Search Results (14)

The study examines economic and non-economic endeavors to explore the association between family involvement and financial decisions within family firms. The non-economic factors of a family drive the need to analyze the impact of socioemotional factors on the financial policies of the family firms. The study explores the impact of family ownership, family management, and family control drawn from agency theory and socioemotional wealth perspectives on the financial decisions of family firms. Our findings in support of the socioemotional wealth perspective show a positive relationship between family ownership and debt financing with a desire to finance growth and avoid control dilution, with an increase in the level of debt. However, the involvement of family members in management and the top management team leads to an adverse relationship between family ownership and debt level, exhibiting the risk-averse behavior of a firm, which drives firms to reduce debt levels. Overall, our findings suggest that the perceptions of the socioemotional wealth theoretical paradigm are important in determining capital structure decisions in family enterprises. The results are resilient to potential endogeneity and heterogeneity difficulties, which may assist scholars and practitioners in assessing capital structure decisions in emerging economies. Full article

Drawing on a panel dataset of 27,972 firm-year observations from Chinese A-share listed companies spanning 2009 to 2022, this study employs fixed-effects models to examine the nonlinear relationship between firm-level climate policy uncertainty (FCPU) and corporate green governance expenditure (GGE). The results reveal a robust inverted U-shaped pattern: moderate levels of FCPU encourage firms to increase GGE, while excessive uncertainty discourages it. Financing constraints mediate this relationship; specifically, FCPU exhibits a U-shaped impact on financing constraints, initially easing and then tightening them. Older top management teams accelerate the GGE downturn, while government environmental expenditure delays it, acting as a buffer. Heterogeneity analyses reveal the inverted U-shaped effect is more pronounced for non-polluting firms and state-owned enterprises (SOEs). This study highlights the complex dynamics of FCPU on corporate green behavior, underscoring the importance of climate policy stability and transparency for advancing corporate environmental engagement in China. Full article

In the context of global climate change and resource scarcity, corporate environmental, social, and governance (ESG) performance, as well as corporate green innovation, have emerged as pivotal drivers for fostering sustainable development. The heterogeneity of the top management team (TMT) significantly influences the direction and effectiveness of both ESG performance and corporate green innovation. Drawing on upper echelon theory, information decision-making theory, and social categorization theory, this paper conducts an empirical study using a sample of 314 manufacturing enterprises and employs multiple linear regression analysis to uncover the impact of TMT heterogeneity on corporate green innovation and the mediating role of ESG performance. The findings from this research suggest that TMT heterogeneity exerts a notable positive influence on green innovation (including green technological innovation and green product innovation), and that ESG performance plays a partial mediating role between TMT heterogeneity and green innovation. This research enriches the theoretical foundation of corporate green innovation from the perspective of TMT heterogeneity and offers pertinent suggestions for enterprises to advance their green innovation development. Full article

The challenge of balancing economic and social benefits has emerged as a critical issue for corporate sustainable development. Environmental, social, and governance (ESG) criteria are key considerations for enterprises aiming to enhance both social and economic benefits simultaneously. Based on the upper echelons theory, differences in cognitive foundations and values brought about by top management team heterogeneity can influence corporate decisions. Taking A-share listed companies in China from 2011 to 2022 as samples, we construct a two-way fixed-effects model by firm and year to explore the impact of top management team heterogeneity on corporate ESG performance, and we introduce top management incentives as a moderating variable to further analyze the underlying mechanisms. Our results demonstrate that the gender heterogeneity, functional background heterogeneity, and overseas background heterogeneity of top management teams have significant positive impacts on corporate ESG performance, and monetary compensation incentives and control incentives to top management teams play a positive moderating role, while equity incentives exhibits a negative moderating effect. These findings remain robust across alternative measures of corporate ESG ratings and monetary and control incentives, and through the SYS-GMM model test and instrumental variable approach to address endogeneity. This research contributes to the literature on corporate ESG by validating and extending the understanding of how top management team characteristics affect organizational outcomes, and it provides practical guidance for enhancing corporate ESG practices. The implications of this study suggest that to enhance corporate ESG performance, enterprises should prioritize the promotion of top management team heterogeneity and tailor their incentive mechanisms accordingly. Full article

In recent years, there has been growing recognition that the stability of the top management team (TMT) significantly impacts the operation and management of companies. However, few studies have focused on the impact of TMT stability on innovation sustainability. Therefore, based on the upper echelon theory and the faultline theory, this paper takes China’s A-share listed companies from 2010 to 2022 as a sample to explore the impact of TMT stability on corporate innovation sustainability, as well as the moderating effect of executive faultlines on this impact. The results indicate that TMT stability is positively correlated with corporate innovation sustainability, whereas the executive faultlines significantly weaken this correlation. The mechanism test reveals that a stable senior management team can reduce an enterprise’s operational risk through the management functions of executives, alleviate the financing constraints serving as a stability signal sent by the company to investors and creditors, and thus promote the sustainability of innovation. Heterogeneity analysis demonstrates that the influence of TMT stability on corporate innovation sustainability is more pronounced in companies with a high percentage of executive shareholdings, non-state ownership, and CEOs possessing technical expertise. This paper combines the overall stability of the executive team with the differentiation of its internal subgroups, broadens the research perspective of the upper echelon theory, and serves as a valuable reference for the development of corporate executive teams. Full article

This study aims to investigate the impact of regional big data application capability (RBDAC) on the green technology innovation (GTI) of manufacturing firms. Based on the data from Shanghai and Shenzhen A-share listed manufacturing firms in China from 2010 to 2020, the difference-in-differences method is used for the analysis. The results show that RBDAC can significantly improve the GTI in manufacturing firms. Further research shows that government subsidy and analyst coverage have strengthened the positive effect of RBDAC on GTI. Extensive analysis validates the heterogeneity of RBDAC in influencing the GTI based on financial constraints, tax administration strengths, regions, property rights, and top management team. The economic outcome test shows that RBDAC also improves firms’ environmental, social, and governance performance. Our findings contribute to the literature on big data application capability and GTI, as well as provide practical enlightenment for manufacturing firms to engage in digital and green practices. Full article

Employee compensation is an often-neglected but essential part of corporate social responsibility which emphasizes caring for the needs of all stakeholders, including employees. In order to address pressure from stakeholders to strengthen prosocial acts, CEOs might prefer to raise employee compensation. However, other top executives are often reluctant to do so due to the concern that it reduces firm profits. In this paper, we propose that appointment-based CEO connectedness (ABCC) has a positive effect on employee compensation as it facilitates CEOs gaining support from the top management team to raise employee compensation. We employ multivariate linear regression as our research approach and find supportive evidence using data from Chinese listed firms during 2011–2020. Our results are robust to endogeneity concerns and survive an array of robustness checks. Heterogeneity tests show that this impact is stronger for firms facing less market competition and firms with low financial constraints. We further show that greater ABCC is associated with higher CSR scores of non-shareholders responsibility dimensions. Overall, our study suggests ABCC is conducive to the fulfillment of corporate social responsibility towards non-shareholders. Full article

Improving the level of digital industry innovation is of great significance to enhance the competitiveness of China’s digital industry and improve the sustainable development advantages of the digital economy. Based on the technology–organization–environment framework (TOE framework), this paper establishes a multi-stage configuration analysis model of factors affecting enterprises’ digital innovation, selects six antecedent variables from technology, organization, and environment, including R&D investment, high-level talents, organizational size, top management team heterogeneity, industrial development speed, and regional digitalization level, and explores the driving path for improving enterprise digital innovation intention and digital innovation performance under the influence of multi-factor combinations. The results are presented that single factors or single dimensions struggle to stimulate high digital innovation, and the combination configuration of multiple factors has a significant impact. The configuration paths that drive high digital innovation intention include the technology–environment type (TE type) driven by technological and environmental factors, organization–environment type (OE type) driven by organizational and environmental factors and technology–organization–environment type (TOE type) driven by technological, organizational, and environmental factors; the configuration paths that drive high digital innovation performance include the technology–organization type (TO type) driven by technological and organizational factors, organization–environment type (OE type) driven by organizational and environmental factors. With the evolution of time, there are configuration solutions with relatively high stability, such as the TE type and OE type that drive high digital innovation intention, and the TO type that drives high digital innovation performance. The results provide a reference for revealing the key influencing factors and driving paths of enterprise digital innovation, and improving the vitality and output level of enterprise digital innovation. Full article

The current world’s green economy and digital economy collide at an accelerated pace, and the deep integration of digitalization and greening has become a new requirement for high-quality industrial transformation. Premised on a socio-technical system (STS)’s theoretical viewpoint from Chinese manufacturing firms, the study examines the effects of the digital transformation (DT) of companies on green transformation (GT), as well as the function of channels and processes. This study uses 70 samples of A-share-listed Chinese manufacturing companies from 2013 to 2020; a combination of linear regression and fsQCA is used to empirically test the research model and analyze the equivalence path. It is found that (1) DT significantly drives the GT of manufacturing firms; (2) DT influences the GT of manufacturing firms by alleviating the information asymmetry problem; and (3) executive team heterogeneity plays a positive mechanism role in the relationship between DT and GT. The qualitative comparative analysis yields two types of paths: (1) the main constructs that shape high GT levels are high DT intensity, low information asymmetry, and high TMT gender heterogeneity, which mainly arise in the eastern region and play a more pronounced role in state-owned enterprises, heavy pollution, and high-tech industries; (2) for non-state-owned enterprises in traditional industries in the central and western regions with average digital development, high TMT gender heterogeneity is the key to GT. The study expands the application of related theories and has practical implications for how Chinese manufacturing enterprises can effectively promote GT. Full article

Digitalization has brought great changes to economic and social development, and corporate digital transformation has gradually become the focus of academic attention. We explore the economic impacts of digital transformation from the perspective of corporate innovation utilizing a sample of China’s A-share listed manufacturing firms from 2008 to 2020, depending on the quasi-natural experiment of “Integration of Informatization & Industrialization”. Using the difference-in-differences (DID) model, we document that the growth of innovation considerably tends to rise via corporate digital transformation, and top management team (TMT) heterogeneity plays a positively moderating role in this process. The findings are still reliable after the parallel trend test, PSM-DID, placebo test, and the test of excluding alternative explanations. Extended analyses find that the innovation incentive effect of digital transformation will enhance corporate value in the later stage. Our findings not only contribute to the advancement of the study in digital transformation, but also offer theoretical support and useful advice for furthering corporate digitalization and upgrading the mechanism for creative growth. Full article

Although the importance of the top management team’s characteristics has attracted research attention, its influence remains undetermined. This paper considers the influence of top management team characteristics’ heterogeneity on GEM-listed enterprises’ innovation performance. From the perspective of optimizing top management team human capital and improving enterprise innovation performance, this study analyzes 634 GEM-listed enterprises via regression analysis. The results show that the effect of shallow-factor heterogeneity of the top management team on enterprise innovation performance is not significant, but deep-factor heterogeneity has a negative effect. These findings indicate that deep-factor heterogeneity is more important than shallow-factor heterogeneity and is subject to closer attention. If a GEM-listed enterprise wants to set up a strong top management team that contributes to the improvement of enterprise innovation performance, it must examine the consistency of each member’s educational background and extend their tenure as long as possible without considering the gender ratio or age structure of the team. Full article

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters. Full article

Organizations are increasingly aware of the importance of managing the acquisition processes of new and sustainable knowledge, which allows them to increase performance. These knowledge-acquisition processes require top management teams to focus on the external environment to search for sustainable opportunities and initiatives. This spurs top teams to make strategic decisions that require more comprehensive managerial information, which is provided by management accounting systems. Our research analyzes how top management team composition facilitates the acquisition of new knowledge. Our management accounting paper also analyzes the mediating effect of the interactive use of management accounting systems (MASs) and their impact on sustainable firm performance. A survey was conducted among the main manufacturer firms in the Republic of Ecuador. Results were analyzed by using the partial least squares methodology, and they showed a positive effect for the interactive use of management accounting systems on sustainable knowledge-acquisition processes. Results also showed that knowledge acquisition increased firm performance through an interactive use of MASs. Full article

Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. We show that a fire forecast system based on a numerical weather prediction (NWP) model coupled with a wildland fire behavior model can provide this forecast. This was illustrated with the Chimney Tops II wildland fire responsible for large socio-economic impacts. The system was run at high horizontal resolution (111 m) over the region affected by the fire to provide a fine representation of the terrain and fuel heterogeneities and explicitly resolve atmospheric turbulence. Our findings suggest that one can use the high spatial resolution winds, fire spread and smoke forecast to minimize the adverse impacts of wildland fires. Full article