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As China is on an inevitable march into the digital era, firms have accumulated abundant digital assets, such as algorithms and data. Facing the possibility of using digital assets as a new type input, besides traditional inputs such as capital and labor, would powerful managers perform better? Would managerial power help managers increase the efficiency of how a firm combines traditional and digital inputs and converts them into outputs? Thus, the purpose of this study is to investigate whether powerful managers promotes corporate productivity by using digital assets as a new input.

Using data from listed Chinese firms between 2008 and 2020, the authors constructed panel regressions with three-way fixed effects to examine whether and how managerial power influences corporate productivity in the current digital context, particularly under market uncertainty.

The findings reveal no consistent relationship between managerial power and corporate productivity. The results explain this from two contrasting effects: while managerial power promotes technological change it hinders technical efficiency – two components of total productivity. Moreover, this study identifies market uncertainty as a significant external contingency. In uncertain markets, strong managerial power positively impacts corporate productivity.

The results extend extant theoretical insights in the literature on how managerial power might influence corporate productivity.

Green funds represent a hybrid approach that integrates both environmental and financial considerations. Firms also strive to balance social benefits with economic performance. This study aims to analyze how green fund shareholdings impact firms’ dual performance and explores the underlying mechanisms and boundary conditions.

This study uses a sample of A-share companies listed on China’s exchanges from 2008 to 2022. A fixed effects model is used to assess the dual value of green funds in enhancing both environmental and financial performance while also exploring viable pathways to achieve a “win-win” outcome.

Green fund shareholdings significantly enhance both financial and environmental performance, with corporate reputation and corporate transparency acting as mediators. Media oversight and executive compensation positively moderate the relationship between green fund shareholdings and dual performance. In competitive industries, the influence of green fund shareholdings on environmental performance is more pronounced than their effect on financial performance. In the context of politically connected firms, green fund shareholdings have a reduced impact on financial performance, with no significant difference in environmental performance. In addition, the impact of green funds on ownership structure is heterogeneous, promoting dual performance in private firms but not in state-owned enterprises.

This study enhances the understanding of green funds’ dual investment logic, provides deeper insights into their role in fostering sustainable corporate development and extends the application of institutional logic in enterprise management.

Metadiscourse is an important dialogue technique used in productive knowledge building to help a group evaluate and advance their knowledge progress. Previous studies have identified and defined various types of metadiscourse. However, there is scant knowledge about how different metadiscourse types emerge among different groups or what implicit correlations lie between progressive discourse and metadiscourse. Moreover, research on how different types of metadiscourse influence groups' knowledge advancement and artifacts is still inadequate. Therefore, this study aims to further examine the roles that different types of metadiscourse play in the collaborative knowledge building community on both a fine-grained (i.e. progressive discourse) and coarse-grained (i.e. group knowledge advancement and group artifacts) level.

Data for this study are drawn from the behaviour of undergraduate students participating in a 12-week course at a key university in China. On the fine-grained level, epistemic network analysis (ENA) is applied to illustrate how metadiscourse promotes the development of progressive discourse. On the coarse-grained level, two different chi-square tests are conducted to examine the roles of different types of metadiscourse in groups' knowledge advancement and artifacts.

The analysis allowed several conclusions to be drawn. First, the types of metadiscourse that students most often adopted were reflecting on ideas development (RD) and commenting on ideas (CI); they less frequently adopted setting group goals (SG) and making group plans (MP). Second, most types of metadiscourse correlated with developments in progressive discourse, particularly RD and CI. Third, the metadiscourse types RD, CI and coordinating group efforts (CE) played essential roles in knowledge advancement. Fourth, higher-quality artifacts could be created by using the metadiscourse type reviewing the state of knowledge building progress (RP).

A more profound comprehension of the role that metadiscourse plays in the collaborative knowledge building community not only contributes to the literature in the knowledge building field but also carries a significant meaning in regulating community, promoting learner agency and sustained knowledge, and consequently improving collaborative learning performance.

As China’s industrial structure continues to upgrade and optimize, the consumption capacity of rural residents gradually improves and the role of consumption in economic growth is increasingly prominent. Against the background of weak external demand, the untapped potential of rural consumption has become a key force in expanding domestic demand. As one of the important means that the government has long relied on, fiscal support for agriculture has played a crucial role in activating the rural consumer market. This manuscript aims to explore the impact of local fiscal support for agricultural expenditure (FSAE) on rural consumption in China, as well as to examine the mediating role of the level of rural financial development.

In this manuscript, the authors use the provincial panel data of 31 provinces in China from 2000 to 2020. The data of all variables mainly come from China Statistical Yearbook and China Rural Statistical Yearbook. According to the variable selection above, 651 sample data of 31 provinces and cities across China from 2000 to 2020 are organized. In terms of methodology, multiple fixed-effects panel model is applied to regression.

Firstly, FSAE varies significantly, while rural consumption slowly but steadily rises, with a relatively stable consumption structure. Secondly, FSAE has a significant positive effect on rural consumption. Thirdly, mediation testing indicates that mechanisms such as income, uncertainty and financial development have significant positive mediating effects on rural consumption. Thirdly, there is evident regional heterogeneity in FSAE’s impact on rural consumption. The Western regions, under government fiscal support, show a more significant effect on the elevation of rural consumption levels, while the role of FSAE in optimizing the consumption structure of rural residents in eastern and central regions is more pronounced.

Firstly, a systematic examination of local FSAE and rural consumption has been conducted, enriching relevant theories. Secondly, utilizing econometric empirical methods to research the relationship between local FSAE and rural consumption provides an exploratory extension to empirical studies on rural consumption in China. This offers empirical evidence for local fiscal support in agricultural development and the promotion of rural consumption.

The purpose of this article is to examine the effects of green internal orientation and external orientation on firm performance and to evaluate the mediating role of green absorptive capacity in this effect. It also investigates the moderating role of environmental culture in the relationship between green absorptive capacity and firm performance.

This study was conducted based on field research with surveys conducted on 343 technology-oriented companies operating in Istanbul. SmartPLS 4 statistical software was used to analyze the data.

Research results show that green internal and external orientation has a positive and significant effect on firm performance. Additionally, green absorptive capacity has a mediating role in this effect. It has been determined that environmental culture plays a moderating role in the relationship between green absorptive capacity and firm performance.

This study has some limitations; for example, the fact that the data was obtained only from technology-focused companies in Istanbul may limit generalizability. However, this research makes important contributions to our understanding of the effects of green internal and external orientation on firm performance and the mediating effect of green absorptive capacity.

This study shows that strengthening businesses’ green internal and external orientations and improving their green absorptive capacity can increase firm performance. It is also an important warning to businesses that environmental culture can play an important role in these relationships.

Strengthening green internal and external orientation and improving green absorptive capacity can provide social benefits through their contributions to environmental sustainability. This study can help businesses take steps to reduce their environmental impact and achieve a sustainable future.

This study is one of the few studies examining the effects of green internal and external orientation on firm performance and the mediating role of green absorptive capacity. Additionally, it makes a unique contribution by focusing on the moderating role of environmental culture in these relationships. This study can be a valuable guide for businesses in developing environmental sustainability strategies.

In the context of the environmental degradation challenge in manufacturing firms, greening the supply chain (SC) is the most widely endorsed method to mitigate the adverse repercussions of climate change. Based on organizational learning and resource dependence theories, the aim of this research is to know how green supply chain external integration (GSCEI) and green supply chain internal integration (GSCII) influence ambidextrous green innovation (AGI). It also examines the mediating roles of green absorptive capacity (GAC) and green knowledge integration capability (GKIC), as well as the moderating role of green technology dynamism (GTD).

To assess the hypothesized model, data were obtained with 386 questionnaires from managers employed in manufacturing firms in Pakistan applying a cross-sectional approach. A partial least square structural equation modeling technique was implemented to evaluate the data.

The results revealed that GSCEI and GSCII substantially impact AGI. Moreover, GAC and GKIC serve as mediators between GSCEI and AGI. GAC and GKIC also intervene in the relationship between GSCII and AGI. GTD was significant as a moderator for the correlation between GSCEI and AGI. However, it does not moderate the relationship between GSCII and AGI.

This research offers significant comprehension and an innovative approach for manufacturing organizations to curb environmental corrosion by stimulating AGI through green SC integration. It suggests to practitioners that integrating internal knowledge with external partners expands communication and collaboration to ensure that resources connected with environmental preservation flow smoothly.

This research is a valuable addition to the field, as it explores for the first time the missing link among the studied constructs. It opened the black box of how knowledge-related capabilities facilitate knowledge resources to elicit AGI, an area that has not yet been explored.

The impact of enterprise social media (ESM) on employees is not always beneficial. The constant connectivity of ESM can trigger emotional issues, threatening employees' psychological states. This study aims to explore the potential risks of ESM usage on thriving at work by examining how ESM usage affects thriving and for whom this relationship is amplified.

Drawing on Affect Event Theory, this study proposes a research model for investigating the underlying mechanisms and boundary conditions of the negative impacts of different ESM usage patterns on employees' thriving at work. This study employed Smart-PLS 4.0 with structural equation modeling to analyze multi-wave survey data from 255 employees, testing the proposed model.

The results indicate the following: (1) ESM usage triggers employees' workplace fear of missing out (WFoMO), increasing their online vigilance and consequently hindering their thriving at work. (2) The impact of ESM usage on employees' WFoMO and thriving at work varies with their ambition levels, with high-ambition employees being more susceptible to negative effects and (3) Different types of ESM usage exert distinct effects on employees.

This study substantiates the potential negative impact of ESM usage on employees' thriving at work, contributing to the literature on the “dark side” of ESM usage and thriving at work. This study confirms the critical mediating role of emotion, offering a novel theoretical perspective on understanding the mechanisms linking ESM usage and its outcomes. Additionally, this study identifies the moderating role of employee ambition, complementing the boundary conditions of ESM usage.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Visualizing relations of textual data requires dimension reduction to increase the interpretability of output. However, traditional dimension reduction methods have some limitations, such as the loss of feature information during extraction or projection in dimension reduction and uncertain results due to the mixture of word labels. In this study, we develop the textual data visualization algorithm using statistical methods to present statistical inferences on the data. We also construct the algorithm in a way that the user can analyze textual data easily.

Unstructured data, such as textual data, is sensitive to choosing analysis methods. In addition, textual data is generally large-sized and sparse. Considering such characteristics, we applied latent Dirichlet allocation to separate data to minimize the loss of information, and false discover rate (FDR) control to reduce dimension in a statistical way.

The relation of textual data can be derived in a one-click way, and the output can be interpreted without background information, with separated topics.

The algorithm is constructed based on the Korean language. However, any language can be used without linguistic information. This study can be an example of usage and flow, which using not well-known dimension reduction methods can replace traditional methods.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.