Innovation and new business through mutually beneficial collaboration and proactive procurement

ABSTRACT This dissertation focuses on new technology commercialization, innovation and new business development. Industry-based novel technology may achieve commercialization through its transfer to a large research laboratory acting as a lead user and technical partner, and providing the new technology with complementary assets and meaningful initial use in social practice. The research lab benefits from the new technology and innovation through major performance improvements and cost savings. Such mutually beneficial collaboration between the lab and the firm does not require any additional administrative efforts or funds from the lab, yet requires openness to technologies and partner companies that may not be previously known to the lab- Labs achieve the benefits by applying a proactive procurement model that promotes active pre-tender search of new technologies and pre-tender testing and piloting of these technological options. The collaboration works best when based on the development needs of both parties. This means that first of all the lab has significant engineering activity with well-defined technological needs and second, that the firm has advanced prototype technology yet needs further testing, piloting and the initial market and references to achieve the market breakthrough. The empirical evidence of the dissertation is based on a longitudinal multiple-case study with the European Laboratory for Particle Physics. The key theoretical contribution of this study is that large research labs, including basic research, play an important role in product and business development toward the end, rather than front-end, of the innovation process. This also implies that product-orientation and business-orientation can contribute to basic re-search. The study provides practical managerial and policy guidelines on how to initiate and manage mutually beneficial lab-industry collaboration and proactive procurement.

Rate and predictors of service disengagement in an epidemiological first-episode psychosis cohort.

OBJECTIVES: To assess the prevalence and predictors of service disengagement in a treated epidemiological cohort of first-episode psychosis (FEP) patients. METHODS: The Early Psychosis Prevention and Intervention Centre (EPPIC) in Australia admitted 786 FEP patients from January 1998 to December 2000. Treatment at EPPIC is scheduled for 18 months. Data were collected from patients' files using a standardized questionnaire. Seven hundred four files were available; 44 were excluded, because of a non-psychotic diagnosis at endpoint (n=43) or missing data on service disengagement (n=1). Rate of service disengagement was the outcome of interest, as well as pre-treatment, baseline, and treatment predictors of service disengagement, which were examined via Cox proportional hazards models. RESULTS: 154 patients (23.3%) disengaged from service. A past forensic history (Hazard ratio [HR]=1.69; 95%CI 1.17-2.45), lower severity of illness at baseline (HR=0.59; 95%CI 0.48-0.72), living without family at discharge (HR=1.75; 95%CI 1.22-2.50) and persistence of substance use disorder during treatment (HR=2.30; 95%CI 1.45-3.66) were significant predictors of disengagement from service. CONCLUSIONS: While engagement strategies are a core element in the treatment of first-episode psychosis, particular attention should be paid to these factors associated with disengagement. Involvement of the family in the treatment process, and focusing on reduction of substance use, need to be pursued in early intervention services.

Preuve par l'ADN: la génétique au service de la justice

[Sommaire] Introduction. - Concepts biochimiques de base. - Concepts génétiques de base. - Quelques méthodes d'analyse de base en biologie. - Analyses génétiques - La caisse à outils de base. - Analyses génétiques - La caisse à outils spécialisée. - Les traces biologiques dans leur utilisation pratique. - Tests indicatifs et recherche des traces. - Quelques éléments d'évaluation probabiliste. - Au-delà du laboratoire. - Glossaire. - Index

Designing business models of cloud platforms

Cloud computing and its three facets (Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS)) are terms that denote new developments in the software industry. In particular, PaaS solutions, also referred to as cloud platforms, are changing the way software is being produced, distributed, consumed, and priced. Software vendors have started considering cloud platforms as a strategic option but are battling to redefine their offerings to embrace PaaS. In contrast to SaaS and IaaS, PaaS allows for value co-creation with partners to develop complementary components and applications. It thus requires multisided business models that bring together two or more distinct customer segments. Understanding how to design PaaS business models to establish a flourishing ecosystem is crucial for software vendors. This doctoral thesis aims to address this issue in three interrelated research parts. First, based on case study research, the thesis provides a deeper understanding of current PaaS business models and their evolution. Second, it analyses and simulates consumers' preferences regarding PaaS business models, using a conjoint approach to find out what determines the choice of cloud platforms. Finally, building on the previous research outcomes, the third part introduces a design theory for the emerging class of PaaS business models, which is grounded on an extensive action design research study with a large European software vendor. Understanding PaaS business models from a market as well as a consumer perspective will, together with the design theory, inform and guide decision makers in their business model innovation plans. It also closes gaps in the research related to PaaS business model design and more generally related to platform business models.