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Enhancing total factor productivity through digital transformation is a crucial pathway for the high-quality development of manufacturing enterprises. This research aims to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity in the context of digital transformation.

Using the data from 536 Chinese listed manufacturing enterprises from 2018 to 2021, this research divides digital transformation into two dimensions (i.e. digital transformation breadth and digital transformation depth) and examines their impacts on total factor productivity as well as the mediation effects of innovation capability and reconfiguration capacity.

It is found that digital transformation breadth, digital transformation depth and their interaction can positively affect manufacturing enterprises’ total factor productivity. The innovation capability and reconfiguration capacity of manufacturing enterprises act as mediators between digital transformation breadth and total factor productivity, as well as between digital transformation depth and total factor productivity.

This study is one of the first attempts to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity from the perspective of digital transformation breadth and depth.

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.

The purpose of this study is to explore how to select a suitable supply chain collaboration paradigm (SCCP) based on the intelligent manufacturing model (IMM) of enterprises.

Given the fit between internal collaboration and external collaboration, we propose a model to select a suitable SCCP based on two-sided matching between SCCPs and IMMs. In this decision problem, we invited five university scholars and seven related consultants to evaluate SCCPs and IMMs based on the regret theory, which is used to obtain the perceived utility and matching results. The evaluation values are comfortably expressed through probabilistic linguistic term sets (PLTSs). Also, we set the lowest acceptance threshold to improve the accuracy of matching results.

The findings indicate that the characteristics of IMMs can significantly influence the selection of SCCPs, and an SCCP is not suitable for all IMMs. Interestingly, the study findings suggest that the selection of SCCP is diverse and multi-optional under the constraints of IMMs.

Existing studies have explored supply chain collaboration (SCC) in Industry 4.0 to improve supply chain performance, but less attention has been paid to the impact of the match between SCCPs and IMMs on supply chain performance. And even fewer studies have addressed how to select a suitable SCCP in different IMMs. This study provides a unique contribution to the practice of SCC and expands the understanding of supply chain management in Industry 4.0.

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.

This study aims to explore key questions within the context of Asian countries: How do artificial intelligence (AI) and blockchain adoption in accounting influence enterprise risk management and environmental, social and governance (ESG) performance? What role does enterprise risk management have as a mediator in this relationship? In addition, how does environmental uncertainty shape the interplay between AI and blockchain adoption in accounting, enterprise risk management and ESG performance?

The authors collected data from Thomson Reuters Eikon Datastream, initially targeting the 20 Asian countries with the highest gross domestic product (GDP) per capita. Using stringent selection criteria, the research sample included 22,212 firms from these countries: Bahrain, China, Hong Kong, Indonesia, Israel, Japan, Jordan, Kazakhstan, South Korea, Kuwait, Lebanon, Malaysia, Oman, Qatar, Saudi Arabia, Singapore, Sri Lanka, Thailand, the United Arab Emirates and Vietnam. After a rigorous screening process, the final sample comprised 1,742 firms, representing 17,420 firm-year observations over the 2014–2023 period. This paper applied maximum likelihood structural equation modeling to analyze the data.

The findings reveal that both AI and blockchain adoption in accounting, along with enterprise risk management, positively impact ESG performance in the Asian context. Enterprise risk management serves as a mediating factor between AI and blockchain adoption in accounting and ESG performance. In addition, environmental uncertainty significantly moderates the relationships between AI and blockchain adoption in accounting and enterprise risk management, as well as between enterprise risk management and ESG performance.

This study uncovers the interplay between internal factors – such as AI and blockchain adoption in accounting and enterprise risk management – and external factors, notably environmental uncertainty, in fostering sustainable value for Asian firms. Internal factors enable firms to integrate ESG considerations into their operations, facilitating risk mitigation and enhancing ESG performance. Meanwhile, heightened environmental uncertainty drives the adoption of sustainable practices. Consequently, Asian Governments should prioritize the development of regions characterized by high environmental uncertainty to advance national sustainable development goals and encourage responsible business practices.

This study contributes to the existing literature by uncovering the combined effects of internal and external factors on ESG performance, offering empirical evidence from Asian countries with high GDP per capita. Specifically, it underscores the efficacy of AI and blockchain adoption in accounting and enterprise risk management, as well as the moderating role of environmental uncertainty, within the Asian context.

The principal objective of this current research is to explore and test an underlying mechanism to solve the inconsistent relationship between supervisors’ developmental feedback and employee creativity. This study also tests the moderating role of absorptive capacity in fully understanding the relationship between two constructs.

A time-lagged survey was utilized to collect data from 317 employees working at information technology (IT) organizations in Vietnam. This research conducted a hierarchical regression analysis to examine the hypotheses.

This research found that employees’ operational skills fully mediated the link between supervisors’ developmental feedback and employees’ creative performance. Moreover, absorptive capacity positively moderated the relationship between supervisors’ developmental feedback and employees’ operational skills as well as the relationship between employees’ operational skills and employee creativity.

This research is one of the first papers to discuss the mediating role of employees’ operational skills in associating supervisors’ developmental feedback with employee creativity in response to the calls of previous studies. To fully comprehend the indirect impact of supervisors' developmental feedback on workers' creative performance, this research also examines the moderating influence of absorptive capacity at the individual level.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

This study aims to examine the relationship between audit partner tenure and earnings management of companies listed on Vietnamese stock exchanges.

This study uses a sample of 1,363 observations from 2016 to 2019. This study manually collects data on audit partner tenure. Using Datastream financial data, this study calculates abnormal accruals using the modified-Jones models (Jones, 1991; Dechow et al., 1995; Kothari et al., 2005), which are used as the proxy for earnings management. This study runs Ordinary Least Squares regressions to test this study’s hypothesis.

The results show that audit partner tenure is positively related to abnormal accruals. Cross-sectional analyses indicate that the relationship between audit partner tenure and abnormal accruals is more pronounced for firms that are audited by non-Big Four auditors and for firms that have chief executive officer-chairperson duality, suggesting that weak corporate governance is a channel for the established relationship. The evidence also shows that audit partner tenure is negatively associated with the magnitude of income-decreasing accruals but has no relationship with income-increasing accruals. This study’s findings are robust for several tests, including using the propensity score matching approach.

To the best of the authors’ knowledge, this study is the first to provide evidence of the relationship between audit partner tenure and earnings management in Vietnam.

The study was conducted to determine the relationship between three factors: work motivation (intrinsic and extrinsic), work efficiency, and job satisfaction of employees at the software company in Ho Chi Minh, Vietnam. Survey data collected from 300 employees working at the software company show that with the specific work characteristics of the information technology industry, the stronger the employee’s motivation to work, especially the intrinsic motivation, the greater efficiency and job satisfaction. At the same time, the higher the work efficiency, the more positive the employee’s job satisfaction will be. This study also draws some managerial implications that are valuable for reference for information technology enterprises to find solutions to increase work motivation, efficiency, and staff satisfaction, thereby minimizing layoffs, stabilizing personnel, and increasing competitiveness.

This study explores digital transformation in the tea supply chain within developing economies, with a focus on smallholder tea producers in Sri Lanka. Tea is one of the most widely consumed beverages in the world. Among the tea producers, smallholder tea producers account for a substantial portion of total tea production in several countries. Mobile phones play a significant role in providing smallholder producers with access to crucial agricultural information, markets and financial services.

This study adopts a deductive approach, analysing mobile phone ownership, literacy, experience and perception among smallholder tea producers. The chi-squared test of independence and hierarchical clustering methods were used to test the hypotheses and address the research questions.

The study identifies four clusters of smallholder tea producers as Basic Tech Adopters, Digital Laggards, Skeptical Feature Phone Users and Tech-savvy Adopters based on their characteristics towards mobile-based technologies. Approximately 75% of the surveyed sample, which included both tech-savvy and basic-tech adopters, showed a positive attitude toward adopting mobile-based agricultural technologies.

The study suggests developing targeted strategies and policies to enhance the productivity of the smallholder tea production process in developing economies. The study highlights the importance of awareness, access, affordability and availability when implementing digital services for businesses at the base of the pyramid, such as tea smallholdings in developing economies.

The present study aims to address the lack of data-driven empirical studies on the use of mobile phones in smallholder settings. The findings of this study enable the enhancement of entrepreneurship within the tea production supply chain, especially, within stakeholders who deliver digital transformation support services.