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Awarded at the 2008 Frontiers in Service Conference

Best Practitioner Paper

Awarded by School of Management, George Mason University

MBA Faculty of the Year Award

Awarded by Goizueta Business School, Emory University

Eller Fellowship Faculty Award

Association for Information Systems : member

Institute for Operations Research and Management Sciences : member

American Economic Association : member

Customized Bundling and Consumption Variety of Digital Information Goods

Bundling Effects on Variety Seeking for Digital Information Goods

Overcoming Free-Riding in Information Goods: Sanctions or Rewards?

Problem-Solving Effort and Success in Innovation Contests: The Role of National Wealth and National Culture

Heterogeneous Submission Behavior and its Implications for Success in Innovation Contests with Public Submissions

Hi, I'm Jesse, a Professor of Information Systems and Operations Management and Senior Associate Dean of Graduate Programs at Emory University's Goizueta Business School. My research focuses on how technology impacts markets and people. Most of my research involves machine learning, econometrics, and conducting randomized experiments.

I teach classes across several programs (BBA, MBA, MSBA, MAF, Exec Ed) on AI, machine learning, data visualization, analytics, and technology strategy. Previously, I've taught classes on business operations, systems analysis & design, data networking and security, web programming, and eCommerce strategy. 

I've held faculty positions at the University of Arizona and George Mason University. I completed my PhD at the University of Minnesota, Carlson School of Management in 2008, my MS in engineering from Minnesota in 2004, and my BS in engineering from the University of Wisconsin in 1999. Before Academia, I worked as a management consultant at Accenture (I was there when it transitioned from Andersen Consulting) and as a research staff member at IBM Research. I love tech, am an active angel investor, and am currently on the board of directors of the Atlanta Technology Angels. 

I like to run, hike, ski, play/coach/watch soccer, play the guitar, and spend time with my family outdoors.

Machine Learning

Data Science

Data Visualization and Statistics

Electronic Commerce

Behavioral Economics

Online Consumer Behavior

Crowdsourcing

Cybersecurity

IT Evolution

Microeconomics

Online Privacy

Personalization Systems

Social Engineering

Personal Website

CNN Newsroom | What to know re: New E.U. Privacy Law (GDPR)

Apple Music Launch: Too Bad Steve Jobs Is Not Around

How Virtual Recommendations Shape Your Music Preferences

Emory University professors incorporating AI into learning

Faculty from various disciplines apply aspects of behavioral research to their work. Learn more about key research happening in this area. 

A Focus on BDI Research

Effort in Global Online Crowdsourced Markets

Heterogeneous Submission Behavior in Unblind Crowdsourcing Contests

Online Recommendations and Consumer Decision Making

Behavioral Economic Issues in IT-Mediated Environments

De-biasing Consumer Preferences in Recommender Systems with Interface Design

why-online-recommendations-make-it-easier-to-hit-buy

Senior Associate Dean for Graduate Programs; Roberto C. Goizueta Professor in Business Technology

When it is time to buy something online, perhaps a coffee maker, you might head to Amazon and browse items for sale. One particular model might spark interest. The product page may contain recommendations for other goods: complementary products such as coffee filters; or recommendations for different, competitor coffee maker brands offering unique features and prices.

E-commerce websites commonly use product recommendations — called co-purchase and co-view recommendations — to keep users locked into the sales funnel and increase customer retention. But what impact do these types of recommendations actually have on consumers? How do they influence one’s willingness to pay for the original product searched?

In fact, the level of influence depends on how close a consumer is to making that purchase, says Jesse Bockstedt, associate professor of information systems & operations management at Emory’s Goizueta Business School. In addition, what type of recommendation the consumer sees plays a role in purchasing as well.
To shed empirical light on this, Bockstedt teamed with Mingyue Zhang from the Shanghai International Studies University.

 “We were curious. We knew that recommendation systems are integral to how consumers discover products online – a good 35 percent of Amazon sales can be attributed to recommendations, for instance,” Bockstedt says. “But we knew a lot less about how recommendations change consumer behavior in relation to a focal product.”

Specifically, the researchers were interested in looking at the effect of complementary versus substitutable products, and what impact the price of these types of products had on consumer behavior. They also wanted to know whether these effects were more or less amplified depending on whether consumers were at the exploratory phase in the buying process or ready to go ahead and make the purchase.

To unpack the dynamics at play, Bockstedt and Zhang ran two experiments that simulated the online purchasing experience. The researchers had volunteers go through the process of evaluating different products and then report back on how much they were willing to pay for each.

“We asked volunteers to look at a product page for a computer mouse, and we randomly assigned different recommendations to that page – some that were for other mice, and others that were for goods and products that would complement the original mouse. Going through the experiment, we also manipulated the price that volunteers saw on different pages, both for the recommended substitute and complementary products,” he says.

“Finally, we looked at the effect of timing and the sales funnel. In one case we had volunteers look for a highly specific mouse and recommended a particular product page to them. To simulate the more exploratory phase, we gave them many pages and asked them to click on the one they found most interesting.”

In total, Bockstedt and Zhang put 200+ volunteers through the replica virtual purchasing experience and recorded their willingness to pay the advertised price for the focal product on scale of 0 to 100, depending on what they had seen and the point in the sales funnel they had seen the recommendations.

If you are looking to learn more about this research and the results, Emory has a full article published for reading and review.

 If you are a journalist looking to cover this topic or if you are simply interested in learning more, then let us help.

Jesse Bockstedt, associate professor of information systems and operations management at Emory’s Goizueta Business School. He is available to speak with media, simply click on his icon now – to book an interview today.



Why online recommendations make it easier to hit “buy”

online-ratings-systems-shouldnt-just-be-a-numbers-game

When you’re browsing the internet for something to buy, watch, listen, or rent, chances are that you will scan online recommendations before you make your purchase. It makes sense. With an overabundance of options in front of you, it can be difficult to know exactly which movie or garment or holiday gift is the best fit.

Personalized recommendation systems help users navigate the often-confusing labyrinth of online content. They take a lot of the legwork out of decision-making. And they are an increasingly commonplace function of our online behavior. All of which is in your best interest as a consumer, right?

Yes and no, says Jesse Bockstedt, associate professor of information systems and operations management at Emory’s Goizueta Business School. Bockstedt has produced a body of research in recent years that reveals a number of issues with recommendation systems that should be on the radar of organizations and users alike.

 While user ratings, often shown as stars on a five- or ten-point scale, can help you decide whether or not to go ahead and make a selection, online recommendations can also create a bias towards a product or experience that might have little or nothing to do with your actual preferences, Bockstedt says. Simply put, you’re more likely to watch, listen to, or buy something because it’s been recommended. And, when it comes to recommending the thing you’ve just watched, listened to, or bought yourself, your own rating might also be heavily influenced by the way it was recommended to you in the first place.

“Our research has shown that when a consumer is presented with a product recommendation that has a predicted preference rating—for example, we think you’ll like this movie or it has four and a half out of five stars—this information creates a bias in their preferences,” Bockstedt says. “The user will report liking the item more after they consume it if the system’s initial recommendation was high, and they say they like it less post-consumption, if the system’s recommendation was low. This holds even if the system recommendations are completely made up and random. So the information presented to the user in the recommendation creates a bias in how they perceive the item even after they’ve actually consumed or used it.”

This in turn creates a feedback loop which can reflect authentic preference, but this preference is very likely to be contaminated by bias. And that’s a problem, Bockstedt says.

“Once you have error baked into your recommendation system via this biased feedback loop, it’s going to reproduce and reproduce so that as an organization you’re pushing your customers towards certain types of products or content and not others—albeit unintentionally,” Bockstedt explains. “And for users or consumers, it’s also problematic in the sense that you’re taking the recommendations at face value, trusting them to be accurate while in fact they may not be. So there’s a trust issue right there.”

 Online recommendation systems can also potentially open the door to less than scrupulous behaviors, Bockstedt adds.

Because ratings can anchor user preferences and choices to one product over another, who’s to say organizations might not actually leverage the effect to promote more expensive options to their users? In other words, systems have the potential to be manipulated such that customers pay more—and pay more for something that they may not in fact have chosen in the first place.

Addressing recommendation system-induced bias is imperative, Bockstedt says, because these systems are essentially here to stay. So how do you go about attenuating the effect?

His latest paper sheds new and critical light on this. Together with Gediminas Adomavicius and Shawn P. Curley of the University of Minnesota and Indiana University’s Jingjing Zhang, Bockstedt ran a series of lab experiments to determine whether user bias could be eliminated or mitigated by showing users different types of recommendations or rating systems. Specifically they wanted to see if different formats or interface displays could diminish the bias effect on users. And what they found is highly significant.

Emory has published a full article on this topic – and its available for reading here:

 If you are a journalist looking to cover this topic or if you are simply interested in learning more, then let us help.

Jesse Bockstedt, associate professor of information systems and operations management at Emory’s Goizueta Business School. He is available to speak with media, simply click on his icon now – to book an interview today.



Online ratings systems shouldn’t just be a numbers game

the-tug-between-protecting-privacy-and-building-brand-loyalty

Charles H. Kellstadt Chair Emeritus of Marketing

 The coronavirus pandemic has put much of normal life on hold, but it hasn’t stopped hackers. According to Securityboulevard.com, in the first quarter of 2020, more than 8.4 billion records from healthcare institutions, technology, software, social media, and meal delivery companies were exposed — a 273 percent increase from Q1 2019.

While data breaches are costly to companies — a recent Ponemon Institute data breach report found that data breaches cost organizations an average of $7 million in the U.S. — their frequency is enough to cause some consumers to wonder if their private information is safe with their favorite brands.

 The increase in data breaches is concerning, noted Jesse Bockstedt, associate professor of information systems & operations management, but several studies have found that the out-of-pocket expense to consumers due to identity theft is less than $1,000. “Which isn’t zero, but it’s not like a few years ago when [identity theft] ruined your life and destroyed your credit,” Bockstedt said. As for the companies, he added, “It’s not a brand killer anymore.” Yet despite consumers’ growing unease, Goizueta faculty say the relationship between privacy and brand loyalty is a bit more intricate. While a data breach can nick a firm’s reputation, it’s the data that is purposely collected beyond the name and vital statistics that worry consumers more.

Our experts found the following key points were necessary when it comes to finding the safe ground between privacy and brand loyalty. In fact, we have an expert from Goizueta who can explain each one:

Building digital trust “Companies are increasingly worried that people will buy less from their brand if they’re perceived to be fast and loose with customer data,” said Daniel McCarthy, assistant professor of marketing. For instance, after political data-analytics firm Cambridge Analytica secretly collected data on roughly 87 million Facebook users, back-lash followed. In an effort to regain users’ trust, Facebook founder and CEO Mark Zuckerberg laid out a “privacy-focused vision” for Facebook, but those efforts were widely criticized as not going far enough. Advertising boycotts followed.

Trust: the key to customer loyalty Minus regulatory guardrails, the differentiating factor is trust, explained Jagdish Sheth, the Charles H. Kellstadt Chair in Marketing. 

“Trust is built over time by doing what you promise to do and by company behavior that is considered appropriate or right,” Sheth said. Loyalty programs such as those with airlines, hospitality companies and grocery stores are founded on a relationship between a consumer and a brand. “Loyalty programs mean relationships, and in all relationships, trust and commitment are key,” he added. Let’s make a deal “Brands that are able to deliver a personalized experience in a privacy-friendly manner will have a competitive advantage,” explained David Schweidel, professor of marketing, in a recent “Goizueta Effect” podcast. “Putting a premium on privacy means forgoing the benefits that come from allowing organizations to collect data they use to deliver a better experience. From a commercial standpoint, the onus is on the marketers to make the case that the benefits outweigh privacy concerns.”

 We’ve attached a full article with even more advice and helpful information from our experts – but if you are looking to learn more or cover this topic, we can help.

All of our faculty are available to speak with media, simply click on either expert’s icon now – to book an interview today.



The tug between protecting privacy and building brand loyalty

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