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This study aims to explore the entrepreneur–venture nexus in transnational entrepreneurial ventures conducting operations in culturally similar countries. Theoretically, smaller cultural differences imply fewer challenges inherent to the liability of outsidership, thus facilitating cultural arbitrage activities. However, these minor differences can also hinder learning and increase competition with local firms, threatening the survival of ventures in the host country. To clarify this perspective, the research looks at how entrepreneurs’ resources affect venture survival, focusing on transnational entrepreneurs operating in countries that have relatively small cultural differences from their own.

This research is outlined by the capital approach in entrepreneurial opportunity development through a qualitative configurational design.

The results indicate that there are four strategies for achieving venture survivability for transnational entrepreneurship operations in culturally similar countries. This is reflected in both the core configurations associated with long-term venture survivability and in the model of venture survivability for cultural arbitrage opportunity development.

This study’s findings, which include the configurations and the model, speak to the burgeoning conversation of cultural arbitrage by illuminating venture survival strategies for international entrepreneurs operating in countries similar to their home countries. They are relevant for the international entrepreneurship discourse and the larger conversation in entrepreneurship, focusing on the entrepreneur–venture nexus.

The aim of this study is to enhance the performance of cloth recommendation systems by proposing a hybrid adversarial network called Cloth-Net, which integrates Dense Vision Transformers for effective 2D-3D image classification.

Cloth-Net combines the strengths of adversarial networks with Dense Vision Transformers to process both 2D and 3D images for improved classification. The model was trained on a large-scale dataset of clothing images, using a hybrid adversarial approach that enhances both feature extraction and image classification accuracy. The methodology also includes data augmentation and transfer learning techniques to optimize the model’s generalization capability.

Experimental results demonstrate that Cloth-Net significantly outperforms traditional convolutional neural network-based methods in terms of accuracy, precision, and recommendation quality. The hybrid adversarial framework, together with Dense Vision Transformers, enables the model to better understand complex clothing images, leading to more accurate and personalized recommendations.

This study introduces a novel hybrid adversarial model, Cloth-Net, that uniquely combines Dense Vision Transformers with traditional adversarial networks for the first time in the context of 2D-3D image classification. The findings present a substantial improvement in the performance of cloth recommendation engines, making the proposed model valuable for both academic research and practical applications in fashion technology.

In a dynamic and uncertain business environment, it is necessary for companies to adapt to be capable of addressing the changing circumstances and ensure business continuity. Hence, companies are employing advanced Industry 4.0 (I4.0) technologies. This study aims to explore the role of advanced I4.0 technologies in facilitating companies’ development towards supply chain resilience (SCR).

The study adopted a structured approach of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to identify 234 articles. Under PRISMA, we adopted a three-phase methodology of planning, conducting and reporting the review results. The data of these articles were synthesized using a Scopus database to investigate the relationship between I4.0 technologies and SCR.

The study’s findings map the technologies appropriate in different supply chain nodes and bridge the literature gap in the intersection of I4.0 technologies and SCR. The study results indicate the role of I4.0 technologies role in achieving resilience in key functions of an organization through an I4.0 technologies-enabled sourcing, manufacturing, distribution and return resilience (I-SMDRR) framework. The I-SMDRR framework also depicts the relationship being moderated by internal and external risk factors.

The study offers a unique framework by mapping sourcing, manufacturing, distribution and return resilience through I4.0 technologies. Additionally, the review delineates the theory-mapped research gaps helpful for future researchers. In summary, this systematic review of the literature identifies the components that lead to developing SCR.

The aerodynamic load caused by high-speed train operation may lead to severe vibration of the pedestrian bridge, thus causing great safety hazards. Therefore, this study aims to investigate the aerodynamic loading characteristics of a pedestrian bridge when a high-speed train passes over the bridge, as well as to evaluate the vibration response of the aerodynamic loads on the bridge structure.

High-speed trains are operated at three different speeds. The aerodynamic pressure load characteristics of high-speed trains crossing a pedestrian bridge are investigated by combining a nonconstant numerical simulation method with a dynamic modeling test method, and the vibration response of the bridge is analyzed.

The results show that when a high-speed train passes through the pedestrian bridge, the pedestrian bridge interferes with the attenuation of the pressure around the train, so that the pressure spreads along the bridge bottom, and the maximum positive and negative pressure peaks appear in the center area of the bridge bottom, while the pressure fluctuations in the bridge entrance and exit areas are smaller and change more slowly, and the pressure attenuation of the bridge bottom perpendicular to the direction of the train’s operation is faster. In addition, the pressure fluctuation generated by the high-speed train will lead to a larger vertical response of the bridge structure in the mid-span position, and the main vibration frequency of the bridge structure ranges from 8 to 10 Hz, and the maximum value of the vertical deformation amplitude is located in the mid-span region of the bridge.

This paper analyzes the flow field distribution around the train and at the bottom of the bridge for the evolution of the flow field when the train passes through the bridge at high speed, and conducts a finite element dynamic analysis of the bridge structure to calculate the vibration response of the bridge when the train passes through at high speed, and to evaluate the comfort of the passengers passing through the high-speed railroad bridge.

The idea of value co-creation involves the benefit actors gain from integrating resources through activities and interactions within a service network, with the environment enabling high-quality collaboration. This paradigm highlights customers’ ability to co-create value with service providers and other customers. This idea is gaining traction in health care. These days, patients are no longer passive recipients of health-care services; rather they have started taking proactive roles in their self-health management. This study aims to understand the phenomenon of value co-creation among patients within online health communities (OHCs).

A systematic literature review of papers published from 2003 to 2024 in Web of Science-indexed journals was conducted. The review highlights theories, contexts, characteristics and methodologies in this area, synthesizing insights from previous research and presenting a future research agenda for underexplored and unexplored contexts using emerging theoretical perspectives and analytical methodologies.

The review illuminates theoretical and empirical studies on value co-creation among patients in OHCs. Previous research shows that value co-creation among patients leads to cognitive, affective and physical benefits such as reduced anxiety and stress, increased assurance and self-confidence, improved quality of life, enhanced patient empowerment, acceptance of disease and treatment effectiveness and a sense of self-worth and well-being.

This review synthesizes insights from previous works and outlines a research agenda for future studies in underexplored and unexplored contexts using new theoretical perspectives and methodologies. Considering the role social media plays in an individual’s life, this work will help in deep diving into the role of such online communities in the health-care sector.

Ergonomic practices significantly enhance productivity, reduce injuries and improve worker satisfaction in construction sites. However, a key issue in the Nigerian construction industry is the limited awareness and implementation of these practices. This study addresses this issue by raising awareness and driving ergonomic improvements to advance occupational health and safety standards.

A quantitative research method was employed, using close-ended questionnaires distributed to construction professionals, including architects, builders, engineers and quantity surveyors. The data collected were analyzed through statistical tools such as percentages, frequencies and mean scores. The Kruskal–Wallis H-test was used to determine significant differences in respondents’ opinions.

The study found that construction professionals have limited knowledge of ergonomic practices, and the implementation of these practices within the industry is minimal, raising concerns about occupational health and safety.

This article provides a comprehensive roadmap for developing economies like Nigeria to improve the understanding of ergonomic practices among construction professionals. It also outlines how collaborative efforts among key stakeholders, including higher education institutions, government, policymakers, lawmakers, financial institutions and professional bodies, can enhance the awareness and implementation of these innovative practices.

While extensive research has been conducted on ergonomic practices in developed countries, few studies have examined their awareness and implementation in developing nations, particularly Nigeria. This study addresses this gap from the perspective of construction professionals.

The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and geometry information of the object, the failure to deeply explore the contributions of the features from different regions to the pose estimation, and the failure to take advantage of the invariance of the geometric structure of keypoints, the performances of the most existing methods are not satisfactory. This paper aims to design a high-precision 6D pose estimation method based on above insights.

First, a multi-scale cross-attention-based feature fusion module (MCFF) is designed to aggregate the appearance and geometry information by exploring the correlations between appearance features and geometry features in the various regions. Second, the authors build a multi-query regional-attention-based feature differentiation module (MRFD) to learn the contribution of each region to each keypoint. Finally, a geometric enhancement mechanism (GEM) is designed to use structure information to predict keypoints and optimize both pose and keypoints in the inference phase.

Experiments on several benchmarks and real robot show that the proposed method performs better than existing methods. Ablation studies illustrate the effectiveness of each module of the authors’ method.

A high-precision 6D pose estimation method is proposed by studying the relationship between the appearance and geometry from different object parts and the geometric invariance of the keypoints, which is of great significance for various robot applications.

The study explores how mobile governance affects the administrative burden on older adults, focusing on learning, psychological and compliance costs.

Using attribution theory, the research employs a quantitative research design, utilizing surveys to gather data from 516 older adults across three cities in China: Quzhou, Wuhan and Shanghai. The study examines how intrinsic factors and extrinsic factors of m-government interfaces impact older adults’ administrative burden.

Perceived complexity increases learning, psychological and compliance costs for older adults. Personalization and high-quality information decrease these costs, enhancing user satisfaction. Visual appeal decreases anxiety and psychological costs.

This research links attribution theory with m-government’s administrative burden on older adults, offering new insights into optimizing m-government to serve older adults better.

Metaverse technology has attracted much attention in many contexts, including industry, education, marketing and business. Some recent studies have focused on qualitative studies based on the actual definition of the metaverse. However, practical research related to metaverse platforms remains in its infancy. This study provides actionable insights into the determinants of metaverse adoption by using perceived fluidity.

A two-stage structural equation modeling (SEM) approach and Hayes’ Macro approach are used to examine the proposed hypotheses.

Results show that technology features (e.g. real-time rendering, interactivity and immersion) increase users’ perceived fluidity, which in turn leads to positive intentions to use the metaverse. A high level of perceived realism is not an advantage for metaverse technology and plays a negative moderating role in this mechanism. The interaction of awe with technological features can enhance the negative moderating effects of realism.

This study pioneers the examination of perceived fluidity as a key determinant of metaverse adoption, offering a novel perspective beyond traditional factors. It uniquely identifies the paradoxical role of perceived realism, demonstrating its potential negative impact on user experience. In addition, the research highlights the reinforcing effect of awe on this relationship.

Driven by the rapid expansion of online retail and the surge in livestream commerce, the impact of different livestream mode on brand and platform performance has become a critical issue. This paper analyzes the impact of artificial intelligence (AI) and key opinion leader (KOL) livestream on the profitability of brands and the platform, incorporating the effects of horizontal interactions to identify the optimal livestream mode.

This paper develops a model of a platform supply chain involving two brands and a platform, where each brand independently decides whether to utilize KOL or AI livestream. Applying Stackelberg game approach, the study derives equilibria for various livestream scenarios, identifying the optimal livestream mode for both parties. Additionally, the model is extended to incorporate asymmetric market potential and network externality to evaluate their impact on a brand’s choice of livestream mode.

Several interesting and important results are derived in this paper. Firstly, it is found that AI livestream enables brands to leverage network externality and mitigate the market disadvantage, thereby gaining a competitive advantage. Secondly, while KOL livestream promotes trust, the medium KOL commission rates could cause brands to be trapped in a prisoner’s dilemma, and excessively high commission rates may render them less profitable. Thirdly, the KOL commission rate, network externality intensity, horizontal interactions and market disadvantage are critical determinants influencing a brand’s choice of livestream mode.

This study is the first to investigate the effects of horizontal interactions, asymmetric market potential and asymmetric network externality on livestream mode selection by brands within a platform supply chain. The research provides valuable insights into optimizing livestream strategies to enhance brand profitability.