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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.

Because of the complexity of relationship between surface tension and its decisive factors, such as temperature, concentration, electronic density, molar atomic volume and electro-negativity, a reasonable predicting model of surface tension of Sn-based solder alloys has not been developed yet. The paper aims to address the surface tension issue that has to be considered if the new lead free solder will be applied for electronics.

Using an artificial neural network (ANN) model with back-propagation (BP) algorithm, the surface tension for Sn-based binary solder alloys was simulated, and the comparison between the simulating results and data from experiments and literatures was analyzed as well. In addition, the relationship between surface tension and its decisive factors would be discussed based on the ANN and orthogonal design methods.

It is shown that the predicting model of surface tension of Sn-based solder alloys is constructed according to the BP–ANN theory, and the predicted value from the BP–ANN is in excellent agreement with the experimental results. The surface tension of Sn-based solders is determined by five factors, i.e. temperature, concentration, electronic density, molar atomic volume and electro-negativity. Among of the factors, molar atomic volume is major factor, and the order of degree of influence on surface tension is molar atomic volume > electro-negativity > electronic > density > concentration > temperature. Moreover, a simply reasonable equation is proposed to estimate the surface tension for Sn-based solders.

The five decisive factors of surface tension for Sn-based binary solder alloys have been analyzed theoretically, and a reasonable model of surface tension for Sn-based binary solder alloys is proposed as well. It is shown that ANN theory will be applied well to simulate the surface tension of Sn-based lead free solder.

The purpose of this article is to explore the impact of open innovation on international revenues, as well as the moderating role of digital transformation and strategic differentiation in the relationship.

This study develops a theoretical framework to specify a group of hypotheses. A two-way fixed effect model is used to analyze the relationship between open innovation and international revenues, as well as the moderating effects of digital transformation and strategic differentiation, using panel data of Chinese multinational firms.

Results revealed that open innovation is statistically significantly positive when related to international revenues. Based on the quantitative analysis, the correlation is stronger in sample enterprises with higher digital transformation and strategic differentiation.

This study highlights how open innovation drives international revenues for Chinese listed firms, advancing resource-based view theory in emerging market countries. Introducing digital transformation and strategic differentiation as boundary conditions addresses research gaps and offers practical insights for supporting open innovation for practitioners.

The purpose of this paper is to provide a novel training-responding controlling approach for human–robot interaction. The approach is inspired by the processes of muscle memory and conditioned reflex. The approach is significant for dealing with the problems of robot’s redundant movements and operator’s fatigue in human–robot interaction system.

This paper presented a directional double clustering algorithm (DDCA) to achieve the training process. The DDCA ensured that the initial clustering centers uniformly distributed in every desired cluster. A minimal resource allocation network was used to construct a memory responding algorithm (MRA). When the human–robot interaction system needed to carry out a task for more than one time, the desired movements of the robot were given by the MRA without repeated training. Experimentally demonstrated results showed the proposed training-responding controlling approach could successfully accomplish human–robot interaction tasks.

The training-responding controlling approach improved the robustness and reliability of the human–robot interaction system, which presented a novel controlling method for the operator.

This approach has significant commercial applications, as a means of controlling for human–robot interaction could serve to point to the desired target and arrive at the appointed positions in industrial and household environment.

This work presented a novel training-responding human-robot controlling method. The human-robot controlling method dealt with the problems of robot’s redundant movements and operator’s fatigue. To the authors’ knowledge, the working processes of muscle memory and conditioned reflex have not been reported to apply to human-robot controlling.

The purpose of this paper is to study the effects of relational mechanisms and market contracts on cross‐enterprise knowledge trading in supply chain and to examine the role of market contracts. Relational mechanism is categorized into indirect and direct relational mechanism in this paper. Cross‐enterprise knowledge trading is categorized into explicit and tacit knowledge trading. The indirect relational mechanism is mainly expressed by knowledge brokers, while the direct relational mechanism consists of shared goals and trust.

Multiple regression analysis was performed on questionnaire data from 256 Chinese manufacturing enterprises in supply chain in order to assess the relationships between relational mechanisms, market contracts and cross‐enterprise knowledge trading.

The results show that knowledge brokers and market contracts have significant and positive effects on explicit knowledge trading, but the effects on tacit knowledge trading are not significant. Shared goals and trust have significant and positive effects not only on explicit knowledge trading but also on tacit knowledge trading, while trust has a stronger positive effect on tacit knowledge trading than explicit knowledge trading. Finally, the moderating effects of market contracts are proven in the relationships between relational mechanisms and knowledge trading, excluding the relationship between knowledge brokers and tacit knowledge trading.

Previous studies about the cross‐enterprise knowledge trading in supply chain focused on theoretical research which did not match with reality, especially in China, where the relational mechanism in trading activities is strong. Based on relational exchange theory and transaction cost theory, a conceptual model for the effects of relational mechanisms and market contracts on cross‐enterprise knowledge trading in supply chain is proposed in this paper, and then empirically tested using the data collected from 256 Chinese manufacturing enterprises in supply chain with multiple regression models. The findings provide a theoretical basis for knowledge trading participants selecting an appropriate governance mechanism to promote knowledge trading, and these also guide the knowledge trading among members of supply chain in practice.

This study aims to explore the effect of chief executive officer education on firms’ action timing and acquisition performance in industry merger waves. In addition, this study investigated the moderating influence of CEO duality and firm cash flow on the relationship between education and entry timing.

Following the methodology for determining merger waves in previous studies, the authors identified 16 industry merger waves of Chinese listed firms from 2008 to 2019. Multiple linear regression was employed to examine the hypotheses.

The results showed that higher CEO education was associated with early participation in merger waves. CEO duality negatively moderated the education-entry timing relation. The effect of CEO education on entry timing was more pronounced when firms had higher cash flow. Moreover, more educated CEOs materially enhanced acquisition performance in merger waves.

Entry timing in industry merger waves has important implications, as early movers establish competitive advantages and achieve higher acquisition performance. However, the managerial characteristics determining entry timing have not received adequate attention. Meanwhile, studies examining the effect of CEO education on acquisitions are limited. This study explored the effect of CEO education on firms’ entry timing and acquisition performance in merger waves, thereby contributing to the literature on merger waves and managerial characteristics. This study’s findings regarding the moderators of the education-entry timing relation enrich the literature on corporate governance and agency theory.

Because of their large field of view, omnistereo vision systems have been widely used as primary vision sensors in autonomous mobile robot tasks. The purpose of this article is to achieve real-time and accurate tracking by the omnidirectional vision robot system.

The authors provide in this study the key techniques required to obtain an accurate omnistereo target tracking and location robot system, including stereo rectification and target tracking in complex environment. A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

The experiments are conducted with all the necessary stages involved in obtaining a high-performance omnistereo vision system. The proposed correction algorithm can process the image in real time. The experimental results of the improved tracking algorithm are better than the original algorithm. The statistical analysis of the experimental results demonstrates the effectiveness of the system.

A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

The purpose of this study is to examine how cloud computing assimilation reduces supply chain financing (SCF) risks of small and medium enterprises (SMEs). This study also investigated the mediating roles of internal and external supply chain integration between cloud computing assimilation and the SCF risks of SMEs, as well as the moderating role of environmental competitiveness.

Data was collected from surveys of SMEs located in China. Multiple regression analysis was used to validate the proposed theoretical model and research hypotheses.

The findings show that cloud computing assimilation could reduce the SCF risks of SMEs directly. The results also indicate that both internal and external supply chain integration mediate the relationship between cloud computing assimilation and SCF risks. Furthermore, environmental competitiveness inhibits the effects of cloud computing assimilation on SCF risks.

To our best knowledge, this is the preliminary study to explore the role of cloud computing assimilation in reducing the SCF risks of SMEs. Also, this study attempted to investigate the process by which cloud computing assimilation affects the SCF risks of SMEs.

This paper aims to investigate the impact of open innovation (OI) in Chinese enterprises on enterprise value (EV). At the same time, this research explores the effects of OI in environmental, social and governance (ESG) and also reveals the intermediary role of ESG in the impact of OI on improving EV.

This study builds a theoretical framework to define a set of hypotheses verified in empirical research. Based on the panel data of Chinese listed companies removing missing data from 2011 to 2020, a two-way fixed effect model is used to study the relationship between OI, ESG and EV.

The research shows that OI of enterprises has effectively improved their value. At the same time, OI can also improve the ESG of enterprises. That OI increases EV is partly by the mediating path of ESG.

This study determines the influence of OI on EV from a stakeholder and sustainable development perspective, clarifying the mediating pathway of ESG in the relationship. Doing so could inspire companies to improve their OI and desire to enhance competitiveness. It will also prompt enterprises to pay attention to social responsibility activities.