# Enhancing User Acceptance of an AI Agent's Recommendation in Information-Sharing Environments
## 1 Introduction
### Related Work  
A significant body of research has focused on how to establish human-computer trust [61; 62; 63]. [PERSON]'s and [PERSON]'s [49] model discusses the two primary components of HCT affecting overall perceived trust, described as \"cognitive-based trust\" (understanding) and \"affect-based trust\" (feelings). The study of [PERSON] et al. [64] identifies three perceptual factors that impact online trust: perception of credibility, ease of use, and risk. [PERSON] et al. [65] examined how customers might respond to AI. Although AI may be more accurate and reliable than humans, users may have reservations about AI, and these reservations tend to increase as AI moves toward context awareness [66]. The extent to which a user is confident in, and willing to act based on, the recommendations, actions, and decisions of an artificially intelligent decision aid is influenced by psychological and behavioral parameters [67]. According to the Technology and Acceptance Model (TAM), an information technology framework for understanding users' adoption and use of emerging technologies, particularly in the workplace environment, a person's intent to use (acceptance of technology) and usage behavior (actual use) of a technology are predicated by the person's perceptions of the specific technology's usefulness (benefit from using the technology) and ease of use [68; 69]. Simply, users are more likely to adopt a new technology with a high-quality user experience (UX) design, i.e., it is perceived as usable, useful, desirable, and credible [70]. For example, using an anthropomorphic design with human traits for a recommender system for open positions increased job seekers' acceptance of the underlying system. Prior research showed that, in some cases, individuals would rather trust humans than algorithms for recommending decisions [71; 72]. Another interesting angle is the excatitude of the recommendation vs. the willingness of users to be convinced. Some studies even proposed guidelines for suboptimal advice in an attempt to increase intuitiveness and thereby make it more appealing to humans [73; 74]. This insight aligns with re-orienting the conventional AI paradigm from finding ways to make the machine smarter to exploring ways to augment human capability, paving the way to unlock far greater potential in machine learning [75]. Scholarly research has found that multiple factors influence human-machine cognitive engagement, and categorizes them into usage-related (e.g., perceived usefulness and perceived ease of use), agent-related (e.g., visual attractiveness and empathy), user-related (e.g., demographic and technology expertise), and other categories [76]. User acceptance is mainly driven by usage benefits for the specific user, which are influenced by agent and user characteristics. Time limits also influence decision making. Under low time pressure, individuals have plenty of time to think carefully about which pieces of information to share [77]. It has been shown that personality trait variations significantly affect end-users' risk-taking behavior because users' online personality is linked to their offline traits, especially conscientiousness [78]. Additionally, this effect was more substantial when users' personality was correlated with the factors of internet proficiency, gender [79], age, and online activity, especially age and self-claimed proficiency (both IT and usage) impacting behavior [80]. Designing intelligent decision support systems that are cost-effective, provide tangible benefits, and produce results accepted by humans is challenging [81]. For example, it has been shown that minor adjustments to a website's design can cause dramatic behavioral changes [82]. Due to its influence on user behavior, the agent's design is crucial to convincing a user of its efficiency, defined as \"to reach an informed decision with the least possible effort\" [27]. The design, which includes many elements, should be believable, trustworthy, attractive, easy to understand, professional, relevant, and enjoyable [83].