Managing skunkworks to achieve ambidexterity: The Robinson Crusoe effect

Literature on ambidexterity emphasizes the need for both exploration and exploitation. Large firms usually choose to implement structural ambidexterity to separate both activities. We here focus on an extreme case of such structural partitioning implemented as a secret skunkworks project in a large French automotive company. A qualitative survey using both primary and secondary data shows that the major basic and initial characteristics of a skunkworks (i.e. secrecy, urgency, and autonomy) created favorable conditions for the technological exploration. However, exploitation failed due to precisely those same characteristics coupled with the fact that the project did not respond to a specific market demand: The skunkworks suffered from what we call the "Robinson Crusoe effect." We therefore contribute to the literature on skunkworks, which have remained understudied in the academic literature, as well as on ambidexterity, by showing how the interrelation between different factors is crucial for structural ambidexterity to be successful     

Exploring the characteristics of rotating electric machines with factor analysis

Applications of multivariate statistics in engineering are hard to find, apart from those in quality control. However, we think that further insight into some technological cases may be gained by using adequate multivariate analysis tools. In this paper, we propose a review of the key parameters of rotating electric machines with factor analysis. This statistical technique allows not only the reduction of the dimension of the case we are analysing, but also reveals subtle relationships between the variables under study. We show an application of this methodology by studying the interrelations between the key variables in an electric machine, in this case the squirrel-cage induction motor. Through a step-by-step presentation of the case study, we deal with some of the topics an applied researcher may face, such as the rotation of the original factors, the extraction of higher-order factors and the development of the exploratory model. As a result, we present a worthwhile framework to both confirm our previous knowledge and capture unexplored facts. Moreover, it may provide a new approach to describing and understanding the design, performance and operating characteristics of these machines.     

A comparison of classification models to identify the Fragile X Syndrome

The main models of machine learning are briefly reviewed and considered for building a classifier to identify the Fragile X Syndrome (FXS). We have analyzed 172 patients potentially affected by FXS in Andalusia (Spain) and, by means of a DNA test, each member of the data set is known to belong to one of two classes: affected, not affected. The whole predictor set, formed by 40 variables, and a reduced set with only nine predictors significantly associated with the response are considered. Four alternative base classification models have been investigated: logistic regression, classification trees, multilayer perceptron and support vector machines. For both predictor sets, the best accuracy, considering both the mean and the standard deviation of the test error rate, is achieved by the support vector machines, confirming the increasing importance of this learning algorithm. Three ensemble methods - bagging, random forests and boosting - were also considered, amongst which the bagged versions of support vector machines stand out, especially when they are constructed with the reduced set of predictor variables. The analysis of the sensitivity, the specificity and the area under the ROC curve agrees with the main conclusions extracted from the accuracy results. All of these models can be fitted by free R programs.     

Reducing differences between profiles of weights: A “peer-restricted” cross-efficiency evaluation

This paper deals with the selection of the profiles of weights to be used in cross-efficiency evaluations. In an attempt to prevent unrealistic weighting schemes, one of the issues of main interest that we address here is that of the zero weights, since their use implies that some of the variables considered are excluded from the assessments to be made. In the calculation of cross-efficiency scores, we propose to ignore the profiles of weights of the DMUs that cannot make a choice of non-zero weights among their alternate optima. The different units are therefore assessed in a peer-evaluation that does not consider the profiles of weights of some inefficient DMUs. This approach is referred to as “peer-restricted” cross-efficiency evaluation. Aside from avoiding zero weights, the choice of weights that we make also seeks to reduce the differences between the weights profiles selected as much as possible. Thus, in the “peer-restricted” cross-efficiency evaluation in the present paper we also try to avoid that the different DMUs attach very different weights to the same variable. Finally, we extend this approach to derive a common set of weights by exploiting the idea of similarity between profiles of weights.     

Ethnic Transformation in Rural California: Looking Beyond the Immigrant Farmworker

Abstract Communities in rural California are becoming increasingly Latino. Using a quantitative database of 288 rural communities, together with qualitative data collected in the San Joaquin Valley, we examine the processes through which this ethnic transformation is occurring. Most studies have focused on Latino immigration as the cause of changing ethnic composition. We find that non-Latino population growth, as well as Latino population growth, accounts for the relative differences in changing community ethnicity. Most important for explaining migration among Latinos are housing costs and year-round job availability. Among white non-Latinos, ethnic conflict and perceptions of community deterioration better explain migration decisions. As a result of these changes, places in rural California are becoming increasingly economically and ethnically differentiated.     

Risk of Error and the Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification

The accuracy of a binary diagnostic test is usually measured in terms of its sensitivity and its specificity, or through positive and negative predictive values. Another way to describe the validity of a binary diagnostic test is the risk of error and the kappa coefficient of the risk of error. The risk of error is the average loss that is caused when incorrectly classifying a non-diseased or a diseased patient, and the kappa coefficient of the risk of error is a measure of the agreement between the diagnostic test and the gold standard. In the presence of partial verification of the disease, the disease status of some patients is unknown, and therefore the evaluation of a diagnostic test cannot be carried out through the traditional method. In this paper, we have deduced the maximum likelihood estimators and variances of the risk of error and of the kappa coefficient of the risk of error in the presence of partial verification of the disease. Simulation experiments have been carried out to study the effect of the verification probabilities on the coverage of the confidence interval of the kappa coefficient.     

Educational responses to immigrant students in Madrid

During the last few years, changes in the Spanish educational system have had a far reaching impact. Several factors have been critical: (1) the progressive decentralization of education by the government, now a responsibility of regional governments; (2) the extension of compulsory education to the age of 16; and finally (3) the increase in the number of immigrant students, which has significantly added to the existing cultural diversity. The enrolment of immigrant students in Spanish schools is a relatively recent phenomenon (starting 15 years ago) when compared with other European countries. Madrid is the region in Spain with the largest number of students. As most of these students were born outside of Spain, we cannot yet speak of a second generation. The research described here investigated how the educational system in Madrid confronted the challenge of an increasing number of immigrant students. The institutional response was the Madrid Regional Government’s Compensatory Education Regional Plan. This encompasses several measures, which are described here. After a critical analysis of educational policies pertaining to immigrant pupils, the paper suggests ways of overcoming a purely compensatory approach. Intercultural education plays a key role is such considerations.