Who knows what? Reconfiguring the governance and the capabilities of the supply chain between physical and digital processes in the fashion industry

Abstract

On the basis of an exploratory multiple-case qualitative longitudinal study in the fashion industry, this article describes how digital technologies enabled choices among different types of supply chain governance. In doing so, this study responds to calls for governance-oriented longitudinal explorations and suggests an alternative path of governance choices in which the digital technologies become the platform that allows a dynamic discourse between outsourcer and outsourcee. Our results show that, when firms decide to leverage their performances through the adoption of information technology, they need to define their knowledge transfer capacities in order to adopt the appropriate type of governance model. With this article, we contribute to the global value chain literature adding to the knowledge transfer dimension.     

Different Factors for Different Causes: Analysis of the Spatial Aggregations of Fire Ignitions in Catalonia (Spain)

The present study analyzes the effects of different socioeconomic factors on the frequency of fire ignition occurrence, according to different original causes. The data include a set of documented ignition points in the region of Catalonia for the period 1995–2008. The analysis focused on the spatial aggregation patterns of the ignitions for each specific ignition cause. The point‐based data on ignitions were interpolated into municipality‐level information using kernel methods as the basis for defining five ignition density levels. Afterwards, the combination of socioeconomic factors influencing the ignition density levels of the municipalities was analyzed for each documented cause of ignition using a principal component analysis. The obtained results confirmed the idea that both the spatial aggregation patterns of fire ignitions and the factors defining their occurrence were specific for each of the causes of ignition. Intentional fires and those of unknown origin were found to have similar spatial aggregation patterns, and the presence of high ignition density areas was related to high population and high unemployment rates. Additionally, it was found that fires originated from forest work, agricultural activities, pasture burning, and lightning had a very specific behavior on their own, differing from the similarities found on the spatial aggregation of ignitions originated from smokers, electric lines, machinery, campfires, and those of intentional or unknown origin.     

Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach

This article presents a Bayesian semi-parametric approach for modeling the occurrence of cesarean sections using a sample of women delivering in 20 hospitals of Sardinia (Italy). A multilevel logistic regression has been fitted on the data using a Dirichlet process prior for modeling the random-effects distribution of the unobserved factors at the hospital level. Using the estimated random effects at the hospital level, a partition of the hospitals in terms of similar medical practice has been obtained that identifies different profiles of hospitals in terms of caesarean section risks. The limited number of clusters may be useful for suggesting policy implications that help to reduce the heterogeneity of caesarean delivery risks.     

New ways of specifying data edits

Summary.   Data editing is the process by which data that are collected in some way (a statistical survey for example) are examined for errors and corrected with the help of software. Edits, the logical conditions that should be satisfied by the data, are specified by subject-matter experts with a procedure which could be tedious and could lead to mistakes with practical implications. To render the process of edit specification more efficient we provide a new step—the definition of the so-called abstract data model of a survey—which describes the structure of the phenomenon that is studied in a survey. The existence of this model enables experts to identify all combinations of variables which should be checked by edits and to avoid the definition of conflicting edits. Furthermore, we introduce an automatic data validation strategy—TREEVAL—that consists of fast tree growing to derive automatically the functional form of edits and of a statistical criterion to clean the incoming data. The TREEVAL strategy is cast within a total quality management framework. The application of the methodologies proposed is demonstrated with the help of a real life application.