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The motivation of value co-creation among the multi-actor in sharing economy was an important topic in interactive marketing communication research. This study investigated how customer gratitude expression leads to value co-creation of PSPs in the sharing economy, and also investigates the moderating effect of platform benevolent climate.

A three-wave field survey (Study 1) and two experiments (Studies 2 and 3) were given to respondents with sharing economy practitioners.

First, customer gratitude expression positively influenced PSP's perceived meaningful work, which in turn enhanced their value co-creation intention. Second, PSP's perceived platform benevolent climate moderated the relationship between customer gratitude expression and PSP's perceived meaningful work.

Prior research discussed PSPs' value co-creation intention mainly from the perspective of platforms and PSPs, but few considered customer-PSP interaction perspective. This study revealed how customer gratitude expression influences PSP's value co-creation intention in highly interactive digital business context, examined the boundary condition of gratitude expression, and extended the application scenarios of social information processing theory.

This study aims to explore work–life integration (WLI) as a crucial mechanism underpinning the transformation of the hospitality and tourism industry from shifts in work paradigms, including rapid technological advancements, flexible work forms, quiet quitting, increasing awareness of well-being, cultural diversity and gender disparity. It further develops the coping strategies and research agenda.

Through the bridged lens of the work–life boundary theory and conservation of resources theory, this research critically analyzes the literature on emerging work paradigms and WLI and develops a conceptual framework guiding the systematic examination of the WLI mechanisms underpinning the influence from work paradigm shifts.

The analysis reveals the underpinning mechanisms in managing WLI, highlighting the importance of systematic and customized work–life management strategies in adapting to evolving work paradigms. An integrated conceptual framework is developed to guide future research and practical applications.

This study underscores the critical need for strategic WLI management, emphasizing its importance for sustainable industry development. It also proposes actionable work–life management strategies for stakeholders, aiming for positive outcomes at personal, organizational and industrial levels amid the paradigm shifts.

The comprehensive conceptual framework uniquely bridged two theories that afford a novel, interconnected and in-depth approach to analyzing how work paradigm shifts reshape the industry. It further broadens the research agenda on WLI, with promising research directions proposed for advancing the understanding of the ever-evolving hospitality and tourism work landscapes.

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 COVID-19 pandemic and the corresponding control measures have harmed the mental health of professionals working in the construction industry. Existing research has also indicated that demographic characteristics are leading variables causing differences in individual’s perceptions on mental health and psychosocial hazardous factors. Combining these, this research aims to compare the differences and similarities of the perceived mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic.

Using a questionnaire survey, data were collected from 531 construction professionals working in Chinese construction companies, which were analyzed quantitatively using mean score comparative analysis, Mann–Whitney U test and Kruskal–Wallis H test, as well as Spearman’s correlation analysis.

The results indicate that construction professionals with different ages, years of working experience and positions are exposed to different psychosocial hazards, resulting in different mental health conditions during the pandemic. Age and years of working experience are also strong predictors of the level of depression and anxiety experienced by construction professionals; that is, mental ill health tends to decrease with the increase of age and experience. Male department/unit heads, working in a company office environment for a private company and aged 31–40 years old with 11–20 years of working experience, tend to have the best mental health condition. In contrast, psychosocial hazards are more likely to produce the most serious impact on male site-based construction professionals working for a state-owned company, either with less than one year of working experience or in a senior management position.

Despite the significant contribution of the construction industry to the global economy, the differences and similarities of the mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic remain unknown. This research, therefore, reveals the mental health outcomes and psychosocial hazard impacts among different types of construction professionals during the pandemic. Specifically, this research unveils the important personal characteristics which are closely associated with poor mental health and the stronger impacts of psychosocial hazards on the mental health of construction professionals during the pandemic. The results are valuable for governments and construction companies to formulate targeted mental health intervention strategies during future public health emergencies.

As the search engine platform, Baidu has already developed keyword advertising as one of its main business scopes, while in-feed advertising is emerging as another intelligent choice for the company. Our purpose is to validate the effectiveness of keyword and retargeted in-feed advertising on offline sales and whether the effectiveness of these two advertising strategies relies on keyword attributes work.

We utilize data from the ad campaigns of a prominent manufacturer within the machinery and equipment (hereinafter referred to as “the company”) on Baidu. To scrutinize the research hypotheses, we have employed linear regression models. Subsequently, we address potential endogeneity issues and use various techniques to ascertain the reliability of the results.

Empirical evidence indicates that both keyword and in-feed advertising enhance offline sales. Upon examining the moderating role of keyword attributes (specificity and length), we observe that specific keywords (price and word-of-mouth (WOM)) accelerate the boosting effect of advertising on sales; similarly, the longer the keywords, the more obvious the enhanced impact of advertising on sales. Moreover, the positive influence of specific keywords (price and WOM) on advertising effectiveness is more outstanding when the keywords are longer.

To our knowledge, no empirical investigation has yet to analyze keyword and retargeted in-feed advertising concurrently within the search engine context. Our research is the inaugural work to reveal that they serve as mutual substitutes regarding their impact on sales. Furthermore, this paper pioneers examining the moderating effects exerted by keyword attributes (specificity and length) on the effectiveness of these two ad types. The findings presented herein offer valuable insights into the harmonious coexistence and collaboration among companies, advertisers, users and search engine platforms.

The purpose of this paper is answering two research questions: What are the trajectories of persistence in exporting followed by different groups of firms? What factors relate to each trajectory of persistence in exporting? The authors propose and test a framework that links operational and marketing firms’ capabilities to different trajectories of persistence in exporting.

Using a dataset of 2,913 firms over 14 years from the annual manufacturing survey in Colombia (AMS), the authors explored the trajectories of persistence in exporting. The authors applied data envelopment analysis to measure operational and marketing capabilities and group-based trajectory modeling to discover and link such trajectories to firms’ capabilities.

The authors identified four trajectories of persistence in exporting. Also, the authors found that while the interplay between marketing and operational capabilities relates positively to the non-exporting trajectory and negatively to the persistent trajectory, operational capabilities relate positively to the erratic trajectory and negatively to the slow growth trajectory of persistence in exporting. Meanwhile, marketing capabilities do not relate to any trajectory.

Policymakers should help firms develop marketing and operational capabilities to compete globally to motivate them to export and persist in exporting. Policymakers should avoid stimulating firms to reinforce learned and familiar capabilities that cannot leverage desirable trajectories of persistence in exporting.

The authors introduce the trajectories of persistence in exporting, providing a fresh perspective for analyzing exporting behavior over time. The authors have also proposed and tested a unique framework that links operational and marketing firms’ capabilities to these trajectories, thus contributing to the existing body of knowledge on exporting behavior by firms from emerging markets.

The success of public–private partnership (PPP) projects is highly correlated to the successful management of risks encountered during the operation phase. PPP projects are especially exposed to risk due to the long operation period over which revenues need to be generated to recoup substantial initial investment and operational running costs. Despite the critical impact of risk exposure, limited research has been specifically undertaken on the matter of operational risk management. This study seeks to address this oversight by identifying and evaluating operational risk management strategies for PPPs.

Vulnerability theory is the theoretical lens used, with context drawn from Chinese PPP projects. Based on the data collected from expert interviews and questionnaires, 28 operational risk management strategies are identified. A fuzzy synthetic method is employed to analyze the effectiveness of the 28 strategies.

The findings reveal that providing an exit mechanism clause into the contract, establishing a comprehensive performance evaluation mechanism and developing a clear compensation mechanism are the top three effective strategies. This study also reveals that risk mitigation approaches that reduce vulnerability prove more effective than attempts to reduce external threats. Specifically, strategies aimed at managing contract, political, technical and financial risk are the most effective.

The findings of this study extend current knowledge regarding the risk management of PPP projects. They also offer a reference by which practitioners may select effective operational risk management pathways and thereby, galvanize the sustainable development of PPPs.

The quality of the unstructured mesh has a considerable impact on the stability and accuracy of aerodynamic simulation in computational fluid dynamics (CFD). Typically, engineers spend a significant portion of their time on mesh quality evaluation to ensure a valid, high-quality mesh. The extensive manual interaction and a priori knowledge required to undertake an accurate and timely evaluation process have become a bottleneck in the idealized efficient CFD workflow. This paper aims to introduce a neural network-based quality evaluation approach for unstructured meshes to enable higher efficiency and the level of automation.

The paper investigates the capability of deep neural networks for the quality evaluation of unstructured meshes. For training the network, we build a training dataset for mesh quality learning algorithms. The dataset contains a rich variety of unstructured aircraft meshes with different mesh sizes, densities, cell distribution, growth ratios and cell numbers to ensure its diversity and availability. We also design a neural network, AircraftNet, to learn the effect of mesh quality on the convergent properties of the numerical solutions. The proposed network directly manipulates raw point data in mesh source files rather than passing it to an intermediate data representation. During training, AircraftNet extracts non-linear quality features from high-dimensional data spaces and then automatically predicts the overall quality of the input unstructured mesh.

The paper provides a series of experimental results on GPUs. It shows that AircraftNet is able to effectively analyze the quality-related features like mesh density and distribution from the extracted features and achieve high prediction accuracy on the proposed dataset with even a small number of training runs.

Because of the limited training dataset, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The paper publishes a benchmarking dataset for mesh quality learning algorithms and designs a novel neural network approach for unstructured mesh quality evaluation.

This study aims to contribute to the existing institutional literature by examining the impact of regional social trust on foreign firm performance.

In this study, 10,286 annual observations of foreign firms in China from 2008 to 2020 are used to test the research hypotheses using an ordinary least square regression model.

Social trust can improve foreign firm performance by reducing operating costs and increasing risk-taking capacity, while formal institutions and economic policy uncertainty strengthen the positive relationship between social trust and foreign firm performance. Moreover, the results of the heterogeneity test show that the positive relationship is only manifested in the samples from the eastern region and is more pronounced in the samples of small- and medium-sized foreign firms and non-manufacturing foreign firms.

This study makes a theoretical contribution to the literature on institutional theory and foreign firm performance, as well as providing practical guidance to foreign firms on how to improve their performance.

The high-load operation of the engine crankshaft causes severe wear and fatigue. This study aims to prepare in situ textures with effective density and study their wear mechanism on the surface of ductile cast iron, which optimizes the tribological properties of engine crankshafts and reduces wear.

A new method was proposed based on the hardness difference in graphite removal to form an in situ texture. The friction performance was evaluated using a combination of computational fluid dynamics and tribological testings. The influence of the texture characteristic parameters on the bearing capacity of the oil film was analyzed. The surface wear morphology was studied by scanning electron microscopy.

The texture density significantly affected the oil film bearing capacity. The surface texture can reduce the average friction coefficient (COF) by more than 35% owing to the oil film bearing and storage capacity. Specifically, the 13% texture density exhibited the lowest wear rate and COF under all three experimental conditions. The reduction in abrasive particles in the wear area of the textured surface indicates that the surface texture can improve the lubrication mechanism.

This study systematically explored the influence of the weight of each model parameter on tribological properties. Subsequently, focusing on the critical parameter (texture density), detailed tribological testings were carried out to reveal the specific effect of texture density on the wear mechanism under different working conditions, and the optimal texture density to achieve the optimal tribological performance was determined accordingly.