Unveiling the affecting mechanism of digital transformation on total factor productivity of Chinese firms
Ontology highlight
ABSTRACT:
SUBMITTER: Fu Z
PROVIDER: S-EPMC10849404 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Drawing upon panel data spanning the years 2011 to 2022 and encompassing 30 provinces across China, this research employs empirical methodologies, specifically the difference GMM and system GMM methods, to scrutinize the impact of the digital economy on the total factor productivity (TFP) within the agricultural sector. The study reveals a significant augmentation of China's agricultural TFP attributable to the digital economy, a finding robust to various methodological examinations. Notably, the influential role of the digital economy on agricultural TFP is more conspicuous in the central and western regions, as well as in locales characterized by lower productivity levels. Mechanistic analysis underscores that the digital economy exerts a positive influence on agricultural TFP through the stimulation of innovation and marketization effects. Furthermore, strategic recommendations emerge from this study, advocating for the reinforcement of institutional and mechanistic reforms to cultivate an enabling external milieu for the digital economy to propel agricultural TFP. It is posited that regional development strategies should be tailored based on individual resource endowments and the extent of digital economic development. Additionally, there is a call to refine mechanisms promoting high-quality development in agriculture, with an overarching goal of comprehensively elevating agricultural TFP. The implications of this research extend to the imperative need for a nuanced and context-specific approach to advancing agricultural productivity across diverse regions in China.
| S-EPMC11884720 | biostudies-literature
Project description: Not available
| S-EPMC10954155 | biostudies-literature
Project description:Digital transformation plays a crucial role in improving the quality development of companies in this era of digital economy with ever-changing technologies. This paper empirically investigates the impact of corporate digital transformation on total factor productivity and the mechanism of action, using A-share listed companies in Shanghai and Shenzhen from 2011-2021 as the research sample, and found that the digital transformation of companies significantly improves total factor productivity, with the plausibility of the findings being verified by a series of robustness tests. Based on the heterogeneity study, it is found that such effect is stronger for private companies, non-high-tech companies, and companies with a high degree of industry competition. The mechanism test indicates that digital transformation facilitates total factor productivity through four ways: strengthening company technological innovation, reducing operational costs, increasing resource allocation efficiency, and improving human capital structure. The findings of this paper support a better understanding of the micro effects of digital transformation and provide empirical evidence for policy formulation and adjustment.
| S-EPMC10578585 | biostudies-literature
Project description:This study provides evidence for the supply network position influencing innovation performance and the moderating effect of digital transformation. Supply chain relationships have been evaluated in earlier research to demonstrate how concentrations of customers and suppliers may either favorably or adversely impact innovation. These metrics, however, only take into account how closely a firm is connected to its direct customers or suppliers. This study integrates the top five suppliers and customers of Chinese listed manufacturing firms and considers the relationship embeddedness of each firm's direct customers and suppliers, as well as the structure embeddedness among the customers' customers, customers' suppliers, suppliers' customers, and suppliers' suppliers to reveal the true impact of supply chain relationships on innovation performance. The top five suppliers and consumers of each firm are chosen to build a supply network for each year using panel data of listed Chinese manufacturing firms from 2013 to 2020. Social network analysis is used to determine network centrality and structural holes. The results show that in the supply network, network centrality and structural holes are significantly negatively correlated with innovation performance, especially in small and medium-sized firms, non-state-owned firms, and firms in recession phase. According to the moderating effect model, digital transformation is an efficient way to reduce the negative effect of supply network position on innovation performance. The research results will further improve the supply network cooperation mechanism, which is of great significance for improving supply chain resilience and firms' innovation.
| S-EPMC9754206 | biostudies-literature
Project description:In this study, based on the data of the Chinese listed firms, the effect of digital transformation on capital mismatch was examined. And the potential mechanism was also further discussed. It was found that digital transformation can significantly suppress capital mismatch, especially for non-state-owned enterprises, mature enterprises, and regions with high marketization and financial technology level. In addition, management capability and information environment are potential influencing mechanisms of digital transformation to suppress capital mismatch. These findings have important implications for revealing the relationship between enterprise digital transformation and capital mismatch, provides new ideas for improving the efficiency of capital allocation, and also provides important insights for enterprises to accelerate digital transformation and promote the high-quality development of enterprises.
| S-EPMC11790084 | biostudies-literature
Project description:The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.
| S-EPMC10906909 | biostudies-literature
Project description:Research backgroundM&A (Mergers and acquisitions) is a strategic measure for enterprises to expand their scale, enhance their competitiveness and improve productivity in the market competition. As a new factor of production, data is changing the factor input model and value creation path of enterprises.Research objectivesFrom the perspective of serial M&A, this study explores the impact of serial M&A on enterprises' TFP (total factor productivity) and the mechanism of digital transformation between them.Research methodsTake the serial M&A transactions of China's A-share listed companies from 2010 to 2019 as samples, using the theory of organizational learning to analyze the relationship among serial M&A, enterprises' TFP and the degree of digital transformation. Three-step regression is used to construct a model that serial M&A indirectly affects enterprises' TFP through intermediary variable digital transformation.Research findingThere is a significant inverse U-shaped relationship between serial M&A and enterprises' TFP, and digital transformation plays a mediating role in this relationship. The impact of serial M&A on enterprises' TFP shows an upward trend at first and then a downward trend and this relationship is indirectly realized through digital transformation. The results are still valid after considering the change-explained variables, lag test, Sobel-Goodman test, and Bootstrap test. Heterogeneity analysis shows that for enterprises with non-state-owned property rights, smaller enterprise scale, and higher business environment index, serial M&A has a more obvious effect on TFP indirectly through the degree of digital transformation.Research valueIt further enriches the existing literature on the decision-making of M&A from the perspective of serial M&A and profoundly reveals the mechanism of the degree of digital transformation in the relationship between serial M&A and enterprises' TFP. The research provides theoretical support and empirical evidence for enterprises to achieve high-quality development.
| S-EPMC11578476 | biostudies-literature
Project description:This study examines the influence of digital government initiatives on corporate total factor productivity (TFP). Employing a difference-in-differences (DID) methodology and analyzing data from publicly listed companies spanning the period 2010 to 2020, we investigate the impact of digital governance on corporate TFP. Our findings reveal a noteworthy positive effect, with an average TFP increase of 5%. Further exploration through heterogeneity analysis indicates that this impact is particularly pronounced in regions with robust network infrastructure, increased marketization, and decreased economic uncertainty, particularly among privately-owned enterprises. Moreover, we identify key mechanisms through which digital governance fosters this enhancement in TFP, including the facilitation of technological innovation, efficient allocation of high-skilled labor, and improved investment efficiency. Our research underscores the significant role of digital government initiatives in bolstering corporate TFP and contributes to a deeper understanding of the mechanisms underlying this relationship.
| S-EPMC11392415 | biostudies-literature
Project description:To provide evidence at the micro level for cracking the Solow productivity paradox, this paper deeply studies the impact of enterprise digital transformation on green innovation. In terms of theoretical research, three potential mechanisms are excavated for the first time; considering empirical research, a series of strict causal effect identification strategies are carried out. The results show that enterprise digital transformation can significantly promote green innovation, and it passes a series of robustness tests and endogenous tests. According to the theoretical and empirical results, the policy suggestions mainly include five points: helping enterprises to accelerate digital transformation; strengthening the green innovation ability of enterprises; reducing internal and external costs and promoting the professional division of labor; piloting the digital transformation policy; enhancing corporate social responsibility. It provides a reference of experience and a path for other countries to follow in implementing a digital transformation strategy and green sustainable development strategy.
| S-EPMC9269481 | biostudies-literature
Project description:Continuous resource misallocation not only results in total factor productivity loss but also leads to ecological degradation. Therefore, in the process of changing from extensive growth to intensive growth, Chinese agriculture should pay attention to the problem of resource misallocation. There is currently a lack of relevant research, especially concerning the spatial spillover effects of resource misallocation at the city level. To fill this gap, we employ a spatial panel model for empirical testing on the basis of measuring agricultural green total factor productivity (GTFP) in 306 cities in China from 1996-2017. We found that there is positive spatial autocorrelation in Chinese agricultural GTFP, but it decreases year by year. Misallocation in land, labor, machinery and fertilizer all directly hinder the local GTFP. The eastern is mainly negatively affected by neighbor resource misallocation, while the central and western are mainly negatively affected by local resource misallocation. Finally, the indirect effect of neighbor resource misallocation on GTFP gradually shifts from inhibiting effect to a facilitating effect with increasing spatial distance. These findings have clear policy implications: Chinese government should strengthen agricultural green technology innovation and diffusion, strengthen environmental regulation and promote the free movement of labor between regions and sectors.
| S-EPMC9736431 | biostudies-literature