The business model ontology a proposition in a design science approach

The goal of this dissertation is to find and provide the basis for a managerial tool that allows a firm to easily express its business logic. The methodological basis for this work is design science, where the researcher builds an artifact to solve a specific problem. In this case the aim is to provide an ontology that makes it possible to explicit a firm's business model. In other words, the proposed artifact helps a firm to formally describe its value proposition, its customers, the relationship with them, the necessary intra- and inter-firm infrastructure and its profit model. Such an ontology is relevant because until now there is no model that expresses a company's global business logic from a pure business point of view. Previous models essentially take an organizational or process perspective or cover only parts of a firm's business logic. The four main pillars of the ontology, which are inspired by management science and enterprise- and processmodeling, are product, customer interface, infrastructure and finance. The ontology is validated by case studies, a panel of experts and managers. The dissertation also provides a software prototype to capture a company's business model in an information system. The last part of the thesis consists of a demonstration of the value of the ontology in business strategy and Information Systems (IS) alignment. Structure of this thesis: The dissertation is structured in nine parts: Chapter 1 presents the motivations of this research, the research methodology with which the goals shall be achieved and why this dissertation present a contribution to research. Chapter 2 investigates the origins, the term and the concept of business models. It defines what is meant by business models in this dissertation and how they are situated in the context of the firm. In addition this chapter outlines the possible uses of the business model concept. Chapter 3 gives an overview of the research done in the field of business models and enterprise ontologies. Chapter 4 introduces the major contribution of this dissertation: the business model ontology. In this part of the thesis the elements, attributes and relationships of the ontology are explained and described in detail. Chapter 5 presents a case study of the Montreux Jazz Festival which's business model was captured by applying the structure and concepts of the ontology. In fact, it gives an impression of how a business model description based on the ontology looks like. Chapter 6 shows an instantiation of the ontology into a prototype tool: the Business Model Modelling Language BM2L. This is an XML-based description language that allows to capture and describe the business model of a firm and has a large potential for further applications. Chapter 7 is about the evaluation of the business model ontology. The evaluation builds on literature review, a set of interviews with practitioners and case studies. Chapter 8 gives an outlook on possible future research and applications of the business model ontology. The main areas of interest are alignment of business and information technology IT/information systems IS and business model comparison. Finally, chapter 9 presents some conclusions.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

Validity, reliability and responsiveness of the French language translation of the Western Ontario Shoulder Instability Index (WOSI).

BACKGROUND: The WOSI (Western Ontario Shoulder Instability Index) is a self-administered quality of life questionnaire designed to be used as a primary outcome measure in clinical trials on shoulder instability, as well as to measure the effect of an intervention on any particular patient. It is validated and is reliable and sensitive. As it is designed to measure subjective outcome, it is important that translation should be methodologically rigorous, as it is subject to both linguistic and cultural interpretation. OBJECTIVE: To produce a French language version of the WOSI that is culturally adapted to both European and North American French-speaking populations. MATERIALS AND METHODS: A validated protocol was used to create a French language WOSI questionnaire (WOSI-Fr) that would be culturally acceptable for both European and North American French-speaking populations. Reliability and responsiveness analyses were carried out, and the WOSI-Fr was compared to the F-QuickDASH-D/S (Disability of the Arm, Shoulder and Hand-French translation), and Walch-Duplay scores. RESULTS: A French language version of the WOSI (WOSI-Fr) was accepted by a multinational committee. The WOSI-Fr was then validated using a total of 144 native French-speaking subjects from Canada and Switzerland. Comparison of results on two WOSI-Fr questionnaires completed at a mean interval of 16 days showed that the WOSI-Fr had strong reliability, with a Pearson and interclass correlation of r=0.85 (P=0.01) and ICC=0.84 [95% CI=0.78-0.88]. Responsiveness, at a mean 378.9 days after surgical intervention, showed strong correlation with that of the F-QuickDASH-D/S, with r=0.67 (P<0.01). Moreover, a standardized response means analysis to calculate effect size for both the WOSI-Fr and the F-QuickDASH-D/S showed that the WOSI-Fr had a significantly greater ability to detect change (SRM 1.55 versus 0.87 for the WOSI-Fr and F-QuickDASH-D/S respectively, P<0.01). The WOSI-Fr showed fair correlation with the Walch-Duplay. DISCUSSION: A French-language translation of the WOSI questionnaire was created and validated for use in both Canadian and Swiss French-speaking populations. This questionnaire will facilitate outcome assessment in French-speaking settings, collaboration in multinational studies and comparison between studies performed in different countries. TYPE OF STUDY: Multicenter cohort study. LEVEL OF EVIDENCE: II.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species.

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Mutagenesis and modelling of the alpha(1b)-adrenergic receptor highlight the role of the helix 3/helix 6 interface in receptor activation.

Computer simulations on a new model of the alpha1b-adrenergic receptor based on the crystal structure of rhodopsin have been combined with experimental mutagenesis to investigate the role of residues in the cytosolic half of helix 6 in receptor activation. Our results support the hypothesis that a salt bridge between the highly conserved arginine (R143(3.50)) of the E/DRY motif of helix 3 and a conserved glutamate (E289(6.30)) on helix 6 constrains the alpha1b-AR in the inactive state. In fact, mutations of E289(6.30) that weakened the R143(3.50)-E289(6.30) interaction constitutively activated the receptor. The functional effect of mutating other amino acids on helix 6 (F286(6.27), A292(6.33), L296(6.37), V299(6.40,) V300(6.41), and F303(6.44)) correlates with the extent of their interaction with helix 3 and in particular with R143(3.50) of the E/DRY sequence.