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This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

As after-hours technology-mediated work (ATW) becomes common in organizations, the increased workload and interference to life caused by ATW has induced employee turnover. This research develops a mediated moderation model to explain how employees' intrinsic and extrinsic motivations for ATW affect their turnover intention through work–life conflict.

A survey was conducted to collect data of 484 employees from Chinese companies. Partial Least Square was used to perform data analysis.

The results show that intrinsic motivation for ATW has an indirect negative impact on turnover intention via work–life conflict, whereas extrinsic motivation for ATW has both a positive direct impact and a positive indirect impact (via work–life conflict) on turnover intention. This study also helps find that time spent on ATW can strengthen the positive impact of extrinsic motivation for ATW on turnover intention but has no moderation effect on the impact of intrinsic motivation for ATW. Furthermore, this study reveals that the interaction effect of time spent on ATW and extrinsic motivation on turnover intention is mediated by employees' perceived work–life conflict.

By discovering the distinct impact of employees' intrinsic and extrinsic motivations for ATW on turnover intention, this research provides a contingent view regarding the impact of ATW and offers guidance to managers regarding how to mitigate ATW-induced turnover intention through fostering different motivations.

The critical chain project buffer monitor process addresses uncertainty and variability in project duration. However, classical buffer monitor methods only consider buffer consumption, while the dynamic allocation of buffer zones and the buffer consumption trend of activities are ignored. This paper presents the innovative framework for dynamic monitoring of project buffer which covers the dynamic buffer allocation, predictive analytics of buffer utilization and a new monitoring technique based on control chart graph.

First, a dynamically buffer allocation model is framed, and buffer zones are given to the activities considering risks. Then, a predictive model amalgamating Bayesian Optimization, Convolutional Neural Networks, and Long Short-Term Memory networks (BO-CNN-LSTM) is framed. Finally, a new buffer monitor framework is constructed that takes into account historical information about buffer usage and utilizes two thresholds derived from control chart theory.

This approach is empirically tested on a representative agricultural website project in China. The results show that, first, the dynamic buffer allocation makes better use of the project buffer, reduces buffer waste and increases the possibility of timely completion of the project. Second, the BO-CNN-LSTM model predicts better than Long Short-Term Memory (LSTM) and Grey Neural Network Model (GNNM), providing project managers with new management insights and perspectives. Third, the novel monitoring procedure makes the leveraging of historical data possible in the control of the schedule deviations, allowing for more timely interventions in the course of the implementation of the project.

A new project buffer monitoring method suitable for uncertain project environments is proposed.

The purpose of this study is to examine the effect of collaborative innovation networks on new product development (NPD) performance in small and medium-sized enterprises (SMEs). It also investigates the mediating role of business model innovation and moderating role of collaboration experience and external information technology (IT) capability in the above relationship.

To test the research hypotheses about the relationships above, survey data were collected from 209 Chinese manufacturing SMEs. Multiple hierarchical regressions analysis was used to examine the hypotheses.

Results reveal that collaborative innovation networks have positive impacts on NPD performance in SMEs. Moreover, business model innovation mediates and collaboration experience and external IT capability positively moderate the relationship between collaborative innovation networks and NPD performance in SMEs.

This study paints a more complete picture of the relationship between collaborative innovation networks and NPD performance in SMEs and advances the understanding of how and when SMEs enhance their NPD performance through collaborative innovation networks.

The impact of digital technology in the wider travel field has been substantial and significant, changing both the competitive landscape of businesses and the behavior of travelers. However, we are now on the cusp of new digital developments encompassing cloud computing, blockchain, internet of things (IoT) and above all, artificial intelligence (AI), which are predicted to disrupt the business and consumer sides of several industries – travel included. This study aims to frame this upcoming digital transformation in travel within a human-centered approach where the critical understanding of digital humanism principles will enrich social scientists’ research agenda in the coming years.

The viewpoint follows a structure based on (i) setting the scene for the rise of digital transformation, (ii) the historical perspective on digital transformation in travel, (iii) the pandemic’s impact and (iv) future development and research agenda.

It is only by fostering a human-centered digital transformation perspective that social science researchers in travel can realize the “high-tech for high-touch” promise of fostering a human-to-human encounter empowered and possibly hampered, by digital technologies. This work proposes to start from the concepts of digital technology control, participation, education and ethics to design a research agenda with a 2050 horizon.

This work has been designed to shift the attention of researchers toward a human-centered digital transformation approach to reflect on the human-machine relationship for a better society. Due to its inner characteristics the travel field can foster a reflection on this topic by reaffirming the centrality of human beings and their authorship in the travel product creation.

数字技术在更广泛的旅游领域产生了巨大而显著的影响, 改变了企业的竞争图景和游客行为。然而, 我们现在正处于新的数字发展的风口浪尖, 包括云计算、区块链、物联网以及最重要的人工智能, 预计这些技术将颠覆包括旅游业在内的多个行业的企业端和消费端。这项研究建议将即将到来的旅行数字化转型纳入以人为本的方法, 对数字人文主义原则的批判性理解将丰富社会科学家未来几年的研究议程。

该观点遵循基于以下方面的一种结构, (i)为数字化转型的兴起设定场景, （ii）旅行数字化转型的历史视角, (iii)疫情的影响和（iv）未来发展和研究议程。

只有培养以人为中心的数字化转型视角, 旅游领域的社会科学研究人员才能实现“高科技换高品质”的承诺, 培养一种被数字技术赋能但也可能受到阻碍的人与人之间的接触。这项工作建议从数字技术控制、参与、教育和伦理的概念出发, 设计一个2050年的研究议程。

这项工作旨在将研究人员的注意力转移到以人为中心的数字化转型方法上, 以反思更美好社会的人机关系。由于旅游领域的内在特征, 它可以通过重申人在旅游产品创造中的中心地位和作者身份来促进对这一主题的反思。

El impacto de la tecnología digital en el ámbito de los viajes en general ha sido sustancial y significativo, cambiando tanto el panorama competitivo de las empresas como el comportamiento de los viajeros. Sin embargo, en la actualidad estamos en la cúspide de nuevos desarrollos digitales que abarcan la nube informática, blockchain, IoT y, sobre todo, la inteligencia artificial, que se prevé que alteren los aspectos comerciales y de consumo de varias industrias, incluidos los viajes. Esta investigación propone enmarcar esta próxima transformación digital en los viajes dentro de un enfoque centrado en el ser humano, donde la comprensión crítica de los principios del humanismo digital enriquecerá la agenda de investigación de los científicos sociales en los próximos años.

El presente trabajo presenta una estructura basada en (i) el establecimiento del escenario para el auge de la transformación digital, (ii) la perspectiva histórica de la transformación digital en los viajes, (iii) el impacto de la pandemia y (iv) el desarrollo futuro y líneas de investigación.

Únicamente si se fomenta una perspectiva de transformación digital centrada en el ser humano podrán los investigadores de las ciencias sociales en el ámbito de los viajes hacer realidad la promesa de “alta tecnología para un alto contacto”, promoviendo un encuentro entre seres humanos potenciado, y posiblemente obstaculizado, por las tecnologías digitales. Este trabajo propone partir de los conceptos de control de la tecnología digital, participación, educación y ética para diseñar una agenda de investigación con horizonte 2050.

Este trabajo ha sido diseñado para desplazar la atención de los investigadores hacia un enfoque de transformación digital centrado en el ser humano para reflexionar sobre la relación ser humano-máquina para una sociedad mejor. Por sus características internas, el ámbito de los viajes puede propiciar una reflexión sobre este tema reafirmando la centralidad del ser humano y su autoría en la creación del producto turístico.

This study focuses on the relationship between top management teams' (TMTs) digital experience and company innovation efficiency. In addition, this study examines the mechanism role of digital transformation processes, including basic capability, chief information officer (CIO) appointments, process management and business management, on the relationship between TMTs' digital experience and innovation efficiency.

Based on the sample of China's A-share listed companies from 2011 to 2021, this article employs data envelopment analysis (DEA) and PSM-DID models to examine the effect of TMTs' digital experience on company innovation efficiency.

The TMTs' digital experience was positively correlated with company innovation efficiency. Artificial intelligence and digital platform are two key technologies that TMTs' digital experience effects company innovation efficiency. The financial ability of companies is a key factor to ensure the effectiveness of these two technologies. Digital foundation only played an important role in the early stages. In addition, TMTs' digital experience is more focused on technological management, not process management and business management. We also found that the CIO did not play a positive role on digital innovation. Finally, within the manufacturing industry and non-high-tech companies, the TMTs' digital experience has proven to be notably more effective in enhancing innovation efficiency.

This study advises that companies in the initial stages of digital transformation should give precedence to recruiting managers with digital experience into TMT, as their role is pivotal in propelling digital transformation in innovation. Futhermore, it is suggested that in the selection of TMT members, firms could consider dual appointments, such as CIO combined with other roles. Finally, this study recommends that TMTs with a digital background should pursue deeper technological competencies before embarking on corresponding business and process managements.

First, this study constructs a theoretical framework for examining how TMTs' digital experience influences innovation efficiency. Second, this study embedded the digital technology into the company innovation input index, elucidating how TMTs' digital experience directly enhances innovation efficiency. Finally, this study reveals how basic capability, CIO appointments, process management and business management affect the relationship between TMT digital experience and innovation efficiency.

Scientific impact is traditionally assessed with citation-based metrics. Recently, altmetric indices have been introduced to measure scientific impact both within academia and among the general public. However, little research has investigated the association between the linguistic features of research article titles and received online attention. To address this issue, the authors examined in the present study the relationship between a series of title features and altmetric attention scores.

The data included 8,658 titles of Science articles. The authors extracted six features from the title corpus (i.e. mean word length, lexical sophistication, lexical density, title length, syntactic dependency length and sentiment score). The authors performed Spearman’s rank analyses to analyze the correlations between these features and online impact. The authors then conducted a stepwise backward multiple regression to identify predictors for the articles' online impact.

The correlation analyses revealed weak but significant correlations between all six title features and the altmetric attention scores. The regression analysis showed that four linguistic features of titles (mean word length, lexical sophistication, title length and sentiment score) have modest predictive effects on the online impact of research articles.

In the internet era with the widespread use of social media and online platforms, it is becoming increasingly important for researchers to adapt to the changing context of research evaluation. This study identifies several linguistic features that deserve scholars’ attention in the writing of article titles. It also has practical implications for academic administrators and pedagogical implications for instructors of academic writing courses.

This paper aims to develop an innovative 3D printer based on material extrusion to expand applied material field and shorten the production cycle. The developed 3D printer can fabricate products directly using various powders, including polymers and fillers. In addition, the influence of extrusion on the orientation of thermal conductive filler is also investigated.

To ensure the plasticizing effect and the mixing ability, the printing head is a conical twin-screw extruder, which have a smaller volume. PA12 and h-BN powders were selected for printing as matrix and filler, respectively. The properties of printing products were characterized.

The results show that the new printer can fabricate products directly using polymer powders because of the mixing ability of the twin-screw. The h-BN filler orient in the PA12 matrix and form thermal conduction paths due to the extrusion process, which make the printed samples have an anisotropic thermal conductivity.

The innovative 3D printer provides a method of printing products directly using powders, which can expand material field and shorten the production cycle. For composites, the extrusion process can make fillers orient in the matrix to fabricate products with anisotropic characteristics.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

This study aims to provide a reference basis for waterproofing for the long-term safe operation of shield tunnels. Shielding subways in the long-term operation of tunnel tube seams leads to opening, dislocation and other issues, which in turn cause the tube sealing gasket to break and ultimately cause water seepage, and the existing symmetrical sealing gasket arrangement cannot meet the waterproofing requirements of the tunnel structure.

First, we carry out an indoor “one-seam” hydrostatic test to quantitatively determine the waterproofing performance of symmetric and four asymmetric arrangements of gaskets. And the arrangement with the best stability and waterproofing performance is selected. Second, we establish a three-dimensional numerical seepage model for the waterproof failure of gaskets with different arrangements, which mechanistically explains the whole course of the gradual failure of the waterproof performance of gaskets with the wedging of water. Finally, we compare and analyze the experimental results with the numerical results to verify the reliability of the different analysis methods.

The results of the research show that the gasket will undergo four stages: the initial stage, deformation stage, wedging stage, and breakthrough stage during the continuous wedging process of the water body. Compared with the symmetric arrangement of the gasket, the asymmetric arrangement of the effective contact part of the gasket stress wave peaks and troughs is smaller, the deformation stage of the ability to resist the deformation of the water pressure is stronger, and the role of the water pressure between the two sealing gaskets of the stress path is less likely to be damaged.

The current test can't fully reproduce real engineering site conditions as it ignores factors like temperature, time and aging during waterproofing tests and lacks tests based on actual application. Only one – seam test is done, lacking research on other seams. The current seepage model has difficulty reflecting some details and needs refinement.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.

The study focuses on the tube sheet joint problem in underground tunnels and proposes four asymmetric gasket arrangements, which are tested and analysed using a variety of methods. The results show that the asymmetric arrangement has a slower decline in waterproofing capacity and better stability, providing a new method and basis for solving tunnel waterproofing problems.