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This study explores how establishing an agile supply chain ecosystem (ASCE) during the COVID-19 pandemic impacts three listed auto firms’ financial and customer performance in China. Drawing on the theory of supply chain resilience, responsiveness and restoration (3R capabilities), we collect, mine and analyze unstructured social media data from Dongfeng Motor Corporation (DFM), BYD Company Limited (BYD) and SAIC Motor Corporation Limited (SAIC), all of which have production plants in Wuhan.

Data mining was conducted on 3,528 comments involving DFM, BYD and SAIC from 190 uploaders in the stages of “Early COVID-19 Period (January 23-May 31, 2020)” and “Enhanced Preparedness and Vaccine Launch Period (September 8, 2020–January 15, 2021)”. Our conceptual model of an ASCE was tested by adopting structural equation modelling and verified by the DFM case study.

The study finds that the 3R capabilities of the supply chain have a positive effect on establishing an automotive agile supply chain ecosystem. Establishing the automotive agile supply chain ecosystem has promoted corporate financial performance and customer performance, but the mechanism of action presents a delayed feature.

This study refines the definitions and application of the 3Rs concerning an ASCE in the automotive industry. Through text mining technology and statistical analysis methods, we uncover and discuss the impact of COVID-19 on the performance of three auto companies in China and examine how ASCE establishment plays a role. Constructing an ASCE provides a theoretical basis and realistic approach for the long-term and healthy development of the automotive supply chain.

According to the resource dependence theory and peer effect theory, this paper explores the mechanism of the focal firm’s knowledge coherence on technological distance among its supply chain partners.

Based on patent data of 301 Information Technology and Communication Services firms over 18 years from 1998 to 2015, this paper conducts panel regression analysis.

Empirical results find the positive relationship between the focal firm’s knowledge coherence and technological distance among its supply chain partners. Furthermore, conclusions indicate that the focal firm’s degree centrality and ego network density negatively and positively moderate this relationship respectively.

The findings of this paper contribute to the existing research in supply chain network innovation and offers guidelines to choose partners with varying technological compositions according to their knowledge bases and network structure characteristics.

Human resources transformation is an essential concept that significantly changes how organizations manage their personnel in an ever-changing business environment. This research intends to explore the potential of artificial intelligence (AI) technologies to facilitate human resource (HR) transformation in automobile companies in China.

The research employs qualitative approaches such as interviews and case studies to gain insights and viewpoints from key stakeholders. The interviews were semi-structured and carried out in person, enabling an in-depth investigation of their experiences and perspectives. The duration of each interview ranged from 30 to 40 minutes.

The study explored three main themes: employee acceptability, career growth and performance, using system thinking theory. The findings highlight the various effects of AI technology on HR changes in automobile businesses. First, AI helps increase employee approval by simplifying activities and improving user experience. Second, it greatly impacts professional advancement by providing tailored learning and skill-building chances. Third, AI integration has a favorable effect on organizational performance indicators such as efficiency, agility and adaptability.

This research's findings significantly contribute to scholars and practitioners engaged in HR reforms. Comprehending the subtle impacts of AI technology on employee acceptability, career advancement and organizational effectiveness helps guide strategic decision-making. Furthermore, these results emphasize the need to connect AI-based HR initiatives with overall corporate goals to enhance impact and efficiency

This research provides new insights into how AI technologies interface with HR transformation. The study offers a thorough understanding of intricate dynamics and identifies new possibilities for using AI in HR practices via system thinking theory. The research enhances academic discussion on HR transformation in the age of AI-driven innovation.

This paper aims to explore the underlying mechanisms and boundary conditions through which equipment manufacturing enterprises can capture market value from digital transformation, with a specific focus on the roles of knowledge search and knowledge recombination.

This study uses a double fixed-effects model to test the hypotheses, using a unique data set of “firm-year” observations from 739 publicly listed equipment manufacturing companies in China, spanning the period from 2018 to 2022.

Digital transformation drives market value creation in equipment manufacturing enterprises through both breakthrough knowledge recombination (BKR) and progressive knowledge recombination (PKR). In addition, the analysis of marginal conditions reveals that diversified knowledge search serves as a substitute for digital transformation in promoting BKR, while also positively moderating the relationship between digital transformation and PKR.

Grounded in the knowledge-based view theoretical framework, this study introduces the novel concepts of BKR and PKR and systematically examines how digital transformation impacts market value in equipment manufacturing enterprises.

This study aims to better understand advanced maternal age (AMA) and its impact on maternal and perinatal health by investigating the prevalence, sociodemographic profiles and adverse pregnancy outcomes of AMA in Abu Dhabi, UAE.

This study is a part of a large multicenter cross-sectional study. The project aimed to improve maternal and early child health in Abu Dhabi, UAE. It was conducted at seven healthcare centers located in the urban, suburban and rural areas of Abu Dhabi. The project’s data included all mothers (i.e. Emirati and non-Emirati mothers) who came to the centers looking for healthcare follow-up.

From the total of 1,710 enrolled mothers with complete data, 379 were with AMA, having a prevalence of 22.2% (95% confidence interval [CI] = 20.2, 24.2). Among 379 advanced maternal age (AMA) mothers, 328(84.5%), and 51(15.5) mothers were between 35 and 39 years, and =40 years, respectively. The mean (SD) of maternal age was 30.0(5.2) years. No significant association was found between AMA, and parent’s education, family income, child gender, preterm birth and low birth weight. In multivariable logistic regression analysis, factors significantly associated with AMA were higher parity (adjusted odd ratio [AOR] = 2.24, 95%CI = 2.00, 2.50), p-value < 0.001, non-Arab mothers (AOR = 2.44, 95%CI = 1.83, 3.24), p-value < 0.001, maternal employment (AOR = 1.37, 95%CI = 1.05, 1.77), p-value = 0.019, and cesarean delivery (AOR = 2.44, 95% CI = 1.83, 3.24), p-value = 0.011, pre-pregnancy maternal overweight (AOR = 1.49, 95%CI = 1.12, 1.98), p-value = 0,006, and pre-pregnancy maternal obesity (AOR = 2.15, 95%CI = 1.35, 3.44), p-value = 0.001.

First, unlike other studies, not all obstetrical outcomes (e.g. maternal and perinatal mortality and morbidity) were included as the current study data were part of a large project, and such limitations are expected; second, only quantitative method was used in the present study, it is clear from the literature AMA is a dilemma. Therefore, a qualitative study and mixed methods (both quantitative and qualitative) will provide a better explanation for such an understudied topic. Therefore, further research is recommended to overcome the abovementioned limitations aiming to explore the complexity of AMA, including studying its social determinants. Furthermore, it is worth emphasizing that future research involves more concerned parties, including health, culture and planning authorities, to better understand AMA issues.

The study provided valuable information regarding AMA issues in the UAE. Such information is of paramount importance to all involved parties, including policymakers, to take action regarding improving the health of mothers with advanced ages. Specific actions can be taken through thorough counseling of all expectant mothers with advanced ages before pregnancy, throughout pregnancy and delivery and beyond, by dealing with existing medical conditions and highlighting possible risks of AMA on pregnancy outcomes and how to overcome them in advance. In addition, further research is recommended to explore the social determinants of AMA.

This study aims to investigate the temperature rise characteristics of vibrating rolling bearings under the influence of the polarization force of unbalanced eccentric blocks. A thermal-fluid-solid mechanics coupled finite element model is established to analyze the effects of different loads and rotational speeds on bearing temperature to prevent overheating, wear and thermal damage.

A thermal-fluid-solid mechanics coupled finite element model of the vibrating rolling bearing is developed based on the principles of heat transfer. Finite element analysis software is used to conduct numerical simulations and study the temperature distribution of the bearing system under different loads and speeds. The model’s accuracy is verified by experimentally measuring the actual temperature of the bearing under the same working conditions.

This study successfully established a thermal-fluid-solid mechanics coupled finite element model of a vibrating rolling bearing, verifying its accuracy and reliability. The research results provide an essential reference for optimizing bearing design, preventing overheating and extending service life.

By analyzing the temperature rise characteristics under various load and rotational speed conditions, the law governing the internal temperature distribution of bearings is revealed. This finding offers a theoretical foundation for comprehending the thermal behavior of bearings.

This study offers a scientific foundation for the maintenance and fault diagnosis of shaker rolling bearings, aiding in the timely identification and resolution of thermal damage issues. Through the optimization of bearing design and usage conditions, the equipment’s lifespan can be prolonged, maintenance expenses can be minimized and production efficiency can be enhanced.

A thermal-fluid-solid mechanics coupled finite element model of a vibrating rolling bearing was established, considering the interaction of multiple physical fields. The influence of the polarization force from the unbalanced eccentric block on the bearing temperature is analyzed in detail, which is close to the actual working conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0396/