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Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

The Penrose effect, or the limitation of the growth rate during expansion due to managerial constraints, has been examined in the context of business diversification, withdrawal and MNE’s foreign direct investment, primarily in the for-profit sector. However, insufficient attention has been paid to its impact on professional service firms, particularly in the context of their expansion through service exports. The main purpose of this study is to examine the Penrose effect in the internationalization of professional service firms through service exports.

This study focuses on large Japanese patent firms as traditional professional service firms and constructs panel data for 48 large patent firms over the observation period from 2002 to 2010 to test our hypotheses.

Our results demonstrate a negative relationship between degree of internationalization and international business growth, thus confirming the Penrose effect. Furthermore, we found that the degree of internationalization has a curvilinear relationship with international business growth and that institutional distance does not have a negatively moderating effect on the relationship between the degree of internationalization and international business growth.

This study made a theoretical contribution to Penrose's growth theory and previous studies on international management and professional service firms and international management by showing that the Penrose effect can be observed in the international expansion of professional service firms through service exports. Moreover, this study identifies the factors that modify the Penrose effect, thereby making a significant theoretical contribution.

This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

The purpose of this paper is to investigate user satisfaction with digital health solutions by identifying and prioritizing different service attributes on the basis of their impact on improving user satisfaction.

Through a literature review and interviews with health professionals and patients, 20 attributes of digital health services provided in Italy have been identified. User satisfaction with these attributes has been evaluated by adopting the Kano model’s continuous and discrete analyses.

The findings reveal the essential attributes of digital health services that meet users' expectations, identify the attributes that users appreciate or dislike having and highlight unexpected attributes that lead to a significant boost in satisfaction when provided.

This study demonstrates the efficacy of the Kano model in assessing the nonlinear correlation between user satisfaction and the quality of digital health services, thus contributing to fill a gap in the literature in this area. The main limitation of this work is the use of a non-probabilistic sampling method.

This research suggests healthcare institutions and organizations consider user preferences when designing digital health solutions to increase their satisfaction. The results indicate different effects on user satisfaction and dissatisfaction for different categories of attributes in the Italian context.

Previous works studied customer satisfaction with digital health, assuming a linear relationship with service quality, or investigated consumer adoption intentions focusing on the technological factors. This work advances available knowledge by analyzing the nonlinear relationship between digital health attributes and users’ satisfaction and dissatisfaction.

This paper aims to examine how bank stress tests affect bank tax planning.

The study uses US bank stress test bank size thresholds and a regression discontinuity design to investigate the effect of the Dodd-Frank Act and the instituted bank stress tests on bank tax planning. We use different measures of tax planning, including bank-specific measures and measures of tax avoidance, tax aggressiveness, and effective tax planning from recent literature. Our regression discontinuity and difference-in-differences regression analyses include bank and year fixed-effects and lagged bank characteristics to control for potential endogeneity.

This study finds that stress tests have the unintended consequences of intensifying tax planning and increasing tax avoidance. Stress-test banks increase tax avoidance by accelerating charge-offs, net interest, and non-interest expenses. However, this increase in tax planning is not optimally maximized, leading to lower effective tax planning compared to non-stress-test banks. Banks with a substantial increase in tax avoidance under the Dodd–Frank Act tend to increase their risk, investing in high-risk-weight assets and lending in riskier loan categories. These findings are consistent with tax minimization conditions under added regulatory attention and policy uncertainty.

Literature on bank tax planning is limited. Most tax avoidance literature excludes financial institutions such as bank holding companies mainly due to differences in business practices and regulatory frameworks. This study is the first to investigate tax planning behavior among US banks. The current study thus extends the research field by examining the effect of bank transparency regulations, such as bank stress tests, on bank tax planning activities. Our findings have a direct bank policy implication. They show that stress testing has the unintended consequences of increasing tax planning activities and consequently increasing risk-taking on banks with high tax avoidance, which goes against the goals of stress testing regulations.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

This study aims to explore the concept of institutional orientation for B2B firms, defined as their ability to understand and adapt to the institutional factors of foreign markets. It examines how internal and external moderators influence the relationship between institutional orientation and export performance.

This study used a purposive sampling survey targeting B2B 257 exporting firms, ensuring representation across company sizes, industries and international experience. The firms come from France and Tunisia, chosen for their institutional, cultural and economic differences.

This study finds that the relationship between institutional orientation and export performance is not supported. However, the study demonstrates that this relationship becomes significant when moderated by channel networking capability, structural organicity, customer dynamism and competitive intensity.

This study contributes to the literature by providing a more nuanced view of the relationship between institutional orientation and export performance in B2B firms and by exploring how internal and external contextual factors moderate it, an area that has not been thoroughly investigated.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

The present article aims at systematizing the literature on EM that spans over three decades (1987–2023) to analyze the growth and development in the EM research following changes in reporting standards, economic conditions and legislations over the period.

The study covers 3,742 articles on earnings management (EM) indexed in SCOPUS and the Web of Science databases from 1987 to 2023. The study aims at the systematization of bibliometric data using R Studio, Biblioshiny and VOS Viewer software.

The study reveals that the research in the pre-SOX era gave more thrust to the development of cross-sectional models for the detection of accrual EM proxies, whereas research on EM had shifted to managerial discretions based on real transactions in the post-SOX era. Later, in the post-GFC era, the focus of EM research was redefined towards investor protection due to the collapse of the global economy that led to the erosion of investors’ wealth. In the modern era, research on EM focuses on ethical aspects such as CSR compliance, ESG framework and so on.

The findings of the study will aid the policymakers in addressing the EM based on real transactions and also incorporate the variations and changes in multiple green reporting standards to ensure fairness and transparency in reported figures.

The study contributes to the existing literature by quadrisecting the entire research on earnings management to analyze the growth and development in EM research and makes novel suggestions that future research on earnings management can be expanded towards the role of non-financial disclosure in managerial discretions and also the insider biases in green reporting.

The purpose of this paper is to review how knowledge transfer, including knowledge integration, absorptive capacity and reverse knowledge transfer (RKT) in cross-border acquisitions, is examined in existing research work. The authors also propose directions to advance research in cross-border acquisitions.

A systematic literature review is conducted, and related propositions are advanced based on scientometric and bibliometric analysis of 146 papers published over 10 years about tacit knowledge transfer, innovation activities, industrial policy effect on merger decisions, top management experience and value creation in cross-border acquisition. First, the authors searched major themes with the help of Scopus, and later, the authors analysed all received literature with the help of VOS Viewer.

This review facilitates us to identify six clusters and main author keywords. These six clusters are the underlying six research streams, including RKT, cultural distances, value creation, absorptive capacity, innovation and reference to India and China.

Despite knowledge transfer constituting important antecedents and critical factors for the success of cross-border acquisitions, knowledge management in the acquired company through proper knowledge transfer and knowledge integration is not given enough attention. Current literature still fails to provide a holistic picture of how firms strategically manage knowledge post-acquisition. To the best of the authors’ knowledge, this study is the first to analyse the dynamics of knowledge transfer in cross-border acquisitions. The study is a novel attempt to relate current research themes to emerging areas of cross-border acquisitions.