GenD An Evolutionary System for Resampling in Survey Research

The paper is a preliminary research report and presents a method for generating new records using an evolutionary algorithm (close to but different from a genetic algorithm). This method, called Pseudo-Inverse Function (in short P-I Function), was designed and implemented at Semeion Research Centre (Rome). P-I Function is a method to generate new (virtual) data from a small set of observed data. P-I Function can be of aid when budget constraints limit the number of interviewees, or in case of a population that shows some sociologically interesting trait, but whose small size can seriously affect the reliability of estimates, or in case of secondary analysis on small samples. The applicative ground is given by research design with one or more dependent and a set of independent variables. The estimation of new cases takes place according to the maximization of a fitness function and outcomes a number as large as needed of ‘virtual’ cases, which reproduce the statistical traits of the original population. The algorithm used by P-I Function is known as Genetic Doping Algorithm (GenD), designed and implemented by Semeion Research Centre; among its features there is an innovative crossover procedure, which tends to select individuals with average fitness values, rather than those who show best values at each ‘generation’. A particularly thorough research design has been put on: (1) the observed sample is half-split to obtain a training and a testing set, which are analysed by means of a back propagation neural network; (2) testing is performed to find out how good the parameter estimates are; (3) a 10% sample is randomly extracted from the training set and used as a reduced training set; (4) on this narrow basis, GenD calculates the pseudo-inverse of the estimated parameter matrix; (5) ‘virtual’ data are tested against the testing data set (which has never been used for training). The algorithm has been proved on a particularly difficult ground, since the data set used as a basis for generating ‘virtual’ cases counts only 44 respondents, randomly sampled from a broader data set taken from the General Social Survey 2002. The major result is that networks trained on the ‘virtual’ resample show a model fit as good as the one of the observed data, though ‘virtual’ and observed data differ on some features. It can be seen that GenD ‘refills’ the joint distribution of the independent variables, conditioned by the dependent one. This paper is the result of deep collaboration among all authors. Cinzia Meraviglia wrote § 1, 3, 4, 6, 7 and 8; Giulia Massini wrote §5; Daria Croce performed some elaborations with neural networks and linear regression; Massimo Buscema wrote §2.     

Strategies for improving new product adoption in uncertain environments: A selective review of the literature

The literature on new product development is replete with studies on new product performance and success. But there is not yet a coherent theoretical framework for understanding strategies for increasing new product adoption in uncertain environments. This conceptual paper reviews the findings about new product performance in the literature and conceptualizes a framework and its related propositions for improving new product adoption. The proposed framework integrates the concept of continuous learning to market orientation to enhance upgrading of capabilities for new product development and extensive communications in uncertain environments. It is also suggested that customer commitment offers a new theoretical insight for improving new product adoption from trust in product-user interface and cooperation with internal and external customers in the new product development process. Furthermore, customer commitment provides a long-term perspective for effective targeting of customers that differentiates low and high commitment customers.     

The priorities dilemma in the EU preventive restructuring directive: Absolute or relative priority rule?

The EU Directive on Preventive Restructuring Frameworks gives the EU Member States (“MSs”) the choice between implementing two fairness rules in cross‐class cram‐down: the US‐style absolute priority rule (“APR”) or the newly conceived relative priority rule (“RPR”). This article argues that there is no good reason for the MSs to implement the RPR in domestic law. While the APR effectively protects the rights of the dissenting classes to get what they are entitled to, the RPR increases moral hazard and opportunism. Also, it might make debt investments in the EU unattractive. On top of that, this article shows that the RPR lacks a clear theoretical justification. One of the main reasons why the RPR was introduced in the Directive alongside the APR is that the RPR was thought to provide a solution to some of the APR's problems. This article considers three of those problems (i.e., the “valuation problem”, the “hold‐out problem” and the “problem of the relevant shareholders”) and explains the reasons why the RPR is not an appropriate solution for these. Among these three problems, the most troublesome one, from the perspective of the EU, is that the APR makes it difficult to award value to the equity of SMEs (the “problem of the relevant shareholders”). This article argues that using the RPR to deal with this problem would incentivize the shareholders to behave opportunistically and to orchestrate the restructuring. Instead of the RPR, this article suggests two alternative techniques which MSs can enact to better address the issue: the new value exception “in kind” and the disposable income method.     

Towards an open innovation ecosystem in the cultural industry: The bright side of trust and the dark side of measurement

The concept of open innovation ecosystems is receiving increasing attention in academic literature, but its application to the cultural industries and, more specifically, to philanthropically-funded cultural initiatives, remains a largely unexplored domain. This study, leveraging on in-depth interviews with stakeholders, observation and immersion in the field, employs primary qualitative data from a philanthropically-funded cultural initiative and applies the ecosystem-as-structure conceptual framework to study the factors that have enabled a nascent open innovation ecosystem in the cultural industries to emerge. Findings point to a number of essential components and characteristics of the emergent ecosystem that are crucial elements of success in the view of key stakeholders. The findings consequently shed light on the managerial practices and strategy that facilitate the success of a philanthropically-funded artistic initiative which fosters the creation of a new open innovation arts ecosystem.