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Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

This study explores entrepreneurial orientation (EO) on project portfolio success in new product development projects, with the moderating effects of digitalization capability and modularization process.

The sample data of 204 firms was used to analyze the research hypotheses. This study adopted hierarchical regression to test the theoretical conceptual model incorporating EO, digitalization capability, modularization process, and project portfolio success.

These results indicate that EO positively affects project portfolio success. More importantly, digitalization capability and modularization process positively moderate the relationship between EO and project portfolio success.

Prominent studies have focused on different antecedent and consequence factors of project portfolio success; however, the impacts of EO still need to be noticed. This study makes a pioneering effort to make up this gap and investigate the effects of EO on project portfolio success, digitalization capability, and modularization process as moderators, which can enrich the current literature on project portfolio management.

Given innovation's significance, this research examines the link between empowered leadership and frugal innovation. The research also explores how collaborative cultures and organizational commitment mediate empowered leadership's effect on frugal innovation.

Quantitative method is used with the approach of hierarchical regression to test the hypotheses with data obtained from Pakistani small- and medium-sized enterprises (SMEs) through the questionnaire from 288 participants.

The results of this study show that empowered leadership has a considerable impact on the firm's capacity for frugal innovation. Additionally, this study shows that organizational commitment and collaborative culture significantly moderate the association between empowering leadership and frugal innovation.

Future studies should examine mediating factors, including employment experience, education and perceived organizational support, and moderating variables like employee psychological empowerment and leadership styles.

This research advises SMEs in developing nations to utilize frugal innovation since they cannot afford to spend extensively on technologies that add creativity and innovation to goods and services.

This study advances how leadership both directly and indirectly helps organizations strengthen their capacity for frugal innovation through the mediating roles of collaborative culture and organizational commitment.

The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

The investigation of pro-environmental behaviors (PEB) among college students is essential for future sustainability endeavors. Existing research seldomly concentrated on college students and their PEB. This study aims to address the gap in understanding PEB among college students.

This study constructed an integrated model combining the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory, with the novel addition of environmental risk perception. Through an empirical study involving 844 college students, this research analyzed the data with the structural model.

The authors identified that environmental values, attitudes, perceived behavioral control, subjective norms and risk perception play crucial roles in shaping PEB. This study also revealed age-related differences, highlighting that older students might be less influenced by attitudes and subjective norms due to more established habits. Findings underscore the importance of fostering PEB through environmental education, promotion of low-carbon lifestyle choices and incentives. This investigation not only enriches the theoretical framework for PEB but also offers practical insights for policymakers and educators to enhance sustainable practices among the youth.

Though the authors offer valuable findings, this research has two key limitations: the use of observational data for hypothesis testing, which weakens causal inference, and the collection of data through questionnaires, which may be biased by social desirability. Respondents of self-report tend to behave in the socially desired ways. Consequently, they usually exaggerate their pro-environmental intention or PEB. To comprehend the influencing aspects more thoroughly, future research should consider incorporating experimental methods and objective data, such as digitalized data.

The findings provide valuable evidence for guiding college students’ PEB, including strengthening environmental education, promoting of low-carbon fashion and providing incentives for PEBs.

First, the authors examine the internal factors influencing PEB among Chinese university students within the “dual-carbon” initiative framework. Second, this research pioneers the use of structural equation modeling to merge TPB and VBN theories, offering a predictive model for university students’ PEB. Third, the authors introduce “environmental risk perception” as a novel variable derived from both TPB and VBN, enhancing the model’s explanatory power.

Prioritising economic, environmental and social requirements of society has become imperative for attaining a competitive edge in the global marketplace. This study examines how internal and external factors influence sustainable strategic orientations of Chinese small technology enterprises. It uses an initial framework grounded in institutional theory and resource-based view (RBV).

The study surveyed small technology enterprises using quantitative techniques. A combination of partial least squares structural equation modelling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) was used to analyse 402 valid samples with statistical significance.

The PLS-SEM results show that absorptive capacity, innovation culture and coercive pressure positively influence small enterprises’ sustainability orientation. Additionally, the fsQCA method identifies five complex combinations of causal antecedents that can facilitate small enterprises’ adoption of sustainability orientation.

The findings reaffirm the importance of maintaining ideal environmental cognition and external institutional pressure. Specifically, enterprises need to strategically allocate their limited resources to optimal levels and combinations to achieve sustainability orientation.

This study enhances the understanding of sustainability orientation by integrating the RBV and institutional theory, highlighting various factors that promote sustainability at the firm level. Furthermore, asymmetric research perspectives show the triggers of sustainability orientation from both symmetrical and asymmetrical perspectives. Set theory approaches represent a pioneering approach that captures the intricacies of sustainability orientation, advancing beyond previous variance-based findings by providing optimal mix solutions.

The purpose of this study is to investigate the determinants of job retention intention among healthcare workers (HCWs) in Canada during the COVID-19 pandemic.

Data are from a large nationally representative cross-sectional survey conducted by the Canadian National Statistics Agency. Ordered logistic regression is estimated to find an association between job retention and its main determinants as gleaned from the literature while controlling for a wide range of pertinent covariates. Odds and standardized odds are reported and discussed.

The results suggest that worsening working conditions, changes in health and well-being and lack of organizational support weaken intentions regarding job retention. Being employed rather than self-employed and working as a nurse also weakens job retention.

This is the first research on the determinants of intentions regarding job retention in Canada using nationally representative data. It allows us to test and confirm the results of previous studies on a large sample of Canadian HCWs. The paper also discusses the implications of the findings for health management and administration.

The objective of the research is to evaluate the carbon footprint of the green asparagus (Asparagus officinalis L.) supply chain, encompassing the agricultural production to the packaging stage in Italy, as it is the sixth largest producer and the second largest in Europe. It provides an assessment in the province of Foggia and highlights the global perspective of the carbon footprint application in agro-food systems.

The carbon footprint (ISO 14067:2018) considers 1 t of packaged fresh asparagus as a functional unit in the agricultural production and packaging stage and is based on primary data collected in one of the leading companies of asparagus production in the province of Foggia, which markets about 0.21 kt of asparagus per year produced in about 31 ha. Data were integrated with face-to-face in-depth interviews and pre-filled checklists.

Findings show that the carbon footprint of 1 t of packaged fresh asparagus is equivalent to 335.31 kgCO2eq, of which 61% in the agricultural stage and 39% in the packaging one. The majority of the emissions are associated with the fertigation and the diesel consumption for the transportation of workers. Farmers should adopt green electricity so as to reduce the emissions associated with the electric pump for the extraction of water from artesian wells. Moreover, it would be desirable to replace mineral urea phosphate with organic fertilizers.

To the best of the authors’ knowledge, scholars have not yet investigated the environmental impacts of the green asparagus supply chain, even if it represents one of the most cultivated vegetables worldwide, with a global production that amounts to 8.5 Mt per year.

This study aims to develop a low-cost additive-subtractive hybrid machine equipped with reverse scanning to fabricate high-precision dental surgical guides. The major focus of developing the hybrid additive manufacturing technology is to achieve clinical precision of dental tools at an affordable price.

The designed machine consists of a self-developed vat-photopolymerization-based 3D printer that can fabricate dental surgical guides. The 3D printer is integrated with a self-developed 3D scanner that will analyze the fabricated part and evaluate the dimensional discrepancies. Based on the data provided by the scanner, the integrated secondary milling process will successfully machine the part to meet the clinical precision and standard.

The efficacy of the newly developed hybrid machine is demonstrated with the fabrication of complex part, lower and upper dental surgical guides with the mean dimensional deviations of 198.1, 136.6 and 117.9 µm, respectively. The integration of the secondary scanning and machining system successfully enhanced the mean dimensional deviation of upper and lower guides by 11.88% and 28.75%, respectively. Furthermore, this study also benchmarked the dimensional accuracies achieved by this low-cost technology with the high-end commercial 3D printers. The overall cost of the machine is evaluated to be $2,399.

This paper proposes a novel hybrid additive manufacturing process with integrated reverse scanning and machining modules to fabricate high-precision dental guides. The developed machine is a low-cost alternative to the existing high-end commercial counterparts. The developed machine has the potential to make endodontic treatments more affordable.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.