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This article aims to examine the boundary conditions of the relationship between career planning and turnover intention and the joint moderating effects of career plateau and risk-taking propensity on the relationships between career plateau and turnover intention.

The data of this paper is collected from 231 employees from Taiwanese organizations with more than four years of work experience.

The results indicate that career plateau significantly moderates the relationships between career planning and turnover intention. The relationships between career planning and turnover intention are weaker when career plateau and risk-taking propensity are low in the three-way interaction effect.

The article examined the moderated moderation model of career planning and turnover intention using career plateau and risk-taking propensity as moderators.

The job content plateau (JCP) is a condition in which employees regard their jobs as routine, and it has been linked to employee turnover intentions and organisational dysfunctional consequences. As a result, the purpose of this study is to see how negative effects of JCP can be avoided.

A conceptual framework has been created and empirically tested that takes into account intervening elements such as job crafting (JCF) and proactive personality (PP) between JCP and career commitment (CC). In this study, survey data from professionals in a variety of businesses were gathered based on position level, total years of experience and educational level. To assess the efficacy of the suggested model and test the hypothesis proposition, confirmatory factor analysis and multiple regressions were used.

The study’s findings demonstrated that JCF plays a mediating function between JCP and CC, allowing proactive executives to lead their CC while minimizing JCP’s negative effects.

This study will have interesting implications and recommendations for practitioners and strategy makers when it comes to assessing the impact of JCP on enhancing career commitment among Indian executives.

This study investigates the antecedents of career initiative, a proactive behavior, whereby individuals engage in activities to promote their career development. The authors first argue that organizational tenure – the length of time employed within a specific organization – will exhibit a curvilinear or inverted-U-shaped relationship with career initiative. In the early years of an employment relationship, career initiative gradually increases as employees overcome the initial challenges of joining a new organization. However, career initiative will plateau and eventually decline as employees struggle to envision further development.

This study uses a survey design with data collected from the North American operations of a large global telecommunications company.

This study identifies two key mechanisms, both concerning relational context, that drive the curvilinear relationship between organizational tenure and career initiative: mentoring and barriers to networking. Specifically, increased mentoring and reduced barriers to networking both significantly weaken the curvilinear effect.

The results suggest that organizations can promote proactive behaviors through employee mentoring and by removing network barriers, particularly for those most at risk for reduced career initiative: early- and especially later-tenure employees.

Career initiative is a valued behavior among employees, but individual-level phenomena can be fostered, or inhibited, by relational context. So, while some scholars have found a trend toward “boundaryless” careers, this study reveals the importance of considering how the boundaries and social context within organizations can create an environment in which employee proactivity can flourish.

This study addresses a central research question: Does employees' personal initiative, with a benevolent political will, lead to career growth prospects in a work environment replete with perceived organizational politics? Drawing upon self-determination, signalling, and social cognitive theories, the authors examine how perceptions of organizational politics operate to limit the influence of benevolent political will – induced personal initiative on career growth prospects.

This research adopts a quantitative research design. This multi-wave, multi-sample and multi-source investigation includes 730 subordinate-supervisor dyads from India's information technology, education and manufacturing companies. The sample comprises 236 full-time faculty members from higher educational institutions and 496 mid-level managers from technical and service departments of information technology and manufacturing companies.

The results indicate that benevolent political will is significantly related to career growth prospects. In addition, perceptions of organizational politics shows a crossover interaction effect. The findings reveal that the indirect relationship between benevolent political will and career growth prospects changed significantly from those with a low perception of organizational politics to significantly negative among those perceiving organizational politics as high.

This study provides several implications for practice regarding personal initiative, benevolent political will and perceptions of organizational politics.

The significant contributions of this study are to provide new insights into the relationship between benevolent political will and career growth prospects and to unravel the paradoxical nature of the personal initiative phenomenon.

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

In recent years, the trend of hiring external CEOs has become increasingly prevalent. However, the impact of these CEOs' prior experiences in different firms on the innovation of their successor firms has not received sufficient attention. Drawing on upper echelons theory and management power theory, this study explores the non-linear relationship between prior CEO experience and breakthrough innovation, as well as the moderating effects of different types of CEO power.

We selected China’s A-share listed manufacturing companies as samples and used zero-inflated Poisson regression to verify the hypothesis. We employed instrumental variable methodology to address potential endogeneity issues and conducted robustness tests by substituting core variables, changing measures, adding additional control variables, and shrinking the core variables.

We conclude that there exists an inverted U-shaped relationship between prior CEO experience and breakthrough innovation. Furthermore, we analyze the effects of formal and informal CEO power on the role of prior CEO experience in breakthrough innovation and find that the inverted U-shaped relationship is contingent upon the level of CEO power.

The findings extend research on CEO succession and offer a reference for firms aiming to hire external CEOs with prior experience to foster breakthrough innovation.

The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.

In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.

Based on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.

With the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

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.