The purpose of this paper is to increase the understanding of how knowledge and uncertainty can affect relationship commitment between companies in the international market. Therefore, knowledge accumulation, the various levels of uncertainties and the creation of business relationships will be examined. In order to achieve the purpose of the study the main research problem is formulated: How do Knowledge and uncertainty levels related to relationship commitment of internationalized firms?This thesis is based on the qualitative method since the aim of this study is to understand and analyse a phenomenon not quantitatively to measure it. This research is further based on the deductive approach since we have formulated our main research question on the basis of previous theory and apply it on real life cases. The empirical data is collected through a multiple case study with three companies active on the international market.The study's conclusion indicates that relationship commitment has an impact on knowledge and uncertainty in the international context. We demonstrate this by proving that companies can through relationships accumulate the necessary knowledge needed to reduce uncertainty in international business. Thus reducing the gap between knowledge possessed by the firm and knowledge needed to perform an international activity.

Human Activity Recognition (HAR) field studies the application of artificial intelligence methods for the identification of activities performed by people. Many applications of HAR in healthcare and sports require the safety-critical performance of the predictive models. The predictions produced by these models should be not only correct but also trustworthy. However, in recent years it has been shown that modern neural networks tend to produce sometimes wrong and overconfident predictions when processing unusual inputs. This issue puts at risk the prediction credibility and calls for solutions that might help estimate the uncertainty of the model’s predictions. In the following work, we started the investigation of the applicability of Non-Bayesian Uncertainty Estimation methods to the Deep Learning classification models in the HAR. We trained a Convolutional Neural Network (CNN) model with public datasets, such as UCI HAR and WISDM, which collect sensor-based time-series data about activities of daily life. Through a series of four experiments, we evaluated the performance of two Non-Bayesian uncertainty estimation methods, ODIN and Deep Ensemble, on out-of-distribution detection. We found out that the ODIN method is able to separate out-of-distribution samples from the in-distribution data. However, we also obtained unexpected behavior, when the out-of-distribution data contained exclusively dynamic activities. The Deep Ensemble method did not provide satisfactory results for our research question. / Inom området Human Activity Recognition (HAR) studeras tillämpningen av metoder för artificiell intelligens för identifiering av aktiviteter som utförs av människor. Många av tillämpningarna av HAR inom hälso och sjukvård och idrott kräver att de prediktiva modellerna har en säkerhetskritisk prestanda. De förutsägelser som dessa modeller ger upphov till ska inte bara vara korrekta utan också trovärdiga. Under de senaste åren har det dock visat sig att moderna neurala nätverk tenderar att ibland ge felaktiga och överdrivet säkra förutsägelser när de behandlar ovanliga indata. Detta problem äventyrar förutsägelsernas trovärdighet och kräver lösningar som kan hjälpa till att uppskatta osäkerheten i modellens förutsägelser. I följande arbete inledde vi undersökningen av tillämpligheten av icke-Bayesianska metoder för uppskattning av osäkerheten på Deep Learning-klassificeringsmodellerna i HAR. Vi tränade en CNN-modell med offentliga dataset, såsom UCI HAR och WISDM, som samlar in sensorbaserade tidsseriedata om aktiviteter i det dagliga livet. Genom en serie av fyra experiment utvärderade vi prestandan hos två icke-Bayesianska metoder för osäkerhetsuppskattning, ODIN och Deep Ensemble, för upptäckt av out-of-distribution. Vi upptäckte att ODIN-metoden kan skilja utdelade prover från data som är i distribution. Vi fick dock också ett oväntat beteende när uppgifterna om out-of-fdistribution uteslutande innehöll dynamiska aktiviteter. Deep Ensemble-metoden gav inga tillfredsställande resultat för vår forskningsfråga.