Rajiv Garg

Associate Professor of Information Systems & Operations Management Emory University, Goizueta Business School

  • Atlanta GA

Expert on AI, Digital Strategy, and the Economics of Human–Machine Interaction

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Biography

Dr. Rajiv Garg is an Associate Professor of Information Systems and Operations Management at Goizueta Business School at Emory University. His research examines how digital technologies, algorithms, and artificial intelligence shape human decision-making, learning, and economic outcomes. Using interdisciplinary methods from economics, statistics, and computer science, his work focuses on information flow in digital platforms, human–AI interaction, and the unintended consequences of algorithmic systems across domains such as education, marketing, labor markets, disaster response, and emerging technologies.

Dr. Garg’s research has been published in leading academic journals including Management Science, MIS Quarterly, Information Systems Research, Production and Operations Management, and the Journal of Management Information Systems, among others. His scholarship has been supported by more than $1 million in competitive research funding and has been presented widely at international conferences and universities across the globe. Beyond academia, his work has informed industry practice and public policy, and has received coverage in major media outlets such as Forbes, Fortune, CIO, and national news organizations.

At Emory, Dr. Garg plays a central leadership role in advancing AI education and research. He serves as Chair of the Goizueta AI Taskforce, Faculty Advisor to the Emory Center for AI in Learning (CAIL), and Faculty Advisor to the Goizueta Center for AI in Finance (GCAIF). He is also a member of the U.S. AI Safety Institute Consortium (US AISIC), where he contributes to national efforts on AI risk management, model evaluation, and safety standards. As ISOM PhD Program Area Coordinator, he oversees doctoral training, mentoring, and placement of PhD students.

Dr. Garg is an award-winning educator who has taught thousands of students across undergraduate, graduate, executive, and doctoral programs at Emory University, The University of Texas at Austin, and Carnegie Mellon University. His teaching has been recognized with multiple Distinguished Educator Awards, including Emory University’s Provost Distinguished Teaching Award for Excellence in Graduate and Professional Education. He is also a frequent keynote speaker and public intellectual, regularly engaging global audiences on artificial intelligence, analytics, and the future of work.

Education

Carnegie Mellon University

PhD

Information Systems & Management

Carnegie Mellon University

M.Phil.

Public Policy & Management

University of Southern California

MS

Computer Science (Databases & Networks)

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Areas of Expertise

Artificial Intelligence (AI) for Humanities
Economics of Information
Social Media
Digital Marketing
Mobile Economy
Business Analytics
Network Data Science
Labor Market
Digital Entrepreneurship
Artificial Intelligence

Publications

Domain anchorage in LLMs: Lexicon profiling and unintended information leakage

Data & Policy

Lekha Challappa , Zijin Zhang, Rajiv Garg

2025-10-27

This study investigates unintended information flow in large language models (LLMs) by proposing a computational linguistic framework for detecting and analyzing domain anchorage. Domain anchorage is a phenomenon potentially caused by in-context learning or latent “cache” retention of prior inputs, which enables language models to infer and reinforce shared latent concepts across interactions, leading to uniformity in responses that can persist across distinct users or prompts. Using GPT-4 as a case study, our framework systematically quantifies the lexical, syntactic, semantic, and positional similarities between inputs and outputs to detect these domain anchorage effects. We introduce a structured methodology to evaluate the associated risks and highlight the need for robust mitigation strategies. By leveraging domain-aware analysis, this work provides a scalable framework for monitoring information persistence in LLMs, which can inform enterprise guardrails to ensure response consistency, privacy, and safety in real-world deployments.

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Working Papers/Projects

The Impact of Generative Artificial Intelligence on Higher Education: Disruption or Seamless Integration?

AI in Education

2024-08-01

Despite widespread adoption of generative artificial intelligence (GenAI) in higher education, theory and evidence remain limited regarding where and how AI integration enhances learning outcomes. Drawing on Cognitive Load Theory and Dual-Process Theory, I develop a framework explaining how content generation source (human versus AI) and delivery mode (human instructor versus AI avatar) jointly influence learning. I test this framework using a randomized field experiment (N=107) with 2×2 factorial design manipulating content source and delivery mode in an online database programming course. Results strongly support hypothesized main effects: human-generated content outperforms AI-generated content by 6.7%, while AI delivery outperforms human delivery by 3.6%. The optimal configuration of human-generated content delivered by AI avatar yields 8.4% higher performance than fully AI-generated instruction, demonstrating complementarity in bundled instructional design. Content analysis reveals that AI-generated materials exhibit uniformly high linguistic complexity while human-generated materials show pedagogical variability, explaining performance differences through cognitive load mechanisms. Practically, findings demonstrate that GenAI's value lies not in replacing human pedagogy but in augmenting it through consistent, scalable delivery of human-designed content, suggesting a strategic division of labor where humans design and AI executes.

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Research Spotlight

5 min

Expert Insight: The Voice of Alexa: How Speech Characteristics Impact Consumer Decisions

In the 2020 film “Superintelligence,” an all-powerful artificial intelligence attempts to take over the world, and it studies an average person, played by Melissa McCarthy, to decide if humanity is worth saving. The AI is voiced by James Corden—a voice it chooses because it knows it’s one McCarthy’s character will engage with. Rajiv Garg, associate professor of Information Systems & Operations Management at Emory’s Goizueta Business School, shows the “Superintelligence” trailer before his research presentations to set the tone. Garg conducts research that explores the impact of artificial intelligence voices on consumer behavior and purchase intent, along with Haris Krijestorac, a professor at HEC Paris, and Vijay Mahajan, a professor from The University of Texas at Austin. Garg’s research began when Amazon launched celebrity voices for its Alexa device in 2019. From Samuel L. Jackson to Shaquille O’Neal, users can now get their news and entertainment, while interacting with their favorite superstars. “I questioned if certain voices could get more engagement or more purchases from consumers,” Garg says. If Alexa starts talking to you in Samuel L. Jackson’s voice, will you continue the conversation? What could Samuel L. Jackson’s voice sell you that you would buy?   Garg and his team began their research by collecting more than 300 celebrity voice samples, which they analyzed based on their sound characteristics, such as amplitude, frequency, and entropy. They looked at 20 sound characteristics and identified that all the voices could be segmented into six clusters: ostentatious, colloquial, friendly, authoritative, seductive, and suave. The team then created advertisements for select products using computer generated voices for each of the six clusters, opting for artificial intelligence-created speech instead of celebrity deep fakes due to permission legalities. They chose a shoe and an office chair as their products, and created two different advertisements for each product. One ad was simple, denoting the shoe as comfortable for all-day wear and the office chair as comfortable for sitting in for extended time periods. The other ad was hedonic, denoting the shoe as crafted with Italian leather and the office chair equipped with several massage features. They recorded the four advertisements using both a female and male voice for all six voice clusters. Study participants listened to each of the four advertisements in one of the 12 voices, which was randomly selected. After the advertisement was played, participants were asked if they wanted more information, and later, if they wanted to buy the product (omitting the price as to not add another factor to their decision making). Influencing Consumer Behavior For simple, utilitarian products, they found no significant effect of voice on information seeking behavior. Garg says once participants hear this type of advertisement, they simply decide to purchase or move on. Participants do, however, engage more in information seeking behavior for hedonic products when the voice is ostentatious, seductive, or authoritative. The team also found men were more likely than women to engage with ostentatious or seductive voices, and women were more likely to engage with friendly or colloquial voices. Overall, they found participants did not seek information with male voices. For information seeking, men and women only engage if the voices are female, which is somewhat intuitive. The industry is doing this—Alexa, Google, and Siri all have a female voice. In terms of purchase intention, they found ostentatious voices have higher yields for utilitarian products. Men, especially, were more likely than women to purchase a utilitarian product advertised in an ostentatious voice. Think about advertising a stapler. It’s a stapler—it staples paper—but you advertise it in a French accent to make it sound interesting. Conversely, for hedonic products, an ostentatious voice has a negative effect on purchase intent because Garg says it can make the product sound gimmicky. Their research shows colloquial voices do the best here because people focus more on the advertisement’s content. Across the board, they found seductive voices have a negative effect on purchase intent, but more so on utilitarian products compared to hedonic ones. Men were more likely than women to respond positively to seductive and suave voices. Applying the results Voices are another way smart device companies can personalize their customers’ experiences. Garg says these companies should be aware that there may be a certain voice that will garner the best engagement. Their findings are not isolated to business, but may apply to other industries, such as the media. Garg says, for example, if publications intend to increase reader curiosity and engagement, they should use a female colloquial voice on “click to listen” features. Although not yet tested, Garg says he wouldn’t be surprised if their results extend to real-world settings with real human voices as well. During their research, Garg’s team asked participants if they had heard the advertisement voices before, and about 15 percent of respondents says they had. "These were voices we’d created for the first time,” Garg says. “If they say they’ve heard the voice before, that means they were thinking of them as human voices. Although we didn’t study it that way, I do believe what we’re seeing will be relevant for actual human being’s voices and interactions.” Having researched this for years, Garg says every time he listens to a voice, whether a customer service representative or podcast host, he questions whether or not it is impacting his behavior. A lot of times when I’m making a decision, I know that I’m making that decision passively because of the voice. “I’m acting 50 percent based on the rational information in the voice, but the other 50 percent I just want to listen more. There is an inherent desire for a certain voice.” Garg says his favorite part of the research are those “aha moments,” whether they be the influence of voice in his own life or in the industry—such as large companies using female voices in their products to draw engagement. He says he hopes to continue doing this kind of research to help startups and other companies perform better, as AI-powered voices continue to change the way people interact with technology and consume information. “We’re finding these interesting phenomena that can help create new products that are more effective,” Garg says. “I am trying to increase the economic surplus, in some ways to improve society, and this technology presents numerous opportunities.” Looking to know more?  Rajiv Garg from Emory’s Goizueta Business School is available to speak with media – simply click on his icon now to arrange an interview today.

Rajiv Garg

2 min

Personality matters: the tie between language and how well your video content performs

Why does one piece of online video content perform better than another? Does it come down to its relevance, production values, and posting and sharing strategies? Or are other dynamics at play? There are plenty of theories about what, when and how to post if you want to drive the performance of your video. But new research by Goizueta’s Rajiv Garg, associate professor of information systems and operations management, sheds empirical and highly nuanced new light on the type of language to inject in a content if you really want to accelerate consumption. And it turns out that a lot of it depends on personality. Together with Haris Krijestorac of HEC Paris and McCombs’ Maytal Saar-Tsechansky, Garg has run a large-scale study, analyzing the words spoken and used in speech-heavy videos posted to YouTube, and then organizing those words by personality – how they “score” in terms of the so-called Big Five personality traits. “The Big Five is a system or taxonomy that has been used by psychologists and others since the 1980s to organize different types of personality traits. These traits are extroversion, agreeableness, openness, conscientiousness, and neuroticism,” says Garg. “In previous research into video content performance, we’ve looked into mechanisms such as posting and re-posting on different channels and how they impact the virality of one video over another. But we were intrigued by the role of language and how different words map to these personality traits, which in turn might have an impact on user emotion or response.” Emory has this entire comprehensive article that includes more details on the Big Five and it is available for reading here: If you are a journalist looking to cover this topic – then let our experts help with your story. Rajiv Garg from Emory’s Goizueta Business School is available to speak with media – simply click on his icon now to arrange an interview today.

Rajiv Garg

5 min

Why posting to multiple channels drives virality of online videos

Back in the summer of 2012, South Korean pop star Psy released a music video on YouTube. Running at just under four minutes, “Gangnam Style” rapidly became a global sensation. Within just two months of its release, the video was attracting a daily average of nine million viewers. In late September, Guinness World Records confirmed it to be the “most liked” video on YouTube. By December it had become the first piece of content on the platform to garner more than one billion views. As of 2020, the Gangnam Style video has been seen by more than 3.7 billion people around the world. Pys’s official YouTube channel has around 14.1 million followers—a significant user base. But even assuming that each one of these followers had watched the video several times and shared it with others, it still doesn’t account for the sheer volume of views the video has racked up over time. So what’s going on? What is behind the super virality of Gangnam Style and other pieces of content that, like it, appear to defy the rules of probability on the social web? Rajiv Garg, associate professor of information systems & operations management at Emory’s Goizueta Business School, has put a new hypothesis to the test. And he’s found that there’s a clear link between virality and what he calls the “spillover effect” of posting content onto multiple platforms at specific times. “We know that when celebrities and popular figures post videos, there’s likely to be a strong response from their follower base, depending on the content. But over time, user consumption reaches a saturation point—the novelty simply wears off. And this happens around 10 days after a video is posted,” Garg said. “Yet some videos just keep on going, getting successive waves of views on the same platform in quantities that eclipse the follower base. We hypothesized that this is affected by launching on different sites and platforms, but we really wanted to understand the mechanisms behind this and figure out why this activity was occurring on the original platform as well as others—as in the Gangnam case.” Together with Vijay Mahajan (McCombs, UT Austin) and Haris Krijestorac (HEC Paris), Garg looked at the diffusion patterns for viral content on the social web. First analysis confirmed that content sharing by users was the chief primary driver of virality; indeed, views typically increased after a video would appear on a second or third platform. But this didn’t explain why those views were growing back on the original platform too. In fact, the finding ran contrary to the established view that platforms compete for content—that posting to one platform leeches user views from another. “The reasoning until now has been that social platforms cannibalize content. In other words, when you post Gangnam Style onto Vimeo, you’ll get fewer views on YouTube as a result,” Garg said. “Users will move to the other platform and watch it there instead.” But in fact, the opposite was happening. Intrigued, Garg and his coauthors deployed synthetic control—a comparative statistics methodology—to test the causal effects of sharing content to one platform versus posting it to multiple sites. This methodology involved posting 381 viral videos on 26 video-hosting sites. In addition, they ran a randomized field experiment with 30 videos that were randomly seeded onto new platforms at random times. The results across both methods were consistent. Users who were finding the videos once they had been posted to a second (or third, or fourth) platform were still sending viewers to the original platform to view the content. And viewers were coming in droves. “What seems to be happening is that content is going viral as it’s consumed on the original platform—YouTube, say—and then shared to other channels. Here, on the second channel—Vimeo, Daily Motion or others—these videos reach new audiences,” explained Garg. “But for whatever reason, once they’ve discovered the video, many of these new users prefer to go to the original channel and watch it there. And this is happening consistently and in highly significant numbers of users.” This spillover effect could be due to a number of things, says Garg. It could be that for certain audiences, content is simply more readily discoverable on certain platforms—but that these platforms are not the first choice in viewing preferences. It could also be that the content is visible to users but not viewable on the second platform. “Say Gangnam Style is seen on YouTube by a viewer and shared. It then appears on Vimeo, and a second user discovers it; but maybe this user doesn’t like Vimeo or perhaps Vimeo isn’t available in their region or country. What happens then?” noted Garg. “The simple answer is that these new users end up Googling Gangnam Style and find it on YouTube—the original platform. The novelty and virality of the first wave of users has died down, but this new wave of users comes in, creating a spillover effect that boosts the popularity of the video all over again.” Looking again at the results of their analyses, Garg and his colleagues were able to determine that the spillover effect is strongest immediately after a video is introduced onto a secondary platform, as well as at the 18and 42-day marks. “We analyzed the effect of introducing a video onto a new platform on the increase in views it generates on the original platform over time,” said Garg. “It appears the spillover mechanism is strongest during the first week but experiences spikes later on. In the long-run, we were able to generate twice as many views back on the original platform as we would otherwise have expected. So the effect really is huge.” It is also limited, however. The researchers found diminishing impact in posting content to a succession of different platforms. By the time the video is shared to a fourth or fifth platform, Garg and his coauthors saw no returns. The findings are nonetheless hugely significant for content creators, he says. “We’ve seen that content shared on different platforms sends users back to the original, and that debunks the idea that online channels cannibalize each other’s content,” Garg noted. “And we’re able to say with precision that this effect is strongest during the first week with later spikes, suggesting these may be the best times to introduce content onto new platforms.” Content creators looking to ‘viralize’ their material would do well to take a strategic, omni-channel approach based on these insights, says Garg. Multi-platform sharing is an effective way of spreading word of mouth content and reaching new audience bases—and not just nationally, he stresses. “The effect is not limited to borders or languages. Savvy content creators can create their first ripple on a YouTube or Vimeo and, as the views start falling off, go on to propagate to a second or third channel, including foreign ones,” he said. “The spillover effect is just the same. Staging and staggering your content this way, you reach completely new audiences, many of whom will spill over onto your original platform.” If you are a journalist looking to cover this topic – then let our experts help with your story. Rajiv Garg from Emory’s Goizueta Business School is available to speak with media – simply click on his icon now to arrange an interview today.

Rajiv Garg

In the News

The Symbiotic Relationship of Humans and AI

ORMS Today  print

2025-03-05

This article argues that AI should augment rather than replace humans, framing work on a continuum between no and full automation. It explains how effective human-AI collaboration depends on three reinforcing elements – engagement, trust, and mutual learning – which together improve productivity, worker well-being, adaptability, and long-term decision quality.

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AI in the Classroom: Shaping the Future of Business Education

ORMS Today  print

2024-12-10

The article argues business schools must deeply integrate AI across teaching, research, and administration. It proposes four levels of AI fluency for students, highlights AI’s transformative yet risky role in business research, and frames AI as augmenting—not replacing—administrative work to create AI-fluent, ethically grounded, human-centered business leaders.

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The ethical pros and cons of Meta’s new Llama 3 open-source AI model

Fast Company  online

2024-04-20

Experts say that while open-source could accelerate innovation, it also could make deepfakes easier.

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