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This paper analyzes the influence of intellectual capital on firms’ technological innovation, and the intermediary effect of supply chain learning in the relationship between different dimensions of intellectual capital and technological innovation. Using a questionnaire to survey 167 Chinese high and medium-high technological manufacturing firms, our research provides a new insight with interesting results. (1) Among the four dimensions of intellectual capital, only two dimensions, internal social capital and external social capital, exert positive effect on technological innovation; (2) Among the two dimensions of supply chain learning, learning from both suppliers and customers exerts a significant effect on technological innovation, and learning from the customer has a more significant effect; (3) A complete intermediary effect occurs from supply chain learning in the relationship among human capital, structural capital and technological innovation, while an incomplete intermediary effect occurs from supply chain learning in the relationship among external social capital, internal social capital, and technological innovation.

This paper aims to investigate the moderating effects of contextual factors which are environmental uncertainty on the relationship between human resource (HR) exploitation and HR exploration with new product development (NPD) performance and the mediating role of cross-functional integration between them. The main question this study wants to answer is how a firm implements HR practices to gain better performance under different environment factors. This study is the first empirical research which testifies the influence of HR exploitation and HR exploration on NPD performance.

This study uses regression analysis to examine the moderating effect of environment uncertainty and structural equation modeling to test mediating effect of cross-functional integration.

The result shows that HR exploitation influences NPD performance to a higher degree when environmental uncertainty is low than high. And HR exploration plays a more important role when environmental uncertainty is high; HR exploitation influences internal integration significantly, and the effect of HR exploration on external integration is significant; and internal integration and external integration mediate the relationship between HR exploitation/exploration and NPD performance, respectively.

These findings not only contribute to the literature but also provide a view for organizations in making the right decision of exploitative or explorative practices under the giving environment factor which organization facing and achieving better performance.

The purpose of this study is to examine the impact of chief executive officer (CEO) hometown identity on company audit fees in the Chinese setting.

This study uses data from Chinese public companies in the Shanghai Stock Exchange and the Shenzhen Stock Exchange for the period 2008–2019. This study investigates the impact path of CEO hometown identity on company audit fees and further examines the moderating role of internal and external governance level.

This study finds that CEO hometown identity is significantly and negatively related to company audit fees. In addition, CEO hometown identity can reduce audit fees by alleviating agency risk and litigation risk. Moreover, the negative effect of CEO hometown identity on audit fees is more pronounced in companies with a higher percentage of institutional investors shareholding and more analysts tracking quantity.

This study may provide new references for executives’ selection, auditors’ optimization decisions and regulators’ information disclosure system.

This study contributes to the literature by exploring the effect of CEO hometown identity on audit fees in the context of China.

The conventional pedestrian detection algorithms lack in scale sensitivity. The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection, based on deep residual network (DRN), to address such lacks.

First, the “Edge boxes” algorithm is introduced to extract region of interests from pedestrian images. Then, the extracted bounding boxes are incorporated to different DRNs, one is a large-scale DRN and the other one is the small-scale DRN. The height of the bounding boxes is used to classify the results of pedestrians and to regress the bounding boxes to the entity of the pedestrian. At last, a weighted self-adaptive scale function, which combines the large-scale results and small-scale results, is designed for the final pedestrian detection.

To validate the effectiveness and feasibility of the proposed algorithm, some comparison experiments have been done on the common pedestrian detection data sets: Caltech, INRIA, ETH and KITTI. Experimental results show that the proposed algorithm is adapted for the various scales of the pedestrians. For the hard detected small-scale pedestrians, the proposed algorithm has improved the accuracy and robustness of detections.

By applying different models to deal with different scales of pedestrians, the proposed algorithm with the weighted calculation function has improved the accuracy and robustness for different scales of pedestrians.

Limited research has paid attention to the physical attractiveness stereotype in peer-to-peer sharing accommodation settings. Since the high-risk situations in sharing accommodations, this paper aims to exam whether beauty premium is still relevant in peer-to-peer (P2P) accommodation.

The mixed method, including 2,506 secondary data analysis and two scenario experiments, is carried out to test the research framework.

The results show that both beauty premium and beauty penalty exist in the e-commerce context. Excessively high attractiveness and plain looking of hosts are likely to decrease consumers’ booking decision while moderately attractive hosts will stimulate more booking behaviors. Moreover, perceived trustworthiness mediates the effect of physical attractiveness on booking decision. Additionally, similarity between hosts and consumers plays a moderating role in the relationship between physical attractiveness and perceived trustworthiness.

This study reveals the physical attractiveness stereotype effects in P2P accommodation and carry implications to P2P platforms and hosts for providing moderately attractive profile photos, enhancing trustworthiness and similarity between hosts and consumers. Further studies can investigate the robustness of the findings as well as more possible reasons for its occurrence.

The research provides a clearer understanding of physical attractiveness stereotype effect in peer-to-peer sharing accommodation platforms. Besides, the linkage between physical attractiveness and perceived trustworthiness is dynamic; a high host – consumer similarity weakens the negative impact of both excessively high attractiveness and plain looking on consumers’ perceived trustworthiness.

Drawing upon organizational support theory and family-like exchange perspective, this paper aims to investigate whether mentoring influences protégés’ work engagement, and the roles of perceived organizational support (POS) and family-like employee-organization relationship (FEOR) between mentoring and protégés’ work engagement.

Matched data were collected from 290 protégés and their mentors in two large state-owned enterprises in Northwest China. Multiple regression analyses and bootstrapping methods were used to test the hypotheses.

The results show that mentoring is positively related to protégés’ work engagement, and POS and FEOR play multiple mediation roles in the relationship between mentoring and protégés’ work engagement.

The primary contribution of this study is exploring the impact of mentoring on protégés’ work engagement. Additionally, this study uses organizational support and family-like exchange perspective to understand how mentoring influences protégés’ work engagement.

Despite a few studies examining the effect of mentoring on protégés’ work engagement, but focusing excessively on organizational socialization and social exchange, as such, limited attention has been given to the role of emotions. This is, to the best of the authors’ knowledge, the first study to investigate the effect of emotional factors (including POS and FEOR) on the relationship between mentoring and protégés’ work engagement in Chinese organizational culture.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.

In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.

The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.

It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.

Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.

The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.

The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.

The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

The purpose of this paper is to provide a comprehensive and unified presentation of recent developments in skid-steer wheeled mobile robots (SSWMR) with regard to its control, guidance and navigation for the researchers who wish to study in this field.

Most of the contemporary unmanned ground robot’s locomotion is based upon the wheels. For wheeled mobile robots (WMRs), one of the prominent and widely used driving schemes is skid steering. Because of mechanical simplicity and high maneuverability particularly in outdoor applications, SSWMR has an advantage over its counterparts. Different prospects of SSWMR have been discussed including its design, application, locomotion, control, navigation and guidance. The challenges pertaining to SSWMR have been pointed out in detail, which will seek the attention of the readers, who are interested to explore this area.

Relying on the recent literature on SSWMR, research gaps are identified that should be analyzed for the development of autonomous skid-steer wheeled robots.

An attempt to present a comprehensive review of recent advancements in the field of WMRs and providing references to the most intriguing studies.