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Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

The international mentoring literature predominantly features traditional company-assigned expatriates as protégés overlooking other types of global talent, such as immigrants, refugees, and international graduates, who may help organizations gain long-term IHRM competitive advantages. We integrate multidisciplinary research to better understand the role of mentoring as a global talent management tool, identify research gaps, and propose future research directions.

We draw on an integrative review of 71 academic journal articles published between 1999 and 2024 to explore the role of mentoring in managing global talent (i.e. expatriates, immigrants, refugees, and international students and graduates).

We found that research has identified and examined relationships between various antecedents and outcomes of mentoring but mainly treating mentoring as a talent development tool. Less is known about the role of mentoring as a recruitment and selection tool in the pre-employment context. Mentoring is an important HRM tool that contributes to managing a global talent pool and developing existing employees.

The review contributes to a better understanding of the characteristics and processes involved in mentoring in a global context by proposing a framework that incorporates antecedents of mentoring, characteristics of the mentoring process, and mentoring outcomes. It highlights the value of mentoring as a recruitment and selection tool supporting global talent management and identifies avenues for future research.

This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

Based on stakeholder, agency and institutional theory, this study aims to examine the role of institutional ownership in the relationship between environmental, social and governance practices and CEO compensation.

Utilizing a fixed-effect panel regression analysis, this research utilized a panel data approach, analyzing data spanning from 2014 to 2021, focusing on US companies listed on the S&P500 stock market index. The dataset encompassed 219 companies, leading to a total of 1,533 observations.

The analysis identified that environmental scores significantly impact CEO equity-linked compensation, unlike social and governance scores. Additionally, it was found that institutional ownership acts as a moderating factor in the relationship between the environmental score and CEO equity-linked compensation, as well as the association between the social score and CEO equity-linked compensation. Interestingly, the direction of these moderating effects varied between the two relationships, suggesting a nuanced role of institutional ownership.

This research makes a unique contribution to the field of corporate governance by exploring the relatively understudied area of institutional ownership's influence on the ESG practices–CEO compensation nexus.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.