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The capacity of organizations to adapt swiftly and implement innovative approaches has ignited discussions about which human resource management (HRM) practices can foster creativity and innovative work behavior (IWB) among employees. Research suggests that IWB thrives in an environment where HRM acts as the architect, influencer and developer. However, our understanding of the specific HRM practices that promote innovative work behaviors remains limited. Existing studies offer scattered and occasionally conflicting insights, particularly concerning measurements and theoretical frameworks that could enable greater generalizability. Consequently, a comprehensive review of the relationships between HRM, innovation and IWB could provide clearer evidence about how HRM impacts innovation. This research presents a bibliometric analysis of research on the relationship between HRM and IWB. The purpose of this analysis is to provide an in-depth overview of the current state and future prospects of HRM and IWB by examining past and current research trends and predict future research directions.

The study used a bibliometric approach to collect 280 journal articles from the Web of Science database. The study identifies the most influential publications, outlines the knowledge structure and forecasts future trends using co-citation and co-word analysis.

The results of the co-citation and co-word analysis revealed the existence of four clusters. While acknowledging some limitations, this review sheds light on the expanding field of HRM and IWB research.

The study provides a comprehensive understanding of HRM and IWB as well as insights into future advancements in the field.

This is the first study to use bibliometric analysis based on the Web of Science (WOS) database to conduct a quantitative evaluation of the HRM practice and IWB literature.

This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology.

Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control.

A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios.

This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.

With multiple-related organizations, worldwide infections, deep economic recession and public disorder, and large consumption amount of anti-epidemic resources, the coronavirus disease 2019 (COVID-19) has been defined as a public health emergency of international concern (PHEIC). Nowadays, Wuhan has recovered from the pandemic disaster and reentered normalization. The purposes of this study are to (1) summarize organization collaboration patterns, successful experience and latent defects under across-stage evolution of Wuhan's cooperation governance mode against the pandemic, and on the basis, (2) reveal how the COVID-19 development trends and organizations' collaborative behaviors affected each other.

Detailed content analysis of online news reports covering COVID-19 prevention and control measures on the website of Wuhan Municipal Government was adopted to identify organizations and their mutual collaborative interrelationships. Four complex network (CN) models of organization collaboration representing the outbreak, preliminary control, recession and normalization stages, respectively, were established then. Time-span-based dynamic parameter analyses of the proposed networks, comprising network cohesiveness analysis and node centrality analysis, were undertaken to indicate changes of global and local characteristics in networks.

First, the definite collaborative status of Wuhan Headquarters for Pandemic Prevention and Control (WHPPC) has persisted throughout the period. Medical institutions and some other administrations were the most crucial participants collaborating with the WHPPC. Construction-industry organizations altered pandemic development trends twice to make the situation controllable. Media, large-scale enterprises, etc. set about underscoring themselves contributions since the third stage. Grassroots cadres and healthcare force, small and medium-sized enterprises (SMEs), financial institutions, etc. were essential collaborated objects. Second, four evolution mechanisms of organization collaboration responding to the COVID-19 in Wuhan has been proposed.

First, universality of Wuhan-style governance experience may be affected. Second, the stage-dividing process may not be the most appropriate. Then, data source was single and link characteristics were not considered when modeling.

This study may offer beneficial action guidelines to governmental agencies, the society force, media, construction-industry organizations and the market in other countries or regions suffering from COVID-19. Other organizations involved could also learn from the concluded organizations' contributions and four evolution mechanisms to find improvement directions.

This study adds to the current theoretical knowledge body by verifying the feasibility and effectiveness of investigating cooperation governance in public emergencies from the perspectives of analyzing the across-stage organization collaboration CNs.

This study aims to investigate the effect of sandwich-generation caregiving (caregiving for elders and children simultaneously) on employed caregivers’ job satisfaction when compared with non-sandwich caregiving patterns of no caregiving, children-only caregiving and elders-only caregiving. This study also aims to explore whether depression mediates this effect and whether three types of caregivers-friendly work time (less work-time length, less nonstandard work-time schedule and more work-time autonomy) buffer these direct and indirect effects.

A sample of 7,571 Chinese employees is chosen from the 2020 China Family Panel Studies through a multistage stratified sampling design.

After controlling for employees’ sociodemographic, work and other caregiving characteristics, this study finds that sandwich-generation caregiving is indeed more likely to negatively affect employees’ job satisfaction when compared with no caregiving and elders-only caregiving, but to the same extent as children-only caregiving. This study also suggests that the effect of sandwich-generation caregiving on job satisfaction is mediated by employees’ depression and that three types of caregiver-friendly work time help to weaken the negative effects on employees’ depression and job satisfaction.

To the best of the authors’ knowledge, this is the first study to explore the negative spillover effect and its mechanisms of caregiving on employees’ job satisfaction through focusing on a special caregivers group: employed sandwich-generation caregivers. These results shed light on the importance of extending caregiver studies to the workplace and provide implications for organization managers and human resources practitioners to design caregiver-friendly workplace policies to maintain employed caregivers’ work-family balance.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

The purpose of this paper is to develop and test an empirically grounded comprehensive framework of e-quality that is the composite of the relationship between e-satisfaction and e-loyalty. This study’s objectives were: first, to develop a comprehensive measurement scale of perceived quality from an operations perspective, based on the classic Engel-Kollat-Blackwell (EKB) decision-making process; second, to develop a framework that integrates dimensions of quality and measures perceptions of e-quality during the customer’s decision-making process; and third, to examine the predictive capability of quality attributes in relation to service operations that rely on customer satisfaction and customer loyalty.

Following review of the related literature, focus group protocols were developed and interviews conducted. Based on the focus group input, surveys were developed and administered. Survey data from 717 online customers allowed testing the research hypotheses, and structural equation modeling allowed validation of the research framework.

The study determined that “e-channel quality”, “e-service quality” (including “web site quality” and “transaction quality”), and “product quality” positively influence customer e-satisfaction within an online operation. These constructs, in turn, influence customer e-loyalty in the e-commerce domain. The findings contribute valuable theoretical and managerial implications that can improve e-service operations.

The paper fills a relevant gap in the e-commerce and services operations literature by empirically developing and validating a new and robust quality measurement scale based on the EKB consumer decision-making process. The study also makes an important research contribution by providing empirical evidence that quality is pivotal in gaining customer loyalty and a competitive e-commerce edge.

This paper aims to propose a robust and efficient method for vanishing point detection in unstructured road scenes.

The proposed method includes two main stages: drivable region estimation and vanishing point detection. In drivable region estimation stage, the road image is segmented into a set of patches; then the drivable region is estimated by the patch-wise manifold ranking. In vanishing point detection stage, the LSD method is used to extract the straight lines; then a series of principles are proposed to remove the noise lines. Finally, the vanishing point is detected by a novel voting strategy.

The proposed method is validated on various unstructured road images collected from the real world. It is more robust and more efficient than the state-of-the-art method and the other three recent methods. Experimental results demonstrate that the detected vanishing point is practical for vision-sensor-based navigation in complex unstructured road scenes.

This paper proposes a patch-wise manifold ranking method to estimate the drivable region that contains most of the informative clues for vanishing point detection. Based on the removal of the noise lines through a series of principles, a novel voting strategy is proposed to detect the vanishing point.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

This paper examines the direct relationship between perceived corporate citizenship (CC) and purchase intention. This study also tests the mediating role of brand trust (BT), consumer–brand identification (CBI) and the moderating effect of personal norms and fear to coronavirus disease 2019 (COVID-19) by contributing social exchange theory (SET), brand relationship theory, social cognitive theory (SCT) and fear appeal theory.

Quantitative research was carried out by means of a survey with a sample of 411 regular consumers who work for national retail brands, where the unit of analysis was an individual. The study analysed the data to test the research hypotheses using SPSS and SMART partial least squares (PLS).

This study found a significant positive impact of perceived CC on purchase intention (direct path), and furthermore, a partial mediation was shown for the indirect approach. In addition, personal norms and fear to COVID-19 have a significant impact on the relationships between perceived CC on purchase intention, BT on purchase intention and CBI on purchase intention.

This study provides useful insights for managers to implement CC strategies to enhance consumer purchase intention and brand relationship in the retail sector within the COVID-19 pandemic.

The current study is perhaps the first to investigate the impact of perceived CC on purchase intention across BT, CBI, personal norms and fear to COVID-19 in the retail industry, period of COVID-19 pandemic. The study also makes some significant theoretical contributions and previously did not shed light on customer behaviour in this context.

The purpose of this study is to analyse user-generated content and firm-generated content on perceived quality and brand trust, and eventually how it impacted brand loyalty with pandemic fear as the moderator.

This study employed an online survey questionnaire method in the Facebook online shopping groups to collect the data. The filter question technique was adopted to verify the respondent's validity. A total of 434 samples was collected using purposive sampling. The data were then analysed using SmartPLS 3.0.

The analysis showed that firm-generated content appeared to have a stronger positive relationship on perceived quality and brand trust than on user-generated content. Brand trust and perceived quality are found to influence brand loyalty positively. However, pandemic fears only moderate the relationship between firm-generated content and brand trust and perceived quality. This study revealed that the main components in social media communication do not influence perceived quality and brand trust to the same extent.

This study contributes to the knowledge of social media communication during the pandemic period that has not been studied empirically in the Malaysian context. The main components in social media communication were delineated and the impact of pandemic fears on how they would possibly affect the established relationships in the literature were examined. This study enables the researchers and practitioners to take a closer look at how the pandemic crisis could provide a shift on what has been understood so far.