Search results

The purpose of this teaching case is to introduce students to an alternative business model as practiced by the Haier Group, a Chinese company, and how that model was used to turn around an acquired company in the United States of America (USA). The explicit teaching objectives are threefold. First, the case provides insights into Haier’s unique employee-centered culture. Second, Haier’s efforts to transfer parts of its culture to the newly acquired General Electric (GE) Appliance Division are discussed. Third, to improve the performance of the acquired division, Haier had to invest in people, equipment and new product technology.

This case is based on both primary and secondary research. The authors interviewed the chief executive officer. In addition, one of the authors spent a week in China observing and participating in meetings at the Haier Group. Both before and after the interviews, the authors searched both print and electronic media for additional information.

The case describes how Haier, a large Chinese appliance manufacturer, learned from its own difficult growth in building an employee- and client-driven organization. Haier was able to transfer key aspects of its culture, management practices and technology to help turnaround the well-known American appliance brand GE. The results of the first five years have been financially successful and investment in new product technology (Smart Home) bodes well for the future.

This case represents only one successful turnaround of one acquisition, GE Appliances, by the Haier Group in the USA. Whether the changes introduced would work with other acquisitions in other countries is unknown. Thus, additional follow-up research needs to be done.

This case brings into question the traditional, hierarchical, bureaucratic, spreadsheet-driven model of management that is the predominant model taught and practiced in the USA. Others, such as Gary Hamel and Michele Zanini, have also raised the same question. This case introduces readers to another way and is just one more example of how managers who practice a high engagement style and share financial success with workers can outperform those who do not.

This case documents the introduction of nontraditional management and human resource management practices by a foreign, Chinese company, into what at one time had been a highly regarded division of a traditional major American corporation. GE had become known for strident labor-management relationships. The employees’ acceptance of the alternative approach to management was in doubt prior to the acquisition, but over the past five years, the labor–management relationship appears to have improved as well as the profitability of the division.

Although the acquisition of the GE Appliance Division by the Haier Group has been widely reported in the press, knowledge of how the acquisition was successfully turned around from a money-losing GE Division to a profitable and growing portion of the Haier Group has been largely ignored in both the popular press and in instructional material for the classroom. This case study seeks to ameliorate the latter deficiency.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

The purpose of this paper is to examine the potential relationship between green finance and greenwashing to rationalize resource allocation better. Additionally, it explores the interconnections among various subgroups of green finance products (GFPs) and identifies any overlooked or underrepresented subgroups.

This paper uses a mixed-method exploratory sequential design. Initially, the authors collected a sample of 313 relevant documents. Thematic analysis and hierarchical coding were then performed using NVivo software to uncover correlations between various nodes and address our research questions. Additionally, a word cloud analysis was conducted to assess the potential research value of stakeholders as moderating variables. Following this, the role of stakeholders was reevaluated, leading to the selection of 58 samples for separate content analysis.

First, there is a negative relationship between green finance and greenwashing. Second, a negative relationship is observed between GFPs and greenwashing. The authors’ correlation coefficient analysis suggests that environmental, social and governance funds, as a non-mainstream research focus within GFPs, deserve further in-depth investigation.

While a significant portion of the existing literature focuses on the relationship between green bonds and greenwashing, a noticeable gap exists regarding the broader spectrum of GFPs and their potential association with greenwashing. The lack of a direct connection between broader GFPs and greenwashing suggests that this area is underexplored in literature. This paper fills this gap by investigating the role of broader GFPs in either promoting or mitigating greenwashing.

The fluctuation of construction fatalities is influenced by both urbanization and economic levels. This study aims to understand the impact of Chinese construction economy development on construction accidents, providing valuable insights for enhancing construction safety and promoting sustainable development in construction.

The Kuznets curve model, multiple linear regression model, and data envelopment analysis (DEA) model are employed to process data sets spanning from 1992 to 2021 for examining the relationship between construction fatalities and the construction economy in China.

Significant correlations have been found between construction fatalities and the construction economy in China. Over the past three decades, as the total output value of construction increased, there have been upward, downward, and downward trends in per capita construction area, the mortality rate per million square meters, and the mortality rate per ten thousand persons respectively. However, it is worth noting that since 2015, there has been a slight upward trend in the fitted U-shaped curve depicting the relationship between the mortality rate per ten thousand persons and the construction economy. This specific trend necessitates the attention of construction safety policymakers. The growth of the construction economy is found to exhibit negative, positive, and positive correlations with the number of construction fatalities, construction area, and the number of employed persons respectively. The changing trends observed in the Kuznets curve model analysis align with the evaluation results obtained from the DEA-based model.

The research offers effective means to identify superior and inferior performance in macro construction safety, providing valuable references for construction safety policymakers to design effective safety strategies and enhance work safety conditions.

This paper provides a thorough examination of the advancements and impacts of artificial intelligence (AI) on construction management (CM) over the past five years, particularly focusing on its role in mitigating prevalent challenges such as inefficiency and ensuring quality. By methodically reviewing and synthesizing the body of research conducted in this period, it underscores key contributions and breakthroughs in the application of AI within construction management (AICM). Additionally, the study aims to shed light on emerging trends and forecast future directions for technological innovation in the construction management sector.

Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, this research conducts a bibliometric analysis of 176 relevant publications from the past five years. The analysis focuses on the adoption of AICM across three critical areas: construction equipment management, improvement of construction safety and construction cost optimization. Additionally, the study systematically identifies and examines 14 emerging themes within this domain, ensuring a comprehensive exploration aligned with PRISMA guidelines.

This manuscript summarizes recent research from the past five years in three key areas: construction equipment management, construction safety management and construction cost management within the realm of AICM. It identifies key gaps and outlines future research directions, including enhancing AI-driven equipment integration, developing sophisticated AI-based safety systems and optimizing cost management with advanced data analytics. These findings and directions are essential for steering the field toward greater digital innovation and sustainability.

This research provides a detailed analysis of the literature within the AICM domain, thoughtfully compiling significant findings and highlighting the importance of addressing user needs. The insights and recommendations shared aim to be beneficial for both academic researchers and industry professionals, contributing to the ongoing development of AICM as it moves toward a future characterized by digital innovation and sustainability.

This study investigates the role of interlocking director networks (IDN) in driving corporate digital transformation (CDT) and explores the moderating role of agency costs, diversification and financial distress.

The analysis uses data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2006 to 2021. A two-way fixed-effects model is employed to assess the impact of IDNs on CDT.

The results indicate that IDNs positively affect CDT. Furthermore, this effect is enhanced by agency costs and financial distress, while diversification acts as a negative moderator.

Informal institutions such as IDNs play a significant role in corporate governance in China’s relational society. This study focuses on the influence of informal institutions on digital transformation, expanding the understanding of the economic consequences of IDNs and enriching the literature on factors influencing digital transformation.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

Information overload has become ubiquitous during a public health emergency. The research purpose is to examine the role of mixed emotions in the influence of perceived information overload on individuals’ information avoidance intention and the state of fear of missing out.

A mixed-methods approach was used in this study: a qualitative study of 182 semi-structured interviews and a quantitative study of 309 surveys.

The results show that perceived information overload negatively affects peace of mind and positively affects fatigue and fear. Emotions with a low activation level (peace of mind and fatigue) promote emotions with a high activation level (hope and fear), and peace of mind negatively influences fatigue. Additionally, peace of mind negatively affects information avoidance intention, while hope positively affects the state of fear of missing out. These two information processing outcomes are positively impacted by fatigue and fear.

This study extends existing knowledge by uncovering the underlying influence of mixed emotions on individuals’ different information processing outcomes caused by perceived information overload. It provides practical insights for online media platforms and Internet users regarding how to process overwhelming information during a public health emergency.

The purpose of the study is to investigate and analyze the dynamics of the government-enterprise grain joint storage mechanism, particularly, focusing on profit-driven speculative behaviors exhibited by enterprises within this context. The study aims to understand the various factors influencing the behavior of stakeholders involved in grain storage, including government storage departments, agent storage enterprises and quality inspection agencies.

The study employs a tripartite evolutionary game model to investigate profit-driven behaviors in government-enterprise grain joint storage. It analyzes strategies of government departments, storage enterprises and quality inspection agencies, considering factors like supervision costs and speculative risks. Simulation analysis examines tripartite payoffs, initial probabilities and the impact of digital governance levels to enhance emergency grain storage effectiveness.

The study finds that leveraging digital governance tools in government-enterprise grain joint storage mechanisms can mitigate risks, enhance efficiency and ensure the security of grain storage. It highlights the significant impact of supervision costs, speculative risks and digital supervision levels on stakeholder strategies, offering guidance to improve the effectiveness of emergency grain storage systems.

The originality of this study lies in its integration of digital governance tools into the analysis of the government-enterprise grain joint storage mechanism, addressing profit-driven speculative behaviors. Through a tripartite evolutionary game model, it explores stakeholder strategies, emphasizing the impact of digital supervision levels on outcomes and offering insights crucial for enhancing emergency grain storage effectiveness.

The purpose of this paper is to verify the performance and function of the scale-up prototypes by predicting the material and energy consumption on the basis of dimension-reduced prototypes. Additive manufacturing (AM) costs determine carbon emissions in total life cycle, among which material and energy consumption are major components. Predicting material and energy consumption is fundamental to reducing costs.

This paper presents a material and energy co-optimization method for AM via multiple layers prediction (MLP). Material and energy consumption are predicted to reduce the AM costs. In particular, scalable, complex curved surface component is used to improve forecasting efficiency. Subsequently, the back pressure distribution is obtained by scale-up specimens, which can lay the foundation for the ergonomic conceptual design.

Taking evolutionary ergonomic product as an example, the relative gravity direction of backrest is calculated. The material and energy consumption are predicted with low deviation. Physical experiments were carried out to validate information. Digital and physical tests have revealed that material and energy co-optimization improves manufacturing efficiency.

The innovatively proposed MLP method predicts material and energy consumption of scale-up prototypes to reduce the costs. It is propitious to improve the carbon emission efficiency in life cycle of AM. The originality may be widely adopted alongside increasing environmental awareness.