Research

Published work

Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls

Haris Krijestorac, Rajiv Garg, and Vijay Mahajan

Published in Information Systems Research

https://pubsonline.informs.org/doi/10.1287/isre.2019.0897

Decisions Under the Illusion of Objectivity: Digital Embeddedness and B2B Purchasing

Published in Production and Operations Management

Haris Krijestorac, Rajiv Garg, and Prabhudev Konana

https://onlinelibrary.wiley.com/doi/10.1111/poms.13363

Working Papers

Personality-Based Content Engineering for Rich Digital Media

Haris Krijestorac, Rajiv Garg, and Maytal Saar-Tsechansky

Invited for major revision at MIS Quarterly

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3366561

In Progress

To Use or Not to Use Samuel L. Jackson? Understanding the Role of “Voice of Machine” in Consumer Decisions

Haris Krijestorac, Rajiv Garg, and Vijay Mahajan

Abstract:

The rise of voice-driven personal assistants such as Amazon Alexa and Apple Siri has created opportunities to promote products through these channels. In response, firms have attempted to boost sales by investing in celebrity voiceovers, as initiated by Amazon’s commissioning of Samuel L. Jackson for Alexa. Yet, it remains uncertain what benefits are reaped from such investments. Moreover, it is unclear whether such benefits can be attributed to the celebrity themselves, i.e., Jackson, or whether there are inherent properties of a voice, such as its frequency, volume, or inflections, that make it effective. To help firms better hone their voice selection, we investigate the role of different voice types on purchase behavior. Based on over 600 voice samples from celebrities, google voices, and voice actors, we identify four voice clusters – authoritative, motivational, seductive, and ostentations – based on eight stochastic acoustic properties of the voice, including frequency, amplitude, and harmonics-to-noise ratio. In addition to estimating the effectiveness of each cluster on the purchase of hedonic and utilitarian goods, we explore the benefits of celebrity in each of these categories. We hypothesize that while utilitarian products likely benefit from the trustworthiness reflected in authoritative and motivational voices, such as that of Samuel L. Jackson, sales of hedonic products are likely to increase with the use of voices in the ostentations or seductive categories, such as that of Will Smith. Moreover, we expect that celebrity status will be beneficial only in the sale of hedonic products, whose sales require more emotional stimulation.


The Streisand Effect: Understanding the Effects of De-Platforming in Social Networks

Haris Krijestorac, Matthew Yeaton, and Sri Kudaravalli

Abstract:

With the Internet’s catalyzation of crowd-driven content of potentially dubious veracity, policy makers and online platforms alike struggle with how to manage the diffusion of content deemed pernicious. Although the legality of such measures is a common topic of discourse, the effectiveness of de-platforming measures is arguably less scrutinized. Indeed, numerous anecdotes illustrate the potential for information suppression in social networks to backfire. One such example includes Barbara Streisand’s hiding of her Malibu beach house in 2003, which allegedly provoked even greater interest from the public. Our research extends this analogy to the context of online social networks, whose information diffusion patterns are differ from those of offline networks. Specifically, we examine the effects of Twitter bans on the diffusion of associated content. For example, was the ban of Alex Jones effective in diminishing the types of content produced by his account, or did the backlash from his disgruntled followers lead to even more such information being disseminated? We hypothesize that this backlash effect may be more likely coming from accounts with greater followings, as well as greater betweenness centrality in the network.


The Effects of Algorithmic Transparency and Personalization on Information Disclosure: A Cross-Cultural Analysis from a Global Field Experiment

Cathy Yang, Ai-Ting Goh, Xitong Li, and Haris Krijestorac

Abstract:

Given the prevalence of algorithms assisting humans in making decisions, it is imperative that companies understand whether and why individuals are willing to adopt algorithmic advice. With the introduction of data protection policies such as GDPR and the California Privacy Act, companies are required to disclose what information is used by an algorithmic advisor, and for what purpose. Thus, firms would benefit from understanding what individuals are willing to provide, as so as to better promote their algorithmic advisors. Our study examines two salient dimensions of the privacy tradeoff: transparency, or the extent to which the firm admits to leveraging user-level information, and personalization, or the extent to which the company discloses to their user that their individual-level information is utilized. To explore this tradeoff along these dimensions, we conduct a large-scale global field experiment on Facebook in which we manipulate the transparency and personalization components in the context of promoting admissions to a master’s degree program in a highly-ranked university. Specifically, we are interested in understanding first, whether an increase in algorithmic transparency increases potential candidates’ willingness to adopt an algorithmic advisor and/or interest in the programs offered by an educational institution. Second, we want to explore whether conditional on using algorithmic advisor, transparency in algorithm encourage users to disclose information in exchange for advice in their application to the institution. Finally, given the large scale and global level of our study, we explore whether cross-cultural differences may explain individuals' willingness to share personal information.