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This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Safety cognitive ability is a key factor influencing unsafe behavior. However, the existing achievements have not yet involved the division of the hierarchical relationship of factors influencing safety cognition and lack a quantitative evaluation system of safety cognitive ability. The purpose of this paper is to find out the deficiencies in the safety cognition of workers in high-risk construction positions and to provide practical suggestions for improving their safety cognitive ability and reducing unsafe behavior.

Based on the iceberg model, the factors influencing the safety cognitive ability of workers in high-risk construction positions and their hierarchical relationship were determined, and an evaluation index system consisting of four primary indicators and 20 secondary indicators was constructed. The game theory algorithm was used to optimize the subjective and objective weights of the indicators calculated by the sequential analysis method (G1) and the entropy weighting method (EWM) to obtain the optimal combination weight value. The Matlab software was used for cloud mapping and similarity calculation to determine the safety cognitive ability level of the object to be evaluated.

The research results indicate that the comprehensive level of safety cognitive ability of scaffolders in this construction project is at “Level III”, the fundamental factors and compliance factors are at “Level IV”, the auxiliary factors and driving factors are at “Level III”. This conclusion aligns with the situation learned from the previous field investigation, which validates the feasibility and scientificity of the proposed evaluation method.

Considering that the safety cognitive ability of construction workers is constantly changing, this study has not yet delved into the specific impacts of various influencing factors on the level of safety cognitive ability. Future research can utilize simulation software, such as MATLAB and Vensim, to construct dynamic simulation models that accurately simulate the changing rules of construction workers’ safety cognitive ability under the influence of different factors.

This research broadens the application scope of the iceberg model, enriches the analysis model of the factors influencing the safety cognitive ability of workers in high-risk construction positions and provides a novel perspective for similar research. The safety cognitive ability evaluation method proposed in this paper can not only accurately evaluate the safety cognitive ability level of workers in high-risk positions such as scaffolders but also provide practical suggestions for improving the safety cognitive ability of workers, which is of great significance to improve the safety management level and reduce unsafe behavior in the construction field.

This research fills the research gap of workers in high-risk construction positions and the quantification of safety cognitive ability. The iceberg model is used to realize the hierarchical division of the factors influencing safety cognitive ability. Additionally, an evaluation method for the safety cognitive ability of workers in high-risk construction positions based on the game theory combination weighting method and cloud model is proposed, which realizes the quantitative evaluation of safety cognitive ability.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

In order to make the construction industry develop in the direction of greening, this paper analyzes whether the application of intelligent technology in prefabricated buildings can achieve carbon emission reduction, starting from the problems of weak technology and insufficient encouragement policies in the prefabricated building industry. It also designs dynamic and adjustable incentives for the smart transformation of prefabricated buildings and makes recommendations to facilitate the transformation of assembly manufacturers into “smart factories”.

This paper takes the intelligent technology for carbon reduction, energy efficiency and policy design in the prefabricated buildings industry as the starting point. Based on in-depth expert interviews and questionnaire survey data, a linear multiple regression model is used to establish an association network of intelligent technology in the production and transportation, construction, operation and maintenance, demolition and scrapping stages. On this basis, an evolutionary game theory is used to construct a smart transformation and carbon reduction utility game model between the government and manufacturers, and relevant suggestions for smart empowerment of green construction development technology combinations and policy settings are proposed.

An assembly manufacturing plant with smart empowerment is an important way to achieve green and sustainable development in the construction industry. Among them, BIM and IoT have made a greater impact on carbon emission reduction of prefabricated buildings in all stages of the whole life cycle. The government’s proposed energy efficiency incentives and environmental tax amount will effectively increase companies' motivation for smart transformation of prefabricated buildings. However, when the environmental tax amount is low, the government should strengthen the regulation of the industry in order to increase the speed of smart transformation of assembly manufacturers. Therefore, a reasonable setting of the environmental tax rate and energy-saving incentives and flexible adjustment of the regulatory efforts can maximize the functional utility of the government in the process of smart transformation.

This paper focuses on the impact of intelligent technologies on the overall carbon emissions of the industry and provides an evolutionary analysis of the strategic game between the government and assembly manufacturers, the main players in the smart transformation process of prefabricated buildings. However, smart technologies for different categories of assembly manufacturing plants and strategic options for a wider range of stakeholders have not been examined in depth.

Different from existing research, this study focuses on exploring the strategic game between the government and assembly manufacturers in the smart transformation of prefabricated buildings. It provides an innovative explanation of the connection between intelligent technology and carbon emissions. The study develops an evolutionary game model for both parties, addressing the research gap on the combined effects of policy incentives and intelligent technology on carbon reduction and efficiency improvement in the prefabricated buildings industry. This research not only offers practical reference for the government in designing incentive mechanisms and establishing regulatory systems but also provides feasible practical guidance for the smart transformation and carbon reduction efforts of assembly manufacturing plants.

The study presented healthcare service quality, lean thinking and Six Sigma to enhance patient satisfaction. Moreover, the notion of machine learning is combined with lean service quality to bring about the fundamental benefits of predicting patient waiting time and non-value-added activities to enhance patient satisfaction.

The study applied the define, measure, analyze, improve and control (DMAIC) method. In the define phase, patient expectation and perception were collected to measure service quality gaps, whereas in the measure phase, quality function deployment (QFD) was employed to measure the high-weighted score from the patient's voice. The root causes of the high weighted score were identified using a cause-and-effect diagram in the analysis phase.

The study employed a random forest, neural network and support vector machine to predict the healthcare patient waiting time to enhance patient satisfaction. Performance comparison metrics such as root-mean-square error (RMSE), mean absolute error (MAE) and R2 were accessed to identify the predictive model accuracy. From the three models, the prediction performance accuracy of the support vector machine model is better than that of the neural network and random forest models to predict the actual data.

Lean service quality improvement using DMAIC, QFD and machine learning techniques can be generalized to predict patient waiting times. This study provides better realistic insights into patient expectations by announcing waiting times to enable data-driven service quality deliveries.

Prior studies lack lean service quality, Six Sigma and waiting time prediction to reduce healthcare waste. This study proposes lean service quality improvement through lean Six Sigma (LSS), i.e. DMAIC and machine learning techniques, along with QFD and cause-and-effect diagram.

This study attempted to measure life satisfaction and developed a scale to measure its dimensions with the required psychometric properties (validity and reliability). In today’s scenario, organizations are focusing on life satisfaction by helping employees to maintain their work-life balance. Therefore, this paper aims to develop a robust scale of life satisfaction.

The methodology consists of three broad stages: item generation, scale development, and validity. Using the sample of 198 working executives, this study used the exploratory factor analysis (EFA) and concluded the four dimensions of life satisfaction: work satisfaction, family satisfaction, societal satisfaction and self-satisfaction.

The results provide academicians and practitioners with new insight and dimensions of life satisfaction. The result of this study shows that life satisfaction has societal satisfaction, family satisfaction, job satisfaction and self-satisfaction dimensions.

This study will provide practitioners with new dimensions to measure life satisfaction. They can help employees achieve life satisfaction across four different factors. This will enable more employee satisfaction, an increase in retention rate and an increase in employee performance. Furthermore, this study provides implications from a policy perspective to design the business policy by considering life satisfaction as an important part of formulating and implementing human resource policies.

This study is unique in terms of exploring the dimensions of life satisfaction in a structured manner and establishing the psychometric properties as construct, content, and criterion validity along with reliability. This scale can be further used in future research to measure the life satisfaction construct.

Digital transformation provides a new impetus for the development of declining firms. However, there is currently a lack of sufficient research on whether digital transformation is beneficial for the turnaround of declining firms. This paper aims to explore the relationship between digital transformation and the turnaround of declining firms.

Drawing on the theoretical foundations of the resource-based view and dynamic capabilities theory, this research uses a comprehensive dataset of Chinese A-share listed companies from 2010–2021 to explore the influence of digital transformation on the turnaround of declining firms.

The research findings show that digital transformation contributes to the turnaround of declining firms. Mechanism analyses demonstrate that digital transformation enhances dynamic capabilities and attracts more analysts, thereby facilitating the turnaround process. Moreover, the moderation analysis reveals that CEO equity incentives strengthen the positive correlation between digital transformation and the turnaround of declining firms. Heterogeneity analysis indicates that the association between digital transformation and the turnaround of declining firms is particularly significant for firms with low financing constraints and high-tech firms. Moreover, this research reveals that digital transformation can facilitate the turnaround of firms in deep and long-term decline.

This research contributes to the literature on the digital transformation of enterprises and provides important insights for the turnaround of declining firms.

This study aims to explore the relationship between proactive socialization behaviors (PSBs) and work engagement, with a specific focus on the mediating role of social capital resources (SCRs). Additionally, it investigates how proactive personality and perceived organizational support (POS) moderate the organizational assimilation of newcomers in Punjab, Pakistan, through the lens of self-determination theory.

A cross-sectional survey design was employed, with data collected from diverse organizations across cities in Punjab, Pakistan, including Bhimber, Gujrat, Gujranwala, Lahore, Jhelum, Kharian, Kotla, Mandi Bahauddin, and Sialkot. A total of 1,000 questionnaires were distributed, resulting in 619 valid responses. The sample primarily consisted of individuals with varying levels of work experience, contributing to the diversity of the data. SEM was utilized to analyze the relationships between the studied variables.

Results indicated a significant positive association between PSBs and work engagement among newcomers. SCRs were found to mediate this relationship, suggesting that proactive behaviors help newcomers accumulate social capital, which enhances work engagement. Moreover, the study reveals that proactive personality and POS significantly moderate the relationship between PSBs and work engagement, with stronger positive associations observed among proactive individuals and those perceiving higher organizational support.

The study underscores the importance of encouraging proactive socialization behaviors and strengthening organizational support systems to foster newcomer engagement and facilitate successful assimilation.

Understanding these dynamics can lead to the creation of more inclusive and supportive organizational environments, enhancing both individual well-being and organizational effectiveness.

This research offers new insights into the mechanisms through which PSBs impact work engagement, particularly through the accumulation of SCRs, and highlights the importance of proactive personality and organizational support as moderators.

This study aims to determine the impact of corporate digital transformation on the audit opinions of auditors in A-share nonfinancial listed companies in China. It also examines how corporate internal control and corporate social responsibility (CSR) disclosure levels moderate this effect. This study fills a gap in the literature by investigating the impact of digital transformation on business performance, especially in the Chinese context, where digital transformation is rapidly progressing. This study also offers practical guidance for practitioners on whether and how to undergo a digital transformation and enhance their internal governance and social responsibility practices.

This study uses a sample of 2,637 Chinese A-share nonfinancial listed companies from 2009–2022, after excluding firms with ST, ST* or PT status; negative revenue; and missing data for three or more consecutive years. Digital transformation index data is collected from firms’ annual reports, and the other microlevel data from the Wind and CSMAR databases. The authors winsorize the data at 1% for outliers, resulting in 17,305 firm-year observations. This study uses fixed-effects logistic regression with clustered robust standard errors to analyze the binary dependent variable. This study also performs various robustness checks, such as probit model, multilevel fixed effects model and IV 2SLS estimations, to confirm the validity of the results.

This study reveals that digital transformation leads to standard unqualified audit opinions, meaning that companies that invest more in digital technologies and capabilities has more tendency to receive standard unqualified audit opinions, which signify the reliability and credibility of their financial reporting. This study also finds that corporate internal control and CSR disclosure levels positively moderate the effect of digital transformation on audit opinions. This study further conducts heterogeneity analysis and shows that the positive effect is originated by the state-owned enterprises, firms audited by non-Big4 auditing firms, firms with high internal control levels and firms with low CSR disclosure levels. The results are robust to different econometric methods.

This study contributes to the literature by providing empirical evidence on how digital transformation influences audit quality and credibility and how internal governance and social responsibility practices strengthen this influence. This study also has practical implications for practitioners by providing advice on whether and how to pursue a digital transformation and improve their internal governance and social responsibility practices. This study demonstrates its originality by reviewing the existing literature from three theoretical perspectives: stakeholder, signaling and reputation, and identifying the research gap that the study addresses. This study also compares its findings with previous studies and discusses the implications and limitations of its research. This study also proposes directions for future research based on its findings.

This article reviews the intercultural adaptability (IA) development models for students in South Korea and China in response to the growing internationalisation of higher education. The article provides significant implications for Confucian heritage culture (CHC) countries and others that wish to enhance in higher education.

This scoping review systematically investigates the literature on different IA development strategies for students in higher education across these two Asian countries.

South Korea has promoted the self-growth model reflected in internationalised programmes such as English as a Medium of Instruction (EMI), an international learning environment with an increasing number of international students and student exchange programmes. Through these initiatives, domestic students’ identity and intercultural learning dynamics are enriched in an intercultural learning environment. Meanwhile, China has adopted the “Outward-oriented” higher education internationalisation model to attract international students to study in Chinese universities and entice Chinese returnees through its International Talent Training Programmes. In this strategy, international students are exposed to Chinese language and cultural knowledge development programmes, and Chinese students are motivated to learn the English language and cultures through interactions with their international peers.

Student IA development models in CHC countries remain underexplored. This article responds to the need of higher education institutions in Asian countries that wish to access and learn from effective IA models from other CHC countries. The study makes a valuable contribution by putting forward significant recommendations for developing students’ IA in CHC countries.