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This paper aims to explore the impact of Sc element on the microstructure and corrosion properties of Mg-0.5Zn alloy.

Three kinds of Mg-0.5Zn-xSc (x = 0.1, 0.3 and 0.5 Wt.%) alloys were obtained, and the microstructure and corrosion properties were both analyzed.

As the Sc concentration increases, the corrosion resistance of the alloys initially improves and subsequently deteriorates. The trace addition of Sc can effectively reduce the grain size of Mg-Zn-Sc alloys and enhance the density of the corrosion products film. Consequently, an appropriate amount of Sc can reduce the corrosion rate of Mg-0.5Zn alloy.

However, the addition of Sc also introduces the second phase particles in the alloy, leading to galvanic corrosion, which adversely affects the corrosion resistance of Mg-0.5Zn alloy. Therefore, the amount of Sc added should be carefully controlled.

This paper seeks to explore how contextual factors influence the effectiveness of government support initiatives in enhancing the international performance (IP) of small and medium-sized enterprises (SMEs). Addressing the fragmented nature of international business literature regarding institutional context, this study adopts an institution-based view (IBV) to examine how specific components of institutional context act as moderators in the relationship between government support and SME performance in international markets.

The study is grounded in empirical research, utilizing data collected from 257 exporting SMEs in the Caucasus region through a random sampling method, achieving a response rate of 57.1%, comparable to similar studies in international business. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both direct effects and moderating relationships.

The results confirm that government support, whether informational or experiential, significantly enhances SMEs’ performance in international markets. However, the relationship between government support and IP is moderated by the stability and specificity of the institutional environment. Institutional enforceability does not exhibit a significant moderating effect. Notably, institutional predictability moderates only the relationship between informational support and SMEs’ IP, highlighting the nuanced role of institutional context.

This research contributes to international business literature by applying an IBV, emphasizing the critical role of context in interactions among market stakeholders. It provides novel insights into how institutional context shapes the effectiveness of government support initiatives in fostering international success for SMEs, particularly in emerging economies. These findings advance the understanding of institutional context influences on SME internationalization and highlight the importance of tailoring government support in accordance with institutional context.

In an era marked by artificial intelligence (AI), virtual reality (VR) and augmented reality (AR), this study presents a research paradigm centered on nurturing fundamental skills crucial for effective digital leadership in a paradoxical age where leaders are ambitious as well as skeptical for the adoption of such technologies. This study offers a strategic framework to seamlessly integrate diverse technologies into leadership development; the objective is to bridge the divide between theoretical understanding and practical implementation, especially through the lens of paradox theory.

This conceptual study delineates essential attributes that digital leaders must cultivate, drawing insights from the corpus of literature encompassing leadership, technology and organizational advancement. Synthesizing theoretical perspectives, the study proposes a comprehensive research framework that provides a systematic approach to harnessing the potential of AI, VR and AR to enhance leadership competencies. This conceptual study significantly contributes to paradox theory through method of “theory adaptation” as elaborated in the literature.

The study unveils a spectrum of foundational proficiencies, including technological acumen, adaptability, strategic acumen, effective communication, collaborative aptitude and ethical acumen, among others. These competencies underscore the multifaceted skill set required of digital leaders. To adeptly traverse the intricate digital terrain, foster innovation and align technological advancements with organizational objectives, these proficiencies are imperative for digital leaders to possess.

The distinctiveness of this study lies in its all-encompassing approach to digital leadership development by offering a paradoxical perspective and hence making a contribution to the body of knowledge for paradox theory. By amalgamating AI, VR and AR into a cohesive framework, the study enhances the comprehension of how these technologies collaboratively nurture leaders capable of cultivating organizational triumph in the digital age. This proposed paradigm serves as a bridge between cutting-edge digital technology usage and leadership proficiency paradox, furnishing pragmatic insights to benefit both academic researchers and industry practitioners.

Building on resource dependence theory and the dynamic institution-based view, this paper examines the influence of government affiliations on firm product innovation in a dynamic institutional environment.

Using unique panel data of Chinese manufacturing firms covering a period of 12 years (1998–2009) with 2,564,547 firm-year observations, this study chooses the panel Tobit model with random effects to explore the influence of government affiliations on firm product innovation, followed by an analysis to test the moderation effects of dynamic institutional environments.

The study findings suggest that Chinese firms with higher-level government affiliations have a relatively high product innovation performance. It finds that this innovation stimulating effect is contingent on the dynamic nature of the institutional environment. To be specific, a high speed of institutional transition may depress the positive innovation effects of government affiliations, while a more synchronized transition speed of institutional components may enhance the positive innovation effects of firms' government affiliations.

This study adds to a better understanding of the drivers of product innovation in Chinese firms that are situated in environments that are characterized by institutional change, using and contributing to resource dependence theory and the dynamic institution-based view.

This study aims to examine how Confucianism influences corporate digital transformation and explore the underlying mechanisms. Meanwhile, this study also seeks to analyze whether the relationship between Confucianism and corporate digital transformation significantly varies under different contextual conditions.

This study utilizes a sample of Chinese listed firms from 2012 to 2021 to empirically examine how Confucianism influences corporate digital transformation and validate the mechanisms of informal hierarchies, agency costs and financing constraints. Moreover, it explores the moderating effects of political connection and overseas culture. Subsample regressions assess the influence of corporate internationalization, property rights and regional marketization.

The findings of this study highlight the crucial role of Confucianism in driving corporate digital transformation. Confucianism contributes to corporate digital transformation by clarifying informal hierarchies, reducing agency costs and alleviating financing constraints. Nevertheless, political connection and overseas culture weaken the positive impact of Confucianism on corporate digital transformation. Further evidence indicates that Confucianism's influence on digital transformation is particularly pronounced in environments characterized by limited internationalization, heightened marketization and among non-state-owned enterprises.

This study elucidates the role of informal institutions in driving corporate digital transformation, enriching the literatures on the intersection of Confucianism and corporate digitalization. Our findings offer a novel perspective and contribute to management practice by exploring the mechanisms and contextual conditions.

The purpose of this paper is to overcome the inherent lack of precision in commonly used interpolation procedures when solving the mathematical model of turbofan engines, as well as to address the issue that the theoretical variogram model in traditional Kriging models is prone to subjective selection bias, which makes it impossible to accurately capture the inherent fluctuation patterns in compressor data.

To mitigate this challenge, based on the spatial distribution characteristics of the compressor characteristic data of a certain type of turbofan engine, the input and output dimensions of the model are defined. By determining the stable operating region from the original component data, the authors use the proposed Kriging method improved with a support vector machine model to reconstruct the characteristics at unknown speeds within this region. The effectiveness of the proposed method is evaluated using the established assessment metrics.

Experimental results demonstrate that the proposed method exhibits significant advantages over the conventional Kriging approach. Specifically, it leads to a substantial reduction in root mean square error and mean absolute error by 0.0153/0.0118 (low speed), 0.1306/0.0362 (medium speed) and −0.0066/0.2366 (high speed).

This refined approach not only offers notable engineering applicability but also contributes significantly to the enhancement of aerospace engine model solutions’ precision.

Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.

With the exponential growth of mobile applications, recommending suitable mobile applications to users becomes a critical challenge. Although existing methods have made achievements in mobile application recommendation by leveraging graph convolutional networks (GCNs), they suffer from two limitations: the reliance on a singular acquisition path leads to signal sparsity, and the neighborhood aggregation method exacerbates the adverse impact of noisy interactions. This paper aims to propose SMAR, a self-supervised mobile application recommendation approach based on GCN, which is designed to overcome existing challenges by using self-supervised learning to create an auxiliary task.

In detail, this method uses three distinct data augmentation techniques node dropout, edge dropout and random walk, which create varied perspectives of each node. Then compares these perspectives, aiming to ensure uniformity across different views of the same node while maintaining the differences between separate nodes. Ultimately, auxiliary task is combined with the primary supervised task using a multi-task learning framework, thereby refining the overall mobile application recommendation process.

Extensive experiments on two real datasets demonstrate that SMAR achieves better Recall and NDCG performances than other strong baselines, validating the effectiveness of the proposed method.

In this paper, the authors introduce self-supervised learning into mobile application recommendation approach based on GCNs. This method enhances traditional supervised tasks by using auxiliary task to provide additional information, thereby improving signal accuracy and reducing the influence of noisy interactions in mobile application recommendations.

This study aims to identify key contributors, research themes, research gaps, and future directions in hospitality innovation by conducting bibliometric and content analyses of peer-reviewed articles in this field.

A bibliometric analysis was conducted using VOSviewer software on 2,698 peer-reviewed English-language articles retrieved from the Web of Science database, published between 1995 and 2023. Key contributors were identified based on publication volume, citation, and co-citation analysis. Co-occurrence analysis of index keywords and content analysis of influential articles were used to identify research themes.

The study identified four distinct research themes in hospitality innovation: (1) digital technology adoption primarily among customers, (2) innovation management within hospitality firms, focusing on knowledge management and eco-innovation, (3) service innovation primarily among employees, and (4) business model innovation involving multiple stakeholders. Additionally, the study determined key contributors, highlighted research gaps, and provided suggestions for future research directions.

This study contributes to the existing literature by providing a systematic and in-depth review of hospitality innovation research. It identifies key contributors, research themes, and potential gaps for future research, offering valuable insights for both industry practitioners and scholars.

Recently, mHealth apps for COVID-19 have emerged as a new research area due to the diverse efforts to control the COVID-19 epidemic. Although there are many bibliometric studies on mHealth and its applications, no bibliometric study sheds light on mHealth apps for COVID-19 as a new research area. To address the above-mentioned research gap, the current study conducts a bibliometric analysis of research in mHealth apps for COVID-19. It aims to provide a comprehensive overview of the new area and its directions.

The study uses a bibliometric approach to provide an analysis of the overall status of research in mHealth apps for COVID-19. The Scopus database provided by Elsevier was used to extract the analyzed data in this study. SciVal was used to perform the analyses, while VOSviewer was used for scientific mapping.

A total of 457 publications were published between 2020 and 2021 (until Tuesday, June 1) and cited 3,559 times. Publications were written by 2,375 authors, with an average of 5.20 authors per publication. Articles play a pivotal role in the literature on mHealth apps for COVID-19 in terms of production and impact. The research area of mHealth apps for COVID-19 is multidisciplinary. The United States made the largest contribution to this area, while the UK was the most influential. This study reveals the most productive and influential sources, institutions and authors. It also reveals the research hotspots and major thematic clusters in mHealth apps for COVID-19, highly cited publications and the international collaboration network.

mHealth apps for COVID-19 are gaining more and more importance due to their influential role in controlling the COVID-19 epidemic. Using bibliometric analysis, the study contributes to defining the knowledge structure of global research in mHealth apps for COVID-19 as a new, interdisciplinary area of research that has not previously been studied. Therefore, the study results and the comprehensive picture obtained about research in mHealth apps for COVID-19, especially at the level of Internet of Things (IoT) and artificial intelligence applications, make it an effective supplement to the expert evaluation in the field.