Design and Appropriation of Knowledge and AI Systems
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ItemFacing the Artificial: Understanding Affinity, Trustworthiness, and Preference for More Realistic Digital Humans( 2020-01-07)In recent years, companies have been developing more realistic looking human faces for digital, virtual agents controlled by artificial intelligence (AI). But how do users feel about interacting with such virtual agents? We used a controlled lab experiment to examine users’ perceived trustworthiness, affinity, and preference towards a real human travel agent appearing via video (i.e., Skype) as well as in the form of a very human-realistic avatar; half of the participants were (deceptively) told the avatar was a virtual agent controlled by AI while the other half were told the avatar was controlled by the same human travel agent. Results show that participants rated the video human agent more trustworthy, had more affinity for him, and preferred him to both avatar versions. Users who believed the avatar was a virtual agent controlled by AI reported the same level of affinity, trustworthiness, and preferences towards the agent as those who believed it was controlled by a human. Thus, use of a realistic digital avatar lowered affinity, trustworthiness, and preferences, but how the avatar was controlled (by human or machine) had no effect. The conclusion is that improved visual fidelity alone makes a significant positive difference and that users are not averse to advanced AI simulating human presence, some may even be anticipating such an advanced technology.
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ItemInspired by Emotions, Guided by Knowledge: Which Emotional Cues Dominate Knowledge Management Research?( 2020-01-07)Knowledge, being context-specific and bound to individuals, is strongly related to human emotions such as joy or fear. Although emotions play an important role to articulate knowledge in text, KM research only offers insight on emotions from specific angles, neglecting a holistic view. Applying a sentiment analysis, this study closes the aforementioned gap by investigating the occurrence of emotions in KM publications. Based on general sentiment dictionaries, we (1) develop a dictionary aligned with KM, and (2) apply it to KM publications to determine the presence of positive and negative emotions and categorize them according to an emotion scale. Our results reveal that a variety of emotions is expressed in KM studies, both positive and negative, proving its relevance for this domain. We find that there is high term diversity, but also the need for consolidation of terms as well as emotion categories in KM.
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