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This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Drones will become part of daily lives similar to smartphones becoming a staple of modern living. Nonetheless, only several past studies investigated the intention to utilise drones for parcel delivery however, the intention to use drones among online shoppers was not fully explored. The study attempts to investigate the factors influencing the intention to use drones for last-mile delivery.

A total of 292 data were gathered via an online survey among online shoppers applying a snowball sampling method. Since the study operationalised the measures as composites, a combination of reflective and formative measurement, and the study focusses on predictive purposes, partial least squares structural equation modelling with SmartPLS 4 was applied to test the model developed based on the stimulus-organism-response model.

The analysis found that all the direct hypotheses were found supported. Moreover, Green support, green desire and pro-environmental behaviour positively and sequentially mediated future orientation and intention, whereas technology anxiety and perceived safety moderated the relationship between pro-environmental behaviour and intention.

The respondents only limit to the online shoppers in Malaysia which based on purposive sampling method, thus the findings cannot be generalized to another countries.

Besides enriching the literature on drone studies, the findings provided practical insights to online platforms and drone operators to develop an effective strategy to encourage online shoppers to shift from conventional delivery to drone delivery.

The study developed a new model for drone delivery studies using the S-O-R model in introducing orientation towards the future and green support as the stimulus, green desire as an organism and pro-environmental behaviour and usage intention as a response. The study introduced multiple sequential mediators, also contributing to the S-O-R model to predict online shoppers' behaviour towards drones as a tool for last-mile delivery. Another important contribution, technology anxiety and perceived safety were confirmed to have a moderation effect for the relationship between pro-environmental behaviour and intention to use drones for last-mile delivery.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

Along with the national government's expectation transformation, administrative system reform, economic transition, social demand structure's upgrading and population change, these negative effects are turning increasingly obvious and thus become huge powers that push the reform of traditional elite sports development mode forward. Against this background, in order to make this reform better adapted to China's reality and future development, the chapter suggests that Chinese traditional elite sports development mode should shift its driving forces of development from single to multiple, change its administrative system from government-oriented to society-oriented, develop its training concepts from instrumentalism to humanism, improve its construction of development from unbalanced to balanced and alter its effectiveness of development from extensive to intensive so as to achieve sustainable development.

Based on upper echelon theory and signaling theory, we aim to examine the impact of returnee executives on firms’ relative exploratory innovation focus and the moderating effect of economic policy uncertainty on this relationship.

Using panel data of Chinese listed companies from 2009 to 2020, we obtained empirical evidence to support our arguments.

Returnee executives positively influence firms’ relative exploratory innovation focus. This means that firms with returnee executives will shift the focus of their innovation activities toward exploratory innovation more than exploitative innovation. In addition, we find that economic policy uncertainty strengthens this relationship.

First, by showing how returnee executives positively influence firms’ shift in focus to exploratory rather than exploitative innovation, we expand our understanding of firms’ trade-offs between exploratory and exploitative innovation. Second, this study examines how returnee executives influence the relative importance that firms place on exploratory and exploitative innovation, allowing us to build a realistic and nuanced view of how returnee executives influence firms’ strategic choices. Finally, this study expands the strategic leadership literature and responds directly to the call for studies focusing on how institutional environmental conditions and executive characteristics work together to shape firm outcomes.

This study explores how the effects of youth-oriented values influence the behavioural attitudes towards online conspicuous activities during travel, leading to their further conspicuous consumption travel behaviour.

This study applied a mixed-methods approach. Semi-structured interviews were conducted with 20 young tourists to extract the youth-oriented value measurement scale. A systematic survey was conducted in a renovated heritage site in China. PLS-SEM analysis was performed on 322 responses to test the proposed hypotheses.

The results of semi-structured interviews supplement seven new items and form a measurement scale of youth-oriented values with 16 items in three dimensions (self-identification, peer-identification and eagerness to change). The results of PLS-SEM analysis reveal that all three youth-oriented values significantly influence attitudes towards information searching and content generation, and these two behavioural attitudes are positively related to continuous conspicuous consumption travel. The openness trait moderates the relationship between self-identification and content generation.

The study contributes to youth tourism research in conspicuous consumption travel behaviour. It provides insights to tourism operators to formulate strategies to develop the young tourist market in a cultural heritage tourism context.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Urban villages are prevalent informal settlements within Chinese cities, arising from urban expansion. These areas frequently face systematic demolition during urban renewal due to their disorderly layout and outdated appearance. Urban village renovation (UVR) entails balancing diverse interests and navigating complex conflicts, particularly within China’s dual property rights system encompassing urban and rural land. The purpose of this study is to avoid the fierce interest conflict of UVR.

This study utilized the theoretical framework of value co-destruction. Initially, text mining and literature analysis were employed to identify concept nodes and interaction relationships. Subsequently, the structural equation model (SEM) was used to verify the causal model. Finally, the fuzzy cognitive map (FCM) was developed to dynamically simulate value co-destruction scenarios within UVR across various hypothetical situations.

The concept nodes influencing value co-destruction in UVR form a complex system with multiple levels. This includes three cause nodes and one result node. Among these, actor-to-actor emerges as a primary and underlying cause influencing value co-destruction in these projects. Furthermore, strategies for UVR should prioritize integrated interventions that enhance actor-to-actor relationships.

This study introduced a novel mixed methodology aimed at systematically simulating the dynamic process of value co-destruction during UVR. It also provided a fresh perspective on reverse assessment to mitigate the prevalent interest conflicts in UVR, thereby contributing to theoretical advancements and practical strategies for UVR.

Society is critically dependent on an adequate supply of hospital doctors to ensure optimal health care. Voluntary turnover amongst hospital doctors is, however, an increasing problem for hospitals. The aim of this study was to systematically review the extant academic literature to obtain a comprehensive understanding of the current knowledge base on hospital doctor turnover and retention. In addition to this, we synthesise the most common methodological approaches used before then offering an agenda to guide future research.

Adopting the PRISMA methodology, we conducted a systematic literature search of four databases, namely CINAHL, MEDLINE, PsycINFO and Web of Science.

We identified 51 papers that empirically examined hospital doctor turnover and retention. Most of these papers were quantitative, cross-sectional studies focussed on meso-level predictors of doctor turnover.

Selection criteria concentrated on doctors who worked in hospitals, which limited knowledge of one area of the healthcare environment. The review could disregard relevant articles, such as those that discuss the turnover and retention of doctors in other specialities, including general practitioners. Additionally, being limited to peer-reviewed published journals eliminates grey literature such as dissertations, reports and case studies, which may bring impactful results.

Globally, hospital doctor turnover is a prevalent issue that is influenced by a variety of factors. However, a lack of focus on doctors who remain in their job hinders a comprehensive understanding of the issue. Conducting “stay interviews” with doctors could provide valuable insight into what motivates them to remain and what could be done to enhance their work conditions. In addition, hospital management and recruiters should consider aspects of job embeddedness that occur outside of the workplace, such as facilitating connections outside of work. By resolving these concerns, hospitals can retain physicians more effectively and enhance their overall retention efforts.

Focussing on the reasons why employees remain with an organisation can have significant social repercussions. When organisations invest in gaining an understanding of what motivates their employees to stay in the job, they are better able to establish a positive work environment that likely to promote employee well-being and job satisfaction. This can result in enhanced job performance, increased productivity and higher employee retention rates, all of which are advantageous to the organisation and its employees.

The review concludes that there has been little consideration of the retention, as opposed to the turnover, of hospital doctors. We argue that more expansive methodological approaches would be useful, with more qualitative approaches likely to be particularly useful. We also call on future researchers to consider focussing further on why doctors remain in posts when so many are leaving.