Microenterprise success and failure factors

The purpose of this study is to identify factors contributory to success or failure of a microenterprise. Microenterprise is an enterprise with less than10 employees. About 99 % of all Finnish enterprises fall in this category. Earlier studies do not provide a comprehensive view on microenterprise success and failure factors. The theoretical part discusses the definition of success and failure, previous research and results already established about the topic and categories of business environment. The empirical part is founded on quantitative survey results from 204 Finnish microenterprises. The questions of the survey were based on previous surveys, literature and intuition. Both success and failure factors were enquired. Summary of the results was made and the results were compared among successful and unsuccessful enterprises. In open-end questions, the most important factors mentioned to affect enterprise performance positively were "Employees", "Customers" and "Skills, knowledge, education and experience." The most important factors affecting enterprise performance negatively were "Economical situation", "Employees availability and attitudes" as well as "Political decisions and passed laws". In Likert-scale set of questions, the most significant factors from the point of view of enterprise performance were "Product and / or service good quality", "Good reputation of the company" and "Staff's strong skills". The least significant factors were "Effect of marketing and promotion", "Conflicts" and "Differences of points of views of different generations". By Likert-scale set of questions, it was also tested which operations the enterprises perform, and according to the results, successful enterprises found "Performing a market research", "Use of distribution channel in sales" and "Expanding to new markets" less important than unsuccessful enterprises. The tests proved that the age of the enterprise correlates with the level of success of the enterprise: The younger the enterprise, the more successful it is. In addition, the results show that if the enterprise is family based, the less successful it is. In addition, there was also slight correlation between success and the level of growth, indicating that higher the level of growth the more successful the enterprise is. From the business environment point of view, the key finding was that internal factors affect more on the success of an enterprise than external factors, and that external factors affect more on the failure of an enterprise than internal factors.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

An examination of factors influencing ERP key-user satisfaction: A qualitative case study

Several companies are trying to improve their operation efficiency by implementing an enterprise resource planning (ERP) system that makes it possible to control the resources of the company in real time. However, the success of the implementation project is not a foregone conclusion; a significant part of these projects end in a failure, one way or another. Therefore it is important to investigate ERP system implementation more closely in order to increase understanding about factors influencing ERP system success and to improve the probability of a successful ERP implementation project. Consequently, this study was initiated because a manufacturing case company wanted to review the success of their ERP implementation project. To be exact, the case company hoped to gain both information about the success of the project and insight for future implementation improvement. This study investigated ERP success specifically by examining factors that influence ERP key-user satisfaction. User satisfaction is one of the most commonly applied indicators of information system success. The research data was mainly collected by conducting theme interviews. The subjects of the interviews were six key-users of the newly implemented ERP system. The interviewees were closely involved in the implementation project. Furthermore, they act as representative users that utilize the new system in everyday business processes. The collected data was analyzed by thematizing. Both data collection and analysis were guided by a theoretical frame of reference. This frame was based on previous research on the subject. The results of the study aligned with the theoretical framework to large extent. The four principal factors influencing key-user satisfaction were change management, contractor service, key-user’s system knowledge and characteristics of the ERP product itself. One of the most significant contributions of the research is that it confirmed the existence of a connection between change management and ERP key-user satisfaction. Furthermore, it discovered two new sub-factors influencing contractor service related key-user satisfaction. In addition, the research findings indicated that in order to improve the current level of key-user satisfaction, the case company should pay special attention to system functionality improvement and enhancement of the key-users’ knowledge. During similar implementation projects in the future, it would be important to assure the success of change management and contractor service related processes.

Translational models of coronary artery disease, myocardial infarction and heart failure. Development, validation and in vivo imaging studies using positron emission tomography

Coronary artery disease is an atherosclerotic disease, which leads to narrowing of coronary arteries, deteriorated myocardial blood flow and myocardial ischaemia. In acute myocardial infarction, a prolonged period of myocardial ischaemia leads to myocardial necrosis. Necrotic myocardium is replaced with scar tissue. Myocardial infarction results in various changes in cardiac structure and function over time that results in “adverse remodelling”. This remodelling may result in a progressive worsening of cardiac function and development of chronic heart failure. In this thesis, we developed and validated three different large animal models of coronary artery disease, myocardial ischaemia and infarction for translational studies. In the first study the coronary artery disease model had both induced diabetes and hypercholesterolemia. In the second study myocardial ischaemia and infarction were caused by a surgical method and in the third study by catheterisation. For model characterisation, we used non-invasive positron emission tomography (PET) methods for measurement of myocardial perfusion, oxidative metabolism and glucose utilisation. Additionally, cardiac function was measured by echocardiography and computed tomography. To study the metabolic changes that occur during atherosclerosis, a hypercholesterolemic and diabetic model was used with [18F] fluorodeoxyglucose ([18F]FDG) PET-imaging technology. Coronary occlusion models were used to evaluate metabolic and structural changes in the heart and the cardioprotective effects of levosimendan during post-infarction cardiac remodelling. Large animal models were used in testing of novel radiopharmaceuticals for myocardial perfusion imaging. In the coronary artery disease model, we observed atherosclerotic lesions that were associated with focally increased [18F]FDG uptake. In heart failure models, chronic myocardial infarction led to the worsening of systolic function, cardiac remodelling and decreased efficiency of cardiac pumping function. Levosimendan therapy reduced post-infarction myocardial infarct size and improved cardiac function. The novel 68Ga-labeled radiopharmaceuticals tested in this study were not successful for the determination of myocardial blood flow. In conclusion, diabetes and hypercholesterolemia lead to the development of early phase atherosclerotic lesions. Coronary artery occlusion produced considerable myocardial ischaemia and later infarction following myocardial remodelling. The experimental models evaluated in these studies will enable further studies concerning disease mechanisms, new radiopharmaceuticals and interventions in coronary artery disease and heart failure.

Voimalaitosten kattilaputkien sisäpuolisten kerrostumien paksuuden mittaaminen ultraäänimenetelmällä

Höyryvoimalaitoksen käyttöönotossa muodostuu kattilaputkien sisäpinnoille niitä korroosiolta suojaava ohut metallioksidikerros. Tämän kerroksen päälle kasvaa kattilan käytön aikana haitallista kerrostumaa paikallisen korroosion tai kattilavedessä olevien epäpuhtauksien kerääntymisen tai kiteytymisen seurauksena. Kerrostuma haittaa lämmönsiirtoa tulipesästä putkiseinämän läpi kattilaveteen. Putkien lämpötilan nousu suunniteltua korkeammaksi kasvattaa putkivaurioiden ja sisäpuolisen korroosion riskiä. Tästä johtuen paksuksi kasvaneet kerrostumat pyritään poistamaan happokäsittelyllä eli peittauksella ennen vaurioiden syntyä. Perinteisesti kerrostumapaksuus on määritetty kattilasta irrotetuista näyteputkista mikroskoopilla. Työn tavoitteena oli tutkia uudenlaisen ultraäänimittauksen teoriaa ja selvittää sen toimivuus höyrystinputkien kerrostumapaksuusmittauksissa. Lisäksi tavoitteena oli tutkia voimalaitoksen höyrystimen sisäpuolisten kerrostumien muodostumista ja niiden vaikutuksia sekä kattilan peittaustarpeen arviointia. Höyrystimen kerrostumien kasvunopeuteen vaikuttavat eniten voimalaitostyyppi, käytetty vesikemia ja kattilaveteen kulkeutuvien epäpuhtauksien määrä. Kasvunopeus vaihtelee laitosten välillä suuresti ja eroaa myös tulipesän eri kohdissa. Kattilaveden epäpuhtauspitoisuus ja kerrostumapaksuus vaikuttavat molemmat korroosiovaurioiden todennäköisyyteen. Peittauspaksuuden ohjearvoissa tulisi huomioida kattilan käyttöpaine, kattilatyyppi ja riski kattilaveden laadun heikkenemiselle. Putkinäytteistä ja laitoksilla suoritettujen mittauksien perusteella uusi ultraäänitekniikka tuottaa luotettavia tuloksia tavanomaisten kerrostumien mittauksessa. Vain yhdellä laitoksella esiintyi irtonaisen sakan kaltaista kerrostumaa, jota mittaus ei kyennyt havaitsemaan. Mittaustulokset kerrostumista tulipesän eri osissa antavat hyvän perustan peittaustarpeen arviointiin.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Acquisition Failure: The case of a multinational enterprise from an emerging market failing in Indonesia.

The escalation in the number of mergers and acquisition transactions involving emerging market firms is a relatively recent phenomenon; as a consequence academic research in such topic is rather limited. The purpose of this research study was to discuss the possible reasons that led the acquisition failure of an emerging multinational firm and an Indonesian player. Extensive theoretical research was performed and it had been achieved, based on this, the finding of a framework that facilitated to understand the way in which the concepts of cultural distances and relate liabilities of foreignness in the process of acquisitions of foreign companies in emerging markets. The theoretical background collects literature related to acquisitions, models of cultural studies between nations and liabilities of foreignness. It has been generated a variety of frameworks that aid to understand the way that the institutional distance and cultural factors together with the concept of liabilities of foreignness can affect the process of market entry of an emerging multinational company to the extent that the best way to stop losing money is to abandon the project. The empirical research consisted of selective semi-structured interviews and an extensive research in available public data on the chosen study case of this research. There were several factors that were identified as the cause of the failure in the market entry of a Mexican multinational firm in Indonesia. The weakness shown by the local government authorities was used by the local community leaders who rioted because of discomfort. These groups were the ones who made the government submit to the extent that the agreements reached at the beginning of the deal were either canceled or modified in a way that favored always the local community. The contributions of this study fall into the knowledge field of emerging multinational firms and market entry process.

Finding Methods for Recognizing and Classifying Spare Part Criticality – Case Full Deposit Stripping Machines

The aim of this Master’s thesis is to find a method for classifying spare part criticality in the case company. Several approaches exist for criticality classification of spare parts. The practical problem in this thesis is the lack of a generic analysis method for classifying spare parts of proprietary equipment of the case company. In order to find a classification method, a literature review of various analysis methods is required. The requirements of the case company also have to be recognized. This is achieved by consulting professionals in the company. The literature review states that the analytic hierarchy process (AHP) combined with decision tree models is a common method for classifying spare parts in academic literature. Most of the literature discusses spare part criticality in stock holding perspective. This is relevant perspective also for a customer orientated original equipment manufacturer (OEM), as the case company. A decision tree model is developed for classifying spare parts. The decision tree classifies spare parts into five criticality classes according to five criteria. The criteria are: safety risk, availability risk, functional criticality, predictability of failure and probability of failure. The criticality classes describe the level of criticality from non-critical to highly critical. The method is verified for classifying spare parts of a full deposit stripping machine. The classification can be utilized as a generic model for recognizing critical spare parts of other similar equipment, according to which spare part recommendations can be created. Purchase price of an item and equipment criticality were found to have no effect on spare part criticality in this context. Decision tree is recognized as the most suitable method for classifying spare part criticality in the company.

Failure modes of passive decay heat removing safety systems of modern nuclear power plants

The purpose of this master’s thesis is to gain an understanding of passive safety systems’ role in modern nuclear reactors projects and to research the failure modes of passive decay heat removal safety systems which use phenomenon of natural circulation. Another purpose is to identify the main physical principles and phenomena which are used to establish passive safety tools in nuclear power plants. The work describes passive decay heat removal systems used in AES-2006 project and focuses on the behavior of SPOT PG system. The descriptions of the main large-scale research facilities of the passive safety systems of the AES-2006 power plant are also included. The work contains the calculations of the SPOT PG system, which was modeled with thermal-hydraulic system code TRACE. The dimensions of the calculation model are set according to the dimensions of the real SPOT PG system. In these calculations three parameters are investigated as a function of decay heat power: the pressure of the system, the natural circulation mass flow rate around the closed loop, and the level of liquid in the downcomer. The purpose of the calculations is to test the ability of the SPOT PG system to remove the decay heat from the primary side of the nuclear reactor in case of failure of one, two, or three loops out of four. The calculations show that three loops of the SPOT PG system have adequate capacity to provide the necessary level of safety. In conclusion, the work supports the view that passive systems could be widely spread in modern nuclear projects.