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Economic evaluations often utilise individual-patient data (IPD) to calculate probabilities of events based on observed proportions. However, this approach is limited when interest is in the likelihood of extreme biomarker values that vary by observable characteristics such as blood glucose in gestational diabetes mellitus (GDM). Here, instead of directly calculating probabilities using the IPD, we utilised flexible parametric models that estimate the full conditional distribution, capturing the non-normal characteristics of biomarkers and enabling the derivation of tail probabilities for specific populations. In the case study, we used data from the Born in Bradford study (N = 10,353) to model two non-normally distributed GDM biomarkers (2-hours post-load and fasting glucose). First, we applied fully parametric maximum likelihood to estimate alternative flexible models and information criteria for model selection. We then integrated the chosen distributions in a probabilistic decision model that estimates the cost-effective diagnostic thresholds and the expected costs and quality-adjusted life years (QALYs) of the alternative strategies (‘Testing and Treating’, ‘Treat all’, ‘Do Nothing’). The model adopts the ‘payer’ perspective and expresses results in net monetary benefits (NMB). The log-logistic and Singh-Maddala distributions offered the optimal fit for the 2-hours post-load and fasting glucose biomarkers, respectively. At £13,000 per QALY, maximum NMB with ‘Test and Treat’ (−£330) was achieved for a diagnostic threshold of fasting glucose >6.6 mmol/L, 2-hours post-load glucose >9 mmol/L, identifying 2.9% of women as GDM positive. The case study demonstrated that fully parametric approaches can be implemented in healthcare modelling when interest lies in extreme biomarker values.

For many predominantly white arts institutions in the United States, the murders of Breonna Taylor and George Floyd in the summer of 2020 prompted externally initiated calls for equity work. Many of these organizations crafted equity statements, engaged in trainings, and made public displays of their intent to do different and better – however, many did not follow through on those promises (Heidelberg, 2020). While many organizations have indeed engaged in fakequity or “equity talk with no action” (Okuno Consulting, 2017), this may not explain every instance of stalled or incomplete equity action within the arts sector. In the case of fakequity, the remedy is to actually do the work of creating a more inclusive and equitable organization, rather than simply talk about it. However, if there are root causes for stalled equity action aside from fakequity, then organizations are left without guidance on how to identify and address that cause/those causes and move forward. This case examines the primary research question: what organizational practices contribute to stalled equity efforts other than fakequity? Investigating this question led to a secondary research question: what conditions help organizations move beyond stalled equity efforts? In order to address this question, I conducted a single-case study (Yin, 2014) of a midwestern museum to offer a contextualized understanding of identifying and addressing organizational elements that contribute to false starts in equity work within predominantly white arts institutions.

This chapter examines three common fintech use cases transforming the financial industry. First, the chapter introduces fintech's role in enhancing financial services and promoting financial inclusion, especially through digital platforms. Second, it investigates various fintech applications that support financial institution management by harnessing the power of artificial intelligence (AI) and machine learning (ML). Finally, the chapter explores fintech use cases related to the regulatory environment, including regulatory technology (regtech), blockchain technology, and cryptocurrencies. The insights presented in this chapter cater to researchers and practitioners keen on better understanding fintech's diverse applications in the ever-evolving financial industry landscape.

This study explores the profound influence of social and cultural factors on the financial conduct of indigenous tribes and groups. Anchored in Vygotsky's sociocultural theory, the analysis delves into the intricate interplay between cultural elements, such as bricolage, and the immediate availability of financial resources, illuminating their collective impact on the tribes' financial behaviour. Typically residing in proximity, these communities exhibit homogeneity by forming groups exclusive to their clans, lacking access to conventional financial services and tangible assets that dissuade banks from extending loans. Crucially, the social capital embedded within the group dynamics, often referred to as the peer mechanism, emerges as a pivotal conduit for members to secure capital and bank credit. The synergy of bricolage, representing the adept use of available social capital, facilitates access to finance and credit. Despite the existence of social capital and financial literacy programmes, a stark reality persists – a significant proportion of indigenous people remain financially excluded. This chapter endeavours to scrutinise the ramifications of these factors on tribal financial behaviour, employing the Partial Least Squares Structural Equation Modelling (PLS-SEM) method. Proposing a paradigm shift in financial attitudes, the research underscores the imperative of fostering financial inclusion within indigenous tribes and communities.

Kaber Ali, the second-generation successor of NGB, had returned home to Bangladesh upon completing his undergraduate studies at Taylor's University, Malaysia, in March 2020. With the plan to take over his father, Jaber, by the end of 2020, he joined as a full-time director in May 2020, working alongside his father. The intention for succession and the complexity of the business and operations led to restructuring of NGB to ensure smooth succession. The father and son worked closely in restructuring the business, while going through the COVID-19 pandemic and departure of several of Jaber's cousins. Despite the challenges faced during the COVID-19 pandemic, Kaber strived to upkeep the family value by ensuring all employees were still receiving their wages, especially the garment factory workers, despite temporary closure of factories without profit. Jaber also ensured that no layoff occurred during restructuring of the business. Following his father's step in helping the needy communities, Kaber had also started his own social work with fundraising. Kaber was looking forward to take over the business fully, while anticipating possible changes to the family dynamics when the family had found him a bride and possible return of his uncles to the business.

The growth and promotion of green tourism destinations can have many potential benefits from artificial intelligence (AI). The literature on AI and applications of AI in promoting green destinations is very less. The major areas of research in this direction are related with nature-based tourism or sustainable tourism. There is a great potential to research in this area as AI can play an important role in promoting green destinations. Simultaneously, AI can play the role of enabler to achieve environmental targets by promoting various green destinations. The major finding of this chapter is that the research in this area is majorly revolving around tourist destinations and sustainable development. Another area of research where AI is used is eco-tourism and sustainable tourism. With the help of various decision support systems, sustainable tourism can be promoted. Social media platforms and digitalization of tourism is a great enabler of using AI in the field of tourism.

This study examines how asking employees to self-assess their performance during the compensation setting process, when they are unaware of their marginal contribution to firm profit, affects employer welfare. Previous research suggests that giving employees a voice in the compensation setting process can positively affect employee performance and firm profit (Jenkins & Lawler, 1981; Roberts, 2003). However, the study proposes that asking employees to assess their own performance as part of the compensation setting process can have unintended consequences that ultimately lead to higher employee compensation demands. This is because asking employees to assess their performance increases their overconfidence in their own performance and their compensation demands. As a result, employers may face the dilemma of whether to meet these higher compensation demands or risk economic losses due to employee retaliation if their demands are not met. Through experimental evidence comparing a control condition without self-assessments and three self-assessment reporting conditions, the study provides evidence that supports the notion that eliciting employee self-assessments as part of the compensation process reduces employer welfare. Data on employee perceptions of performance further support the notion that asking employees to evaluate their performance leads to an inflated perception of their performance. These findings provide a theory-based explanation of why, in practice, many companies disentangle employee performance assessments from the compensation setting process and that companies are well advised in doing so.