Search results

This chapter presents several approaches for identifying and dating the speculative bubble on real estate market. Using the real estate price index (IPAI), statistical and structural approaches were combined in order to detect the existence of a bubble on the Moroccan real estate market. The results obtained affirm that the Moroccan real estate market experienced a speculative bubble during the period 2006–2008 explained mainly by the boom of credit during the same period. The use of the Markov switching model affirmed that the speculative bubble on Morocco is cyclic and consequently corroborates the critic formulated by Evans (1991) concerning the traditional approaches for the detection of financial bubbles. Thus, the analysis of the series of the bubble, extracted using the Kalman filter, affirms the existence of two regimes, namely an explosive regime and a normal regime. The first regime describes the periods of explosion of the bubble and lasts for about 9 quarters, while the second, lasting for 14 quarters, describes the periods of return to the average cycle.

The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided.

Epidemiology is often described as “the basic science of public health” (Savitz, Poole & Miller, 1999; Syme & Yen, 2000). This description suggests both a close association with public health practice, and the separation of “pure” scientific knowledge from its application in the messy social world. Although the attainability of absolute objectivity is rarely claimed, epidemiologists are routinely encouraged to “persist in their efforts to substitute evidence for faith in scientific reasoning” (Stolley, 1985, p. 38) and reminded that “public health decision makers gain little from impassioned scholars who go beyond advancing and explaining the science to promoting a specific public health agenda” (Savitz et al., 1999, p. 1160). Epidemiology produces authoritative data that are transformed into evidence which informs public health. Those data are authoritative because epidemiology is regarded as a neutral scientific enterprise. Because its claims are grounded in science, epidemiological knowledge is deemed to have “a special technical status and hence is not contestable in the same way as are say, religion or ethics” (Lock, 1988, p. 6). Despite the veneer of universality afforded by its scientific pedigree, epidemiology is not a static or monolithic discipline. Epidemiological truth claims are embodied in several shifting paradigms that span the life of the discipline. Public health knowledges and practices, competing claims internal and external to epidemiology, and structural conditions (such as current political economies, material technologies, and institutions) provide important contexts in which certain kinds of epidemiological knowledge are more likely to emerge.

This study investigates the effect of volatility scaling on valuing financial assets by examining the long-term return properties of the spot USD/AUD. Tests are conducted for evidence of a scaling law in USD/AUD returns. The economic implications of dependence and non-normality of the distribution of returns are explored using the Garman and Kohlhagen modified Black–Scholes model for valuing foreign currency options. The results suggest that the USD/AUD does not conform to a stable distribution and that as a result of differential scaling laws, Garman and Kohlhagen option values using implied annual volatility will be consistently too high or too low.

The purpose of this chapter is to contribute to the theory building of transformative university by delivering two conceptual models for legitimizing entrepreneurial university with a transformative role in quadruple helix for sustainability governance structures as being nominated as “responsible facilitator.” This chapter draws many theoretical insights sourced from Entrepreneurial University (Etzkowitz, 1988), Institutional Theory (Scott, 1987), quadruple/quintuple helix (Carayannis & Campbell, 2009a, b) and Responsible Innovation (Owen, Macnaghten, & Stilgoe, 2012) in order to frame the resulting conceptual models of transformative university for sustainability and quadruple helix for sustainability governance. The conceptual models offer a new paradigm discussion for the changing role of universities in knowledge economy and opens up for further investigation of quadruple helix actors namely as transformative university, society, industry and government for strategic capital, social capital, economic capital and culture–human capital interventions in sustainability governance. The second model illustrates the key interventions of quadruple helix actors in four pillars of capital delivering concrete examples of activities. The originality of this chapter lies in its discourse articulating a multilayered approach for the institutionalization of entrepreneurial university embedded in a responsible innovation ecosystem based on individual, organizational and macro-level perspectives. Quadruple helix actors are nominated as “responsible facilitator,” “hybrid hub,” “agile regulator” and “pressure beneficiary” roles for their relevant place in sustainability governance structure.

Teams focus on a common and valued goal, and effective teams are able to alter their behaviors in pursuit of this goal. When teams are viewed in the context of a dynamic environment, they must adapt to challenges in the environment in order to maintain team effectiveness. In this light, we describe various sources of team variation and how they combine with individual-level, team-level, and dynamical mechanisms for maintaining team effectiveness in a dynamic environment. The combination of these elements produces a systems view of team effectiveness. Our goals are to begin to define, both in words and in operational terms, team effectiveness from this perspective and to evaluate this definition in the context of team training using intelligent tutoring systems (team ITS). In addressing these goals, we present an example of real-time analysis of team effectiveness and some challenges for team ITS training based on a dynamical systems view of team effectiveness.