IT Enabled Collaboration for Development
Permanent URI for this collectionhttps://hdl.handle.net/10125/112409
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Item type: Item , Too Many LLMs to Choose? Design and Trust in Multi-AI Collaboration Systems(2026-01-06) Zeng, Ang; Cheng, Xusen; De Vreede, TriparnaAlthough generative artificial intelligence (GAI) has demonstrated potential in many fields, it still struggles with issues such as limited knowledge and hallucinations. To address these issues, we present a multi-AI collaboration system. Our study takes a multimethod approach, combining design science research (DSR) with quantitative methods. Our aim is to explore the design of effective multi-AI systems and how these affect team trust compared to single-AI setups. We also aim to investigate the differences in users' collaboration experiences between the two models. Our results reveal that multi-AI systems can significantly enhance users' trust in AI, particularly with regard to perceived ability. Users also reported a higher willingness to use multi-AI collaboration and greater satisfaction with it. However, they also noticed more perceived conflict when working with multiple AIs. These findings will help to create more reliable and user-friendly AI collaboration tools, improving the way in which humans and AI can work together effectively.Item type: Item , Collaborative Use of Information Systems: Joint IT Use Analysis Framework and Typology(2026-01-06) Tchanou, Armel Quentin; Léger, Pierre-Majorique; Fredette, MarcInformation systems (IS) often involve joint use of information technology (IT), named joint IT use, where multiple users share an IT interface. However, IS literature suggests gaps in understanding group-level joint IT use patterns and their antecedents, consequences, and mechanisms. This paper proposes a holistic framework of joint IT use, offering a typology based on technical elements (system and task) and action levels (user and user group). The framework, inspired by the Input-Mediators-Output-Input model, consists of three layers. (1) Inputs, including characteristics at system, task, individual, group, and organization levels, plus IS triggers; (2) Mediators, made of individual-level, group-level, and cross-level configurations of emotions, cognitions, and behaviors, system attributes, and task configurations; (3) Outcomes, made of consequences of the Mediators layer, which influence the Inputs layer. This framework supports future research in IT use, including on multilevel view of IT use and real-time IT use patterns.Item type: Item , Self-censorship on Online Misinformation: Understanding the Complexity of Individual Mental Process(2026-01-06) Zhu, Zhe; Chen, Anying; Zhang, Nan; Yan, ChangwuThe management of social media misinformation has become a critical governance priority for all governments. Self-censorship as an autonomous governance mechanism for internet users has been empirically demonstrated to constitute an effective strategy for mitigating online misinformation dissemination. Motivated by deficiencies in proactive self-censorship behaviors within current misinformation management practices and corresponding theoretical gaps, this study develops a comprehensive self-censorship framework to systematically explicate the complex individual mental process in which distinct actor groups conceptualize, interpret, and respond to self-censorship behaviors. The framework identifies five interconnected determinants: guanxi networks, individual attributes, cognitive schemas, motivational drivers, and contextual contingencies, which collectively influence self-censorship propensity in misinformation containment. These factors subsequently shape reactive behavioral outcomes, with guanxi moderating the relationship between self-censorship behaviors and reactive behaviors. This research advances theoretical understanding of individual mental process through its process-oriented examination of self-censorship dynamics while providing practical insights for developing culturally-attuned misinformation intervention strategies.Item type: Item , Introduction to the Minitrack on IT Enabled Collaboration for Development(2026-01-06) Cheng, Xusen; Yan, Xiangbin; Fan, Weiguo (Patrick)