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The overall goal of this chapter is twofold. First, the authors aim to identify indigenous phenomena that influence employee turnover and retention in the Chinese context. Second, the authors link these phenomena to the contextualization of job embeddedness theory. To achieve the goal, the authors begin by introducing three macro-level forces (i.e., political, economic, and cultural forces) in China that help scholars analyze contextual issues in turnover studies. The authors then provide findings in the literature research on employee retention studies published in Chinese academic journals. Next, the authors discuss six indigenous phenomena (i.e., hukou, community in China, migrant workers, state-owned companies, family benefit prioritization, and guanxi) under the three macro-level forces and offer exploratory propositions illustrating how these phenomena contribute to understanding employee retention in China. Finally, the authors offer suggestions on how contextualized turnover studies shall be conducted in China.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

This chapter examines how the breakthrough of Zhang Ziyi's depiction of a female kung fu master in The Grandmaster (2013) transforms the figure of the heroine in Chinese action films. Zhang is well known for her acting in action films conducted by renowned directors, such as Ang Lee, Zhang Yimou and Wong Kar-wai. After winning 12 different Best Actress awards for her portrayal of Gong Ruomei in The Grandmaster, Zhang announced that she would no longer perform in any action films to show her highest respect for the superlative character Gong. Tracing Zhang's transformational portrait of a heroine in The Grandmaster alongside her other action roles, this analysis demonstrates how her performance projects the directors' distinctive gender viewpoints. I argue that Zhang's characterisation of Gong remodels heroine-hood in Chinese action films. Inheriting the typical plot of a daughter's use of martial arts for revenge for her father's death, Gong breaks from conventional Chinese action films that highlight romantic love during a woman's adventure and the decisive final battle scene. Beyond the propensity for sensory stimulation, Gong's characterisation enables Zhang to determine that women can really act in action films – demonstrating their inner power and ability to create multi-layered characters – not merely relying upon physical action. This chapter offers a relational perspective of how women transform the action film genre not merely as gender spectacles but as embodied figures that represent emerging female subjectivity.

This case highlights the green agriculture initiative of the Honliv High Tech Agricultural Development Co., Ltd. towards agriculture environmental protection and better life. In this process, the Honliv Company confronted many difficulties, such as addressing the green transformation concerns of the team, identifying the sustainable agriculture target, the project selection of red globe grape products, the planning of a demonstration vineyard and green marketing. Nevertheless, with the Red Globe Grape of the “Honliv brand” had won the certification of China’s “Grade A” green food mark, and its green marketing initiatives, such as the “picking and demonstration model” had won people’s identification. The company was awarded the honorary titles of “Red Globe grape planting demonstration base with the Chinese characteristics” and “pollution-free science and technology demonstration unit of Chinese fruits and vegetables.” By discussing the Honliv Company’s experience and difficulties during the sustainable agriculture transformation process, one can realize the importance of green agriculture initiatives for creating a better life for humans.

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.

This chapter elaborates on entrepreneurship in developed and developing countries and focuses on the optimization of entrepreneurial activities. Various scenarios are considered: independent functioning of the market, integration in the form of reorganization (mergers and acquisitions), integration in the form of clustering, and integration in the form of innovational networks and technological parks. The optimal structure of the integration processes and best-case scenarios for its implementation to accelerate the rate and increase the quality of economic growth are substantiated. The potential for uptake of integration processes in stimulating economic growth through entrepreneurship is determined by the level of institutionalization in an economy. In developed countries, all forms of company integration are characterized by the high level of institutionalization, which allows for their effective use for economic growth. Independent companies, mergers, and acquisitions restrain economic growth and reduce its quality, while clusters, technological parks, and innovational networks accelerate the rate of economic growth and increase its quality. In developing countries, integration processes in entrepreneurship have a different influence on economic growth and require further institutionalization.

The intensifying effects of climate change and the growing concentration of population in hazardous locations mean that, for many communities, disasters are increasingly becoming not only foreseeable, but inevitable. While much attention is, and should be, focused on what these foreseeable disasters require in terms of disaster planning and mitigation, attention should also be focused on a related and equally pressing phenomena: mismanagement of disaster response, particularly as climate proves an increasing stressor. Like disasters themselves, disaster mismanagement – while not entirely predictable – may exhibit some predictable patterns. This chapter explores past disaster management failures, considers how climate change may alter or exacerbate certain response pathologies, and evaluates some potential remedies that might mitigate these challenges.