257 research outputs found
Monetary policy stabilization in a new Keynesian model under climate change
No full textWe address the question of whether monetary policy is affected by the detrimental impact of
climate change on an economy’s productivity and, if so, whether policymakers should take it into
account when designing policies to stabilize the business cycle. To do this, we develop a new
Keynesian dynamic stochastic general equilibrium model of a closed economy which incorporates
a climate module that interacts with the economy. In this framework, monetary authorities choose
the nominal interest rate on government bonds. The model is solved numerically using parameter
values calibrated to the US economy. Our results, which are robust to both extensions and a large
number of sensitivity checks, suggest non-trivial implications for the design of optimal monetary
policy irrespectively of whether the shocks hitting the economy are standard economic shocks,
climate shocks, or shocks to the price of energy
Competition Policy in Network Industries: An Introduction
No full textThe author discusses issues of the application of antitrust law and regulatory rules to network industries. In assessing the application of antitrust in network industries, we analyze a number of relevant features of network industries and the way in which antitrust law and regulatory rules can affect them. These relevant features include (among others) network effects, market structure, market share and profits inequality, choice of technical standards, relationship between the number of active firms and social benefits, existence of market power, leveraging of market power in complementary markets, and innovation races. The author finds that there are often significant differences on the effects of application of antitrust law in network and non-network industries.
Temporal learning analytics visualizations for increasing awareness during assessment
No full textLes representacions visuals de dades de traces generades per l’alumnat durant les activitats d’aprenentatge ajuden tant els estudiants com els professors a interpretar-les intuïtivament i a percebre’n amb rapidesa aspectes amagats. En aquest treball descrivim la visualització de dades de traces temporals durant el procés d’avaluació. L’estudi tenia un doble objectiu: a) descriure la implicació dels estudiants en el procés d’avaluació pel que fa a temps esmerçat i factors temporals associats amb característiques concretes de l’aprenentatge, i b) explorar els factors que influeixen en la intenció comportamental del professorat quant a emprar el sistema proposat com a sistema d’informació i les seves percepcions de l’efectivitat i l’acceptació del nostre enfocament. Les visualitzacions proposades s’han examinat en un estudi amb 32 professors d’ensenyament secundari. Vàrem adoptar una metodologia de recerca basada en el disseny i vàrem utilitzar un instrument d’enquesta –basada en el model d’acceptació de l’anàlisi de l’aprenentatge– per mesurar l’impacte esperat de les visualitzacions proposades. L’anàlisi de les troballes indica que a) els factors temporals es poden utilitzar per visualitzar el comportament dels estudiants durant l’avaluació, i b) la visualització de la dimensió temporal del comportament dels estudiants augmenta el coneixement del professor pel que fa al progrés dels alumnes, a possibles conceptes erronis (per exemple, endevinar la resposta correcta) i a les dificultats de la tasca.Visual representations of student-generated trace data during learning activities help both students and instructors interpret them intuitively and perceive hidden aspects of these data quickly. In this paper, we elaborate on the visualization of temporal trace data during assessment. The goals of the study were twofold: a) to depict students’ engagement in the assessment procedure in terms of time spent and temporal factors associated with learning-specific characteristics, and b) to explore the factors that influence the teachers’ Behavioural Intention to use the proposed system as an information system and their perceptions of the effectiveness and acceptance of our approach. The proposed visualizations have been explored in a study with 32 Secondary Education teachers. We adopted a design-based research methodology and employed a survey instrument – based on the Learning Analytics Acceptance Model (LAAM) – in order to measure the expected impact of the proposed visualizations. The analysis of the findings indicates that a) temporal factors can be used for visualizing students’ behaviour during assessment, and b) the visualization of the temporal dimension of students’ behaviour increases teachers’ awareness of students’ progress, possible misconceptions (e.g., guessing the correct answer) and task difficulty. Las representaciones visuales de datos de trazas generados por el alumnado durante las actividades de aprendizaje ayudan tanto a los estudiantes como a los profesores a interpretarlos intuitivamente y a percibir con rapidez aspectos ocultos. En este trabajo, describimos la visualización de datos de trazas temporales durante la evaluación. El estudio tenía un doble objetivo: a) describir la implicación de los estudiantes en el proceso de evaluación en cuanto a tiempo invertido y factores temporales asociados con características concretas del aprendizaje, y b) explorar los factores que influyen en la intención comportamental del profesorado en cuanto a emplear el sistema propuesto como sistema de información y sus percepciones de la efectividad y la aceptación de nuestro enfoque. Las visualizaciones propuestas se han examinado en un estudio con 32 profesores de educación secundaria. Adoptamos una metodología de investigación basada en el diseño y utilizamos un instrumento de encuesta –basada en el modelo de aceptación del análisis del aprendizaje– para medir el impacto esperado de las visualizaciones propuestas. El análisis de los hallazgos indica que a) los factores temporales se pueden utilizar para visualizar el comportamiento de los estudiantes durante la evaluación, y b) la visualización de la dimensión temporal del comportamiento de los estudiantes aumenta el conocimiento del profesor respecto al progreso de los alumnos, posibles conceptos erróneos (por ejemplo, adivinar la respuesta correcta) y dificultad de la tarea.
Enhanced Retention of Historical Information with Empathetic Pedagogical Conversational Agents (PCAs)
No full textEmbodied learning using extended reality (XR) technology can enhance learning and satisfaction during field trips to historical sites. This study evaluates the impact of Empathetic Pedagogical Conversational Agents (PCAs) on historical information retention within a simulated XR environment. PCAs employ verbal and nonverbal behaviors to attract attention, with variations in humorous versus serious tones. These behaviors can evoke emotions that motivate learning and influence cognitive processes. The study used facial expressions and eye tracking to measure the impact on the retention of historical information, comparing attention-grabbing PCAs (AG-PCA) with non-attention-grabbing PCAs (NAG-PCA). Results show that AG-PCAs triggered curiosity and positive emotions, but also some dislike, leading to better retention of historical information among NAG-PCAs, particularly among female participants. The study concludes with recommendations for designing PCAs and multimodal content to enhance historical information retention and learner satisfaction
Game Theoretic Path Selection to Support Security in Device-to-Device Communications
No full textDevice-to-Device (D2D) communication is expected to be a key feature supported by 5G networks, especially due to the proliferation of Mobile Edge Computing (MEC), which has a prominent role in reducing network stress by shifting computational tasks from the Internet to the mobile edge. Apart from being part of MEC, D2D can extend cellular coverage allowing users to communicate directly when telecommunication infrastructure is highly congested or absent. This significant departure from the typical cellular paradigm imposes the need for decentralised network routing protocols. Moreover, enhanced capabilities of mobile devices and D2D networking will likely result in proliferation of new malware types and epidemics. Although the literature is rich in terms of D2D routing protocols that enhance quality-of-service and energy consumption, they provide only basic security support, e.g., in the form of encryption. Routing decisions can, however, contribute to collaborative detection of mobile malware by leveraging different kinds of anti-malware software installed on mobile devices. Benefiting from the cooperative nature of D2D communications, devices can rely on each others’ contributions to detect malware. The impact of our work is geared towards having more malware-free D2D networks. To achieve this, we designed and implemented a novel routing protocol for D2D communications that optimises routing decisions for explicitly improving malware detection. The protocol identifies optimal network paths, in terms of malware mitigation and energy spent for malware detection, based on agame theoretic model. Diverse capabilities of network devices running different types of anti-malware software and their potential for inspecting messages relayed towards an intended destination device are leveraged using game theoretic tools. An optimality analysis of both Nash and Stackelberg security games is undertaken, including both zero and non-zero sum variants, and the Defender’s equilibrium strategies. By undertaking network simulations, theoretical results obtained are illustrated through randomly generated network scenarios showing how our protocol outperforms conventional routing protocols, in terms of expected payoff, which consists of:security damage inflicted by malwareandmalware detection cost
The Economics of the Internet Backbone
No full textThis paper discusses the economics of the Internet backbone. The author discusses competition on the Internet backbone as well as relevant competition policy issues. In particular, he shows how public protocols, ease of entry, very fast network expansion, connections by the same Internet Service Provider ('ISP') to multiple backbones (ISP multi-homing), and connections by the same large web site to multiple ISPs (customer multi-homing) enhance price competition and make it very unlikely that any firm providing Internet backbone connectivity would find it profitable to degrade or sever interconnection with other backbones in an attempt to monopolize the Internet backbone.Technology and Industry, Regulatory Reform
Exploring autonomous learning capacity from a self‐regulated learning perspective using learning analytics
No full textPractising self‐regulated learning (SRL) has been proposed to develop learning autonomy. However, there is lack of empirical evidence on how SRL strategies affect autonomous learning capacity. This study attempts to bridge that gap by utilizing the learners’ trace data for measuring the learners’ autonomous interactions, and investigates the effects of four SRL strategies on learners’ autonomous choices. The goal is to explain how the employed SRL strategies impact autonomous control (in terms of frequencies of self‐enforced decisions, as well as time‐spent on decision making). The results from an exploratory study with undergraduate learners (N = 113) shown that goal‐setting and time‐management have strong positive effects on autonomous control, effort‐regulation moderately positively affects learners’ autonomy, while help‐seeking has a strong negative effect. These findings provide empirical evidence and contribute to clarifying the role of each one of the SRL strategies in the development of autonomous learning capacity, from a learning analytics perspective. Limitations and potential implications for research and practice are also discussed.5063138315
The impact of on‐demand metacognitive help on effortful behaviour: A longitudinal study using task‐related visual analytics
No full textThis longitudinal study investigates the differences in learners' effortful behaviour over time due to receiving metacognitive help—in the form of on‐demand task‐related visual analytics. Specifically, learners' interactions (N = 67) with the tasks were tracked during four self‐assessment activities, conducted at four discrete points in time, over a period of 8 weeks. The considered and coded time points were: (a) prior to providing the metacognitive help; (b) while the task‐related visual analytics were available (treatment); (c) after the removal of the treatment; and (d) while the option to receive metacognitive help was available again. To measure learners' effortful behaviour across the self‐assessment activities, this study utilized learners' response‐times to correctly/wrongly complete the tasks and on‐task effort expenditure. The panel data analysis shown that the usage of metacognitive help caused statistically significant changes in learners' effortful behaviour, mostly in the third and fourth phase. Statistically significant changes were detected also in the usage of metacognitive help. These results provide empirical evidence on the benefits of task‐related visual analytics to support learners' on‐task engagement, and suggest relevant cues on how metacognitive help could be designed and prompted by focusing on the “task”, instead of the “self”.publishedVersion© 2020 The Authors. Journal of Computer Assisted Learning published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited
Exhibiting achievement behavior during computer-based testing: What temporal trace data and personality traits tell us?
No full textPersonalizing computer-based testing services to examinees can be improved by considering their behavioral models. This study aims to contribute towards deeper understanding the examinee’s time-spent and achievement behavior during testing according to the five personality traits by exploiting assessment analytics. Further, it aims to investigate assessment analytics appropriateness for classifying students and generating enhanced student models to guide personalization of testing services. In this study, the LAERS assessment environment and the Big Five Inventory were used to track the response times of 112 undergraduate students and to extract their personality traits respectively. Partial Least Squares was used to detect fundamental relationships between the collected data, and Supervised Learning Algorithms were used to classify students. Results indicate a positive effect of extraversion and agreeableness on goal-expectancy, a positive effect of conscientiousness on both goal-expectancy and level of certainty, and a negative effect of neuroticism and openness on level of certainty. Further, extraversion, agreeableness and conscientiousness have statistically significant indirect impact on students’ response-times and level of achievement. Moreover, the ensemble RandomForest method provides accurate classification results, indicating that a time-spent driven description of students’ behavior could have added value towards dynamically reshaping the respective models. Further implications of these findings are also discussed.7542343
How consumers are affected by TikTok Influencers’ perceived traits, and how these traits are linked to consumers’ purchase intentions
No full textΠτυχιακή εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2025.Η βιβλιοθήκη διαθέτει αντίτυπο της πτυχιακής μόνο σε ηλεκτρονική μορφή.Social media influencers have increasingly become a focal point of investigation in digital marketing
research, as they are recognized as influential tools for shaping consumer behaviors. This paper sought
to address how TikTok, a rapidly growing social media platform, that academic research has not explored
thoroughly, has impacted the perceived traits and image of these influencers. More precisely, this study
investigates how influencer’s characteristics and psychological-related influential factors impact
purchase intentions of consumers. This study seeks to investigate how attitude homophily along the
perceived fit quality between a product and influencer, foster parasocial interactions and shape
perceived source credibility. In turn, it investigates how these factors impact consumer purchasing
intentions. The research was conducted in June 2024 and involves 318 participants that reside in Greece.
Data were collected through the distributions of a self-administered questionnaire. The analysis was
conducted in SPSS, for demographic data analysis, and R for Partial Least Squares Structural Equation
Modeling (PLS-SEM).
Results indicated that attitude homophily and product-influencer fit positively affected both perceived
credibility and parasocial relationship. Moreover, credibility and PSR both translated into a positive
consumer purchase intention. Attitude homophily and product-influencer fit had indirect effects on
purchase intention only when mediated by parasocial relationship. Understanding the dynamics the
findings revealed, are beneficial for content creators, marketers and brands to enhance their digital
marketing strategies and comprehend consumer behavior in TikTok
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