0000000000601276
AUTHOR
Michail N. Giannakos
Seeking Information on Social Commerce: An Examination of the Impact of User- and Marketer-generated Content Through an Eye-tracking Study
Following the growing popularity of social commerce sites, there is an increased interest in understanding how consumers decide what products to purchase based on the available information. Consumers nowadays are confronted with the task of assessing marketer-generated (MGC) as well as user-generated information (UGC) in a range of different forms to make informed purchase-related decisions. This study examines the information types and forms that influence consumers in their decision-making process on social commerce. Building on uses and gratifications and dual-process theories, we distinguish between marketer and user generated content, and differentiate formats into informational and no…
Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach
Social Networking Sites (SNSs) play an important role in our daily lives and the number of their users increases regularly. To understand how users can be satisfied in the complex digital environment of SNSs, this study examines how motivations and emotions combine with each other to explain high satisfaction. Users’ motivations comprise four attributes, entertainment, information, social-psychological, and convenience. Emotions are divided into their two main categories, that is positive and negative emotions. We draw on complexity and configuration theories, present a conceptual model along with propositions and perform a fuzzy-set qualitative comparative analysis (fsQCA). Through an empi…
How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data
This study examines how quickly we can predict users’ ratings on visual aesthetics in terms of simplicity, diversity, colorfulness, craftsmanship. To predict users’ ratings, first we capture gaze behavior while looking at high, neutral, and low visually appealing websites, followed by a survey regarding user perceptions on visual aesthetics towards the same websites. We conduct an experiment with 23 experienced users in online shopping, capture gaze behavior and through employing machine learning we examine how fast we can accurately predict their ratings. The findings show that after 25 s we can predict ratings with an error rate ranging from 9% to 11% depending on which facet of visual ae…
Fitbit for learning: Towards capturing the learning experience using wearable sensing
The assessment of learning during class activities mostly relies on standardized questionnaires to evaluate the efficacy of the learning design elements. However, standardized questionnaires pose additional strain on students, do not provide “temporal” information during the learning experience, require considerable effort and language competence, and sometimes are not appropriate. To overcome these challenges, we propose using wearable devices, which allow for continuous and unobtrusive monitoring of physiological parameters during learning. In this paper we set out to quantify how well we can infer students’ learning experience from wrist-worn devices capturing physiological data. We coll…
Explaining User Experience in Mobile Gaming Applications: An fsQCA Approach
Purpose In the complex ecosystem of mobile applications multiple factors have been used to explain users’ behavior, without though focusing on how different combinations of variables may affect user behavior. The purpose of this paper is to show how price value, game content quality, positive and negative emotions, gender and gameplay time interact with each other to predict high intention to download mobile games. Design/methodology/approach Building on complexity theory, the authors present a conceptual model followed by research propositions. The propositions are empirically validated through configurational analysis, employing fuzzy-set qualitative comparative analysis (fsQCA) on 531 a…
Rethinking Learning Design in IT Education During a Pandemic
Maintaining high-quality teaching and learning in the times of a pandemic poses a huge challenge to education systems. To scaffold adequate practices in our courses during the pandemic, more advanced, and fine-grained “learning design” is needed than providing the learning objectives and learning materials of the course and defining the deliverables and assignments. In this paper, we leverage on our experience with putting into practice different learning designs and technologies, in various information technology (IT) contexts and discuss how IT educators can further reflect on the learning design of their courses and scaffold fully remote or blended learning approaches to accommodate thei…
Systematic literature review of E-learning capabilities to enhance organizational learning
AbstractE-learning systems are receiving ever increasing attention in academia, business and public administration. Major crises, like the pandemic, highlight the tremendous importance of the appropriate development of e-learning systems and its adoption and processes in organizations. Managers and employees who need efficient forms of training and learning flow within organizations do not have to gather in one place at the same time or to travel far away to attend courses. Contemporary affordances of e-learning systems allow users to perform different jobs or tasks for training courses according to their own scheduling, as well as to collaborate and share knowledge and experiences that res…
Technology-Enhanced Organizational Learning: A Systematic Literature Review
Part 9: Learning and Education; International audience; E-Learning systems are receiving ever increasing attention in, academia, businesses as well as in public administrations. Managers and employee who need efficient forms of training as well as learning flow within the organization, do not have to gather in a place at the same time, or to travel far away for attending courses. Contemporary affordances of e-learning systems allow them to perform different jobs or tasks for training courses according to their own scheduling, as well as collaborate and share knowledge and experiences that results rich learning flow within the organization. The purpose of this article is to provide a systema…
How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data
This study examines how quickly we can predict users’ ratings on visual aesthetics in terms of simplicity, diversity, colorfulness, craftsmanship. To predict users’ ratings, first we capture gaze behavior while looking at high, neutral, and low visually appealing websites, followed by a survey regarding user perceptions on visual aesthetics towards the same websites. We conduct an experiment with 23 experienced users in online shopping, capture gaze behavior and through employing machine learning we examine how fast we can accurately predict their ratings. The findings show that after 25 s we can predict ratings with an error rate ranging from 9% to 11% depending on which facet of visual ae…
Utilizing Multimodal Data Through fsQCA to Explain Engagement in Adaptive Learning
Investigating and explaining the patterns of learners’ engagement in adaptive learning conditions is a core issue towards improving the quality of personalized learning services. This article collects learner data from multiple sources during an adaptive learning activity, and employs a fuzzy set qualitative comparative analysis (fsQCA) approach to shed light to learners’ engagement patterns, with respect to their learning performance. Specifically, this article measures and codes learners’ engagement by fusing and compiling clickstreams (e.g., response time), physiological data (e.g., eye-tracking, electroencephalography, electrodermal activity), and survey data (e.g., goal-orientation) to…
Multimodal data as a means to understand the learning experience
Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perf…
Goalkeeper: A Zero-Sum Exergame for Motivating Physical Activity
Incentives and peer competition have so far been employed independently for increasing physical activity. In this paper, we introduce Goalkeeper, a mobile application that utilizes deposit contracts for motivating physical activity in group settings. Goalkeeper enables one to set up a physical exercise challenge with a group of peers that deposit a fixed amount of money for participating. If a peer fails to complete the challenge, Goalkeeper redistributes their deposit to those who managed to complete it (i.e., zero-sum game). We evaluated the potential of Goalkeeper in increasing physical activity with a total of 50 participants over the course of 2 months. Our findings suggest that deposi…