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This chapter presents a multi-criteria portfolio model with the expected return as a performance measure and the expected worst-case return as a risk measure. The problems are formulated as a single-objective linear program, as a bi-objective linear program, and as a triple-objective mixed integer program. The problem objective is to allocate the wealth on different securities to optimize the portfolio return. The portfolio approach has allowed the two popular financial engineering percentile measures of risk, value-at-risk (VaR) and conditional value-at-risk (CVaR) to be applied. The decision-maker can assess the value of portfolio return, the risk level, and the number of assets, and can decide how to invest in a real-life situation comparing with ideal (optimal) portfolio solutions. The concave efficient frontiers illustrate the trade-off between the conditional value-at-risk and the expected return of the portfolio. Numerical examples based on historical daily input data from the Warsaw Stock Exchange are presented and selected computational results are provided. The computational experiments prove that both proposed linear and mixed integer programming approaches provide the decision-maker with a simple tool for evaluating the relationship between the expected and the worst-case portfolio return.

In this chapter, we review recent work examining the influence individual executive characteristics exhibit on acquisition behavior, often in service of their private interests. In doing so, we outline the findings of this limited research, explore possible alternative explanations and factors, and discuss several novel data collection and methodological techniques that scholars have advanced in the upper echelon context, in recent years. As we discuss, we believe that researchers can more fruitfully explore the underlying personal, psychological, and social factors that motivate acquisition activity, by augmenting current techniques with these methodological innovations.

This chapter illuminates the impact of providing informal learning experiences for students pursuing Science, Technology, Engineering, and Mathematics (STEM) teaching careers at a time when there is a considerable shortage of qualified teachers in America's urban centers. Preservice STEM teachers were provided with the opportunity to participate in a National Science Foundation (NSF) grant funded Noyce Internship Program prior to serving as counselors and teaching assistants in a STEM camp for underrepresented middle school students. Through the Noyce Internship Institute, participants were introduced to interactive sessions that model promising teaching practices including inquiry-based and project-based learning. This narrative inquiry examines the impact of these experiences on preservice STEM teachers' self-efficacy and highlights outcomes in three areas: increase of preservice teachers' confidence, classroom management, and strengthening their desire to teach STEM.

This chapter will focus on how inclusive pedagogic practices can be played out in primary and secondary classrooms where the goal is using languages other than the learners’ home language as both the medium and content of learning (i.e. learning to use language and using languages to learn). This requires an approach which is inclusive, flexible and relates to any context – both languages and subject classrooms. The focus will be on how using an integrated approach to the curriculum, in which languages are used as a tool for learning, has the potential to be motivating and accessible to very diverse learners.

The chapter includes two lessons – the primary lesson plan will expand how simple language can be used to develop and enjoy painting and art with young students and the secondary lesson plan will focus on how a visual approach to thematic or cross-disciplinary work, such as natural disasters, can supplement and support deeper understanding of other areas of the curriculum as well as building confidence in communicating in an alternative language.

In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point.

This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.

This chapter argues that there are many, just many many variables which contribute to academic performance as measured in degree outcome, and, as such, simple bivariate analysis is inappropriate. We use structural equation modelling, and explore the contribution of academic behavioural confidence, to make the point that it does contribute to academic performance, but to a lesser extent than self-efficacy theory argues. We suggest that this is because degree outcome is made up of many efficacy variables, which we argue are better captured overall in academic behavioural confidence.

This research aims to investigate the process of value creation, value delivery, and value capture in project-based companies. Most previous research focused on companies that operate regularly and offer manufactured products or services. This research used companies in the field of information technology that developed software to explain how value creation, value delivery, and value capture occurred. A case study with qualitative research was applied to analyze between cases. Empirical findings showed that companies carry out six activities to ensure that value creation, value delivery, and value capture can be realized in the software development process. The six activities were iterative and not a rigid sequence. This research was limited to the software industry, and further research can test the results of this study by using a survey to increase the generalizability theory developed in this study.