Research
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Saffarizadeh, Keil, Boodraj, and Alashoor (2024)
The use of conversational AI agents (CAs), such as Alexa and Siri, has steadily increased over the past several years. However, the functionality of these agents relies on the personal data obtained from their users. While evidence suggests that user disclosure can be increased through reciprocal self-disclosure (i.e., a process in which a CA discloses information about itself with the expectation that the user would reciprocate by disclosing similar information about themself), it is not clear whether and through which mechanism the process of reciprocal self-disclosure influences users’ post-interaction trust. We theorize that anthropomorphism (i.e., the extent to which a user attributes humanlike attributes to a nonhuman entity) serves as an inductive inference mechanism for understanding reciprocal self-disclosure, enabling users to build conceptually distinct cognitive and affective foundations upon which to form their post-interaction trust. We found strong support for our theory through two randomized experiments that used custom-developed text-based and voice-based CAs. Specifically, we found that reciprocal self-disclosure increases anthropomorphism and anthropomorphism increases cognition-based trustworthiness and affect-based trustworthiness. Our results show that reciprocal self-disclosure has an indirect effect on cognition-based trustworthiness and affect-based trustworthiness and is fully mediated by anthropomorphism. These findings conceptually bridge prior research on motivations of anthropomorphism and research on cognitive and affective bases of trust.
Recommended Citation
Saffarizadeh, Kambiz; Keil, Mark; Boodraj, Maheshwar; and Alashoor, Tawfiq (2024) ““My Name is Alexa. What’s Your Name?” The Impact of Reciprocal Self-Disclosure on Post-Interaction Trust in Conversational Agents,” Journal of the Association for Information Systems, 25(3), 528-568.
DOI: 10.17705/1jais.00839
Available at: https://aisel.aisnet.org/jais/vol25/iss3/9
Saffarizadeh, Jabr, and Keil (2018)
Extant literature suggests that faster app evolution (i.e., more updates over a given period of time) leads to a more successful app. But this relationship does not account for users’ limited capacity to absorb and assimilate the changes that result from a continual stream of app updates. In this study, we draw on the innovation diffusion, absorptive capacity, and readiness for change streams of research to advance our understanding of the effect of app evolution on app success. We theorize that the limited absorptive capacity of users leads to an assimilation gap that results in a curvilinear relationship that takes the shape of an inverse-U. Specifically, as app evolution increases app success increases but only to a certain point, after which as app evolution continues to increase, app success begins to decrease. We further argue that users’ readiness for change positively moderates this relationship. We conclude by discussing our theoretical contributions and implications for app developers.
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Saffarizadeh, Boodraj, and Alashoor (2017)
By the end of 2017 more than 33 million voice-based devices will be in circulation, many of which will include conversational assistants such as Amazon’s Alexa and Apple’s Siri. These devices require a significant amount of personal information from users to learn their preferences and provide them with personalized responses. This creates an interesting and important tension: the more information users disclose, the greater the value they receive from these devices; however, due to concerns for the privacy of personal information, users tend to disclose less information. In this study, we examine the role of reciprocal self-disclosure and trust within the novel and emerging context of conversational assistants. Specifically, we investigate the effect of conversational assistants’ self-disclosure on the relationship between users’ privacy concerns and their self-disclosure. Further, we explore the mechanism through which self-disclosure by conversational assistants influences this relationship, namely, the role of cognitive trust and emotional trust.
WORKING PAPERS
- Alashoor, T, Keil, M., Jiang, Z., and Saffarizadeh, K., “Data donations for advancing medical research: Mitigating the negative effect of privacy concerns on data donation decisions,” MIS Quarterly (Conditionally Accepted)
- Saffarizadeh, K., Keil, M., and Maruping, L, “Relationship Between Trust in AI Creator and Trust in AI Systems: The Crucial Role of AI Alignment and Steerability,” Journal of Management Information Systems (Conditionally Accepted)
- Saffarizadeh, K., and Keil, M., “Conversational AI Agents: The Effect of Process and Outcome Variation on Anthropomorphism and Trust,” in preparation for a 3rd round review at Information Systems Journal (Major Revision)
- Miao, Y., He, Q., Kim, S., Saffarizadeh, K. “Unveiling Creative Potential: A Multi-Method Investigation of Generative AI’s Dual Mechanisms of Exploration and Serendipity in Artwork Creation” under review at Journal of Management Information Systems
- Arnold, C., Zhiming, X., Saffarizadeh, K., Madiraju, P., “Generative AI as (Un)welcome Agents in Medical Crowdfunding: The Trust Dilemma and Moral Hazard” under review at Behaviour & Information Technology [NMDSI grant]
Amanda Project
Amanda is a custom-designed conversational assistant that I developed to explore human-AI interaction. The project includes several studies, each focusing on a specific aspect of this interaction. Amanda comprises an Android app paired with a backend administration website. This website not only remotely controls the app but also integrates machine learning and artificial intelligence capabilities to enhance the app’s functionality.
![download download](https://kambizsaffari.com/wp-content/uploads/elementor/thumbs/download-pxkldapxlz3y2ngs7v2q50736jug99s678g8f6nywk.png)
HelpAIGrow Android App
Description: HelpAIGrow is a conversational assistant designed to help researchers study human-AI interaction. The app is available on Google Play Store.
Language: Java
Licence: GPLv3
Source Code: https://github.com/saffarizadeh/HelpAIGrow
![settings settings](https://kambizsaffari.com/wp-content/uploads/elementor/thumbs/settings-1-pxkldapxlz3y2ngs7v2q50736jug99s678g8f6nywk.png)
HelpAIGrow Researcher Dashboard
Description: HelpAIGrow Researcher Dashboard is a server-side software that communicates with HelpAIGrow app. The dashboard enables the researchers to create and customize several types of experiments for the app.
Language: Python
Licence: GPLv3
Source Code: https://github.com/saffarizadeh/HelpAIGrowDashboard
Co-Authors
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Mark Keil
Georgia State University
Personal Website
Google Scholar
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Likoebe M. Maruping
Georgia State University
Personal Website
Google Scholar
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Nicholas Berente
University of Notre Dame
Personal Website
Google Scholar
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Zhenhui (Jack) Jiang
HKU Business School
Personal Website
Google Scholar
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Sung S Kim
University of Wisconsin-Madison
Personal Website
Google Scholar
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Wael Jabr
Penn State University
Personal Website
Google Scholar
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Yumeng Miao
University of Wisconsin-Madison
Personal Website
Google Scholar
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Qinglai He
University of Wisconsin-Madison
Personal Website
Google Scholar
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Tawfiq Alashoor
IESE Business School
Personal Website
Google Scholar
![](https://kambizsaffari.com/wp-content/uploads/2022/08/Boodraj_Maheshwar.jpeg)
Maheshwar Boodraj
Boise State University
Personal Website
Google Scholar
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Hyoungyong Choi
Hankuk University of Foreign Studies
Personal Website
Google Scholar
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Alan Yang
University of Nevada, Reno
Personal Website
Google Scholar