Roles, role performance, and radical innovation competences

Despite the importance of radical innovations (RI), few firms have the capability to develop such innovations internally; success is increasingly linked with relationships and networks. However, the way(s) in which relationships and networks support RI is less clear. Successfully launching RIs requires the development of four distinctive competences; discovery, incubation, acceleration and commercialization. This paper examines how networks support the development of RIs, focusing on when and how network partners become involved and how their role performances support the development of the four competences. The study context is the automotive industry, which is heavily dependent upon RI and complex interrelationships. Data were collected through in-depth interviews with network participants involved in developing several RIs. Five task-oriented and three network-oriented roles are uncovered and the performance of these roles supports the development of different competences, with some roles being exclusively connected with particular competences and others supporting several competences. Focusing on role performance offers a useful means for distinguishing between acontextual actors, and the activities and resources they bring. In doing so, the paper enhances understanding of the links between network participants, role performances and the development of RI competences, and identifies a number of important implications for theory and practice.     

Robust influence diagnostics for generalized linear models with continuous responses

This article introduces two parametric robust diagnostic methods for detecting influential observations in the setting of generalized linear models with continuous responses. The legitimacy of the two proposed methods requires no knowledge of the true underlying distributions so long as their second moments exist. The performance of the two proposed influence diagnostic tools is investigated through limited simulation studies and the analyses of an illustration.     

Integrating expected coverage and local reliability for emergency medical services location problems

Daskin's MEXCLP model [Daskin M. A maximum expected covering location model: formulation, properties, and heuristic solution. Transportation Science 1983;17:48-70] was one of the first efforts to capture the stochastic nature of emergency medical services (EMS) location problems within a mixed-integer formulation. With their subsequent introduction of MALP, ReVelle and Hogan [The maximum availability location problem. Transportation Science 1989;23:192-200] offered two key advances, local vehicle busyness estimates and the α-reliability objective. While these constructs have influenced many subsequent EMS location models, they have been subjected to relatively little empirical analysis. To address this, we introduce the LR-MEXCLP, a hybrid model combining the local busyness estimates of MALP with the maximum coverage objective of MEXCLP. We then solve a series of problems with all three models and employ simulation to estimate aggregate service levels. We find that LR-MEXCLP leads to modest but consistent service gains over both MALP and MEXCLP. These results support the merits of local busyness estimates, but they also suggest that the α-reliability objective may be inappropriate when seeking to maximize aggregate system response capabilities. More generally, our research underscores the utility of (a) linking modeling assumptions and goals with real-world application contexts, and (b) employing simulation or other techniques to validate theoretical results.     

The impact of relative residence times on the distribution of heavy drinkers in highly distinct environments

Alcohol consumption is a function of social dynamics, environmental contexts, individuals' preferences and family history. Empirical surveys have focused primarily on identification of risk factors for high-level drinking but have done little to clarify the underlying mechanisms at work. Also, there have been few attempts to apply nonlinear dynamics to the study of these mechanisms and processes at the population level. A simple framework where drinking is modeled as a socially contagious process in low- and high-risk connected environments is introduced. Individuals are classified as light, moderate (assumed mobile), and heavy drinkers. Moderate drinkers provide the link between both environments, that is, they are assumed to be the only individuals drinking in both settings. The focus here is on the effect of moderate drinkers, measured by the proportion of their time spent in “low-” versus “high-” risk drinking environments, on the distribution of drinkers.A simple model within our contact framework predicts that if the relative residence times of moderate drinkers are distributed randomly between low- and high-risk environments then the proportion of heavy drinkers is likely to be higher than expected. However, the full story even in a highly simplified setting is not so simple because “strong” local social mixing tends to increase high-risk drinking on its own. High levels of social interaction between light and moderate drinkers in low-risk environments can diminish the importance of the distribution of relative drinking times on the prevalence of heavy drinking.