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The 21st century has brought the business model earth-shaking changes, especially since the Corona Virus Disease 2019 (COVID-19) epidemic at the end of 2019. Now, the epidemic normalization is slowing down China's rapid development. However, technological development, like artificial intelligence (AI), is unstoppable and is transforming China's economic growth modes from factor-driven to innovation-driven systems. Therefore, it is necessary to study further the new changes in labor entrepreneurship and innovation business models and their mechanism of action on economic growth.

This work studies how innovative human capital (IHC) uses AI and other scientific and technological (S&T) innovation technologies to promote China's innovation-driven economic growth model transformation from the labor entrepreneurship and innovation perspective.

The research shows that the entrepreneurial innovation ability of IHC can increase marginal return and output multiplier effect. It changes the traditional business model and promotes China's economic growth and innovation development. At the same time, this work analyzes China's inter-provincial panel data through the panel smooth transition regression (PSTR) model. It concludes that there is a nonlinear relationship between IHC and the output of innovative achievements. The main body presents three stages of nonlinear changes: first rising, then slightly declining, and rising so far.

The finding provides a direction for solving the problem of slow economic growth and accelerating the transformation of economic growth mode under epidemic normalization.

The distinct characteristics of slow tourism compared to mass tourism determine the differences between slow travelers and mass travelers. This transformation is essential for potential tourists to transition into slow travelers when promoting slow tourism to them. Can travel vlogs, a “fast” way of promoting, achieve such a “slow” transformation? To address this question, the purpose of this paper is to introduce the resonance theory and investigate the impact of resonance with slow tourism vlogs on potential travelers’ destination image and travel intention.

Through the distribution of online questionnaires on site, this study collected a total of 349 valid questionnaires. Subsequently, the researcher used partial least squares structural equation modeling to analyze the data and test the hypotheses.

The research findings indicate that cognitive resonance only positively affected cognitive image, while emotional resonance was positively associated with both cognitive image and affective image. In addition, it was observed that cognitive image and affective image played a positive mediating role between resonance with slow tourism vlogs and travel intention.

This research is pioneering in quantitatively validating the structure linking resonance, destination image and travel intention in the context of slow tourism. By validating the second-order structure of cognitive and emotional resonance, this study underscores the crucial role of resonance in shaping tourists’ transition toward a slow mindset in slow tourism.

Temperature is an important load for a ballastless track. However, little research has been conducted on the dynamic responses when a train travels on a ballastless track under the temperature gradient. The dynamic responses under different temperature gradients of the slab are theoretically investigated in this work.

Considering the moving train, the temperature gradient of the slab, and the gravity of the slab track, a dynamic model for a high-speed train that runs along the CRTS III slab track on subgrade is developed by a nonlinear coupled way in Abaqus.

The results are as follows: (1) The upward transmission of the periodic deformation of the slab causes periodic track irregularity. (2) Because of the geometric constraint of limiting structures, the maximum bending stresses of the slab occur near the end of the slab under positive temperature gradients, but in the middle of the slab under negative temperature gradients. (3) The periodic deformation of the slab can induce periodic changes in the interlayer stiffness and contact status, leading to a large vibration of the slab. Because of the vibration-reduction capacity of the fastener and the larger mass of the concrete base, the accelerations of both the slab and concrete base are far less than the acceleration of the rail.

This study reveals the influence mechanism of temperature gradient-induced periodic deformation in the dynamic responses of the train-track system, and it also provides a guide for the safe service of CRTS III slab track.

This study aims to explore the dimensional structure of hotel digital intelligence capability and develop a measurement scale.

This study adopts qualitative and quantitative approaches to conduct an exploratory inquiry into the structural dimensions of hotel digital intelligence capability with the help of grounded theory. Based on this, several questionnaires were developed to test the measurement scale and verify its validity.

The results reveal that hotel digital intelligence capability comprises four dimensions: data collection and processing capability, customer service personalization capability, digital intelligence decision support capability and sustainable development capability. The measurement scale consists of four factors and 13 items, with reliability and validity tests demonstrating ideal levels.

This study not only provides a new perspective to understand hotel digital intelligence capability but also develops a corresponding measurement scale, laying a solid theoretical basis for hotel managers to scientifically evaluate this capability to achieve sustainable competitive advantage.

本研究旨在探索酒店数智化能力的维度结构，并开发其测量量表

本研究采用定性和定量的方法，对酒店数智化能力的结构维度进行探索性研究。在此基础上，编制了调查问卷以检验量表并验证其有效性

研究结果表明，酒店数智化能力包括四个维度：数据收集与处理能力、客户服务个性化能力、数智化决策支持能力以及可持续发展能力。测量量表由四个因子和13个条目组成，信度和效度测试表明其达到了理想水平

本研究不仅为理解酒店数智化能力提供了新的视角，还开发了相应的测量量表，为酒店管理者科学评估该能力以实现可持续竞争优势奠定了坚实的理论基础

In this study, we examine the relation between employee treatment and annual report readability, which is measured as a reading difficulty score.

We use regression analysis to explore the impact of employee treatment on annual report reading difficulty.

We find a significant negative relation between employee treatment and reading difficulty, which suggests that annual reports of firms with better employee treatment are easier to read and understand (i.e. more readable).

Our study contributes to a more thorough knowledge of annual report readability and our findings may be of relevance to accounting standard setters and investors.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

With the development of artificial intelligence, proximity sensors show their great potential in intelligent perception. This paper aims to propose a new planar capacitive sensor for the proximity sensing of a conductor.

Different from traditional structures, the proposed sensor is characterized by sawtooth-structured electrodes. A series of numerical simulations have been carried out to study the impact of different geometrical parameters such as the width of the main trunk, the width of the sawtooth and the number of sawtooths. In addition, the impact of the lateral offset of the approaching graphite block is investigated.

It is found that sensitivity is improved with the increase of the main trunk with, sawtooth width and sawtooth number while a larger lateral offset leads to a decrease in sensitivity. The performance of the proposed planar capacitive proximity sensor is also compared with two conventional planar capacitive sensors. The results show that the proposed planar capacitive sensor is obviously more sensitive than the two conventional planar capacitive sensors.

In this paper, a new planar capacitive sensor is proposed for the proximity sensing of a conductor. The results show that the capacitive sensor with the novel structure is obviously more sensitive than the traditional structures in the detection of the proximity conductor.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

The purpose of this paper is to investigate the impact of design features in in-feed advertising on its effectiveness. Previous research on various forms of advertising has demonstrated that design features can influence advertising effectiveness. However, given the distinct presentation mode and content of in-feed advertising compared to traditional forms, it is crucial to examine whether the effects of design features differ for this type of advertising. Through two studies, we examined how five specific design features affect consumers' purchase intention within the context of in-feed advertising. The mediating role of perceived value and the moderating role of product involvement are also proved.

Using the methods of online survey and online experiment, the author conducted two empirical studies. In study 2, the authors adopted the orthogonal array design to simplify experimental grouping.

The findings demonstrate that akin to conventional Internet advertising, the informational content, credibility and entertainment value of in-feed advertising exert a positive influence on its efficacy. Notably, the interactive nature of in-feed advertising significantly enhances users' inclination toward making purchases. Conversely, any form of interference can detrimentally impact its utility.

The study demonstrates five design characteristics that may impact the effectiveness of in-feed advertising, expanding the relevant theories about in-feed advertising. At the same time, this study contributes to the understanding of consumer responses to advertising. However, the two studies in this paper are conducted within the framework of WeChat, a popular Chinese social media platform, with the participants consisting exclusively of Chinese users.

Considering the rapid development of in-feed advertising in terms of quantity, content and form, the author believes that the results of this paper can help advertisers in their design thinking. The moderating effect of product involvement can be applied to optimize personalized advertising delivery schemes.

This paper focuses on a practical problem, that is, how to improve the effectiveness of in-feed advertising by modifying advertising design features.

This study aims to investigate the effect of high-performance HR practices (HPHRP) on the innovative work behaviour (IWB) of employees. Drawing on social exchange theory, when employees perceive their exchange relationship in terms of HPHRP and leadership support as fair, we hypothesize that employees will demonstrate greater IWB. However, drawing on social identity theory, we hypothesize that when the attitude of employees towards their employer with best employer practices is favourable, the impact of HPHRP mediated by organizational pride and organizational identification, has a greater impact on employee IWB.

Survey research was used to empirically validate the study involving employees (n = 370) who belong to the best employer brands in India. The data was analysed using Process Macro Models 7 for moderated mediation and Model 6 for serial mediation using bootstrapping procedures.

The results suggest that perceived leadership support moderated the indirect effect of HPHRP on IWB through organizational pride. Again, organizational pride and identification partially and serially mediated the impact of HPHRP on IWB.

Organizations must invest in HPHRP, with supportive leadership practices that can foster an emotional attitude of pride and a cognitive attitude of organizational identification to be an employer of choice resulting in employees’ IWB.

The study investigating the mediating impact of the emotional and cognitive attitudes of pride and organizational identification has not been previously explored, in the relationship between HPHRP and IWB, from a social identity perspective.