A new rank correlation measure

A new rank correlation measure β _n is proposed, so as to develop a nonparametric test of independence for two variables. β _n is shown to be the symmetrized version of a measure earlier proposed by Borroni and Zenga (Stat Methods Appl 16:289–308, 2007). More specifically, β _n is built so that it can take the opposite sign, without changing its absolute value, when the ranking of one variable is reversed. Further, the meaning of the population equivalent of β _n is discussed. It is pointed out that this latter association measure vanishes not only at independence but, more generally, at indifference, that is when the two variables do not show any “tendency” to positive or negative dependence. The null distribution of β _n needs an independent study: hence, the finite null variance and a table of critical values are determined. Moreover, the asymptotic null distribution of β _n is derived. Finally, the performance of the test based on β _n is evaluated by simulation. β _n is shown to be a good competitor of some classical tests for the same problem.     

A politico-economic model of aging,technology adoption and growth

This paper provides a politico-economic theory that explains how an economy evolves when the longevity of its citizens is jointly determined with the process of economic development. We propose a three-period overlapping generation model where agents’ decisions embrace two dimensions: a private choice about education and a public one on innovation policy. We find that (a) poverty traps can emerge in human capital accumulation, (b) higher life expectancy increases the incentive to innovate for both young and adults, (c) different political configurations can arise depending on endogenous demographic structures and (d) the steady state can entertain both innovation and its absence.     

Governance Considerations for Seeker–Solver Relationships: A Knowledge‐Based Perspective in Crowdsourcing for Innovation Contests

The need to solve innovation problems and insource knowledge has led to an increasing number of organizations engaging in crowdsourcing activities and subsequently establishing working relationships with winning solution providers. Using a knowledge‐based view and the problem‐solving perspective, we develop a theoretical framework suggesting how specific innovation problem attributes (i.e. the decomposability, formulation and search space of the problem) influence the governance decision (unilateral vs. bilateral) of seekers to manage the relationship with winning solvers. We empirically analyse the framework using 582 challenges broadcast on the NineSigma crowdsourcing platform. Our results indicate that problem attributes – the formulation and search space of the problem – have a positive effect on seekers’ preference towards unilateral governance structures. However, we did not find any empirical confirmation of the effect that the decomposability of the innovation problem has on seekers’ preference towards unilateral governance structures. This study offers several contributions to the crowdsourcing literature, and also has important implications for managers of organizations aiming to insource knowledge through crowdsourcing for innovation contests.     

Biopharmaceutical and chemical firms’ R&D disclosure,and cost of equity: The impact of the regulatory regime

Based on the proprietary costs theory, this paper aims to survey whether the regulatory regime (mandatory versus voluntary) of research and development (R&D) narrative disclosures impacts, by the means of a reduced information asymmetry, on the cost of equity capital. In order to construct a disclosure index to investigate the extent and the comprehensiveness of R&D information, the methodology adopted was the content analysis of 77 biopharmaceutical and chemical listed companies’ management reports from eight Western European countries across the period 2005–2009. Hence, we obtained an (unbalanced) panel data of 309 observations. The cost of capital has subsequently been regressed on the disclosure index. Results confirm a larger amount of R&D disclosures whereas information is more regulated, but they do not confirm an inverse relation with the cost of capital.     

Intervention Research as Management Research in Practice: Learning from a Case in the Fashion Design Industry

Research in the field of management and organizational sciences has yielded a deeper understanding of many emerging business issues. However, the relevance of the contributions has been increasingly criticized, in both the academic and public spheres. We propose the intervention research approach – originally developed by the research group at Ecole des Mines de Paris – as a design science approach able to address both the relevance gap issue and the growing complexity of management practice. It is argued that increasing our understanding of management requires research that is more insightful, influential and immediately applicable. This in turn requires closer collaboration between management and researchers during the inquiry process, which is not always easy to achieve. An illustrative case study of an intervention research project focusing on creativity, conducted in Italy in collaboration with a fashion company, demonstrates how intervention research can be rigorous and relevant to practitioners, and how it can advance theoretical knowledge in management science.     

2MTO,a new mapping tool to achieve lean benefits in high-variety low-volume job shops

This paper proposes the ‘Mapping Tool for Make-To-Order companies’ (2MTO), a new approach to map, analyse and achieve lean benefits in high-variety-low-volume job shops. The focus is, mainly, on the transitional phase from push to pull, when there is a compelling need to acquire a deep understanding of the current state of the system under analysis. Sometimes, in this initial phase, a pure pull approach cannot be developed and a job shop configuration must be maintained. If so, 2MTO allows selecting a suitable hybrid Production Planning and Control system to control Work-In-Process and to limit lead times, so as to quickly achieve lean benefits, without significantly altering the original layout. 2MTO is fully explained making reference to an Italian precision mechanic company, where the application of the method led to a successful implementation of a Workload Control system.     

Inequality indices: theoretical and empirical aspects of their asymptotic behaviour

The authors use a Berry-Esseen type bound to identify the factors which influence the speed of convergence to the normal distribution of the indices of Gini, Piesch and Mehran. To empirically confirm the conclusions reached, a Monte Carlo experiment is performed for a log-logistic distribution.     

Evaluating industrial technological forecasting

This article discusses an approach to the application of technological forecasting among a group of Italian companies. The information upon which the article is based has been obtained from a preliminary study. However, it should prove of interest to manufacturing organizations where similar problems necessitate a constructive approach to forecasting.     

Algebraic statistics of level codings for fractional factorial designs

We discuss the applications of algebraic statistics to fractional factorial design with special emphasis on the choice of level coding. In particular, we deal with the theory of Bayley's level codings in that framework.     

Comparing and generating Latin Hypercube designs in Kriging models

In Computer Experiments (CE), a careful selection of the design points is essential for predicting the system response at untried points, based on the values observed at tried points. In physical experiments, the protocol is based on Design of Experiments, a methodology whose basic principles are questioned in CE. When the responses of a CE are modeled as jointly Gaussian random variables with their covariance depending on the distance between points, the use of the so called space-filling designs (random designs, stratified designs and Latin Hypercube designs) is a common choice, because it is expected that the nearer the untried point is to the design points, the better is the prediction. In this paper we focus on the class of Latin Hypercube (LH) designs. The behavior of various LH designs is examined according to the Gaussian assumption with exponential correlation, in order to minimize the total prediction error at the points of a regular lattice. In such a special case, the problem is reduced to an algebraic statistical model, which is solved using both symbolic algebraic software and statistical software. We provide closed-form computation of the variance of the Gaussian linear predictor as a function of the design, in order to make a comparison between LH designs. In principle, the method applies to any number of factors and any number of levels, and also to classes of designs other than LHs. In our current implementation, the applicability is limited by the high computational complexity of the algorithms involved.