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Drawing on social information processing theory, this study constructs a multilevel moderated mediation model. This model seeks to delve into the intricate and previously overlooked interplay between supervisor bottom-line mentality (BLM) and knowledge hiding. Within this context, we introduce self-interest as a mediating factor and incorporate performance climate as a team-level moderating variable.

The time-lagged data involve 336 employees nested in 42 teams from 23 automobile sales companies in five regions of China. The analysis was meticulously executed using Hierarchical Linear Modeling, complemented by bias-corrected bootstrapping techniques.

The findings reveal that self-interest acts as a full mediator in the positive link between supervisor BLM and knowledge hiding. Furthermore, the performance climate plays a moderating role in both the relationship between supervisor BLM and self-interest, and the entire mediation process. Notably, these relationships are intensified in environments with a high performance climate compared to those with a low one.

This research stands as one of the pioneering efforts to integrate supervisor BLM into the discourse on knowledge hiding, elucidating the underlying psychological mechanisms and delineating the boundary conditions that shape the “supervisor BLM–knowledge hiding” relationship. Further, our insights provide organizations with critical guidance on strategies to curtail knowledge hiding among their employees.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

This study aims to identify the key factors that influence household pro-environmental behaviors (HPEBs) and explore the differences caused by the same influencing factors between household waste management behavior (HWM) and household energy-saving behavior (HES).

A meta-analysis was conducted on 90 articles about HPEBs published between 2009 and 2023 to find the key factors. HPEBs were further categorized into HWM and HES to investigate the difference influenced by the above factors on two behaviors. The correlation coefficient was used as the unified effect size, and the random-effect model was adopted to conduct both main effect and moderating effect tests.

The results showed that attitude, subjective norms, and perceived behavioral control all positively influenced intention and HPEBs, but their effects were stronger on intention than on HPEBs. Intention was found to be the strongest predictor of HPEBs. Subjective norms were found to have a more positive effect on HES compared to HWM, while habits had a more positive effect on HWM. Furthermore, household size was negatively correlated with HWM but positively correlated with HES.

The same variables have different influences on HWM and HES. These results can help develop targeted incentives to increase the adoption of HPEBs, ultimately reducing household energy consumption and greenhouse gas emissions and contributing to the mitigation of global warming.

This study aims to improve the acceleration in the additive manufacturing (AM) process. AM tools, such as extrusion heads, jets, electric arcs, lasers and electron beams (EB), experience negligible forces. However, their speeds are limited by the positioning systems. In addition, a thin tool must travel several kilometers in tiny motions with several turns while realizing the AM part. Hence, acceleration is a more significant limiting factor than the velocity or precision for all except EB.

The sawtooth (ST) scanning strategy presented in this paper minimizes the time by combining three motion features: zigzag scan, 45º or 135º rotation for successive layers in G00 to avoid the CNC interpolation, and modifying these movements along 45º or 135º into sawtooth to halve the turns.

Sawtooth effectiveness is tested using an in-house developed Sand AM (SaAM) apparatus based on the laser–powder bed fusion AM technique. For a simple rectangle layer, the sawtooth achieved a path length reduction of 0.19%–1.49% and reduced the overall time by 3.508–4.889 times, proving that sawtooth uses increased acceleration more effectively than the other three scans. The complex layer study reduced calculated time by 69.80%–139.96% and manufacturing time by 47.35%–86.85%. Sawtooth samples also exhibited less dimensional variation (0.88%) than zigzag 45° (12.94%) along the build direction.

Sawtooth is limited to flying optics AM process.

Development of scanning strategy for flying optics AM process to reduce the warpage by improving the acceleration.

The study specifically seeks to comprehend the impact of online corporate social responsibility (CSR) initiatives on consumer behaviour, with a focus on Generation Y consumers. It also aims to examine how, particularly within Ghanaian manufacturing firms, the views of Generation Y consumers regarding digital social responsibility (DSR), and how it moderates the relationship between brand loyalty and purchase intention.

This study takes a quantitative approach, using information gathered via a survey questionnaire from 611 Generation Y consumers in Ghana. Examining the connections between DSR, customer engagement, brand loyalty and purchase intention is the main goal of the investigation. Structural equation modelling (SEM) methods are used in the study to examine the data gathered and verify the proposed linkages.

The study reveals a strong positive relationship between corporate social responsibility (DSR) and purchase intention, mediated by consumer engagement and brand loyalty. However, it does not suggest Generation Y's attitudes towards DSR moderating this relationship. The study underscores the importance of DSR for Ghanaian manufacturing businesses.

By studying the relatively unexplored idea of DSR and its effects on consumer behaviour in developing nations – especially in the context of Ghanaian manufacturing enterprises – this study adds to the body of current work. This study sheds light on the ways in which DSR affects Generation Y customers' intentions to buy by examining the mediating roles of brand loyalty and consumer engagement.

Progress in agriculture significantly relies on the adoption of innovative farm practices by farmers. Being proactive and risk-taking catalyses this innovativeness. Progressive farmers in general are proving to be effective in developing their farms along entrepreneurial lines. The paper aims to examine the relationship between the entrepreneurial traits of risk-taking, proactiveness, innovativeness and entrepreneurial intention (EI) of progressive farmers in Kashmir.

A quantitative research approach was used to evaluate how innovativeness, risk-taking and proactiveness affect EI. The data was collected from registered progressive farmers using a structured questionnaire via both online and offline means. 203 useable responses were received. The data was then analysed using partial least squares structural equation modelling (PLS-SEM).

The results reveal that progressive farmers' EI is influenced by innovativeness, risk-taking and proactiveness. As hypothesized, a significant and positive relationship was found between entrepreneurial orientation (EO) traits of risk-taking, proactiveness and innovativeness and EI.

The study adds to the existing body of knowledge on agri-entrepreneurship by conceptualizing EO traits influencing EI of progressive farmers and offering insightful advice to policymakers on how to improve progressive farmers' entrepreneurial abilities and in turn convert their EI into agro venture establishment in Kashmir.

This study makes advancements in the field of farming-related EO by examining the EI of progressive farmers. This study covers a knowledge gap as there aren't many empirical studies on agricultural entrepreneurship that concentrate on the EO of progressive farmers and how it influences the EI in general in India and the Kashmir valley in particular.

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.

The primary objective of this paper is to determine the factors that affect the entrepreneurship propensity of students undergoing compulsory entrepreneurship education courses at various universities.

A research instrument was developed and implemented on a sample of 380 students who were offered compulsory entrepreneurship education courses at six major universities in the Jammu and Kashmir region of India. The study employed multiple cross-sectional designs with a simple random sampling technique to gather data. The collected data was subjected to descriptive statistics and structural equation modeling using SMART-PLS (Version 4).

The findings reveal that conceptualization, opportunity identification and implementation are the three antecedents of entrepreneurship propensity. The results indicate that the conceptualization factor is one of the most important predictors of entrepreneurship propensity, followed by opportunity identification, whereas implementation through education has the weakest influence on students' entrepreneurship propensity.

This research provides important insights to universities for designing and developing entrepreneurship courses that can foster the start-up culture. The results will be helpful for policymakers to devise various programs to boost entrepreneurship.

The study integrated the theories of planned behavior and human capital to evaluate the effectiveness of entrepreneurship courses at the university level. The three factors, namely, conceptual factors, actualization factors and implementation factors of entrepreneurship propensity are under-researched.

Since there is lack of studies in determine factors that affecting enjoyment sentiment when using online learning system, this study aims to explore the antecedents of perceived online learning enjoyment by using extended technology acceptance model (TAM) and its effect on behavioral intentions (BIN) among higher education institutions students.

The research framework was empirically evaluated using a cross-sectional research design and the data was collected from 715 undergraduate students from public higher education institutions in Malaysia using an online survey method. A structural equation modeling using partial least square method was used to examine the hypothesized model.

The results of partial least squares structural equation modeling indicated that the main predictive variables of TAM along with the extended variables were significantly influence the perceived online learning enjoyment. Meanwhile, the analysis also identified that perceived online learning enjoyment can significantly generate positive BIN for using online learning platforms as well as it also plays as a significant mediator role.

This study has significant implications for higher education institutions that wish to develop online learning environment for their students by providing answers to higher education institutions on how to successfully use the learning management system to assist students' learning performance from the aspect of online learning enjoyment sentiment.

This study is remarkable because it is the first attempt to explore the effect of these five predictors on students' learning enjoyment toward online learning platforms and subsequently on BIN to use this learning platforms, especially in the context of Malaysian higher education system. It is also unique in the way to extend the use of TAM predictive variables with others variables to produce more informative results about the study. Hence, this study also has a new contribution in the literature in the domain of digital learning.