Differentiated pathways for enhancing green innovation quality in the automotive industry guided by ESG frameworks and energy policies

The purpose of the thesis is to analyze how the automotive manufacturing companies being active in Hungary operate in global value chains, with a particular focus on suppliers. Although the topic of GVC is widespread and discussed in international literature, there is a gap in relation to the Hungarian automotive manufacturing industry, especially in the current situation when the COVID-19 pandemic affects the operation of the multinational enterprises. The main identified research question is the following: What is the value creation of the automotive manufacturing industry in Hungary within global value chain? The research process started with a comprehensive literature review and theoretical background analysis about the GVC concept (including the introduction of ‘Smile-curve’) and FDI investment in Central and Eastern Europe (including the characterization of near-shoring activities) and continued with conducting a sample survey and semi-structured interviews with the key car parts suppliers. Executive board, managerial level and engineers were the target persons both for the survey and for interviews. Based on the literature review, I formulated two hypotheses: 1. The theory of ‘Smile curve’ is also valid in case of the Hungarian automotive manufacturing industry, typically low value-added production processes take place in the country. 2. In addition to the central location, the cheap and skilled Hungarian labour was the most important factor in the near-shoring activities of multinational companies expanding to Hungary. In order to be able to accept or reject the first hypothesis about the relevance of the so called ‘Smile curve’ in the Hungarian automotive manufacturing industry, to define position of the automotive manufacturer companies being active in Hungary in the global automotive manufacturing value chain and to create an in-depth understanding about investment incentives of the Western European firms in the country, I prepared an online survey. To test my second hypothesis about the reasons of near-shoring activity in Hungary, I conducted 3 interviews with industry experts from TIER 1 companies of different size. The targeted automotive parts manufacturers are all suppliers of the 5 OEMs present in Hungary (Audi, BMW, Mercedes, Opel and Suzuki) among others. The new results of the doctoral dissertation are the following: I can reject the first hypothesis about the relevance of ‘Smile curve’ in the Hungarian automotive manufacturing industry, because beside manufacturing activities with low added value typically, also research and development activities take place at bigger multinational companies with higher added value. I can accept the second hypothesis about near-shoring in Hungary, because beside the ‘proximity to export markets’, the cheap but skilled labour was decisive when multinationals decided to invest in the country. The ‘positive support system’, ‘favourable tax conditions’, ‘government policy’ and ‘proximity to HQ’ were aspects that companies used, but they are rather neutral factors. The ‘good infrastructure’ is not so good in the real life and the ‘cheap raw material’ is not cheap, because firms have to deal with world market prices, thus, these were not attractive to investors. Further results about the business operations of the analyzed supplier companies: The purchasing decisions for the Hungarian production happens locally decisively, either independently or with involving the headquarter. The manufactured products are typically drive chains, body parts and electric sensors and the proportion of products designated by OEMs is rather high. Western Europe is the biggest export market of the companies analysed, followed by China, North-America and the Central Eastern European region. Relocation processes are not characteristic of the firms. If so, only from other country to Hungary and it is also determined by OEMs providing new opportunities for them. In some cases, wage costs and logistics also play a role in the relocation process. Electromobility and autonomous driving are the most affecting trends in the automotive manufacturing industry. The semiconductor shortage as a serious downside risk is also the result of the pandemic. The effects of COVID-19 are becoming less pronounced today, but the semiconductor crisis is continuing. Favourable tax conditions and higher value added are the success criteria that will help the Hungarian automotive manufacturing industry to remain competitive in the future. Professional trainings, more support for SMEs and favourable legal conditions are also important aspects. Today, the CEE region, including Hungary is a net exporter of knowledge-intensive goods. To improve its global competitiveness and to be able to move into higher-value-added goods and services, the region should invest more in R&D, infrastructure, education and collaboration between companies and universities. The key players in the automotive part manufacturing has realized that value added is a very important factor in the success of an industry and it can be increased due to investment in research and development and innovation. As revealed by the research, they have already established R&D centers and joint projects with universities (e.g. departments), so companies are well on their way to producing higher added value.

Objective: To systematically evaluate the incidence of low back pain (LBP) and analyze its main influencing factors among automobile manufacturing workers in China. Methods: In March 2024, literatures related to LBP of workers in the automotive manufacturing industry were retrieved from China National Knowledge Infrastructure (CNKI) , VIP China Science and Technology Journal Database, Wanfang Database, PubMed, and Web of Science Database. The search time range was from the establishment of the database to March 2024, and the literature was screened according to the inclusion and exclusion criteria. After evaluating the quality of the article using the quality evaluation criteria recommended by the Agency for Healthcare Research and Quality in the United States, Stata 17.0 software was used for analysis. Random effects models or fixed effects models were selected based on the degree of heterogeneity to calculate the combined effect size, and subgroup analysis and analysis of influencing factors of LBP were conducted. Results: A total of 16 articles were included, with a total sample size of 22245 people. The literature quality score ranged from 6 to 8 points. The results of the Meta-analysis showed that the incidence of LBP among automobile manufacturing workers in China was 32% (95%CI: 22%, 42%) . The results of subgroup analysis showed that the incidence of LBP among automotive manufacturing workers aged ≥30 years was 39% (95%CI: 22%, 57%) , which was higher than that among automotive manufacturing workers aged <30 years (24%, 95%CI: 17%, 32%) . The incidence of LBP among automotive manufacturing workers with a length of service of ≥5 years was 40% (95%CI: 23%, 56%) , which was higher than that among automotive manufacturing workers with a length of service of <5 years (24%, 95%CI: 16%, 33%) . The incidence of LBP reported from 2011 to 2017 (39%, 95%CI: 18%, 60%) was higher than that from 2018 to 2023 (28%, 95%CI: 20%, 36%) . Working in an uncomfortable posture (OR=3.72, 95%CI: 2.05, 6.77) , standing for a long time while working (OR=1.97, 95%CI: 1.61, 2.42) , carrying heavy objects (OR=1.93, 95%CI: 1.63, 2.30) , bending over while working (OR=1.86, 95%CI: 1.60, 2.17) and frequent overtime work (OR=2.38, 95%CI: 1.44, 3.92) were both risk factors for LBP among workers in the automotive manufacturing industry (P<0.05) , while sufficient rest time (OR=0.55, 95%CI: 0.48, 0.63) was a protective factor (P<0.05) . Conclusion: The incidence of LBP among workers in China's automotive manufacturing industry is relatively high. Working in an uncomfortable posture, standing for a long time, carrying heavy objects, bending over for work, frequent overtime work and sufficient rest time are the influencing factors of LBP among workers in the automotive manufacturing industry. Preventive measures should be actively taken in response to the above influencing factors to effectively reduce the incidence of LBP among workers in China's automotive manufacturing industry.

To succeed in the rapidly advancing technological environment driven by Industry 4.0, automotive manufacturers need to swiftly embrace new technologies. Moreover, the ability to introduce innovations to the market more quickly and sustainably hinges on the integration of Industry 4.0 technologies. The automotive industry plays a crucial role in boosting the economy, generating a multiplier impact in Türkiye, much like it does in various countries around the world. Therefore, keeping a close eye on the digital transformation of the automotive industry is critical for establishing a cost-efficient, productive, and competitive market in a rapidly developing market. This study aims to indicate the importance level of Industry 4.0 technologies for automotive manufacturers operating in Türkiye. The Analytic Hierarchy Process (AHP) method based on Pythagorean fuzzy sets was employed to achieve this aim. Pythagorean fuzzy sets are a contemporary fuzzy approach that gives experts more freedom to express their judgments regarding uncertainty and ambiguity in decision-making problems. The study results reveal that the top three most important technologies in the automotive manufacturing industry are “simulation and modeling”, “autonomous robots”, and “big data and analytics”, respectively. However, blockchain technology ranked lowest in terms of importance level. The proposed approach will serve as a guide for decision-makers in selecting the appropriate Industry 4.0 technology in the automotive industry.

As the nexus of the "innovation-driven" and "green development" national strategies, green technology innovation resonates with China's dual carbon targets and represents an essential pathway toward achieving high-quality development. Existing literature has seldom employed a holistic framework to investigate the complex causal mechanisms through which technological, organizational, and environmental conditions influence green technology innovation efficiency, thereby largely overlooking the configurational effects among these antecedent conditions. To further advance green technology innovation and enhance its efficiency, this study examines China's industrial sectors. Drawing on the Technology-Organization-Environment (TOE) framework, we utilize both Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of 38 industrial sectors above a designated size. The analysis explores how six antecedent conditions across the technological, organizational, and environmental dimensions combine to impact green technology innovation. The findings are threefold. First, no single antecedent condition is necessary for achieving high green technology innovation efficiency, although technological factors exert a relatively strong constraint. Second, three distinct configurational pathways lead to high efficiency: a "technology-led, government-supported" path, a "technology-led, independent-innovation" path, and an "environment-technology-organization synergy" path. Third, in an otherwise favorable market environment, ill-suited environmental regulations can suppress innovation efficiency, a context where even strong market demand fails to be effective, suggesting that the impact of organizational conditions is subject to a threshold.

Study on whole-life cycle automotive manufacturing industry CO2 emission accounting method and Application in Chongqing

Visual inspection workers are always performing under various ergonomic factors and are more vulnerable to the effects of physical and organizational aspects, since their work deals highly with cognitive functions and the impact of ergonomic factors has to be identified in automotive industries to improve work performance. To study the combined effect of ergonomic factors that may have an impact on the performance of visual inspection workers in the automotive industry. In this experimental study combined factors such as postures (standing, sitting, Sit-stand) and work shifts (A shift, B shift, C Shift) have been studied at three levels. The study was conducted among selected employes (n = 10) in the automotive manufacturing industry in 2023. During the study, the visual inspectors' work performance was measured using the error study, and the cognitive functions of visual inspectors' were evaluated by taking the Digit Symbol Substitution Test (DSST). The study established that postures significantly impact work performance at 40.08% and cognitive functions at 36.25%. Work shifts significantly impact work performance with 18.18% and cognitive functions with 26.62% of visual inspectors' in the automotive industry. The combined effect of postures and work shifts has significantly impacted the visual inspectors' performance with 13.29% on work performance and 10.12% on cognitive functions. This study draws the inference that individual and combined factors (Posture and Work shift) both possess a significant impact on the work performance and cognitive functions of visual inspectors' in the automotive manufacturing industry.

At present, the digital economy is developing rapidly worldwide, and the global economic system is undergoing profound changes. As an important pillar of traditional industries, the transformation and upgrading of the automotive manufacturing industry's supply chain have a profound impact on the reshaping of the global industrial chain. Although China's automotive manufacturing industry has significant advantages in terms of the length of the industrial chain and the degree of technological integration, its position in the global value chain still faces many challenges, such as insufficient international competitiveness of domestic brands, lagging core technological innovation capabilities, and the immaturity of the development of the new energy vehicle field. This study is based on the data of A-share automotive manufacturing enterprises from 2009 to 2022. It employs a two-way fixed effects model for empirical analysis, aiming to comprehensively evaluate the mechanism of the digital economy's impact on the automotive manufacturing supply chain in terms of efficiency improvement, cost optimization, technological innovation, and green sustainable development. At the same time, the differences in supply chain development of enterprises with different property rights and scales under the background of the digital economy were also explored. The aim is to provide solid academic support and practical suggestions for the high-quality development of supply chains in China's automotive manufacturing industry in the digital economy era, and thereby enhance the competitiveness and status of China's automotive manufacturing industry in the global industrial chain.

Necessary condition analysis (NCA) understands cause–effect relations in terms of “necessary but not sufficient.” This means that without the right level of the cause, a certain effect cannot occur. This is independent of other causes; thus, the necessary condition can become a single bottleneck, critical factor, constraint, disqualifier, or so on that blocks the outcome when it is absent. NCA can be used as a stand-alone method or in multimethod research to complement regression-based methods such as multiple linear regression (MLR) and structural equation modeling (SEM), as well as methods like fuzzy set qualitative comparative analysis (fsQCA). The NCA method consists of four stages: formulation of necessary condition hypotheses, collection of data, analysis of data, and reporting of results. Based on existing methodological publications about NCA, guidelines for good NCA practice are summarized. These guidelines show how to conduct NCA with the NCA software and how to report the results. The guidelines support (potential) users, readers, and reviewers of NCA to become more familiar with the method and to understand how NCA should be applied, as well as how results should be reported. NCA’s rapid diffusion and broad applicability in the social, technical, and medical sciences is illustrated by the growth of the number of article publications with NCA, the diversity of disciplines where NCA is applied, and the geographical spread of researchers who apply NCA.

Against the backdrop of green transformation as a strategic priority in the hotel industry, the employee-driven mechanisms of green service innovation (GSI) remain insufficiently understood. Existing studies largely rely on linear pathways and fail to capture the complex interactions among ability, motivation, and opportunity. Drawing on the ability-motivation-opportunity framework, this study integrates necessary condition analysis (NCA), fuzzy-set qualitative comparative analysis (fsQCA), and artificial neural networks (ANN) to analyze two-wave survey data from frontline employees in Chinese upper-midscale hotels. The results reveal that no single factor constitutes a necessary condition for GSI. High-level GSI can be achieved through two sufficient pathways: "autonomy support and capability-driven pathway" and "organizational support and motivation-driven pathway". ANN sensitivity analysis further confirms the important role of green human resource management (GHRM). This study advances beyond linear perspectives by uncovering the asymmetric configurational mechanisms of GSI and offers practical implications for integrating systematic GHRM practices with employee autonomy to foster sustainable green innovation.

The coordinated development of green technology innovation performance among regions is of great significance for China’s sustainable development. Therefore, it is necessary to explore the evaluation and promotion modes for green technology innovation performance. Based on the data of 30 provinces in China, this paper comprehensively evaluates regional green innovation performance and determines the factor configurations that can achieve high green technology innovation performance. This paper constructs an index system of green technology innovation from four aspects (economy, innovation, environment, and social welfare) and measures the results by the entropy weight method. The results show that green technology innovation performance decreases from the eastern region to the western region. At the province level, Ningxia, Qinghai, and Xinjiang have lower performance, while Beijing, Zhejiang and Shanghai have higher performance. The fuzzy set qualitative comparative analysis method reveals five configurations that achieve high green technology innovation performance. Specifically, regions with high green technology innovation performance tend to be those with high environmental regulation intensity and high innovation input. When environmental regulation and innovation input are both high, regions should fully consider the technology spillover effect of foreign direct investment. When energy prices is high, regions should give full play to the technology spillover effect of FDI and the transformation of market demand to reduce the pressure of production costs.

Management of quality and productivity as key performance areas in automotive industry is an important aspect for the success of companies in the industry. Any conflict between these two key performance areas that if left unmanaged, can lead to overall deterioration of objectives and failure to protect the interests of investors and customers. Product recalls from customers and warranty costs are an embodiment of this conflict and costly to all involved stakeholders. This study uses data from automotive components manufacturing company to determine the nature and strength of a relationship between quality and productivity, with a attention to the automotive industry. In a research that includes primary data collection and analysis, as well as semi-formal interviews with stratified randomly selected participants, results obtained show a positive relationship between the two variables: quality and productivity in the automotive industry. The results obtained can be used to direct management efforts in automotive companies in areas of quality and productivity for optimum results, investor confidence boost, and customer satisfaction/retention.

A framework for determining the impacts of a multiple relationship network on green innovation

This paper focuses on exploring the role of digital finance in corporate technological innovation, how it influences technological innovation, and the manifestation of green development in this process. The study centers on China's listed automotive manufacturing enterprises and is based on data from Chinese A-share automotive manufacturing companies listed from 2011 to 2023, examining the mechanism by which digital finance affects technological innovation and the moderating effect of green development level. The research results obtained in this paper show that: (1) Digital finance can promote technological innovation in enterprises, and the effect is obvious. It achieves this by optimizing the allocation of resources to make them more reasonably distributed and reducing the cost required for enterprise financing. In this way, digital finance can effectively enhance the technological innovation capabilities of automotive manufacturing enterprises. (2) Its core mechanism is mainly to alleviate the constraints that enterprises encounter in financing, provide very crucial financial support for the innovation activities of typical capital-intensive industries such as the automotive manufacturing industry, and enable these innovation activities to proceed smoothly. (3) The level of green development of enterprises will positively regulate this relationship. In the automotive manufacturing industry, where the pressure of green transformation is relatively high, the better the environmental performance of those enterprises, the more obvious the impact of digital finance.

The automotive manufacturing industry plays a pivotal role in global economic development, providing transportation solutions while simultaneously facing multifaceted challenges related to environmental health and safety (EHS) practices. This review investigates the indispensable role of EHS practices within the automotive manufacturing sector, highlighting their significance in mitigating environmental impact, ensuring workplace safety, and complying with regulatory standards. Effective EHS practices are integral to managing environmental sustainability within automotive manufacturing. These practices encompass waste management, emissions reduction, and resource conservation strategies aimed at minimizing the industry's ecological footprint. Additionally, the adoption of eco-friendly technologies and processes, such as renewable energy integration and material recycling, contributes to the industry's overall environmental stewardship. Furthermore, prioritizing workplace safety is imperative in the automotive manufacturing sector due to its inherently hazardous operational environments. EHS initiatives focus on risk assessment, hazard identification, and the implementation of preventive measures to safeguard employees from occupational injuries and illnesses. Moreover, fostering a culture of safety awareness through training programs and regular audits promotes a conducive working environment conducive to employee well-being and productivity. Compliance with regulatory standards is a cornerstone of EHS management in the automotive manufacturing industry. Adherence to local, national, and international regulations ensures operational legality and enhances corporate reputation. Through continuous monitoring and assessment, automotive manufacturers strive to stay abreast of evolving regulatory frameworks, thereby aligning their practices with industry best practices and societal expectations. The integration of robust EHS practices is indispensable for sustainable operations and corporate responsibility within the automotive manufacturing industry. By addressing environmental concerns, ensuring workplace safety, and meeting regulatory requirements, automotive manufacturers can uphold their commitment to environmental stewardship and social accountability while maintaining operational efficiency and competitiveness in the global market. Keywords: Environment, Health, Safety, Automotive, Manufacturing, Industry.

Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path