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Ethics is fundamentally a science of social and collective responsibility. Ethics concerns human behavior as responsible or accountable. Because of the nature of social interaction, certain members of the society will bear greater authority, and hence, greater individual and social responsibility than others. In our world, personal responsibility and social responsibility are hardly separable. Personal responsibility becomes responsibility for the world because the person and the world are inseparable. In this chapter, we use the term responsibility from a legal, ethical, moral, and spiritual (LEMS) standpoint as some promise, commitment, obligation, sanctioned by self, morals, law, or society, to do good, and if harm results, to repair harm done on another. Hence, responsibility from a moral perspective is trustworthiness and dependability of the agent in some enterprise. Its inverse is exoneration – the extent to which one is excused from commitment and repairing the harm done to others by one’s actions. We apply the theories and constructs of executive responsibility to two contemporary cases: (1) India’s Super Rich in 2014 and (2) the Fall and Rise of Starbucks. After exploring the basic notion of responsibility, we present a discussion on the nature and obligation of corporate responsibility into three parts: Part I: Classical Understanding and Discussion on Corporate Responsibility; Part II: Contemporary Understanding and Discussion on Corporate Responsibility, and Part III: A synthesis of classical and contemporary views of responsibility and their applications to corporate executive responsibility.

The purpose of this paper is to report on the aspects of an exploratory investigation into the scale and nature of the financial abuse of adults lacking mental capacity.

It uses mixed-methods study which comprises of: a review of safeguarding adults’ statistics; analysis of court case findings; classification of types of financial abuse, victims and perpetrators; qualitative exploration of professional views of the nature of financial abuse of those lacking mental capacity; and a consideration of policy implications.

It demonstrates the significance of financial abuse within the spectrum of abuse experienced by adults at risk; the wide range of both victims lacking capacity being abused and type of financial abuse; its often hidden nature embedded within the family; and the limitations of processes designed to protect.

The investigation reveals the paucity of statistical data available on the nature of financial abuse and the outcomes of official investigations into reported cases, both of which limit analysis and understanding of the phenomenon.

It demonstrates the need for greater transparency and consistency in the reporting of safeguarding and legal processes to enable practitioners and policymakers to fully understand the nature and significance of this abuse for both victims and society.

It questions the extent to which existing protective processes are sufficient in terms of safeguarding victims and deterring perpetrators.

It involves original research that brings together data from a range of sources involved in the protection of a particular hard-to-reach group of individuals (those lacking capacity) from a particular type of risk (financial abuse) of increasing social significance.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.