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Although regulations to prevent financial wrongdoing exist in the USA, whistleblowing (WB) remains important in deterring unethical corporate behavior. The purpose of this study is to address the tension in the audit continuance literature by examining auditor resignations following a WB allegation.

WB allegation data is obtained from the US Occupational Safety and Health Administration and through manually collecting media-reported WB allegations. The authors use a difference-in-difference research design to examine auditor resignations around WB allegations. The test sample is compared against a propensity-score-matched (PSM) sample of control firms.

Auditors are significantly less likely to resign from an engagement following a WB allegation. Auditors prefer to increase their fees and continue with the engagement rather than resign, even when faced with the heightened risks posed by WB allegations. These findings suggest that auditors value the ability to earn a consistent stream of increased revenue from their clients, in agreement with the risk-spreading theory of audit continuance literature.

To the best of the authors’ knowledge, this is the first study that examines auditor resignations following a WB allegation. These findings contribute to the existing WB and auditing literature and add to the auditor acceptance/continuance literature. The findings are meaningful to shareholders, managers, researchers and especially to standard setters who are tasked with ensuring that auditors provide assurances about the accuracy of financial statements.

This study aims to examine the relationship between managerial ability and innovation efficiency, the mediating effect of digital transformation and the moderating effect of internal control.

This study collected A-share manufacturing listed companies in China from 2008 to 2019 and analyzed the data by means of multiple regression analysis, mediating effect test, moderating effect test and heterogeneity test. Finally, the authors conducted robustness test by remeasuring key variables and adding control variables.

The empirical results show that the higher managerial ability can improve innovation efficiency, internal control has a positive moderating effect and digital transformation plays a partial mediating effect on the relationship between managerial ability and innovation efficiency. Specially, it is found that the mediating effect of digital transformation is not significant in non-state-owned firms.

This study suggests that it is necessary to focus on the managerial ability in terms of both cultivation and supervision, to further deepen the digital transformation from the aspects of firms, government and society, especially to support the digital transformation of non-state-owned firms, and to make efforts to improve the corporate governance mechanism and internal control system, so as to better comprehensively realize the improvement of enterprise innovation efficiency.

Based on the mediating effect analysis of digital transformation and the moderating effect analysis of internal control, this study explores the role of managerial ability on innovation efficiency from a new perspective, expanding the related theoretical framework and research boundaries.

The purpose of this study is to examine the role of venture capital (VC) in supporting corporate growth and innovation through participation in private placements. While VC provides essential financial support to companies, it remains unclear whether this involvement serves a strategic investment role or a purely financial one. This study seeks to elucidate the role of VC by analyzing changes in the price discount of private placements following VC participation.

The authors take the private placement events of China A share listed companies from April 2005 to January 2023 as the sample, and examine the influence of VC subscriptions on price discount rate.

VC subscriptions to private placements increase information asymmetry, consequently raising the discount rate. This relationship is influenced by the transaction characteristics and information environment. Specifically, VC subscriptions further elevate the discount rate when VC are geographically dispersed from the issuers, possess industry expertise in the issuers’ sector, allocate raised funds for asset restructuring or non-digital investments and when the issuers are in their growth stages. Moreover, the positive correlation between VC subscriptions and the discount rate is more pronounced under conditions of lower internal control quality and weaker external media supervision. Higher discount rates in VC-subscribed private placements result in lower R&D investment and investment efficiency by the issuers, leading to larger-scale VC sell-offs and ultimately diminishing the market and financial performance of the issuers.

The issuers should diligently assess the behaviors and motives of VC and selectively choose issuance targets and methods to manage risks associated with price deviations in private placements. Additionally, this study recommends that regulatory authorities develop a more detailed regulatory framework that considers transaction characteristics and the information environment. This strategy should help optimize external regulatory measures like media coverage and protect the interests of small and medium-sized investors.

This study extends research on the “name chasing” motive and certification effect of VC in private placements, enriches the literature on the mechanisms forming discount rates and provides insights for refining regulatory policies on private placements.

Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most existing detection methods primarily focus on capturing language features from news content. However, these methods neglect the varying importance of different news entities. Additionally, these methods tend to overlook the auxiliary role of external knowledge, resulting in an incomplete understanding of the entity. To address these issues, this paper aims to propose a Dual-layer Semantic Information Extraction Network with External Knowledge (DSEN-EK) for fake news detection.

This approach is proposed to comprise three parts: Dual-layer Semantic Information Extraction Network, Entity Integration Network with External Knowledge and Classifier. Specifically, Dual-layer Semantic Information Extraction Network is designed to enhance relationships between entities and the influence of important entity representations. The Entity Integration Network with External Knowledge is proposed to extract entity descriptions from external knowledge bases.

The DSEN-EK model performs well on the Liar, Constraint, Twitter15 and Twitter16 data sets, achieving accuracy of 98.02%, 94.61%, 90.09% and 93.65%, respectively. These results highlight its effectiveness in detecting fake news across different types of content.

The Dual-layer Semantic Information Extraction Network is proposed to capture the relationships between entities and enhance the continuous semantic information of the news. The Entity Integration Network with External Knowledge is designed to enrich entity descriptions, leading to a more comprehensive capture of semantic details.

This study aims to investigate how digital technology influences the happiness of villagers in traditional ethnic minority communities, with Waipula Village as a focal case study. Recognized as a forerunner in achieving the United Nations Sustainable Development Goals, Waipula Village exemplifies how digital innovation can transform rural communities.

Using an exploratory case study approach, the research reveals that digital technology enhances villagers’ happiness by improving market access, mitigating geographical limitations and fostering entrepreneurship and innovation.

In addition, digital technology has led to new consumption patterns and cultural values, significantly contributing to the village’s sustainable development and overall well-being.

This analysis expands the understanding of the role of digital technology in ethnic minority villages and offers valuable insights for rural revitalization strategies, highlighting its importance in enhancing happiness and preserving cultural heritage.

Quality function deployment (QFD) has been widely applied in new product development, but existing research on QFD has some limitations. Primarily, QFD lacks the capability to provide feedback on the satisfaction degree of customer requirements (CRs) according to the actual values of engineering characteristics (ECs). In addition, QFD does not quantitatively consider the interrelationships among ECs. Reverse QFD (R-QFD) was introduced to implement the feedback process. On this basis, this paper quantitatively considers the interrelationships among ECs in the R-QFD model and extends these relationships to encompass combinations of multiple ECs, aiming to improve the inference accuracy of the model.

A nonlinear regression model was established between CRs and ECs, aiming to infer the satisfaction degree of CRs based on the implementation status of ECs. This model considers the interdependencies among ECs and extends the consideration of pairwise EC correlations from every two to every fifteen. Lingo Software is utilized to seek solutions for this program. To facilitate the implementation of the program, a directive to simplify the solution has been proposed.

The experimental results indicate that the interrelationships among ECs significantly affect the inference accuracy of the R-QFD model, thereby verifying the necessity of considering higher-order interrelationships among ECs within the R-QFD framework. Based on the results from data experiments, this paper also proposes research recommendations pertaining to ECs hierarchy for varying quantities of ECs.

The outcomes of this study have further refined the R-QFD model, addressing its limitations of ignoring the interrelationships among ECs. This transformation elevates the R-QFD model from a relatively simple linear model to a nonlinear model formed through modeling, thereby enhancing its accuracy and applicability. In practical terms, this study provides case support for the application of the R-QFD model in manufacturing industry.

Unpredictable economic landscapes have led to a continuous escalation in global economic policy uncertainty (EPU). Improving risk management and sustainability in an environment with high macro risk is critical for business development. This study aims to explore the impact of corporate sustainable development on corporate tax risk.

After using a sample of companies that were A-share listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2021, this paper applies ordinary least squares and a moderate effect model.

Better environmental, social and governance (ESG) performance can weaken corporate tax risk by improving green innovation capability, reputation and information transparency. Meanwhile, the restraining effect of ESG on tax risk was more significant amid high EPU. These impacts were amplified amid higher market competition, lower tax supervision and a lower degree of corporate digital transformation.

The findings emphasize the need for the government to establish a healthy business and tax environment so that enterprises can improve sustainable development and increase their risk management abilities, especially post-COVID-19.

This study guides enterprises and the entirety of society to in paying attention to and promoting ESG practices, which can enhance enterprise tax management.

This study expands the research on the economic consequences of sustainable development and the factors influencing corporate tax risk and EPU.

Drawing upon compensatory ethics theory, this study explored the underlying mechanisms through which gossipers engaged in helping behavior as a form of compensation after initiating negative workplace gossip.

Through a two-wave field study of 394 Chinese employees, this study tests theoretical hypotheses using path analysis and bootstrapping methods.

The findings suggested that negative workplace gossip positively influenced the gossipers’ helping behavior through moral self-image and guilt. Moreover, moral reflectiveness not only positively moderated the impacts of negative workplace gossip on moral self-image and guilt but also positively moderated the mediating effects of moral self-image and guilt in the relationship between negative workplace gossip and helping behavior.

These results enrich the theoretical research on negative workplace gossip, offer new perspectives for studying this phenomenon and provide a theoretical basis and decision-making reference for management practices.

Implementing green innovation is crucial for firms to build or sustain competitive advantages within the context of the sustainable development goals. Academic research has broadly explored how firms can induce green innovation behavior (GIB), examining external factors, but few studies in the current literature have deeply investigated unabsorbed slack as an internal antecedent of GIB. Drawing upon the behavioral theory of the firm and integrating it with dynamic capabilities theory, this study aims to address this deficiency by investigating the impact of unabsorbed slack on GIB and the mediating roles of two dimensions of capability reconfiguration: capability evolution and capability substitution in the relationship between unabsorbed slack and GIB. Furthermore, this study also discusses the moderating effects of consumer green pressure on the relationship between unabsorbed slack and capability evolution/substitution.

Survey data were collected from 286 Chinese technology-intensive manufacturing firms to empirically test the relationships among the variables.

The results reveal that unabsorbed slack has a positive influence on GIB. Furthermore, capability evolution and substitution both play mediating roles in the relationship between unabsorbed slack and GIB. Comparative analysis showed that the mediating effect of capability substitution is stronger than that of capability evolution. Moreover, consumer green pressure strengthens the positive relationship between unabsorbed slack and capability evolution/substitution.

This study enriches the research on the driving forces of GIB and contributes to providing managerial implications for firms to launch green innovation activities.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.