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The purpose of this paper is to investigate how mentors affect the career satisfaction of protégés. Drawing from the Elaboration Likelihood Model (ELM), the authors propose a dual processing model that considers both cognition and emotion in the relationship between mentoring and the career satisfaction of protégés.

The study employed a three-stage questionnaire survey to collect data from employees in Chinese enterprises, resulting in a total of 329 valid responses.

The results showed that mentoring had a significant positive impact on protégé career satisfaction. Additionally, role clarity and positive affect of protégés played dual-mediating effects between mentoring and protégé career satisfaction. Moreover, as a non-mentoring behavior, mentor advice-seeking behavior strengthened the positive impact of mentoring on role clarity and positive affect.

In the study, the authors utilize the ELM as a new perspective to construct a dual-mediating model of protégé role clarity and positive affect to illuminate the mechanism of mentoring on protégé career satisfaction, advancing the literatures of mentoring relationship and career development. Further, the authors put forward the moderating effect of mentor advice-seeking behavior by considering it as a non-mentoring behavior of mentors to deepen the understanding of mentorships. Moreover, the authors attempt to propose the long-term cumulative effect of the dual processing to expand the application of the ELM in interpersonal processes.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

A Chinese study revealed that mentoring had a significant positive impact on protégé career satisfaction. Additionally, the role clarity and positive affect of protégés played dual-mediating effects between mentoring and protégé career satisfaction. A particularly innovative aspect of the research was including the moderating effect of mentor advice-seeking behavior, which strengthened the positive impact of the mentoring.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The purpose of this paper is to find a new method to evaluate the hydrogen embrittlement performance of heterogeneous materials and thin film materials.

The changes of hydrogen embrittlement properties of steel were studied by electrochemical hydrogen charging test and scratch test. The microstructure and properties of the alloy were analyzed by hardness tester, scanning electron microscope and three-dimensional morphology. The fracture toughness before and after hydrogen charging was calculated based on the scratch method.

The results showed that the hydrogen-induced hardening phenomenon occurs in the material after hydrogen charging. The scratch depth and width increased after hydrogen charging. The fracture toughness obtained by the scratch method showed that hydrogen reduces the fracture toughness of the material. The comparison error of fracture toughness calculated by indentation method was less than 5%.

The results show that the scratch method can evaluate the hydrogen embrittlement performance of the material. This method provides a possibility to evaluate the hydrogen embrittlement of thin-film and heterogeneous materials.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.