DATA ANALYTICS AND PERSUASIVE TECHNOLOGY TO PROMOTE STUDENTS’ ENGAGEMENT AND LEARNING

The use of interactive systems and internet technology nowadays enhance the process of learning as they allow educational resources to be effectively distributed and delivered to students. This gives students the opportunity to learn at their own pace and convenience. Hence, universities employ these computing technologies to aid in teaching and learning in order to meet the needs of diverse learners. Thus, students could engage in learning activities at any time and even outside the four walls of universities. Despite the usefulness of these systems, students find it hard to engage for a long time with these learning resources. They are distracted by so many activities such as chatting, playing games, listening to music, watching movies, etc. As a result, a wide gap exists in academic performance between successful students and unsuccessful one (those that drop out of universities). Therefore, there is a need for research on how to increase students’ motivation to learn. The level of motivation of students to learn and progress in their education determine the length of time they spend on learning-related activities. This research investigated the use of persuasive technology in encouraging students to spend quality time in their learning resources. Persuasive technology describes computer applications which change users’ behaviour or opinion without using coercion or deception. Specifically, this research examined the effect of three social influence strategies of persuasive technology (social comparison, social learning, and competition) on students’ engagement in their learning activities. Socially-oriented strategies recognize the fact that humans are socially-driven and thus, our feeling, behaviour or opinion is affected by that of others (social influence). The strategies were operationalized in a persuasive system as three versions of visualization using students’ assessment grades. The persuasive system was applied to a real university course-based setting to determine its effect on students’ engagement in their learning activities. Quantitative and qualitative approaches were used in determining the effectiveness of the persuasive system versions implementing the three strategies in motivating the students to engage actively in learning activities. The results of this research show that the three socially-oriented strategies of persuasive technology employed can be used in educational software to influence students to achieve a positive goal in their learning. Precisely, the persuasive system attracted and motivated students to spend more time in their learning activities

Wearables at work:preferences from an employee’s perspective

This exploratory study aims to obtain a first impression of the wishes and needs of employees on the use of wearables at work for health promotion. 76 employ-ees with a mean age of 40 years old (SD ±11.7) filled in a survey after trying out a wearable. Most employees see the potential of using wearable devices for workplace health promotion. However, according to employees, some negative aspects should be overcome before wearables can effectively contribute to health promotion. The most mentioned negative aspects were poor visualization and un-pleasantness of wearing. Specifically for the workplace, employees were con-cerned about the privacy of data collection

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

A Taxonomy of Web Personalization

Web personalization has become an important way to provide individualized user experiences. As a fragmented use of the term “Web personalization” and a lack of a common framework potentially hinder the establishment of a cumulative body of research, we develop a taxonomy of Web personalization. Bringing together research from information systems, computer science, and marketing, we develop a taxonomy focusing on the meta-characteristics user modeling (with the dimensions type of data, acquisition method, and life span of data) and system adaptation (with the dimensions object, volatility, scope, and control of adaptation). We demonstrate an application of our taxonomy by analyzing a sample of articles published in premier information systems journals and present some exemplary use cases to demonstrate how the taxonomy could be applied in practical contexts

ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.Comment: 17 pages, 12 figure

Thinking Outside the Black-Box: The Case for "Algorithmic Sovereignty" in Social Media

This article is an interdisciplinary critical analysis of personalization systems and the gatekeeping role of current mainstream social media. The first section presents a literature review of data-driven personalization and its challenges in social media. The second section sheds light on increasing concerns regarding algorithms' ability to overtly persuade—and covertly manipulate—users for the sake of engagement, introducing the emergence of the exclusive ownership of behavioral modification through hyper-nudging techniques. The third section empirically analyzes users' expectations and behaviors regarding such data-driven personalization to frame a conceptualization of users' agency. The fourth section introduces the concept of "algorithmic sovereignty." Current projects that aim to grant this algorithmic sovereignty highlight some potential applications. Together this novel theoretical framework and empirical applications suggest that, to preserve trust, social media should open their personalization algorithms to a social negotiation as the first step toward a more sustainable social media landscape. To decentralize the immense power of mainstream social media, guarantee a democratic oversight, and mitigate the unintended undesirable consequences of their algorithmic curation, public institutions and civil society could help in developing and researching public algorithms, fostering a collective awareness so as to eventually ensure a fair and accountable "algorithmic sovereignty.

Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions

An online recommendation system (RS) involves using information technology and customer information to tailor electronic commerce interactions between a business and individual customers. Extant information systems (IS) studies on RS have approached the phenomenon from many different perspectives, and our understanding of the nature and impacts of RS is fragmented. The current study reviews and synthesizes extant empirical IS studies to provide a coherent view of research on RS and identify gaps and future directions. Specifically, we review 40 empirical studies of RS published in 31 IS journals and five IS conference proceedings between 1990 and 2013. Using a recommendation process theoretical framework, we categorize these studies in three major areas addressed by RS research: understanding consumers, delivering recommendations, and the impacts of RS. We review and synthesize the extant literature in each area and across areas. Based on the review and synthesis, we surface research gaps and provide suggestions and potential directions for future research on recommendation systems