Exploring and Visualizing Spatial Effects and Patterns in Ride-Sourcing Trip Demand and Characteristics

The complex demand pattern of ride-sourcing remains to be a challenge to transportation modeling practitioners due to the infancy and the inherently dynamic nature of the ride-sourcing system. Spatial effects exploration and analysis protocols can provide informative insights on the underlying structure of demand and trip characteristics. Those protocols can be thought of as an opportunistic strategy to alleviate the complexity and help specifying the appropriate econometric models for the system. Spatial effects exploration is comparable to point pattern analysis, in which, signals from spatial entities, like census tracts, can be analyzed statistically to reveal whether a specific phenomenon respective signal distribution is a completely random process or if it follows some regular pattern. The results of such analysis help to explore the investigated phenomenon and conceptualize its causal forces. In this paper, we apply spatial pattern analysis edge methods integrated into a visual analytics framework to: (1) test the null hypothesis of system demand complete randomness; (2) further analyze and explain this demand in terms of the origin-destination (OD) flow and trips characteristics, i.e., length and duration; and (3) develop a pattern profile of the demand and trip characteristics to provide potential directions to modeling and predictive analytics approaches. This framework helps explain the ride-sourcing system demand and trip characteristics in space and time to fill the gap in integrating the system in multimodal transportation frameworks. We use the ride-sourcing trip dataset released from the City of Chicago, USA, for the year 2019 to showcase the proposed methods and their novelty in capturing such effects as well as explaining the underlying complexities in a streamlined workflow. The ride-sourcing demand hotspots were explored and identified in the city’s central business district. A novel method to capture and analyze the origin-destination flowlines was developed and implemented. Finally, a complementary trip characteristics pattern analysis was conducted to fully comprehend the system and validate the findings from the system demand points and OD-flowlines

Analysing the Design of Privacy-Preserving Data-Sharing Architecture

Privacy has become an essential software quality to consider in a software system. Privacy practices should be adopted from the early stages of the system design to safeguard personal data from privacy violations. Privacy patterns are proposed in industry and academia as reusable design solutions to address different privacy issues. However, the diverse types and granularity of the patterns lead to difficulty for the practitioner to select and adopt them in the architecture. First, the fragmented information about the system actors in the patterns does not align with the regulatory entities and interactions between them. Second, these privacy patterns lack architectural perspectives that could help weave patterns into concrete software designs. Third, the consequences of applying the patterns have not covered the impacts on software quality attributes. This thesis aims to provide guidance to software architects and practitioners for considering and applying privacy patterns in their design, by adding new perspectives to the existing patterns. First, the research provides an analysis of the relationships between regulatory entities and their responsibility in adopting the patterns in a software design. Then, the research reports studies that were conducted using architectural-level modelling-based approaches, to analyse the architectural views of privacy patterns. The analyses aim to improve understanding of how privacy patterns are applied in software designs and how such a design affects software quality attributes, including privacy, performance, and modifiability. Finally, in an effort to harmonise and unite the extended view of privacy patterns that have a close relation to system architecture, this research proposes an enhanced pattern catalogue and a systematic privacy-by-design (PbD) pattern-selection model that aims to aid and guide software architects in pattern selection during software design. The enhanced pattern catalogue offers consolidated information on the extended view of privacy patterns. The selection model provides a structured way for the practitioner to know when and how to use the pattern catalogue in the system-design process. Two industry case studies are used to evaluate the proposed pattern catalogue and selection model. The findings demonstrate how the proposed frameworks are applicable to different types of data-sharing software systems and their usability in supporting pattern selection decisions in the privacy design

ONLINE LEARNING EXPERIENCES AND PERCEIVED OUTCOMES WITH KEY OPINION LEADEARS: A TWO-PHASE STUDY

Classroom teaching has been undergoing a digital transformation in the last decade and is now being amplified by Educational Key Opinion Leaders (Edu-KOLs). The research aims to investigate the relationship between learners’ perceived outcomes, motivation, and the selection preferences of Edu-KOLs. This paper presents insights gained from a two-phase study. The first phase we conducted through an online questionnaire completed by 186 parents in China whose children are studying or have recently studied online. The second phase we interviewed parents to deep dive into their thinking process behind their choices of Edu-KOLs. By utilizing the PLS-SEM method, this research has proposed and verified six hypotheses that e-learning platforms, student engagement scores and perceived outcomes strongly correlate with the perception of Edu-KOLs. However, parents’ educational level or occupation have less impact on the choices of Edu-KOLs. There are also positive relationships among Edu-KOLs, customer advocacy and future purchase intention

European Distance and E-Learning Network (EDEN). Conference Proceedings

Erasmus+ Programme of the European UnionThe powerful combination of the information age and the consequent disruption caused by these unstable environments provides the impetus to look afresh and identify new models and approaches for education (e.g. OERs, MOOCs, PLEs, Learning Analytics etc.). For learners this has taken a fantastic leap into aggregating, curating and co-curating and co-producing outside the boundaries of formal learning environments – the networked learner is sharing voluntarily and for free, spontaneously with billions of people.Supported by Erasmus+ Programme of the European Unioninfo:eu-repo/semantics/publishedVersio

How to design for persistence and retention in MOOCs?

Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme. In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context

Applying Social Network Analysis to Monitor Risk in Project Management

In today’s business environment it is often argued that if organizations want to achieve sustainable competitive advantages or even just survive, they must excel in performance and innovation to meet complex and unpredictable market demands. Often organizations alone do not always have the necessary resources such as brilliant minds, technologies, know-how, financial support, just to name a few, to properly respond to such market demands. To overcome such constraints organizations usually engage in collaborative working models (such as open innovation (Chesbrough, 2003)), which essentially consist in strategic partnerships with other entities such as other business partners, public institutions, universities, and development centers, just to name a few, whereby the collaborative exchange of resources and capabilities enables achieving their objectives in a faster and more efficient way. However, it is often argued that the lack of effective models to support collaborative initiatives is the biggest obstacle for organizations to engage in a higher frequency in collaborative working models. In project management, one of the biggest challenges that organizations face today as they deliver projects is to distinguish project critical success factors from project critical failure factors regarding how project stakeholders collaborate across the different phases of a project lifecycle. This challenge has been a growing concern particularly in organizations that deliver projects, essentially due to the potential high impact (both, negative and positive) in economic, environmental, and social dimensions. More concretely, this challenge is essentially related to how the dynamic interactions between the different project stakeholders - characterized by the mix of formal and informal networks of relationships that emerge and evolve across the different phases of a project lifecycle, and how these may or not impact project outcomes (success or failure). In this work a heuristic two-part model to address the mentioned challenge is proposed. The development of the proposed model is supported by three distinct but interrelated scientific fields. They are: (1) project management - which contributes with the definitions and structure of a project lifecycle, (2) risk management - which contributes with the standard risk management process framework, and (3) social network analysis - which provides the tools & techniques to identify and quantify the collaborative interactions between entities throughout a project lifecycle. The proposed model was developed to identify and quantitatively measure the extent to which such project participant´s dynamic interactions (also called as dynamic behaviors), influence project outcomes (usually classified as successfully or unsuccessfully delivered). The proposed model in this work named POL Model (which stands for the Project Outcome Likelihood model), has two parts. In part one the proposed model will analyze five key project collaboration types ((1) Communication and Insight, (2) Internal and Cross Boundaries-Collaboration, (3) Know-how sharing and Power, (4) Clustering (variability effect—PSNVar), and (5) Teamwork efficiency) that emerge and evolve in each project phase of a given project lifecycle, by accessing, analyzing and interpreting project data-related collected in three different sources ((1) project meetings, (2) project emails, and (3) through the application of a SNA-based survey) from successfully and unsuccessfully delivered projects. The model will search in both successfully and unsuccessfully delivered projects for unique repeatable behavioral patterns (RBPs) regarding each one of the five key project collaboration types. If the model identifies different RBPs in projects that were successfully delivered from those that were unsuccessfully delivered, such RBPs are classified as critical success factors (CSFs). If not, then no CSFs are identified. If the latter outcome is the case, then, according to the proposed model in this work, collaborative projects outcomes (successful or unsuccessful) are not influenced by the dynamic interactions of project participants that emerge and evolve across the different phases of a given project lifecycle. Once part 1 of the POL model is concluded, and if CSFs have been found, then part two can initiate. In part two the POL model will provide guidance to an ongoing or upcoming project by analyzing the deviation between an actual project evolution (actual state), and the CSFs identified in part 1 regarding each one of the already mentioned five key project collaboration types.No atual ambiente económico e social, é muitas vezes afirmado que se as organizações pretendem alcançar vantagens competitivas sustentáveis ou simplesmente sobreviver, elas têm de ser capazes de atingir elevados níveis de performance e inovação. No entanto, a maioria das organizações, por si só, nem sempre têm as capacidades necessárias e suficientes para eficazmente responder ás crescentes atuais e futuras necessidades dos mercados. Tais capacidades como, mentes brilhantes, tecnologias de ponta, acesso a informação mais restrita e vital, conhecimento adquirido, experiência em várias dimensões, entre outras, normalmente só estão ao alcance de algumas organizações. Para tentarem ultrapassar este obstáculo, as organizações que por si só não dispõem ou não consegue adquirir as tais capacidades necessárias e suficientes para eficazmente responder ás tais exigências por parte do ecossistema dos mercados, procuram encontrar soluções por outras formas. Uma das formas que ao longo dos últimos anos tem tido uma crescente procura consiste essencialmente em partilhar recursos e capacidades através do estabelecimento de parcerias estratégicas com outras organizações, tais como universidades, institutos, parceiros de negócio, ou mesmo concorrentes diretos e indiretos. Estas tais parcerias estratégicas são essencialmente denominadas de modelos organizacionais colaborativos que permitem ás organizações participantes obter benefícios que de uma forma individual nunca conseguiriam atingir (Camarinha-Matos, & Afsarmanesh, 2006; Arana & Castellano, 2010). Um modelo que se tornou muito popular nos últimos anos, é o modelo de inovação aberta (Open Innovation, ou simplesmente OI”) proposto por Chesbrough, (2003). Chesbrough defende que para que as organizações consigam atingir resultados mais positivos e mais rápidos estas deveriam optar por trabalharem em conjunto (colaborarem) no desenvolvimento e comercialização de ideias e inovações, tendo por base essencial, a troca supervisionada de informação, ideias, recursos (materiais e imateriais) entre as organizações participantes. E de referir ainda que este modelo de colaboração que potencialmente trás consideráveis benefícios ás organizações tais como a partilha de riscos e oportunidades, um acelerado time-to-market de produtos e serviços desenvolvidos, otimização ou criação de produtos e serviços a um preço muito mais baixo, entre muitos outros, é contrário ao modelo que ainda é tradicionalmente adotado pela maioria das organizações que assenta essencialmente num processo de inovação fechada em que as organizações não partilham recursos e capacidades no processo de desenvolvimento e comercialização de ideias e inovações. No entanto, a realidade mostra que não só potenciais benefícios resultam dessas parcerias estratégicas. De acordo com literatura consultada, de um modo geral, são muitas as organizações, que ainda têm receio de optar por estes modelos de parcerias estratégicas que envolvem a partilha ativa e supervisionada de informação, ideias, e recursos, essencialmente devido á falta de modelos que permitam uma eficiente gestão das diferentes dinâmicas colaborativas que existem dentro, e entre diferentes organizações (Santos et al., 2019; Nunes & Abreu, 2020(a); Nunes & Abreu, 2020 (b)). Este aspeto, de acordo com a literatura consultada, tem ainda mais peso na limitação da entrada das organizações em modelos colaborativos como o Open Innovation, do que propriamente aspetos técnicos (Deichmann et al., 2017). De acordo com varia literatura consultada um dos maiores desafios que as organizações atualmente enfrentam, é a capacidade de identificar fatores críticos relacionadas com a colaboração que levam projetos e operações a ter um desfecho com sucesso (Workday studios, 2018; Arena, 2018; Nunes & Abreu, 2020(c); Nunes & Abreu, 2020). Na verdade, esta preocupação tem crescido exponencialmente ao longo dos últimos anos essencialmente devido á crescente perceção dos elevado impactos (negativos e positivos) que este fator projeta no seio das organizações. No entanto, embora este tema está ainda muito pouco explorado, em gestão de projetos, cada vez mais cresce o interesse de perceber a relação entre o sucesso e o insucesso de projetos com as diferentes interações dinâmicas que emergem e evoluem entre pessoas, grupos, departamentos e organizações que executam projetos (Santos et al., 2019; Nunes & Abreu, 2020(a); Nunes & Abreu, 2020 (b)). Dada a importância deste aspeto, é proposto neste trabalho um modelo que tem como principal objetivo contribuir para a identificação de fatores críticos de sucesso relativos á gestão das interações dinâmicas entre organizações em ambientes de projetos. Neste trabalho é apresentado um modelo heurístico composto de duas partes (parte 1 e parte 2), onde o seu desenvolvimento foi apoiado em três áreas científicas ((1) gestão de projetos, (2) gestão do risco, e (3) análise de redes socias) e que tem como principal objetivo a identificação da importância (de uma forma mensurável) das diferentes interações dinâmicas entre pessoas que trabalham num ambiente de projetos no desfecho desses mesmos projetos. Cada uma das áreas científicas acima mencionadas contribui de forma única para o modelo proposto neste trabalho. A área científica de gestão de projetos (1), contribui para o modelo proposto neste trabalho com as definições e estrutura de um projeto, onde inclui as definições de projeto, gestão de projeto, fases de um projeto, ciclo de vida de um projeto, entre outras. A área científica de gestão do risco (2), contribui para o modelo proposto neste trabalho com as definições de risco, e gestão de risco, e com os processos e estrutura de análise mais utilizados na identificação, tratamento e controle do risco. Finalmente, a área científica de análise de redes socias (3), contribui para o modelo proposto neste trabalho com as definições e características de rede social, capital social, redes colaborativas, e ainda com as ferramentas e técnicas de análise para quantitativamente medir as interações dinâmicas entre pessoas, grupos, departamentos de uma dada organização, ou mesmo entre organizações diferentes que colaboram na execução de projetos. O modelo proposto neste trabalho de nome POL Model (project outcome likelihood), tem duas partes – parte 1 e parte 2. Na primeira parte o modelo vai analisar cinco tipos dinâmicas chave que emergem e se desenvolvem numa dada rede social de um projeto ao longo das diferentes fases do ciclo de vida de um projeto. Estes cinco tipos chave de dinâmicas são: (1) comunicação, (2) intra e intercolaboração organizacional, (3) partilha de conhecimento e poder, (4) variabilidade de participação ativa em reuniões de projetos, e (5) eficiência do trabalho em equipa. Para analisar os cinco tipos de dinâmicas chave, o modelo proposto neste trabalho vai utilizar informação recolhida em reuniões de projetos, emails que contenham informação relacionada com tarefas e atividades de projetos, e questionários estrageiros endereçados aos elementos que participam num dado projeto. Uma vez recolhida toda a informação necessária o modelo vai aplicar um serie de técnicas e ferramentas desenvolvidas com base na área científica da análise de redes socias identificar padrões de comportamento de uma forma quantitativa, associados a projetos que tiveram um desfecho com sucesso, e associados a projetos que tiveram um desfecho sem sucesso, relativamente aos cinco tipos genéricos de colaboração dinâmica acima mencionados. Estas técnicas e ferramentas consistem essencialmente em métricas que medem a centralidade de uma rede social apoiadas na teoria das grafos (matemática discreta). Se os resultados da aplicação do modelo mostrarem evidentes diferentes padrões de comportamentos relativos as cinco dinâmicas chave de projetos em projetos que tiverem um desfecho com sucesso, de projetos que tiverem um desfecho sem sucesso, conclui-se que foram encontrados fatores críticos de sucesso. Uma vez terminada a parte 1 do modelo, e se fatores críticos foram encontrados, pode-se iniciar a parte 2 do modelo POL. Se por outo lado não forma encontrados fatores críticos, então a segunda parte do modelo não pode ser executada. Na segunda parte (parte 2), o modelo POL essencialmente vai monitorizar o quanto um projeto que esteja em execução está ou não desalinhado com os fatores críticos identificados na parte 1. Na segunda o modelo vai primeiro efetuar uma análise aos cinco tipos chave de colaboração dinâmica ((1) comunicação, (2) intra e intercolaboração organizacional (3) know-how, partilha de informação e poder, (4) variabilidade de participação ativa em reuniões de projetos, e (5) eficiência do trabalho em equipa) de um projeto que esteja a atualmente decorrer e comparar os resultados obtidos com os fatores críticos identificados na parte 1 do modelo. Por fim em função da quantidade de fatores (métricas) que estejam ou não alinhados com os fatores críticos de sucesso, o modelo calcula uma probabilidade de desfecho (sucesso ou insucesso) do projeto que esta a ser executado. Para efeitos da ilustração do funcionamento, aplicação e validação do modelo proposto neste trabalho, é apresentado no capítulo 6 deste trabalho um caso de estudo de uma real aplicação do modelo POL na execução de um projeto com a participação de várias pessoas com diferentes competências, ao longo de uma especifica fase de um projeto colaborativo. Ao longo do capítulo 6 é possível observar que o modelo proposto neste trabalho identifica de uma forma simples e eficiente diferentes padrões de comportamento existentes em redes colaborativas, o que permite ás organizações correlacionar resultados obtidos da aplicação do modelo, com os diferentes desfechos de projetos (sucesso ou insucesso) e dessa forma identificar quais os fatores críticos de sucesso

Transition Process and Performance in IT Outsourcing: Evidence from a Field Study and Laboratory Experiments

In this dissertation, complementing the strategic and economic studies on interorganizational relationships and IT outsourcing, we focus on the operational execution challenges inherent in these relationships by examining the transition stage, which starts immediately after contract signing and involves the critical transfer of knowledge, experience and routines related to outsourced activities from client to vendor firm. We focus on the transition stage due to its significance for outsourcing success, its complexity and theoretical richness, and its limited current understanding. Utilizing both a longitudinal field study and laboratory experiments to investigate transition, this dissertation generates important theoretical contributions and practical implications. In the first study (see Chapter 4), adopting a longitudinal perspective, we capture a real-life transition as it unfolds over time between a Utility company (Saturn) and a Global IT vendor (Apollo). Adopting the qualitative data analysis techniques and process theorizing guidelines, we inductively develop, explain and illustrate the transition process model consisting of three phases – transfer, adapt and routinize. For each phase, we illustrate the triggering conditions, key activities and outcomes for progression to the next phase. In the second study (see Chapter 5), building on the findings from the longitudinal qualitative field-study (Chapter 4), we focus on the transfer phase, which represents the most fundamental phase and largely determines the success of not only transition but also overall IT outsourcing relationship. To determine the influence of this phase on transition performance, we develop a novel experiment that captures outsourcing and transition scenarios in the laboratory. Using this experimental setting, we focus on understanding the relationship between transfer mechanisms (i.e

Learning for a Better Future

Various international scholars and associates of the PASCAL (Place, Social Capital and Learning Regions) International Observatory (Africa hub), under the auspices of the Centre for Local Economic Development (CENLED) based at the University of Johannesburg (UJ), have contributed chapters in this scholarly book. The book aims to demonstrate how a combination of globalisation, pandemics and the impact of innovation and technologies are driving towards a world in which traditional ideas are being challenged. The book carries forward a dual context and relevance: to South African social, educational, economic and cultural development, and the broader international context and action directed at how lifelong learning for all can be fostered in communities as a foundation for a just, human-centred, sustainable world. The distinctive contribution of this book to the production of a local body of knowledge lies in the symbiotic relationships between these objectives, so that South Africa could serve as a test case in working towards approaches that have a wider international significance

Examining SCRM approach and its role in facilitating tacit knowledge sharing & creation, and exploring its integration effects

Tacit knowledge resides in a person's mind, which is highly personal and very difficult to interpret & transfer. It can be transferred if individuals reside in the same environment for an extended period of time i.e. through shared experiences. Therefore, finding the right method to acquire tacit knowledge has always been difficult for organizations, as face-to-face interactions or sharing experiences in the same environment is not achievable all the time (due to geographic constraints, lack of mobility, etc.). Over and above that, tacit knowledge sharing through Information Technology (IT) and IT tools is extremely limited or totally impossible. But with the rise of the social web (online collaboration tools, social media, discussion forums, interactive blogs, etc.), tacit knowledge can be created and shared frequently. The main objective of this research is to investigate Social Customer Relationship Management (SCRM) approach and its role in facilitating tacit knowledge sharing & creation and the modification it brings about, due to the integration of social media activities. Based on literature review, in-depth interviews (qualitative approach) and data analysis, this research revisits and explains the concept of SCRM approach, and examines the key enablers required for SCRM approach to exist. Following the above starting point, this research explores if SCRM approach facilitates tacit knowledge creation and if yes, how. To do so, this research adopts Nonaka's 'Dynamic Theory of Organizational Knowledge Conversion & Creation' and investigates if SCRM approach facilitates all the four modes – Socialization, Externalization, Combination, and Internalization – in the SECI model (Micro-Level Analysis) and if yes, how. This study also examines online collaborative platforms or 'online spaces' implemented under SCRM approach that facilitate tacit knowledge sharing and creation. Simultaneously, this study explores the relationship of online spaces with BA', the shared context for knowledge creation (Micro-Level Analysis). This research adopts 'Mechanism of Co-ordination' to examine the effects of social media on R&D department's structure. It also describes new processes that are integrated within the New Product Development (NPD) process (Meso-Level Analysis). The integration of SECI model of knowledge creation and BA' in an SCRM setting is useful both to academia and practitioners. This research adds to the existing literature, which believes social media can facilitate tacit knowledge sharing and creation, and also helps practitioners understand the importance of generating customer knowledge through social media rather than relying on historical and transactional data