Overview and details of the sessions of this conference. Please select a date or room to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
This agenda is preliminary and subject to change.
Please note that all times are shown in the time zone of the conference. The current conference time is: 19th May 2024, 11:58:46pm CST
Session Chair: Hui Yan, Renmin University of China
Location:Room 5
Events V on 3F
3F沙龙V
Presentations
Understanding Researchers’ Data-Centric Tasks: A Classification of Goals, Gaps, and Resources
G. Sun1, C. Liu2, S. Peng2, Q. Li3
1National Institute of Education, Nanyang Technological University, Singapore; 2Wuhan University School of Information Management, Wuhan, China; 3Department of Information Resources Management, Nankai University Business School, Tianjin, China
In an era where data reuse is increasingly central to research efficacy, this study delves into the granularity of data-centric work tasks and addresses task goals, the challenges researchers encounter (i.e., the gaps), and the essential resources for these tasks. Utilizing a systematic literature review, we articulate a classification framework that identifies four distinct goal families and twelve goal categories. Within the goal families of “Research” and “Data”, goals are further characterized as either exploratory, confirmatory, or balanced. Our results demonstrate that the nature of goals has implications for how researchers anticipate gaps and resources. Specifically, those with more defined (confirmatory and balanced) goals predict the hurdles they will face and are proactive in identifying resources, whereas those with exploratory goals show less foresight in challenges but seek a wider range of potential resources. This study enhances our understanding of the complex interplay among goals, gaps, and resources in data-centric research tasks, offering avenues for more targeted research support services.
Heuristic intervention for algorithmic literacy: From the perspective of algorithmic awareness and knowledge
J. Liu1, G. Sun1, D. Wu1,2
1Wuhan University, China, People's Republic of; 2Center of Human-Computer Interaction and User Behavior Wuhan University
Improving algorithmic literacy empowers people to engage with algorithm-driven products across myriad applications with material impact. However, there remains a shortage of interventions aimed at nurturing algorithmic lit-eracy within everyday life. With algorithmic awareness and algorithmic knowledge serving as vital pillars for algorithmic literacy, we initiated a heuristic intervention. This intervention drew upon official information from platforms including Taobao, Bilibili, and Weibo, integrating stimulus questions and heuristic materials. We conducted online experiments, amassing a dataset of 622 responses. The outcomes of our data analysis substantiated the efficacy of the heuristic intervention in bolstering algo-rithmic awareness and advancing algorithmic knowledge. Moreover, the re-sults indicated a positive correlation between users' algorithmic knowledge and their algorithmic awareness, suggesting that as users' algorithmic knowledge increased, so did their level of algorithmic awareness. These findings hold substantial significance for guiding future practices in the cultivation of algorithmic literacy.
Understanding Users’ Decision-making on Privacy Disclosure From a Configurational Perspective
X. Chen, R. Yang
Shanghai University, China
Based on the privacy calculus model, users traditionally made decisions about privacy disclosure by weighing perceived values against privacy concerns. However, recent studies indicate that users’ cognitive style and trust also play significant roles in a social media context. This study examines how these factors collectively influence users’ privacy disclosure behavior from a configurational perspective. Through an online survey, we collected data from 452 respondents on a Chinese social media platform. The results reveal that users’ decision-making on privacy disclosure is a complex process with various configurations. For individuals with a field-dependent cognitive style, cognitive style is more important than the trade-off between perceived values and privacy concerns. On the other hand, for field-independent individuals, decisions are not only influenced by the trade-off between perceived values and privacy concerns but also by trust. We finally discuss the theoretical and practical implications of these findings.
