## 2 Literature Review and Theoretical Foundation  
### Chatbots as Recommendation Agents and Users' Privacy Concerns  
Recommender systems have been used by companies in a plethora of industries for a long time [41]. Recently, the same ability to recommend products and services has been given to chatbots, known as \"recommendation agents\", and employed by e-commerce organizations [42]. These systems powered by AI perform by using algorithms combining data collected from users and the company's databases with pattern matching, machine learning, and natural language to provide personalized recommendations to users [43].  
Data is collected from multiple sources, including the direct messages exchanged between the chatbot and the user [43].  
Most of the earlier research on recommender systems and chatbots focused exclusively on delivering the right recommendation to the user [44; 45; 46]. However, it was later found that these agents, because of the way they operate, increase privacy concerns, which in turn negatively impacts user experience. [PERSON] and [PERSON] [12] found that perceived privacy risk reduces the level of users' satisfaction with chatbots. [PERSON] et al. [13] (p. 11) established that \"the respondents generally had privacy concerns, which negatively affected the intended usage frequency of chatbots.\"  
Privacy concerns refer to the \"users' uncertainty about using chatbot services because of potential negative outcomes associated with the revealing of customers' information\" [12] (p. 6)--such as phone numbers, names, or addresses--which can be exploited by companies and/or shared with unauthorized third parties [47].  
Therefore, tension exists between the firm's business need for collecting and analysing consumer data in order to customize experiences on the one hand, and the users' desire for privacy on the other [48]. This phenomenon is known in the marketing literature as the _personalization-privacy trade-off_[48]. Chatbots used to personalize the experience embody this dichotomy: when customers use chatbots as recommender systems, they are placed in a trade-off situation between personalized product recommendations and privacy invasion [47].  
Research shows that privacy concerns negatively impact users' information disclosure to chatbots [18]. Knowing this, research has studied strategies to decrease users' privacy concerns and in turn, increase users' disclosure to chatbots. Strategies studied include giving the chatbot anthropomorphic cues such as adapting the chatbot's messages to evoke emotion to build rapport with users [18] or giving the chatbot a human name and qualities to increase the sense of social presence [49]. However, these strategies are centered on the business need, where the goal is to gain more data from customers (e.g., [50]). The _status quo_ is that information disclosure is unilateral from the user to the chatbot. Each time a user engages with a chatbot, the information asymmetry as well as the chatbot's power increases [51]. This represents a problem as \"the party with less information, [the user], may not make fully informed choices or may have made different choices if they had the same information as the other party in the exchange\" [51] (p. 928). However, the study of information disclosure from a user's perspective--so as to make users aware of and potentially decrease their disclosures to chatbots--has been overlooked in the literature.