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For many of the current leadership theories, models, and approaches, the answer to the question posed in the title, “Is leadership more than ‘I like my boss’?,” is “no,” as there appears to be a hierarchy of leadership concepts with Liking of the leader as the primary dimension or general factor foundation. There are then secondary dimensions or specific sub-factors of liking of Relationship Leadership and Task Leadership; and subsequently, tertiary dimensions or actual sub-sub-factors that comprise the numerous leadership views as well as their operationalizations (e.g., via surveys). There are, however, some leadership views that go beyond simply liking of the leader and liking of relationship leadership and task leadership. For these, which involve explicit levels of analysis formulations, often beyond the leader, or are multi-level in nature, the answer to the title question is “yes.” We clarify and discuss these various “no” and “yes” leadership views and implications of our work for future research and personnel and human resources management practice.

The purpose of this paper is to explore how curved road and lane-changing rates affect the stability of traffic flow.

An extended two-lane lattice hydrodynamic model on a curved road accounting for the empirical lane-changing rate is presented. The linear analysis of the new model is discussed, the stability condition and the neutral stability condition are obtained. Also, the mKdV equation and its solution are proposed through nonlinear analysis, which discusses the stability of the extended model in the unstable region. Furthermore, the results of theoretical analysis are verified by numerical simulation.

The empirical lane-changing rate on a curved road is an important factor, which can alleviate traffic congestion.

This paper does not take into account the factors such as slope, the drivers’ characters and so on in the actual traffic, which will have more or less influence on the stability of traffic flow, so there is still a certain gap with the real traffic environment.

The curved road and empirical lane-changing rate are researched simultaneously in a two-lane lattice hydrodynamic models in this paper. The improved model can better reflect the actual traffic, which can also provide a theoretical reference for the actual traffic governance.

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.

Given that a prerequisite for COVID-19 transmission is the interaction between individuals, it is reasonable to suspect that transportation networks may have contributed to the spread of COVID-19. This study uses the air transportation network to quantify the risk of COVID-19 spread in the United States. The proposed model is applied at the county level and identifies the risk of importing COVID-19-infected passengers into a given county. We also undertake an examination of the factors influencing the spread of COVID-19 in relation to air travel. Utilizing an extensive dataset encompassing various socioeconomic, demographic, and healthcare-related variables, our results indicate a positive relationship between these factors and the relative risk of COVID-19 spread, highlighting the pronounced impact of population density, air travel volume, and larger household sizes on increasing travel-related risk. Conversely, greater healthcare capacity, particularly in terms of hospital and intensive care unit (ICU) beds, is associated with reduced risk. We provide estimates of expected relative risk for each county and a ranking that can be useful for informing public health policies to stem the spread of the virus by devoting resources such as screening and enhanced travel protocols to airports located in at-risk counties.

In the era of connected and autonomous vehicles, a large amount of surrounding vehicular information can be acquired by the focal vehicle in real time using vehicle-to-vehicle communication technology, such as the core variable of electronic throttle opening angle. Meanwhile, the traffic jerk, such as the non-compliance of drivers and pedestrians, worsens the chaos of the surrounding traffic environment. To reflect the future traffic environment, the authors simultaneously incorporate the electronic throttle (ET) and traffic jerk into the traditional continuum model. The authors derive the stability criterion of the enhanced continuum model via the perturbation method.

To facilitate insight into the propagation and evolution mechanism of traffic jam near the stability condition, the authors use the nonlinear stability analysis method to derive the KdV-Burgers equation of proposed continuum model.

The new item of ET opening angle and traffic jerk have a positive impact on suppressing traffic congestion and improving road robustness.

The research on autonomous continuum models is rare. This model can better reflect the actual traffic, which can also provide a theoretical reference for the future traffic governance.

This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.

Status, which is based on differences in esteem and honor, is an ancient and universal form of inequality which nevertheless interpenetrates modern institutions and organizations. Given its ubiquity and significance, we need to better understand the basic nature of status as a form of inequality. I argue that status hierarches are a cultural invention to organize and manage social relations in a fundamental human condition: cooperative interdependence to achieve valued goals with nested competitive interdependence to maximize individual outcomes in the effort. I consider this claim in relation to both evolutionary arguments and empirical evidence. Evidence suggests that the cultural schema of status is two-fold, consisting of a deeply learned basic norm of status allocation and a set of more explicit, variable, and changing common knowledge status beliefs that people draw on to coordinate judgments about who or what is more deserving of higher status. The cultural nature of status allows people to spread it widely to social phenomena (e.g., firms in a business field) well beyond its origins in interpersonal hierarchies. In particular, I argue, the association of status with social difference groups (e.g., race, gender, class-as-culture) gives inequalities based on those difference groups an autonomous, independent capacity to reproduce themselves through interpersonal status processes.

Despite the rising interest in eco-innovation, few studies have examined how open innovation (OI) actually increases eco-innovation performance. Drawing on capabilities theory, this study aims to investigate how two specific organizational capabilities (alliance management capability and absorptive capacity) individually complement OI strategies (inbound and outbound) to increase eco-innovation performance, while taking into consideration high and low levels of environmental uncertainty.

To test the hypotheses, the authors used a primary survey and secondary proxy data sources from 232 Taiwan-based manufacturing firms. The authors collected survey data for measuring OI strategies, followed by secondary proxy data for measuring alliance management capability, absorptive capacity, environmental uncertainty and eco-innovation performance.

The results indicate that in highly dynamic environments, alliance management capability complements inbound/outbound strategies to increase eco-innovation performance. However, absorptive capacity complements only inbound strategies, not outbound strategies.

These findings have important implications for managers attempting to increase eco-innovation performance by using OI in dynamic environments.

The findings provide new evidence that configurations of OI alone are not enough for increasing eco-innovation performance. Instead, firms’ eco-innovation benefits more when OI are complemented by alliance management capability.

This study makes an original contribution to the eco-innovation literature by demonstrating how organizational capabilities complement OI to increase eco-innovation performance in dynamic environments.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.