Purpose This study aims to revisit the expropriation and bonding effects of specific asset investment (SAI) in B2B marketing channels by considering the dual pathways of channel role behaviors (suppliers’ in-role contract enforcement and distributors’ extra-role altruistic behavior) between SAI and cooperative performance and examines how suppliers’ fairness perceptions (distributive and procedural fairness) moderate these relationships. Design/methodology/approach Drawing on agency theory and equity theory, this study develops a comprehensive model connecting SAI, channel role behaviors and fairness perceptions. The hypotheses were tested using survey data collected from 580 home appliance distributors in mainland China. Findings The results reveal that SAI positively influences both suppliers’ in-role contract enforcement and distributors’ extra-role altruistic behavior, with both channel role behaviors positively contributing to cooperative performance in B2B relationships. Fairness perceptions moderate these relationships differently: distributive fairness enhances the relationship between SAI and distributors’ extra-role altruistic behavior, while procedural fairness strengthens the relationship between SAI and suppliers’ in-role contract enforcement. Channel role behaviors partially mediate the relationship between SAI and cooperative performance. Originality/value This study contributes to B2B marketing channel literature by revisiting SAI from the perspective of channel role behaviors, providing a novel lens to understand the expropriation and bonding effects in industrial exchange relationships. By identifying how suppliers’ SAI simultaneously influence different types of channel role behaviors, this study reveals important mechanisms through which these investments transmit to cooperative performance. This study also demonstrates how different dimensions of fairness shape these behavioral pathways in B2B contexts, offering new insights for channel relationship management beyond traditional perspectives.

The oil and gas industry remains central to global production and consumption, wielding significant economic and political power. This power stems not only from the strategic value of its resources but also from the industry’s embedded social power. This paper conceptualizes and analyzes this social embeddedness through the lens of elite network embeddedness. It opens up the black box of the firms at the top of the oil and gas industry and highlights the importance of human agency, in particular the corporate directors in charge of the firms. Building on economic geography and economic sociology literature, we explore how lead oil and gas (O&G) firms are socially and territorially embedded through their directors’ elite ties, focusing on the O&G sector of the UK Continental Shelf in the North Sea (UKCS). Sourcing firm and people data from BoardEx (2016–2020), we utilize social network analysis and complementary qualitative research (e.g. semi-structured interviews). We find that lead firms with significant control of O&G assets in the UK are embedded in extensive national and transnational corporate networks and extensively connected to politics and policy planning through elite networks. We also find US elite ties to be dominant across these three domains, highlighting the continuing influence of US elite power structures within the UKCS oil sector even as US O&G firms have significantly reduced their operations in the North Sea. This study hence offers a new perspective on how firm-territory relations of the UKCS O&G industry are geographically and societally embedded through elite networks.

Workplace conflicts are an inevitable aspect of organizational dynamics, directly influencing employee performance and overall productivity. This study examines the effectiveness of conflict management practices in modern industrial relations system and its impact on employee satisfaction, motivation, and performance. Adopting a quantitative research methodology, data was collected through structured questionnaire and analyzed using SPSS to identify key conflict triggers as well as resolution strategies. The findings indicate that poor communication, role ambiguity, and leadership styles are the primary contributors to workplace conflicts. Moreover, statistical analysis explicates a significant correlation between effective conflict resolution strategies and enhanced employee performance, emphasizing the critical role of structured conflict management in fostering a positive work environment. The study further highlights the importance of training programs, organizational policies, and leadership interventions in mitigating workplace conflicts. These insights contribute to the broader discourse on industrial relations and provide practical recommendations for organizations to implement robust conflict management frameworks, ultimately enhancing workforce harmony and productivity.

Protein-starch interaction in encapsulation systems: From functional group distribution to physicochemical and release properties.

ABSTRACT The debate on whether national industrial relations (IR) are experiencing convergence is a long‐standing one. Recently, scholars argue that we are witnessing a neoliberal convergence of national IR, understood as an increase in employers’ discretion. Much of this discussion has focused on countries of the European Union (EU) as empirical test cases. Hence, this debate has intertwined with that on the effects of the process of European integration on national IR. Drawing insights from the economic geography literature and the perspective of ‘variegated neoliberalisation’, this article argues that neoliberalisation and Europeanisation on IR should be seen as intertwined processes, which are, however, constitutively variegated. Through a multi‐level research design, the article applies this theoretical framework to one key function of national IR, that is, wage regulation, with a focus on Ireland and Italy.

Purpose Textile industries in developing countries face the major challenge of fulfilling customer demand at competitive prices. Therefore, it becomes inevitable for industry to reduce its production costs by minimizing process waste to survive in the global market. Design/methodology/approach This research focuses on the identification and elimination of Lean wastes from the sewing production lines in the apparel industry through value stream mapping supported by “Define, Measure, Analyze, Improve and Control” (DMAIC) methodology of Six Sigma. The analysis has been further supported by discrete event simulation (DES) to investigate the current situation and map the future state of the system. The analytic hierarchy process, integrated with the Grey relational analysis approach, is practiced exploring the best optimum solution from the various feasible solutions suggested by DES. Findings VSM and DES highlighted the multiple non-value-added (NVA) activities, including the high level of work in process within the production line, unnecessary operations, bottleneck workstations and manual operations that could be automated with less effort. Lean Six Sigma (LSS) has proven to be an effective method for eliminating the identified NVA activities from the production line. Quantitative analysis showed an improvement in productivity of 9.3% and a lead time reduction of 73%. Additionally, the amount of clothing produced per shift has increased by 6.8%, which in the initial situation was not even enough to meet the daily demand from customers. Research limitations/implications By adapting the integrated LSS, DES and multi-criteria decision-making paradigm to other labor-intensive industries, this study increases its generalizability in the context of Industry 4.0 and creates opportunities for future research. Its concentration on a single denim manufacturing scenario, however, may limit its wider applicability in the absence of additional validation across pertinent industries. Originality/value The proposed methodological framework, based on different industrial engineering tools and techniques, provides a strategic roadmap to not only the apparel industry but also the related industries that are suffering from low productivity and high lead time.

Abstract Dairy cattle systems and their related industry are important to food security, nutrition, and employment worldwide. Over the last decades, there has been an increase in global demand for milk and dairy products, due to both a growing human population and the increasing demand in emerging economies. Concern has been put on the unsustainability of conventional dairy systems, which significantly contribute to greenhouse gas (GHG) and nutrient emissions, and show low profitability, high dependence on external inputs and subsidies, and animal welfare concerns. This review aims to show the central challenges and alternatives regarding the sustainability of dairy farming systems, based on a review of studies carried out in recent decades and years. We identified environmental, social, and economic challenges, as well as options for dealing with them. In environmental terms, we especially highlight GHG and nutrient emissions, with options to address the challenges based on diet changes, rumen bacterial modifiers, plant extracts, herd and manure management, genetic selection, strategic fertilization, urease and nitrification inhibitors, and regulations and taxes on emissions. In social aspects, challenges such as animal welfare, low associativity, and early retirement of dairy farmers can be addressed with pasture-based systems, improved barn design, avoiding overstocking, and strengthening of milk producer associations. In the economic field, rising labor costs, market liberalization, low economic margins, competition from plant-based alternatives and reduced genetic diversity can be addressed through options such as automated milking, subsidies to small/medium-scale farms, limiting herd sizes, innovation, organic or locally based dairy products and increased crossbreeding. We present selected examples of frameworks and case studies aimed at evaluating the sustainability of contrasting dairy systems. Organic or alternative systems had generally better values in environmental and social indicators, such as lower GHG and nutrient emissions, improved nutrient balances, and reduced external input dependence, with similar technical efficiency compared to conventional farms. Likewise, small-scale dairy systems in developing countries using minimum external inputs have been rated positively in environmental indicators related to emissions and soil quality, but their economic performance is dependent on access to fair markets. It is concluded that productivity improvements in the modern dairy industry have been associated with strong negative socioenvironmental impacts. To construct a more sustainable dairy sector, intersectoral dialogue will be central, sharing knowledge, expertise, and skills.

Abstract Our paper analyzes science–industry (S-I) relations through the prism of firms’ academic patents, defined here as patents filed exclusively by firms and including at least one public researcher as an inventor. While previous research has mainly explored either other S-I linkages or the value of academic patents, this study focuses on the determinants of the occurrence of these patents in the healthcare field in France. Our contribution covers two main aspects. First, from a methodological point of view, we develop a method for identifying academic inventors using French data, following the “inventor-author” approach. This method is applied on a corpus of 3122 French priority patents belonging to the A61 international patent classification (IPC) and published in 2013. Second, our empirical results highlight the significant role of firm size, as SMEs are found to be suitable units for diffusing scientific knowledge through academic patents. Finally, our analysis adds empirical support to the decisive role of geographical proximity and territorial features, such as urban agglomerations, in shaping the dynamics of S-I links. These results are of great interest for innovation and science policies.

Industry 5.0, characterized by the deep integration of the Internet of Things (IoT), artificial intelligence (AI), digital twins, and edge computing, represents a new paradigm for intelligent manufacturing and human–machine collaboration. By enabling real-time interaction between physical and cyber-physical systems, Industry 5.0 fosters personalized, adaptive, and sustainable production environments. However, the growing diversity of industrial products and user requirements presents challenges in effectively matching industrial users with suitable design solutions or manufacturing resources. Recommendation systems, particularly those based on Collaborative Filtering (CF), have emerged as powerful tools to address this issue by leveraging user preferences and behavioral data. Nevertheless, traditional CF algorithms often encounter efficiency bottlenecks when processing the high-dimensional, heterogeneous, and dynamic data typical of Industry 5.0 environments.To overcome these limitations, this paper proposes a clustering-based CF algorithm that improves recommendation efficiency by incorporating industrial user relationship modeling. Furthermore, a Density-Sensitive Distance Transformer (DSD-Transformer) framework is developed to enhance clustering precision and recommendation accuracy. Experimental evaluations conducted on real industrial datasets demonstrate that the proposed model significantly outperforms existing methods in both prediction accuracy and computational efficiency, making it well suited for Industry 5.0-oriented intelligent recommendation and decision-support applications.

ABSTRACT Despite the growing presence of trade unions (TUs) in sustainability discussions, academic research on their role is still scattered. This article presents an integrative review of 110 peer‐reviewed English‐language academic articles on this topic, indexed in Scopus and Web of Science and published between 1997 and early 2025. Through an iterative coding process, we identified five meta‐theoretical perspectives: socio‐technical transitions (MLP); imperial mode of living/treadmill of production (IML‐ToP); industrial relations (IR); Marxist/critical political economy; and degrowth/ecological political economy. We then used these perspectives to map the conceptual architecture of the field. Theoretical orientation significantly influences the interpretation of TU strategies, ranging from obstructive and reformist to transformative. Existing tensions between labour's material dependence on unsustainable systems and its potential as an ecological agent of change are variously theorised, yet rarely resolved. By highlighting the associations between conceptual frameworks and evaluative conclusions, we provide a lens through which to reflectively assess this emerging field and outline avenues for more pluralistic and theoretically grounded research into labour‐environment relations.

One of the core objectives of the Healthy China 2030 Planning Outline is to build a harmonious doctor-patient relationship, and the key element in exploring the factors affecting this relationship lies in intergroup emotions. However, current research on such issues remains incomplete. Therefore, this study adopts the basic concept of social work-"individuals exist in a specific environment"-to investigate how intergroup emotions influence the ways of doctor-patient communication and response. First, relevant literature was reviewed to synthesize domestic and international research findings. Then, empirical data were used to illustrate how intergroup emotions affect and reshape the doctor-patient relationship through two approaches: passive adaptation and active construction. Additionally, this study analyzed how these emotions further expand their influence via four channels: online public opinion, self-sustaining positive cycles, story reshaping, and environmental impact. Second, corresponding solutions were proposed based on the needs of relevant groups and the roles of social organizations, covering aspects such as emotional competence cultivation and regulation, improvement of intergroup communication skills, and optimization of medical service environments. Furthermore, a diversified cooperation mechanism was put forward to resolve conflicts and related issues. Finally, this study aims to provide deeper insights for the research on intergroup emotion theory, promote studies on doctor-patient relationships in the medical industry, improve the quality of medical services, and reduce the occurrence of doctor-patient conflicts.

As public attention to environmental issues grows, enterprises have begun implementing environment-centered business management. Achieving environmental sustainability requires the participation of all organizational members. This study was conducted in Chinese food manufacturing small and medium-sized enterprises located in Guangdong and Jiangsu provinces, China, and employed a three-wave, time-lagged survey design to collect and match data from team leaders and employees. Hierarchical linear modeling was used to test the cross-level hypotheses, and the indirect effect was assessed using Bayesian multilevel mediation analysis. Using cross-level data from both team leaders and team members, this study examines how green transformational leadership impacts employees' pro-environmental behavior. In addition, this study examines the mediating role of employee value-action barriers and the moderating role of green brand image. The results indicate that (1) green transformational leadership positively influences employee pro-environmental behavior, (2) employee value-action barriers mediate the relationship between green transformational leadership and employee pro-environmental behavior, and (3) green brand image moderates both the correlation between green transformational leadership and employee pro-environmental behavior and the relationship between employee value-action barriers and employee pro-environmental behavior. These findings provide empirical support for the application of social learning theory and offer managerial insights into how managers can more effectively enhance their employees' pro-environmental behavior. Future research may further test the robustness and applicability of these relationships in other industries and in different regional and national contexts.

ABSTRACT This study examines the spillover effects of China’s STAR Market registration reform on the default risk of Main Board firms operating in related industries. Using a panel dataset of A-share listed firms from 2014–2022 within a difference-in-differences analytical framework, the evidence indicates that the introduction of the registration-based IPO system generates favorable externalities by improving information transparency and strengthening operational stability, thereby reducing the default risk of peer enterprises listed on the main board. Further heterogeneity analyses demonstrate that this mitigating effect is more evident among firms with greater media exposure, higher institutional ownership, and lower levels of industry competition. These findings provide new insights into how capital market reforms influence corporate behavior and risk, enriching the literature on corporate default risk and institutional changes in emerging economies.

The article examines the key problems of ensuring the efficiency of Ukrainian enterprises during the period of the Russian Federation’s large-scale military aggression in Ukraine. Based on the analysis of key performance indicators of enterprises in the leading sectors of Ukraine’s economy, it is established that the obstacles faced by national producers – such as electricity blackouts, blocking of the Ukrainian border, price increases, destruction of supply chains, loss of counterparties, and reduced demand for goods and services in the domestic market – are only some of the problems that have been a challenge for Ukrainian enterprises over the past two years of their activities. The article proves that full-scale military aggression and its consequences force Ukrainian enterprises to look for new solutions not only to ensure the continuity of their core business processes, but also to mitigate the possible risks of war, such as destruction from military operations, losses from the destruction of assets, migration of the working population abroad, cyberattacks on enterprise management systems, etc. It is established that, in such conditions, almost all business entities need additional financial support from the State. The authors identify the main areas of state support for domestic enterprises and the support provided by foreign partners of the national economic system in 2022-2024. Considering the deficit of national financial resources, in the short term Ukraine’s economy will not be able to rebuild its capacity and overcome the challenges caused by military aggression without the support of foreign partners. According to the authors, one of the priority areas of cooperation in the area of post-war rebuilding and support of the Ukrainian economy today should be close cooperation between our country and the European Union. Combining the efforts of Ukraine and the EU countries will contribute to the economic reconstruction of our country. It is determined that the strategic task of rebuilding Ukraine’s economy can be realized only if the business activity of enterprises in key industries is renewed. It is the industries that have high growth potential in the Ukrainian economy that will provide opportunities for the growth of related industries, increase employment, help strengthen the country’s defense capabilities, and in the future contribute to Ukraine’s high geopolitical status.

Recent advances in Citri Reticulatae Pericarpium polysaccharides: Extraction, structure, bioactivity, and applications: A review.

Rheology-driven approaches in slurry transportation: Influence of bimodal mixtures, additives, and modelling perspectives.

Psychological factors that influence the nature-health relationship in the agricultural industry; insights for providing more effective mental health support to farmers

The dynamics of sustainability: CEO celebrity as a moderator on the ESG and firm performance relationship in the restaurant industry

Bacillus cereus is a Gram-positive rod-shaped bacterium widely distributed in natural environments. Strains of this opportunistic pathogen can cause a range of illnesses, including bacteremia, food poisoning, localized infections, and severe systemic diseases, leading to substantial economic losses and adverse effects on related processing industries and livestock farming. Consequently, the establishment of a rapid detection method for Bacillus cereus is urgently needed to facilitate timely and effective prevention and control of this pathogen. In this study, polyclonal antibodies and colloidal gold solutions were prepared, followed by optimization of spray concentrations for the control line (C line) and test line (T line). Various types of nitrocellulose (NC) membranes were also evaluated. After assembly, the test strips were systematically assessed. The results indicate that the optimal detection was achieved when the antigen was labeled at 10 μg/mL, with the C line sprayed at 1 mg/mL and the T line at 0.5 mg/mL. Sartorius CN140 was identified as the most suitable NC membrane. The colloidal gold test strip showed no cross-reactivity with Bacillus thuringiensis, Bacillus mycoides, Bacillus subtilis, Bacillus licheniformis, Staphylococcus aureus, or Escherichia coli. Validation experiments confirmed that the test strips exhibited high sensitivity, specificity, and stability. When challenged with closely related species and other genera, the immunochromatographic strip demonstrated excellent specificity and sensitivity, offering a reliable tool for rapid clinical detection of Bacillus cereus infections.

As a preprocessing step of spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, electrophoresis, etc., the baseline correction is very important for improving the signal quality, thereby ensuring the reliability and accuracy of the data analysis. Methods such as polynomial fitting, wavelet transforms, and frequency-domain filtering are widely used for baseline correction, effectively reducing interference and enhancing the reliability of signal analysis. However, these methods have certain limitations: (i) Polynomial fitting faces challenges in determining the optimal order, which may affect the fitting quality, (ii) wavelet transforms are complex and require fine adjustments, and (iii) frequency-domain filtering may cause signal distortion. These shortcomings affect the implementation of the algorithm in spectral related industries. Therefore, finding an appropriate algorithm to optimize baseline removal is crucial for the development of automated spectral analysis equipment. Here, we propose a rolling ball baseline removal algorithm based on morphological operations. With its simple implementation and excellent baseline removal performance, this method effectively avoids the overfitting problems. It is suitable for baseline correction in not only Raman spectroscopy, but also various other types of spectral data. In all, this approach offers a convenient and efficient general solution for the processing of various spectral data.