## 2 Literature Review and Theoretical Foundation
### Mediating Effect of Emotional Response  
Emotions are an important component of both human-human communication and human-machine interaction [101; 102]. Any interface that disregards a user's emotional state or fails to display the proper emotion risks being viewed as \"cold, socially inept, untrustworthy, and incompetent\" [102] (p. 14). Taken from the psychology literature, the Affect Infusion Model (AIM) explains that people use their emotional state as data when making a judgment [32]. In other words, it explores how emotions are infused into thoughts as people process information, which results in response behaviors during interactions with others. An emotion is defined as a brief but powerful feeling resulting from a clear cause and cognitive content [32]. For example, \"if a situation makes you feel scared (an intense feeling that has clear cause and cognitive content), then you interpret the situation as being dangerous (short lived until out of danger)\" [103] (p. 19). The emotional response is described in a two-dimensional space that is spanned by the two dimensions, \"valence\" and \"arousal\", which are known to be distinct from one another [104]. Arousal assesses the intensity of an emotional state, whereas emotional valence specifies whether an emotion is positive or negative. AIM argues that the extent to which emotional response dictates judgment depends on the individual's motivation level going into the judgment. When motivation is low or judgments are made fast, this model predicts that mood will greatly affect judgment. This type of processing is known as Heuristic processing or Affect-as-information [105; 106]. Referring to the ELM proposed by [PERSON] and [PERSON] [28] and discussed above, the heuristic processing is comparable to the peripheral route to processing information [32]. This processing happens because people often want to achieve judgment with the minimum possible effort, which could include considering only a small portion of the available data and relying on whatever shortcuts or simplifications they can find in a given situation [107]. For example, when asked to form an opinion about a suggested product, individuals can base their judgment on the simple question \"How do I feel about it?\" rather than recalling the features of the target [106]. Thus, in this case, affect--the emotions felt in the moment--becomes information and impacts judgment. This research uses peripheral cues to influence users' information disclosure behaviors. Based on the AIM, these cues would be processed heuristically by users. Specifically, in the face of these cues, users will be less inclined to judge extensively whether to answer the questions being asked by the chatbots. In other words, users would simply rely on their emotional state in response to the available cues in the interaction environment--such as the information disclosure nudges presented in this research--to base their decision on whether to disclose information or not. Although research on users' emotional response in chatbot interactions has been conducted, few employ the AIM to ground their work. Moreover, the contexts that have been studied do not include question sensitivity and information disclosure behaviors in an e-commerce setting. For example, [PERSON] and [PERSON] [108] underlined that the mood of the chatbot itself may have an impact on the users' inclination to continue the interaction in a context where chatbots are used as pedagogical agents to children in primary school. On the other hand, [PERSON] et al. [60] discovered that when a chatbot providing support in a mental health context uses language that conveys emotional states, it draws users' cognitive attention to the social component of their interaction partner, increasing the feeling of co-presence. Similarly, [PERSON] and [PERSON] [33] studied the role of empathy in chatbots' ability to provide comforting medical information. Finally, in the context of customer service, [PERSON] et al.