Search results

Foreign subsidiaries of multinational enterprises (MNEs) operate in complex and competitive international environments, implement market and non-market strategies, manage resources and value-added activities and contribute to the overall performance of their parent firms. Thus, the research question on the determinants of MNE foreign subsidiaries’ performance is of interest to managers and academic researchers. The empirical literature has flourished over the recent decades; however, the domains are fragmented, and the findings are inclusive. The purpose of this study is to systematically review, analyse and synthesize the empirical articles in this area, identify research gaps and suggest a future research agenda.

This study uses the qualitative content analysis method in reviewing and analysing 150 articles published in 24 scholarly journals during the period 2000–2023.

The literature uses a variety of theoretical perspectives to examine the key determinants of subsidiary performance which can be grouped into six major domains, namely, home- and host country-level factors; distance between home and host countries; the characteristics of parent firms and of subsidiaries; and governance mechanisms (the establishment modes and ownership strategy, subsidiary autonomy and the use of home country expatriates for transferring knowledge from the headquarters and controlling foreign subsidiaries). A range of objective and subjective indicators are used to measure subsidiary performance. Yet, the research shows a lack of broader integration of theories and presents inconsistent theoretical predictions, inconclusive empirical findings and estimation bias, which hinder our understanding of how the determinants independently and jointly shape the performance of foreign subsidiaries.

This study provides a comprehensive, nuanced and systematic review that synthesizes and clarifies the determinants of subsidiary performance, offers deeper insights from both theoretical, methodological and empirical aspects and proposes some promising avenues for future research directions.

This paper aims to evaluate the effect of commitment misperception on channel conflict. It highlights the importance of trust and transaction-specific investments for business marketing strategies.

This paper develops a concept framework to understand how the direction (overestimated vs underestimated) and extent of commitment misperception influence channel conflict. The model is tested using dyadic data from 212 distributors and manufacturers across several industries in China.

The results show that the direction of commitment misperception affects trust, transaction-specific investments and channel conflict. Overestimated commitment induces positive illusion and enhances trust and transaction-specific investments and reduces channel conflict, whereas underestimated commitment induces negative illusion and reduces trust and transaction-specific investments and enhances channel conflict. Trust and transaction-specific investments mediate the impact of the direction of commitment misperception on channel conflict. The extent of commitment misperception plays the moderating influence on the direction of commitment misperception.

This study reveals the mechanisms and boundary conditions by exploring the mediating influence of trust and transaction-specific investments and the moderating effects of the extent of commitment misperception.

The purpose of this study is to present a bibliometric and network analysis that uses the Scopus and Dimension databases to provide new insights into the progression toward the study of sustainable economic development.

This analysis has been drawn on 665 papers published between 2015 and 2023. Bibliometric analysis characterizes a research topic by identifying leading nations, the most significant authors and expressive publications. Network analysis revealed keyword evolution over time, co-citation patterns and study grouping. Content analysis was used to identify major topic in the discipline, with a focus on their interrelationships. Each publication in the data set is briefly described, along with its methodological approach.

The results of this study show that green finance plays a major role in long-term economic growth, having a significant influence on the preservation of environmental quality, economic efficacy and a more comprehensive economic system. Financial technology also accelerates the transition to a carbon-neutral economy by enhancing the beneficial effects of green finance on aspects of the economic system and environmental conservation.

The investigation is based only on Scopus and Dimensions-indexed journal articles. However, additional studies should incorporate publications from other reputable databases, such as Web of Science, PubMed and Science Direct, for the bibliometric analysis, so that the findings of the model analysis become more reliable and valid with examination of more documents. The visualization of similarity viewer was used for data analysis in the study, there is a scope for using other tools such as Biblioshiney and CitNet Explorer.

To support long-term economic growth, authorities should encourage Fintech companies to actively participate in various green finance initiatives and environmental conservation businesses. Financial managers should facilitate the integration of technology and green finance for financial services. It is important to encourage institutional and individual investors alike to look into more environmentally friendly ways to invest and save money. Policymakers should provide a platform for global awareness and government agencies should enhance their recommendations to state governments to increase the efficacy of green finance.

This study contributes to the literature by investigating the relationship between Fintech and green financing. This study holds significance for financial intermediaries, industrialists, investors and policymakers by providing insights into the integration of Fintech with green finance for sustainable development. These findings affirm the pivotal role of Fintech and green finance in fostering sustainable economic development. The novelty of the topic and the variety of publications in which it has been published demonstrate that sustainable economic development has piqued the interest of a wide range of areas.

The objective of this study is to examine how emotions play a role in the firm’s reaction to disruptions in the supply chain. Drawing on the upper echelons theory, we evaluate whether managers’ perception of collective emotions (CEs) in the supply environment affects the execution of specific organisational responses (bridging and buffering) to disruptive events. Furthermore, we investigate to what extent companies' own capabilities, such as supply chain resilience, influence this relationship.

A web-based survey was distributed among managers involved in supply chain relationship management (e.g. supply chain or purchasing managers). LinkedIn was used to identify and contact adequate respondents, and 221 valid responses were collected. The proposed theoretical model was empirically tested using structural equation modelling based on partial least squares (PLS-SEM).

Results suggest that emotions can shape a firm's response to supply chain disruptions. In fact, managers are more likely to pursue both bridging and buffering strategies as their perception of CEs increases. However, the intensity and underlying motivations for pursuing each strategy differ.

When CEs are perceived by buyer managers, stronger supply chain resilience incentivises the choice of cooperative practices within existing suppliers, thereby reinforcing pre-existing links. We conclude that combining companies' inherent variables or capabilities with managerial cognition and perceptions can improve our understanding of decision-making processes and buyer–supplier relationships.

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

Philanthropy has developed into a trillion-dollar industry with substantial transnational funds. Scholarly research on philanthropic leadership has experienced substantial growth since the 1990s, but as an academic field, it remains ill-defined. The purpose of this study is to examine the current state of the literature on philanthropic leadership to determine the extent to which the field needs to be further specialized.

Relying on the VOSviewer software version 1.6.15, the authors conducted a bibliometric analysis of 470 identified articles published between 1991 and 2021 to uncover the most influential articles, academic outlets and scholars in the field.

There is a noticeable lack of literature that accurately reflects the overall practice of philanthropic leadership. Most specialized research concentrates on the influence of corporate leaders in using philanthropic activities as a means of achieving business objectives. However, it is essential to recognize that leadership plays a critical role in effective philanthropy, which benefits various stakeholders and produces favorable spillover effects. The findings indicate that existing literature tends to focus on the influence of corporate leaders on philanthropic activities and their correlation with business outcomes.

This study contributes to the field by offering insights into the intellectual structure of the field and assists with the identification of new research directions within the philanthropic leadership domain. Further scholarly consideration is needed to understand the practice of philanthropic leadership.

This paper aims to examine the impacts of unstable off-farm employment on the probability and stability of farmland rent-out and explore its mechanisms.

The paper adopts Ordinary Least Squares (OLS), Probit, Tobit, Order probit models with two-way fixed effects to conduct empirical analysis based on the balanced panel data collected in 2016 and 2023 with a national representativeness sample of 1,206 rural households in 100 villages across 5 provinces in China.

The empirical results showed that unstable off-farm employment had negative effects on the probability of farmland rent-out, but it had no effects on the stability of farmland rent-out. The mechanism analysis showed that unstable off-farm employment affected the probability of farmland rent-out by decreasing the probability of purchasing houses in city and endowment insurance with high pension. Heterogeneity analysis indicated that the negative effect of unstable off-farm employment was much larger for the households with higher share of labor engaging in off-farm employment outside home county, elder members in the households and those located in the villages of mountain areas.

This paper is the first to define the unstable off-farm employment from the perspective of incontiguous off-farm employment for several years, which could capture the normality rather than particular case in a certain year of off-farm employment among rural labors. Using these new measurements of unstable off-farmland, this paper examined the impacts and mechanisms of share of unstable off-farm employment on the probability and stability of farmland rent-out.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

This paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.

The MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.

Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.

The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.

The objective of this study is to investigate how the implementation of an Emission Trading Scheme (ETS) influences an ETS-regulated firm’s level of earnings smoothness.

Using a staggered difference-in-differences model based on China’s ETS pilots commencing in 2013, this study investigates how the implementation of ETS pilots affects regulated firms’ earnings smoothing relative to non-regulated firms. The sample period spans from 2008 to 2019. This model incorporates time-invariant firm-specific heterogeneity, time-specific heterogeneity, and a series of firm characteristics to establish causality. Robustness tests justify findings.

The results show that after implementing an ETS pilot, regulated firms increase their earnings smoothness relative to non-regulated firms. Regulated firms strategically smooth their earnings to obtain additional financial resources and meet compliance costs arising from an ETS. Further analysis reveals that regulated firms’ earnings smoothing activity is a function of environmental regulations, managerial integrity, and capital market incentives.

This study deviates from past research focusing on the environmental consequences of ETS by indicating that an ETS affects regulated firms’ financial reporting decisions. Specifically, regulated firms resort to earnings smoothing as a short-term exit strategy from financing concerns arising from environmental regulations. This finding expands prior literature primarily focusing on the effect of tax and financial reporting regulations on earnings smoothness. This study also indicates that firms utilize earning smoothing to lower their short-term cost of capital, which enables them to access additional financing at a lower cost and reconfigure their operations to meet stakeholder environmental demands.