Search results

The purpose of this study is to assess the effects of fused filament fabrication (FFF) printing parameters on the surface quality and dimensional accuracy of FFF-fabricated copper green parts using the appropriate filaments. The orthogonal experiments were implemented and the errors in length, width and height were measured and analyzed. The results of range analysis and variance analysis indicated the orders of effect factors. Dissolvent debinding combined with thermal debinding was adopted to remove the binders inside the green part by calculating debinding rate. The influence mechanism of sintering temperatures on the microstructure and shrinkage was elaborated.

The extrusion-based FFF in manufacturing copper parts can overcome shortcomings for high reflectivity and heat dissipation in laser powder bed fusion process at cost-saving and materials saving. This study makes an attempt to prepare copper/binder composite filaments through mixing, extrusion and flowability evaluation.

The results showed that the suitable composite filaments applied for FFF should balance rigidity and plasticity. The combination of printing speed and heating temperature impacts on the surface quality significantly, and the major factor in determining the dimensional accuracy is layer thickness. Two-stage debinding procedure was beneficial for binder removal and sintering process. The higher sintering temperature results in less voids, sizes shrinkage and densified microstructure, which is attributed to the occurrence of sintering neck among the fused copper powders.

The self-prepared copper/binder composite filaments were successfully manufactured using the FFF process. This study provides unique approach and print guidance for fabricating complex structures of pure copper components.

This study examines the impact of climate legislation on green agricultural production and tests the heterogeneous impact of different types of climate legislation on agricultural green production.

In this study, the super-slacks-based measure (super-SBM) model is used to calculate agricultural green total factor productivity (AGTFP). The impact of climate legislation (including legislative acts and executive orders) on AGTFP is examined through regression analysis. The transmission mechanism of climate legislation affecting agricultural green production is further investigated.

This study shows that climate legislation has a positive long-term effect on AGTFP. It stimulates innovation in agricultural green technology but has a negative impact on resource allocation efficiency. Executive orders have a more significant effect on AGTFP than climate legislative acts. The effectiveness of climate legislation is more significant in countries with stronger legislation. Moreover, climate legislation reduces AGTFP in low-income countries while enhancing AGTFP in high-income countries. This effect is most prominent in upper-middle-income countries.

This study examines the different effects of various types of climate legislation, considering the level of economic development and the strength of the legal system on AGTFP. The findings can offer a global perspective and insights for China’s policymaking.

As China is on an inevitable march into the digital era, firms have accumulated abundant digital assets, such as algorithms and data. Facing the possibility of using digital assets as a new type input, besides traditional inputs such as capital and labor, would powerful managers perform better? Would managerial power help managers increase the efficiency of how a firm combines traditional and digital inputs and converts them into outputs? Thus, the purpose of this study is to investigate whether powerful managers promotes corporate productivity by using digital assets as a new input.

Using data from listed Chinese firms between 2008 and 2020, the authors constructed panel regressions with three-way fixed effects to examine whether and how managerial power influences corporate productivity in the current digital context, particularly under market uncertainty.

The findings reveal no consistent relationship between managerial power and corporate productivity. The results explain this from two contrasting effects: while managerial power promotes technological change it hinders technical efficiency – two components of total productivity. Moreover, this study identifies market uncertainty as a significant external contingency. In uncertain markets, strong managerial power positively impacts corporate productivity.

The results extend extant theoretical insights in the literature on how managerial power might influence corporate productivity.

This paper explores the engagement of architecture and design-related disciplines within the built environment with social media. The study aims to identify key themes, assess the methodologies used and compare the approaches across different disciplines.

The research employs a semi-systematic literature review coupled with thematic analysis. This approach allows for a comprehensive examination of existing studies, identifying key themes, trends and gaps in the current knowledge base. Through this methodology, the paper synthesises diverse perspectives and insights from a wide range of sources.

Social media data is a valuable tool for understanding architecture and the built environment, but there is a significant divergence in methodologies across disciplines. There is a growing advocacy for mixed-method approaches that would benefit from critical engagement with data that’s underpinned by theoretical frameworks. Such approaches enhance research robustness, support multidisciplinary collaboration and improve the applicability of social media data in design practices.

Despite the proliferation of literature on social media and the built environment, there remains a lack of nuanced understanding regarding thematic variations within individual design disciplines and the specific research methodologies they employ. This paper critically reviews existing research to offer current insights, research gaps and potential future research directions on social media and architecture/design-related disciplines in the built environment.

This study focuses on the context of interactions in online medical-consultation services. It integrates theories of value co-creation and social support, and it introduces customer-perceived doctor’s contribution behaviors (DCBs) as mediators to investigate the impact of customer value co-creation behavior (CVCB) on customer service well-being (CSW). This study also explores the moderating roles of communication and task characteristics of interactive medical interfaces.

A survey of 353 patients with online consultation experience were conducted. Structural equation modeling and PROCESS macro technology were used to test the hypotheses.

CVCB positively affects CSW through the mediating role of DCBs, including cure-based knowledge contribution behavior (KCB) and care-based emotional supportive behavior (ESB). The difference in task-oriented communication styles between customers and doctors weakens the positive effect of CVCB on perceived doctor’s cure-based KCB and care-based ESB. However, task complexity diminishes the positive effect of CVCB on perceived doctor’s cure-based KCB.

Managers of online medical-consultation platforms can optimize service design and enhance positive doctor–patient interactions to improve CSW and promote sustainable platform growth. This can effectively address social equity issues associated with the uneven distribution of medical resources and enhance societal well-being.

There is insufficient attention to the perceived well-being caused by the service context and customer–employee interactions, especially in online environments. This study contributes to value co-creation theory and customer well-being research by exploring factors influencing CSW in online medical care.

A popular production model for high-tech manufacturers is that they move most production lines abroad to produce formal products for sale and just keep a few production lines in headquarters to manufacture sample products for new product development. Under such a production model, the paper aims to develop a selection model of International Air Express (IAE) for high-tech manufacturers in airfreight of sample products using the fuzzy best-worst method (BWM).

In this paper, an assessment model based on the fuzzy BWM approach is proposed for high-tech manufacturers in selecting airfreight carriers for the shipping of sample products. Further, one high-tech electronic manufacturer in Taiwan was empirically investigated to validate the assessment model.

The result indicates that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for the transportation of sample products. Based on the findings, some practical management implications were discussed.

Some literature limitations should be addressed. Initially, the adoption of the fuzzy BWM assumes independence among criteria. Nonetheless, this assumption is not yet to confirm in this study. Accordingly, this limitation leaves room for improvement in future studies. Further, in this paper, five experienced experts from the Radiant Opto-Electronics Corporation (ROEC) case were empirically surveyed. To ensure the validity of the surveying, this paper adopted an interviewing survey instead of a traditional mailed survey. However, more representative samples are still necessary to confirm the empirical results in future research.

Firstly, the proposed research model provides a systematic framework to the decision-making process, which assists high-tech manufacturers in identifying the most suitable IAEs based on multiple criteria. It has been illustrated that high-tech companies deliver their sample products requiring timely and secure means of transport. In practice, manufacturers can assess various IAEs considering some main factors, such as Operational Flexibility (OF), Partner Relationship (PR), Transportation Capability (TC) and Management, using fuzzy BWM. This process ensures the selection of IAEs aligning with their logistical needs and business priorities, ultimately enhancing operational efficiency and customer satisfaction. Secondly, empirical results from the ROEC case indicate that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for transportation of sample products. In other words, ROEC should consider establishing long-term contracts with preferred IAEs (i.e. FedEx) to secure favorable rates and service commitments. On top of that, results not only provide practical information for manufacturers in selecting IAEs but also for IAE partners to improve their service policies.

The results not only provide practical information for high-tech manufacturers in selecting airfreight carriers but also for the airfreight carriers to improve their service quality.

This study aims to examine the effects of the characteristics of apparel professionals on their attitude toward sustainability-related technology in the context of a developing country, Bangladesh.

A quantitative approach was used to investigate the apparel professionals’ perception of sustainability-related technology. A survey was conducted, and 204 valid responses were used in data analysis. The structural equation modeling technique was used to analyze the data.

The findings demonstrate that apparel professionals’ personal innovativeness positively impacts their knowledge of apparel technology. Knowledge of apparel technology and environmental issues in apparel manufacturing both significantly and positively impact their level of awareness of sustainability-related technology in apparel manufacturing. The findings also suggest that managers’ level of awareness of sustainability-related technology has a significant positive impact on their attitude toward sustainability-related technology.

Fishbein’s attitude theory was applied to examine how the various characteristics of apparel professionals (i.e. personal innovativeness in technology, knowledge of apparel technology, knowledge of environmental issues of apparel manufacturing) affect their awareness of and attitude toward sustainability-related technology. This study expands our understanding of the causal flow among cognitive variables of apparel professionals, including their innovativeness, knowledge, awareness and attitudes. The findings of the study can be helpful to the apparel industry to improve apparel professionals’ adoption of sustainable technology.

The purpose of this study is to map the intellectual structure of sustainable finance and accounting (SFA) literature by identifying the influential aspects, main research streams and future research directions in SFA.

The results are obtained using bibliometric citation analysis and content analysis to conduct a bibliometric review of the intersection of sustainable finance and sustainable accounting using a sample of 795 articles published between 1991 and November 2023.

The most influential factors in the SFA literature are identified, highlighting three primary areas of research: corporate social responsibility and environmental disclosure; financial and economic performance; and regulations and standards.

SFA has experienced rapid development in recent years. The results identify the current research domain, guide potential future research directions, serve as a reference for SFA and provide inspiration to policymakers.

SFA typically encompasses sustainable corporate business practices and investments. This study contributes to broader social impacts by promoting improved corporate practices and sustainability.

This study expands on previous research on SFA. The authors identify significant aspects of the SFA literature, such as the most studied nations, leading journals, authors and trending publications. In addition, the authors provide an overview of the three major streams of the SFA literature and propose various potential future research directions, inspiring both academic research and policymaking.

This study aims to investigate the role of emoji and the moderating effects of recycling messages in encouraging customers’ attitudes toward recycling and their recycling intentions via processing fluency.

Two 2 × 2 between-subject experiments are conducted to investigate the role of emojis, the moderating role of recycling messages and the mediating role of processing fluency on customers’ recycling experiences.

Study 1 identifies that customers’ attitudes toward recycling are enhanced when a positive emoji is used with a promotion-focused message, compared to a prevention-focused message. Study 2 finds that customers’ attitudes and recycling intentions are enhanced when a positive emoji is used with an upcycling message, compared to a recycling message. Both Study 1 and Study 2 identify the mediating role of processing fluency.

Results suggest strategically using emojis and various recycling messages to enhance customers’ recycling experience.

This study investigates the role of emojis as an effective recycling communication strategy.

The application of steel fiber reinforced concrete (SFRC) beams is limited in practice, partially due to the lack of accurate shear strength prediction models. This study aims to develop a reliable shear strength prediction model for SFRC beams.

In this study, an artificial neural network was employed to predict the shear strength of SFRC beams, utilizing a comprehensive database of 562 experimental studies. Multiple neural networks were established with varying hyperparameters, and their performance was evaluated using statistical parameters.

The neural network with 11 neurons showed superior results than other networks. The performance evaluation, efficiency and accuracy of the selected neural network were examined using margin of deviation, k-fold cross-validation, Shapley analysis, sensitivity analysis and parametric analysis. The proposed artificial neural network model accurately predicts the shear strength and outperforms other existing equations.

This research contributes to overcoming the limitations of existing prediction models for shear strength of SFRC beams without stirrups by developing a highly accurate model based on ANN. Utilizing a comprehensive database and rigorous evaluation techniques enhances the reliability and applicability of the proposed model in practical engineering applications.