Background: In the United States, increasing ethnic diversity has been apparent. However, the epidemiology and trends of BKV genotypes remain unclear. This meta-analysis was conducted with the aim to assess the prevalence of BKV genotypes among kidney transplant (KTx) recipients in the United States. Methods: A comprehensive literature review was conducted through October 2018 utilizing MEDLINE, Embase, and Cochrane Database to identify studies that reported the prevalence of BKV subtypes and/or subgroups in KTx recipients in the United States. Pooled prevalence rates were combined using random effects, generic inverse variance method. The protocol for this study is registered with PROSPERO (no. CRD42019134582). Results: A total of eight observational studies with a total of 193 samples (urine, blood, and kidney tissues) from 188 BKV-infected KTX recipients were enrolled. Overall, the pooled estimated prevalence rates of BKV subtypes were 72.2% (95% confidence of interval [CI]: 62.7-80.0%) for subtype I, 6.8% (95% CI: 2.5-16.9%) for subtype II, 8.3% (95% CI: 4.4-15.1%) for subtype III, and 16.1% (95% CI: 10.4-24.2%) for subtype IV, respectively. While metaregression analysis demonstrated a significant positive correlation between year of study and the prevalence of BKV subtype I (slopes = +0.1023, P =.01), there were no significant correlations between year of study and percentages of BKV subtype II-IV (P >.05). Among KTx recipients with BKV subtype I, the pooled estimated percentages of BKV subgroups were 22.4% (95% CI: 13.7-34.5%) for subgroup Ia, 30.6% (95% CI: 17.7-47.5%) for subgroup Ib1, 47.7% (95% CI: 35.8-59.9%) for subgroup Ib2, and 4.1% (95% CI:1.2-13.3%) for subgroup Ic, respectively. Conclusion: BKV subtype I is the most prevalent subtype among KTx recipients in the United States and its prevalence seems to increasing overtime. Subgroup Ib2 is the most common subgroup among BKV subtype I.

Context. There has been a remarkable growth of the mobile game industry since the raging pandemic covid-19 destroyed many businesses across several industries [1]. Nowadays mobile gaming has been one of the highest performing industries globally, raking in more billions in revenue [1,2]. Understanding the direction and aspects to improve the quality of products and reduce the cost is important for a mobile gaming company to stand out. There is a plethora of literature on how to improve the related product quality [3]. One of them is to analyze and optimize the various requirements in each version update, and how these requirements could be elicited from the company’s development plan and user feedback. Specifically, mobile game companies would review the user comments of their products from various application platforms such as Google Play and Apple store, select the informative comments with specific user requirements according to their own standard, and finally elicit and then implement these requirements in the follow-up version updates. During this process, it is important to control the lead time---the time cost for mobile game companies to review and select the valuable user comments, make decisions to apply the changes, make a development plan afterwards and finally put it into action. In the current increasingly intense competitive environment, time-based dimensions of a product such as the lead-time are becoming an increasingly important component in assessing strategic advantage, since having products early increases the possible market introduction window. Meanwhile, traditional long lead times and high inventory levels may be less appropriate and more costly endeavors that may not even achieve product parity [4]. To compress the product lead time was the priority task to help companies keep their competitiveness [5]. To fulfil this aim, fundamental changes must be made in every function that affects the delivery of the product. However, most existing literature focuses on the lead time in the traditional software industry, which can be different in the case of the mobile game apps. We herein in this paper explore the contents of lead time in the mobile gaming industry. We designed a series of steps to explore the real situation of lead time in the mobile gaming industry. Differences between mobile gaming and traditional software industries are also of interest to be explored.       Objectives. The main purpose of our research was to study the lead time which would be caused during the process of implementing users’ requirements. We tried to achieve the purpose from two aspects: First, we investigated whether there were differences in the lead time of different requirement types. Second, we investigated whether the lead time differences existed in different types of mobile games.   Methods. Our group used Case Study as the main research method to investigate the lead time in real cases.   Results. .First, there were differences in the lead time of implementing different types of requirements. Such as the lead time of bug fix types of requirements would be shorter than feature added types of requirements. Second, different types of mobile game apps had differences in the lead time. For example, MOBA games would take longer time on Function update or Feature request types of requirements, and FPS games would take longer time on exclusive event types of requirements. The details would be shown in part 4.2 and 4.3.   Conclusions. Two research questions in our thesis were answered. When mobile game companies dealt with requirements in user feedback, the lead time objectively existed. We could calculate the length of the lead time of different types of requirements. Moreover, different types of requirements had various lead times. For example, the lead time caused by bug fixing requirement would be shorter compared with that of adding new functions. And this research provided some fundamental results to both academic field and mobile game industry field.   Keywords: Mobile game apps, User reviews, User requirements, Lead time

In this study we gather customer reviews from Prisjakt, a Swedish price comparison site, with the goal to study the relationship between review and rating, known as sentiment analysis. The purpose of the study is to evaluate three different supervised machine learning models on a fine-grained dependent variable representing the review rating. For classification, a binary and multinomial model is used with the one-versus-one strategy implemented in the Support Vector Machine, with a linear kernel, evaluated with F1, accuracy, precision and recall scores. We use Support Vector Regression by approximating the fine-grained variable as continuous, evaluated using MSE. Furthermore, three models are evaluated on a balanced and unbalanced dataset in order to investigate the effects of class imbalance. The results show that the SVR performs better on unbalanced fine-grained data, with the best fine-grained model reaching a MSE 4.12, compared to the balanced SVR (6.84). The binary SVM model reaches an accuracy of 86.37% and weighted F1 macro of 86.36% on the unbalanced data, while the balanced binary SVM model reaches approximately 80% for both measures. The multinomial model shows the worst performance due to the inability to handle class imbalance, despite the implementation of class weights. Furthermore, results from feature engineering shows that SVR benefits marginally from certain regex conversions, and tf-idf weighting shows better performance on the balanced sets compared to the unbalanced sets.

Avec le développement du commerceélectronique, les clients ont publié de nombreuxcommentaires de produit sur Internet. Ces donnéessont précieuses pour les concepteurs de produit, carles informations concernant les besoins de client sontidentifiables. L'objectif de cette étude est dedévelopper une approche d'analyse automatique descommentaires utilisateurs permettant d'obtenir desinformations utiles au concepteur pour guiderl'amélioration et l'innovation des produits.L’approche proposée contient deux étapes :structuration des données et analyse des données.Dans la structuration des données, l’auteur proposed’abord une ontologie pour organiser les mots et lesexpressions concernant les besoins de client décrientdans les commentaires. Ensuite, une méthode detraitement du langage naturelle basée des règleslinguistiques est proposé pour structurerautomatiquement les textes de commentaires dansl’ontologie proposée.Dans l’analyse des données, deux méthodes sontproposées pour obtenir des idées d’innovation et desvisions sur le changement de préférence d’utilisateuravec le temps. Dans ces deux méthodes, les modèleset les méthodes traditionnelles comme affordancebasedesign, l’analyse conjointe, et le Kano modelsont étudié et appliqué d’une façon innovante.Pour évaluer la praticabilité de l’approche proposéedans la réalité, les commentaires de client de liseusenumérique Kindle sont analysés. Des pistesd’innovation et des stratégies pour améliorer leproduit sont identifiés et construites. / With the development of e-commerce,consumers have posted large number of onlinereviews on the internet. These user-generated dataare valuable for product designers, as informationconcerning user requirements and preference can beidentified.The objective of this study is to develop an approachto guide product design by analyzing automaticallyonline reviews. The proposed approach consists oftwo steps: data structuration and data analytics.In data structuration, the author firstly proposes anontological model to organize the words andexpressions concerning user requirements in reviewtext. Then, a rule-based natural language processingmethod is proposed to automatically structure reviewtext into the propose ontology.In data analytics, two methods are proposed based onthe structured review data to provide designers ideason innovation and to draw insights on the changes ofuser preference over time. In these two methods,traditional affordance-based design, conjointanalysis, the Kano model are studied andinnovatively applied in the context of big data.To evaluate the practicability of the proposedapproach, the online reviews of Kindle e-readers aredownloaded and analyzed, based on which theinnovation path and the strategies for productimprovement are identified and constructed.

Indiana University-Purdue University Indianapolis (IUPUI) / The purpose of this work is to prove that the visualization is at least as powerful as the best automatic feature selection algorithms. This is achieved by applying our visualization technique to the online review classification into fake and genuine reviews. Our technique uses radial chart and color overlaps to explore the best feature selection through visualization for classification. Every review is treated as a radial translucent red or blue membrane with its dimensions determining the shape of the membrane. This work also shows how the dimension ordering and combination is relevant in the feature selection process. In brief, the whole idea is about giving a structure to each text review based on certain attributes, comparing how different or how similar the structure of the different or same categories are and highlighting the key features that contribute to the classification the most. Colors and saturations aid in the feature selection process. Our visualization technique helps the user get insights into the high dimensional data by providing means to eliminate the worst features right away, pick some best features without statistical aids, understand the behavior of the dimensions in different combinations.

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Background Today the hotel industry has become very digitalized compared to when there was only a travel catalogue to look in. Nowadays people search for information about hotels and read reviews from past guests online. There are different kinds of accommodations and lately it has become popular with shared services like Airbnb. With the rise of digitalization the hotel market competition has increased tremendously, therefore it is important for the hotels to keep a good reputation. Services like a hotel stay can be experienced differently from person to person and thus it is important for the service personnel to possess emotional intelligence.  Purpose The purpose with this study is to find out how the consumer behavior changes depending on the reviews. The study should give knowledge to hotel management about how it is possible to improve their relationship with guests by service, reviews and the use of digitalization. This study wants to update the research on the topic reviews and how it affects the consumers when booking a hotel with new information since the technology changes constantly and thus some research about the field in the past might not mirror the reality today.  Method This study was conducted using a quantitative method survey study in the form of an internet questionnaire. This questionnaire contained mostly closed ended questions however there were some open questions that allowed the participants to name a factor that was important for them. The survey used convenience sampling to be able to receive a lot of answers quickly. To ensure that the answers were of good quality anonymous answers has been applied and there was a qualification question that can sort out unqualified and spam answers to obtain the best quality. To analyze the results from the survey, descriptive analytics and statistical inference will be used and compared with previous research. The statistical analysis will be done by SPSS (Statistical Package for the Social Sciences) and Excel.  Conclusion  The study has come to the conclusion that reviews affect and have an impact on all the different stages in the consumer decision making process. The study also conclude that it is important for the hotel to try to increase the number of reviews since with the rise of reviews the more trustworthy the reviews will be. The study also concluded that it has become important with digitalization in the hotel industry and that 29,5% of the study participants use the mobile app when booking a hotel. Thus, the hotel management must follow and adapt the hotel to the new way of communication.

This thesis is situated in the research field of electronic commerce, specifically the aspect of fast fashion brands. This has drawn consumer interest because they find struggles when shopping for clothes online due to their inability to try the product before purchasing from sellers, which tends to result in information asymmetry. Therefore, they may be more hesitant to purchase online due to the perceived risk that results in low level of trust while shopping clothes online; therefore, businesses should strive to alleviate their concerns. Previous research has shown that electronic word of mouth (e-WOM) can guide and increase confidence.  The purpose of this study is to gain a better understanding of how consumers perceive online reviews as ways of reducing information asymmetry and reduce risk in order to ensure that they will be satisfied with their purchase. Furthermore, many factors identified in previous research that could influence the use of online reviews were identified and analyzed in the context of fast fashion for this study. Therefore, the study discovered several factors that influenced the use of online reviews.  This study investigates the role of trust as a mediator between customers' perception of electronic word-of-mouth (eWOM) and their subsequent actions. Specifically, the study focuses on the influence of the perceived usefulness of online reviews on customer trust in fast fashion websites. To gain a comprehensive understanding of consumers' opinions on online reviews, a qualitative research approach employing semi-structured interviews was conducted. The interviews provided participants with the opportunity to elaborate on their responses and provide nuanced insights. The findings indicate that fast fashion brands should prioritize the inclusion of online reviews and enhance their mechanisms based on the factors identified in this study. By recognizing the importance of customer trust and addressing the perceived usefulness of online reviews, fast fashion brands can improve their relationship with customers and foster positive consumer actions. This study contributes to the existing literature on eWOM, trust, and online reviews, offering practical implications for fast fashion brands aiming to optimize their online platforms.

In the digital age, with the accelerating pace of e-commerce, online platforms such as Amazon, Yelp, TripAdvisor, Facebook, Netflix, Uber and others have gained in prominence. Furthermore, in the wake of the COVID-19 pandemic, even businesses which were heretofore primarily brick-and-mortar have had to shift to a strong online presence in order to adapt and survive; which, while beneficial to all stakeholders, has resulted in dire challenges for the producers/service providers, platform owners, as well as consumers. In my first essay, I investigate the challenges faced by mobility as a service (MaaS) platforms such as Uber and Lyft for managing their demand and the pool of available drivers. On one hand, driver compensation issues in MaaS platforms is a highly discussed topic. On the other hand, the MaaS platforms are expanding to encompass several external businesses in search of profitability. In this chapter, I focus primarily on driver compensation issues in MaaS platforms when the platforms engage in external businesses. I find that in the majority of instances, the driver compensation reduces when the platforms get involved in external businesses; however, there are a few cases, where it leads to an increment in driver compensation, thus benefiting them. The second essay is on the impact of online reviews from digital platforms such as Yelp and TripAdvisor on business performance. Using a data set from Yelp, first, I study the interaction of average rating and number of reviews on business performance; second, how competition affects the interaction effect of the average rating and number of reviews on the focal business' performance. I find that the impact of the interaction of average rating and number of reviews on business performance is different at various levels of average ratings, and the inclusion of competition negatively influences the interaction effect of the average rating and number of reviews on the performance of the focal restaurant. In my third essay, I analyze how the interaction of supplier encroachment and consumer showrooming impacts an omnichannel retailer and her upstream manufacturer, who encroaches the downstream retailer's market with an online direct sales channel. I identify different scenarios in a covered market where either the retailer, or the manufacturer, or both will be better off. Taken together, these three essays provide valuable managerial insights for real world business problems, which will empower researchers in academia and industry managers, and help them improve their businesses and maximize their operational performance. / Business Administration/Marketing

Idag finns det stora mängder användar-skapat data i form av texter från spelomdömen till åsikter i mikro-bloggar som Twitter. Att analysera detta data kan vara utav värde för både företag och akademisk forskning men är väldigt omfattande. Med hjälp av attitydanalysen kan detta utföras automatiskt och spara resurser, men vilka algoritmer presterar bäst? Med hjälp av en förstudie och ett par kvantitativa tester kunde dem mest populära tillvägagångsätten inom attitydanalysen genom att analysera spelomdömen från plattformen Steam. I testning har det visat sig att maskininlärningsalgoritmer både presterar bättre och är enklare att komma igång i jämförelse med lexikonbaserade algoritmer som knappast uppnår tröskelvärdet för pålitlighet vid klassifikation av omdömen som positiva eller negativa. Men det är fortfarande viktigt anpassa attitydanalysen för just det specifika problemet eftersom båda dessa har sina brister eftersom båda dessa tillvägagångsätt hade en dålig prestation i förhållande till sarkastiska omdömen. / Today there exist a huge amount of user created content in the shape of text from game reviews to opinions in microblogs like Twitter. To analyze this data could be of value for both companies and data scientists but remains to be very daunting. With the help of sentiment analysis this could be achieved automatically and save resources, but the question remains which algorithms have the best performance? With the help of a study and a couple of tests the most popular approaches in sentiment analysis could be compared by analyzing game reviews from the platform Steam. Through testing it has showed that machine learning based algorithms have the best performance and are easier to start with in comparison to lexicon-based approaches, which barely even reach the threshold for reliability in classifying reviews to be positive or negative. But it’s still important to plan and consider which algorithm one chooses for sentiment analysis as both approaches have their flaws and had a weak performance regarding sarcastic reviews.