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The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.

This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.

The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.

This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.

The increasing focus of consumers on health and environmental sustainability continues to drive the demand for organic food. Despite the recognized importance of health and environmental concerns, the differential impact of these factors on organic food purchasing decisions is evident, indicating the presence of moderating variables. This investigation attempts to delineate these contingencies within the realms of socio-environmental and individual factors, paying particular attention to subjective norms, uncertainty, and egoistic values.

Using the convenience sampling method, the primary data sample was collected by a professional market research consulting firm and included 1876 usable respondents from China. Hierarchical multiple regression analysis was utilized to verify the model and test the relationships between the constructs.

The results indicated that the path from environmental concern to organic food purchase intention was significantly influenced by subjective norms and uncertainty, both of which enhance this relationship. In contrast, egoistic values appeared to dampen this effect. Uncertainty also emerged as a key factor in the link between health concerns and organic food purchase intention, albeit with an opposite impact, weakening the relationship.

This study provides useful insights for academics and marketers to understand the complex phenomenon of organic consumer behavior. This result indicates that marketers can target reference groups to develop organic food marketing strategies.

Few studies have proposed and validated a model with these moderating factors collectively to study the purchase intention of organic food consumers in China.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

Members in the international joint ventures (IJVs) for high-speed rail (HSR) projects usually engage in coopetition interactions to create common benefits (CB) and simultaneously safeguard private benefits (PB). Previous studies of coopetition and performance using variance-based methods usually ignore the combinational influence of diverse coopetition constructs on performance, which can be effectively compensated by adopting a configuration perspective. Therefore, this research aims to ascertain various combinations of three coopetition constructs (coopetition relationship, coopetition capability and coopetition strategy) that lead to high IJVs’ performance through a configuration approach.

First, the research framework of coopetition configuration was established, and the key constructs were operationalized, which were validated by expert interviews. Then the information on 12 HSR IJVs was collected and quantified through nine rounds of interviews and a questionnaire survey. Later, the fuzzy-set qualitative comparative analysis (fsQCA) was applied to explore what coopetition configurations benefit the CB or PB achievement.

Configuration results indicate that six coopetition configurations lead to CB outcome and seven configurations lead to PB outcome. Based on the results, coopetition contexts are divided into four categories: firm-based coopetition, project-based coopetition, firm-project-based coopetition and none-based coopetition. Then, a selection scheme for coopetition strategies in various contexts has been developed. The results also show that the core conditions mostly appear in the coopetition relationships and coopetition strategies dimensions, and the optimal coopetition strategies vary in different contexts.

This study enhances the theoretical understanding of coopetition in HSR IJVs and assists relative HSR industrialists, as well as the mega infrastructure project managers, in IJVs’ implementation. The configuration perspective of this paper also contributes to a systemic and holistic view of coopetition in HSR IJVs.

Based on upper echelons theory and social contagion theory, we investigate how environmental leadership affects GIC via green human resource management (GHRM) and examine the moderating role of environmental climate.

We conduct hierarchical regression and use the bootstrap method to analyze the two-waved data from 317 Chinese manufacturers in order to verify the hypotheses.

The results indicate that GHRM mediates the impacts of environmental leadership on green human capital, structural capital and relational capital. In addition, environmental climate strengthens the positive impact of environmental leadership on GHRM.

Our study enriches the literature on GIC by uncovering the “black box” between environmental leadership and GIC, providing a logical framework opposite to mainstream GIC research, and expanding the boundary condition for GIC accumulation. This study provides more logical paths for enterprises and governments to increase the accumulation of GIC and promote green intellectual economy development.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

This study investigates two internationalization strategies, foreign direct investment (FDI) expansion and export expansion, and their effects on the innovation performance of Chinese multinational enterprises (MNEs). Additionally, it explores the moderating roles of both formal and informal political connections in the relationship between these internationalization strategies and innovation performance.

The hypotheses were tested using the Poisson panel model and data on 2,106 observations from 645 Chinese-listed firms between 2010 and 2017.

FDI expansion significantly enhances the innovation performance of Chinese MNEs compared to export expansion. Moreover, formal and informal political connections are found to have distinct moderating effects: formal political connections amplify the positive effect of FDI expansion on firm innovation, whereas informal political connections tend to weaken it.

This study contributes to the discourse on innovation and political connections, especially in the context of EMNEs. It enriches the theoretical understanding of internationalization strategies and innovation performance in EMEs, contrasting with the technology-utilization motives observed in MNEs from developed economies.

This study aims to obtain governance strategies for managing the complexity of megaprojects by analyzing the impact of individual factors and their configurations using the fuzzy-set qualitative comparative analysis (fsQCA) method and to provide references for project managers.

With the continuous development of the economy, society and construction industry, the number and scale of megaprojects are increasing, and the complexity is becoming serious. Based on the relevant literature, the factors affecting the complexity of megaprojects are determined through case analysis, and the paths of factors affecting the complexity are constructed for megaprojects. Then, the fsQCA method is used to analyze the factors affecting the complexity of megaprojects through 245 valid questionnaires from project engineers in this study.

The results support the correlation between the complexity factors of megaprojects, with six histological paths leading to high complexity and seven histological paths leading to low complexity.

It breaks the limitations of the traditional project complexity field through a “configuration perspective” and concludes that megaproject complexity is a synergistic effect of multiple factors. The study is important for enriching the theory of megaproject complexity and providing complexity governance strategies for managers in megaproject decision-making.

The purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.

In this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem.

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

The proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity.

The decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.