Financial information use in parliamentary debates in a changing context

Applying quantitative and qualitative content and sentiment analysis, this study investigates the use of financial information by politicians in the Portuguese parliament, in a changing context. There is clear preference for Budgetary Information. A change in the political majority in Parliament and an improvement in the country’s economic and financial situation affect the intensity and intentions of using financial information by politicians – the use with positive sentiments of those government-related, to legitimize their policies, and with negative sentiments when they pass to the opposition, using it as ammunition or to divert attention from measures implemented when they were in power.info:eu-repo/semantics/publishedVersio

Promoting Data Journalism with Purpose-Made Systems: A case study of the benefits of purpose-made data journalism systems among Norwegian Data Journalists

The research project presented in this thesis is a case study investigating the usefulness of purpose-made data journalism systems. The study consists of two investigations, the first informal and exploratory, and the other more extensive and rigorous. The study features interviews with Norwegian data journalists based in the city of Bergen, which constitutes the main source of data. As part of the research, a prototype purpose-made data journalism system has been developed, based on preliminary findings from the exploratory investigation. The research carried out indicates that there is potential for developing computer systems designed to solve certain specific data journalism systems, concluding with a proposed application.Masteroppgave i informasjonsvitenskapINFO390MASV-IKTMASV-INF

Evaluating intuitive interactions using image schemas

Intuitive use is a desirable feature in interface design. In the last decade, researchers have made considerable progress in developing approaches for design for intuitive use. Most of the current approaches, however, have been limited to qualitative exploration, in the form of providing guidelines aimed to help designers to design products that are intuitively useable. This thesis focusses on developing a quantitative approach for evaluating intuitive interaction with the help of image schemas. An image schema is a recurring structure within the human cognitive system that establishes patterns of reasoning with the physical world. Researchers have found this concept to be very useful in capturing human interaction with products; it is also suitable for analysing product features. The approach developed in this thesis measures intuitive interaction based on the degree of match (known in this study as quantification, ‘Q’) between the designer’s intent and the users’ interaction both expressed through image schemas. The value of the proposed approach is in the provision of measurable outputs for evaluating intuitive interaction. The proposed approach is evaluated through an empirical study designed to test the validity of ‘Q’ as a measure of intuitive use expressed through task completion time, errors and cognitive effort. The results reveal that participants with high ‘Q’ value were significantly quicker, made fewer errors, and expended less cognitive effort while completing all subtasks with the three products used in the study. Secondly, the thesis addresses the limitation of other studies in the applicability of the methods used for extracting image schemas. Previous approaches have predominantly relied on the expertise of the researcher conducting the study in extracting the identified image schemas from the utterances of the users. This could introduce subjective bias, thereby reducing the reliability of the method for extracting image schemas. This study addressed this limitation by developing a systematic approach based on a novel algorithm for extracting image schemas from users’ utterances. This enhanced image schema extraction method is based on the use of two ontologies: a lexical ontology and a domainspecific ontology (image schema ontology). The domain-specific ontology was purpose-built for the needs of this study. The independent evaluation study conducted to evaluate the algorithm revealed a substantial strength with an overall k of 0.67 across the 3 products used in the study. Previous studies have predominantly focussed on the cognitive aspect of intuitive use. A limited amount of work has explored the affective aspect of intuition, and integrated it into the evaluation of intuitive use. This study addressed this limitation by developing a novel approach for assessing the affective aspect of the intuitive use of products. This novel approach incorporates the enhanced algorithm for extracting image schemas that was developed for this study. In addition, a sentiment analysis on the affective words linked to the image schemas employed in the task is used as part of the evaluation process. The proposed approach is evaluated through an empirical study based on the sentiments used in describing the image schemas employed for the interaction. The results show that the approach links the image schemas used for the completion of a task to the affective experiences of the users. This has potential to lead to significant improvements in design for intuitive use because it allows experiences to be linked directly to the specific image schemas employed in the design. Overall, the study contributes significantly to the knowledge in this field by validating a new quantitative approach for evaluating intuitive interactions with physical objects. The approaches developed in this study will enable designers to evaluate intuitive usage at different phases of the design process

Adoption Factors of Artificial intelligence in Human Resource Management

Tesis por compendio[ES] El mundo es testigo de nuevos avances tecnológicos que afectan significativamente a las organizaciones en diferentes departamentos. La inteligencia artificial (IA) es uno de estos avances, visto como una tecnología revolucionaria en la gestión de recursos humanos (RRHH). Profesionales y académicos han discutido el brillante papel de la IA en RRHH. Sin embargo, el análisis profundo de esta tecnología en el proceso de RRHH es aún escaso. Con todo ello, el objetivo principal de esta tesis es investigar el estado de la IA en RRHH y así identificar factores clave de implementación concretos. Primero, construyendo un marco académico para la IA en RRHH; segundo, analizar las aplicaciones de IA más utilizada en los procesos de RRHH; tercero, identificar las formas óptimas de transferir el conocimiento en los procesos de implementación de IA. La metodología utilizada para la investigación combina la revisión sistemática de la literatura y técnicas de investigación cualitativa. Como base y medida preparatoria para abordar las preguntas de investigación, se llevó a cabo un extenso análisis de la literatura en el campo AI-RRHH, con un enfoque particular en las publicaciones de algoritmos de IA en HRM, análisis de HR-Big data, aplicaciones/soluciones de IA en HRM e implementación de IA. En la misma línea, el autor publicó artículos en varias conferencias que contribuyeron a mejorar la madurez de las preguntas de investigación. Con base en este conocimiento, los estudios publicados ilustraron la brecha entre la promesa y la realidad de la IA en RRHH, teniendo en cuenta los requisitos técnicos de la implementación de la IA, así como las aplicaciones y limitaciones. Posteriormente, se entrevistó a expertos en recursos humanos y consultores de IA que ya habían adquirido experiencia de primera mano con los procesos de recursos humanos en un entorno de IA para descubrir la verdad de la aplicación de la IA dominante en el proceso de RRHH. Los principales hallazgos de esta tesis incluyen la derivación de una definición completa de IA en RRHH, así como el estado de las estrategias de adopción de aplicaciones de IA en RRHH. Como resultado adicional, se explora la utilidad y las limitaciones de los chatbots en el proceso de contratación en la India. Además, factores clave para transferir el conocimiento del proceso de implementación de IA a los gerentes y empleados de recursos humanos. Finalmente, se concluye identificando desafíos asociados con la implementación de IA en el proceso de recursos humanos y el impacto de COVID-19 en la implementación de IA.[CA] El món és testimoni de nous avanços tecnològics, que afecten significativament les organitzacions en diferents departaments. La intel·ligència artificial (IA) és un d'aquests avanços que s'anuncia àmpliament com una tecnologia revolucionària en la gestió de recursos humans (HRM). Professionals i acadèmics han discutit el brillant paper de la IA en HRM. No obstant això, encara és escàs l'anàlisi profund d'aquesta tecnologia en el procés de HRM. Per tant, l'objectiu principal d'aquesta tesi és investigar l'estat de la IA en HRM i derivar factors clau d'implementació concrets. Primer, construint un marc acadèmic per a la IA en HRM; segon, analitzar l'aplicació de IA més utilitzada en el procés de recursos humans; tercer, identificar les formes òptimes de transferir el coneixement dels processos d'implementació de IA. La metodologia utilitzada per a la investigació es combina entre una revisió sistemàtica de la literatura i una tècnica d'investigació qualitativa. Com a base i mesura preparatòria per a abordar les preguntes d'investigació, es va dur a terme una extensa anàlisi de la literatura en el camp IA-HRM, amb un enfocament particular en les publicacions d'algorismes de IA en HRM, anàlisis de HR-Big data, aplicacions/soluciones de IA en HRM i implementació de IA. En la mateixa línia, l'autor va publicar articles en diverses conferències que van procedir a millorar la maduresa de les preguntes d'investigació. Amb base en aquest coneixement, els estudis publicats van illustrar la bretxa entre la promesa i la realitat de la IA en HRM, tenint en compte els requisits tècnics de la implementació de la IA, així com les aplicacions i limitacions. Posteriorment, es va entrevistar experts en recursos humans i consultors de IA que ja havien adquirit experiència de primera mà amb els processos de recursos humans en un entorn de IA per a descobrir la veritat de l'aplicació de la IA dominant en el procés de recursos humans. Les principals troballes d'aquesta tesi són la derivació d'una definició completa de IA en HRM, així com l'estat de les estratègies d'adopció d'aplicacions de IA en HRM. Com a resultat addicional, explore la utilitat i les limitacions dels chatbots en el procés de contractació a l'Índia. A més, factors clau per a transferir el coneixement del procés d'implementació de IA als gerents i empleats de recursos humans. També es van concloure els desafiaments associats amb la implementació de IA en el procés de recursos humans i l'impacte de COVID-19 en la implementació de IA.[EN] The world is witnessing new technological advancements, which significantly impacts organizations across different departments. Artificial intelligence (AI) is one of these advancements that is widely heralded as a revolutionary technology in Human Resource Management (HRM). Professionals and scholars have discussed the bright role of AI in HRM. However, deep analysis of this technology in the HR process is still scarce. Therefore, the main goal of this thesis is to investigate the status of AI in HRM and derive concrete implementation key factors. Through, first, building an academic framework for AI in HRM; second, analyzing the most commonly used AI applications in HR process; third, identifying the optimal ways to transfer the knowledge of AI implementation processes. The methodology used for the investigation combines a systematic literature review and a qualitative research technique. As a basis and preparatory measure to address the research questions, an extensive literature analysis in the AI-HRM field was carried out, with a particular focus on publications of AI in HRM, HR-Big data analysis, AI applications/solutions in HRM and AI implementation. Along similar lines, the author published papers in several conference proceedings to improve the maturity of research questions. Based on this work, the published studies illustrate the gap between the promise and reality of AI in HRM, taking into account the requirements of AI implementation as well as the applications and limitations. Subsequently, HR experts and AI consultants, who had already gained first-hand experience with HR processes in an AI environment, were interviewed to find out the truth of the dominant AI's application in HR process. The main findings of this thesis are the derivation of a complete definition of AI in HRM as well as the status of the adoption strategies of AI applications in HRM. As a further result, it explores the usefulness and limitations of chatbots in the recruitment processes in India. In addition, derived the key factors to transfer the knowledge of AI implementation process to HR managers and employees. Challenges associated with AI implementation in the HR process and the impact of COVID-19 on AI implementation were also concluded.Tuffaha, M. (2022). Adoption Factors of Artificial intelligence in Human Resource Management [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185909Compendi

Fine-grained position analysis for political texts

Meinungsanalyse auf politischen Textdaten hat im Bereich der Computerlinguistik in den letzten Jahren stets an Bedeutung gewonnen. Dabei werden politische Texte zumeist in voneinander diskrete Klassen unterteilt, wie zum Beispiel pro vs. contra oder links vs. rechts. In den Politikwissenschaften dagegen werden bei der Analyse von politischen Texten Positionen auf Skalen mit fließenden Werten abgebildet. Diese feingranulare Darstellung ist für die dort gegebenen Fragestellungen erforderlich. Das Feld der “quantitativen Analyse” - der automatisierten Analyse von Texten - die der traditionellen qualitativen Analyse gegenüber steht, hat erst kürzlich mehr Beachtung gefunden. Bisher werden Texte dabei zumeist lediglich durch Worthäufigkeiten dargestellt und ohne jegliche Struktur modelliert. Wir entwickeln in dieser Dissertation Ansätze basierend auf Methoden der Computerlinguistik und der Informatik, die gegeignet sind, politikwissenschaftliche Forschungsfragen zu untersuchen. Im Gegensatz zu bisherigen Arbeiten in der Computerlinguistik klassifizieren wir nicht diskrete Klassen von Meinungen, sondern projizieren feingranulare Positionen auf fließende Skalen. Darüber hinaus schreiben wir nicht Dokumenten ganzheitlich eine Position zu, sondern bestimmen die Meinungen zu den jeweiligen Themen, die in den Texten enthalten sind. Diese mehrdimensionale Meinungsanalyse ist nach unserem Kenntnisstand neu im Bereich der quantitativen Analyse. Was unsere Ansätze von anderen Methoden unterscheidet, sind insbesondere folgende zwei Eigenschaften: Zum Einen nutzen wir Wissen aus externen Quellen, das wir in die Verfahren einfließen lassen - beispielsweise integrieren wir die Beschreibungen von Ministerien des Bundestags als Definition von politischen Themenbereichen, mit welchen wir automatisch Themen in Parteiprogrammen erkennen. Zum Anderen reichern wir unsere Verfahren mit linguistischem Wissen über Textkomposition und Dialogstruktur an. Somit gelingt uns eine tiefere Modellierung der Textstruktur. Anhand der folgenden drei Fragestellungen aus dem Bereich der Politikwissenschaften untersuchen wir die Umsetzung der oben beschriebenen Methoden: 1. Multi-Dimensionale Positionsanalyse von Parteiprogrammen 2. Analyse von Themen und Positionen in der US-Präsidentschaftswahl 3. Bestimmen von Dove-Hawk-Positionen in Diskussionen der amerikanischen Zentralbank Wir zeigen, dass die vorgestellten Lösungen erfolreich feingranulare Positionen in den jeweiligen Daten erkennen und analysieren Möglichkeiten sowie Grenzen dieser zukunftsweisenden Verfahren