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Realistic composite vehicles with 2, 3, 5 and 9 axles, consisting of a truck with one or two trailers, are addressed in this paper by computational models for vehicle–bridge interaction analysis.

The vehicle–bridge interaction (VBI) models are formed by sets of 2-D rigid blocks interconnected by mass, damping and stiffness elements to simulate their suspension system. The passage of the vehicles is performed at different speeds. Several rolling surface profiles are admitted, considering the maintenance grade of the pavement. The spectral density functions are generated from an experimental database to form the longitudinal surface irregularity profiles. A computational code written in Phyton based on the finite element method was developed considering the Euler–Bernoulli beam model.

Several models of composite heavy vehicles are presented as manufactured and currently travel on major roads. Dynamic amplification factors are presented for each type of composite vehicle.

The VBI models for compound heavy vehicles are 2-D.

This work contributes to improving the safety and lifetime of the bridges, as well as the stability and comfort of the vehicles when passing over a bridge.

The structural response of the bridge is affected by the type and size of the compound vehicles, their speed and the conservative grade of the pavement. Moreover, one axle produces vibrations that can be superposed by the vibrations of the other axles. This effect can generate not usual dynamic responses.

The paper examines whether special purpose governments follow the pecking order model when raising capital, replacing firm equity in the original model with local intergovernmental revenues. Special purpose governments are a relatively underexamined component of state and local governments, and their capital structure choices have important implications for America’s aggregate fiscal health. This paper seeks to illuminate special purpose governments’ choices among available forms of capital.

To address our research inquiry regarding whether special-purpose governments adhere to the pecking order theory, we narrow our focus to a specific group of transit entities. Our investigation utilizes data sourced from two distinct repositories: the United States Census of Governments and the National Transit Database. To facilitate integration of these disparate datasets, we establish a correspondence between them using the Federal Information Processing Standard and the transit identification number. Initially, our analysis employs maximum likelihood estimation, comparing these estimates to those generated by a naïve model. Subsequently, we derive parameter estimates through the application of a bivariate probit model.

The paper supports the pecking order hypothesis where internal funds are consumed first, debt is consumed next and IGR is consumed last. The results are robust and are not influenced by simultaneity bias.

The paper uses transit special purpose governments as the special purpose government of interest. These transit special purpose governments have high fixed costs that may inform their capital structure decisions relative to non-transit special purpose governments. Additionally, transit special purpose governments often have a profit-maximizing objective that may not uniformly apply to other special purpose governments, such as school districts, who lack such an incentive.

Our research should grab the attention of state and local politicians, voters, policy experts and scholars. Special-purpose governments, a key part of our governmental system, are on the rise. Understanding them better can guide decision-making on how to allocate resources, set policies and plan strategically. This knowledge boosts financial reporting quality, transparency, accountability and public confidence. Journalists can leverage our findings to ensure accurate and thorough reporting, fostering accountability and trust. Additionally, debt markets and analysts can factor this information into their risk assessments.

The paper enhances our understanding of how special purpose governments interact with existing models of corporate finance when making capital structure decisions.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

The main purpose of this study is to fill the research gap on how B2B global service firms integrate dynamic capabilities within their omnichannel management to influence positive word of mouth (WOM), customer engagement (CE) and customer equity.

Drawing on the dynamic capability and WOM theories, a model has been developed that defines the subjects of the empirical test. The paper reports on data collected from 312 service-oriented global firms in Australia, through a cross-sectional survey. Data were analyzed using structural equation modeling.

The findings suggest that content management (i.e. information consistency, source trustworthiness and endorsement) and concerns management (i.e. privacy, security and recovery) capabilities are the two significant antecedents of positive WOM within a B2B omnichannel setting in international marketing. The findings also confirm the key mediating role of CE between positive WOM and customer equity.

The findings extend dynamic capability theory in the context of international marketing by linking WOM, CE and customer equity. The findings add further theoretical rigor by establishing the nomological chain between positive WOM and customer equity, in which CE plays a key mediating role.

Work-based learning is critical for enhancing employees’ skills and contributing to the firm’s performance. This paper aims to establish the effects of needs assessment on the relationship between training intensity as part of learning and how employees’ skills are reflected in firm performance.

The paper used the World Bank Tanzania Employees Skills Survey (TESS) dataset, which contains 424 firms. This paper estimated the moderated mediation model through partial least squares structural equation modelling (PLS-SEM) and employed the index of moderated mediation to determine if the model was correctly specified.

The results show that among three skills, i.e. technical, interpersonal/communication and work ethic skills, only the level of technical skills mediated the relationship between training and the firm’s performance. The index of moderated mediation suggests a threshold point for the firm’s training needs, above which the indirect effect of training on performance through technical skills starts to decrease. The negative correlation between the firm’s training needs and the indirect effect suggests that employees’ essential human capital qualities, viewed from the angle of their training needs, are among the key factors for executing effective training.

This paper’s conceptual model is limited because it does not incorporate an education variable for the trained employees. In addition, it only conceptualized the perceived most important skills of interpersonal communication, technical skills and work ethic, despite there being other skills that could have been considered. Moreover, the data only measured the present skill level at three on the Likert scale, providing limited room for skill level variance.

Those who decide which training programme deserves priority given limited resources and the firm’s goals need to understand that training is an addition to what their employees already have and, thus, should make extra efforts to equip them with more knowledge relating to their assignments. Moreover, this understanding should extend to the employees themselves.

The paper introduced and showed the necessity of training needs assessment to increase the value of training in enhancing the firm’s performance. We propose a model for assessing training intensity through process analysis. The respective model depicts a threshold point for the firm’s training needs, below which the training will work.

The purpose of this research is to investigate the differences between financial crises of fear and confidence and the differential behavior between downtrends and recovery.

Five national stock markets have been analyzed – the USA (SP500), China (Hang Seng), Spain (IBEX 35), Japan (Nikkei) and Germany (DAX) – through the evolution of three world economic crises: the mortgage bubble crisis of 2007 in the first place, with special attention to the bankruptcy of Lehman Brothers, which will be treated as an independent crisis process, and the crisis caused by COVID-19. The behavioral finance theory, with the support of the complexity theory in the field of risk management, will establish the different behavioral biases that explain the differences between the two types of crises, fear and confidence, when confronted with risk.

Economic crises resulting from a shocking event, addressed as crises of fear in this research, such as Lehman Brothers or COVID-19, are fast-moving; all the economies analyzed show a common pattern of evolution. The difference is found in the recovery periods in which the previous parallelism does not continue. Crisis events that arise from a social context, addressed as crises of trust in this research, follow similar patterns in their evolution; nonetheless, the start date presents higher variations than those originated by a shock. These crises also lack parallelism between fall and recovery.

Understanding crisis process patterns may help to prevent them and alleviate their effects when they occur.

Understanding crisis process patterns may help to prevent them and alleviate their effects when they occur. This constitutes an original field of research.

The study has practical implications for decision-makers in that increasing board competence and expertise through training on environmental issues will promote green policy-making.

This study included 655 firm-year observations from companies listed on the Indonesia Stock Exchange between 2017 and 2021. Panel data regression analysis is used to investigate the hypotheses. Additionally, a robustness test is conducted to validate the consistency of the primary test results.

The results demonstrate that green theme training from the board of directors, board of commissioners and independent commissioners has a positive and significant impact on the implementation of green innovation at each level of the board. This result is aligned with the robustness test performed.

This study is restricted by the fact that the only data sources used to examine the board’s green training are publication reports and other reports that disclose the board’s training activities. Therefore, future research can be done by considering other methods, such as surveys to trace green training followed by the board. Additional research may also examine green theme training in the corporate governance structure from a different theoretical angle, such as agency theory and human capital theory.

In practice, the study has implications for decision-makers in that increasing board competence and expertise through training on environmental issues will be able to promote green policy-making.

This study concentrates on Indonesia with two-board governance characteristics: the board of directors and the board of commissioners. Several scholars have examined the board of directors in light of resource dependence theory. To the best of the authors’ knowledge, no research has explained the supervisory board within the context of two-board governance. In addition, the authors have not found research that analyzes board training activities related to the environment.