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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.

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.

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.

This study aims to investigate the relationship between inventory flexibility, digital transformation, supply chain concentration, and productivity in the context of Chinese manufacturing enterprises.

Empirical analysis was conducted using data from listed Chinese manufacturing firms spanning from 2013 to 2022. The study employs a moderated model to examine how digital transformation influences the connection between inventory flexibility and productivity. Additionally, a moderated moderation model is utilized to explore the role of supply chain concentration in moderating the relationship among inventory flexibility, digital transformation, and productivity.

The study reveals a significant positive correlation between inventory flexibility and productivity, underlining the importance of flexible inventory management. Digital transformation moderates this relationship, with digital transformation enhancing the impact of inventory flexibility on productivity. Supplier and customer concentration also positively moderate this connection, suggesting a complementary relationship with digital transformation.

These findings offer valuable insights for managers and policymakers, emphasizing the need for a flexible approach to inventory management that considers the evolving digital landscape and supply chain dynamics.

This study contributes to the literature by providing empirical evidence of the nuanced relationship between inventory flexibility, digital transformation, supply chain concentration, and productivity in Chinese manufacturing enterprises. It underscores the importance of integrating digital transformation and supply chain concentration initiatives with flexible inventory management to optimize productivity in the business landscape.

The purpose of this study was to examine the factors that influence the information seeking behaviors of ChatGPT users. Specifically, we investigated how ChatGPT self-efficacy, ChatGPT characteristics and ChatGPT utility affect the frequency and duration of information seeking via ChatGPT. We also tested the mediating roles of ChatGPT characteristics and utility in the relationship between ChatGPT self-efficacy and information-seeking behaviors.

This study adopts a quantitative approach and collects data from 403 ChatGPT users using an online questionnaire. The data are analyzed using linear regression and structural equation modeling (SEM).

The linear regression analyses revealed that ChatGPT self-efficacy is positively and significantly related to the information seeking behaviors in ChatGPT. Second, mediation analyses also showed that ChatGPT characteristics and utility significantly mediate the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT independently and sequentially.

This study is the first to investigate the factors and mechanisms that influence information-seeking behaviors in ChatGPT, a new phenomenon in the media landscape. The findings in this study suggest that ChatGPT self-efficacy acts as an important motivator for information-seeking behaviors in ChatGPT and that ChatGPT characteristics and utility provide information regarding potential mechanisms in the relationship between ChatGPT self-efficacy and information-seeking behaviors in ChatGPT. The study contributes to the literature on information seeking, self-efficacy and generative AI.

Ascorbic acid, a water-soluble antioxidant, is an essential component of the human diet and is known for its potent antioxidant properties against several diseases. In recent years, there has been increasing interest in the development of nonenzymatic sensors due to their simplicity, efficiency and excellent selectivity. The aim of this study is to present a selective and sensitive method for the detection of ascorbic acid in aqueous system using a new electrochemical non-enzymatic sensor based on a gold nanoparticles Au-NPs-1,3-di(4-bromophényl)-5-tert-butyl-1,3,5-triazinane (DBTTA) composite.

Using the square wave voltammetry (SWV) technique, a series of Au-NPs-DBTTA composites were successfully developed and investigated. First, DBTTA was synthesized via the condensation of tert-butylamine and a4-bromoaniline. The structure obtained was identified by IR, 1H NMR and 13C NMR analysis. A glassy carbon electrode (GCE) was modified with 10–1 M DBTTA dissolved in an aqueous solution by cyclic voltammetry in the potential range of 1–1.4 V. Au-NPs were then deposited on the DBTTA/GCE by a chronoamperometric technique. SWV was used to study the electrochemical behavior of the modified electrode (DBTTA/Au-NPs/GCEs). To observe the effect of nanoparticles, ascorbic acid in a buffer solution was analyzed by SWV at the modified electrode with and without gold nanoparticles (Au-NPs).

The DBTTA/Au-NPs/GCE showed better electroanalytical results. The detection limit of 10–5 M was obtained and the electrode was proportional to the logarithm of the AA concentration in the range of 5 × 10⁻³ M to 1 × 10⁻¹ with very good correlation parameters.

It was also found that the elaborated sensor exhibited reproducibility and excellent selectivity against interfering molecules such as uric acid, aspartic acid and glucose. The proposed sensor was tested for the recognition of AA in orange, and satisfactory results were obtained.