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This chapter explores the significance and implementation of family planning practices, emphasizing their vital role in enhancing reproductive health outcomes. This chapter conducts an in-depth examination of family planning practices within marginalized communities in Bangladesh, shedding light on the unique challenges and opportunities faced by these populations. By uncovering the nuances of family planning practices in marginalized contexts, this chapter underscores the need for tailored and culturally sensitive family planning programs. It advocates for program designs that facilitate the effective adoption of family planning practices among marginalized communities, ultimately promoting better reproductive health outcomes. Furthermore, this chapter highlights the importance of empowerment strategies in reaching and engaging marginalized communities. Through practice enhancement initiatives, it seeks to empower marginalized populations with the knowledge and resources necessary to make informed decisions about family planning, thereby contributing to improved reproductive health and overall well-being.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

Exposing the hidden lives of female public relations (PR) practitioners requires deep connection with those lives. Stories need to be uncovered, interrogated, and ultimately told, to shine a light on the lived experiences of those working in PR. The methods used to collect these stories require deep immersion in the field and the ethnographic method is ideal for this. Ethnographic research methods have long been utilised to gain insights into the lived experiences of individuals and communities. This chapter provides an understanding of the strengths and limitations of ethnographic research methods in capturing the nuances of women's experiences of working in PR.

Organisational ethnography is an established field in business studies and has been used to investigate disciplines cognate to PR, including advertising and media, but, to date, has failed to be fully explored in PR research. This chapter examines the potential for ethnography to open new areas of PR theory and considers its potential as a means of bridging the gap between PR theory and practice.

Ethnography is not without its limitations; key concerns surround objectivity, the role of the researcher, and that of the participant, and ethics. Nonetheless, this method would appear to offer huge potential for the study of PR practices; the diverse nature of the sector makes it a rich area to study.

This chapter explores the potential of this method to offer an opportunity to investigate areas such as working practices, ethics in practice, power, gender, diversity, and culture.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Purpose of This Chapter: This research aims to study the need for more age of qualified talent. The evolving corporate needs, education, and curriculum require urgent reform. Current university methods do not align with corporate demands due to outdated content and ineffective pedagogy.

Design / Methodology / Approach: Drawing on established research, this study delves into 7 prominent training strategies across 14 sectors. A survey of 53 HR professionals and managers forms the basis for employing the non-linear best–worst method (BWM) and the Fuzzy BWM to discern the most effective training and development (T&D) modules. This comprehensive methodology ensures a nuanced analysis of T&D practices and insights for businesses seeking to align with Industry 5.0 demands.

Findings: On-the-job training emerges as the most impactful method, followed by case studies, interactive group learning, and more. These methods enhance employee skills in Industry 5.0.

Research Limitations: Limited by a small sample, future research should expand participant diversity for robustness.

Practical Implications: The study holds significance for bridging the skill gap between academic institutions and Industry 5.0. Aligning strategies with industry needs reduces skill disparities, fuels growth, and addresses employability. The study’s impact extends to society, lowering unemployment and shaping a resilient, adaptable workforce for Industry 5.0.

Originality: This innovative research examines decision-making for implementing T&D strategies in the emergence of Industry 5.0, aligning with a human-centric approach to business transformation.

This chapter discusses various aspects of family planning initiatives on a global scale, with a specific focus on their influence in Asian contexts, including Bangladesh. This chapter examines the diverse range of family planning programs and policies implemented worldwide, in Asia and Bangladesh considering cultural factors that significantly shape family planning decisions. By highlighting the unique approaches and challenges faced in different regions, it provides valuable insights into the evolving landscape of family planning efforts and their impact on population dynamics.

This chapter aims to estimate the impact of the use of an innovative cultivation method on the social, economic and environmental aspects in the French region Aix-en-Provence, by using the survey data for 200 heterogeneous vegetable producers (organic and conventional). It distinguishes three types of producers in the French region Aix-en-Provence. First, conventional producers (n = 100) who used a high level of mechanization, better access to water, high yield, high labor costs. Second, certified organic producers (n = 70) who used organic technologies such as biotechnology and rotation, low yield, high organic product price compared to conventional products, a family workforce and high transport. Third, noncertified organic producers (n = 30) have used the same technologies as certified organic producers, while they sell their products at the same price as conventional products. Labor is the member of the family. These noncertified farms are marked by high operating and transport costs and low yield compared to conventional producers or certified organic producers. The results show that this cultivation method has a positive effect on the environmental aspect, however a negative one on the social and economic aspect.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.