A new metric scale for measuring trust towards holographic intelligent agents

Trust is an essential attitude in social relationships, but it also mediates our approach to certain technology. The definition of interpersonal trust, however, is too wide to expound our understanding of how trust impedes such interaction with technology, and the lack of an applicable quantifiable model in particular presents an obstacle to our quest of building reliable, trusted, and intelligent holographic agents. In this paper, we therefore develop a novel metric scale to measure trust. We identify, select, and refine over a hundred items related to trust, check their precision and validity with the help of a judges panel, and select polarising items that are able to bring out the distinctive characteristics regarding people’s trust towards intelligent agents. Our findings indicate that an assessment of trust involves looking at the user’s belief about the agent’s competence, integrity, benevolence, and compassion, which drive the attitude of trust, influenced by the user’s general propensity to trust. Trust then drives intention to engage and ultimately engagement, which, if successful, results in the establishment of a trust relationship with the agent. While we propose an item-response scale for measuring this model of trust, we also add our deliberations on how elements of it could be replaced with alternative means that possibly offer more immediacy than self-inspection, discussing in particular alternatives for measuring elements of compassion, competence, and social relationships

Characterising Learners in Online Communities Based on Actor-Artefact Relations

Online communities are of huge interest in terms of learning and knowledge creation because of the potential to distribute knowledge among possibly large audience independently from time and place. In this context, various forms of online learning have developed over time ranging from small learning groups to massive open online courses (MOOCs) with thousands of participants. In order to support learning in those settings an increased understanding of specific characteristics of learners in online communities is necessary. Thus, dedicated means to gather valuable information from data produced in online learning environments have to be developed. This cumulative dissertation includes five publications aiming to make progress in this direction with a particular focus on the advancement of methods to analyse activity and interaction data of learners. The methodological foundation of the work is (social) network analysis, which provides a well-grounded set of methods for structural analysis of relational data. Network analysis is especially suited since the collected data about actors (in this thesis mostly learners) who create and consume digital content (artefacts) can be modelled as actor-artefact networks. Those actor-artefact networks denote the starting point of all analyses presented in this dissertation, which target different aspects of learning in online communities, in particular the usage of learning resources, emergence of interest profiles, and information exchange between learners. In the course of this work, stable artefacts that are not assumed to have changing content over time are distinguished from time-evolving dynamic artefacts (typically user generated content). In the case of stable artefacts, affiliations of learners to learning resources in online courses are analysed by identifying mixed clusters of learners and resources using network clustering algorithms. The evolution of these learner-resource clusters over time is investigated in detail leading to discoveries of typical resource access patterns that characterise learners regarding their interests in provided learning materials. The approach is further extended and combined with content analysis techniques to analyse thematic development in discussion forums. Discussion forums are also the subject of two other studies investigating information exchange between learners in MOOCs. The evolving discussion threads are considered as dynamically evolving artefacts that are used to extract social networks reflecting information exchange between forum users. These networks are analysed to uncover different roles of forum users with respect to their positions in the network. For this task different approaches are described that are capable of modelling structural characteristics of the information exchange network over time and further take discussion topics as additional information into account.Online-Gemeinschaften sind aufgrund der Möglichkeiten Wissen zeit- und ortsunabhängig unter einer großen Menge von Adressaten zu verbreiten von großem Interesse bezüglich Lernens und Wissenskonstruktion. In diesem Kontext haben sich über die Zeit verschiedene Formen des Online-Lernens entwickelt, von kleinen Lerngruppen bis zu “Massive Open Online Courses” (MOOCs) mit tausenden von Teilnehmern. Um das Lernen in diesen Bereichen zu unterstützen, ist ein besseres Verständnis spezifischer Charakteristiken von Lernenden in Online-Gemeinschaften notwendig. Um nützliche Informationen aus Daten zu gewinnen, die in Online-Umgebungen anfallen, sind dezidierte Methoden wichtig. Diese kumulative Dissertation beinhaltet Publikationen die auf Fortschritte in diesem Bereich abzielen. Ein besonderer Fokus liegt dabei auf der Weiterentwicklung von Methoden zur Analyse von Aktivitäts- und Interaktionsdaten von Lernern in Online-Gemeinschaften. Das methodische Fundament ist dabei die (Soziale) Netzwerkanalyse, welche fundierte Methoden zur strukturellen Analyse von relationalen Daten bereitstellt. Netzwerkanalyse ist besonders geeignet, da Daten über Akteure (hier meistens Lernende), die digitale Inhalte (Artefakte) erstellen und konsumieren, als Akteur-Artefakt-Netzwerk modelliert werden können. Solche Akteur-Artefakt-Netzwerke sind der Ausgangspunkt aller in dieser Dissertation vorgestellten Analysen, die auf verschiedene Aspekte des Lernens in Online-Gemeinschaften abzielen, insbesondere die Nutzung von Lernressourcen, die Entwicklung von Interessensprofilen und Wissensaustausch zwischen Lernenden. Im Verlauf dieser Arbeit wird zwischen stabilen Artefakten, die sich über die Zeit nicht verändern und sich über die Zeit entwickelnden dynamischen Artefakten (typischerweise Nutzergenerierte Inhalte) unterschieden. Im Fall von statischen Artefakten werden Affiliationen von Lernenden zu Lernressourcen in Online-Kursen untersucht, indem gemischte Cluster aus Lernenden und Lernressourcen mittels Netzwerk-Clusteringalgorithmen identifiziert werden. Die Evolution dieser Lerner-Ressourcen-Cluster wird eingehend untersucht, woraus Erkenntnisse über typische Ressourcennutzungsmuster gewonnen werden, die die Lernenden in Online-Gemeinschaften bezüglich ihrer Präferenzen zu Lernmaterialien charakterisieren. Dieser Ansatz wird zudem weiterentwickelt und mit Techniken der Inhaltsanalyse kombiniert, um thematische Entwicklungen in Diskussionsforen zu analysieren. Diskussionsforen sind auch Gegenstand zweier weiterer Studien, die den Austausch von Informationen zwischen Lernenden in MOOCs zu untersuchen. Die einzelnen Diskussionsstränge werden dabei als dynamische Artefakte angesehen, die dann dazu genutzt werden um soziale Netzwerke zu extrahieren, die den Informationsaustausch zwischen Lernenden abbilden. Diese Netzwerke werden dahingehend analysiert, unterschiedliche Rollen von Forumsnutzern bezüglich ihrer Position in dem Netzwerk zu identifizieren. Dazu werden verschiedene Ansätze vorgestellt, die die strukturellen Charakteristiken des Informationsaustauschnetzwerks über die Zeit darstellen, sowie Diskussionsthemen als zusätzliche Informationen berücksichtigen

Text analytics in business environments: a managerial and methodological approach

O processo de tomada de decisão, em diferentes ambientes gerenciais, enfrenta um momento de mudança no contexto organizacional. Nesse sentido, Business Analytics pode ser visto como uma área que permite alavancar o valor dos dados, contendo ferramentas importantes para o processo de tomada de decisão. No entanto, a presença de dados em diferentes formatos representa um desafio. Nesse contexto de variabilidade, os dados de texto têm atraído a atenção das organizações, já que milhares de pessoas se expressam diariamente neste formato, em muitas aplicações e ferramentas disponíveis. Embora diversas técnicas tenham sido desenvolvidas pela comunidade de ciência da computação, há amplo espaço para melhorar a utilização organizacional de tais dados de texto, especialmente quando se volta para o suporte à tomada de decisões. No entanto, apesar da importância e disponibilidade de dados em formato textual para apoiar decisões, seu uso não é comum devido à dificuldade de análise e interpretação que o volume e o formato de dados em texto apresentam. Assim, o objetivo desta tese é desenvolver e avaliar um framework voltado ao uso de dados de texto em processos decisórios, apoiando-se em diversas técnicas de processamento de linguagem natural (PNL). Os resultados apresentam a validade do framework, usando como instância de demonstração de sua aplicabilidade o setor de turismo através da plataforma TripAdvisor, bem como a validação interna de performance e a aceitação por parte dos gestores da área consultados.The decision-making process, in different management environments, faces a moment of change in the organizational context. In this sense, Business Analytics can be seen as an area that leverages the value of data, containing important tools for the decision-making process. However, the presence of data in different formats poses a challenge. In this context of variability, text data has attracted the attention of organizations, as thousands of people express themselves daily in this format in many applications and tools available. Although several techniques have been developed by the computer science community, there is ample scope to improve the organizational use of such text data, especially when it comes to decision-making support. However, despite the importance and availability of textual data to support decisions, its use is not common because of the analysis and interpretation challenge that the volume and the unstructured format of text data presents. Thus, the aim of this dissertation is to develop and evaluate a framework to contribute with the expansion and development of text analytics in decision-making processes, based on several natural language processing (NLP) techniques. The results presents the validity of the framework, using as a demonstration of its applicability the tourism sector through the TripAdvisor platform, as well as the internal validation of performance and the acceptance by managers

Corporate Governance: Theory and Practice

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