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Middle-aged and elderly users become an important group on short-video platforms, however, the research on determinants of their video-creating intention is limited. Based on lifespan development theories, this study examines the impact of aging experiences on their video-creating intention, considering internal generative motivations as mediators and age as a moderator.

To test this study’s hypotheses, survey data from 321 Chinese middle-aged and elderly short-video users were collected and partial least square-structural equation modelling (PLS-SEM) approach was used to analyze these data.

Middle-aged and elderly users' aging experiences of social loss and personal growth are positively related to their video-creating intention. Aging experiences (i.e. physical loss, social loss, and personal growth) are positively related to internal generative motivations (i.e. need to be needed and symbolic immortality), and need to be needed is positively related to video-creating intention. Via the mediation of need to be needed, physical loss and personal growth are indirectly positively related to video-creating intention. Personal growth strengthens the relationship between physical loss and symbolic immortality, but weakens the associations of social loss with need to be needed and symbolic immortality. Age weakens the relationship between symbolic immortality and video-creating intention.

This study is the first wave to introduce and integrate lifespan theories such as selective optimization with compensation model, socioemotional selectivity theory, and generativity theory to explore the impacts of aging experiences on middle-aged and elderly users' video-creating intention by considering generativity motivations as mediators and age as a moderator.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Family businesses are essential to the global economy but often grapple with family-related issues, especially during succession. This study explores how governance tools like the family protocol (FP) mitigate conflicts by setting standards for family firm management and continuity. Pioneering the use of game theory and adverse selection setups in family business governance, this research uncovers FP determinants.

This research employs game theory and adverse selection setups to delve into the strategic decision-making processes of stakeholders in family firms. The authors break new ground by applying principal–agent theory (PAT) to family business governance structures. This innovative approach uncovers the determinants of the FP, enhancing the authors’ understanding of family firm dynamics.

The authors emphasize the importance of custom governance structures, such as the FP, in managing complex family-business interactions. These structures mitigate conflicts and promote smoother transitions during succession, ensuring family firm continuity. This study identifies key determinants, and these results will aid founders, families and practitioners in achieving smoother transitions, ensuring family firm continuity.

This research pioneers game theory and PAT applications in family business governance, shedding light on the effectiveness of customized governance mechanisms. By identifying FP determinants, the authors contribute to a deeper understanding of family firm dynamics. The findings have practical implications for founders, families, practitioners and consultants, promoting the long-term success and harmony of family firms in the global economy.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Based on dual information asymmetry, the two-stage segmented compensation mechanism for technological innovation of civilian enterprises’ participation in military (CEPIM) has been discussed.

On the basis of the traditional principal-agent problems, the incentive compatibility condition is introduced as well as the hybrid incentive compensation model is established, to solve optimal solution of the compensation parameters under the dynamic contract condition and the validity is verified by numerical simulation.

The results show that: (1) The two-stage segmented compensation mechanism has the functions of “self-selection” and “stimulus to the strong”, (2) It promotes the civilian enterprises to obtain more innovation benefit compensation through the second stage, (3) There is an inverted U-shaped relationship between government compensation effectiveness and the innovation ability of compensation objects and (4) The “compensable threshold” and “optimal compensation threshold” should be set, respectively, to assess the applicability and priority of compensation.

In this paper, through numerical simulation, the optimal solution for two-stage segmented compensation, segmented compensation coefficient, expected returns for all parties and excess expected returns have been verified under various information asymmetry. The results show that the mechanism of two-stage segmented compensation can improve the expected returns for both civilian enterprises and the government. However, under dual information asymmetry, for innovation ability of the intended compensation candidates, a “compensation threshold” should be set to determine whether the compensation should be carried out, furthermore an “optimal compensation threshold” should be set to determine the compensation priority.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

This paper aims to explore the determinants of the level of minibonds issued by companies. In doing so, it discusses the importance of minibonds in providing a market-based funding source. In the empirical analysis, special attention is paid to the study of the recovery from the COVID-19 crisis.

The analysis is carried out through an econometric approach, on the basis of a high-quality empirical dataset related to the Italian small- and medium-sized enterprises (SMEs). The reference period covers the recent pandemic. From a theoretical point of view, a regression model is implemented, including a multicollinearity analysis and an outlier detection procedure.

The results of the study indicate that factors such as leverage, cash flow, firm collaterals and seniority can explain the amount of minibonds issued. These findings provide valuable insights into the drivers of minibond issuance and highlight the potential benefits of minibonds as a funding option for Italian SMEs.

Importantly, results highlight relevant managerial implications at two levels. On one side, we carry on a managerial discussion about the worthiness of accessing the minibonds market; on the other side, we give insights on the managerial implications related to the features of the companies issuing minibonds.

The paper investigates an innovative financial instrument that has been introduced recently and has not yet been studied in depth. To the best of our knowledge, this is the first contribution assessing the main drivers for minibonds issuance level, which is a timely and relevant managerial research topic. In addition, this study also takes into account the impact of the COVID-19 pandemic on minibond issuance, making the analysis appropriate for explaining the current economic context.

The manufacturing industry and the producer service industry have a high degree of industrial correlation, and their integration will cause changes in the complex industrial network topology, which is an important reason for the synergistic effect. This paper describes the topology of industrial systems using complex network theory; further, it discusses how to identify the criticality and importance of industrial nodes, and whether node characteristics cause synergistic effects.

Based on the input-output data of China in 2007, 2012 and 2017, this paper constructs the industrial complex network of 30 Chinese provinces and cities, and measures the regional network characteristics of the manufacturing industry. The fixed-effect panel regression model is adopted to test the influence of agglomeration degree and centrality on synergies, and its adjustment mechanism is explored.

The degree of network agglomeration in the manufacturing industry exerts a negative impact on the synergistic effect, while the centrality of the network exerts a significant promoting effect on the synergistic effect. The results of adjustment mechanism test show that enhancing the autonomous controllable ability of the regional industrial chain in the manufacturing industry can effectively reduce the effect of network characteristics on the synergistic effect.

Based on input-output technology, this paper constructs a complex industrial network model, however, only basic flow data are used. Considerable in-depth and detailed research on the economic and technological connections within the industry should be conducted in the future. The selection of the evaluation index of the importance of industrial nodes also needs to be further considered. For historical reasons, it is also difficult to obtain and process data when carrying out quantitative analysis; therefore, it is necessary to make further attempts from the data source and the expression form of evaluation indicators.

In a practical sense this has certain reference value for the formulation of manufacturing industrial policies the optimization of regional industrial layout and the improvement of the industrial development level. It is necessary to formulate targeted and specialized industrial development strategies according to the characteristics of the manufacturing industry appropriately regulate the autonomous controllable ability of the industrial chain and avoid to limit the development of industries which is in turn limited by regional resources. Industry competition and market congestion need to be reduced industry exchanges outside the region encouraged the industrial layout optimized and the construction of a modern industrial system accelerated.

The above research results hold certain reference importance for policy formulation related to the manufacturing industry, regional industrial layout optimization and industrial development level improvement. Targeted specialized industrial development strategies need to be formulated according to the characteristics of the manufacturing industry; the autonomous controllability of the industrial chain needs to be appropriately regulated; limitation of regional resources needs to be avoided as this restricts industrial development; and industry competition and market congestion need to be reduced. Agglomeration of production factors and optimization of resource allocation is an important part of a beneficial regional economic development strategy, and it is also an inevitable choice for industrialization to develop to a certain stage under the condition of a market economy. In alignment with the research conclusions, effective suggestions can be put forward for the current major industrial policies. In the process of promoting the development of the manufacturing industry, it is necessary for regional governments to carry out unified planning and guidance on the spatial layout of each manufacturing subsector. Regional governments need to effectively allocate inter-industry resources, better share economies of scale, constantly enhance the competitive advantages and competitiveness of development zones and new districts and promote the coordinated agglomeration and development of related industries with input industries. Industrial exchanges outside the region should be encouraged, the industrial layout should be optimized and the construction of a modern industrial system should be accelerated.

Complex network theory is introduced to study the industrial synergy effect. A complex industrial network of China's 30 regions is built and key network nodes are measured. Based on the dimensionality of the “industrial node – industrial chain – industrial complex network”, the research path of industrial complex networks is improved.

Although outcome sharing in construction is a well-established concept in the literature, there is still an ongoing debate on the most effective approach for distributing project outcomes between an owner and downstream contracting parties (DCPs). To address this issue, this paper aims to investigate an optimal framework for distributing project outcomes among various levels of subcontracting in construction projects. The framework includes contractors, subcontractors, sub-subcontractors and other related parties.

To formulate the optimization problem, the principal–agent model is utilized. The theoretical development is validated through an experiment conducted with employees from road construction companies.

When distributing outcomes among various levels of subcontracting, the sharing should be determined by their contribution to the outcome, effort costs, level of outcome uncertainty and risk preference.

This paper expands on the existing principal–agent theory by incorporating multiple levels of agents, transforming the conventional view of outcome sharing among downstream subcontracting levels into testable hypotheses and well-defined concepts. The paper has practical implications for industry practitioners seeking to effectively allocate benefits and costs throughout a project's subcontracting chain.