Intimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers
By Youngme Moon. 2000
891 citations recorded by the source corpus.
Subfields: Marketing, Organizational Behavior and Human Resource Management, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/10b9509d6deac5f88746e8ffbe2e3b3834dede4c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Marketing
- Alessandro Acquisti
- George Loewenstein
- Claudio Alvarez
- Paul Benjamin Lowry
- Dhruv Grewal
- Susan Fournier
- Leslie K. John
- Heng Xu
In Organizational Behavior and Human Resource Management
- Claudio Alvarez
- Susan Fournier
- Robert W. Palmatier
- Marios Koufaris
- Tom L. Roberts Jr.
- Clay Posey
- P. Aggarwal
- D. Harrison McKnight
In Sociology and Political Science
- Alessandro Acquisti
- Heng Xu
- Paul Benjamin Lowry
- Tamara Dinev
- George Loewenstein
- Jeremy N. Bailenson
- Sarah Spiekermann
- Leslie K. John
Citations in the corpus
- Proposing the online community self-disclosure model: the case of working professionals in France and the U.K. who use online communities (2010, background): “In communication, ‘there is substantial evidence that people will engage in intimate self-disclosure – even with relative strangers – if they first become the recipients of such disclosure from their conversational partners’ (Moon, 2000, p. 324).”
- E-mail interviewing in qualitative research: A methodological discussion (2006, neutral): “There is evidence that people will engage in more self-disclosure when they first become recipients of such self-disclosure from their inter-viewers (Moon, 2000).”
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot (2018, background): “…ways to how they reciprocate another person ’ s disclosures, and behave in more polite ways to a computer if that computer asks questions about its own performance compared to when a di ff erent computer asks about its performance (Moon, 2000; Nass, Fogg, & Moon, 1996; Nass, Moon, & Carney, 1999).”
- Automatic prediction of frustration (2007, background): “Results from studies of human–human interaction can usefully inform the design of human–computer interaction, e.g. Reeves and Nass (1996) and Moon (2000).”
- Interactions With Robots: The Truths We Reveal About Ourselves (2017, background): “Similar to findings on racial prejudice (Brown 1995), one study found that people rated computer agents with a face of the same ethnicity as them to be more attractive, trustworthy, persuasive, and intelligent than agents of another ethnicity (Nass & Moon 2000).”
- Data Privacy: Effects on Customer and Firm Performance (2017, neutral): “…on customer privacy perceptions, empirical studies of customer data management have primarily addressed how customers disclose personal information (Moon 2000; White 2004) and begin to trust fi rms as a result of their data management processes (Bart et al. 2005; Schlosser, White, and Lloyd 2006).”
- Customers’ Willingness to Disclose Personal Information throughout the Customer Purchase Journey in Retailing: The Role of Perceived Warmth (2020, background): “Willingness to disclose personal information Self-disclosure is defined as the voluntary communication of personal information such as one’s name, preferences, and demographics to one or more recipients (Moon 2000; Mothersbaugh et al. 2012; Nguyen, Bin, and Campbell 2012; Li, Lin, and Wang 2015).”
- The Role of Trust as a Mediator Between System Characteristics and Response Behaviors (2015, background): “Within this line o f research the authors have shown that participants demonstrate gender stereotypes toward computers (Nass, Moon, & Green, 1997), politeness toward computers (Nass, Moon, & Carney, 1999), reciprocal selfdisclosure toward computers (Moon, 2000), and “similarity-attraction,” where”
- Brands as Relationship Partners: Warmth, Competence, and In-Between (2012, positive): “Technological products are especially conducive to anthropomorphism (Mick & Fournier, 1998; Moon, 2000; Turkle, 1984) since artificial intelligence and overt actions readily enable inferences of volition, motivation, and feeling on the part of the inanimate brand.”
- How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents (2020, background): “…of computers emerged in Nass and Moon ’ s work whose research found that users tend to apply gender and ethnicity stereotypes as well as social behaviors, rules, expectations, and reciprocity during their interaction with computers (Moon, 2000; Nass and Moon, 2000; Pfeuffer et al., 2019).”
- Is That Car Smiling at Me? Schema Congruity as a Basis for Evaluating Anthropomorphized Products (2007, neutral): “as a Basis for Evaluating Anthropomorphized”
- Consumer–brand relationship quality: When and how it helps brand extensions (2014, background): “Further, marketers often attempt to induce consumers to think of their brands as a living by employing personification, animism, and anthropomorphosis as well as using human characters and other attributes (Aggarwal & McGill, 2007; Moon, 2000).”
- Marketing in Computer-Mediated Environments: Research Synthesis and New Directions (2014, neutral): “•New models: Closed-form analytical models (see Moorthy 1993) frequently appear in the literature on CMEs.”
- The behavioural sequence of the financial services industry in Taiwan: Service quality, relationship quality and behavioural loyalty (2006, background): “Reciprocity is not only identified as a key feature explaining the duration and stability of relationships [Larson, 1992 ] but is also considered one of the most robust effects found in psychological literature [Moon, 2000 ].”