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The R environment for statistical computing and graphics (R Development Core Team, 2008) offers practitioners a rich set of statistical methods ranging from random number generation and optimization methods through regression, panel data, and time series methods, by way of illustration. The standard R distribution (base R) comes preloaded with a rich variety of functionality useful for applied econometricians. This functionality is enhanced by user-supplied packages made available via R servers that are mirrored around the world. Of interest in this chapter are methods for estimating nonparametric and semiparametric models. We summarize many of the facilities in R and consider some tools that might be of interest to those wishing to work with nonparametric methods who want to avoid resorting to programming in C or Fortran but need the speed of compiled code as opposed to interpreted code such as Gauss or Matlab by way of example. We encourage those working in the field to strongly consider implementing their methods in the R environment thereby making their work accessible to the widest possible audience via an open collaborative forum.

Higher education in Botswana is believed to transform life through the provision of job opportunities for those with the privilege to access it. Parents believe that when their children graduate with degrees, this will alleviate them from poverty, and hence encourage their children to work hard and perform to their best ability. Higher education is viewed as the pinnacle a good life – an assurance of a better future for the extended family kinship.

Unfortunately, access to higher education institutions is a prerogative of those who can attain high marks in their last national or international examinations. When students do well, they receive full scholarship from the Botswana Government to attend any institution of higher learning of their choice. However, most students from the marginalized or minority groups tend to fail to access higher education due to various socio-economic challenges they face.

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 Current university methods do not align with corporate demands due to outdated content and ineffective pedagogy.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.

A common approach to dealing with missing data is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. We derive a new probit type estimator for models with missing covariate data where the dependent variable is binary. For the benchmark case of conditional multinormality we show that our estimator is efficient and provide exact formulae for its asymptotic variance. Simulation results show that our estimator outperforms popular alternatives and is robust to departures from the parametric assumptions adopted in the benchmark case. We illustrate our estimator by examining the portfolio allocation decision of Italian households.

From the early 1980s until the late 1990s the term structure of interest rates in Chile was usually downward sloping, particularly for long maturities. We postulate that the explanation is behind liquidity premium of the term structure of interest rates. Based upon a parsimonious theoretical model, we show that the sign of liquidity premium depends on both expected return and risk.

For our sample period 1983–1999, investors were willing to hold long-term assets even though their return was relatively lower. This appears to be a consequence of indexation, which reduced risk of long-term bonds as their return was linked to past inflation.

This chapter examines a multifactor model for stock returns in nine Asian markets (Japan, China, South Korea, Hong Kong, Taiwan, Singapore, Indonesia, Malaysia, and Thailand). The authors develop a model using the market risk premium, size, book-to-market, profitability, investment, momentum, price-to-earnings ratio, and dividend yield factors for each market. The empirical results suggest that this eight-factor model can better explain the variations of stock returns than the original Fama–French three-factor model. Factor-based models using local data outperform those using data from US markets. In addition, the evidence suggests that the eight-factor model can better explain stock returns when the market is under stress.

The COVID-19 pandemic and its related economic meltdown and social unrest severely challenged most countries, their societies, economies, organizations, and individual citizens. Focusing on both more and less successful country-specific initiatives to fight the pandemic and its multitude of related consequences, this chapter explores implications for leadership and effective action at the individual, organizational, and societal levels. As international management scholars and consultants, the authors document actions taken and their wide-ranging consequences in a diverse set of countries, including countries that have been more or less successful in fighting the pandemic, are geographically larger and smaller, are located in each region of the world, are economically advanced and economically developing, and that chose unique strategies versus strategies more similar to those of their neighbors. Cultural influences on leadership, strategy, and outcomes are described for 19 countries. Informed by a cross-cultural lens, the authors explore such urgent questions as: What is most important for leaders, scholars, and organizations to learn from critical, life-threatening, society-encompassing crises and grand challenges? How do leaders build and maintain trust? What types of communication are most effective at various stages of a crisis? How can we accelerate learning processes globally? How does cultural resilience emerge within rapidly changing environments of fear, shifting cultural norms, and profound challenges to core identity and meaning? This chapter invites readers and authors alike to learn from each other and to begin to discover novel and more successful approaches to tackling grand challenges. It is not definitive; we are all still learning.

Regionalism implying some form of city-region or metropolitan-level planning and governance has long been promoted for multiple reasons albeit with varied success. Experiencing a resurgence in 1990s, regional coordination and cooperation has proven effective in pursuing economic development and bolstering competitiveness. Unfortunately, other voices, such as those promoting regional scale land use planning and management to cultivate more sustainable urban form and settlement patterns became comparatively crowded out. With climate change-related environmental and ecological pressures mounting, the chapter suggests it is time to frame regions as socio-ecological rather than mere socio-economic spaces, thereby placing greater emphasis on ecosystems and ecological land management and a circular, regenerative economy. Using the city-region of Stuttgart (Germany) as exemplar, our contribution initiates an exploration into whether statutory regional planning in combination with various informal tools and a multi-level governance framework allows actors to begin to embed and implement these emerging ecological sustainability concepts.