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From the perspective of local government tax administration, the impact of geographic dispersion on the corporate tax burden is investigated in this paper.

Using unbalanced panel data with a sample of listed companies from 2003 to 2020 in China, this paper focuses on the effect of geographic dispersion on corporate tax burden and the mechanisms.

It is found that corporate tax burden is positively related to geographic dispersion. It is also found that geographic dispersion affects the corporate tax burden by increasing the effort of local government tax administration. In addition, the relation between geographic dispersion and corporate tax burden is more pronounced for local SOEs prior to the implementation of Golden Tax Project III and in cases where local governments face stronger financial pressure to obtain revenue.

This study has important implications for the promotion of the coordinated development of the regional economy, as well as the legalization, modernization and informatization of tax administration.

The purpose of this study is to explore the mechanism of digitalized transformation in organizations’ human resource management (HRM). This study summarizes three basic factors driving the digital transformation process in China: level of perception, level of application and speed of transformation.

This study analyzes the strategic transformation process of HRM in Haier, Hisense and Chambroad to explore the human resource digital transformation mechanism in Chinese enterprises.

The results of this study show that three HR value chain models can be constructed based on how well HRM deals with business: the efficiency-oriented HRM value chain, quasi-business-oriented HRM value chain and business-oriented HRM value chain. The basic factors – level of perception, level of application and speed of transformation – are observed in the entire HRM digital transformation process.

This study provides theoretical and empirical insights for enterprises to explore the value of digital technology in HRM and facilitate the digital transformation of HRM.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

The digital economy has become a key force supporting the high-quality development of tourism. This paper discusses the coupling coordination relationship and spatiotemporal evolution path of digital economy and tourism in China's provinces.

This paper uses the entropy method to measure the development level of digital economy and tourism, and establishes coupling coordination model and spatial autocorrelation model to study the interaction between the two industries.

Results show that the development levels of the two industries are rising, which spatially show a progressively decreasing pattern of east-middle-northeast-west. The coupling coordination degrees of the two industries have increased steadily, but the overall level is still near maladjusted. Spatially, the positive correlation is increasing, but the incongruity of spatial agglomeration is still significant. The coupling coordination evolution path in the provinces shows differentiated characteristics. The migration path is mainly concentrated in Zones I and II. The eastern region has an obvious trend of extending to Zone III, where the tourism industry was the most affected by the pandemic.

The study helps clarify the industrial coupling and coordination relationship in various regions and formulate regional tourism digital transformation strategies to promote the high-quality development of China's tourism industry.

This paper enriches the research on the relationship between digital economy and tourism from the perspective of industrial integration. The development commonality of China's tourism digital transformation summarized provides theoretical reference and demonstration for the coordinated development of China's tourism.

As China's economy begins to transform into a high-quality development, and under the national “carbon peak and carbon neutral” target, all sectors of society and industries need to transform to green development to varying degrees, coupled with the catalyst of epidemics and other factors, new development requirements are put forward for enterprises to better fulfill their climate risk disclosure behaviors. Thus, it is clear that improving corporate climate risk disclosure is of far-reaching significance to both countries and enterprises.

This study incorporates management science, psychology and other related knowledge fields, based on stakeholder theory and media dependency theory, and aims to improve the level of corporate compliance with climate risk disclosure, suggesting the influence of entrepreneurs' visibility on corporate climate risk disclosure; on this basis, the role of entrepreneurs' visibility and media attention on corporate climate risk disclosure is verified through an empirical model; finally, targeted and effective response strategies are proposed to improve corporate climate risk disclosure, set reasonable media attention and increase the effectiveness of entrepreneurs' visibility.

This paper establishes a multiple regression model using A-share listed companies in China from 2016 to 2022 as the research sample, verifies the intrinsic association between entrepreneurial visibility and corporate climate risk climate disclosure through empirical analysis, and further examines the mediating role of media attention in the relationship between the two. The results show that entrepreneurs' visibility is positively related to the level of corporate climate risk disclosure, with media attention playing a part in mediating the relationship between the two. Increasing entrepreneurs' visibility is conducive to increasing the level of corporate climate risk disclosure. Therefore, it contributes to the dual incentive effect of reputation and compensation.

This study incorporates management science, psychology and other related knowledge fields, based on stakeholder theory and media dependency theory, and aims to improve the level of corporate compliance with climate risk disclosure, suggesting the influence of entrepreneurs' visibility on corporate climate risk disclosure; on this basis, the role of entrepreneurs' visibility and media attention on corporate climate risk disclosure is verified through an empirical model; finally, targeted and effective response strategies are proposed to improve corporate climate risk disclosure, set reasonable media attention and increase the effectiveness of entrepreneurs' visibility.

Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.

The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.

The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.

The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.

Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.

As China’s e-commerce and cross-border e-commerce rapidly develop, the cross-border e-commerce supply chain exhibits characteristics of globalized development scale, collaborative multiparty participation, streamlined management processes, digitalized production and trade and flexible strategic choices. It tends toward data-driven intelligence, interoperable information collaboration, personalized order responses, sustainable supply chain management and secure blockchain technology. These characteristics and trends provide critical references for businesses, governments and investors.

In response to issues such as inconsistent legal regulations, imbalanced logistics and transportation, imperfect payment settlements and opaque supply chains.

It is recommended to take measures to strengthen cooperation and communication, optimize logistics, reduce customs clearance difficulties, reinforce safeguard measures and promote sustainable development, collectively fostering the healthy growth of cross-border e-commerce.

With the rapid development of cross-border e-commerce, green and low-carbon initiatives have become a significant trend in this sector. The cross-border e-commerce supply chain refers to the mechanism that reduces environmental impacts and enhances resource efficiency from manufacturers to consumers. It primarily involves manufacturers, e-commerce platforms, logistics companies and payment and settlement processes. The cross-border e-commerce supply chain is gradually becoming a highlight in China’s foreign trade, supporting the concept of “buying globally and selling globally” and connecting the “world’s factory” with the “world’s market.”

This paper aims to examine whether chief executive officer’s (CEO) information technology (IT) background can affect enterprises’ continuous green innovation (CGI).

This study uses the data of China’s listed enterprises from 2011 to 2019.

The statistical results reveal that when a company hires a CEO with an IT background, its CGI can be higher. Firm ownership, firm digitization and industry bias alter the impact of CEO’s IT background on firms’ CGI. This effect is most pronounced in non-state-owned enterprises (non-SOEs), high-digitalized enterprises and skill-biased industries, while not in SOEs, low-digitalized enterprises and labor-biased industries.

This study has practical implications, as it measures CGI of enterprises. It also points to the necessity for a CEO’s IT background to enhance CGI.

The findings provide new strategies for incentivizing sustainable development and green innovation.

To the best of the authors’ knowledge, this study is the first to discuss the association between CEO’s IT background and enterprises’ CGI. The conclusions enrich both upper echelons theory and enterprise green innovation literature.

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.