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 one thousand voice samples from celebrities, google voices, and voice actors, we identify clusters of voices based on eight stochastic acoustic properties of voice, including frequency, amplitude, quality, and tempo. Using generated voice samples for both celebrities and non-celebrities corresponding to each identified voice cluster, we explore the effectiveness of both cluster (e.g., authoritative, seductive) and celebrity status on purchase of hedonic and utilitarian goods.
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 Role of Emotional Arcs in Generating User Engagement on Social Media Platforms
Reza Alibakshi, Haris Krijestorac, and Shirish Srivastava
The rise of "story" features on various social media platforms has made it more imperative that content creators craft well-designed narratives to attract user attention. Such narratives require creators to think beyond one-dimensional content features (e.g., positive or negative sentiment) and focus more on dynamic features (e.g., emotional highs and lows) that truly stimulate an audience. To explore how to craft such narratives, we explore which emotional properties of Instagram posts lead to greater engagement. Examining the emotional variability and inertia of five key emotions (happiness, surprise, anger, fear, sadness) of Instagram posts from Fortune 500 firms, we find that the shapelets corresponding to these emotions can predict the performance of posts with high accuracy, and can be identified to prescribe content creation strategies. Our findings contribute to content engineering literature by showing that for rich, complex media such as stories and videos, empirical insights can be extracted to inform intuition used by content creators to achieve greater audience engagement.
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.