field. Accordingly, our study is precious to young scholars researching sustainability who want to use the term in an informed and meaningful way.

The purpose of this paper is twofold: (1) to map and analyze existing complexity typologies and (2) to develop a framework for characterizing complexity-based approaches.

This study was conducted in three stages: (1) initial identification of typologies related to complexity following a structured procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol; (2) backward and forward review to identify additional relevant typologies and (3) content analysis of the selected typologies, categorization and framework development.

Based on 17 selected typologies, a comprehensive overview of complexity studies is provided. Each typology is described considering key concepts, contributions and convergences and differences between them. The epistemological, theoretical and methodological diversity of complexity studies was explored, allowing the identification of the main schools of thought and authors. A framework for characterizing complexity-based approaches was proposed including the following perspectives: ontology of complexity, epistemology of complexity, purpose and object of interest, methodology and methods and theoretical pillars.

This study examines the main typologies of complexity from an integrated and multidisciplinary perspective and, based on that, proposes a novel framework to understanding and characterizing complexity-based approaches.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

The purpose is to use co-citation analysis of servant leadership (SL) research to investigate the evolution of the field, its subfields, gaps and opportunities for future research in a systematic manner.

A document co-citation technique and three clustering algorithms (latent semantic index (LSI), the log-likelihood ratio (LLR) and the mutual information (MI) index) were employed to analyse 24,030 references from 549 articles spanning a period of 50 years.

Cluster analyses reveal that SL research consists of eight distinct subfields: (1) conceptualisation and measurement of SL; (2) SL and related theories; (3) methodological foundations and empirical expansion of SL research; (4) individual-level cognitive effects of SL and related theories; (5) “Warmth effects” of leadership behaviour; (6) antecedents of effective leadership; (7) SL, marketing, sales management and ethics and (8) SL, job design and work engagement. Important gaps and opportunities for future research are identified.

The analyses do not show a complete picture of research on SL. Interesting works used by subgroups of SL researchers may not have enough citations to be included in the results. Moreover, bibliometric analyses do not explain the impact of books, journals and articles on the practice of SL. The authors welcome future analyses of the most influential sources of SL practice. The authors expect that managerial and practice-oriented books and journals, such as the International Journal of Servant Leadership and the Servant Leadership Theory and Practice, would play a central role in such analyses.

The discussions of the nature of SL, its effects and antecedents are useful to leaders who want to develop a SL style or assist others in developing it. For researchers and doctoral students, the cluster analyses of co-citations give an overview of the subfields of SL research and reveal important knowledge gaps in the literature.

SL has several favourable effects on the motivation and psychological well-being of followers. Also, followers tend to adopt a willingness to serve.

Previous research has categorised SL research into three broad categories or phases. The cluster analyses of the co-citations reported here reveal a meaningful structure of eight distinct subfields. Knowledge gaps within the subfields represent novel opportunities for future research on SL. The authors also suggest a new subfield of SL research: pedagogical approaches to the motivation and development of SL skills.

The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.

From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.

The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.

KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

This paper analyses the establishment and evolution of a public oversight body (POB) – the Egyptian Audit Oversight Unit (AOU) – and its implications for local auditing firms and practices.

Primary data were gathered from 34 semi-structured interviews (including follow-up ones) between 2014 and 2020. Secondary data was obtained through publicly available documents and internal memos. Drawing on Debord's (1967) Society of the Spectacle, the insights focus on the POB's conception, materialisation and evolution in a context characterised by weak regulatory structures.

Through a series of acts, the findings reveal how the AOU first accepted the image of “international best practice” oversight (the “metaphorical”), followed by the construction of the local structure and décor replicating a United States (US) style POB archetype (the “transformational”) by primarily relying on visible processes/procedures. Yet, these mechanisms emphasised the spectacular nature of oversight, with little improvement for practice and limiting itself to “cracking down” on smaller local firms. A final stage (the “performative”) reveals how the AOU seeks to expand its activities beyond its original mandate without challenging the image-driven nature of its oversight.

The paper offers two key contributions. First, it reveals how actors, through a combination of symbolic and tangible measures, create a new performative reality of public oversight. Second, it advocates Debord's “spectacle” to complement other theoretical lenses, with a view to illuminating the materialisation stages that bridge the gap between proclaimed oversight policies and actual practices (including conscious and unconscious omissions) within a given political economy context.