Kambiz Saffarizadeh

Assistant Professor of Management at Marquette University

Research

Relevant Outlets

"Be disciplinary while being interdisciplinary!"

My Contributions

Conversational Assistants:
Investigating Privacy Concerns, Trust, and Self-Disclosure

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.

Update Assimilation in App Markets:
Is There Such a Thing as Too Many Updates?

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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Leveraging Customer Feedback Through App Reviews

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.

Supporting Methods and Techniques

Econometrics

Structured Equation Modeling

ANOVA

Deep Learning

Topic Modeling

Support Vector Machine

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ResearchGate
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