The dialectics of globalization: what are the effects for management and organization in Germany and Japan

The globalization literature has been limited cumulative. Different disciplines have different accents, use different definitions, rely on divergent theories and focus on different units of analysis. As a corollary, they have come to divergent opinions on the concept of globalization, on its broader consequences as well as on the more specific consequences for organization and management. This article contributes to the literature by proposing a societal theory of institutional change to analyze globalization effects in a more systematical way. It explores the effects of globalization forces on organization and management in Germany and Japan in comparison to the US and the UK, by tracing their impact on the systems of corporate governance, industrial relations and human resources. The article shows that different patterns of change exist at different levels of analysis. These differences help us explain why no generalized pattern of convergence towards the same economic model should be expected.     

Risks in new product development: devising a reference tool

This paper describes the development and applicability of a risk reference framework (RRF) for diagnosing risks in technological breakthrough projects. In contrast to existing risk identification strategies, the RRF centers on an integral perspective on risk (i.e. business, technological and organizational) and the assessment of risks in ongoing projects. The resulting RRF consists of 12 main risk categories and 142 connected critical innovation issues and has been developed for a globally operating company in the fast-moving consumer goods industry. Our analyses show that to some extent different project members identified the same risks and that saturation occurred in the number of new risk-issues brought to light. We conclude that the success of breakthrough innovation projects improves through formal risk-assessment.     

Trust‐Preferred Securities and Insurer Financing Decisions

We analyze insurance holding company (IHC) issuance of trust‐preferred securities (TPS) from 1994 to 2013. We find that larger and more financially levered IHCs issued TPS in 1996 and 1997, as well as those that obtained financial strength ratings from A.M. Best. Abnormal stock price returns are positively related to financial distress costs, growth opportunities, and tax burden, but negatively related to size. Consistent with the pecking order theory, intent to use TPS proceeds to retire debt is positively related to abnormal stock returns, whereas intent to use proceeds to retire preferred equity is negatively related to abnormal stock returns.     

What Causes the Positive Price-Turnover Correlation in European Housing Markets?

This paper examines what determines the correlation between prices and turnover in European housing markets. Using a panel vector autoregressive model, we find that there is a particularly strong feedback mechanism between prices and turnover. Momentum effects are another important reason why prices and turnover are correlated. Common underlying factors, such as GDP and interest rates, also explain part of the price-turnover correlation. The results in this paper imply that, to understand price and turnover dynamics, it is important to model prices and turnover as two interdependent processes. There is a considerable bias in the coefficient estimates of standard house price models if this dependency is not explicitly taken into account.     

Testing Alternative Regression Frameworks for Predictive Modeling of Health Care Costs

Predictive models of health care costs have become mainstream in much health care actuarial work. The Affordable Care Act requires the use of predictive modeling-based risk-adjuster models to transfer revenue between different health exchange participants. Although the predictive accuracy of these models has been investigated in a number of studies, the accuracy and use of models for applications other than risk adjustment have not been the subject of much investigation. We investigate predictive modeling of future health care costs using several statistical techniques. Our analysis was performed based on a dataset of 30,000 insureds containing claims information from two contiguous years. The dataset contains more than 100 covariates for each insured, including detailed breakdown of past costs and causes encoded via coexisting condition flags. We discuss statistical models for the relationship between next-year costs and medical and cost information to predict the mean and quantiles of future cost, ranking risks and identifying most predictive covariates. A comparison of multiple models is presented, including (in addition to the traditional linear regression model underlying risk adjusters) Lasso GLM, multivariate adaptive regression splines, random forests, decision trees, and boosted trees. A detailed performance analysis shows that the traditional regression approach does not perform well and that more accurate models are possible.     

The Dilemma of Accountability for Professionals: A Challenge for Mainstream Management Theories

Professional institutions are increasingly confronted by fiscal constraints and political pressures to improve and increase their accountability in a competitive consumer-driven market. Accordingly, the need to ensure efficiency and accountability is of strategic importance. This article reports on a qualitative study of medical professionals that assessed the utility of financial incentives and external control methods derived from agency theory to ensure accountability of professionals. The authors argue that approaches derived from stewardship and institutional theories can extend the principal–agent perspective to sustain greater social and ethical accountability.     

Visual displays: analytical study and applications to graphs and real data

Principal axis methods such as principal component analysis (PCA) and correspondence analysis (CA) are useful for identifying structures in data through interesting planar graphic displays. However, some kinds of data sets can be dealt alternatively with PCA or CA. This paper focuses on methods, such as PCA and CA, and on visual displays. Our aim is to illustrate the implications for a potential user of selecting either method, and its advantages and disadvantages, from an applied point of view. This is a matter covered broadly in textbooks and elsewhere considering theoretical arguments. Our purpose is to contribute to the comparison between these methods, over the same data set, in order to illustrate them for the practitioner. In the first part of this paper we present a novel analytical study of a binary matrix associated with a non-oriented axis-symmetric graph and show that CA outperforms standardized PCA for the reconstitution and visualization of such kind of graphs. In the second part we present a case using real data dealing with the distribution of employees in different economic sectors for the countries of the European Union, analyzed by means of standardized PCA and two-way CA, in order to see the differences between the two methods in practice.     

Asymptotic behaviour of tests for a unit root against an explosive alternative

We compare the asymptotic local power of upper-tail unit root tests against an explosive alternative based on ordinary least squares (OLS) and quasi-differenced (QD) demeaning/detrending. We find that under an asymptotically negligible initialisation, the QD-based tests are near asymptotically efficient and generally offer superior power to OLS-based approaches; however, the power gains are much more modest than in the lower-tail testing context. We also find that asymptotically non-negligible initial conditions do not affect the power ranking in the same way as they do for lower-tail tests, with the QD-based tests retaining a power advantage in such cases.     

The impact of population growth and climate change on food security in Africa: looking ahead to 2050

Providing nutritious and environmentally sustainable food to all people at all times is one of the greatest challenges currently facing society. This problem is particularly acute in Africa where an estimated one in four people still lack adequate food to sustain an active and healthy life. In this study, we consider the potential impact of future population growth and climate change on food security in Africa, looking ahead to 2050. A modelling framework termed FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation) was used which was characterized to model the impacts of future climate changes (utilizing the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios projections) and projected population growth on food availability and subsequent undernourishment prevalence in 44 African countries. Our results indicate that projected rapid population growth will be the leading cause of food insecurity and widespread undernourishment across Africa. Very little to no difference in undernourishment projections were found when we examined future scenarios with and without the effects of climate change, suggesting population growth is the dominant driver of change. Various adaptation options are discussed, such as closing the yield gap via sustainable intensification and increasing imports through trade and aid agreements. These strategies are likely to be critical in preventing catastrophic future food insecurity.