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Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

This study investigated what are the effective leadership styles and practices that boost employees’ work outcomes during the COVID-19 pandemic from the perspective of facilities management professionals (FMPs).

Three predominant leadership styles (transformational, transactional contingent reward and disaster management) were operationalized into 38 leadership practices (X variables) and 8 work outcomes (Y variables). The explanatory sequential research design was adopted. Online questionnaire survey was first conducted on FMPs who managed facilities during the critical periods of COVID-19 pandemic in Singapore. In-depth interviews were then carried out with subject matter experts to elaborate on the quantitative findings.

During the pandemic, FMPs were significantly stressed at work, but also experienced significant job satisfaction and satisfaction with their leaders/supervisors. Statistical results revealed a range of leadership practices that are significantly correlated with FMPs’ work outcomes. One leadership practice is critical as it affects 4 of the 8 FMPs’ work outcomes - frequently acknowledging employees’ good performance during the pandemic.

The study explored 3 leadership styles. There are other styles like laissez faire and servant leadership that might also affect work outcomes.

Based on the findings, suggestions were provided to organizations that employ FMPs on how to improve their work outcomes during a crisis such as a pandemic.

The novelty is the discovery that in the context of a global disaster such as the COVID-19 pandemic, the most relevant leadership styles to boost employees’ work outcomes are transactional contingent reward and disaster management leadership. The study adds to knowledge by showing that not one leadership style is superior – all 3 styles are complementary, but distinct, forms of leadership that need to work in tandem to boost FMPs’ work outcomes during a crisis such as a pandemic.

Online games are widely adopted electronic applications that facilitate flow experiences, which is a highly enjoyable experience for players, thus motivating further engagement in online gameplay. During gameplay, players set gaming goals, and they must make cognitive efforts to achieve these goals. However, we do not know how goal-setting and cognitive gaming elements (game complexity and game familiarity) create flow, indicating a research gap. To fill this gap, we use the cognitive gaming elements in the literature and the theoretical elements of goal-setting theory to build a model.

Conducting a large-scale online survey, we collect 3,491 responses from online game players and use structural equation modeling for data analysis.

We find that challenging goals, game complexity, game familiarity and telepresence are positively linked to player-perceived flow, explaining 45% of the variance. The new finding is that challenging goals can strengthen the link between game complexity and flow. We also find that telepresence can strengthen the link between game familiarity and flow.

Our study provides the novel insight that gaming goals and cognitive gaming elements can generate player-perceived flow. This insight can help game makers design gaming elements to accommodate players' cognitive efforts to achieve in-game goals, thus creating flow and effectively increasing players' game engagement.

In the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling.

Data collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data.

The obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration.

This finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way.

The recognition of housing as an essential requirement in enhancing the quality of life of an individual has increasingly captivated scholars’ interest, particularly within the context of sustainability. However, the identification of suitable attributes of sustainable housing to be prioritized encountered challenges due to a lack of effective approach in addressing uncertainties and stakeholders’ interests. This study attempts to identify critical attributes of sustainable housing in rural areas and explore their interrelationships.

Six dimensions and 54 criteria are proposed and validated using the expert linguistic preferences through the Fuzzy Delphi Method. The Fuzzy Decision-Making Trial Evaluation Laboratory is also applied to determine the interrelationship between those attributes.

The result demonstrates that economic benefits strongly impacted social implications for sustainable housing. The top criteria, including government participation, reduced life cycle cost, environmental protection and local authorities’ participation, are considered to assist housing stakeholders for better sustainable practices.

To the best of the authors’ knowledge, this is one of the first studies addressing the interrelationship among sustainable housing attributes through linguistic preferences in the context of rural areas.

This study aims to investigate the causal complexity of ECF investors’ peer effect through two different paths of structural social influence.

Using the fuzzy-set qualitative comparative analysis (fsQCA) approach, we employ 157 samples from a Chinese ECF source to explore how peer-effect are caused by both informational and normative mechanisms.

The findings suggests that there are multiple configurations could lead to ECF investors’ high level peer-effect through both informational and normative mechanisms, and the informational mechanism' role depends on the normative mechanism, while the normative mechanism could lead to peer-effect independently.

The findings enrich the literature on ECF investors’ behaviors by revealing the diverse configurations resulting in investors’ peer-effect and shedding new light on investigating the decision-making driven by information asymmetry and relationship settings for individuals at a disadvantage.

This is the first study that investigates the multiple-driven of ECF investors’ decision-making and the importance of mutual norms in individuals' decision-making by complex network analysis approach and qualitative comparative analysis from the perspective of complexity. The results reveal the complexity of investors’ decision-making in ECF.

Unethical pro-organizational behavior (UPB) harms organizations’ long-term development; hence, all sectors of society view it as highly concerning. Optimizing leadership and curbing this behavior is a key managerial challenge. This study takes the relationship between temporal leadership and UPB as its object and examines the direct and indirect paths of temporal leadership’s influence on UPB based on the conservation of resources theory. It further dissects the mediating mechanism of emotional exhaustion and the regulating mechanism of job complexity and constructs the mechanism through which temporal leadership affects UPB.

Data gathered from a sample of 380 employees in 24 provinces and cities were employed for empirical testing using validated factor analysis, hierarchical regression analysis, and a bootstrap method.

The results show that temporal leadership inhibits UPB, while emotional exhaustion partially mediates the relationship between temporal leadership and UPB. That is, temporal leadership inhibits pro-organizational unethical behavior by alleviating emotional exhaustion. In addition, job complexity negatively moderates the relationship between emotional exhaustion and UPB and positively moderates the mediating role of emotional exhaustion between temporal leadership and UPB.

First, although the data used in the study were collected at two different times, they were obtained through self-assessment; therefore, the subjective component and the potential problem of common method bias is evident. Second, the study’s sample size and types of respondents are limited.

1. This study found that temporal leadership can inhibit UPB by reducing employee emotional exhaustion. Therefore, organizations should place greater emphasis on the time factor. 2. In terms of emotional factors, organizations should actively focus on the impact of emotional exhaustion on employees' UPBs. 3. In management practice, managers should adjust their leadership modeling behaviors according to the different degrees of job complexity to replace UPBs with conscious and rational behaviors.

The study reveals how temporal leadership affects UPB and provides a theoretical basis for organizations to mitigate employees' UPB by optimizing their leadership style.

Current research on temporal leadership primarily focuses on the positive predictive effects on individual behaviors and attitudes (Zhang and Ling, 2016), but neglects its effects on negative behaviors. This study’s results complement research on the relationship between temporal leadership and employees' negative behaviors and responds to the call by Zhang and Ling (2015) to conduct research related to temporal leadership in China. On the other hand, current research on employees’ UPB largely focuses on its causative factors, while less research has been conducted on the disincentives for UPB, which to some extent limits systematic and sound research on UPB.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

This paper aims to identify a suitable convolutional neural network (CNN) model to analyse where void(s) are formed in asymmetrical flip-chips with large amounts of the ball-grid array (BGA) during underfilling.

A set of void(s)-filled through-scan acoustic microscope (TSAM) images of BGA underfill is collected, labelled and used to train two CNN models (You Look Only Once version 5 (YOLOv5) and Mask RCNN). Otsu's thresholding method is used to calculate the void percentage, and the model's performance in generating the results with its accuracy relative to real-scale images is evaluated.

All discoveries were authenticated concerning previous studies on CNN model development to encapsulate the shape of the void detected combined with calculating the percentage. The Mask RCNN is the most suitable model to perform the image segmentation analysis, and it closely matches the void presence in the TSAM image samples up to an accuracy of 94.25% of the entire void region. The model's overall accuracy of RCNN is 96.40%, and it can display the void percentage by 2.65 s on average, faster than the manual checking process by 96.50%.

The study enabled manufacturers to produce a feasible, automated means to improve their flip-chip underfilling production quality control. Leveraging an optimised CNN model enables an expedited manufacturing process that will reduce lead costs.

BGA void formation in a flip-chip underfilling process can be captured quantitatively with advanced image segmentation.