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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 poor capacity of prefabricated construction cost estimation is the essential reason for the low profitability of the general contractor. Therefore, this study aims to focus on the cost estimation of prefabricated construction as the research object. This research aims to enhance the accuracy of total project cost estimation for general contractors, ultimately leading to improved profitability.

This study used Vensim PLE software to establish a system dynamics model. In the modeling process, a systematic research review was used to identify cost-influencing factors; ABC classification and the analytic hierarchy process were used to score and determine the weights of influencing factors.

The total cost error obtained by the model is less than 2% compared with the actual value. It can be used to cost estimation and analysis. The analysis results indicate that there are 7 key factors, among which the prefabrication rate has the most significant impact. Furthermore, the model can provide the extreme range cost; the minimum cost can reduce by 13% from the value in the case. The factor's value can compose a cost control strategy for general contractors.

The cost of prefabricated buildings can be estimated well, and deciding the prefabrication rate is crucial. The cost can be declined by correct cost control strategies when bidding and subcontracting are in process. The strategies can follow the direction of the model.

A systemic, quantitative and qualitative analysis of cost estimation of prefabricated buildings for general contractors has been conducted. A mathematical model has been developed and validated to facilitate more effective cost-control measures.

Social media usage has been documented to affect the psychological well-being of its users. This study aims to examine how social media overload influences cognitive fatigue among individuals in Malaysia.

This study employed a comprehensive research framework based on the stressor-strain-outcome (SSO) model to examine how perceived overload affects social media cognitive fatigue through emotional exhaustion and anxiety. Survey data were gathered from 451 social media users in Malaysia, and data analysis was performed using PLS-SEM.

The findings revealed that information overload, communication overload and interruption overload are antecedents of emotional exhaustion. Communication overload, interruption overload and cognitive overload were identified as antecedents of anxiety, while emotional exhaustion and anxiety were confirmed as predictors of social media cognitive fatigue. However, pathway analysis indicated no relationship between emotional exhaustion and anxiety.

Our study contributes to the literature on media technology and media psychology by examining the psychological mechanisms (emotional exhaustion and anxiety). The findings offer implications for service providers, practitioners and social media users, as they facilitate measures and strategies to mitigate the adverse effects of social media while elevating psychological well-being.

The rise of digitalization brings new opportunities and challenges to female entrepreneurship (FE). This paper aims to systematically review the intellectual landscape of FE research, identifying gaps and proposing future directions.

A systematic literature review was conducted on 1,918 highly relevant articles on FE, which were retrieved from the Scopus and Web of Science up to 2023.

While cyberfeminism is a rising perspective, the impact of digitalization on FE remains underexplored. Antecedents of FE are predominantly examined at the individual, family, organizational and environmental levels, overlooking team-level factors. Studies predominantly focus on outcomes at a single level and within a country, neglecting FE’s cross-level and cross-national complexity and diversity. The static analysis of mediating mechanisms in existing research fails to capture the dynamic process of FE. Furthermore, contextualization research often isolates family, institutional, social and mixed embeddedness, neglecting the simultaneous multiple contexts in which female entrepreneurs operate.

To bridge gaps in existing literature, this paper introduces an integrated research framework for FE. It suggests future research directions encompassing the impact of digital intelligence, heterogeneity of different groups of female entrepreneurs, the multiple contextual embedded nature of FE, dynamic process mechanisms for FE and cross-cultural comparisons. The proposed framework aims to inspire new insights and contribute to the evolving field of FE research.

The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.

Through an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.

The subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.

This paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.

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.

Supply chain risk (SCR) has been extensively explored in various sectors, yet there is a notable scarcity of SCR studies in the dairy industry. This study aims to identify the primary and distinctive risks in the dairy supply chain (DSC), propose a typological model for SCR, highlight challenges specific to the DSC and offer mitigation strategies.

We employ a systematic literature review to collect and review relevant research articles published between 2010 and 2019 to identify the main risks and mitigation strategies associated with the DSC, enabling the construction of a typological model of DSC risks.

Results of the systematic review of the SCR literature show that the main DSC risks include on-farm risk (e.g. risks originating from the farming system), off-farm risk (e.g. supply risk, demand risk and manufacturing risk) and inherent SCR (e.g. logistics risk, information risk and financial risk). Notably, we find that the farming system plays a key role in today’s agricultural supply chain operations, indicating the importance of considering on-farm risk in the entire DSC. Additionally, mitigation strategies are located in response to the identified DSC risks by the typology of DSC risks.

This paper is the first attempt to develop a typological model of SCR for the dairy industry by a systematic literature review. The findings contribute to providing a comprehensive understanding of DSC risks by bridging the gap of ignoring the on-farm risks of the DSC in the existing literature. The typology may serve as a guide in practice to develop mitigation strategies in response to DSC risks.

This study aims to design a novel seasonal discrete grey model for forecasting monthly natural gas consumption by incorporating damping accumulation and time-polynomial term.

Considering the principle of new information priority and nonlinear patterns in the original series of monthly natural gas consumption, we establish a novel discrete seasonal grey model by adding the damping accumulation and time-polynomial term into the existing model. In addition, the order of damping accumulation and the coefficient of time-power term can be determined by the moth flame optimization (MFO) algorithm.

The empirical cases show that the proposed model has a better prediction performance when compared with other benchmark models, including six seasonal grey models, one statistical model and one artificial intelligent model. Based on forecasts, the proposed model can be considered a promising tool for monthly natural gas consumption (NGC) in US.

By combining the damping accumulation and the time-polynomial term, a new discrete seasonal grey model for improving the prediction performance of the existing grey model is proposed. The properties of the proposed model are given, and the newly-designed model is initially applied to predict monthly NGC in US.

The paper proposes a two-level closed-loop supply chain (CLSC) dynamic competitive model based on different competitive cooperation situations, and explores the impact of competitive cooperation methods on the pricing strategies, recycling and remanufacturing strategies and competitive model selection strategies of supply chain firms.

This paper establishes a CLSC game consisting of a manufacturer and two retailers. Firstly, five CLSC models are established in both horizontal and vertical dimensions, each of which competes with one another. Secondly, the recycling and remanufacturing pricing strategies are analyzed under different competition or cooperation models. Finally, the results are verified through numerical analysis.

The overall profitability of the CLSC is highest when the manufacturer–retailer partnership alliance is in place. The relationship between retailers and manufacturers is also found to be the best way to achieve overall optimization of the CLSC.

The paper investigates the relationship between the competitive partnership and the total profit of the CLSC, taking into account how to optimize the overall benefit, and focusing on how to optimize the individual interests of each participating enterprise. The results can provide basis and guidance for managers' pricing decision and competition cooperation.

Flexibility and efficiency are dual attributes of the organizational structure that are crucial for project-driven enterprises to achieve sustainable development in a dynamic environment. However, there is a lack of research on the patterns by which the dual attributes of a project-driven enterprise’s organizational structure affect business model innovation. Employing organizational theory, this study aims to assess the mediating mechanisms and dynamic capabilities through which the dual attributes of the organizational structure influence business model innovation in project-driven enterprises.

Data were collected from 242 employees from four project-driven companies across 26 cities (e.g. Beijing, Tianjin, Guangzhou and Shenzhen) in China. Structural equation modeling revealed the relationship between organizational structure’s dual attributes and business model innovation.

The findings show that the dual attributes (flexibility and efficiency) of the organizational structure have positive impacts on business model innovation. Moreover, dynamic capabilities mediate the relationship between the dual attributes and business model innovation in project-driven enterprises.

This study provides contributions to innovation research in the context of project-driven enterprises by revealing the influence of organizational structure on business model innovation through the firms’ dynamic capabilities. Such knowledge can enable managers of project-driven enterprises to develop effective interventions to promote business model innovation.