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Identifying autism spectrum disorder (ASD) in children’s early years significantly impacts their academic performance and future life. It allows them to receive special support services and the required early intervention. Preschool teachers spend considerable time with their students, which allows them to observe their behaviors in different settings like classrooms and playgrounds. However, teachers need to be knowledgeable of ASD early assessment to detect them. Whereas, all well-known ASD assessment tools are designed for populations from different cultures than the Gulf Cooperation Councils' (GCC) culture. This study emphasizes the importance of preschool age, from 3 to 5, in indicating children's early signs of ASD. This paper aims to indicate the early signs of ASD in preschool children in the UAE as a model for other GCC countries.

A qualitative method consisting of interviews with preschool teachers and the ASD children’s parents was conducted to investigate the ASD early symptoms in children.

The findings of the study show that there is a direct connection between the opinions expressed by preschool teachers and the parents of children with ASD (Autism Spectrum Disorder). These results also highlight the presence of early indications of ASD among preschool-aged children in the United Arab Emirates, such as difficulties in communication, repetitive actions, delayed speech, excessive focus on tablets, trouble paying attention, hyperactivity, sleep problems, and aggressive behavior.

The outcomes suggest a strong connection between the feedback from preschool teachers and the parents of children with ASD. The study uncovers initial indications of ASD in preschool-aged children from the United Arab Emirates, such as difficulties in communication, repetitive actions, delayed speech, excessive fascination with tablets, problems with attention, hyperactivity, sleep disturbances, and aggressive behavior.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

Suicide prevention and intervention in prisons is a challenge. Prisons were not designed to be clinical facilities, yet with the growing numbers of people who face mental health challenges in prisons, staff require knowledge and skills to adequately address mental health crises, especially suicide. This study aims to: describe trends in suicide attempts and completions within one state’s prison system and measure staff knowledge and preparedness to address suicide.

This research uses a nonexperimental research design and two data sources. Administrative data from 2000 to 2017 on serious suicide attempts and completions were analyzed, and all correctional staff employed in the state’s Department of Corrections were surveyed at one point in time. Univariate and bivariate analyses were conducted.

The number of serious suicide attempts trended up but completed suicides decreased. Correctional staff demonstrated high suicide knowledge of risk factors and warning signs of suicide. Staff who viewed a media-based suicide training displayed significantly more knowledge of suicide and perceived greater preparedness compared to staff who did not or did not recall viewing the training.

Corrections staff play a key role in preventing suicides in prison. Innovative intervention is needed to increase suicide awareness, improve communication and enhance prevention skills.

The learning outcomes are as follows: to be able to evaluate early warning signs/red flags through financial statement analysis; to be able to analyse company’s credit or debt servicing using a thorough process of fundamental analysis; to be able to analyse and decode the financial health of an organization through different financial tools applicable according to the industry such as default probability and financial ratios; and to be able to synthesize credit rating framework and role of credit rating agencies in the bond market.

In late January 2019, the allegation by an online investigative portal about the misuse of the Dewan Housing Finance Corporation Ltd. (DHFL) money by its promoter for buying asset abroad was the start of the fall of the non-banking finance company giant. This was followed by a series of downgrade by credit rating agencies on its debt and eventual default on its interest payment on 4 June 2019 which upset multiple portfolio investors and the regulators. Investors became sceptical about the regulator’s policy and inefficiencies of credit rating agencies in predicting the default along with asset management houses which were expected to guard investors’ interest. One investor, Shikhar Pachori, decided to scrutinize all hidden information on DHFL to investigate if DHFL crisis arises because of unknown factors which was not in control of management or if it a clear negligence on the part of all involved parties. The case tries to emphasize the aspect of Asset-Liability Management and process of credit analysis while looking for red flags which aids in identifying any stress in company’s financial or any potential default by company.

This case can be used in the advance level of post-graduate finance course or MBA program for elective/specialization courses such as Financial Statement Analysis, Financial Institutions and Market and Fixed Income.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

This study aims to analyze the perceptions of smallholder farmers on climate change and events and further explores climate change adaptation strategies and associated challenges. The findings provide useful information for enhancing the adaptive capacity of smallholder farmers to adjust to climate-related hazards and improve their resilience and disaster preparedness in northern Ghana.

This study uses a multistage sampling procedure and sample size of 150 farmers, the Binary Probit Model (BPM), to identify and examine the determinants of climate change adaptation strategies adopted by smallholder farmers. Also, the constraints of adaptation were analyzed using Kendall’s coefficient of concordance.

The results from the BPM and statistics of Kendall’s coefficient revealed that the farm risk level, ability to adapt, farmer’s income, age, farming experience, climate change awareness and extension visits were factors that significantly influenced the adaptation strategies of smallholder farmers (in order of importance). The majority (60%) of the farmers ranked farm risk level as the major constraint to adopting climate change strategies.

The findings of this study enhance understanding on access to relevant and timely climate change adaptation information such as an early warning to farmers during the start of the farming/rainy season to support their adaptive responses to climate change.

This research has been conducted with a view to creating a framework to integrate risk management based on building information modeling (BIM) information.

In this research, all the information related to the construction of a residential project including 3D, 4D and 5D BIM models and the execution and control phases information was collected, and the risk list was determined for each activity accordingly.

The present study has suggested a framework for risk management in order to optimize project changes.

The lack of integration between 3D, 4D and 5D modeling besides execution information is a fundamental problem in many projects. The gap between these two groups of information will lead to improper management and late decisions, eventually imposing unforeseen delays and cost overruns. Risk management by the means of adopting a new approach has been addressed in recent studies using new methods, such as BIM and its associated technologies, some of which were mentioned in the review of theoretical literature in this research.

According to the Project Management Institute, 70% of projects fail globally. The causes of project failure in many instances can be identified as non-technical or behavioral in nature arising from interactions between participants. These intangible risks can emerge in any project setting but especially in project settings having diversity of cultures, customs, beliefs and traditions of various companies or countries. This paper provides an objective framework to address these intangible risks.

This paper presents a structured approach to identify, assess and manage intangible risks to enhance a project team’s ability to meet its objectives. The authors propose a user-friendly framework, Intangible Risk Assessment Methodology for Projects (IRAMP), to address these risks and the factors that cause them. Meta-network (e.g., a network of networks) simulation and established social network analysis (SNA) measures provide a quantitative assessment and ranking of causal events and their influence on the intangible behavior centric risks.

The proposed IRAMP and meta-network approach were utilized to examine the project delivery process of an international energy firm. Data were gathered using structured interviews, surveys and project team workshops. The use of the IRAMP to highlight intangible risk areas underpinned by the SNA measures led to changes in the company’s organizational structure to enhance project delivery effectiveness.

This work extends the existing project risk management literature by providing a novel objective approach to identify and quantify behavior centric intangible risks and the conditions that cause them to emerge.