Finnish buyers’ perceptions of Indian textile and clothing suppliers: Country of origin in focus

Choosing the right supplier is crucial for long-term business prospects and profitability. Thus organizational buyers are naturally very interested in how they can select the right supplier for their needs. Likewise, suppliers are interested in knowing how their customers make purchasing decisions in order to effectively sell and market to them. From the point of view of the textile and clothing (T&C) industry, regulatory changes and increasing low-cost and globalization pressures have led to the rise of low-cost production locations India and China as the world’s largest T&C producers. This thesis will examine T&C trade between Finland and India specifically in the context of non-industrial T&C products. Its main research problem asks: what perceptions do Finnish T&C industry buyers hold of India and Indian suppliers? B2B buyers use various supplier selection models and criteria in making their purchase decisions. A significant amount of research has been done into supplier selection practices, and in the context of international trade, country of origin (COO) perceptions specifically have garnered much attention. This thesis uses a mixed methods approach (online questionnaire and in-depth interviews) to evaluate Finnish T&C buyers’ supplier selection criteria, COO perceptions of India and experiences of Indian suppliers. It was found that the most important supplier selection criteria used by Finnish T&C buyers are quality, reliability and cost. COO perceptions were not found to be influential in purchasing process. Indian T&C suppliers’ strengths were found to be low cost, flexibility and a history of traditional T&C expertise. Their weaknesses include product quality and unreliable delivery times. Overall, the main challenges that need to be overcome by Indian T&C companies are logistical difficulties and the cost vs. quality trade-off. Despite positive perceptions of India for cost, the overall value offered by Indian T&C products was perceived to be low due to poor quality. Unreliable delivery time experiences also affected buyer’s reliability perceptions of Indian suppliers. The main limiting factors of this thesis relate to the small sample size used in the research. This limits the generalizability of results and the ability to evaluate the reliability and validity of some of the research instruments.

Model-based estimation of liquid flows in the blast furnace hearth and taphole

This thesis presents a set of methods and models for estimation of iron and slag flows in the blast furnace hearth and taphole. The main focus was put on predicting taphole flow patterns and estimating the effects of various taphole conditions on the drainage behavior of the blast furnace hearth. All models were based on a general understanding of the typical tap cycle of an industrial blast furnace. Some of the models were evaluated on short-term process data from the reference furnace. A computational fluid dynamics (CFD) model was built and applied to simulate the complicated hearth flows and thus to predict the regions of the hearth exerted to erosion under various operating conditions. Key boundary variables of the CFD model were provided by a simplified drainage model based on the first principles. By examining the evolutions of liquid outflow rates measured from the furnace studied, the drainage model was improved to include the effects of taphole diameter and length. The estimated slag delays showed good agreement with the observed ones. The liquid flows in the taphole were further studied using two different models and the results of both models indicated that it is more likely that separated flow of iron and slag occurs in the taphole when the liquid outflow rates are comparable during tapping. The drainage process was simulated with an integrated model based on an overall balance analysis: The high in-furnace overpressure can compensate for the resistances induced by the liquid flows in the hearth and through the taphole. Finally, a recently developed multiphase CFD model including interfacial forces between immiscible liquids was developed and both the actual iron-slag system and a water-oil system in laboratory scale were simulated. The model was demonstrated to be a useful tool for simulating hearth flows for gaining understanding of the complex phenomena in the drainage of the blast furnace.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Design and validation of stateful composite RESTful web services

A web service is a software system that provides a machine-processable interface to the other machines over the network using different Internet protocols. They are being increasingly used in the industry in order to automate different tasks and offer services to a wider audience. The REST architectural style aims at producing scalable and extensible web services using technologies that play well with the existing tools and infrastructure of the web. It provides a uniform set of operation that can be used to invoke a CRUD interface (create, retrieve, update and delete) of a web service. The stateless behavior of the service interface requires that every request to a resource is independent of the previous ones facilitating scalability. Automated systems, e.g., hotel reservation systems, provide advanced scenarios for stateful services that require a certain sequence of requests that must be followed in order to fulfill the service goals. Designing and developing such services for advanced scenarios with REST constraints require rigorous approaches that are capable of creating web services that can be trusted for their behavior. Systems that can be trusted for their behavior can be termed as dependable systems. This thesis presents an integrated design, analysis and validation approach that facilitates the service developer to create dependable and stateful REST web services. The main contribution of this thesis is that we provide a novel model-driven methodology to design behavioral REST web service interfaces and their compositions. The behavioral interfaces provide information on what methods can be invoked on a service and the pre- and post-conditions of these methods. The methodology uses Unified Modeling Language (UML), as the modeling language, which has a wide user base and has mature tools that are continuously evolving. We have used UML class diagram and UML state machine diagram with additional design constraints to provide resource and behavioral models, respectively, for designing REST web service interfaces. These service design models serve as a specification document and the information presented in them have manifold applications. The service design models also contain information about the time and domain requirements of the service that can help in requirement traceability which is an important part of our approach. Requirement traceability helps in capturing faults in the design models and other elements of software development environment by tracing back and forth the unfulfilled requirements of the service. The information about service actors is also included in the design models which is required for authenticating the service requests by authorized actors since not all types of users have access to all the resources. In addition, following our design approach, the service developer can ensure that the designed web service interfaces will be REST compliant. The second contribution of this thesis is consistency analysis of the behavioral REST interfaces. To overcome the inconsistency problem and design errors in our service models, we have used semantic technologies. The REST interfaces are represented in web ontology language, OWL2, that can be part of the semantic web. These interfaces are used with OWL 2 reasoners to check unsatisfiable concepts which result in implementations that fail. This work is fully automated thanks to the implemented translation tool and the existing OWL 2 reasoners. The third contribution of this thesis is the verification and validation of REST web services. We have used model checking techniques with UPPAAL model checker for this purpose. The timed automata of UML based service design models are generated with our transformation tool that are verified for their basic characteristics like deadlock freedom, liveness, reachability and safety. The implementation of a web service is tested using a black-box testing approach. Test cases are generated from the UPPAAL timed automata and using the online testing tool, UPPAAL TRON, the service implementation is validated at runtime against its specifications. Requirement traceability is also addressed in our validation approach with which we can see what service goals are met and trace back the unfulfilled service goals to detect the faults in the design models. A final contribution of the thesis is an implementation of behavioral REST interfaces and service monitors from the service design models. The partial code generation tool creates code skeletons of REST web services with method pre and post-conditions. The preconditions of methods constrain the user to invoke the stateful REST service under the right conditions and the post condition constraint the service developer to implement the right functionality. The details of the methods can be manually inserted by the developer as required. We do not target complete automation because we focus only on the interface aspects of the web service. The applicability of the approach is demonstrated with a pedagogical example of a hotel room booking service and a relatively complex worked example of holiday booking service taken from the industrial context. The former example presents a simple explanation of the approach and the later worked example shows how stateful and timed web services offering complex scenarios and involving other web services can be constructed using our approach.

Evaluating land-use related environmental impacts of biomass value chains for decision-support : Comparison and testing of methodologies proposed for environmental life cycle assessment

Life cycle assessment (LCA) is one of the most established quantitative tools for environmental impact assessment of products. To be able to provide support to environmentally-aware decision makers on environmental impacts of biomass value-chains, the scope of LCA methodology needs to be augmented to cover landuse related environmental impacts. This dissertation focuses on analysing and discussing potential impact assessment methods, conceptual models and environmental indicators that have been proposed to be implemented into the LCA framework for impacts of land use. The applicability of proposed indicators and impact assessment frameworks is tested from practitioners' perspective, especially focusing on forest biomass value chains. The impacts of land use on biodiversity, resource depletion, climate change and other ecosystem services is analysed and discussed and the interplay in between value choices in LCA modelling and the decision-making situations to be supported is critically discussed. It was found out that land use impact indicators are necessary in LCA in highlighting differences in impacts from distinct land use classes. However, many open questions remain on certainty of highlighting actual impacts of land use, especially regarding impacts of managed forest land use on biodiversity and ecosystem services such as water regulation and purification. The climate impact of energy use of boreal stemwood was found to be higher in the short term and lower in the long-term in comparison with fossil fuels that emit identical amount of CO2 in combustion, due to changes implied to forest C stocks. The climate impacts of energy use of boreal stemwood were found to be higher than the previous estimates suggest on forest residues and stumps. The product lifetime was found to have much higher influence on the climate impacts of woodbased value chains than the origin of stemwood either from thinnings or final fellings. Climate neutrality seems to be likely only in the case when almost all the carbon of harvested wood is stored in long-lived wooden products. In the current form, the land use impacts cannot be modelled with a high degree of certainty nor communicated with adequate level of clarity to decision makers. The academia needs to keep on improving the modelling framework, and more importantly, clearly communicate to decision-makers the limited certainty on whether land-use intensive activities can help in meeting the strict mitigation targets we are globally facing.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.

Business models for Finnish biobanks

This study concentrates on developing a suitable business model for Finnish biobanks, with particular emphasis on value creation to stakeholders. The sub-objective of this thesis are to map the commercial possibilities of biobanks and potential barriers for business development. The study approaches the subject from the biobanks’ as well as the stakeholders’ point of view, integrating their hopes and needs considering current and future co-operation into the findings. In 2013 the Biobank Act came into effect, after which six biobanks have been established and several other pending biobank projects are in process. There is relatively little research in regard to the commercial opportunities of this newcomer of the biomedical industry, and particularly in the Finnish markets. Therefore, the aim of this study is to partially fill the research gap of the commercial potential of biobanks and particularly outline the problematic elements in developing business. The theoretical framework consists of a few select theories, which depict business modeling and value creation of organizations. The theories are combined to form a synthesis, which best adapts to biobanks, and acts as a backbone for interviews. The empirical part of the study was conducted mainly by seven face-to-face interviews, and complemented by two phone interviews and an e-mail questionnaire with four responses. The findings consist mainly of the participants’ reflections on the potential products and services enabled by consumer genomics, as well as perceptions on different obstacles for biobanks’ business development. The nature of the study is tentative, as biobanks are relatively new organizations in Finland, and their operation models and activities are still molding. The aim is to bring to surface the hopes and concerns of biobanks’ representatives, as well as the representatives of stakeholders, in order to transparently discuss the current situation and suggestions for further development. The study concludes that in principle, the interviewees’ agree on the need for development in order not to waste the potential of biobanks; regardless, the participants emphasize different aspects and subsequently lean on differing methods.

Business models for Finnish biobanks

This study concentrates on developing a suitable business model for Finnish biobanks, with particular emphasis on value creation to stakeholders. The sub-objective of this thesis are to map the commercial possibilities of biobanks and potential barriers for business development. The study approaches the subject from the biobanks’ as well as the stakeholders’ point of view, integrating their hopes and needs considering current and future co-operation into the findings. In 2013 the Biobank Act came into effect, after which six biobanks have been established and several other pending biobank projects are in process. There is relatively little research in regard to the commercial opportunities of this newcomer of the biomedical industry, and particularly in the Finnish markets. Therefore, the aim of this study is to partially fill the research gap of the commercial potential of biobanks and particularly outline the problematic elements in developing business. The theoretical framework consists of a few select theories, which depict business modeling and value creation of organizations. The theories are combined to form a synthesis, which best adapts to biobanks, and acts as a backbone for interviews. The empirical part of the study was conducted mainly by seven face-to-face interviews, and complemented by two phone interviews and an e-mail questionnaire with four responses. The findings consist mainly of the participants’ reflections on the potential products and services enabled by consumer genomics, as well as perceptions on different obstacles for biobanks’ business development. The nature of the study is tentative, as biobanks are relatively new organizations in Finland, and their operation models and activities are still molding. The aim is to bring to surface the hopes and concerns of biobanks’ representatives, as well as the representatives of stakeholders, in order to transparently discuss the current situation and suggestions for further development. The study concludes that in principle, the interviewees’ agree on the need for development in order not to waste the potential of biobanks; regardless, the participants emphasize different aspects and subsequently lean on differing methods.

Public-private partnership investments in dry ports – Russian logistics markets and risks

The investments have always been considered as an essential backbone and so-called ‘locomotive’ for the competitive economies. However, in various countries, the state has been put under tight budget constraints for the investments in capital intensive projects. In response to this situation, the cooperation between public and private sector has grown based on public-private mechanism. The promotion of favorable arrangement for collaboration between public and private sectors for the provision of policies, services, and infrastructure in Russia can help to address the problems of dry ports development that neither municipalities nor the private sector can solve alone. Especially, the stimulation of public-private collaboration is significant under the exposure to externalities that affect the magnitude of the risks during all phases of project realization. In these circumstances, the risk in the projects also is becoming increasingly a part of joint research and risk management practice, which is viewed as a key approach, aiming to take active actions on existing global and specific factors of uncertainties. Meanwhile, a relatively little progress has been made on the inclusion of the resilience aspects into the planning process of a dry ports construction that would instruct the capacity planner, on how to mitigate the occurrence of disruptions that may lead to million dollars of losses due to the deviation of the future cash flows from the expected financial flows on the project. The current experience shows that the existing methodological base is developed fragmentary within separate steps of supply chain risk management (SCRM) processes: risk identification, risk evaluation, risk mitigation, risk monitoring and control phases. The lack of the systematic approach hinders the solution of the problem of risk management processes of dry port implementation. Therefore, management of various risks during the investments phases of dry port projects still presents a considerable challenge from the practical and theoretical points of view. In this regard, the given research became a logical continuation of fundamental research, existing in the financial models and theories (e.g., capital asset pricing model and real option theory), as well as provided a complementation for the portfolio theory. The goal of the current study is in the design of methods and models for the facilitation of dry port implementation through the mechanism of public-private partnership on the national market that implies the necessity to mitigate, first and foremost, the shortage of the investments and consequences of risks. The problem of the research was formulated on the ground of the identified contradictions. They rose as a continuation of the trade-off between the opportunities that the investors can gain from the development of terminal business in Russia (i.e. dry port implementation) and risks. As a rule, the higher the investment risk, the greater should be their expected return. However, investors have a different tolerance for the risks. That is why it would be advisable to find an optimum investment. In the given study, the optimum relates to the search for the efficient portfolio, which can provide satisfaction to the investor, depending on its degree of risk aversion. There are many theories and methods in finance, concerning investment choices. Nevertheless, the appropriateness and effectiveness of particular methods should be considered with the allowance of the specifics of the investment projects. For example, the investments in dry ports imply not only the lump sum of financial inflows, but also the long-term payback periods. As a result, capital intensity and longevity of their construction determine the necessity from investors to ensure the return on investment (profitability), along with the rapid return on investment (liquidity), without precluding the fact that the stochastic nature of the project environment is hardly described by the formula-based approach. The current theoretical base for the economic appraisals of the dry port projects more often perceives net present value (NPV) as a technique superior to other decision-making criteria. For example, the portfolio theory, which considers different risk preference of an investor and structures of utility, defines net present value as a better criterion of project appraisal than discounted payback period (DPP). Meanwhile, in business practice, the DPP is more popular. Knowing that the NPV is based on the assumptions of certainty of project life, it cannot be an accurate appraisal approach alone to determine whether or not the project should be accepted for the approval in the environment that is not without of uncertainties. In order to reflect the period or the project’s useful life that is exposed to risks due to changes in political, operational, and financial factors, the second capital budgeting criterion – discounted payback period is profoundly important, particularly for the Russian environment. Those statements represent contradictions that exist in the theory and practice of the applied science. Therefore, it would be desirable to relax the assumptions of portfolio theory and regard DPP as not fewer relevant appraisal approach for the assessment of the investment and risk measure. At the same time, the rationality of the use of both project performance criteria depends on the methods and models, with the help of which these appraisal approaches are calculated in feasibility studies. The deterministic methods cannot ensure the required precision of the results, while the stochastic models guarantee the sufficient level of the accuracy and reliability of the obtained results, providing that the risks are properly identified, evaluated, and mitigated. Otherwise, the project performance indicators may not be confirmed during the phase of project realization. For instance, the economic and political instability can result in the undoing of hard-earned gains, leading to the need for the attraction of the additional finances for the project. The sources of the alternative investments, as well as supportive mitigation strategies, can be studied during the initial phases of project development. During this period, the effectiveness of the investments undertakings can also be improved by the inclusion of the various investors, e.g. Russian Railways’ enterprises and other private companies in the dry port projects. However, the evaluation of the effectiveness of the participation of different investors in the project lack the methods and models that would permit doing the particular feasibility study, foreseeing the quantitative characteristics of risks and their mitigation strategies, which can meet the tolerance of the investors to the risks. For this reason, the research proposes a combination of Monte Carlo method, discounted cash flow technique, the theory of real options, and portfolio theory via a system dynamics simulation approach. The use of this methodology allows for comprehensive risk management process of dry port development to cover all aspects of risk identification, risk evaluation, risk mitigation, risk monitoring, and control phases. A designed system dynamics model can be recommended for the decision-makers on the dry port projects that are financed via a public-private partnership. It permits investors to make a decision appraisal based on random variables of net present value and discounted payback period, depending on different risks factors, e.g. revenue risks, land acquisition risks, traffic volume risks, construction hazards, and political risks. In this case, the statistical mean is used for the explication of the expected value of the DPP and NPV; the standard deviation is proposed as a characteristic of risks, while the elasticity coefficient is applied for rating of risks. Additionally, the risk of failure of project investments and guaranteed recoupment of capital investment can be considered with the help of the model. On the whole, the application of these modern methods of simulation creates preconditions for the controlling of the process of dry port development, i.e. making managerial changes and identifying the most stable parameters that contribute to the optimal alternative scenarios of the project realization in the uncertain environment. System dynamics model allows analyzing the interactions in the most complex mechanism of risk management process of the dry ports development and making proposals for the improvement of the effectiveness of the investments via an estimation of different risk management strategies. For the comparison and ranking of these alternatives in their order of preference to the investor, the proposed indicators of the efficiency of the investments, concerning the NPV, DPP, and coefficient of variation, can be used. Thus, rational investors, who averse to taking increased risks unless they are compensated by the commensurate increase in the expected utility of a risky prospect of dry port development, can be guided by the deduced marginal utility of investments. It is computed on the ground of the results from the system dynamics model. In conclusion, the outlined theoretical and practical implications for the management of risks, which are the key characteristics of public-private partnerships, can help analysts and planning managers in budget decision-making, substantially alleviating the effect from various risks and avoiding unnecessary cost overruns in dry port projects.

Customer Discovery and Customer Validation in Lean Software Startups

The traditional business models and the traditionally successful development methods that have been distinctive to the industrial era, do not satisfy the needs of modern IT companies. Due to the rapid nature of IT markets, the uncertainty of new innovations‟ success and the overwhelming competition with established companies, startups need to make quick decisions and eliminate wasted resources more effectively than ever before. There is a need for an empirical basis on which to build business models, as well as evaluate the presumptions regarding value and profit. Less than ten years ago, the Lean software development principles and practices became widely well-known in the academic circles. Those practices help startup entrepreneurs to validate their learning, test their assumptions and be more and more dynamical and flexible. What is special about today‟s software startups is that they are increasingly individual. There are quantitative research studies available regarding the details of Lean startups. Broad research with hundreds of companies presented in a few charts is informative, but a detailed study of fewer examples gives an insight to the way software entrepreneurs see Lean startup philosophy and how they describe it in their own words. This thesis focuses on Lean software startups‟ early phases, namely Customer Discovery (discovering a valuable solution to a real problem) and Customer Validation (being in a good market with a product which satisfies that market). The thesis first offers a sufficiently compact insight into the Lean software startup concept to a reader who is not previously familiar with the term. The Lean startup philosophy is then put into a real-life test, based on interviews with four Finnish Lean software startup entrepreneurs. The interviews reveal 1) whether the Lean startup philosophy is actually valuable for them, 2) how can the theory be practically implemented in real life and 3) does theoretical Lean startup knowledge compensate a lack of entrepreneurship experience. A reader gets familiar with the key elements and tools of Lean startups, as well as their mutual connections. The thesis explains why Lean startups waste less time and money than many other startups. The thesis, especially its research sections, aims at providing data and analysis simultaneously.

Customer Discovery and Customer Validation in Lean Software Startups

The traditional business models and the traditionally successful development methods that have been distinctive to the industrial era, do not satisfy the needs of modern IT companies. Due to the rapid nature of IT markets, the uncertainty of new innovations‟ success and the overwhelming competition with established companies, startups need to make quick decisions and eliminate wasted resources more effectively than ever before. There is a need for an empirical basis on which to build business models, as well as evaluate the presumptions regarding value and profit. Less than ten years ago, the Lean software development principles and practices became widely well-known in the academic circles. Those practices help startup entrepreneurs to validate their learning, test their assumptions and be more and more dynamical and flexible. What is special about today‟s software startups is that they are increasingly individual. There are quantitative research studies available regarding the details of Lean startups. Broad research with hundreds of companies presented in a few charts is informative, but a detailed study of fewer examples gives an insight to the way software entrepreneurs see Lean startup philosophy and how they describe it in their own words. This thesis focuses on Lean software startups‟ early phases, namely Customer Discovery (discovering a valuable solution to a real problem) and Customer Validation (being in a good market with a product which satisfies that market). The thesis first offers a sufficiently compact insight into the Lean software startup concept to a reader who is not previously familiar with the term. The Lean startup philosophy is then put into a real-life test, based on interviews with four Finnish Lean software startup entrepreneurs. The interviews reveal 1) whether the Lean startup philosophy is actually valuable for them, 2) how can the theory be practically implemented in real life and 3) does theoretical Lean startup knowledge compensate a lack of entrepreneurship experience. A reader gets familiar with the key elements and tools of Lean startups, as well as their mutual connections. The thesis explains why Lean startups waste less time and money than many other startups. The thesis, especially its research sections, aims at providing data and analysis simultaneously.

Vulnerability to adverse consequences of drinking and problem drinker status as predicted by risky drinking behaviours, drug use, sex differences and affect : a test of multiple models /

Although alcohol problems and alcohol consumption are related, consumption does not fully account for differences in vulnerability to alcohol problems. Therefore, other factors should account for these differences. Based on previous research, it was hypothesized that risky drinking behaviours, illicit and prescription drug use, affect and sex differences would account for differences in vulnerability to alcohol problems while statistically controlling for overall alcohol consumption. Four models were developed that were intended to test the predictive ability of these factors, three of which tested the predictor sets separately and a fourth which tested them in a combined model. In addition, two distinct criterion variables were regressed on the predictors. One was a measure of the frequency that participants experienced negative consequences that they attributed to their drinking and the other was a measure of the extent to which participants perceived themselves to be problem drinkers. Each of the models was tested on four samples from different populations, including fIrst year university students, university students in their graduating year, a clinical sample of people in treatment for addiction, and a community sample of young adults randomly selected from the general population. Overall, support was found for each of the models and each of the predictors in accounting for differences in vulnerability to alcohol problems. In particular, the frequency with which people become intoxicated, frequency of illicit drug use and high levels of negative affect were strong and consistent predictors of vulnerability to alcohol problems across samples and criterion variables. With the exception of the clinical sample, the combined models predicted vulnerability to negative consequences better than vulnerability to problem drinker status. Among the clinical and community samples the combined model predicted problem drinker status better than in the student samples.

Identification, Weak Instruments and Statistical Inference in Econometrics

We discuss statistical inference problems associated with identification and testability in econometrics, and we emphasize the common nature of the two issues. After reviewing the relevant statistical notions, we consider in turn inference in nonparametric models and recent developments on weakly identified models (or weak instruments). We point out that many hypotheses, for which test procedures are commonly proposed, are not testable at all, while some frequently used econometric methods are fundamentally inappropriate for the models considered. Such situations lead to ill-defined statistical problems and are often associated with a misguided use of asymptotic distributional results. Concerning nonparametric hypotheses, we discuss three basic problems for which such difficulties occur: (1) testing a mean (or a moment) under (too) weak distributional assumptions; (2) inference under heteroskedasticity of unknown form; (3) inference in dynamic models with an unlimited number of parameters. Concerning weakly identified models, we stress that valid inference should be based on proper pivotal functions —a condition not satisfied by standard Wald-type methods based on standard errors — and we discuss recent developments in this field, mainly from the viewpoint of building valid tests and confidence sets. The techniques discussed include alternative proposed statistics, bounds, projection, split-sampling, conditioning, Monte Carlo tests. The possibility of deriving a finite-sample distributional theory, robustness to the presence of weak instruments, and robustness to the specification of a model for endogenous explanatory variables are stressed as important criteria assessing alternative procedures.

Short run and long run causality in time series: Inference

We propose methods for testing hypotheses of non-causality at various horizons, as defined in Dufour and Renault (1998, Econometrica). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the U.S. economy.