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The purpose of this paper is to provide a critical historical analysis of the business (mis)behaviors and influencing factors that discourage enduring cooperation between principals and agents, to introduce strategies that embrace the social values, economic motivation and institutional designs historically adopted to curtail dishonest acts in international business and to inform an improved principal–agent theory that reflects principal–agent reciprocity as shaped by social, political, cultural, economic, strategic and ideological forces

The critical historical research method is used to analyze Chinese compradors and the foreign companies they served in pre-1949 China.

Business practitioners can extend orthodox principal–agent theory by scrutinizing the complex interactions between local agents and foreign companies. Instead of agents pursuing their economic interests exclusively, as posited by principal–agent theory, they also may pursue principal-shared interests (as suggested by stewardship theory) because of social norms and cultural values that can affect business-related choices and the social bonds built between principals and agents.

The behaviors of compradors and foreign companies in pre-1949 China suggest international business practices for shaping social bonds between principals and agents and foreign principals’ creative efforts to enhance shared interests with local agents.

Understanding principal–agent theory’s limitations can help international management scholars and practitioners mitigate transaction partners’ dishonest acts.

A critical historical analysis of intermediary businesspeople’s (mis)behavior in pre-1949 (1840–1949) China can inform the generalizability of principal–agent theory and contemporary business strategies for minimizing agents’ dishonest acts.

In the dynamic landscape of 2023, global challenges are amplified by escalating conflicts and the COVID-19 aftermath. Developing nations like Vietnam face a critical juncture, requiring diversified economies for enhanced livelihoods and poverty reduction. However, this growth necessitates increased energy consumption, potentially escalating carbon emissions. Green innovation (GI) emerges as a beacon of hope, offering products and services designed for a minimal carbon footprint. Beyond socio-economic advancement, GI aligns with sustainable development goals. This study aims to  examine the influence of knowledge management (KM) and digitalization (DG) on GI, particularly in sustainable competitive advantage.

Using structural equation modeling and drawing upon a survey administered to 301 Vietnamese enterprises.

The findings illuminate diverse underpinnings between green product and process innovation, unravel the intricate relationship between KM, DG and GI, and provide crucial insights for firms seeking sustainable competitive edges.

This multidimensional approach significantly enriches the understanding of these pivotal elements in contemporary business landscapes.

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.

The emerging economies of Asia have made remarkable economic progress over the past few decades, primarily driven by rapid structural transformation towards industrialization and manufacturing in particular. The share of informal manufacturing sector value added to GDP and of employment in the informal sector in total employment has increased considerably in these countries. Although this shift from agricultural to industrial/manufacturing may be seen as positive for the goals of poverty reduction, increased standard of living, formation of human capital, etc., its impact on the environment is often not free from contention. The aim of the paper is to examine the impact of informal manufacturing sector growth on environmental degradation in emerging Asian economies. Here, we have used CO₂ emissions as an indicator of environmental degradation. The impact of other exogenous variables, such as population growth, energy consumption, trade openness and foreign direct investment, has also been studied. We have employed the fixed effect model and the random effect model on the data spanning from 2000 to 2022. We have also used the Hausman test to check the suitability of the models. The results of the analysis indicate the presence of a U-shaped relationship between CO₂ emissions and informal manufacturing growth, thus refuting the validity of the Environmental Kuznets Curve hypothesis.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This paper investigates the interplay among Finance-Accounting Digitalization (FAD), Advanced Finance-Accounting System (AFAS) and Accounting 2.0 and FinTech (AFT). It aims to elucidate how FAD impacts the adoption of AFAS, the evolution of AFT, and the ensuing sustainable evolution of finance and accounting practices.

A comprehensive survey was conducted from 2021 to 2023, involving 500 companies in Kosovo, spanning various sectors and sizes. The data were meticulously analyzed using SPSS and AMOS software, using structural equation modeling (SEM) to assess hypotheses, model fit and direct and indirect effects.

Significant relationships emerged between FAD, AFAS and AFT, highlighting the importance of computerized financial and accounting systems, the role of AFAS in achieving financial goals and the accessibility and adaptability of accounting data. In particular, the SEM analysis underscored a robust positive correlation between FAD and AFT, highlighting the transformative potential of digitalization. In addition, AFAS as a technological solution facilitates sustainability and operational efficiency. These findings provide crucial insights for future research and advocate for the use of digitalization to improve the efficiency of financial management.

The research is constrained by its focus on Kosovo’s companies, potentially limiting generalizability. Additionally, the study’s reliance on survey data might introduce response bias.

This research contributes to the literature by delineating the intricate relationships between FAD, AFAS and AFT, shedding light on their implications for financial and accounting practices. It underscores the transformative value of digitalization in these domains, offering novel perspectives for academic and managerial discourse.

This study aims to investigate the influence of chief executive officer (CEO) trustworthiness on firm investment efficiency and explores how this relationship varies in different contexts, including product market competition (PMC), institutional investors, media coverage, analyst monitoring and ownership structure.

The authors examined a sample of A-Share non-financial firms listed on the Shanghai and Shenzhen Stock Exchanges from 2005–2018 by using panel date regression techniques. The robustness of the findings is affirmed through alternative measures of investment efficiency and various econometric techniques. Further, various endogeneity tests are conducted to confirm that the findings are not affected by potential bias.

The authors find a significant positive effect of CEO trustworthiness on firms’ investment efficiency and exhibit that CEO trustworthiness mitigates the issue of underinvestment rather than overinvestment. Further, PMC strengthens the association between CEO trustworthiness and investment efficiency. The influence is more pronounced when institutional investors, media and analyst monitoring are low and in non state-owned firms. Likewise, financial reporting quality is found to be an underlying mechanism for the positive association between CEO trustworthiness and investment efficiency.

The reliance on a location-specific index of CEO trustworthiness may obscure its true nature, and caution is warranted when generalizing these results to other regions.

This study suggests that elevating a trustworthy CEO to the firm upper echelon can improve investment efficiency. Policymakers and investors should recognize and leverage the effect of CEO trustworthiness in firms, especially those with weaker governance structures.

This study enriches the literature about investment efficiency by introducing a novel determinant, CEO trustworthiness and establishes that it acts as an informal social institution that improves firms’ resource utilization in emerging economies with weak governing structures.

This study aims to investigate how long commutes negatively affect employees’ creative deviance at work, exploring the mediating role that impaired work–life balance plays in linking commute to restricted creative deviance, as well as examining whether access to flexible work arrangements can alleviate commuting’s detrimental indirect effects.

This study employed a three-wave survey methodology conducted over monthly intervals with 246 participants in China’s Pearl River Delta region. Rigorous screening ensured a demographically diverse sample.

Commuting time negatively affects creative deviance, both directly and indirectly through work–life balance. Flexible work arrangements mitigate the adverse effects of long commutes on work–life balance, subsequently weakening the indirect effect of commuting time on creative deviance through work–life balance.

A holistic approach is suggested for organizations aiming to foster a supportive and ethical work environment, which involves a combination of organizational policies, leadership practices and individual actions to promote both creativity and employee welfare.

This research breaks new ground by identifying commuting time as a key factor influencing creative deviance in the workplace, mediated by work–life balance. It integrates transportation research with organizational behavior, applying an ethics of care perspective to challenge traditional paradigms. The study’s interdisciplinary approach, bridging multiple fields, provides a novel, holistic view of how non-work factors impact workplace innovation.

Building upon the resource-based view (RBV) and related research, this paper empirically examines the impact and specific mechanisms of artificial intelligence transformation on corporate innovation capabilities. It provides micro-level evidence of AI’s influence on innovation behavior.

Drawing upon data from Chinese listed companies spanning the period from 2011 to 2022, this study employs a dual fixed-effects model and a mediation effects model to empirically analyze the influence of enterprise AI transformation on its innovation capability as well as the specific mechanisms involved.

The research reveals that AI transformation significantly enhances the innovation capability of enterprises. Heterogeneity analysis indicates that AI transformation exerts a stronger promoting effect on the innovation capability of non-technology firms, large enterprises and those within the manufacturing sector. Mechanism analysis further reveals that AI transformation enhances innovation capability by boosting enterprise profits, reducing costs and reinforcing internal control mechanisms. Further examination demonstrates that AI transformation elevates the quality, efficiency and eco-friendliness of enterprise innovation.

Firstly, this study employs text analysis methods from machine learning to construct artificial intelligence indicators at the firm level, providing stronger evidence of AI’s impact on corporate innovation capabilities. Secondly, it extends corporate innovation behavior to include innovation quality, efficiency and green innovation practices, offering a more comprehensive validation of AI’s role in fostering corporate innovation.

The purpose of this article is to develop a new model called strategy segmentation methodology (SSM) by combining the Kraljic portfolio matrix (KPM) and the supplier relationship model (SRM) so that the buyer company can effectively conduct its relations with its suppliers.

The importance weights of the criteria defining the dimensions of each model are calculated with the single-valued neutrosophic analytical hierarchy process (SVN-AHP) method. Subsequently, the derived importance weights are employed in the single-valued neutrosophic technique for order preference by similarity to ideal solution (SVN-TOPSIS) method to obtain the scores of the suppliers and their supplied items. In order to illustrate the feasibility of the proposed methodology, a case study in the machinery industry is performed with the related comparative analysis.

The implementation of SSM enables to formulate various strategies to manage suppliers taking into account the items they procure, their capabilities and performance and the supplier–buyer relationship strength. Based on the proposed strategies, it is concluded that the firm in the case study should terminate its relationship with six of its suppliers.

Although KPM has become the basis of purchasing strategies for various businesses, it neglects the characteristics of suppliers and the buyer–supplier relationship. In this study, KPM is integrated with the SRM approach presented by Olsen and Ellram (1997) to overcome these disadvantages of KPM. The novel integration of the two approaches enables the realization of a robust and reliable supplier classification model.