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Functional Architectures for Knowledge Seeking and Discovery
Published in Denise Bedford, Knowledge Architectures, 2020
Different working and social environments give rise to different discovery needs – so the everyday competencies of discovery are increasingly crucial to everyone. It is essential to distinguish between what we know from formal and informal theories of seeking and discovery behavior. The early theories focus on explicit information, final products, and finished and published information. (e.g., Zipf’s Principle of Least Effort, Brenda Dervin’s Sense-Making, and Elfreda Chatman’s Life in the Round) investigate the processes that surround information seeking). A review of the literature on information-seeking behavior shows that information seeking has generally been accepted as dynamic and non-linear (Foster, 2005; Kuhlthau, 2006; Ross, 1983). People experience the information search process as an interplay of thoughts, feelings, and actions (Case, 2007; Kuhlthau, 2006; Robinson, 2010; Wilson 1999); therefore, they developed a nested model of conceptual areas, which visualizes the interrelation of the here-mentioned central concepts. Wilson defines models of information behavior to be ‘statements, often in the form of diagrams, that attempt to describe an information-seeking activity, the causes and consequences of that activity, or the relationships among stages in information-seeking behavior’ (1999, p. 250). These representations are not unlike the use case scenarios described earlier. These theories are groundbreaking, though they do not address the challenge of different levels of quality of information and the means of distinguishing between what to trust and what not to trust. Instead, they focus on the goodness of search and its results. This early research dates back to the early 1950s and 1960s and comes from the early documentalists and engineers. This early research evolved into a focus on library collections and databases of information. From this foundation, our focus on search evolved. This area of practice establishes a good understanding of how people seek and discover explicit information.
Exploring Influence Factors of WeChat Users’ Health Information Sharing Behavior: Based on an Integrated Model of TPB, UGT and SCT
Published in International Journal of Human–Computer Interaction, 2021
Before pursuing research questions about information sharing behavior in social media, first and foremost, the definition of “information behavior” should be discussed. As contended by Wilson (2000), information behavior is “the totality of human behavior in relation to sources and channels of information, including both active and passive information seeking and information use. Thus, it includes face-to-face communication with others, as well as the passive reception of information as in, for example, watching TV advertisements, without any intention to act on the information given (p. 49).” From this definition of information behavior, we contend that information behavior includes information encountering, information seeking, information scanning, information sharing and so on. Information should be obtained first by individuals before they share with others. Social media has created a virtual space for people to obtain and provide information. Information with widely exposed on social media will have more opportunity to be noticed, and then gain more chance to be shared and have great influence. Otherwise, other information without widely shared would not be reached to users and only have little impact (Park, 2019). That means information on social media must satisfy users’ information needs first, then could have the opportunity to be disseminated. Therefore, information encountering, seeking, or scanning are the basis of information sharing, as well as information sharing behavior plays a vital role in information dissemination. Information sharing is considered as a dimension of information use and is an important and hot research topic in the field of knowledge management. It is a voluntary behavior that people can discuss with others who have similar information needs (Jarvenpaa & Staples, 2000).
An empirically grounded sociotechnical perspective on designing virtual agents for older adults
Published in Human–Computer Interaction, 2020
In turn, in this paper we aim to explore the feasibility of one type of information behavior theory, in the form of information models, to inform the sociotechnical approach to the design of user-facing AI systems, especially for marginalized users such as older adults. Information behavior models can provide valuable insight into the processes that users engage in to acquire, manage, process, and understand information. These models have been used extensively in the library and information sciences to explain people’s interactions with information but have been underused in HCI research.