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This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.

This study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.

This study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.

The building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.

This study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.

As a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.

This study aims to propose a cloud platform architecture considering information sharing based on blockchain to realize the security and convenience of enterprise information sharing in the automotive supply chain.

A bilateral matching model considering enterprises information contribution stimulates information sharing and improves the efficiency and quality of supply and demand matching. Three smart contracts are used to complete the information sharing process and match supply and demand in the automotive supply chain.

The system is tested on the local Ganache private chain, and the decentralized web page is designed based on the architecture prototype.

Solve the problem of information island in automobile supply chain.

In response to the complex external structure of high-precision aviation plugs, which makes it difficult to search outside the hole and adjust inside the hole during automated assembly. This paper aims to propose an assembly framework that combines multi-agent search and variable parameter compliant control to solve this problem.

First, a multi-agent search strategy (MAS) based on Gaussian Mixture Model and Deep Q-Network was proposed to optimize displacement direction and actions, thereby improving search speed and success rate. Then, a variable parameter admittance control method (RL-VPA) based on dual delay depth deterministic policy gradient (TD3) was proposed, which dynamically optimized the internal parameters of the admittance controller and adopted state space discretization to improve convergence speed and assembly efficiency.

Compared to spiral search and single-agent search, the average search success rate has improved by approximately 10% and 6.6%. Compared to fixed admittance control and other RL-based methods, the average assembly success rate has increased by approximately 38.6%, 22% and 8.6%. Compared with the training results of the model without state discretization, it was found that state discretization helps the model converge quickly. To verify the generalization ability of the assembly framework, experiments were conducted on three different pin counts of aviation plugs, the assembly success rate reached 86.7%, all of which showed good assembly results. Finally, combining state space discretization to reduce the impact of environmental noise, improve training effectiveness and convergence speed.

MAS has been proposed to optimize displacement direction and action, improving search speed and success rate. RL-VPA is designed to dynamically optimize the internal parameters of the admittance controller, enhancing the robustness and generalization ability of the model. Additionally, state space discretization is combined to improve training effectiveness and convergence speed.

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 aims to propose a moderated mediation model to investigate the moderating effects of environmental, social and governance (ESG) performance on the relationship between inefficient investment and firm performance and the mediating effect of firms that participate in institutional research on the relationship between investment efficiency and performance. This study also analyses the heterogeneity of the corporate nature, intensity of industrial research and development (R&D), industrial competition and regional marketization.

This study uses a panel data fixed-effects model to conduct a regression analysis of 1,918 Chinese listed firms from 2016 to 2020. A Fisher’s permutation test is used to examine the differences between state-owned and nonstate-owned firms.

Inefficient investment negatively impacts corporate performance and higher ESG performance exacerbates this effect by attracting more institutional research which reveals more problems. State-owned enterprises perform significantly better than nonstate-owned enterprises in terms of ESG transformation. Industrial R&D intensity, competition and regional marketization also mitigate the negative effects of inefficient investment on corporate performance.

This study suggests that companies should consider inefficient investments that arise from agency issues in corporate ESG transformation. In addition, state-owned enterprises in ESG transformation should take the lead to achieve sustainable development more efficiently. China should balance regional marketization, encourage enterprises to increase R&D intensity, reduce industry concentration, encourage healthy competition and prevent market monopolies.

This study combines the agency and stakeholder theories to reveal how inefficient investments that arise from agency issues inhibit value creation in ESG initiatives.

The chapter introduced stealth construction as a transformative concept emerging in the construction industry, emphasising resilience and efficiency through innovative technologies, cutting-edge materials, and advanced methodologies. Drawing parallels to stealth aircraft, stealth construction aims to create buildings and infrastructure that blend seamlessly into their environments, minimising environmental impact and enhancing sustainability. This approach integrates smart materials, strategic planning, and covert practices throughout all construction stages, ensuring robust, adaptive, and discreet structures. Stealth construction represents a significant paradigm shift by prioritising energy efficiency, environmental responsibility, and resilience against natural disasters and cyber threats. It combines modern aerospace engineering principles with diverse construction practices to achieve construction invincibility, setting new standards for the industry regarding sustainability, security, and aesthetics.

With an increasing array of new technologies and devices emerging as novel approaches to museum narratives, scholars are intrigued by the narrative effect they produce. Within this context, this study aims to conduct a quantitative case study on the influencing mechanisms among technology embodiment, technology attachment, museum narrative effect and perceived museum image based on embodied cognitive theory (ECT).

By introducing the Opium War Museum, Dongguan City, Guangdong Province, China, as an empirical case study, the authors collected data, through questionnaires, from 425 visitors who had experienced virtual reality (VR) technology in the museum. The data was then analyzed by using the maximum likelihood estimate for structural equation modeling.

Technology embodiment and technology attachment have significant positive correlations with the museum narrative effect. Technology attachment provides a partial mediating effect between technology embodiment and the museum narrative effect. The perceived museum image moderates such mediating effects of technology attachment.

This study brings the emerging idea of developing visitor experiences with VR through expanding the applications of ECT in the museum scenario. First, the authors identified the underlying interaction patterns between various factors that influence the museum narrative effect. Second, the authors verified that technology can optimize the museum narrative effect and help the creative transformation of cultural relics and culture. This study also provides practical recommendations to improve the technological service experience and strengthen the “audience-centric” management concept of museums

随着越来越多的新技术和设备作为博物馆新颖的叙事方法出现, 学者们对它们产生的叙事效果感到兴趣。在这一背景下, 我们基于具身认知理论对技术具身感、技术依恋、博物馆叙事效果和感知博物馆形象之间的影响机制进行了定量案例研究。

通过引入中国广东省东莞市鸦片战争博物馆作为实证案例研究, 我们从体验了博物馆VR技术的425名参观者中收集了问卷数据。然后, 我们使用极大似然法估计结构方程模型对数据进行了分析。

(1) 技术具身感和技术依恋与博物馆叙事效果存在显著正相关关系。 (2) 技术依恋在技术具身感与博物馆叙事效果之间起到了部分中介作用。 (3) 感知博物馆形象调节了技术依恋的上述中介效应。

本研究通过扩展具身认知理论在博物馆场景中的应用, 提出了利用VR优化访客体验的新兴理念。首先, 我们识别了影响博物馆叙事效果的各种因素之间的潜在互动模式。其次, 我们验证了技术可以优化博物馆叙事效果, 并有利于文物和文化的创造性转化。本研究还提出了实际建议, 以改善技术服务体验, 并加强博物馆“以观众为中心”的管理理念。

The purpose of this paper is to examine the effect of chief executive officer (CEO) overconfidence on earnings quality and the moderating role of ownership structure as a crucial corporate governance device.

The paper uses the generalized method of moments (GMM) estimation method to test our models on a sample of 335 French companies between 2009 and 2020, i.e. 4,020 observations.

The results show that CEO overconfidence negatively affects earnings quality. This result supports the predictions of behavioral finance theory and suggests that CEO overconfidence is a behavioral bias that affects the quality of earnings. The authors also examined the effect of different types of ownership structures on this relationship. The results show the significant role of controlling shareholders, owner-managers, families and institutional investors in mitigating the negative effect of CEO overconfidence on earnings quality.

This paper has some limitations. First, other types of ownership structures could have been analyzed such as state ownership. Second, we ignored the role of the board of directors as an important governance mechanism in controlling overconfident CEOs’ actions.

Companies should be aware of the potential risks associated with CEO overconfidence, which can compromise the faithful representation of earnings. This highlights the importance of effective monitoring and internal controls to detect and prevent such practices, which involve the role of ownership structure.

This paper addresses the effect of CEO overconfidence on earnings quality and provides new evidence on the role of different ownership structure types in shaping this relationship. Additionally, this paper sheds new light on how overconfident CEOs may behave in challenging times.

This paper investigates the impact of AI penetration rate on the degree of corporate greenwashing and aims to assess the potential of AI in enhancing firms' environmental performance and reducing false disclosures.

This study employs a year and firm fixed-effects model to analyze data from Chinese listed firms from 2012 to 2022. We use the low-carbon city pilot as a quasi-natural experiment to address endogeneity concerns and conduct a series of robustness tests, including adding control variables and transforming the model.

The results of this paper show that the application of AI can inhibit firms' greenwashing behavior, with green innovation activities further enhancing this inhibitory effect. In state-owned firms and those with Party Organizations, the inhibitory effect of AI on corporate greenwashing is more significant. This reduction in greenwashing is more likely to be observed in firms that are heavily influenced by Confucian culture, receive higher public attention regarding their environmental impact, face less market competition, suffer from more serious pollution and face less financial constraints.

We propose a new research perspective that offers novel insights into promoting the green development of firms by revealing the potential of AI in reducing their greenwashing behavior. Corporate boards can explore specific strategies for applying AI to monitor, prevent and correct greenwashing, thereby enhancing corporate environmental performance and social responsibility.

Customer expectations and preferences may evolve as they experience more. This study aims to analyze the dynamics of customer requirements (CRs) as customer experience familiarity increases, offering insights for enhancing product-service quality.

This study categorizes dynamic requirements into two conversion stages: from new to repeat customers and from repeat to frequent customers. First, crawl online reviews (ORs) and determine each review’s conversion stage. Second, identify product-service attributes from reviews and conduct aspect-level sentiment analysis. Then, examine each attribute’s trend direction and magnitudes in the two stages. Finally, a dynamic-trend importance-performance analysis (DTIPA) model is developed to analyze the dynamic requirements and provide strategies for optimizing product services.

This study identifies eight attributes of buffet restaurants with varying requirement change trends. In particular, customer attention to “waiting time,” “variety of dishes,” “cost performance” and “taste” decreases in the first stage. In the second stage, “environment” and “freshness” increase differently from the first. Satisfaction with “cost performance” increases in the first stage but decreases in the perception of frequent customers. Improvement strategies are also provided based on these trends.

Research on dynamic requirement trends based on ORs and customer experience familiarity is scarce, particularly in the context of buffet restaurants. Moreover, existing methods have their limitations. This study proposes a novel approach for the progressive exploration and extraction of evolving CRs from ORs. By incorporating the trend direction and magnitude of attributes’ importance and satisfaction, DTIPA is developed to form strategies for optimizing buffet restaurant product services.