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This analysis examines the relationship between corruption and firm productivity in Vietnam.

The authors apply the system generalized method of moments estimation approach on a panel dataset constructed from comprehensive enterprise surveys covering all the sectors over the 2011–2020 period.

The results confirm a non-linear relationship between corruption and firm productivity. Where corruption is severe, leaving corruption alone tends to benefit firm productivity because efforts to control corruption are likely to cause greater delays. In less corrupt provinces, corruption appears to harm firm productivity while efforts to control corruption provide significant productivity gains. This U-shaped relationship is confirmed for small firms and those in the private sector sub-samples. Intriguingly, this study reveals that the U-shaped relationship does not apply to micro, medium, large firms, state-owned firms and foreign-invested firms because corruption is found to have no significant impact on productivity among these sub-samples. Changes in regulations after 2014 toward promoting a transparent business environment are shown to foster the positive impact of lowering corruption on firm productivity.

This study suggests that lowering corruption is beneficial for firm productivity at the micro level. However, where corruption is severe, monitoring corruption alone is likely to cause adverse effects on productivity due to increased bureaucratic delays. Institutional reforms might play an important role in leveraging the effects of lowering corruption on productivity in highly corrupt areas.

This paper sheds new light on the relationship between corruption and firm productivity in the broad existing literature and especially in the limited number of studies for Vietnam.

The core of the successful implementation of the integrated project delivery (IPD) mode is to establish a high-quality relationship of cooperation, trust and sharing among participants. This paper proposes recommendations to improve the relationship quality of participants from the owner's perspective. The results provide the theoretical basis and practical guidance for the popularization and application of the IPD mode.

This paper analyzes the dynamic relationship among participants in IPD mode based on supply chain theory and similarity theory. A tripartite game model of the owner, designer and the contractor is constructed to obtain the evolutionary equilibrium strategies under different parameter constraints. Then, numerical simulations under various scenarios are conducted to explore the dynamic evolution and the influencing factors of the relationship quality among the participant in the IPD mode.

The results show that (1) the relationship quality under certain conditions gradually improves as the project progresses until stable and high-quality cooperation is formed and (2) the owner's positive supervision cost, the distribution coefficient of incentive and punishment of participants and the scale of incentive pool are important factors influencing the relationship quality.

This study incorporates the following three innovations. First, analyzing the relationship quality among the participants of IPD mode based on supply chain theory. Second, the evolutionary game theory is applied to the relationship quality analysis. Third, conclusion innovation. The authors conclude that the relationship quality may progress, decrease or cycle with the progress of the project and targeted recommendations are presented based on the results.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

This study aims to investigate the relationship between prevention and panic of COVID-19 and distress tolerance, happiness and emotional intelligence (EI) in undergraduate and postgraduate accounting students in Iraq. In other words, this study seeks to answer whether or not different types of prevention and fear of COVID-19 can lead to improved distress tolerance, happiness and EI.

The study’s statistical population comprises 298 undergraduate and 138 postgraduate students in Iraq who were selected as the sample size using the Cochran sampling method. In this study, partial least squares regression (PLS) have been used to investigate the effect of independent variables on the dependent variable.

The results showed a positive and significant relationship between COVID-19 prevention and distress tolerance and happiness, but no significant relationship was observed between COVID-19 prevention and EI. Also, no significant relationship was observed between fear of COVID-19 and distress tolerance and happiness, but there was a positive and significant relationship between fear of COVID-19 and EI.

The present study’s results can provide valuable information to everyone and help the development of science and knowledge because so far, and to the best of the authors’ knowledge, no research has examined the impact of prevention and panic of COVID-19 on distress tolerance, happiness and EI in students.

This study aims to explore how subsidiary capabilities collectively configure for performance. Additionally, it seeks to examine whether these configurations of capabilities can provide equifinal solutions through developing a comprehensive research framework that focuses on subsidiaries in China.

With a data set collected through a questionnaire from 172 Korean multinational enterprises (MNEs) in China, this study used a fuzzy-set qualitative comparative analysis to detect the capability conditions and configurations. These configurations represent combinations of various subsidiary capabilities linked to high performance.

This study identified several complex pathways with distinct configurations for high subsidiary performance. The findings demonstrate the importance of configurations over individual conditions. Thus, the results highlight that the effectiveness of diverse capabilities, which are widely believed to singularly contribute to the high performance of MNE subsidiaries, depends on how each combines with other capabilities. Overall, the findings provide a richer and fine-grained understanding of the role and relative importance of various forms of MNE subsidiary capabilities and how the joint effect of these subsidiaries contributes to high performance.

This study suggests that MNE managers should comprehensively understand how subsidiary capabilities are configured to produce subsidiary performance outcomes. This specifically illustrates the importance of understanding the mutually conflicting yet collectively exhaustive results of multi-selective solutions and aims to align with China’s industrial and regional heterogeneity.

By examining the role of MNE subsidiary capability configurations, which may collectively influence the subsidiary’s performance, this study contributes to the literature. It elucidates how MNE subsidiaries may achieve superior performance by developing and possessing various capabilities tailored to the local context.

With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.

Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.

The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.

There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

This study aims to focus on how reactive diluents with mono- and di-functionalities affect the properties of resin formulation developed from bioderived precursors. A hydroxyethyl methacrylate (HEMA) terminated urethane acrylate oligomer was synthesized and characterized to study its application in stereolithography 3D printing with different ratios of isobornyl acrylate and hexanediol diacrylate.

Polyester polyol was synthesized from suberic acid and butanediol. Additionally, isophorone diisocyanate, polyester polyol and HEMA were used to create urethane acrylate oligomer. Fourier transform infrared spectroscopy and ¹H NMR were used to characterize the polyester polyol and oligomer. Various formulations were created by combining oligomer with reactive diluents in concentrations ranging from 0% to 30% by weight and curing with ultraviolet (UV) radiation. The cured coatings and 3D printed specimens were then evaluated for their properties.

The findings revealed an improvement in thermal stability, contact angle value, tensile strength and surface properties of the product which indicated its suitability for use as a 3D printing material.

This study discusses how oligomers that have been cured by UV radiation with mono- and difunctional reactive diluents give excellent coating characteristics and demonstrate suitability and stability for 3D printing applications.

This paper aims to clarify the impact of information asymmetry on users' payment rates and examine the role of perceived uncertainty (PU) and acceptable price (AP) in the relationship between information asymmetry and users' payment rates.

To test the influences of information asymmetry on users' payment rates, this paper collects 18,489 transaction data from the Chinese knowledge payment platform Zhihu with a Python crawler. This paper constructs a mediation model to define the relationship between information asymmetry and users' payment rates by introducing PU and AP as the mediators.

Information asymmetry negatively affects users' payment rates. In addition, PU and AP mediate the information asymmetry in users' payment rates bond.

This study only explores the mediators of the information asymmetry users’ payment rates bond, ignoring the effect of potential moderators, which would be an important direction for future research.

The findings of this paper suggest that information communication is essential in knowledge market transactions. Knowledge providers, as well as knowledge platforms, should enhance information exchange with consumers in order to increase product sales.

This paper provides a new perspective for understanding how information asymmetry affects users' payment rates and helps to guide suppliers to improve product quality. The research framework of this paper is universal to a certain extent.

This paper is one of the first to propose using PU and AP to construct a mediation model to study the information asymmetry between users' payment rates relationship. It provides a new perspective for understanding the channel of information asymmetry in customer behavior.

Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social relationship (PSR), this study aims to capture viewers’ lively social feelings toward the streamer as the key factor leading to the purchase behavior of virtual gifts. It also aims to establish a theoretical link between PSR and viewers’ holistic experience in live streaming as captured by cognitive absorption and aims to investigates the role of technological features (i.e. viewer–streamer and viewer–viewer interactivity, streamer-level and viewer-level deep profiling and design aesthetics) in shaping viewers’ experience.

Based on 433 survey responses, this study employs a combination of structural equation modeling and neural networks to offer valuable insights into the relationships between the technological environment, viewer experience and viewer behavior.

Our results highlight the salience of PSR in promoting the purchase of virtual gifts through cognitive absorption and the importance of the technological environment in eliciting the viewer experience. This study sheds light on the development of PSR in a technological environment and its relationship with cognitive absorption.

By applying PSR to conceptualize viewers’ perceived connection with the streamer, this study extends the research on purchase behavior in the non-shopping context by providing an enlightened understanding of virtual gift purchase behavior in live streaming. Moreover, by theoretically linking PSR with cognitive absorption, virtual gift purchase and technological features of live streaming, it enriches the theory of PSR and bridges the gap between the design practice of supporting the IT infrastructure of live streaming and research.