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 (2020)
Decisions Under the Illusion of Objectivity: Digital Embeddedness and B2B Purchasing
Haris Krijestorac, Rajiv Garg, and Prabhudev Konana
Published in Production and Operations Management (2021)
Personality-Based Content Engineering for Rich Digital Media
Haris Krijestorac, Rajiv Garg, and Maytal Saar-Tsechansky
Invited for major revision at MIS Quarterly
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
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 how beneficial these investments are. 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 are more influenced by emotional stimulation.
The Streisand Effect: Understanding the Effects of De-Platforming in Social Networks
Haris Krijestorac, Matthew Yeaton, and Sri Kudaravalli
With the Internet’s catalyzation of crowd-driven content of potentially dubious veracity, policy makers and online platforms alike struggle 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 themselves 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 Reddit bans on the diffusion of content associated with the banned account. We hypothesize that while the suspension may decrease both the sentiment and salience of content promoted by the de-platformed account overall, it will likely backlash within the group of followers associated with the account. Moreover, we expect that the latter effect may be amplified when the banned account has greater centrality in the Reddit network. Lastly, we explore variation in the aforementioned effects across topics associated with the de-platformed account.
The Effects of Algorithmic Transparency and Personalization on Information Disclosure: A Cross-Cultural Analysis from a Global Field Experiment
Cathy Yang, Xitong Li, Ai-Ting Goh, and Haris Krijestorac
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 information individuals are willing to disclose, as so as to better promote their algorithmic advisors. Our study examines two salient dimensions of the privacy-information tradeoff: personalization, or the extent to which data individual-level data about a user is leverages, and transparency, or the extent to which it is disclosed that such information is used. 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 business school. We find that high transparency tends to decrease clickthrough, and that this effect is particularly salient under high personalization. We argue that under conditions of high personalization, users feel more vulnerable to disclosing personalized information, and are thus particularly triggered by high transparency, which reminds them that their information is being used. This unique result is somewhat non-intuitive, as transparency does not have the typical effect of increasing user trust by increasing perceived knowledge over how personal data is used.