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Bakgrund: Regelbaserad redovisning innefattar en tydlig och detaljerad vägledning medans principbaserad redovisning är mer öppen för bedömningar och tolkningar. Valet mellan dessa redovisningsmetoder kan studeras genom mindre aktiebolags val av K-regelverk där K2 är regelbaserat och K3 är principbaserat. Denna studie kan bidra till forskningen genom att studeramönster i beslutet genom en teoretisk modell. Syfte: Syftet med denna uppsats är att förklara vilka faktorer som påverkar beslutsfattarna i valet mellan regelbaserad och principbaserad redovisning. Metod: Denna studie har utförts med en deduktiv forskningsansats där befintliga teorier och tidigare forskning har ut gjort grunden för studien. En teoretisk modell har skapats genom en integration av Positive accounting theory, institutionell teori och beslutsteorier. En kvantitativ undersökning utfördes genom en enkätundersökning där den teoretiska modellen låg till grund för enkätfrågorna, analysen och resultatet i studien. Slutsats: Det går utifrån PAT, IT och Beslutsteori att förklara vad som har påverkat en beslutsfattare i valet mellan regelbaserad och principbaseradredovisning. De faktorer som påverkar en beslutsfattare är revisorns rekommendationer, koncerntillhörighet, resultatet, externa påtryckningar, administrativa kostnader, underlättande av redovisningen och rättvisande bild av företaget.

Bakgrund: Anläggningslokalisering är för de flesta organisationer ett strategiskt beslut och traditionellt sett har dessa beslut beserats på kostnadsorienterade modeller. Dessa modeller kan beskrivas som matematiska eller faktorvärderande. Historiskt sett har dessa modeller använts för att bestämma ekonomiskt lämpliga placeringar men när andra fördelar som miljö och sociala fördelar kan öka en organisations konkurrensfördelar har intresset för hållbar utveckling ökat. I dagsläget finns dock inga modeller där hänsyn tas till hållbar utveckling för anläggningslokalisering. Det framgår att kunskapen om hållbar utveckling vid logistiklokalisering är bristfällig och att det finns ett behov att introducera hållbara aspekter vid beslutsfattande som rör lokalisering. Syfte: Syftet med studien var att kartlägga vilka hållbara faktorer som är viktigast vid logistiklokalisering samt att rangordna dessa med en modell som utgår ifrån de tre dimensionerna av hållbar utveckling; ekonomi, socialt och miljö. Metod: En litteraturstudie gjordes på vetenskapliga artiklar, böcker och dokument som berör de ämnen som varit aktuella för denna studie. För att komplettera teorin gjordes en fallstudie med intervjuer på organisationer som jobbar med regional utveckling samt med en upphovsman av en lokaliseringsmodell som börjat jobba med hållbarhet. Resultat: I resultatet samlades respondenternas synpunkter om hållbarhet och arbetet med hållbar utveckling inom logistiketableringar. Analys och Diskussion: det framkom att respondenterna hade lite olika synvinklar på hållbarhet men att de ändå kom fram till samma slutsats. Samtliga respondenter tog upp hållbara transporter, kompetenstillgången och tillgången på arbetskraft som viktiga aspekter för hållbara etableringar. För att motivera hållbara logistikverksamhets-lokaliseringar visade sig tillgången till dessa faktorer vara viktiga. När analysen sammanställdes visade det sig att sex faktorer trädde fram i intervjusvaren samt även i teorin. Dessa var hållbar infrastruktur, livscykelperspektiv, omgivningens påverkan, logistikparker, arbetskraft och mångfald. Slutsats: De sex kriterierna som togs fram rangordnades med hjälp av en AHP-beslutsmodell och det visade sig att hållbar infrastruktur är viktigast för hållbar utveckling inom logistiklokaliseringar. Därefter kom livscykelperspektiv, logistikparker, arbetskraft, omgivningens påverkan och till sist mångfald. Lärdomarna efter denna studie är att hållbarhet inom lokalisering fortfarande är bristfällig och att lokalisering bör ske utifrån samarbeten och arbetskraftutveckling i större grad. Genom att föreslå viktiga faktorer finns nu ett ramverk att jobba efter när det kommer till hållbara logistiklokaliseringar. / Background: Facility location is for most organizations a strategic decision and traditionally, these decisions have been based on cost-oriented models. These models can be described as mathematical or evaluative factor. Historically, these models are used to determine the appropriate economic placements but other benefits such as environmental and social benefits can increase an organization's competitive advantage when interest in sustainable development has increased. In the current situation, there are no models that take into account sustainable development in facility location. It is clear that knowledge of sustainable development in logistics business location is flawed and that there is a need to introduce sustainable aspects when making decisions related to localization problems. Purpose: The purpose of this study was to identify the most important sustainable factors in logistics business location and to rank them with a model based on the three dimensions of sustainable development; economic, social and environmental. Method: A literature review was conducted based on scientific articles, books and documents related to the topics that have been considered for this study. To complement the theory a case study was implemented based on interviews with organizations that work with regional development as well as an author of a localization model with sustainable aspects. Results: The respondents view on sustainability and sustainability in logistics were collected in this section. Analysis and Discussion: It was revealed that the respondents had slightly different perspectives on sustainability but they still came to the same conclusion. All respondents talked about sustainable transports, availability of labor workers and also availability of skilled personnel as important aspects of sustainable establishments. To motivate sustainable logistics localizations it came clear that access to these factors is important. When the analysis was compiled, it was found that six factors emerged in the interview responses, as well as in theory. These were sustainable infrastructure, life-cycle perspective, influences on the surroundings, logistics parks, labor and diversity. Conclusion: The six criteria that were developed were ranked using an AHP decision model, and it turned out that sustainable infrastructure is the most important factor for sustainable development in logistics localizations. Then came the life-cycle perspective, logistics parks, labor, influences on the surroundings and ultimately diversity. Lessons learned by this study are that the sustainability in localization is still inadequate and that the localization should be based on partnerships and workforce development to a greater degree. By proposing important factors a framework is developed to work by when it comes to sustainable logistics localizations.

This report describes a case study where a multi criteria decision model is used to make decisions regarding asset interventions for four social housing complexes, similar in terms of issues and possible interventions, at Christchurch City Council. The value judgements from the decision makers and their advisors that were necessary for creating the decision model were elicited through three workshops; selecting aspects, weighting and rating and lastly reviewing the output. An analysis performed shows that the decision model is logically consistent and does not suffer from the rank reversal phenomenon. The validation of the model also included creating four individual decision models, one for each social housing complex, comparing the results of applying the joint model and the individual models, and revisiting and reconsidering the value judgments made in the different models when discrepancies were found. This included utility difference analysis and asking trade-off questions to the decision makers. Part of the validation was also to get acceptance of the output of the joint model from the social housing team. Applying the decision model on the four social housing complexes and receiving an output which is accepted by the social housing team suggests that the aggregated model can be used for future decision problems of the same kind, provided that they are within the set level ranges of the aspects. Since the decision model is transparent in terms of which values or priorities have been applied and which prerequisites must be met in order to apply the model to future decisions, it is possible to use the decision model as a ‘live model’ with adjustment being made to it when required.

Medical decision problems are extremely complex owing to their dynamic nature, large number of variable factors, and the associated uncertainty. Decision support technology entered the medical field long after other areas such as the airline industry and the manufacturing industry. Yet, it is rapidly becoming an indispensable tool in medical decision making problems including the class of sequential decision problems. In these problems, physicians decide on a treatment plan that optimizes a benefit measure such as the treatment cost, and the quality of life of the patient. The last decade saw the emergence of many decision support applications in medicine. However, the existing models have limited applications to decision problems with very few states and actions. An urgent need is being felt by the medical research community to expand the applications to more complex dynamic problems with large state and action spaces. This thesis proposes a methodology which models the class of sequential medical decision problems as a Markov decision process, and solves the model using a simulation based reinforcement learning (RL) algorithm. Such a methodology is capable of obtaining near optimal treatment strategies for problems with large state and action spaces. This methodology overcomes, to a large extent, the computational complexity of the value-iteration and policy-iteration algorithms of dynamic programming. An average reward reinforcement-learning algorithm is developed. The algorithm is applied on a sample problem of treating hereditary spherocytosis. The application demonstrates the ability of the proposed methodology to obtain effective treatment strategies for sequential medical decision problems.

In order to cope with the turbulent environments in digital age, an enterprise should response to the changes quickly. Therefore, an enterprise must improve her ability of real-time decision-making. One way to increase the competence of real-time decision-making is to use Real-Time Decision Support Systems (RTDSS). A key feature for a Decision Support Systems (DSS) to successfully support real-time decision-making is to help decision-makers selecting the best models within deadline. This study focuses on developing methods to support the mechanism of model selection in DSS. There are five results in this study. Firstly, we have developed a time-based framework to evaluate models. This framework can help decision-makers to evaluate the quality and cost of model solutions. Secondly, based on the framework of models evaluation, we also developed three models selection strategies. These strategies can help decision-makers to select the best model within deadline. Thirdly, according the definitions of parameter value precision and model solution precision in this study, we conduct a simulation analysis to understand the impacts of the precision of parameter values to the precision of a model solution. Fourthly, in order to understand the interaction among the model selection variables, we also simulate the application of model selection strategies. The results of simulation indicate our study can support models selection well. Finally, we developed a structure-based model retrieval method to help decision-makers find alternative models from model base efficiently and effectively. In conclusion, the results of this research have drawn a basic skeleton for the development of models selection. This research also reveals much insight into the development of real-time decision support systems.

We present an approach based on Model-Driven Development ideas to create Software Product Lines(SPLs). In Model-Driven SPL approaches, the derivation of a product starts from a domain application model. This model is transformed through several stages reusing model transformation rules until a product is obtained. Transformations rules are selected according to variants included in configurations created by product designers. Configurations include variants from variation points, which are relevant characteristics representing the variability of a product line. Our approach (1) provides mechanisms to improve the expression of variability of Model-Driven SPLs by allowing designers to create fine-grained configurations of products, and (2) integrates a product derivation process which uses decision models and Aspect-Oriented Programming facilitating the reuse, adaptation and composition of model transformation rules. We introduce constraint models which make it possible for product line architects to capture the scope of product lines using the concepts of constraint, cardinality property and structural dependency property. To configure products, we create domain models and binding models, which are sets of bindings between model elements and variants and satisfy the constraint models. We define a decision model as a set of aspects. An aspect maintains information of what and when transformations rules that generate commonalities of products must be intercepted (joinpoints) and what transformation rules (advices) that generate variable structures must be executed instead. Our strategy maintains uncoupled variants from model transformation rules. This solves problems related to modularization, coupling, flexibility and maintainability of transformations rules because they are completely separated from variants; thus, they can evolve independently.

The two-stage data mining technique for classifications in tourism recommendation system is necessary to connect user perception, decision criteria and decision purpose. In existed literature, hybrid data mining method combining Decision Tree and K-nearest neighbour approaches (DTKNN) were proposed. It has a high precision rate of approximately 80% in K-nearest Neighbour (KNN) but a much lower rate in the first stage using Decision Tree (Fu & Tu, 2011). It included two potential improvements on two-stage technique. To improve the first stage of DTKNN in precision rate and the efficiency, the amount of questions is decreased when users search for the desired recommendation on the system. In this paper, the researcher investigates the way to improve the first stage of DTKNN for full questionnaires and also determines the suitability of dynamic questionnaire based on its precision rate in future tourism recommendation system. Firstly, this study compared and chose the highest precision rate among Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network (BPNN). The chosen method is then combined with KNN to propose a new methodology. Secondly, the study compared and deter¬mined the suitability of dynamic questionnaires for all three classification methods by decreasing the number of attributes. The suitable dynamic questionnaire is based on the least amount of attributes used with an appropriate precision rate. Tourism recommendation system is selected as the target to apply and analyse the usefulness of the algorithm as tourism selection is a two-stage example. Tourism selection is to determine expected goal and experience before going on a tour at the first stage and to choose the tour that best matches stage one. The result indicates that Multi-staged Bi¬nary Tree has the highest precision rate of 74.167% comparing to Decision Tree with 73.33% then BPNN with 65.47% for full questionnaire. This new approach will improve the effectiveness of the system by improving the precision rate of first stage under the current DTKNN method. For dynamic questionnaire, the result has shown that Decision Tree is the most suitable method given that it resulted in the least difference of 1.33% in precision rate comparing to full questionnaire, as opposed to 1.48% for BPNN and 4% for Multi-staged Binary Tree. Thus, dynamic questionnaire will also improve the efficiency by decreasing the amount of questions which users are required to fill in when searching for the desired recommendation on the system. It provides users with the option to not answer some questions. It also increases the practicality of non-dynamic questionnaire and, therefore, affects the ultimate precision rate.

The 2019 Dallas Tornado struck a densely populated area, was the costliest tornado in Texas history, and had minimal warning lead time, yet there were no serious injuries or fatalities. To understand why, this study examines individuals' decision-making processes during this tornado using the protective action decision model (PADM). Specifically, it investigates the factors affecting threat belief and evaluation, the facilitators and impediments to protective action, and the effects on future risk perception and hazard adjustment measures. Semi-structured telephone interviews were conducted with 23 survivors to explore their experiences and decision-making processes during this tornado. Interviews were analyzed through inductive coding and a constant comparative approach. Key findings of this study suggest that clear and direct warning messages, coupled with rapid, heuristic-driven reactions, can overcome the impediment of a short-fuse warning time and motivate those at risk to take protective action. Additionally, this study identifies condominium owners as a housing population with unique needs and impediments in the tornado recovery process. Furthermore, results illustrate how the hazard scenario and contemporary technological culture nuance protective action decision-making and future hazard adjustment measures.

Det svenska sjukvårdssystemet är konstant under hög press och situationen benämns ofta i media som en sjukvårdskris. Radiologin är en av de medicinska discipliner som drabbats av en kontinuerligt ökande arbetsbelastning och personalbrist. Detta sätter sjukvården i en situation att konstant tvingas väga effektivitet mot kvalitet. Trots höga förväntningar på att innovationer som Artificiell Intelligens (AI) ska kunna bistå behoven, används AI idag i en mycket begränsad utsträckning. Denna studie syftar till att utreda påverkande faktorer för införandet av AI inom radiologin. För att besvara arbetets forskningsfråga har HA Adoption-Decision Model, en modifierad version av det väletablerade Technology-Organization-Environment Framework (TOE), tillämpats. Ramverket innefattar tre kontexter; teknologisk, organisatorisk och extern kontext. Varje kontexts delaspekter, så kallade faktorer, följer under respektive kontext. Dessa tio faktorer utvärderades för att besvara studiens forskningsfråga om vilka faktorer som har störst påverkan på införande av AI inom radiologi. Genom att förena tidigare forskning med resultatet från sex intervjuer visade sig affärsvärde , strategisk lämplighet , ledningsstöd och reglering av datahantering ha störst påverkan. Avslutningsvis presenteras ett förslag om att introducera en elfte faktor, IT-mognad, till ramverket.