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Key core technology is the most important weapon of the country, and breaking through the “strangled” problem is one of the real problems that China’s emerging industries and enterprises must solve. Accurately identifying the “strangled” problem will help China accelerate the realization of high-level scientific and technological self-reliance and win the battle against key core technologies.

Combined with the characteristics of key core technologies, the key core technology evaluation system was constructed from four dimensions: technology innovation, technology radiation, technology economy and technology safety. We adopt the entropy TOPSIS method to evaluate the patents, and the patents with the top 5% scores are identified as key core technology patents. Then, this study identifies key core technology “strangled” problems in three dimensions: technology value advantage, competitive advantage and quantitative advantage.

Taking the patent data of the global new generation information technology industry from 2011 to 2023 as a sample, 178 moderately “strangled” technologies and 49 severely “strangled” technologies are selected. The study results are consistent with the current situation of the new generation information technology industry’s development, and verify the feasibility and reliability of the key core technology “strangled” problem identification model.

This study uses patent data to identify key core technologies and “Strangled” in the new generation information technology industry. It can provide a reference for relevant national departments and agencies, as well as universities and enterprises.

The purpose of this study is to examine the effects of servant leadership (SL) and innovative organizational culture (IOC) on employees' innovative work behavior (IWB). In addition, this paper attempts to examine the mediating role of perceived organizational support (POS) in these relationships.

Data were collected from 280 employees working in technopark companies located in Turkey, which require intensive IWB. Structural equation modeling and bootstrapping procedure were used to test the hypothesized relationships.

The findings suggest that SL, and IOC are significantly and positively related to employees' IWB. The results also show that SL and IOC stimulate employees' IWB through POS.

Because this study was carried out by employing a cross-sectional research design with data obtained from the same source, the inferences about the causality among the variables cannot be inferred.

The empirical findings suggest that organizations should make efforts to promote SL and improve IOC in order to harvest IWB from their employees. Moreover, organizations and managers need to recognize the importance of the POS by employees, and therefore form an adequate working environment, create and utilize policies and procedures accordingly.

This study suggests ways for organizations to enhance their innovativeness through IOC and SL applications in pursue of harvesting employees' IWB using POS by employees as mediator. This study is also original, in that no previous studies have investigated the mediating role of POS in the relationship between IOC, SL and IWB.

本研究旨在探討僕人式領導和創新組織文化對僱員的創新工作行為的影響; 研究亦擬探討就上述有關的影響和關聯而言、組織支持感所扮演的調節角色。

研究的數據取自在土耳其的科技園區裡工作的280名僱員 (在科技園區工作，僱員須具備強大的創新工作行為) 。研究採用結構方程模型和拔靴法程式去測試假設的關聯。

研究結果間接表明了僕人式領導和創新組織文化與僱員創新工作行為之間的關聯是正相關的，而且，這相關性頗為顯著。研究結果亦顯示，僕人式領導和創新組織文化均會透過組織支持感、促進僱員的創新工作行為。

由於研究採用橫斷研究設計，而使用的數據又取自同一來源，故變數間的因果關係是無法推斷的。

本研究之經驗結果建議組織應鼓勵推行僕人式領導，亦應努力提昇創新組織文化，以能取得僱員創新工作行為所帶來的成效。而且，組織和管理階層必須明白使僱員感受到組織的支持是很重要的，因此，組織和主管須創造一個良好的工作環境，並制訂適當的政策和處理各種事宜的程式，以能提高組織支持感。

本研究為組織提供了增強創新能力的建議，方法是透過促進創新組織文化和採用僕人式領導，並利用僱員組織支持感的調節作用，以取得僱員創新工作行為所帶來的成效。另外，本研究是獨創的，這是因為從前沒有研究、去探討在創新組織文化和僕人式領導與創新工作行為之間的關聯上、組織支持感所扮演的調節角色。

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

This study aims to examine whether Chinese firms increase their concerns about analysts’ earnings forecasts following the split-share structure reform (SSR) in 2005, which removed trading restrictions on approximately 70% of the shares of listed firms.

Using data from 2002 to 2019, the authors empirically test the association between meeting or beating analysts’ earnings expectations and the implementation of SSR.

The authors find that firms are more inclined to meet analysts’ earnings expectations after the introduction of SSR. Further analysis shows that firms guide analysts to walk their forecasts down by manipulating third-quarter earnings, suggesting enhanced value relevance between analysts’ forecasts and third-quarter earnings management in the postreform period.

The findings reveal an undesirable side effect of SSR and suggest that policymakers and regulators should consider and carefully manage the complex relationships between firms and analysts.

In contrast to prior studies that predominantly focus on the positive effects of the reform, this study reveals the side effects of SSR and provides new evidence on the mechanisms of meeting or beating analysts’ earnings expectations.

This study assesses how relational factors and buyer-supplier relationship commitment (BSRC) influence supply chain integration (SCI) and firm performance in Bangladesh's apparel manufacturing sector. Firm performance includes operational performance and innovation performance.

Grounded in the social exchange theory, a survey data-based structural equation modeling (SEM) approach is applied. Based on two experts and four executives' opinions and an in-depth literature review, 28 measurement items were identified in the close-ended questionnaire design. Further, 144 valid questionnaires from the manufacturer-supplier dyads in Bangladesh were collected and used for SEM analysis.

Our study reveals that relational factors positively influence BSRC. BSRC directly impacts SCI, operational, and innovation performance, whereas SCI is significantly related to operational and innovation performance. Besides, SCI mediates the two relationships: BSRC and operational performance; and BSRC and innovation performance.

Our results contribute to the literature and offer a new way to understand relationships that connect relational factors of BSRC, BSRC, and outcomes not only by examining the focal firm but also by examining its dyadic supplier partner separately. Separate assessment in the dyad displays some similar and dissimilar results. Moreover, we suggest practical implications for managers to enhance firm performance by focusing on the significance of linking relational factors, BSRC, and SCI.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

Storytelling-driven messaging for luxury hotels is a robust social media marketing strategy to boost electronic word of mouth (e-WOM) and induce visit intention. This paper focuses on individual-related characteristics related to imagery processing and explores their relationship with brand trust and narrative transportation. Gender is examined for its potential moderating impact on relationships revolving around e-WOM intent formation.

This study employs the partial least squares-structural equations modelling (SEM) and multi-group analyses (MGA) approaches to examine consumer responses to luxury hotel brands’ Instagram marketing. Data from 268 responses to an online survey was analyzed on Smart PLS4.

Results confirm that comprehension fluency, imagery fluency and narrative transportation are predictors of brand trust. Additionally, brand trust and narrative transportation impact e-WOM intention. The impact of narrative transportation on e-WOM intention is relatively stronger in men; while women exhibit a stronger impact of brand trust on e-WOM intention.

It is recommended that luxury hotel brands create content which is easy to comprehend and also capable of inducing mental imagery, to boost the narrative transportation effect. Content should be tailored to target specific gender segments to enhance e-WOM effectiveness. Detailed strategies for segment-specific content are discussed in the paper.

This study demonstrates how gender differences shape consumer responses to brand storytelling on Instagram, particularly for luxury hotels, filling a notable gap in extant literature.

The sudden COVID-19 epidemic in 2019 has frustrated China's overall economy, and the implementation and development of the National Fitness Program has encountered huge obstacles. At a new historical starting point, in order to realize the dream of becoming a powerful country in sports, it is necessary to transform the successful experience gained since the reform and opening up into regular understanding and systematic theories, so as to make a theoretical response to the new contradictions and challenges faced in development and give full play to the National Fitness has comprehensive values and multiple functions in improving people's health, promoting people's all-round development, promoting economic and social development and demonstrating the country's cultural soft power.

Taking the topic of national fitness as an example, this paper sets out from the three dimensions of knowledge input, knowledge output and knowledge production, using citation analysis, social network analysis, co-word analysis and cluster analysis, to measure the characteristics and knowledge structure of interdisciplinary knowledge exchange.

China's national fitness is still in the primary development stage, and the strong boost of the national top-level policy is the biggest driving force of its development, driven by the policy together with the settlement of many major events, constantly improving and enriching the wings. The main body of knowledge production on the topic of national fitness is mainly colleges and universities, with low participation of government and enterprises, high degree of cooperation among authors, obvious interdisciplinary characteristics and strong application of research themes.

This study provides a strong theoretical basis for the promotion of the Healthy China strategy. Especially under the influence of COVID-19, this paper can contribute to the comprehensive value and multimodal functions of national fitness in improving the health of people, promoting economic and social development and demonstrating the soft power of national culture.

This chapter explores the impact of Entrepreneurial Orientation (EO), Sustainable Orientation (SO), and Human Resource Orientation (HRO) – the key dimensions of the strategic posture known as Humane Entrepreneurship – on the success of 142 equity crowdfunding campaigns hosted on the US-based platform WeFunder. Utilizing text analysis in conjunction with fuzzy set qualitative comparative analysis, a configurational research method, the study identifies diverse combinations of these dimensions that lead to successful funding outcomes. The analysis, encompassing both minimum and maximum funding targets and supplemented by robustness tests, indicates that successful equity crowdfunding campaigns are characterized by pitches displaying high levels of autonomy, innovativeness, proactiveness, and risk-taking, with a lesser focus on competitive aggressiveness. It is also observed that campaigns emphasizing HRO consistently achieve their fundraising goals, independent of the funding target set. Notably, a startup’s SO does not seem to significantly influence campaign success within the equity crowdfunding context.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.