Purpose This research proposes a methodological framework to analyze the impact of blockchain technology (BT) affordances on sustainable–resilient pharmaceutical supply chain performance (SRPSCP). Design/methodology/approach This research uses a mixed-method approach. Sustainability Development Goal#3 (SDG#3) guides the SRPSCP conceptualization. Data from academic and grey literature have been collected for the conceptual framework. Primary qualitative data from 3 groups of 4 experts (totaling 12) and secondary qualitative data from web scrawling have been collected for the analytical framework. Findings Analysis reveals the positive and negative effects of blockchain affordances on cost. Specifically, the affordances of visibility and resilience are most effective in minimizing costs. Also, automation impacts SRPSCP the most, followed by visibility, resilience, aggregation and validation. And visibility emerges as the most influential affordance affecting reliability, while automation, aggregation, validation and resilience contribute to varying extents. Practical implications Managers can justify the initial expenditure on BT to stakeholders by highlighting cumulative advantages and cost reductions over time. BT enabled visibility and automation, contributing highly to maintaining high service levels. BT implementation assures the reliability of pharmaceutical products, streamlines the process of recalling products and enables quick settlement of false accusations by leveraging its improved traceability. Originality/value This work is the first to use a mix of technology affordance, network and transaction cost theory. The proposed framework can help academicians and decision-makers to explore, analyze and rationalize the impact of BT implication on PSC performance when assessed with existing non-Web 3.0 technologies.

Abstract In this conceptual article I aim to advance our understanding of digital genre networks in online science communication. Specifically, I develop genre theory by drawing on the meta-theory of complexity, which involves two highly influential interpretive frameworks, complexity theory and complex systems theory, and their related subfields, complex network theory and complex dynamic systems theory. I conceptualise digital genre networks as analogous to complex, dynamic systems, with their constituent genres acting as interconnected nodes that achieve specific social actions. I explain the theoretical and practical rationales behind this conceptual model and outline how to empirically demonstrate the structured heterogeneity and holistic behaviour of digital genre networks using several constructs from the meta-theory of complexity — non-linearity, adaptability, coevolution, self-organisation and dynamic interactions. The conceptual model also involves methodological developments, which I illustrate using case study research designs. Finally, I suggest some future directions for expanding the field of English for Research Publication Purposes and broader fields, and propose ways of training researchers who need or want to compose digital genre networks to increase the visibility and impact of their work.

Millions of adults suffer from Major Depressive Disorder (MDD) globally. Applying network theory to study functional brain dynamics often uses the fMRI modality to identify the perturbed connectivity in depressed individuals. However, the weak temporal resolution of fMRI limits its ability to access the fast dynamics of functional connectivity (FC). Therefore, electroencephalography (EEG), which can track functional brain dynamics every millisecond, may serve as a diagnostic marker for utilizing the dynamics of intrinsic brain networks at the sensor level. This research proposes a unique neural marker for depression detection by analyzing long-range functional neurodynamics between the default mode network (DMN) and visual network (VN) via optimal EEG nodes. While DMN abnormalities in depression are well documented, the interactions between the DMN and VN, which reflect visual imagery at rest, remain unclear. Subsequently, a novel differential graph centrality index is applied to reduce a high-dimensional feature space representing EEG temporal neurodynamics, which produced an optimized brain network for MDD detection.
The proposed method achieves an exceptional classification performance with an average accuracy, f1 score, and MCC of 99.76\%, 0.998, and 0.9995 for the MODMA and 99.99\%, 0.999, and 0.9998 for the HUSM datasets, respectively.
The findings of this study suggest that a significant decrease in connection density within the beta band (15-30 Hz) in depressed individuals exhibits disrupted long-range inter-network topology, which could serve as a reliable neural marker for depression detection and monitoring. Furthermore, weak FC links between the DMN and VN indicate disengagement between the DMN and VN, which signifies progressive cognitive decline, weak memory, and disrupted thinking at rest, often accompanied by MDD.

Does the structure of dynamic symptom networks depend on baseline psychopathology in students?

Purpose Based on the structural perspective of social network theory, this study aims at investigating how intra-team and inter-team network centralization of cross-functional B2B sales teams as well as their interaction influence team’s customer-oriented selling and sales performance, and the moderating effects of team structure (team instability) and sales complexity (customer purchase complexity) variables. Design/methodology/approach 319 valid responses from 76 cross-functional sales teams are used to test the hypotheses, and a structural equation model and hierarchical regression are employed to examine the proposed main and moderating effects. Findings The results indicate that intra-team centralization positively impacts customer-oriented selling, while inter-team centralization negatively impacts customer-oriented selling, and their interaction negatively impacts customer-oriented selling. Moreover, team instability positively moderates the link between intra-team network centralization and customer-oriented selling, and purchase complexity negatively moderates the relationship between inter-team network centralization and customer-oriented selling. Originality/value From a theoretical perspective, this paper extends the research level of customer-oriented selling from the individual level to the team level and enriches its antecedents from the network perspective. In addition, this study verifies the interaction effect of intra-team and inter-team centralizations on the team’s customer-oriented selling and sales performance, providing more comprehensive insights into the impact of network structure on team-level outcomes. From a practical perspective, the results offer valuable suggestions for enhancing customer-oriented selling and sales performance of B2B sales teams via effectively setting up the centralized intra-team network structure and the decentralized inter-team network structure.

Abstract Local knowledge (LK) refers to the ancestral understanding that Indigenous Peoples and local communities have developed over centuries through trial‐and‐error and hands‐on management of natural resources. LK may provide valuable insights for biodiversity conservation and human well‐being. However, its effectiveness remains under‐explored at large scales, especially where multiple communities manage ecosystems. One example is fisheries, which form complex, interconnected networks where fish move across spatial boundaries between managed areas. Fisheries are critical for food security and income, yet face threats from overharvesting. Fisheries Co‐Management (FCM)—a partnership between local communities and governments—leverages LK. However, the value of LK in designing protection strategies remains unclear. Using a process‐based dynamical model parameterized with empirical data, we evaluated FCM strategies for pirarucu ( Arapaima gigas ) fisheries, which form a metapopulation network of protected and unprotected lakes in the Brazilian Amazon. We combined our metapopulation model with LK, fish biology and network theory to assess how lake protection, fishing quotas and illegal fishing impact pirarucu population abundance at the riverscape scale. By analysing 13 FCM‐protected lakes and 18 unprotected lakes, we contrasted six hypothesis‐driven management strategies against the current one, which is based on LK. In all strategies, protected lakes support higher pirarucu populations and buffer against increased fishing pressure, while unprotected lakes face population collapse due to the lack of fishing regulations. While a strategy that provides the best outcomes in terms of metapopulation persistence was based on pirarucu carrying capacity, the currently applied FCM strategy closely matched its efficacy. Synthesis and applications . Our modelling approach allows managers to compare alternative conservation strategies under different socio‐ecological scenarios, highlighting trade‐offs and guiding investment of effort and resources. While immediately valuable for pirarucu management in the Middle Juruá, the framework scales across tiers of applicability, each requiring progressively greater model adaptation: from supporting FCM in other Amazonian regions (with minimal adjustment), to adaptation for other riverine fisheries and ultimately to broader socio‐ecological systems. In this way, we provide both system‐specific insights and a flexible tool for advancing sustainable management of natural resources across contexts.

This study investigates the risk spillover network among major stock market indices of G20 countries from 2003 to 2024. Transfer entropy is employed to measure the asymmetric and nonlinear information flow between stock markets. Based on this key metric, a directed and weighted risk spillover network among stock markets is constructed using the threshold method during periods of extreme events. Utilizing complex network theories, such as PageRank and betweenness centrality, the study analyzes the macro-topological characteristics of the risk spillover network and identifies key nodes. The findings not only demonstrate strong information interaction among G20 stock markets but also show that European and North American markets exhibit regional clustering characteristics, while emerging markets serve as bridging nodes in the risk spillover network. These results offer theoretical and practical insights for portfolio management, risk monitoring, and cross-border financial regulation and crisis management.

This study investigates the structural organization of an email communication network constructed from the SNAP Enron dataset, where nodes represent individual email addresses and edges correspond to communication links between them. Communities within the network were identified using the Label Propagation Algorithm (LPA), yielding 35 distinct groups. To evaluate the structural coherence and significance of these communities, we integrated two complementary analytical frameworks: Persistent Homology, from Topological Data Analysis (TDA), and Modularity, a key metric in network theory. Persistent homology was utilized to detect enduring topological features—such as connected components, loops, and voids—that characterize the intrinsic structure of each community across varying filtration scales. Modularity analysis, in turn, quantified the relative density of intra- and inter-community connections. Combining these approaches enabled the classification of communities as non-significant, significant, influential, or highly influential. The findings reveal a strong correlation between persistent topological features and high modularity scores, offering deeper insights into the stability, cohesion, and influence of communities in large-scale social communication networks.

ABSTRACT Research on scientific collaboration networks demonstrates that co-authorship enhances scientists’ performance, particularly when co-authors are prominent within the network. Yet, prior work largely adopts a static perspective, leaving limited understanding of how changes in co-authors’ prominence affect scientists. When co-authors engage in other research projects, this affects their network prominence and can influence scientists’ performance. Drawing on social network theory and research on scientific collaboration, we theorise that increases in co-authors’ prominence are associated with reduced performance for the focal scientist. We argue that when co-authors increase in prominence, they disperse their effort across projects, allocating less to the joint endeavour. The hypotheses are tested using unique data on 12,023 observations of 1,553 scientists collaborating on 4,433 papers. The results reveal a negative spillover: focal scientists’ performance declines when co-authors rise in prominence. This effect intensifies when collaborators also increase in performance but diminishes with greater knowledge domain overlap.

ABSTRACT In resource‐constrained environments, frugal innovation has emerged as an essential entrepreneurial strategy. It allows firms to develop efficient, affordable and sustainable solutions to meet the needs of low‐income consumer markets. This increasing demand necessitates a deeper understanding of the mechanisms that foster frugal innovation, particularly in emerging economies. Thus, this research aims to investigate the influence of network ties, resource bricolage, institutional support and organizational readiness to develop frugal innovation. For this purpose, a structured questionnaire was developed and data were collected through an online survey of 350 firms across Pakistan. The empirical testing was performed using partial least squares structural equation modelling for data analysis. The findings show that network ties have a positive influence on frugal innovation through significant mediation of resource bricolage. Institutional support and organizational readiness also positively moderate the relationship between resource bricolage and frugal innovation. These findings extend the frugal innovation literature by investigating the underlying mechanisms through which network ties and resource bricolage contribute to innovation outcomes under resource‐constrained environments. The study also advances theoretical perspectives of social network theory and resource‐based view. Moreover, the paper provides significant insights to entrepreneurs, practitioners and policymakers, which enable them to develop cost‐effective products and services under resource‐constrained markets to serve low‐income consumer markets.

Forest recreation is irreplaceable for the protection and sustainable development of urban environments. Understanding the structural characteristics of forest recreation networks in urban areas thus offers valuable theoretical and practical insights. Grounded in social network theory and spatial analysis of recreational behavior, this study leverages point of interest (POI) data for forest attractions, forest land cover data, and user-generated content (UGC) trajectory data to analyze the evolution of the forest recreation network in the Chang-Zhu-Tan Green Heart (CZTGH) of China—the world’s largest metropolitan ecological green heart area. Findings reveal that the forest recreation network of CZTHGH exhibits a multi-center, clustered spatial pattern, with a weakened radiative influence from core to peripheral areas. While recreational behaviors are increasingly fragmented and localized, this has not undermined the network’s overall function; instead, it has fostered systemic adaptability through multiple, functionally complementary clusters, accompanied by a marked shift in activity preference toward ecologically oriented spaces such as arbor forests, shrublands, and scenic forests, alongside a significant decline in non-forest recreation. Furthermore, a high degree of spatial alignment is observed among recreation supply nodes, public demand, and forest resources, indicating synergistic spatial coordination between recreational use and ecological conservation. Findings support an analytical framework integrating recreation supply, recreation demand, and forest resources, providing practical references for the sustainable use of ecological spaces in similar urban areas.

Plant functional traits are important to understanding biodiversity and ecosystem functioning responses to global change. However, traditional plant trait network (PTN) theory does not adequately capture coordinated adjustment mechanisms operating among traits across multiple organs and functional systems. To address this challenge, we introduce the use of plant trait multilayer networks (PTMNs). This innovative framework systematically integrates multilayer network theory with plant functional trait analysis, enabling quantitative assessments of trait relationships across plant organs and functional systems. We applied both traditional PTN and PTMN analyses to a dataset of 76 native and non-native woody species in North American deciduous forests, examining relationships among leaf, stem, and root traits. PTN results showed that non-native species already displayed higher connectivity and integration within single-layer networks, while PTMN analysis further revealed stronger cross-layer links and more efficient coordination across multiple organs and functional systems, indicating enhanced integrative capacity and adjustment potential in non-native species. In conclusion, by analyzing trait interdependencies across organs and functional systems, PTMNs provide a comprehensive framework for understanding the complex ways plants respond to environmental change, thereby enhancing predictions of biodiversity patterns and ecosystem resilience.

ABSTRACT In the ongoing debate surrounding the gender gap in top managerial positions, various possible causes have been explored, with different networking patterns being one of them. However, we still lack detailed insights on how knowledge transfer via networks varies based on gender. Identifying and understanding these gender‐based inequalities in information access is essential for the organization and the individual top executive (e.g., regarding impact on career advancement) likewise. Drawing upon network theories, the present study investigates the direct network ties for innovation‐relevant knowledge among 28 top managers—comprising 13 women and 15 men—in companies situated in the United Kingdom. The results of this Social Network Analysis reveal significant disparities: Women tend to establish small(er) networks with strong(er) ties, whereas men hold large(r) networks with weak(er) ties. The filtering approach indicates a proclivity among women to filter ties ex ante the knowledge transfer while men filter knowledge ex post transfer. A framework highlighting the gender specifics is developed. Overall, the study underscores the necessity to reassess prior understandings of top executives' networking activities considering gender.

Abstract This study examines the decision to flee and the destination preferences of displaced Ukrainians who sought refuge in Berlin, Warsaw, and Budapest following the 2022 full-scale Russian invasion. Drawing on 255 semi-structured interviews and employing an abductive analytical approach, the analysis integrates insights from neoclassical economics of migration, the new economics of migration, and network theory. The findings highlight the importance of perceived agency at the onset of displacement in shaping the types of push and pull drivers that migrants considered relevant. Additionally, the study shows that social contacts influencing the decision to flee differ from those shaping destination preferences: The former are typically strong ties (e.g., close family), while the latter often involve weaker ties (e.g., acquaintances or volunteers). The article concludes by demonstrating the continued relevance and adaptability of classical migration theories for understanding forced migration dynamics.

The purpose of this paper is to combine the cloud model method with the Artificial neural network to predict and evaluate flight delay date. In this paper, the flight delay data of the United States in recent 15 years were collected, involving nine airlines and seven delay causes. Then, the flight delay prediction model is obtained by combining neural network and cloud model theory. 36 sets of data are extracted to verify the accuracy of the prediction model. Finally, the delay rate is predicted and the prediction data is evaluated by cloud model. The results show that the amount of data to be processed is reduced to 20 % after combining the cloud model with the neural network. In the model validation, the average expected value deviation of flight delay prediction model is 23.1 %. In the analysis and evaluation of the prediction results, the delay cause 6 is taken as an example, with the highest delay rate of 7.03 % in December. The delay risk assessment results for all months were general. The method provided in this paper can predict flight delays and provide theoretical support for the delay prevention of airlines.

ABSTRACT Drawing on social network theory, we develop a moderated model in which network embeddedness is an informal governance mechanism to inhibit suppliers' unethical behaviors. Since the relational network is also embedded in a specific business environment and institutional context, we explore the mutually nested role of environmental embeddedness and network embeddedness. We test the model using dyadic data on the buyer–supplier relationship drawn from Chinese household electronics, automotive, and communications suppliers and buyers. The results show that regarding the relational aspect, network embeddedness has an inhibitive role on suppliers' deceitful and subtle practices, and political ties strengthen the inhibitive role of network embeddedness on unethical behaviors, while competitive intensity exerts contingency effects on this role. Our findings prove the value of network embeddedness in managing unethical behavior. We provide evidence for the mutually nested view of embeddedness. Specifically, we reveal that competitive intensity reinforces the inhibited effects of network embeddedness on deceitful practices. In addition, our study reveals that in the Chinese institutional environment, political ties reinforce the inhibited effects of network embeddedness on deceitful and subtle practices.

The rapid integration of Artificial Intelligence (AI) into everyday social systems has reshaped long-standing sociological understandings of power, identity, and human agency. This paper explores AI not merely as a technological tool but as an emerging social actor capable of influencing behaviours, shaping decision-making processes, and redefining institutional practices. Drawing on theories of symbolic interactionism, actor–network theory, and critical sociology, the study examines how AI systems mediate social interactions, produce new forms of algorithmic authority, and contribute to shifting power relations between individuals, organisations, and the state. The analysis highlights how AI-driven classifications, predictions, and automated decisions reshape identities—through profiling, personalisation, and digital surveillance—while also raising concerns over autonomy, inequality, and ethical accountability. By conceptualising AI as a socially embedded actor, the paper argues that AI technologies have begun to co-produce social realities, redistribute control, and challenge the boundaries between human and machine agency. This reconfiguration demands renewed sociological attention toward digital governance, transparency, and the societal impacts of algorithmic systems in increasingly automated environments.

Abstract. Under the backdrop of global climate change, the frequency and severity of drought events are continuously increasing, posing signif-icant challenges to human society, ecosystems, and economic development. Traditional drought simulation methods often overlook the interactions among meteorological, hydrological, and geographic information. Complex network theory offers a new perspective for exploring these interconnections. Graph Neural Networks (GNNs), as a deep learning technique capable of handling geospatial data and complex structures, have advantages in capturing geographic correlation information and network topology. Therefore, combining complex networks and GNNs for drought simulation is of great significance. This study proposes a framework that integrates complex networks and graph learning to identify the spatial structure of drought regions. Using latitude-longitude grids as nodes, topological indicators such as degree, betweenness centrality, and clustering coefficient describe node features, which are combined with SPEI time series statistical features to form multidimensional vectors. These are input into a Graph Convolutional Network (GCN) to obtain low-dimensional embeddings, and clustering is used to divide the space into subregions. Results show that the clustering based on multi-feature combinations exhibits stronger spatial continuity and clearer boundaries. Regions with high degree and betweenness centrality and low clustering coefficient serve as network hubs and information bridges, while medium-feature regions are intermediate connecting zones, and regions with low feature values are peripheral isolated areas. This method offers a novel approach to analyzing drought system structures and regional risk management.

We employ a molecular theory of dynamic polymer networks to describe the viscoplastic response of rubbery FK-800, a thermoplastic copolymer of chlorotrifluoroethylene and vinylidene fluoride, over a broad range of thermal histories. The kinetics of crystallization at different annealing temperatures was modeled using a modified Avrami equation, whose parameters were found to evolve through simple relationships over the full temperature range of the rubbery state. By fitting experimental compression data, we discovered predictable trends for the physical parameters in our mechanical model over its full range of crystallinities (up to ≈20%) and provided insights based on molecular-level physics to justify them. Using this, an end-to-end model was developed to predict the yielding and post-yield behavior of rubbery FK-800 for arbitrary thermal histories. The model successfully predicted the highly nonlinear evolution of characteristic mechanical signatures (stiffness, yield point, post-yield drop) throughout the crystallization process. A statistical analysis of variance test was employed to determine that the measured variations in the mechanical behavior of rubbery FK-800 are primarily dictated by its fractional crystallinity, regardless of its exact thermal history.

This study is a Systematic Literature Review (SLR) that aims to map the utilization of Actor–Network Theory (ANT) in accounting studies published during the 2023–2025 period. The research method employs the PICO framework (Population, Intervention, Comparison, Outcome) to establish inclusion criteria and conduct thematic analysis. The primary data consist of 33 articles that were collected and mapped into a source file. The results show a dominance of qualitative methods (case studies, ethnography, and discourse analysis), a focus on human non-human interactions in accounting practices, and the significant role of nonhuman elements (e.g., information systems, factories, devices) in shaping organizational tensions and outcomes. This SLR provides a conceptual contribution by recommending the integration of ANT with institutional theory and the use of mixed-method research in future studies.