Research
Saffarizadeh, Keil, Boodraj, and Alashoor (2023)
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.
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.
Leveraging Customer Feedback Through App Reviews
Saffarizadeh, Jabr, and Keil (2016)
Online app markets (e.g. Apple App Store) exhibit heavy customer engagement in the form of reviews that could help software developers adjust to user needs and become more competitive in crowded app markets. Using a panel dataset on 12,231 apps and document similarity methods, we develop a model that relates app success to developers’ integration of user feedback.
Amanda Project
Amanda is a custom-designed conversational assistant that I developed to conduct experiments on human-conversational-AI interaction. The project comprises of multiple studies each of which designed to shed light on one aspect of the interaction. Amanda consists of an Android app and a back-end administration website that remotely controls the app and provides machine learning and artificial intelligence capability for the app.
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
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
Mark Keil
Senior Editor, MIS Quarterly
Regents’ Professor of the University System of Georgia and John B. Zellars Professor of Computer Information Systems
Georgia State University
Likoebe M. Maruping
Senior Editor, MIS Quarterly
Professor of Computer Information Systems and a member of the Center for Digital Innovation (CDI) in the J. Mack Robinson College of Business at Georgia State University
Nicholas Berente
Associate Editor, MIS Quarterly
Viola D. Hank Associate Professor of IT, Analytics, and Operations at University of Notre Dame
Zhenhui (Jack) Jiang
Associate Editor, Information Systems Research
Professor and Area Head of Innovation and Information Management at HKU Business School.
Wael Jabr
Assistant Professor
Penn State University
Yumeng Miao
Ph.D. Student
University of Wisconsin-Madison
Tawfiq Alashoor
Assistant Professor
Copenhagen Business School
Maheshwar Boodraj
Assistant Professor
Boise State University
Hyoungyong Choi
Assistant Professor
Hankuk University of Foreign Studies
Alan Yang
Assistant Professor
University of Nevada, Reno