Purpose – The aim of this thesis is to explain the factors that influence consumers’ willingness to contribute to crowdsourcing organizations, by applying the reciprocity theory. Design/methodology/approach – This is a quantitative research which used a cross sectional research design with an explanatory approach. The data was collected with a questionnaire survey that was distributed using face-to-face and online methods. Findings – The findings of this research revealed that social proof is positively influencing willingness to contribute, either direct or indirect, through reciprocity. In addition, trust, commitment and identification were not directly influencing willingness to contribute, however they have an indirect positive impact on willingness to contribute through reciprocity. Research limitations/implications – This study has created a research model by the use of relevant literature in regards to reciprocity and willingness to contribute. Moreover, the limitations of this study are related to the chosen sample, since the generalization of the results is done based on three countries. Practical implications – The study provides some valuable insights for crowdsourcing organizations managers who aim to increase the amount of contributions through their online communities by the use of the reciprocity theory. Detailed explanation goes in the managerial implications section. Originality/value – This research is unique in that it presents a new model that shows reciprocity as a mediating factor for improving online communities’ users attitudes towards contributing to crowdsourcing organizations.

Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-06-05T14:22:04Z No. of bitstreams: 1 MAXWELL GUIMARÃES DE OLIVEIRA - TESE (PPGCC) 2016.pdf: 7339920 bytes, checksum: c917e7c00193e284b46c986eb3d45841 (MD5) / Made available in DSpace on 2018-06-05T14:22:04Z (GMT). No. of bitstreams: 1 MAXWELL GUIMARÃES DE OLIVEIRA - TESE (PPGCC) 2016.pdf: 7339920 bytes, checksum: c917e7c00193e284b46c986eb3d45841 (MD5) Previous issue date: 2016 / CNPq / As cidades em todo o mundo enfrentam muitos problemas diretamente relacionados ao espaço urbano, especialmente nos aspectos de infraestrutura. A maioria desses problemas urbanos geralmente afeta a vida de residentes e visitantes. Por exemplo, as pessoas podem relatar um carro estacionado em uma calçada que está forçando os pedestres a andar na via, ou um enorme buraco que está causando congestionamento. Além de estarem relacionados com o espaço urbano, os problemas urbanos geralmente demandam ações das autoridades municipais. Existem diversas Redes Sociais Baseadas em Localização (LBSN, em inglês) no domínio das cidades inteligentes em todo o mundo, onde as pessoas relatam problemas urbanos de forma estruturada e as autoridades locais tomam conhecimento para então solucioná-los. Com o advento das redes sociais como Facebook e Twitter, as pessoas tendem a reclamar de forma não estruturada, esparsa e imprevisível, sendo difícil identificar problemas urbanos eventualmente relatados. Dados de mídia social, especialmente mensagens do Twitter, fotos e check-ins, tem desempenhado um papel importante nas cidades inteligentes. Um problema chave é o desafio de identificar conversas específicas e relevantes ao processar dados crowdsourcing ruidosos. Neste contexto, esta pesquisa investiga métodos computacionais a fim de fornecer uma identificação automatizada de problemas urbanos compartilhados em mídias sociais. A maioria dos trabalhos relacionados depende de classificadores baseados em técnicas de aprendizado de máquina, como SVM, Naïve Bayes e Árvores de Decisão; e enfrentam problemas relacionados à representação do conhecimento semântico, legibilidade humana e capacidade de inferência. Com o objetivo de superar essa lacuna semântica, esta pesquisa investiga a Extração de Informação baseada em ontologias, a partir da perspectiva de problemas urbanos, uma vez que tais problemas podem ser semanticamente interligados em plataformas LBSN. Dessa forma, este trabalho propõe uma ontologia no domínio de Problemas Urbanos (UIDO) para viabilizar a identificação e classificação dos problemas urbanos em uma abordagem automatizada que foca principalmente nas facetas temática e geográfica. Uma avaliação experimental demonstra que o desempenho da abordagem proposta é competitivo com os algoritmos de aprendizado de máquina mais utilizados, quando aplicados a este domínio em particular. / The cities worldwide face with many issues directly related to the urban space, especially in the infrastructure aspects. Most of these urban issues generally affect the life of both resident and visitant people. For example, people can report a car parked on a footpath which is forcing pedestrians to walk on the road or a huge pothole that is causing traffic congestion. Besides being related to the urban space, urban issues generally demand actions from city authorities. There are many Location-Based Social Networks (LBSN) in the smart cities domain worldwide where people complain about urban issues in a structured way and local authorities are aware to fix them. With the advent of social networks such as Facebook and Twitter, people tend to complain in an unstructured, sparse and unpredictable way, being difficult to identify urban issues eventually reported. Social media data, especially Twitter messages, photos, and check-ins, have played an important role in the smart cities. A key problem is the challenge in identifying specific and relevant conversations on processing the noisy crowdsourced data. In this context, this research investigates computational methods in order to provide automated identification of urban issues shared in social media streams. Most related work rely on classifiers based on machine learning techniques such as Support Vector Machines (SVM), Naïve Bayes and Decision Trees; and face problems concerning semantic knowledge representation, human readability and inference capability. Aiming at overcoming this semantic gap, this research investigates the ontology-driven Information Extraction (IE) from the perspective of urban issues; as such issues can be semantically linked in LBSN platforms. Therefore, this work proposes an Urban Issues Domain Ontology (UIDO) to enable the identification and classification of urban issues in an automated approach that focuses mainly on the thematic and geographical facets. Experimental evaluation demonstrates the proposed approach performance is competitive with most commonly used machine learning algorithms applied for that particular domain.

Les plateformes de crowdsourcing spatial (PCS) sont des systèmes qui permettent à des personnes, appelées commanditaires, de publier des tâches spatiales afin de trouver la main-d’œuvre pour les exécuter. Ces tâches spatiales exigent que leurs exécutants soient à un endroit donné, souvent dans une fenêtre de temps donnée, pour être accomplies. Quelques exemples de PCS sont Uber et TaskRabbit. Les PCS suscitent beaucoup d’intérêt dans la recherche, mais des pistes de recherche sont encore à explorer.Doan et al. [2011] a soutenu que l’objectif réside maintenant dans “la construction de plateformes générales de crowdsourcing qui peuvent être utilisées pour développer rapidement ces systèmes”. Depuis, peu de travaux ont porté sur la conception technique des PCS. En outre, il existe un écart entre ce qui est mis en oeuvre par les PCS de l’industrie et les propositions que l’on trouve dans la littérature scientifique. Nous proposons GENIUS-C, une architecture générique pour les PCS. Nous fournissons une implémentation de référence (IR) pour GENIUS-C, fonctionnant comme un cadre pour le développement de PCS. GENIUS-C et son IR sont destinés à combler les écarts entre le monde académique et industriel, et faciliter la compréhension et le développement rapide de PCS.Nous étudions également l’important problème de l’appariement des exécutants et des tâches d’un PCS. Comment peut-on trouver une ou plusieurs tâches adaptées à un exécutant (et vice versa)? Certains utilisent des techniques de système de recommandation, d’autres des approches d’optimisation. La plupart d’entre eux ne tiennent pas compte des dimensions spatio-temporelles des tâches et des exécutants. Ceux qui en tiennent compte ignorent les préférences des exécutants, des commanditaires ou du système lui-même. Dans ce contexte, nous identifions et modélisons le problème réel et récurrent suivant: une fois que l’exécutant est prêt à accomplir des tâches, quelle est la meilleure séquence de tâches à suivre en respectant ses contraintes spatio-temporelles? Comment cette séquence peut-elle être obtenue en tenant compte des préférences de l’exécutant, des commanditaires, du système lui-même ou d’une combinaison de ceux-ci? Nous nommons cette situation le Problème de la Recommandation de Trajectoire, auquel nous proposons une solution optimale, et étudions des heuristiques d’approximation pour le résoudre. / Spatial Crowdsourcing Platforms (SCP) are systems that allow people, called requesters, to publish spatial tasks in order to find suitable workforce to perform it. These spatial tasks require workers to be at a given location, usually within a given time window, to be accomplished. Some examples of SCPs are: Uber, BlaBlaCar and TaskRabbit. SCPs are source of much interest for academy, however several research opportunities remain.Doan et al. [2011] argued that the race is now on “toward building general crowdsourcing platforms that can be used to develop such systems quickly". Since then, little has been done to investigate the technical design of SCPs precisely. Also, there is a gap between what is done in commercial platform and in scientific literature. We propose GENIUS-C, a generic architecture for SPCs. We provide a reference implementation (RI) for GENIUS-C, that works as a framework for the development of SCPs. GENIUS-C and its RI are meant to fill the gap between the academic and industry world, and facilitate the understanding and the quick development of new SCPs.We also study the important problem of matching workers and tasks. How can we find one or more tasks suitable for a worker (and vice versa)? Some tackle this issue using recommender system techniques, others optimization approaches. Most of them do not take into account the spatiotemporal dimensions of tasks and workers. Others take it into account, but to ignore the preferences of either workers, requesters or the system itself. In this context, we identify and model the following common real-life problem: once a worker is willing to spend sometime accomplishing tasks, what is the best sequence of tasks to be followed respecting their spatiotemporal constraints? How can this sequence be obtained taking into account the preferences of the worker, the requesters, the system itself, or a combination of them? We name this situation the Trajectory Recommendation Problem (TRP), propose a feasible exact solution and study approximation heuristics for it.

Subject of this thesis is creation of a Web application aiming to offer an alternative to desktop and mobile applications testing possibilities. In the theoretical part we acquire a basic overview of the principles of software testing and its possible use in crowdsourcing test projects. In the practical part the functionality of the web application and its data structure is designed. On the basis of the created design and gained knowledge of software testing the application is then implemented.

The evolution of structured data from simple rows and columns on a spreadsheet to more complex unstructured data such as tweets, videos, voice, and others, has resulted in a need for more adaptive analytical platforms. It is estimated that upwards of 80% of data on the Internet today is unstructured. There is a drastic need for crowdsourcing platforms to perform better in the wake of the tsunami of data. We investigated the employment of a monitoring service which would allow the system take corrective action in the event the results were trending in away from meeting the accuracy, budget, and time SLOs. Initial implementation and system validation has shown that taking corrective action generally leads to a better success rate of reaching the SLOs. Having a system which can dynamically adjust internal parameters in order to perform better can lead to more harmonious interactions between humans and machine algorithms and lead to more efficient use of resources.

A pressing need for efficient personalized recommendations has emerged in crowdsourcing systems. On the one hand, workers confront a flood of tasks, and they often spend too much time to find tasks matching their skills and interests. Thus, workers want effective recommendation of the most suitable tasks with regard to their skills and preferences. On the other hand, requesters sometimes receive results in low-quality completion since a less qualified worker may start working on a task before a better-skilled worker may get hands on. Thus, requesters want reliable recommendation of the best workers for their tasks in terms of workers' qualifications and accountability. The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers' capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches (mostly developed for e-commerce markets) no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this research, we reveal our insight into the essential difference between the tasks in crowdsourcing systems and the products/items in e-commerce markets, and the difference between buyers' interests in products/items and workers' interests in tasks. Our insight inspires us to bring up categories as a key mediation mechanism between workers and tasks. We propose a two-tier data representation scheme (defining a worker-category suitability score and a worker-task attractiveness score) to support personalized task and worker recommendation. We also extend two optimization methods, namely least mean square error (LMS) and Bayesian personalized rank (BPR) in order to better fit the characteristics of task/worker recommendation in crowdsourcing systems. We then integrate the proposed representation scheme and the extended optimization methods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top-N recommendation algorithms for crowdsourcing systems: (1) Top-N-Tasks (TNT) recommendation algorithms for discovering the top-N most suitable tasks for a given worker, and (2) Top-N-Workers (TNW) recommendation algorithms for identifying the top-N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.

O presente trabalho versa sobre as narrativas e justificativas relativas ao processo de criação de um mercado de financiamentos coletivos no Brasil a partir de diversos interlocutores: criadores de projetos, criadores de websites e a partir da literatura acadêmica mais comum sobre o tema. Busca-se retratar e discutir a criação deste mercado a partir do estabelecimento de um passado e de uma pedagogia, típicas dos circuitos de consumo relativos ao fenômeno dos crowdfunding, de modo a circunscrever o que meus interlocutores chamam de “cultura do financiamento coletivo”. Este mercado constitui-se em um duplo processo de afastamento e aproximação entre plataformas como forma de delimitar moralmente o que “deve”, ou não, ser este fenômeno. Tal processo revela a emergência de controvérsias, agenciamentos e, principalmente, de sujeitos como “empreendedores” que buscam “fazer a diferença” a partir de projetos e plataformas de financiamento coletivo. No limite, os financiamentos coletivos alinham-se à elaboração de justificativas críticas sobre o papel do Estado e de empresas na constituição dos mercados, ainda que reiterem práticas comuns no campo empresarial, como a manipulação publicitária e a tentativa de criação de vínculos – ou attachments – com o público alvo. Assim, doação e pré-compra confundem-se, bem como as motivações para criação de projetos de financiamento coletivo. Teoricamente, este trabalho sustenta-se nas digressões da Sociologia Pragmática de Luc Boltanski e da compreensão sobre mercados oriunda da Sociologia e Antropologia, principalmente inspirada nos escritos de Michel Callon e Jens Beckert. Busca-se contribuir para o campo da Antropologia a partir da discussão sobre o processo de criação de mercados a partir dos sujeitos que dele fazem parte e de suas narrativas. / This dissertation discusses the narratives and justifications related to the process of creating a market for crowdfunding in Brazil based on the people who create projects and websites, mainly, as well as the most common literature in the field. We intend to portray and discuss the establishment of this market through its past and pedagogy, typical of circuits of commerce in Crowdfunding whose actors intend to circumscribe what they call as a “Crowdfunding culture”. This market is comprised of a double-movement of approximation and distance among platforms as a way to morally determine what is supposed to be a Crowdfunding and what is not supposed to be. This process reveals the emergence of controversies, agencies and, mainly, entrepreneurial subjects that aim at “making the difference” through Crowdfunding platforms and projects. Ultimately, the Crowdfunding is related to the creation of critical justification on the role of the Government and Private sector in the constitution of markets, even if Crowdfunding itself recall common corporate practices as communication strategies, for instance, which aim at creating attachments with the target audience. Therefore, donation and pre-buying mechanisms overlap, as well as the motivations to create such projects. Theoretically, this work is supported by the Pragmatic Sociology of Luc Boltanski and the comprehension of markets based on Sociology and Anthropology, mainly inspired by the point of view of Michel Callon and Jens Beckert. This dissertation intends to contribute to Anthropology by discussing the process of market creation from the point of view of the subjects that work in it and its narratives.

abstract: Traditionally, emergency response is in large part the role and responsibility of formal organizations. Advances in information technology enable amateurs or concerned publics to play a meaningful role in emergency response. Indeed, in recent catastrophic disasters or crises such as the 2010 Haiti earthquake and the 2011 Japan earthquake and nuclear crisis, participatory online groups of the general public from both across the globe and the affected areas made significant contributions to the effective response through crowdsourcing vital information and assisting with the allocation of needed resources. Thus, a more integrative lens is needed to understand the responses of various actors to catastrophic crises or disasters by taking into account not only formal organizations with legal responsibilities, but also volunteer-based, participatory groups who actively participate in emergency response. In this dissertation, I first developed an “event-driven” lens for integrating both formal and volunteer-based, participatory emergency responses on the basis of a comprehensive literature review (chapter 1). Then I conducted a deeper analysis of one aspect of the event-driven lens: relationships between participatory online groups and formal organizations in crisis or disaster situations. Specifically, I explored organizational and technical determinants and outcomes of forming such relationships (chapter 2). As a consequence, I found out three determinants (resource dependence, shared understanding, and information technology) and two outcomes (inter-organizational alignment and the effectiveness of coordinated emergency response) of the relationship between participatory online groups and formal organizations and suggested seven hypotheses. Furthermore, I empirically tested these hypotheses, focusing on the 2015 Nepal earthquake case (chapter 3). As a result, I found empirical evidence that supports that shared understanding and information technology improve the development of the relationship between participatory online groups and formal organizations. Moreover, research findings support that the development of the relationship enhances inter-organizational coordination. Lastly, I provide implications for future research (chapter 4). This dissertation is expected to contribute to bridging the disconnect between the emergency management literature and the crisis informatics literature. The theoretical insight from inter-organizational relations (IOR) theory provides another contribution. / Dissertation/Thesis / Doctoral Dissertation Public Administration 2016

abstract: Road networks are valuable assets that deteriorate over time and need to be preserved to an acceptable service level. Pavement management systems and pavement condition assessment have been implemented widely to routinely evaluate the condition of the road network, and to make recommendations for maintenance and rehabilitation in due time and manner. The problem with current practices is that pavement evaluation requires qualified raters to carry out manual pavement condition surveys, which can be labor intensive and time consuming. Advances in computing capabilities, image processing and sensing technologies has permitted the development of vehicles equipped with such technologies to assess pavement condition. The problem with this is that the equipment is costly, and not all agencies can afford to purchase it. Recent researchers have developed smartphone applications to address this data collection problem, but only works in a restricted set up, or calibration is recommended. This dissertation developed a simple method to continually and accurately quantify pavement condition of an entire road network by using technologies already embedded in new cars, smart phones, and by randomly collecting data from a population of road users. The method includes the development of a Ride Quality Index (RQI), and a methodology for analyzing the data from multi-factor uncertainty. It also derived a methodology to use the collected data through smartphone sensing into a pavement management system. The proposed methodology was validated with field studies, and the use of Monte Carlo method to estimate RQI from different longitudinal profiles. The study suggested RQI thresholds for different road settings, and a minimum samples required for the analysis. The implementation of this approach could help agencies to continually monitor the road network condition at a minimal cost, thus saving millions of dollars compared to traditional condition surveys. This approach also has the potential to reliably assess pavement ride quality for very large networks in matter of days. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-04T18:36:20Z No. of bitstreams: 1 RUAN PIERRE DE OLIVEIRA - DISSERTAÇÃO PPGCC 2015..pdf: 5220589 bytes, checksum: 7a520750a6bc2cf4996b412571399d26 (MD5) / Made available in DSpace on 2018-05-04T18:36:20Z (GMT). No. of bitstreams: 1 RUAN PIERRE DE OLIVEIRA - DISSERTAÇÃO PPGCC 2015..pdf: 5220589 bytes, checksum: 7a520750a6bc2cf4996b412571399d26 (MD5) Previous issue date: 2015-07-31 / Técnicas de crowdsourcing buscam empregar a experiência, a inteligência ou o conhecimento de uma grande população sobre algum assunto, em prol do desenvolvimento ou melhoria potencialmente rápida e econômica de solução de problemas. A participação dos usuários é o ponto principal para o bom funcionamento dessas técnicas, entretanto, manter uma população contribuindo de forma eficaz em uma comunidade não é tarefa simples, razão por que encontramos, cada vez mais crowdsourcing associado a outras técnicas como, por exemplo, gamificação. Como tal, a conjunção de técnicas tem despertado o interesse de pesquisadores para a aplicação em diversos campos, incluindo a área de saúde. No combate à dengue, quando seria útil que o governo se aliasse à população, crowdsourcing e gamificação têm sido pouco utilizados, porém esta dissertação de mestrado buscou contribuir com a aplicação de crowdsourcing e gamificação no combate à dengue, que é uma doença negligenciada e assim chamada por atrair menos investimentos em pesquisas do que a AIDS, o câncer, a tuberculose e a malária, o que torna a dengue mais letal e com taxa crescente de mortalidade. No Brasil, o combate à dengue é tipicamente designada à atribuição de dois órgãos públicos: à Vigilância Ambiental - VA e à Vigilância Epidemiológica – VE, contudo, normalmente esses órgãos apresentam recursos materiais, estruturais e humanos insuficientes para um combate mais eficaz, em termos dos seguintes indicadores de sucesso: número de notificações, tempo de ação e de tomada de decisão e grau de integração da VA/VE. A questão de pesquisa que esta dissertação de mestrado endereça, é: “Um sistema colaborativo baseado nos principais conceitos de crowdsourcing e gamificação influenciará favoravelmente tais indicadores de sucesso?” No caso, o sistema usará os conceitos supracitados e será disponibilizado em plataforma Web com acessos via dispositivos de computação móvel (inclusive). A metodologia utilizada para o desenvolvimento do software foi a XP, que é recomendada quando o objetivo é entregar as funcionalidades de forma rápida e eficiente. Esta pesquisa foi conduzida em parceria com a VA e VE do município de Campina Grande - PB e teve como recurso metodológico alguns estudos exploratórios, como: pesquisa de campo, para conhecer os processos operacionais das equipes da VA e VE; entrevista semiestruturada com membros da VA/VE para identificar os principais pontos críticos nos processos de combate à dengue; entrevista estruturada com 3 usuários do disk dengue; definição das métricas de sucesso em parceria com a equipe da VE; apresentação dos screenshots do sistema para VA/VE; apresentação do protótipo para ambos os órgãos e priorização das funcionalidades e desenvolvimento do sistema. A validação do protótipo indicou melhorias significativas nos indicadores de sucesso quando comparados com as formas de denúncia já existente, disk dengue e dengue zap. / Crowdsourcing techniques try to use the experience, intelligence or knowledge of a large population on a matter, for the development or potentially quick economic improvement and troubleshooting. The participation of users is the main point for proper operation of these techniques. However, keeping a population contributing effectively in a community is not an easy task, so increasingly crowdsourcing is associated with other techniques, such as gamification. As such, the combination of techniques has aroused the interest of researchers for use in various fields, including the health area. In the fight against dengue, when it would be helpful if the government allied with the population, crowdsourcing and gamification have been used very little. This dissertation aimed to contribute to an application of crowdsourcing and gamification in combating dengue. Dengue is a neglected disease, named as such for attracting less investment in research than AIDS, cancer, tuberculosis and malaria that makes dengue more lethal and having an increasing mortality rate. In Brazil, the fight against dengue is typically assigned to two government agencies: Environmental Monitoring - VA and epidemiological surveillance - VE. However, these bodies usually have insufficient materials, structural resources and human workers for a more effective fight in terms of the following indicators of success: number of notifications, time of action and decision-making, and degree of integration of VA / VE. The research question that this Masters Dissertation addresses is: "Would a collaborative system based on key concepts of crowdsourcing and gamification favorably influence such indicators of success?" In the case, the system will use the above concepts and will be available on a Web platform with access via mobile computing devices (inclusive). The methodology used to develop the software was XP, which is recommended when the goal is to deliver the features quickly and efficiently. This research was conducted in partnership with the VA and VE of the Campina Grande - PB city, and had as a methodological feature some exploratory studies, such as field research to meet the operational processes of the VA and VE teams, semi-structured interviews with members of the VA / VE to identify major gaps in the fight against dengue cases; structured interviews with three members of the dengue hotline; defining success metrics in partnership with the team of VE; presentation of system screenshots for VA / VE; presentation of the prototype for both agencies and prioritization of features and system development. The validation of the prototype indicated significant improvements in indicators of success when compared to existing forms of denunciation, disk dengue and dengue zap.