This thesis examines the role of exports and GMO regulations of the exporting country on the adoption of GM crops by its farmers. The analytic sample includes five major exporting countries including Argentina, Brazil, China, India, and Mexico. I focus only on the crop that is widely grown in each country. The crops are cotton, maize, and soybean. GMO regulations indices, which were constructed by Vigani et al, consists of four categories of different regulations related to import of GMO products. Relative impact of different aspects of GMO regulations on exports are assessed to get a better understanding of domestic production decisions of farmers in major exporting countries. The result for the approval process, risk assessment and international agreement suggested that, these regulations are not significant to influence the adoption rate of the GM crops. The exporting countries, especially the developing countries, usually reluctant to implement the GM regulations because of the high cost of completing the procedures.

<p>The cereal couture company, <em>[me] & goji, </em>is the dream of three young entrepreneurs. They were the first online company ever to provide the U.S market with customized cereal mix. This case study finds that online companies with an innovative product such as <em>[me] & goji</em> may benefit from using Social Networks as a marketing channel to communicate their Value Proposition. Supported by Roger's Adoption theory, selected theories on, Value Proposition, Social Networks, and qualitative data gathered from, interviews and surveys several findings were made. The conclusion is that despite offering a relatively non complex product, with a high relative advantage the market may have difficulties with recognizing the value of the product. This is mainly due to the fact that products sold online cannot be tried by the customer until after purchase. This may be perceived as an uncertainty factor for some customers. The main benefit with viral marketing tools such as Social Networks is that they may increase the rate of the market adopting new products.</p>

The cereal couture company, [me] &amp; goji, is the dream of three young entrepreneurs. They were the first online company ever to provide the U.S market with customized cereal mix. This case study finds that online companies with an innovative product such as [me] &amp; goji may benefit from using Social Networks as a marketing channel to communicate their Value Proposition. Supported by Roger's Adoption theory, selected theories on, Value Proposition, Social Networks, and qualitative data gathered from, interviews and surveys several findings were made. The conclusion is that despite offering a relatively non complex product, with a high relative advantage the market may have difficulties with recognizing the value of the product. This is mainly due to the fact that products sold online cannot be tried by the customer until after purchase. This may be perceived as an uncertainty factor for some customers. The main benefit with viral marketing tools such as Social Networks is that they may increase the rate of the market adopting new products.

The field of radiation oncology (RO) involves the use of highly advanced techniques to treat cancer and safely spare healthy organs. The discipline has experienced rapid growth in the past 25 years, with technological advancement as the driving force. Available data and an instrument to effectively measure the accessibility of innovation in the field were lacking. The purpose of this study was to investigate the accessibility of innovative services in RO in the United States and assess possible diffusion patterns. Two hundred and forty medical physicists practicing in RO in the United States completed a custom Internet-based survey. The diffusion of innovation theory was used as the theoretical framework for the study. A quantitative cross-sectional analysis was performed to assess how innovation scores may vary depending on individual and organizational factors. ANOVA, Spearman correlation, and multiple linear regression were used to analyze the data. University affiliation, urbanicity, appreciation, and motivation were found to be statistically significant factors affecting accessibility to innovative services. Statistically significant barriers preventing innovation were lack of evidence, increased complexity, staffing constraints, lack of interest from others, lack of interoperability, and lack of reimbursement. Medical physicists are in a leadership position to influence the adoption of innovative services in RO. Encouraging the utilization of innovative and Food and Drug Administration-approved techniques may improve cancer outcomes and consequently have a positive social change effect on public health.

The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The models’ performance were not significantly improved after optimization and it was also dependent on which corpus the models were trained on. An investigation of how a chatbot would affect Hedvig’s adoption rate was also conducted, mainly by reviewing previous studies about chatbots’ effects on user experience. The potential effects on the innovation’s five attributes, relative advantage, compatibility, complexity, trialability and observability were analyzed to answer the problem statement. The results showed that the adoption rate of Hedvig could be positively affected, by improving the first two attributes. The effects a chatbot would have on complexity, trialability and observability were however suggested to be negligible, if not negative. / Det vetenskapliga området språkteknologi har fått ökad uppmärksamhet den senaste tiden, men mindre fokus riktas på att jämföra modeller som skiljer sig i komplexitet. Den här kandidatuppsatsen jämför Random Forest med LSTM, genom att undersöka hur väl modellerna kan användas för att klassificera ett meddelande som fråga eller icke-fråga. Jämförelsen gjordes genom att träna och optimera modellerna på historisk chattdata från det svenska försäkringsbolaget Hedvig. Olika typer av word embedding, så som Word2vec och Bag of Words, testades också. Resultaten visade att LSTM uppnådde något högre F1 och accuracy än Random Forest. Modellernas prestanda förbättrades inte signifikant efter optimering och resultatet var också beroende av vilket korpus modellerna tränades på. En undersökning av hur en chattbot skulle påverka Hedvigs adoption rate genomfördes också, huvudsakligen genom att granska tidigare studier om chattbotars effekt på användarupplevelsen. De potentiella effekterna på en innovations fem attribut, relativ fördel, kompatibilitet, komplexitet, prövbarhet and observerbarhet analyserades för att kunna svara på frågeställningen. Resultaten visade att Hedvigs adoption rate kan påverkas positivt, genom att förbättra de två första attributen. Effekterna en chattbot skulle ha på komplexitet, prövbarhet och observerbarhet ansågs dock vara försumbar, om inte negativ.