Can digital transformation promote green innovation in enterprises? the moderating effect of heterogeneous environmental regulations.

Currently, digital transformation is having various impacts on enterprises around the world, including the green innovation. However, the current literature on the relationship between digitalization and green innovation in enterprises is scarce. What is the relationship between them, and whether heterogeneous environmental regulation has mediating effects, are questions that are worth exploring. Using a sample of listed manufacturing enterprises in China, this paper empirically tests the impact of digital transformation on enterprise green innovation. The results show that: (1) Digital transformation has a significant positive impact on green innovation, including green innovation output and green innovation capability. (2) Diverse environmental regulation may have mediating effects of digital transformation's influence on green innovation. (3) After a number of robustness tests, the conclusions are still valid. This paper can provide a reference for developing green development strategies for manufacturing enterprises.

Impact of air quality on the health of present-day workers in an Asbestos roof manufacturing industry, Sri Lanka.

The study's objective was to determine the air quality in an asbestos-related industry and its impact on current workers' respiratory health. Seventy-seven air and 65 dust samples were collected at 5-day intervals in an asbestos roofing sheets production factory in Sri Lanka having two production facilities. Sampling was performed in ten sites: Defective sheets-storage, Production-plant, Pulverizer, Cement-silo, and Loading-area. A detailed questionnaire and medical screening were conducted on 264 workers, including Lung Function Tests (LFT) and chest X-rays. Asbestos fibres were observed in deposited dust samples collected from seven sites. Free chrysotile fibres were absent in the breathing air samples. Scanning Electron Microscopy confirmed the presence of asbestos fibres, and the Energy Dispersive X-ray analysis revealed Mg, O, and Si in depositions. The average concentrations of trace metals were Cd-2.74, Pb-17.18, Ni-46.68, Cr-81.01, As-7.12, Co-6.77, and Cu-43.04 mg/kg. The average Zn, Al, Mg, and Fe concentrations were within 0.2-163 g/kg. The highest concentrations of PM2.52.5 and PM1010, 258 and 387 µg/m3, respectively, were observed in the Pulverizer site. Forty-four workers had respiratory symptoms, 64 presented LFT abnormalities, 5 indicated chest irregularities, 35.98% were smokers, and 37.5% of workers with abnormal LFT results were smokers. The correlation coefficients between LFT results and work duration with respiratory symptoms and work duration and chest X-ray results were 0.022 and 0.011, respectively. In conclusion, most pulmonary disorders observed cannot directly correlate to Asbestos exposure due to negligible fibres in breathing air, but fibres in the depositions and dust can influence the pulmonary health of the employees.

Antecedent configuration pathways for manufacturing-enterprise digital servitization: Based on a technology-organization-environment theoretical framework.

The expeditious advancement and elevation of the manufacturing industry's transformation and upgrading represent pivotal strides for China in its ascent toward the upper echelons of the global manufacturing value chain. Currently, China's manufacturing-industry transformation faces the dual-lag quandary of digitalization and servitization. The notion of digital servitization elucidates the interdependent relationship between digitalization and servitization, unveiling the mechanisms underlying the formation of digital servitization. This holds significant implications for advancing the comprehension of digitalization and servitization and, crucially, facilitates the acceleration of China's manufacturing sector transitioning from production-centric to service-centric paradigms. Harnessing the technology-organization-environment (TOE) theoretical framework, we constructed a model elucidating the driving factors underpinning manufacturing digital servitization. By employing the fuzzy-set qualitative comparative analysis (fsQCA), we explored strategic decisions and path dependencies in the transformation of manufacturing digital servitization, offering valuable insights to foster China's manufacturing sector in its digital-servitization journey. The following findings were obtained. (1) A singular condition was insufficient as a prerequisite for manufacturing digital servitization and necessitated the coordinated alignment of multiple variables. (2) Three pathways existed for achieving manufacturing digital servitization: TOE, organization-environment collaborative-oriented, and technology-organization collaborative-oriented. (3) The progression of manufacturing digital servitization resulted from the collective impact of numerous factors, exhibiting a characteristic of different paths leading to the same destination. Various manufacturing enterprises pursued distinct trajectories to achieve digital servitization, contingent upon their unique circumstances. These findings have the potential to provide valuable insights for effectively fostering manufacturing digital servitization.

Mitigating the environmental concerns through total quality management and green manufacturing practices.

This study seeks to explore the intricate relationship between total quality management (TQM) and environmental performance (EP), incorporating the mediating role of green manufacturing practices (GMPs). Additionally, the study examines the moderating impact of environmental strategy on the connections between GMPs and EP. Data were collected through a questionnaire distributed to managers of manufacturing small and medium-sized enterprises (SMEs) and were subjected to analysis using structural equation modeling. The results reveal a positive and significant impact of TQM on EP. Furthermore, the findings suggest that GMPs partially mediate the association between TQM and EP, while the anticipated moderating effect of environmental strategy between GMPs and EP is also supported in this study. These outcomes hold valuable implications for enhancing the environmental performance of SMEs through the integration of TQM and GMPs. It is important to note that this research exclusively focuses on manufacturing SMEs; therefore, future studies should extend their examination of this concept to other industries. Additionally, the study's findings provide a valuable roadmap for SME administrators aiming to elevate their environmental performance.

Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population.

OBJECTIVES: This study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL. DESIGN: Population based, a cross sectional study. SETTING: Han, China. PARTICIPANTS: This study selected 3266 Han male workers from three automobile manufacturing industries. PRIMARY OUTCOME MEASURES: Information including personal life habits, occupational health test information and occupational exposure history were collected and predictive factors of NIHL were screened from these workers. BPNN and logistic regression models were constructed using these predictors. RESULTS: The input variables of BPNN model were 20, 16 and 21 important factors screened by univariate, stepwise and lasso-logistic regression. When the BPNN model was applied to the test set, it was found to have a sensitivity (TPR) of 83.33%, a specificity (TNR) of 85.92%, an accuracy (ACC) of 85.51%, a positive predictive value (PPV) of 52.85%, a negative predictive value of 96.46% and area under the receiver operating curve (AUC) is: 0.926 (95% CI: 0.891 to 0.961), which demonstrated the better overall properties than univariate-logistic regression modelling (AUC: 0.715) (95% CI: 0.652 to 0.777). The BPNN model has better predictive performance against NIHL than the stepwise-logistic and lasso-logistic regression model in terms of TPR, TNR, ACC, PPV and NPV (p<0.05); the area under the receiver operating characteristics curve of NIHL is also higher than that of the stepwise and lasso-logistic regression model (p<0.05). It was a relatively important factor in NIHL to find cumulative noise exposure, auditory system symptoms, age, listening to music or watching video with headphones, exposure to high temperature and noise exposure time in the trained BPNN model. CONCLUSIONS: The BPNN model was a valuable tool in dealing with the occupational risk prediction problem of NIHL. It can be used to predict the risk of an individual NIHL.

An experimental investigation of Lean Six Sigma philosophies in a high-mix low-volume manufacturing environment.

This article experimentally examines methods for implementing the philosophies of Lean Six Sigma (LSS) in a High-Mix Low-Volume (HMLV) manufacturing environment. HMLV environments present unique challenges to LSS paradigms because of the need for extraordinary operational flexibility and customer responsiveness. The subject HMLV manufacturer for this experimentation manufactures (among 8500 others) an example component for which 3 machines work independently to perform the necessary operations to manufacture this component. The experiment that is the subject of this research seeks to adapt LSS philosophies to develop treatments to improve the performance of the manufacturing of this component. These LSS-inspired treatments included 1) using cellular manufacturing methods, and the 3 machines as a single work cell to manufacture the component, and 2) using a single multipurpose machine to perform all operations required to manufacture the component. The results of this experiment demonstrate that the cellular manufacturing method was the most effective to reduce costs, to standardize operations at a process level, and to increase throughput. The single machine processing method improved production rates and on-time delivery relative to the baseline, but greatly increased lead time, thereby increasing total cost per part. These results highlight the importance of critically assessing the application of LSS within HMLV environments compared to the Low-Mix High-Volume (LMHV) environments where LSS is traditionally successful. HMLV manufacturers and researchers can use these findings to identify the most effective methods for their specific needs and to design interventions that will improve system-level manufacturing performance in high mix environments.

Open innovation and confidentiality agreements as key factors of innovative performance in the manufacturing and service industries.

The innovative performance of manufacturing and service companies can be impacted by the existing relationship between open innovation (OI) and the generation of confidentiality agreements (NDAs) as a tool for the protection of intellectual property. Based on the analysis of a cross-sectional sample of 6,798 industrial companies (2019-2020) and 9,304 companies in the service sector (2017-2019) that are part of the directory of the National Administrative Department of Statistics (DANE) in its Technological Innovation and Development Survey (EDIT and EDITS), it can be suggested that the interaction of these two variables (OI and NDAs) generate positive effects for the manufacturing industry but negative ones for the service sector. It could be deduced that the positive effect is due to the greater tradition of OI in the manufacturing industry and the negative effect to the caution that the service sector presents when collaborating with external actors.

How does digital transformation affect the ESG performance of Chinese manufacturing state-owned enterprises?-Based on the mediating mechanism of dynamic capabilities and the moderating mechanism of the institutional environment.

Against the background of sustainable development policies, the ESG performance of Chinese manufacturing enterprises is still generally poor. As the leading enterprises in the manufacturing industry, state-owned enterprises should take the lead in responding to the national call for sustainable development and actively explore the path to improve their ESG performance. This study aims to explore whether and how state-owned manufacturing enterprises can improve their poor ESG performance through digital transformation in the digital economy. This study takes Shanghai and Shenzhen A-share state-owned listed manufacturing enterprises as the research sample and constructs an unbalanced panel. OLS regression analysis is used to empirically test the impact of digital transformation on the ESG performance of the sample firms. Further attempts are made to discuss the influence mechanism of digital transformation from the perspectives of dynamic capabilities and the institutional environment through stepwise and hierarchical regression methods, respectively. The study shows that, firstly, digital transformation is an important influencing factor in promoting the improvement of enterprises' ESG performance, and at the same time, there are significant structural differences in this influence. Second, under the dynamic capability perspective, digital transformation can improve corporate ESG performance through an absorptive feedback mechanism, matching response mechanism, and innovation efficiency enhancement mechanism. Third, from the perspective of the institutional environment, the informal system has a significant positive moderating effect on the relationship between digital transformation and ESG performance, i.e., the informal system and digital transformation have a synergistic governance effect on corporate ESG performance. The moderating effect of the formal institutional environment on digital transformation and ESG performance is not significant. The findings of the study clarify the controversy over the relationship between digital transformation and ESG performance of manufacturing state-owned enterprises and enrich the research on the influencing factors of corporate ESG performance. It also provides a theoretical foundation and empirical evidence for manufacturing SOEs to improve ESG performance and lead to sustainable development.

Research on risk identification of manufacturing enterprises' Internet strategic transformation.

The Communist Party of China's 19th National Congress underlined the necessity of speeding the development of a manufacturing powerhouse and advanced manufacturing sector by supporting the deep integration of the Internet, big data, artificial intelligence, and the real economy. This study employed principal component analysis to extract the prominent risk factors from questionnaire data in order to manage the risks connected with the Internet strategic transformation of manufacturing firms. To confirm the major risk factors, a structural equation modeling was created using Amos-24 software. The findings revealed that risk factors of Internet strategic transformation in manufacturing businesses are mostly expressed in equipment flexibility risks, organizational versatility risks, smart technology risks, Internet technology risks, flexible management risks, and financing management risks. The paper offers useful theoretical and practical insights into the risks of China's manufacturing businesses' Internet strategic transformation. The findings can assist manufacturing firms in better identifying and managing these risks, supporting their smooth transition to the Internet economy.

Impact of pollution prevention practices and green environmental practices on sustainable performance: Empirical evidence from Chinese SMEs.

Adequate protection of the environment is one of the hot spots of concern for all sectors of society due to severe environmental pollution. The solution to this issue is friendly management of the environment. With the rapid growth of Chinese Manufacturing SMEs for economic development, environmental pollution and abuse of resources are arising. To resolve these issues, Chinese manufacturing SMEs are accelerating the implementation of green innovation in their industries. However, it is a complex task that involves enterprise, government, and social considerations. Therefore, it is essential to identify the green drivers for this implementation. With a focus on China's current situation from previous research and views from experts, this study aims to investigate how Chinese Manufacturing Small and Medium-sized Enterprises (SMEs) are responding to resource misuse and environmental pollution by implementing green innovation, emphasising the role of artificial intelligence (AI) in improving environmental performance. This study primarily looks into the factors that influence the adoption of green innovations by analysing the growth paths of Chinese SMEs operating in highly polluting industries over a longer time frame than five years. Artificial Intelligence is a valuable tool for solving the issues of ecological degradation. A quantitative method has been implemented for the Chinese companies' samples from the deeply polluting industries for more than five years. The findings of this paper advise that the average board size, the governing board meetings, and organizational performance are positively connected with the Chinese firms' environmental process. Board independence and diversity of gender have irrelevant associations with ecological performance. A convenient threshold regression model has been used to accumulate the respondents' data. It also reveals that larger board sizes and more frequent governing board meetings are positively associated with improved environmental performance among these firms. The findings state the critical implications for the firm executives, policymakers, environmental activists, and regulators. This result supports the insight drained from the resource dependence, stakeholder, firm agency, and legitimacy theories.

Does environmental innovation have peer effects? The moderating role of slack resources and avoidance goal orientation.

Exploring the interactive patterns of environmental innovation behavior among firms is of great significance for improving the level of environmental innovation in the whole industry and achieving sustainable development. Based on social interaction theory, this study examines the peer effect of a firm's environmental innovation and the moderating effects of slack resources and avoidance goal orientation. A total of 1210 listed companies in China's manufacturing industry from 2015 to 2020 comprised the research sample, and the researchers used multiple regression analysis to analyze the data. The results indicate a peer effect of environmental innovation among firms; that is, firms' environmental innovation will positively impact the environmental innovation of other firms in the industry. Slack resources positively moderate the peer effect of environmental innovation among firms, and firms' avoidance goal orientation weakens that moderating effect. This study reveals the internal mechanism of the peer effect of environmental innovation and provides new management implications for managers' environmental-innovation decision-making.

Will advancement in technologies bring fear and damage human employment? Evidence from China's manufacturing industry.

Advancement in technologies such as robotic industries and artificial intelligence bring fear among human being that jobs will be substituted by robots. Base on the panel data of 28 China's manufacturing industries, this research analyzed the impact of technical progress bias on employment. First, we calculate the technical progress bias index of 28 industries base on the stochastic frontier model with transcendental logarithm function found 16 industries were toward the skilled labor while the remaining 12 industries were toward the unskilled labor. Second, the empirical results show that technical progress bias has a positive impact on the total manufacturing employment and significant positive effect on the unskilled labor, while no significant impact on skilled labor employment. Third, the threshold effect test proves that if taking industry value-added per capita or R&D capital stock as threshold variable, the threshold about the impact exist, making the impact on skilled labor was insignificant.

Does the opening of producer services promote the wage growth of the downstream manufacturing industry?-Empirical evidence from Chinese manufacturing listed companies.

Based on the vertical connection between upstream and downstream industries, a unique theoretical model is constructed to analyse the impact mechanism of the opening of producer services on downstream manufacturing wage growth. The empirical tests are carried out using the data of China's manufacturing listed companies from 1999 to 2020. Our findings indicate that the opening of producer services has an inverted-U-shaped impact on downstream manufacturing wage growth, and the average level of the opening of producer services in the sample period is lower than the corresponding threshold. Overall, it is in the stage of promoting the wage growth of the downstream manufacturing industry. The opening of producer services mainly affects the wage growth of the downstream manufacturing industry through two channels: labour productivity and labour income share. The results of heterogeneity analysis show that the wages of capital and technology-intensive and low-competitive manufacturing industries are relatively strongly promoted by the opening of producer services. Therefore, promoting the orderly opening of producer services and strengthening the technological links between industries will help promote the wage growth of downstream manufacturing industries.

[Application and comparison of three occupational health risk assessment methods in an automobile manufacturing industry].

Objective: Three occupational health risk assessment methods were used to assess the occupational health risk of noise exposed posts in an automobile manufacturing enterprise. According to the results, the selection of risk assessment methods and risk management of such occupational noise enterprises were provided. Methods: Form April to November 2021, The occupational health field survey was carried out in an automobile manufacturing industry in Tianjin. The occupational health MES risk assessment method, occupational health risk index risk assessment method and Australian occupational hazard risk assessment method were used to evaluate the occupational health risk of noise-exposed posts in this enterprise, and the evaluation results of different methods were analyzed and compared. Results: The average value of L(Aeq, 8 h) in the four workshops of automobile manufacturing industry was 82.95 dB (A) , and the noise detection exceeding rate was 22.41% (26/116) . The LAeq, 8h and exceeding rate noise of welding workshop were higher than those of other workshops (χ(2)=23.56, 32.94, P<0.01) . The three occupational health risk assessment methods have the same risk assessment results for the four major workshops. The assembly and painting workshops are level 4 risk (possible risk) , and the stamping and welding workshops are level 3 risk (significant risk) . Conclusion: Occupational noise has certain potential hazards to workers in automobile manufacturing enterprises. Therefore, in the future work, corresponding organizational management measures should be taken to improve the working environment and reduce the actual exposure level of workers in order to protect the health of occupational workers.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta.

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

Characterization of VOC emissions and health risk assessment in the plastic manufacturing industry.

Volatile organic compounds (VOCs) significantly contribute to ozone pollution formation, and many VOCs are known to be harmful to human health. Plastic has become an indispensable material in various industries and daily use scenarios, yet the VOC emissions and associated health risks in the plastic manufacturing industry have received limited attention. In this study, we conducted sampling in three typical plastic manufacturing factories to analyze the emission characteristics of VOCs, ozone formation potential (OFP), and health risks for workers. Isopropanol was detected at relatively high concentrations in all three factories, with concentrations in organized emissions reaching 322.3 µg/m3, 344.8 µg/m3, and 22.6 µg/m3, respectively. Alkanes are the most emitted category of VOCs in plastic factories. However, alkenes and oxygenated volatile organic compounds (OVOCs) exhibit higher OFP. In organized emissions of different types of VOCs in the three factories, alkenes and OVOCs contributed 22.8%, 67%, and 37.8% to the OFP, respectively, highlighting the necessity of controlling them. The hazard index (HI) for all three factories was less than 1, indicating a low non-carcinogenic toxic risk; however, there is still a possibility of non-cancerous health risks in two of the factories, and a potential lifetime cancer risk in all of the three factories. For workers with job tenures exceeding 5 years, there may be potential health risks, hence wearing masks with protective capabilities is necessary. This study provides evidence for reducing VOC emissions and improving management measures to ensure the health protection of workers in the plastic manufacturing industry.

Validating the effectiveness of a self-report tool to predict unsafe behavior of industrial workers: a QEEG analysis.

Objectives. Unsafe behavior (UB) is defined as the likelihood of intentionally or unintentionally deviating from pre-defined plans. This study aims to investigate the validation of a self-report tool for measuring workers' cognitive-based UB using quantitative electroencephalography (QEEG). Methods. The cognitive-based unsafe behavior questionnaire (CUBQ) was completed by 632 front-line workers in a manufacturing industry to identify differences in the backgrounds of the subjects regarding UBs. Two groups were then selected as extreme groups and QEEG was conducted based on the international 10-20 electrode placement. Results. The mean values of absolute power (AP), alpha/beta ratio (ABR) and alpha/gamma ratio (AGR) from brain oscillations in different regions of the cortex were significantly different between the studied groups (p < 0.05). Additionally, these values were found to be significantly correlated with slips, lapses and mistakes, as measured by certain scales of the CUBQ (p < 0.05). Conclusions. The findings of this study indicated differences in brain oscillation activities among industrial workers with different UB backgrounds. These results confirm the effectiveness of CUBQ as a proactive tool for safety practitioners to predict industrial workers' UBs.

A pathway towards high-quality development of the manufacturing industry: Does scientific and technological talent matter?

Against the background of the accelerated evolution of the new round of scientific and technological revolution and industrial change, scientific and technical talents, as essential innovation resources, play an important role in promoting the high-quality development of the manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2021, the article constructs a fixed-effects model and systematically researches the impact of scientific and technological talents on the high-quality development of the manufacturing industry. The results show that scientific and technical talents play a significant role in promoting the high-quality development of the manufacturing industry, and the upgrading of the consumption structure and the accumulation of productive service industries play a mediating role. Heterogeneity analysis found that the promotion effect of scientific and technical talents is more favorable in the eastern region, medium-technology level manufacturing, and labor-intensive manufacturing. Among the three sub-dimensions of scientific and technological talents, the scale of scientific and technical talents has the most significant impact on the development of the manufacturing industry. The analysis of the spatial spillover effect finds that scientific and technological talents will have a positive spillover effect on the development of the manufacturing industry in neighboring areas. The study provides a basis for relevant departments to formulate effective strategies and policies.

Research on the identification and evolution of health industry policy instruments in China.

The application of health industry policies could be discovered more quickly and comprehensively through the automated identification of policy tools, which could provide references for the formulation, implementation, and optimization of subsequent policies in each province. This study applies the Bidirectional Encoder Representation from Transformer (BERT) model to identify policy tools automatically, utilizes Focal Loss to reduce the unbalance of a dataset, and analyzes the evolution of policy tools in each province, which contains time, space, and topic. The research demonstrates that the BERT model can improve the accuracy of classification, that supply and environment policy tools are more prevalent than demand tools, and that policy instruments are organized similarly in four major economic regions. Moreover, the policy's attention to topics related to healthcare, medicine, and pollution has gradually shifted to other topics, and the extent of policy attention continues to be concentrated on the health service industry, with less attention paid to the manufacturing industry from the keywords of the various topics.

Can digital finance development drive green transformation in manufacturing? Evidence from China.

Digital finance is a product of emerging technology-enabled innovation in financial services and has a critical impact on the green transformation of the manufacturing industry. We propose a new efficiency measurement model based on the slacks-based measure (SBM) to measure the efficiency of green transformation on regional manufacturing. Chinese interprovincial data from 2010 to 2019 were obtained for the study. In addition, we estimated the effect of digital finance on green transformation of manufacturing using a benchmark panel model. Finally, considering the regional heterogeneity and spatial effects of green transformation efficiency in the manufacturing industry, we constructed a spatial Durbin model based on an economic-geographic nested spatial weight matrix to analyze the spatial influence of digital finance on green transformation in the manufacturing industry. The results show that the green transformation of the manufacturing industry has significant positive spatial spillover effects owing to the existence of competition, demonstration, and economic correlation effects among regions.