Search results

The goals of this chapter are three-fold: (1) to outline some broad empirical regularities concerning how drug problems evolve over time, (2) to sketch some plausible mechanisms for ways in which aspects of that variation might be endogenous, and (3) to review two classes of dynamic models of drug use that have implications for how policy should vary over a drug epidemic.

Purpose – The purpose of this chapter is to present an investigation on the dynamic linkages between global macro hedge funds and traditional financial assets of developed and emerging markets.

Methodology/approach – To explore relationships among these price indices, we analyse Granger causality and vector autoregression (VAR) dynamics through impulse response functions. Besides, multivariate cointegration is used to know long-term relationships between assets and allows risk-averse investors to reduce uncertainty. Finally, a vector error correction model (VECM) provides active asset managers the opportunity to anticipate short-term price movements.

Findings – Our results show that in a Granger causality sense, we observe long- and short-term relationships between global macro hedge funds and traditional financial assets for Canada, France and Germany. This implies that opportunities for international portfolio diversification are significantly lower for countries having relationships between assets. For Canada, France and Germany, the risk-averse investors can reduce their long-term volatility by investing according to the cointegrating vector, whereas active managers can benefit from the knowledge of short-term asset price movements. The VEC Pairwise Granger causality in the short term confirms our analysis of causality according to VAR models.

Originality/value of paper – These results are original because they help the investor to understand the dynamics of the relationship between global macro hedge funds and traditional financial assets.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

With the introduction of the Teaching Excellence Framework (TEF), the status of teaching has been moved towards the centre of concerns in the UK higher education (HE) sector. This interest develops further the notion of teaching excellence created through various institutional and sectoral schemes such as the Higher Education Academy (HEA) fellowship. Whilst excellence schemes and the TEF all highlight the importance of teaching, they also run the danger of reducing it to lists and simplified proxies.

This chapter argues that reductive characterisations of teaching, through metrics supporting the TEF, such as the national student survey, or ‘idealised’ descriptions of the foundational aspects of ‘excellent practice’, all lead to partial accounts of the teaching process. Such characterisations might lead to creeping performativity and increasing organisational attempts to control. An alternative account of teaching is proposed based on complexity theory. This sees teaching as emergent, multifaceted and contextually based. It refutes notions of ‘best practice’ and argues that any attempt to capture ‘excellent practice’ is to reduce the holistic nature of the processes that bring teaching, learning, curriculum and assessment together.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

Organizational researchers live in two worlds. The first demands and rewards speculations about how to improve performance. The second demands and rewards adherence to rigorous standards of scholarship (March & Sutton, 1997, p. 698).Those of us who study organizations and are professors of management work on the front lines, so to speak, where the beliefs we have about how to improve managerial performance get passed directly on to practitioners. The question is, What right do we have to put our beliefs in a privileged position? Beliefs, by definition, are supposed to be true. According to Webster’s (1996) a belief is a conviction about the truth of some statement and/or reality of some phenomenon, especially when based on examination of evidence. Are all of our lectures based on consensually agreed upon evidentiary standards? What are these standards and who should maintain them?

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.