Knowledge Management in Software Development

Today’s business environment is extremely dynamic and competitive. In order to sustain the pressure and gain a competitive edge, it is imperative for organizations to be creative in their software development efforts. Agile software development has huge potential for nurturing creativity. However, little research has examined creativity in the context of software development projects, particularly those using agile practices. The objective of this paper is to articulate a model that elucidates the relationship between agile practices and creativity. Further, the model tries to provide an understanding of how Knowledge Integration mediates the Relationship between agile practices and team creativity

Team Formation and Performance in Online Crowdsourcing Competitions: The Role of Homophily and Diversity in Solver Characteristics

Rapid advances in Information Technology (IT) have enabled organizations to venture beyond their workforce to seek solutions to vital business problems through online crowdsourcing platforms. Such platforms are characterized by geographically dispersed self-organizing teams that compete with one another to evolve the best solutions to challenging issues that confront organizations. Despite the growing popularity of crowdsourcing, there is a paucity of empirical research on: a) how participants on these platforms form teams; and b) how the composition of these teams affects their performance. In this paper, we investigated solvers’ teaming preferences and their impact on performance in an online crowdsourcing competition platform. Specifically, we explored demographics and acquired characteristics as potential predictors of the choice of a teammate. The findings of this study provide insights on the role of homophily and diversity of solver characteristics on team formation and performance in crowdsourcing competitions

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

The rapid integration of the physical and cyber worlds through the Internet of Things, or IoTs, is transforming our lives in ways that we could not have imagined even five years ago. Although they are still in their infancy, IoTs have already made a significant impact, particularly in the healthcare domain. The purpose of this study is to unravel key themes latent in the sparse but growing academic literature on the application of IoTs in healthcare. Specifically, we performed topic modeling and identified five dominant clusters of research, namely, privacy and security, wireless network technologies, applications, data, and smart health and cloud. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. In addition to categorizing and discussing the topics identified, the paper provides directions for future researc

An Exploratory Study of Airbnb Customer Reviews and Impact of COVID - 19

Airbnb has experienced meteoric growth in the sharing economy business model since it launched in 2009. The Airbnb business model helps exploit underutilized assets and helps travelers stay in a home away from home to fulfill their travel needs at a much cheaper cost. Due to the outbreak of the COVID-19 pandemic, the operations impacted the overall decline in Airbnb activity. We study how Airbnb activity and the rental market have changed during the COVID-19 pandemic using the comprehensive record of Airbnb listings, calendar, and customer reviews. Specifically, we perform exploratory data analysis using natural language processing techniques to compare and contrast changes in Airbnb activities including customer preferences, customer reviews, and pricing. Based on the findings, we point out implications and avenues for future research

Medical Crowdsourcing: Harnessing the “Wisdom of the Crowd” to Solve Medical Mysteries

Medical crowdsourcing offers hope to patients who suffer from complex health conditions that are difficult to diagnose. Such crowdsourcing platforms empower patients to harness the “wisdom of the crowd” by providing access to a vast pool of diverse medical knowledge. Greater participation in crowdsourcing increases the likelihood of encountering a correct solution. However, more participation also leads to increased “noise,” which makes identifying the most likely solution from a broader pool of recommendations (i.e., diagnostic suggestions) difficult. The challenge for medical crowdsourcing platforms is to increase participation of both patients and solution providers, while simultaneously increasing the efficacy and accuracy of solutions. The primary objectives of this study are: (1) to investigate means to enhance the solution pool by increasing participation of solution providers referred to as “medical detectives” or “detectives,” and (2) to explore ways of selecting the most likely diagnosis from a set of alternative possibilities recommended by medical detectives. Our results suggest that our strategy of using multiple methods for evaluating recommendations by detectives leads to better predictions. Furthermore, cases with higher perceived quality and more negative emotional tones (e.g., sadness, fear, and anger) attract more detectives. Our findings have strong implications for research and practice

The Impact of Helping Others in Coopetitive Crowdsourcing Communities

Organizations are increasingly engaging the community through crowdsourcing platforms to evolve innovative solutions to challenging business problems. Participants on such platforms often simultaneously cooperate and compete with one another to earn top honors. This paper addresses the imperative to understand the dynamics of knowledge sharing in such a coopetitive environment. Specifically, our study relies on the conceptual foundations of social exchange and social capital theories to investigate how help rendered (e.g., exchanging ideas or sharing knowledge) by participants in an online coopetitive crowdsourcing setting affects their performance. Furthermore, the study examines the moderating effects of the intensity of competition. Results of our econometrics analyses suggest that help given in a highly competitive contest, as opposed to a less competitive one, is more likely to be reciprocated, but less likely to improve the contributor’s contest performance. In addition, our study found that help received by participants positively impacts their contest performance, and partially mediates the relationship between help rendered and contest performance. This research also provides insight into what motivates participants to share knowledge under conditions of coopetition. The findings of our study have strong implications for both theory and practice

IMU-based Modularized Wearable Device for Human Motion Classification

Human motion analysis is used in many different fields and applications. Currently, existing systems either focus on one single limb or one single class of movements. Many proposed systems are designed to be used in an indoor controlled environment and must possess good technical know-how to operate. To improve mobility, a less restrictive, modularized, and simple Inertial Measurement units based system is proposed that can be worn separately and combined. This allows the user to measure singular limb movements separately and also monitor whole body movements over a prolonged period at any given time while not restricted to a controlled environment. For proper analysis, data is conditioned and pre-processed through possible five stages namely power-based, clustering index-based, Kalman filtering, distance-measure-based, and PCA-based dimension reduction. Different combinations of the above stages are analyzed using machine learning algorithms for selected case studies namely hand gesture recognition and environment and shoe parameter-based walking pattern analysis to validate the performance capability of the proposed wearable device and multi-stage algorithms. The results of the case studies show that distance-measure-based and PCA-based dimension reduction will significantly improve human motion identification accuracy. This is further improved with the introduction of the Kalman filter. An LSTM neural network is proposed as an alternate classifier and the results indicate that it is a robust classifier for human motion recognition. As the results indicate, the proposed wearable device architecture and multi-stage algorithms are cable of distinguishing between subtle human limb movements making it a viable tool for human motion analysis.Comment: 10 pages, 12 figures, 28 reference

Competition matters! Self-efficacy, effort, and performance in crowdsourcing teams

Advances in information technologies (IT) have enabled organizations to seek solutions for their business problems from beyond their own workforce through digital crowdsourcing platforms. In the most common form of crowdsourcing, teams that offer solutions compete for rewards. Thus, a question of interest is whether competition is a key crowdsourcing characteristic that influences how teams allocate their effort and achieve desired performance. Motivated by this question, we investigate how competition moderates the relationship between self-efficacy and effort using comprehensive, time-variant data collected from crowdsourcing teams that completed a project under competitive and non-competitive conditions. Under competitive conditions, self-efficacy shows a positive effect on effort, which in turn, affects performance positively. Whereas, under noncompetitive conditions, self-efficacy has a negative effect on effort and subsequently on performance. Our results also show a recursive relationship between self-efficacy and performance, in which performance subsequently affects self-efficacy positively. Thus, inducing a sense of competition through competitive reward structures and IT-based “gaming elements” helps improve team effort and subsequent performance. We also tested for mediation of team motivation in the self-efficacy and effort relationship, and we found that motivation partially mediates the relationship. Based on our findings, implications for both theory and practice are discussed

Complicações da infecção por EBV em doentes transplantados

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2016Paralelamente à crescente utilização de transplantes de órgão sólido ou de células estaminais no tratamento de uma miríade de doenças, tem crescido a investigação de patologias relacionadas com a imunossupressão associada. As doenças linfoproliferativas pós-transplante (PTLD, do inglês post-transplant lymphoproliferative disorders), frequentemente associadas à reactivação do vírus de Epstein-Barr nestes doentes, podem representar uma complicação grave da imunossupressão. Nesta revisão pretende-se sumarizar o mecanismo de desenvolvimento destas patologias, apresentar as classificações, enumerar os factores de risco, assim como os sinais e sintomas de apresentação de PTLD. Para orientação clínica, expõem-se ainda os exames complementares de diagnóstico úteis para o seu rastreio e monitorização, terminando por discutir os principais esquemas terapêuticos usados com intenção preventiva ou curativa, segundo as guidelines mais actuais.The rising usage of solid organ transplants and stem cell transplant in the treatment of various diseases has been accompanied by a growing research into the consequences of the associated imunossupression. Post-Transplant Lymphoproliferative Disorders (PTLD), frequently linked to the reactivation of the Epstein-Barr virus, may represent a serious complication of immunossupression. This review summarizes the mechanism inherent to the development of these disorders, describes their current classification system, lists the risk factors and the signs and symptoms associated with PTLD. To guide the clinical approach to these disorders, this review scrutinizes the most important diagnostic tests for screening and monitoring the development of PTLD, and also discusses the main therapeutic approach to prevention or cure, according to the most recent guidelines