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.

The prognostic value of health-related quality-of-life data in predicting survival in glioblastoma cancer patients: results from an international randomised phase III EORTC Brain Tumour and Radiation Oncology Groups, and NCIC Clinical Trials Group study.

This is one of the few studies that have explored the value of baseline symptoms and health-related quality of life (HRQOL) in predicting survival in brain cancer patients. Baseline HRQOL scores (from the EORTC QLQ-C30 and the Brain Cancer Module (BN 20)) were examined in 490 newly diagnosed glioblastoma cancer patients for the relationship with overall survival by using Cox proportional hazards regression models. Refined techniques as the bootstrap re-sampling procedure and the computation of C-indexes and R(2)-coefficients were used to try and validate the model. Classical analysis controlled for major clinical prognostic factors selected cognitive functioning (P=0.0001), global health status (P=0.0055) and social functioning (P<0.0001) as statistically significant prognostic factors of survival. However, several issues question the validity of these findings. C-indexes and R(2)-coefficients, which are measures of the predictive ability of the models, did not exhibit major improvements when adding selected or all HRQOL scores to clinical factors. While classical techniques lead to positive results, more refined analyses suggest that baseline HRQOL scores add relatively little to clinical factors to predict survival. These results may have implications for future use of HRQOL as a prognostic factor in cancer patients.

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.

Perceived competition explains regional differences in stereotype content of immigrant groups

This research investigates differences in the stereotype content of immigrant groups between linguistic regions. We expected that immigrant groups who speak the local language of a specific linguistic region would be perceived as more competitive within this region than in another linguistic region. Further, we expected these differences would underlie regional differences in stereotype content, albeit only for the warmth dimension. Predictions were tested in the two largest linguistic regions of Switzerland. As expected, in the German-speaking region, locals perceived German immigrants as more competitive and thus as less warm, whereas in the French-speaking region, locals perceived French immigrants as more competitive and, consequently, as less warm. So, paradoxically, immigrants with strong integration potential are particularly disliked because they are regarded as direct competitors.

vHOG, a multispecies vertebrate ontology of homologous organs groups.

MOTIVATION: Most anatomical ontologies are species-specific, whereas a framework for comparative studies is needed. We describe the vertebrate Homologous Organs Groups ontology, vHOG, used to compare expression patterns between species.¦RESULTS: vHOG is a multispecies anatomical ontology for the vertebrate lineage. It is based on the HOGs used in the Bgee database of gene expression evolution. vHOG version 1.4 includes 1184 terms, follows OBO principles and is based on the Common Anatomy Reference Ontology (CARO). vHOG only describes structures with historical homology relations between model vertebrate species. The mapping to species-specific anatomical ontologies is provided as a separate file, so that no homology hypothesis is stated within the ontology itself. Each mapping has been manually reviewed, and we provide support codes and references when available. Availability and implementation: vHOG is available from the Bgee download site (http://bgee.unil.ch/), as well as from the OBO Foundry and the NCBO Bioportal websites.¦CONTACT: bgee@isb-sib.ch; frederic.bastian@unil.ch.

Smoking and adolescence: Exploring tobacco consumption and related attitudes in three different age groups in Switzerland

The present study constitutes an investigation of tobacco consumption, related attitudes and individual differences in smoking or non-smoking behaviors in a sample of adolescents of different ages in the French-speaking part of Switzerland. We investigated three school-age groups (7th-grade, 9th-grade, and the second-year of high school) for differences in attitude and social and cognitive dimensions. We present both descriptive and inferential statistics. On an inferential level, we present a binary logistic regression-based model predicting risk of smoking. The resulting model most importantly suggests a strong relationship between smoking and alcohol consumption (both regular and sporadic). We interpret this result in terms of both the impact of the actual campaigns and the cognitive processes associated with adolescence.