· · · Institutional Repository

The number of cyber-attacks creates the realization that the vulnerability of critical infrastructures of a country are increasing. The numbers of cyber-attacks are so high that governments fear a cyber war. This makes it important for governments to prepare their nation for cyber war. To be able to make the right preparation and to design the right resilient systems it is necessary to know how dangerous other countries can be by measuring their offensive cyber warfare capabilities. This leads to the design of a model based on offensive cyber warfare attributes and public indicators for the assessment of offensive cyber warfare capabilities. The aim of this research is to provide an approach of a maturity model to assess offensive cyber warfare capabilities of countries based on public data, by which governments can make better decisions and policies to prepare themselves for cyber war. The research has been started with an in depth desk research describing the process of cyber warfare, which resulted in a diagram with 6 categories. These categories have been defined based on some literature about traditional warfare and an analogy about individuals in war. The 6 categories describing the process are: Motivation, Channel, Target, Means, Method and Damage. This diagram shows the difference between traditional and cyber war. Only offensive cyber warfare attributes are specified in this diagram. This was necessary for finding the indicators for offensive cyber warfare capability. From these 6 categories only two have been used to define offensive cyber warfare capability. Motivation level does not contribute to capability level, but to the threat level. If one is motivated, it does not necessarily mean that one has the capability. The channel is the environment where the cyber attack is launched. Having access to the channel, having knowledge about it and skills for operating in this medium is necessary to launch a cyber attack. So channel is an important group to consider for assessing offensive cyber warfare capability. The Target actually does not decide on the capability of another. So this is not important for the design of the model. The Means are very important to assess the offensive capability level, because having the ability to create the means, having access to them and the ability to use them shows how capable one is. The Method is the way how the attack is performed for example from behind or from the front and thus is not contributing to the assessment of the capability level. Also Damage is not contributing to the capability of a country, because anyone can cause damage by hiring others. So based on this analysis Channel and Means are important for assessing the offensive cyber warfare capability level. Based on these 2 classification and their details in the diagram the indicators for offensive cyber warfare capability have been identified. This resulted in a theoretical model showing the relation between the indicators and the cyber warfare attributes. As data to direct indicators are limited, an approach of a model has been given based on proxy variable and indirect indicators for which data was available. Finding data for indirect indicators has been difficult as well, but there are 16 indicators for which data has been found. The dataset for offensive cyber warfare capability was not available, so a proxy variable has been used. The closest proxy variable based on the categories Channel and Means is the ICT development index, which describes the access to, use of and skills in ICT. ICT development has been build from 11 indicators, from which 9 are the same for offensive cyber warfare capability. The assumption has been made that the ICT development index is a data collection method for offensive cyber warfare capability. Using the 16 indicators and the proxy variable the model has been designed following some analysis as is shown in the flowchart. The flowchart describes the statistical analysis in SPSS. On the 16 indicators factor analysis was done, which resulted in 4 factors that are the independent variables to explain offensive cyber warfare as the dependent variable. As there is no such dataset for the dependent variable the ICT development index is used in its place. Based on a linear regression the equation has been found; this is the model to assess offensive cyber warfare capability. Due to limitation only an approach of a model for assessing offensive cyber warfare capability has been given, which is based on proxy variable and indirect indicators. The equation in this report is a first approach of a model assessing offensive cyber warfare capability, on which further research can be conducted. The growth in capability level is described by maturity levels. There are 5 maturity levels defined for offensive cyber warfare capability based on the Channel and Means capability, which are: Beginners, Semi-intermediate, Intermediate, Semi-advanced and Advanced. In chapter 1 an introduction has been given, describing the aim of this research, the research questions and the research methods. In chapter 2 the theoretical background has been built resulting in a diagram describing offensive warfare, maturity levels and a theoretical model for assessing offensive cyber warfare capability. Chapter 3 gives an approach of a model and the statistical analysis to be performed. Chapter 4 has been devoted on reflection and the report ends with conclusions and research relevance.

Economic instability due to the financial crisis has caused major changes in the maritime industry, resulting in high uncertainty for the future. Market trends and developments have become increasingly important indicators for providing expectations for the future of throughput goods in seaports. Managers often use a Forecasting Support System (FSS) to support the forecasting process and create a more substantiated forecast. This paper provides a design for a FSS, based on leading market indicators, for forecasting support in seaport authorities. A Vector Autoregressive (VAR) model has identified and forecasted these indicators. A spreadsheet-based dashboard represents this information and can be used by forecasters. The FSS has provided an accurate representation of the leading indicators for dry bulk goods in the Port of Rotterdam. Further research can focus on splitting up the goods and making a model on a lower level of abstraction.

Patents are often taken as an indicator to measure innovativeness, because they are a lot easier to obtain than most others. There is a complication though, which is that patents are not a direct measure of innovativeness: the fit between patents and innovativeness is biased by differing propensities to patent. The propensity to patent was analyzed in the 1980’s and 1990’s, but results vary and more recent research focuses mostly at specific case studies. With the availability of the CIS (Community Innovation Survey) databases of 2000 and 2004 there is a good opportunity to test again, in a structured way, which factors are important for the propensity to patent. For this research CIS data from three North-Western European countries were analyzed: Belgium, Norway and Germany. Descriptive results show that the propensity to patent varies greatly among different types of innovation, and especially when looking at the difference between goods, services and processes. Process and service innovations turn out to be rarely patented, with average patent propensities of respectively 7.6% and 9.6%. Moreover, descriptive results confirm that the propensity to patent differs across industries. Next to that a logistic regression analysis was performed. This analysis tested existing hypotheses and explored new factors that came available in the CIS questionnaires. By only including those enterprises in the analysis that actually had innovative output, factors could be determined that increase the propensity to patent these innovations. Factors that were found significantly relevant include EU funding (+), having a new to market innovation (+), cooperation arrangements with universities (+), having a local/regional market (-), having a market outside Europe (+) and using private R&D institutions as information sources for innovation (-). On top of those it was confirmed that patent propensity is higher in specific industries and countries, as well as for different types of innovation, even when correcting for the aforementioned factors. Results provide a comprehensive overview of the importance of, and some correlations between, firm level, industry level and country level factors of the propensity to patent. These results can be used to improve patent based innovation measures, as well as to provide additional insights into appropriability conditions between sectors.

There is a growing awareness that road networks, are becoming more and more vulnerable to unforeseen disturbances like incidents and that measures need to be taken in order to make road networks more robust. In order to do this the following questions need to be addressed: How is robustness defined? Against which disturbances should the network be made robust? Which factors determine the robustness of a road network? What is the relationship between robustness, travel times and travel time reliability? Which indicators can be used to quantify robustness? How can these indicators be computed? This paper addresses these questions by developing a consistent framework for robustness in which a definition, terms related to robustness, indicators and an evaluation method are included. By doing this, policy makers and transportation analyst are offered a framework to discuss issues that are related to road network robustness and vulnerability which goes beyond the disconnected definitions, indicators and evaluation methods used so far in literature. Furthermore, the evaluation method that is presented for evaluating the robustness of the road network against short term variations in supply (like incidents) contributes to the problem of designing robust road networks because it has a relatively short computation time and it takes spillback effects and alternative routes into account.

In this research, the central objective was to explore how cultural and political factors might affect participatory practices of policy analysis. For this purpose, initially the concept of policy analysis and its evolutionary process from traditional and expert-based approach to participatory style, and role of context in policy analysis in general and participatory policy analysis in particular are studied. Next, the concept of public participation is elaborated through studying the levels and purposes of participation, type of participants, and the methods or techniques of public participation. Afterwards, risk and challenges of participation are enumerated, and a number of "factors of participation (FP)" which are sensitive to politico-cultural context are identified. Thirteen FPs are introduced and classified into four main categories of factors. Next, the cultural and political indicators which steers these FPs are explored. Hofstede's Theory, World Value Survey (WVS) by Inglehart, Schwartz cultural values orientations, cultural study of GLOBE project and Minkov cultural study, are the cross-cultural theories examined in order to extract measurable cultural indicators to explore the identified FPs. Subsequent to the recognition of cultural indicators three globally reputable research projects, namely Freedom in the World Survey, The Economist Intelligence Unit’s Index of Democracy and Worldwide Governance Indicators (WGI), in which governance indexes and democracy indicators are periodically calculated in the national level, are scrutinized in order to identify the most relevant political indicators to this thesis. Exploiting the relation between cultural and political indicators and factors of participation (FP), a framework is developed for each category of FPs. In this framework, each FP is evaluated by national scores of some cultural and/or political indicators, every FP has two poles which are assigned to extreme scores of relevant indicators and are distinguishable by specific attributes. In order to test the applicability of the developed framework, several national cases have been studied utilizing the framework. A comparative case study for a specific participatory method - consensus conference – would be done for some other countries. The case studies show that the framework can effectively explain the influence of contextual factors. Furthermore, the case studies are also helped to revise and improve the framework in a reciprocal process. It is revealed that the framework can provide awareness for policy analysts who want to employ participatory approach. This is in fact the descriptive application of the framework. Moreover, the framework can have the prescriptive application. Although this application should be elaborated in a separate research, the practical application of the framework initiates at the end of thesis. The implication of each FP's attributes is indicated and accordingly can guide the analysts to select and adapt the purpose and method of participation. Some relevant features of known public participation methods are introduced and a tentative example of practical considerations is demonstrated. The research concludes with an indication of the considerations about the research/framework and gives some suggestions for future research.

Occupants and housing managers deal with several environmental problems in dwellings. To support the diagnosis of cause and effect and to promote good communication about health hazards, indicators of the relationship between physical properties, occupancy patterns and health risks are needed. The research project focussed on the selection of indicators that mark the relationships between physical parameters, user behaviour and health risk. Houses were inspected and occupants interviewed to study environmental conditions. Through modelling the relevance of parameters was evaluated. Performance evaluation in practice suggested the importance of communication and action oriented strategies. A tool was designed, used and evaluated after one year. The results ask for accessible information to support the assessment of performance and a simple action list to remediate major problems.

Occupants in air-tight houses and with poorly maintained mechanical ventilation systems have stimulated the study of health hazards of housing. Five hundred houses were inspected, to diagnose the relation between lay-out, construction, technical services, interior decorations and potential indoor air pollution or problems with noise and safety. The occupants were interviewed about ventilation behaviour and activities that produce pollutants or hazards. The relations between technical performances of houses, occupant behaviour and exposure to health risk were analysed. The result is a list of indicators that mark in particular the exposure to house dust mite, mould, legionella pneumophila bacteria, fine dust, noise, extreme discomfort and safety hazards. Tools for the evaluation of health performances were developed and tested. The strategy and the indicators presented in this thesis are the basis for the Checklist Healthy Housing, available in versions for households and professional users (see www.toetslijstgezondwonen.nl).

In recent years there has been an increasing interest in the relation between levels of domain specific knowledge of company executives and their companies' performance. In researching this relation we have chosen to research internet companies during their emerging stages in a high growth industry. We have opted to select a data sample of 411 internet companies which had their initial public offering between 1997 and 2003. For the levels of domain specific knowledge we used two main metrics for the research: formal: ‘Engineering degrees’, and tacit knowledge ‘Years' experience in the management of technology’. Company performance we had separated into: ‘survival’ and ‘share price performance’. We also investigated for interactions with the independent variable: 'average age of the executive team', 'company size' and the 'degree of digitalization'. Our research concludes the following: 1) Domain specific knowledge of the executive management team of a company increases the chances of survival of a company significantly. 2)There has been no significant correlation between domain specific knowledge and the share price performance of a company (nor the surrogate: revenue performance) 3)There is no significant interaction for age, company size or degree of digitalization.