FDH Directional Distance Functions with an Application to European Commercial Banks

Weextend Free Disposable Hull (FDH) efficiency analysis towardsthe general directional distance function framework. The profitinterpretation of directional distance functions is extendedto the non-convex FDH technologies. In addition, we derive anefficient enumerative algorithm for computing distance measuresin Free Disposable Hull (FDH) technologies, which applies tothe entire (infinitely large) family of directional distancefunctions. A simple numerical example and an application to Europeancommercial banks illustrate the algorithm.     

The interaction of multiple champions in orchestrating innovation networks: Conflicts and complementarities

In networked or open innovation processes, so-called innovation communities have been identified in the innovation champion literature, in which innovation champions from different levels in the innovation system supposedly act as a team. It has however not been studied in detail to what extent and how different champions in innovation communities complement each other and act as a team. Applying the concept of innovation network orchestration to analyze the role and position of different kinds of champions as brokers in innovation networks, the purpose of this paper is to unravel the interaction between champions and what this entails in terms of role complementarities and conflicts as regards innovation network orchestration. This is done by using an explorative multiple case study approach in which three innovation journeys are analyzed. The results indicate that a distinction can be made between primary innovation communities, who act as aggregated orchestrators of the overall innovation network, and who in turn orchestrate secondary innovation communities in certain sub-networks. Here different kinds of champions complement each other and act as a team, but these complementarities are not a given: they are negotiated over time in interaction, and lack of reflection on each other’s roles may result in role conflicts. The main conclusion is that an oversimplified notion of innovation communities as a unified team of champions should be avoided: innovation communities themselves need a form of orchestration.     

Psychometrically and qualitatively validating a cross-national cumulative measure of fear-based xenophobia

The article reports the results of a Mokken Scale Procedure (MSP) developing a hierarchical cross-national scale to measure xenophobia, and a qualitative validation of this scale. A pool of 30 xenophobic scale items were collected from several sources and edited according to established unidimensional criteria. The survey was administered to 608 undergraduate students in the USA, 193 undergraduate students in the Netherlands, and 303 undergraduate students in Norway. Fourteen scale statements measuring perceived threat or fear and meeting the criteria of the Stereotype Content Model (e.g., Fiske et al. in Trends Cogn Sci 11:77–83, 2006) were selected for further analysis. A separate item analysis and subsequently MSP analysis yielded a cumulative scale with the same five items for each of the three samples meeting criteria for homogeneity in all samples with H >.40. The result, a cross-national 5-item scale measuring fear-based xenophobia, was tested by means of the Three-Step Test-Interview (Hak et al. in Surv Res Methods 2:143–150, 2008) with 10 students in The Netherlands and 10 students in Norway. The analysis of these qualitative interviews shows that individual respondents’ criteria for the ranking of the scale items strongly depend on the way immigrants are framed. Ranking according to different levels of fear turned out to be only one criterion out of several possible ones used by individual respondents.     

Towards a Further Understanding of the Antecedents of Retailer New Product Adoption

In addition to the traditionally investigated profit-drivers of retail adoption, such as gross margin, trade support, consumer marketing support, and product uniqueness, the authors investigate to what extent relatively under-investigated variables, such as relationship variables and category variables, are potential antecedents of retailers’ new product adoption decisions. Based on a sample of 392 new product adoption decisions by buyers of a Dutch food retailer, the authors provide evidence that both relationship and category variables do matter in new product adoption decisions by retailers. They show a significant negative effect of relationship dependence, and a significant positive effect of relationship length on a retailer's new product adoption probability. Furthermore, the authors show that higher levels of expected category growth due to the new product introduction are associated with higher levels of retailer adoption probability. Finally, in contrast with all previous retail adoption studies, this study shows a significant, positive relation between the relative gross margin of the new product and the retailer adoption probability.     

The determinants of cleaner energy innovations of the world’s largest firms: the impact of firm learning and knowledge capital*

In this paper, we address the determinants of clean energy inventions by 946 large firms. We use a new set of large firms’ patent portfolios and we broaden and deepen existing literature on this issue in two main ways: first, we conduct our study directly at the firm level and not at the industry or national levels and second, we do not focus on a single industry but encompass all industrial sectors. Drawing on firm (internal and external) knowledge and knowledge accumulation, we show there is a robust positive association between the (past) knowledge accumulated capital related to clean technologies and the number of inventions produced in that field, even after controlling for industry and nation fixed effects and other factors. The same relation works for (past) knowledge-accumulated capital in other (non-clean) technologies. However, the relation’s impact on the number of clean inventions produced is much lower. The magnitudes of our coefficient are in line with that obtained previously on firms in the auto-industry or at the sectoral level.     

Adapting extreme value statistics to financial time series: dealing with bias and serial dependence

We handle two major issues in applying extreme value analysis to financial time series, bias and serial dependence, jointly. This is achieved by studying bias correction methods when observations exhibit weak serial dependence, in the sense that they come from \(\beta\)-mixing series. For estimating the extreme value index, we propose an asymptotically unbiased estimator and prove its asymptotic normality under the \(\beta\)-mixing condition. The bias correction procedure and the dependence structure have a joint impact on the asymptotic variance of the estimator. Then we construct an asymptotically unbiased estimator of high quantiles. We apply the new method to estimate the value-at-risk of the daily return on the Dow Jones Industrial Average index.