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This chapter aims to examine the ways in which gender has featured in Hong Kong’s prison system from its colonial origins to its contemporary form as a politically autonomous region of China. We conclude with a discussion on the reasons for these recent trends of imprisonment.

We draw from the concepts of patriarchy and colonialism to examine how gender has operated and shaped Hong Kong’s prison system. Our analysis is based on historical and contemporary government reports and other documents and secondary data.

Similar to other locales around the world, Hong Kong’s prison system was designed for and by men in its early colonial days, as expected given that most prisoners were male. Although a few prison administrators attempted to provide some programs for women and voiced concern over the conditions of women’s imprisonment to colonial authorities during the latter part of the 1800s, it was not until the 1930s that the first female prison was established. Since then, Hong Kong prison authorities have faced the challenge of a phenomenal and rapid growth in women’s imprisonment, which resulted in a historical reversal of shifting male prisoners to alternate accommodation to make room for their female counterparts.

This study is among the few which have examined how gender operates in the context of imprisonment in a colonial and postcolonial context. This chapter does this by examining how colonial authorities managed competing political debates about the purpose of punishment and cultural understandings of race and difference, and the limited recognition of gender and difference. It also examines how, in postcolonial Hong Kong, authorities have placed gender center stage and the reasons for this in coping and dealing with the growth in women’s imprisonment.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.

Cycle tourism is considered as a trendy opportunity of local development that should be taken into consideration by several destinations to (further) increase tourism according to the sustainable development approach. It is a broad and complex phenomenon that involves various social and economic actors. Cycle tourists are looking for new and deep experiences to better benefit from the local identities and the uniqueness of the landscape of a territory. Cycle tourism gives sustainable access to environmental and cultural resources of territories often neglected. Despite its evident potentialities, the lack of studies represents a drawback that could compromise the local development. The aim of this chapter is first to describe the characteristics of this form of tourism both in terms of its contribution to the sustainable development and of demand and supply features. Second, the study focuses on an Italian area that is strongly investing in the development of this form of mobility: an area called “Insubria,” which is located in the Lombardy region, near the Swiss border and includes, as main cities, Varese and Como. The work explores whether the supply of the tourism product in this area is aligned with the current and future demand trends of cycling and tourism. The analysis ends with some suggestions about possible improvements in the area and for the long-term industry competitiveness.

Rivalry among conference centers has greatly increased in the past few years. It is thus important to adequately comprehend the critical characteristics of conferences, including those associated with their facilities and services. This chapter explores the impacts of conference quality and perceived value on attendees’ behavior in Malaysia. A questionnaire was administered to international attendees of nine academic association conventions held in Malaysia in 2016. Partial least squares structural equation modeling was applied to the collected data using Smart-PLS software. Results capture the positive impacts of service quality and perceived value on behavioral intention. Delegate perspectives regarding the significance of some facilities and services are further appraised.

This chapter reviews the existing empirical evidence on how social insurance affects health. Social insurance encompasses programs primarily designed to insure against health risks, such as health insurance, sick leave insurance, accident insurance, long-term care insurance, and disability insurance as well as other programs, such as unemployment insurance, pension insurance, and country-specific social insurance programs. These insurance systems exist in almost all developed countries around the world. This chapter discusses the state-of-the art evidence on each of these social insurance systems, briefly reviews the empirical methods for identifying causal effects, and examines possible limitations to these methods. The findings reveal robust and rich evidence on first-stage behavioral responses (“moral hazard”) to changes in insurance coverage. Surprisingly, evidence on how changes in coverage impact beneficiaries’ health is scant and inconclusive. This lack of identified causal health effects is directly related to limitations on how human health is typically measured, limitations on the empirical approaches, and a paucity of administrative panel data spanning long-time horizons. Future research must be conducted to fill these gaps. Of particular importance is evidence on how these social insurance systems interact and affect human health over the life cycle.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.