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Read expert insights from Emory University, Goizueta Business School on a wide range of topics and connect with leading experts in their fields.

Expert Spotlights from Emory University, Goizueta Business School

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In advance of our 100th anniversary, Dean Erika James unveils Goizueta Business School's charge to rethink today's image of business by sparking passion that ignites meaningful change.

Emory University’s Goizueta Business School offers a unique, community-oriented environment paired with an academic prestige and rigor shared with the international acclaim of Emory University. Goizueta trains business leaders of today and tomorrow with an Undergraduate degree program, a Master's of Science in Business Analytics (MSBA), a Two-Year Full-Time MBA, a One-Year MBA, an Evening MBA, a MBA for Executives (Weekend and Modular formats), a Doctoral degree and a portfolio of non-degree Emory Executive Education courses. The school is named for the late Roberto C. Goizueta, former Chairman and CEO of The Coca-Cola Company. For more information, visit http://goizueta.emory.edu and follow us on Twitter at @emorygoizueta.
 
Emory University is recognized internationally as an inquiry-driven, ethically engaged, and diverse community whose members work collaboratively for positive transformation in the world through courageous leadership in teaching, research, scholarship, health care, and social action. The University consists of an outstanding liberal arts college, highly ranked professional schools, and one of the larger and more comprehensive healthcare systems in the Southeast.

Goizueta Beyond

The new Roberto C. Goizueta Center for Entrepreneurship & Innovation fosters entrepreneurship and innovation born of critical inquiry, developed to serve fundamental societal needs, executed according to sound business principles, and conceptualized in ways that open possibilities and spark imagination. Learn more at https://emory.biz/innovate #GoizuetaStarts

Emory University’s Goizueta Business School offers a unique, community-oriented environment paired with an academic prestige and rigor shared with the international acclaim of Emory University. Goizueta trains business leaders of today and tomorrow with an Undergraduate degree program, a Master's of Science in Business Analytics (MSBA), a Two-Year Full-Time MBA, a One-Year MBA, an Evening MBA, an Executive MBA, a Doctoral degree and a portfolio of non-degree Emory Executive Education courses. The school is named for the late Roberto C. Goizueta, former Chairman and CEO of The Coca-Cola Company. For more information, visit http://goizueta.emory.edu and follow us on Twitter at @emorygoizueta.
 
Emory University is recognized internationally as an inquiry-driven, ethically engaged, and diverse community whose members work collaboratively for positive transformation in the world through courageous leadership in teaching, research, scholarship, health care, and social action. The University consists of an outstanding liberal arts college, highly ranked professional schools, and one of the larger and more comprehensive healthcare systems in the Southeast.

Fostering Entrepreneurship & Innovation at Goizueta

Effective collaboration is more important to organizations than ever before.  In this session with Andrea Dittmann, you will learn how organizations can shift their cultures to authentically value working together, which can help all employees perform to their full potential on teams – particularly employees who grew up working class.

Emory University’s Goizueta Business School offers a unique, community-oriented environment paired with an academic prestige and rigor shared with the international acclaim of Emory University. Goizueta trains business leaders of today and tomorrow with an Undergraduate degree program, a Master's of Science in Business Analytics (MSBA), a Two-Year Full-Time MBA, a One-Year MBA, an Evening MBA, an Executive MBA, a Doctoral degree and a portfolio of non-degree Emory Executive Education courses. The school is named for the late Roberto C. Goizueta, former Chairman and CEO of The Coca-Cola Company. For more information, visit http://goizueta.emory.edu and follow us on Twitter at @emorygoizueta.
 
Emory University is recognized internationally as an inquiry-driven, ethically engaged, and diverse community whose members work collaboratively for positive transformation in the world through courageous leadership in teaching, research, scholarship, health care, and social action. The University consists of an outstanding liberal arts college, highly ranked professional schools, and one of the larger and more comprehensive healthcare systems in the Southeast.

Harnessing Collaboration to Reduce Social Class Inequality in the Workplace

Every Leader needs a playbook. Join Francisco Crespo, Goizueta Executive-in-Residence, former Chief Growth Officer - Coca-Cola Company as he shares his four disciplines of growth and the tools leaders have to navigate the challenges encountered when executing these disciplines.

Emory University’s Goizueta Business School offers a unique, community-oriented environment paired with an academic prestige and rigor shared with the international acclaim of Emory University. Goizueta trains business leaders of today and tomorrow with an Undergraduate degree program, a Master's of Science in Business Analytics (MSBA), a Two-Year Full-Time MBA, a One-Year MBA, an Evening MBA, an Executive MBA, a Doctoral degree and a portfolio of non-degree Emory Executive Education courses. The school is named for the late Roberto C. Goizueta, former Chairman and CEO of The Coca-Cola Company. For more information, visit http://goizueta.emory.edu and follow us on Twitter at @emorygoizueta.
 
Emory University is recognized internationally as an inquiry-driven, ethically engaged, and diverse community whose members work collaboratively for positive transformation in the world through courageous leadership in teaching, research, scholarship, health care, and social action. The University consists of an outstanding liberal arts college, highly ranked professional schools, and one of the larger and more comprehensive healthcare systems in the Southeast.

The Growth Leader's Playbook - Business over Breakfast

Goizueta Business School Finance Professor, Tom Smith, returns to Business over Breakfast for a special election edition of “The Economy and Me” – checking in on the impact of the 2020 Presidential election, the latest economic news, the impact of the coronavirus, what people are saying, and what it all means for you.

Emory University’s Goizueta Business School offers a unique, community-oriented environment paired with an academic prestige and rigor shared with the international acclaim of Emory University. Goizueta trains business leaders of today and tomorrow with an Undergraduate degree program, a Master's of Science in Business Analytics (MSBA), a Two-Year Full-Time MBA, a One-Year MBA, an Evening MBA, an Executive MBA, a Doctoral degree and a portfolio of non-degree Emory Executive Education courses. The school is named for the late Roberto C. Goizueta, former Chairman and CEO of The Coca-Cola Company. For more information, visit http://goizueta.emory.edu and follow us on Twitter at @emorygoizueta.
 
Emory University is recognized internationally as an inquiry-driven, ethically engaged, and diverse community whose members work collaboratively for positive transformation in the world through courageous leadership in teaching, research, scholarship, health care, and social action. The University consists of an outstanding liberal arts college, highly ranked professional schools, and one of the larger and more comprehensive healthcare systems in the Southeast.

The Economy & Me: Election Edition - Business over Breakfast

Tom Smith, Goizueta Business School Finance Professor, returns to Business over Breakfast focusing on “The Economy and Me” – checking in on the latest economic news, the impact of coronavirus, what people are saying and what it means for you.

Business Over Breakfast Webinar Series with Emory Executive Education featuring Tom Smith

Bridging the Gap: Improving Intergenerational Communication featuring Molly Epstein

From Baby Boomers to Gen Z, your team may include members of up to five generations. Improve team dynamics and increase team productivity by learning how to communicate most effectively across generations. In this seminar, Goizueta Business School Professor Molly Epstein will explore each generations’ influences and develop strategies for you to connect with all your colleagues. She will also discuss how you can apply your intergenerational knowledge to remote teams.

Business Over Breakfast Webinar Series with Emory Executive Education featuring Molly Epstein

Goizueta Business School Dean Erika James explains the school's priorities for training future business leaders and the importance of collaboration to continue the school's rich legacy. 
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Dean James on Goizueta Business School

BENN KONSYNSKI #ICEBUCKETCHALLENGE

To learn more about Core Values at Goizueta Business School: http://goizueta.emory.edu/degree/fulltimemba/life/core_values.html.

Ryan Douglas 13MBA | An Unexpected Value | The Goizueta Experience

"Business school is a great opportunity to take two years and determine: here are the things i'm proud of - here are these things i'd like to grow - and here...

Jill Schreifer 05MBA | Room to Grow | The Goizueta Experience

Amanda Hinderliter 07EvMBA | Working a Network | The Goizueta Experience

David Shanklin 09MBA | The Right Partners | The Goizueta Experience

Hear Professor Shiva Rajgopal share his findings on SEC staffer trading and its implications.

In SEC Dealings: Who Monitors the Monitors?

12-days-of-holiday-experts-goizueta-business-school-sources-for-the-season

12 Days of Holiday Experts - Goizueta Business School Sources for the Season

Goizueta Business School is again offering experts providing expertise on any angle you're covering this holiday season

  It's that time of the year again!  And as Americans get ready for another journey into the festive season, there are always opportunities for stories to be told about shopping, travelling, buying, returning, and making sure you don't get ripped off or scammed during all the hustle and bustle,

Here's a stocking full of topics and expert sources who are here to help with your coverage this holiday!

Gifts, Giving, and all the Costs That Come With It  Economics of the Holiday Season  A successful Q4 makes the difference between annual profitability and loss for many businesses. Professor Tom Smith  is available to discuss seasonal hiring, retail expectations, the impact of tariffs, and the importance of the holiday season to retailers. 



View his profile here Black Friday & Using AI to find the Perfect Gift  Professor Doug Bowman expects to see more Shoppers (esp. Gen Z) experimenting with GenAI for personalization, inspiration, product discovery, summarizing reviews, generating lists, and finding deals.
Results may be mixed, depending on the data the AI was trained on. He also expects more purposeful and complex shopping, with fewer impulse purchases and more searching (both online and in brick-and-mortar stores), due to lower inventory levels/assortments at some retailers.

View his profile here Food and Travel Pricing   Professor Saloni Firasta Vastani can discuss the cost of this year’s holiday dinners. What’s gone up and what’s gone down? She can also discuss the cost of travel this holiday season and offer tips on how consumers can secure a better deal.

View her profile here Avoiding Holiday Overspend   Professor Usha Rackliffe can discuss how holiday shopping can expose consumers to credit products, such as store credit cards, that offer various incentives and often result in overspending. She can discuss the pros and cons of the buy now, pay later offers and how interest rates will play into this year’s holiday shopping and spending.

View her profile here Gift Giving   Professor Ira Bedzow says there are three ways gift-giving can promote both personal growth and professional development.

View his profile here Gifts Express Relationship, Not Reciprocity. Contracts and transactions are about keeping score—I give, you give back. Gifts are about connection. A thoughtful gift doesn’t close a deal; it opens a door. Personally, it reframes love and friendship as ongoing commitments rather than conditional exchanges. Professionally, treating interactions as opportunities to build trust creates loyalty, sparks creativity, and builds a culture no contract can guarantee.  The Art of Perspective-Taking in Choosing Gifts: The best gifts come from stepping outside yourself and asking: What would this person really want? This act of empathy is a skill worth practicing. Personally, it pulls us beyond ego; professionally, it sharpens our ability to anticipate needs, see through others’ eyes, and make decisions aligned with their values—a foundation for real leadership.  Gifts as Lessons in Friendship and Human Connection: True friendship isn’t built on ideology, convenience, or self-interest. It’s rooted in caring for someone simply for who they are. Gift-giving is a rehearsal for that kind of connection. Personally, it reminds us that what we truly want typically comes through relationships, not rivalry. Professionally, it shows that lasting success rests less on shared advantage and more on genuine respect and human connection.   Shopping for Sustainability    Consumers are increasingly seeking eco-friendly products, and brands that emphasize sustainability are likely to see higher sales. Nearly 69% of shoppers prefer to buy from companies committed to ethical practices, such as those that use carbon-neutral shipping and offer recyclable packaging. Professor Dionne Nickerson focuses on how companies can integrate sustainability in their products and why it matters to consumers. 

View her profile here Pressure Purchasing    As the days inch closer to the holidays, shoppers feel the pressure to find a gift. Professor Max Gaerth can discuss how stress, scarcity, and time pressure shape purchasing decisions.

View his profile here Online Shopping and Influencing  AI Changing How We Shop  Professor David Schweidel examines how new AI tools are transforming the shopping experience and the ways brands utilize AI to engage with prospective customers and personalize product recommendations. He can also discuss OpenAI’s Atlas and how it puts ChatGPT directly into your browser.

View his profile here Influencers Influencing Our Purchases   How are creators impacting the economy, and are influencers impacting our purchasing decisions? Professor Marina Cooley looks at the creator economy and how TikTok and Instagram are impacting our holiday wish lists, and what it takes for a product to go from unknown to trending. She can also discuss TikTok Shop (something Instagram has struggled to execute).  

View her profile here How to Attract Customers to the Store this Holiday:   Shopping looks different, and it is up to retailers to stand out not just in the brick-and-mortar world but also online. The success of a business can balance on the customer experience. Professor Reshma Shah can discuss the policies that brick-and-mortar retailers need to have in place to successfully merge online shopping and the in-person shopping experience.

View her profile here  Holiday Scams   Tis The Season for Scams  Bad actors are using AI to scam consumers. From phone calls to emails, Professor Tucker Balch can tell us how to spot a scam and what we can do to protect ourselves.

View his profile here Holiday Returns  Product Returns   Professor Doug Bowman can discuss the retail strategy and the impact of holiday gift returns, comparing online returns to those in brick-and-mortar stores. 

View his profile here He can also weigh in on:
Why are returns so expensive for retailers? 
Online returns vs. brick and mortar returns
Predicting online returns - helping retailers understand how likely it is that a product will be returned.

As well: 
Are retailers still offering free returns? 
What’s this costing them? Is this likely to continue? 
What will they do differently?  If you’re a journalist covering the holiday season, our experts can help shape your story. Use the “Connect” button on any expert’s profile to send an inquiry — all inquiries are monitored by our media team to ensure a quick, timely response.


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#Expert Perspective: When AI Follows the Rules but Misses the Point

Vice Dean for Faculty and Charles Howard Candler Professor of Finance

   When a team of researchers asked an artificial intelligence system to design a railway network that minimized the risk of train collisions, the AI delivered a surprising solution: Halt all trains entirely. No motion, no crashes. A perfect safety record, technically speaking, but also a total failure of purpose. The system did exactly what it was told, not what was meant.

 This anecdote, while amusing on the surface, encapsulates a deeper issue confronting corporations, regulators, and courts: What happens when AI faithfully executes an objective but completely misjudges the broader context? In corporate finance and governance, where intentions, responsibilities, and human judgment underpin virtually every action, AI introduces a new kind of agency problem, one not grounded in selfishness, greed, or negligence, but in misalignment.
 From Human Intent to Machine Misalignment  Traditionally, agency problems arise when an agent (say, a CEO or investment manager) pursues goals that deviate from those of the principal (like shareholders or clients). The law provides remedies: fiduciary duties, compensation incentives, oversight mechanisms, disclosure rules. These tools presume that the agent has motives—whether noble or self-serving—that can be influenced, deterred, or punished. But AI systems, especially those that make decisions autonomously, have no inherent intent, no self-interest in the traditional sense, and no capacity to feel gratification or remorse. They are designed to optimize, and they do, often with breathtaking speed, precision, and, occasionally, unintended consequences.

This new configuration, where AI acting on behalf of a principal (still human!), gives rise to a contemporary agency dilemma. Known as the alignment problem, it describes situations in which AI follows its assigned objective to the letter but fails to appreciate the principal’s actual intent or broader values. The AI doesn’t resist instructions; it obeys them too well. It doesn’t “cheat,” but sometimes it wins in ways we wish it wouldn’t.

When Obedience Becomes a Liability  In corporate settings, such problems are more than philosophical. Imagine a firm deploying AI to execute stock buybacks based on a mix of market data, price signals, and sentiment analysis. The AI might identify ideal moments to repurchase shares, saving the company money and boosting share value. But in the process, it may mimic patterns that look indistinguishable from insider trading. Not because anyone programmed it to cheat, but because it found that those actions maximized returns under the constraints it was given. The firm may find itself facing regulatory scrutiny, public backlash, or unintended market disruption, again not because of any individual’s intent, but because the system exploited gaps in its design.

This is particularly troubling in areas of law where intent is foundational. In securities regulation, fraud, market manipulation, and other violations typically require a showing of mental state: scienter, mens rea, or at least recklessness. Take spoofing, where an agent places bids or offers with the intent to cancel them to manipulate market prices or to create an illusion of liquidity. Under the Dodd-Frank Act, this is a crime if done with intent to deceive. But AI, especially those using reinforcement learning (RL), can arrive at similar strategies independently. In simulation studies, RL agents have learned that placing and quickly canceling orders can move prices in a favorable direction. They weren’t instructed to deceive; they simply learned that it worked.

 The Challenge of AI Accountability  What makes this even more vexing is the opacity of modern AI systems. Many of them, especially deep learning models, operate as black boxes. Their decisions are statistically derived from vast quantities of data and millions of parameters, but they lack interpretable logic. When an AI system recommends laying off staff, reallocating capital, or delaying payments to suppliers, it may be impossible to trace precisely how it arrived at that recommendation, or whether it considered all factors. Traditional accountability tools—audits, testimony, discovery—are ill-suited to black box decision-making.

In corporate governance, where transparency and justification are central to legitimacy, this raises the stakes. Executives, boards, and regulators are accustomed to probing not just what decision was made, but also why. Did the compensation plan reward long-term growth or short-term accounting games? Did the investment reflect prudent risk management or reckless speculation? These inquiries depend on narrative, evidence, and ultimately the ability to assign or deny responsibility. AI short-circuits that process by operating without human-like deliberation.

The challenge isn’t just about finding someone to blame. It’s about whether we can design systems that embed accountability before things go wrong. One emerging approach is to shift from intent-based to outcome-based liability. If an AI system causes harm that could arise with certain probability, even without malicious design, the firm or developer might still be held responsible. This mirrors concepts from product liability law, where strict liability can attach regardless of intent if a product is unreasonably dangerous. In the AI context, such a framework would encourage companies to stress-test their models, simulate edge cases, and incorporate safety buffers, not unlike how banks test their balance sheets under hypothetical economic shocks.

There is also a growing consensus that we need mandatory interpretability standards for certain high-stakes AI systems, including those used in corporate finance. Developers should be required to document reward functions, decision constraints, and training environments. These document trails would not only assist regulators and courts in assigning responsibility after the fact, but also enable internal compliance and risk teams to anticipate potential failures. Moreover, behavioral “stress tests” that are analogous to those used in financial regulation could be used to simulate how AI systems behave under varied scenarios, including those involving regulatory ambiguity or data anomalies.

Smarter Systems Need Smarter Oversight  Still, technical fixes alone will not suffice. Corporate governance must evolve toward hybrid decision-making models that blend AI’s analytical power with human judgment and ethical oversight. AI can flag risks, detect anomalies, and optimize processes, but it cannot weigh tradeoffs involving reputation, fairness, or long-term strategy. In moments of crisis or ambiguity, human intervention remains indispensable. For example, an AI agent might recommend renegotiating thousands of contracts to reduce costs during a recession. But only humans can assess whether such actions would erode long-term supplier relationships, trigger litigation, or harm the company’s brand.

There’s also a need for clearer regulatory definitions to reduce ambiguity in how AI-driven behaviors are assessed. For example, what precisely constitutes spoofing when the actor is an algorithm with no subjective intent? How do we distinguish aggressive but legal arbitrage from manipulative behavior? If multiple AI systems, trained on similar data, converge on strategies that resemble collusion without ever “agreeing” or “coordination,” do antitrust laws apply?

Policymakers face a delicate balance: Overly rigid rules may stifle innovation, while lax standards may open the door to abuse. One promising direction is to standardize governance practices across jurisdictions and sectors, especially where AI deployment crosses borders. A global AI system could affect markets in dozens of countries simultaneously. Without coordination, firms will gravitate toward jurisdictions with the least oversight, creating a regulatory race to the bottom.

Several international efforts are already underway to address this. The 2025 International Scientific Report on the Safety of Advanced AI called for harmonized rules around interpretability, accountability, and human oversight in critical applications. While much work remains, such frameworks represent an important step toward embedding legal responsibility into the design and deployment of AI systems.

The future of corporate governance will depend not just on aligning incentives, but also on aligning machines with human values. That means redesigning contracts, liability frameworks, and oversight mechanisms to reflect this new reality. And above all, it means accepting that doing exactly what we say is not always the same as doing what we mean

Looking to know more or connect with Wei Jiang, Goizueta Business School’s vice dean for faculty and research and Charles Howard Candler Professor of Finance. Simply click on her icon now to arrange an interview or time to talk today.



expert-research-incentives-speed-up-operating-room-turnover-procedures

#Expert Research: Incentives Speed Up Operating Room Turnover Procedures

Researchers have found a simple and cost-effective intervention that reduces the lag between procedures by an average of 6.4 percent.

Asa Griggs Candler Professor of Accounting

  The operating room (OR) is the economic hub of most healthcare systems in the United States today, generating up to 70% of hospital revenue. Ensuring these financial powerhouses run efficiently is a major priority for healthcare providers. But there’s a challenge. Turnovers—cleaning, preparing, and setting up the OR between surgeries—are necessary and unavoidable processes. OR turnovers can incur significant costs in staff time and resources, but at the same time, do not generate revenue. For surgeons, the lag between wheels out and wheels in is idle time. For incoming patients, who may have spent hours fasting in preparation for a procedure, it is also a potential source of frustration and anxiety.

 Reducing OR turnover time is a priority for many US healthcare providers, but it’s far from simple. For one thing, cutting corners in pursuit of efficiency risks patient safety. Then there’s the makeup of OR teams themselves. As a rule, well-established or stable teams work fastest and best, their efficiency fueled by familiarity and well-oiled interpersonal dynamics. But in hospital settings, staff work in shifts and according to different schedules, which creates a certain fluidity in the way turnover teams amalgamate. These team members may not know each other or have any prior experience working together.  

For hospital administrators this represents a quandary. How do you cut OR turnover time without compromising patient care or hiring in more staff to build more stable teams? To put that another way: how do you motivate OR workers to maintain standards and drive efficiency—irrespective of the team they work with at any given time?

 One novel approach instituted by Georgia’s Phoebe Putney Health System is the focus of new research by Asa Griggs Candler Professor of Accounting, Karen Sedatole PhD. Under the stewardship of perioperative medical director and anesthesiologist, Jason Williams MD 02MR 20MBA, and with support from Sedatole and co-authors, Ewelina Forker 23PhD of the University of Wisconsin and Harvard Business School’s Susanna Gallini PhD, staff at Phoebe ran a field experiment incentivizing individual OR workers to ramp up their own performance in turnover processes.

What they have found is a simple and cost-effective intervention that reduces the lag between procedures by an average of 6.4 percent.

Homing in on the Individual  Williams and his team at Phoebe kicked off efforts to reduce OR turnover times by first establishing a benchmark to calculate how long it should take to prepare for different types of procedure or surgery. This can vary significantly, says Williams: while a gallbladder removal should take less than 30 minutes, open-heart surgery might take an hour or longer to prepare.

“There’s a lot of variation in predicting how long it should take to get things set up for different procedures. We got there by analyzing three years of data to create a baseline, and from there, having really homed in on that data, we were able to create a set of predictions and then compare those with what we were seeing in our operating rooms—and track discrepancies, over-, and underachievement.”

 Williams, a Goizueta MBA graduate who also completed his anesthesiology residency at Emory University’s School of Medicine, then enlisted the support of Sedatole and her colleagues to put together a data analysis system that would capture the impact of two distinct mechanisms, both designed to incentivize individual staff members to work faster during turnovers. The first was a set of electronic dashboards programmed to record and display the average OR turnover performance for teams on a weekly basis, and segment these into averages unique to individuals working in each of the core roles within any given OR turnover team. The dashboard displayed weekly scores and ranked them from best to worst on large TV monitors with interactive capabilities—users could filter the data for types of surgery and other dimensions.

Broadcasting metrics this way afforded Williams and his team a means of identifying and then publicly recognizing top-performing staff, but that’s not all. The dashboards also provided a mechanism with which to filter out team dynamics, and home in on individual efforts.

 “If you are put in a room with one team, and they are slower than others, then you are going to be penalized. Your efforts will not shine. Now, say you are put in with a bigger or faster team, your day’s numbers are going to be much higher. So, we had to find a way to accommodate and allow for the team effect, to observe individual effort. The dashboards meant we could do this. Over the period of a week or a month, the effect of other people in the team is washed out. You begin to see the key individuals pop up again and again over time, and you can see those who are far above their peers versus those who, for whatever reason, are not so efficient.” Sharing “relative performance” information has been shown to be highly motivating in many settings. The hope was that it would here, too.

Three core roles: Who’s who in the Operating Room turnover team?   OR turnover teams consist of three roles: circulating nurse, scrub tech, and anesthetist. While other surgery staff might be present during a turnover, depending on the needs of consecutive procedures, these are the three core roles in the team, and they are not interchangeable in any way: each individual assumes the same responsibilities in every team they join. Typically, turnover tasks will include removing instruments and equipment from the previous surgery and setting up for the next: restocking supplies and restoring the sterile environment. Turnover tasks and activities will vary according to the type of procedure coming next, but these tasks are always performed by the same three roles: nurse, scrub tech, and anesthetist, working within their own area of expertise and specialty. OR turnover teams are assembled based on staff schedules and availability, making them highly fluid. Different nurses will work with different scrub techs and different anesthetists depending on who is free and available at any given time. With dashboards on display across the hospital’s surgery department, Williams decided to trial a second motivational mechanism; this time something more tangible.

“We decided to offer a simple $40 Dollar Store gift card to each week’s top performing anesthetist, nurse, or scrub technician to see if it would incentivize people even more. And to keep things interesting, and sustain motivation, we made sure that anyone who’d won the contest two weeks in a row would be ineligible to win the gift card the following week,” says Williams. “It was a bit of a shot in the dark, and we didn’t know if it would work.” Altogether, the dashboards remained in situ over a period of about 33 months while the gift card promotion ran for 73 weeks.   It was important to stress the foundational importance of safety and then allow individuals to come up with their own ways to tighten procedures. This was a bottom-up, grassroots experience where the people doing the work came up with their own ways to make their times better, without cutting corners, without cutting quality, and without cutting any safety measures. Jason Williams MD 02MR 20MBA  Incentives: Make it Something Special and Unique  Crunching all of this data, Sedatole and her colleagues could isolate the effect of each mechanism on performance and turnover times at Phoebe. While the dashboards had “negligible” effect on productivity, the addition of the store gift cards had immediate, significant, and sustained impact on individuals’ efforts. Differences in the effectiveness of the two incentives—the relative performance dashboard and the gift cards—are attributable to team fluidity, says Sedatole.

“It’s all down to familiarity. Dashboards are effective if you care about your reputation and your standing with peers. And in fluid team settings, where people don’t really know each other, reputation seems to matter less because these individuals may never work together again. They simply care less about rankings because they are effectively strangers.”

Tangible rewards, on the other hand, have what Sedatole calls a “hedonic” value: they can feel more special and unique to the recipient, even if they carry relatively little monetary value.

Something like a $40 gift card to Target can be more motivating to individuals even than the same amount in cash. There’s something hedonic about a prize that differentiates it from cash—after all, you will just end up spending that $40 on the electricity bill.  Asa Griggs Candler Professor of Accounting, Karen Sedatole “A tangible reward is something special because of its hedonic nature and the way that human beings do mental accounting,” says Sedatole. “It occupies a different place in the brain, so we treat it differently.”

In fact, analyzing the results, Sedatole and her colleagues find that the introduction of gift cards at Phoebe equates to an average incremental improvement of 6.4% in OR turnover performance; a finding that does not vary over the 73-week timeframe, she adds. To get the same result by employing more staff to build more stable teams, Sedatole calculates that the hospital would have to increase peer familiarity to the 98th percentile: a very significant financial outlay and a lot of excess capacity if those additional team members are not working 100% of the time.

 These are key findings for healthcare systems and for administrators and decision-makers in any setting or sector where fluid teams are the norm, says Sedatole: from consultancy to software development to airline ground crews. Wherever diverse professionals come together briefly or sporadically to perform tasks and then disperse, individual motivation can be optimized by simple mechanisms—cost-effective tangible rewards—that give team members a fresh opportunity to earn the incentive in different settings on different occasions—a recurring chance to succeed that keeps the incentive systems engaging and effective over time.

For healthcare in particular, this is a win-win-win, says Williams.

“In the United States we are faced with lower reimbursements and higher costs, so we have to look for areas where we can gain efficiencies and minimize costs. In the healthcare value model, time and costs are denominators, and quality and service are numerators. Any way we can save on costs and improve efficiencies allows us to take care of more patients, and to be able to do that effectively.

“We made some incredible improvements here. We went from just average to best in class, right to the frontier of operative efficiency. And there is so much more opportunity out there to pull more levers and reach new levels, which is truly encouraging.”

Looking to know more or connect with Asa Griggs Candler Professor of Accounting, Karen Sedatole?  Simply click on her icon now to arrange an interview or time to talk today.




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Why Simultaneous Voting Makes for Good Decisions

Associate Professor of Information Systems & Operations Management

  How can organizations make robust decisions when time is short, and the stakes are high? It’s a conundrum not unfamiliar to the U.S. Food and Drug Administration.

Back in 2021, the FDA found itself under tremendous pressure to decide on the approval of the experimental drug aducanumab, designed to slow the progress of Alzheimer’s disease—a debilitating and incurable condition that ranks among the top 10 causes of death in the United States.

Welcomed by the market as a game-changer on its release, aducanumab quickly ran into serious problems. A lack of data on clinical efficacy along with a slew of dangerous side effects meant physicians in their droves were unwilling to prescribe it. Within months of its approval, three FDA advisors resigned in protest, one calling aducanumab, “the worst approval decision that the FDA has made that I can remember.” By the start of 2024, the drug had been pulled by its manufacturers. Of course, with the benefit of hindsight and data from the public’s use of aducanumab, it is easy for us to tell that FDA made the wrong decision then. 

 But is there a better process that would have given FDA the foresight to make the right decision, under limited information?



The FDA routinely has to evaluate novel drugs and treatments; medical and pharmaceutical products that can impact the wellbeing of millions of Americans. With stakes this high, the FDA is known to tread carefully: assembling different advisory, review, and funding committees providing diverse knowledge and expertise to assess the evidence and decide whether to approve a new drug, or not. As a federal agency, the FDA is also required to maintain scrupulous records that cover its decisions, and how those decisions are made.

The Impact of Voting Mechanisms on Decision Quality  Some of this data has been analyzed by Goizueta’s Tian Heong Chan, associate professor of information systems and operation management. Together with Panos Markou of the University of Virginia’s Darden School of Business, Chan scrutinized 17 years’ worth of information, including detailed transcripts from more than 500 FDA advisory committee meetings, to understand the mechanisms and protocols used in FDA decision-making: whether committee members vote to approve products sequentially, with everyone in the room having a say one after another; or if voting happens simultaneously via the push of a button, say, or a show of hands. Chan and Markou also looked at the impact of sequential versus simultaneous voting to see if there were differences in the quality of the decisions each mechanism produced. Their findings are singular.

 It turns out that when stakeholders vote simultaneously, they make better decisions. Drugs or products approved this way are far less likely to be issued post-market boxed warnings (warnings issued by FDA that call attention to potentially serious health risks associated with the product, that must be displayed on the prescription box itself), and more than two times less likely to be recalled.

The FDA changed its voting protocols in 2007, when they switched from sequentially voting around the room, one person after another, to simultaneous voting procedures. And the results are stunning. Tian Heong Chan, Associate Professor of Information Systems & Operation Management  “Decisions made by simultaneous voting are more than twice as effective,” says Chan. “After 2007, you see that just 3.4% of all drugs and products approved this way end up being discontinued or recalled. This compares with an 8.6% failure rate for drugs approved by the FDA using more sequential processes—the round robin where individuals had been voting one by one around the room.” Imagine you are told beforehand that you are going to vote on something important by simply raising your hand or pressing a button. In this scenario, you are probably going to want to expend more time and effort in debating all the issues and informing yourself before you decide. Tian Heong Chan  “On the other hand, if you know the vote will go around the room, and you will have a chance to hear how others’ speak and explain their decisions, you’re going to be less motivated to exchange and defend your point of view beforehand,” says Chan. In other words, simultaneous decision-making is two times less likely to generate a wrong decision as the sequential approach. Why is this? Chan and Markou believe that these voting mechanisms impact the quality of discussion and debate that undergird decision-making; that the quality of decisions is significantly impacted by how those decisions are made.

Quality Discussion Leads to Quality Decisions  Parsing the FDA transcripts for content, language, and tonality in both settings, Chan and Markou find evidence to support this. Simultaneous voting or decision-making drives discussions that are characterized by language that is more positive, more authentic, and more even in terms of expressions of authority and hierarchy, says Chan. What’s more, these deliberations and exchanges are deeper and more far-ranging in quality.

We find marked differences in the tone of speech and the topics discussed when stakeholders know they will be voting simultaneously. There is less hierarchy in these exchanges, and individuals exhibit greater confidence in sharing their points of view more freely. Tian Heong Chan  “We also see more questions being asked, and a broader range of topics and ideas discussed,” says Chan. In this context, decision-makers are also less likely to reach unanimous agreement. Instead, debate is more vigorous and differences of opinion remain more robust. Conversely, sequential voting around the room is typically preceded by shorter discussion in which stakeholders share fewer opinions and ask fewer questions. And this demonstrably impacts the quality of the decisions made, says Chan.

Sharing a different perspective to a group requires effort and courage. With sequential voting or decision-making, there seems to be less interest in surfacing diverse perspectives or hidden aspects to complex problems. Tian Heong Chan  “So it’s not that individuals are being influenced by what other people say when it comes to voting on the issue—which would be tempting to infer—rather, it’s that sequential voting mechanisms seem to take a bit more effort out of the process.” When decision-makers are told that they will have a chance to vote and to explain their vote, one after another, their incentives to make a prior effort to interrogate each other vigorously, and to work that little bit harder to surface any shortcomings in their own understanding or point of view, or in the data, are relatively weaker, say Chan and Markou.

The Takeaway for Organizations Making High-Stakes Decisions  Decision-making in different contexts has long been the subject of scholarly scrutiny. Chan and Markou’s research sheds new light on the important role that different mechanisms have in shaping the outcomes of decision-making—and the quality of the decisions that are jointly taken. And this should be on the radar of organizations and institutions charged with making choices that impact swathes of the community, they say.

“The FDA has a solid tradition of inviting diversity into its decision-making. But the data shows that harnessing the benefits of diversity is contingent on using the right mechanisms to surface the different expertise you need to be able to see all the dimensions of the issue, and make better informed decisions about it,” says Chan. A good place to start? By a concurrent show of hands.

Tian Heong Chan is an associate professor of information systems and operation management. he is available to speak about this topic - click on his con now to arrange an interview today.


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Expert Perspective: The Hidden Costs of Cultural Appropriation

Assistant Professor of Organization & Management

In our interconnected world, cultural borrowing is everywhere. But why do some instances earn applause while others provoke outrage? This question is becoming increasingly crucial for business leaders who must carefully navigate cultural boundaries.

Take the backlash the Kardashian-Jenner family faced for adopting styles from minority cultures or the controversy over non-Indigenous designers using Native American patterns in fashion. These examples highlight the issue of cultural appropriation, where borrowing elements from another culture without genuine understanding or respect can lead to accusations of exploitation.

Abraham Oshotse, an assistant professor of organization and management at Goizueta Business School, along with Assistant Professor of Sociology and Anthropology at Hebrew University Yael Berda and Associate Professor of Organizational Behavior at the Stanford Graduate School of Business Amir Goldberg, explores this in their research on “cultural tariffing.” They shed light on why high-status individuals, such as celebrities or industry leaders, often come under fire when crossing cultural boundaries.

 The Concept of Cultural Tariffing  Oshotse and coauthors define cultural tariffing as “the act of imposing a social cost on cultural boundary crossing. It is levied on high-status actors crossing into low-status culture, in order to mitigate the reproduction of the status inequality.” This notion suggests that the acceptance or rejection of cultural boundary-crossing is influenced by the perceived costs and benefits.

Cultural appropriation involves taking elements from a culture that one does not belong to, without permission or authority. For example, when Elvis Presley brought African-American music into the mainstream, it was initially seen as elevating the genre. However, in today’s context, such acts might be criticized as appropriation rather than celebration. This research seeks to analyze people’s modern reactions to different examples of cultural boundary-crossing and which conditions induce cultural tariffing.

The Hypotheses The researchers make four hypotheses about participants’ reactions to cultural appropriation:

People will disapprove of cultural borrowing if there’s a clear power imbalance, with the borrowing group having more status or privilege than the group they are borrowing from. Cultural borrowing is more likely to be criticized if the person doing it has a higher socioeconomic status within their social group. Cultural borrowing is more likely to be criticized if the person doing it has only a shallow connection to the culture they’re borrowing from. Cultural borrowing is more likely to be criticized if the person doing it benefits more from it than the people from the culture they are borrowing from.  Put to the Test  Oshotse et al exposed respondents to four scenarios per hypothesis (16 total) with a permissible and a transgressive condition. In the permissible condition, subjects exhibit lower status or socioeconomic standing or a stronger connection to the target culture. Subjects in the transgressive condition exhibit a higher status or socioeconomic standing and less of an authentic connection to the target culture.

  Insights from the Study  Oshotse’s study offers four key insights:

Status Matters: Cultural boundary-crossing is more likely to generate disapproval if there’s a clear status difference favoring the adopter. Superficial Connections: The less authentic the adopter’s connection to the target culture, the more likely they are to face backlash. Socioeconomic Influence: Higher socioeconomic status within the adopter’s social group increases the likelihood of disapproval. Value Extraction: The more value the adopter gains relative to the culture they’re borrowing from, the higher the disapproval.  These insights are crucial for leaders who want to navigate cultural boundaries successfully, ensuring their actions are seen as respectful and inclusive rather than exploitative.

Real-World Implications for Business Leaders  Why does this matter for business leaders? Understanding cultural tariffing is crucial when expanding into new markets, launching multicultural campaigns, or even managing diverse teams. The research suggests that crossing cultural boundaries without deep understanding or respect can backfire. That’s especially true when the adopter holds a higher socioeconomic status.

Consider the example of a luxury brand adopting traditional African patterns without engaging with the communities behind them. In this case, it risks being seen as exploitative rather than innovative. The consequences aren’t just reputational; they can also impact the brand’s bottom line.

This research isn’t just about isolated incidents; it mirrors sweeping societal shifts. Over the past 50 years, Western views have evolved to embrace ethnic diversity and multicultural exchange. But with this newfound appreciation comes a fresh set of challenges. Today’s leaders must navigate cultural interactions with greater care, fully aware of the historical and social contexts that shape perceptions of appropriation.

In today’s global and interconnected business landscape, mastering the subtleties of cultural appropriation and tariffing is crucial. Leaders who tread thoughtfully can boost their reputation and success, while those who falter may face serious backlash. By understanding the hidden costs of crossing cultural boundaries, business leaders can cultivate authentic exchanges and steer clear of the pitfalls of appropriation.

Abraham Oshotse is an assistant professor of organization & management. He is available speak to media regarding  this important topic - simply click on his icon now to arrange an interview today.


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Hiring More Nurses Generates Revenue for Hospitals

Professor of Information Systems & Operations Management

  Underfunding is driving an acute shortage of trained nurses in hospitals and care facilities in the United States. It is the worst such shortage in more than four decades. One estimate from the American Hospital Association puts the deficit north of one million. Meanwhile, a recent survey by recruitment specialist AMN Healthcare suggests that 900,000 more nurses will drop out of the workforce by 2027.

American nurses are quitting in droves, thanks to low pay and burnout as understaffing increases individual workload. This is bad news for patient outcomes. Nurses are estimated to have eight times more routine contact with patients than physicians. They shoulder the bulk of all responsibility in terms of diagnostic data collection, treatment plans, and clinical reporting. As a result, understaffing is linked to a slew of serious problems, among them increased wait times for patients in care, post-operative infections, readmission rates, and patient mortality—all of which are on the rise across the U.S.

Tackling this crisis is challenging because of how nursing services are reimbursed. Most hospitals operate a payment system where services are paid for separately. Physician services are billed as separate line items, making them a revenue generator for the hospitals that employ them. But under Medicare, nursing services are charged as part of a fixed room and board fee, meaning that hospitals charge the same fee regardless of how many nurses are employed in the patient’s care. In this model, nurses end up on the other side of hospitals’ balance sheets: a labor expense rather than a source of income.

For beleaguered administrators looking to sustain quality of care while minimizing costs (and maximizing profits), hiring and retaining nursing staff has arguably become something of a zero-sum game in the U.S.

The Hidden Costs of Nurse Understaffing  But might the balance sheet in fact be skewed in some way? Could there be potential financial losses attached to nurse understaffing that administrators should factor into their hiring and remuneration decisions?

Research by Goizueta Professors Diwas KC and Donald Lee, as well as recent Goizueta PhD graduates Hao Ding 24PhD (Auburn University) and Sokol Tushe 23PhD (Muma College of Business), would suggest there are. Their new peer-reviewed publication* finds that increasing a single nurse’s workload by just one patient creates a 17% service slowdown for all other patients under that nurse’s care. Looking at the data another way, having one additional nurse on duty during the busiest shift (typically between 7am and 7pm) speeds up emergency department work and frees up capacity to treat more patients such that hospitals could be looking at a major increase in revenue. The researchers calculate that this productivity gain could equate to a net increase of $470,000 per 10,000 patient visits—and savings to the tune of $160,000 in lost earnings for the same number of patients as wait times are reduced.

“A lot of the debate around nursing in the U.S. has focused on the loss of quality in care, which is hugely important,” says Diwas KC. But looking at the crisis through a productivity lens means we’re also able to understand the very real economic value that nurses bring too: the revenue increases that come with capacity gains. Diwas KC, Goizueta Foundation Term Professor of Information Systems & Operations Management  “Our findings challenge the predominant thinking around nursing as a cost,” adds Lee. “What we see is that investing in nursing staff more than pays for itself in downstream financial benefits for hospitals. It is effectively a win-win-win for patients, nurses, and healthcare providers.” Nurse Load: the Biggest Impact on Productivity  To get to these findings, the researchers analyzed a high-resolution dataset on patient flow through a large U.S. teaching hospital. They looked at the real-time workloads of physicians and nurses working in the emergency department between April 2018 and March 2019, factoring in variables such as patient demographics and severity of complaint or illness. Tracking patients from admission to triage and on to treatment, the researchers were able to tease out the impact that the number of nurses and physicians on duty had on patient throughput. Using a novel machine learning technique developed at Goizueta by Lee, they were able to identify the effect of increasing or reducing the workforce. The contrast between physicians and nursing staff is stark, says Tushe.

“When you have fewer nurses on duty, capacity and patient throughput drops by an order of magnitude—far, far more than when reducing the number of doctors. Our results show that for every additional patient the nurse is responsible for, service speed falls by 17%. That compares to just 1.4% if you add one patient to the workload of an attending physician. In other words, nurses’ impact on productivity in the emergency department is more than eight times greater.” Boosting Revenue Through Reduced Wait Times  Adding an additional nurse to the workforce, on the other hand, increases capacity appreciably. And as more patients are treated faster, hospitals can expect a concomitant uptick in revenue, says KC.

“It’s well documented that cutting down wait time equates to more patients treated and more income. Previous research shows that reducing service time by 15 minutes per 30,000 patient visits translates to $1.4 million in extra revenue for a hospital.” In our study, we calculate that staffing one additional nurse in the 7am to 7pm emergency department shift reduces wait time by 23 minutes, so hospitals could be looking at an increase of $2.33 million per year. Diwas KC  This far eclipses the costs associated with hiring one additional nurse, says Lee.

“According to 2022 U.S. Bureau of Labor Statistics, the average nursing salary in the U.S. is $83,000. Fringe benefits account for an additional 50% of the base salary. The total cost of adding one nurse during the 7am to 7pm shift is $310,000 (for 2.5 full-time employees). When you do the math, it is clear. The net hospital gain is $2 million for the hospital in our study. Or $470,000 per 10,000 patient visits.” Incontrovertible Benefits to Hiring More Nurses  These findings should provide compelling food for thought both to healthcare administrators and U.S. policymakers. For too long, the latter have fixated on the upstream costs, without exploring the downstream benefits of nursing services, say the researchers. Their study, the first to quantify the economic value of nurses in the U.S., asks “better questions,” argues Tushe; exploiting newly available data and analytics to reveal incontrovertible financial benefits that attach to hiring—and compensating—more nurses in American hospitals.

We know that a lot of nurses are leaving the profession not just because of cuts and burnout, but also because of lower pay. We would say to administrators struggling to hire talented nurses to review current wage offers, because our analysis suggests that the economic surplus from hiring more nurses could be readily applied to retention pay rises also.
Sokol Tushe 23PhD, Muma College of Business

The Case for Mandated Ratios  For state-level decision makers, Lee has additional words of advice.

“In 2004, California mandated minimum nurse-to-patient ratios in hospitals. Since then, six more states have added some form of minimum ratio requirement. The evidence is that this has been beneficial to patient outcomes and nurse job satisfaction. Our research now adds an economic dimension to the list of benefits as well. Ipso facto, policymakers ought to consider wider adoption of minimum nurse-to-patient ratios.” However, decision makers go about tackling the shortage of nurses in the U.S., they should go about it fast and soon, says KC.

“This is a healthcare crisis that is only set to become more acute in the near future. As our demographics shift and our population starts again out, demand for quality will increase. So too must the supply of care capacity. But what we are seeing is the nursing staffing situation in the U.S. moving in the opposite direction. All of this is manifesting in the emergency department. That’s where wait times are getting longer, mistakes are being made, and overworked nurses are quitting. It is creating a vicious cycle that needs to be broken.” Diwas Diwas KC is a professor of information systems & operations management and Donald Lee is an associate professor of information systems & operations management. Both experts are available to speak about this important topic - simply click on either icon now to arrange an interview today.


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#Expert Research: The Use of AI in Financial Reporting

Associate Professor of Accounting

  Artificial intelligence (AI) is developing into an amazing tool to help humans across multiple fields, including medicine and research, and much of that work is happening at Emory University’s Goizueta Business School.

Financial reporting and auditing are both areas where AI can have a significant impact as companies and audit firms are rapidly adopting the use of such technology. But are financial managers willing to rely on the results of AI-generated information? In the context of audit adjustments, it depends on whether their company uses AI as well.
 Willing to Rely on AI?   Cassandra Estep, assistant professor of accounting at Goizueta Business School, and her co-authors have a forthcoming study looking at financial managers’ perceptions of the use of AI, both within their companies and by their auditors. Research had already been done on how financial auditors react to using AI for evaluating complex financial reporting. That got Estep and her co-authors thinking there’s more to the story.


“A big, important part of the financial reporting and auditing process is the managers within the companies being audited. We were interested in thinking about how they react to the use of AI by their auditors,” Estep says. “But then we also started thinking about what companies are investing in AI as well. That joint influence of the use of AI, both within the companies and by the auditors that are auditing the financials of those companies, is where it all started.” The Methodology  Estep and her co-authors conducted a survey and experiment with senior-level financial managers with titles like CEO, CFO, or Controller – the people responsible for making financial reporting decisions within companies. The survey included questions to understand how companies are using AI. It also included open-ended questions designed to identify key themes about financial managers’ perceptions of AI use by their companies and their auditors.

In the experiment, participants completed a hypothetical case in which they were asked about their willingness to record a downward adjustment to the fair value of a patent proposed by their auditors. The scenarios varied across randomly assigned conditions as to whether the auditor did or not did not use AI in coming up with the proposed valuation and adjustment, and whether their company did or did not use AI in generating their estimated value of the patent. When both the auditor and the company used AI, participants were willing to record a larger adjustment amount, i.e., decrease the value of the patent more. The authors find that these results are driven by increased perceptions of accuracy.

It’s not necessarily a comfort thing, but a signal from the company that this is an acceptable way to do things, and it actually caused them to perceive the auditors’ information as more accurate and of higher quality. Cassandra Estep, assistant professor of accounting  “Essentially, they viewed the auditors’ recommendation for adjusting the numbers to be more accurate and of higher quality, and so they were more willing to accept the audit adjustment,” Estep says. Making Financial Reporting More Efficient  Financial reporting is a critical process in any business. Companies and investors need timely and accurate information to make important decisions. With the added element of AI, financial reporting processes can include more external data.

We touched on the idea that these tools can hopefully process a lot more information and data. For example, we’ve seen auditors and managers talk about using outside information. Cassandra Estep  “Auditors might be able to use customer reviews and feedback as one of the inputs to deciding how much warranty expense the company should be estimating. And is that amount reasonable? The idea is that if customers are complaining, there could be some problem with the products.” Adding data to analytical processes, when done by humans alone, adds a significant amount of time to the calculations. Research from the European Spreadsheets Risks Interest Group says that more than 90% of all financial spreadsheets contain at least one error. Some forms of AI can process hundreds of thousands of calculations overnight, typically with fewer errors. In short, it can be more efficient.

Efficiency was brought up a lot in our survey, the idea that things could be done faster with AI. Cassandra Estep  “We also asked the managers about their perspective on the audit side, and they did hope that audit fees would go down, because auditors would be able to do things more quickly and efficiently as well,” Estep says. “But the flip side of that is that using AI could also raise more questions and more issues that have to be investigated. There’s also the potential for more work.” The Fear of Being Replaced  The fear of being replaced is a more or less universal worry for anyone whose industry is beginning to adopt the use of AI in some form. While the respondents in Estep’s survey looked forward to more efficient and effective handling of complex financial reporting by AI, they also emphasized the need to keep the human element involved in any decisions made using AI.

What we were slightly surprised about was the positive reactions that the managers had in our survey. While some thought the use of AI was inevitable, there’s this idea that it can make things better. Cassandra Estep  “But there’s still a little bit of trepidation,” Estep says. “One of the key themes that came up was yes, we need to use these tools. We should take advantage of them to improve the quality and the efficiency with which we do things. But we also need to keep that human element. At the end of the day, humans need to be responsible. Humans need to be making the decisions.”  A Positive Outlook  The benefits of AI were clear to the survey participants. They recognized it as a positive trend, whether or not it was currently used in their financial reporting. If they weren’t regularly using AI, they expected to be using it soon.

I think one of the most interesting things to us about this paper is this idea that AI can be embraced. Companies and auditors are still somewhat in their infancy of figuring out how to use it, but big investments are being made. Cassandra Estep  “And then, again, there’s the fact that our experiment also shows a situation where managers were willing to accept the auditors’ proposed adjustments. This arguably goes against their incentives as management to keep the numbers more positive or optimistic,” Estep continues. “The auditors are serving that role of helping managers provide more reliable financial information, and that can be viewed as a positive outcome.” “There’s still some hesitation. We’re still figuring out these tools. We see examples all the time of where AI has messed up, or put together false information. But I think the positive sentiment across our survey participants, and then also the results of our experiment, reinforce the idea that AI can be a good thing and that it can be embraced. Even in a setting like financial reporting and auditing, where there can be fear of job replacement, the focus on the human-technology interaction can hopefully lead to improved situations.” Cassandra Estep, is an assistant professor of accounting at Goizueta Business School, and a co-author of the forthcoming study looking at financial managers’ perceptions of the use of AI. She's available to speak about this important topic - simply click on her icon now to arrange an interview today.


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Expert Perspective: Mitigating Bias in AI: Sharing the Burden of Bias When it Counts Most

John H. Harland Dean and Professor of Information Systems & Operations Management

  Whether getting directions from Google Maps, personalized job recommendations from LinkedIn, or nudges from a bank for new products based on our data-rich profiles, we have grown accustomed to having artificial intelligence (AI) systems in our lives.

But are AI systems fair? The answer to this question, in short—not completely. Further complicating the matter is the fact that today’s AI systems are far from transparent.  

Think about it: The uncomfortable truth is that generative AI tools like ChatGPT—based on sophisticated architectures such as deep learning or large language models—are fed vast amounts of training data which then interact in unpredictable ways. And while the principles of how these methods operate are well-understood (at least by those who created them), ChatGPT’s decisions are likened to an airplane’s black box: They are not easy to penetrate.

So, how can we determine if “black box AI” is fair? Some dedicated data scientists are working around the clock to tackle this big issue.

One of those data scientists is Gareth James, who also serves as the Dean of Goizueta Business School as his day job. In a recent paper titled “A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification” Dean James—along with coauthors Bradley Rava, Wenguang Sun, and Xin Tong—have proposed a new framework to help ensure AI decision-making is as fair as possible in high-stakes decisions where certain individuals—for example, racial minority groups and other protected groups—may be more prone to AI bias, even without our realizing it. 

In other words, their new approach to fairness makes adjustments that work out better when some are getting the short shrift of AI.

 Gareth James became the John H. Harland Dean of Goizueta Business School in July 2022. Renowned for his visionary leadership, statistical mastery, and commitment to the future of business education, James brings vast and versatile experience to the role. His collaborative nature and data-driven scholarship offer fresh energy and focus aimed at furthering Goizueta’s mission: to prepare principled leaders to have a positive influence on business and society. Unpacking Bias in High-Stakes Scenarios  Dean James and his coauthors set their sights on high-stakes decisions in their work. What counts as high stakes? Examples include hospitals’ medical diagnoses, banks’ credit-worthiness assessments, and state justice systems’ bail and sentencing decisions. On the one hand, these areas are ripe for AI-interventions, with ample data available. On the other hand, biased decision-making here has the potential to negatively impact a person’s life in a significant way. 

In the case of justice systems, in the United States, there’s a data-driven, decision-support tool known as COMPAS (which stands for Correctional Offender Management Profiling for Alternative Sanctions) in active use. The idea behind COMPAS is to crunch available data (including age, sex, and criminal history) to help determine a criminal-court defendant’s likelihood of committing a crime as they await trial. Supporters of COMPAS note that statistical predictions are helping courts make better decisions about bail than humans did on their own. At the same time, detractors have argued that COMPAS is better at predicting recidivism for some racial groups than for others. And since we can’t control which group we belong to, that bias needs to be corrected. It’s high time for guardrails.

A Step Toward Fairer AI Decisions Enter Dean James and colleagues’ algorithm. Designed to make the outputs of AI decisions fairer, even without having to know the AI model’s inner workings, they call it “fairness-adjusted selective inference” (FASI). It works to flag specific decisions that would be better handled by a human being in order to avoid systemic bias. That is to say, if the AI cannot yield an acceptably clear (1/0 or binary) answer, a human review is recommended. 

To test the results for their “fairness-adjusted selective inference,” the researchers turn to both simulated and real data. For the real data, the COMPAS dataset enabled a look at predicted and actual recidivism rates for two minority groups, as seen in the chart below.

 In the figures above, the researchers set an “acceptable level of mistakes” – seen as the dotted line – at 0.25 (25%). They then compared “minority group 1” and “minority group 2” results before and after applying their FASI framework. Especially if you were born into “minority group 2,” which graph seems fairer to you?

Professional ethicists will note there is a slight dip to overall accuracy, as seen in the green “all groups” category. And yet the treatment between the two groups is fairer. That is why the researchers titled their paper “a burden shared is a burdened halved.” 

Practical Applications for the Greater Social Good  “To be honest, I was surprised by how well our framework worked without sacrificing much overall accuracy,” Dean James notes. By selecting cases where human beings should review a criminal history – or credit history or medical charts – AI discrimination that would have significant quality-of-life consequences can be reduced. 

Reducing protected groups’ burden of bias is also a matter of following the laws. For example, in the financial industry, the United States’ Equal Credit Opportunity Act (ECOA) makes it “illegal for a company to use a biased algorithm that results in credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because a person receives public assistance,” as the Federal Trade Commission explains on its website. If AI-powered programs fail to correct for AI bias, the company utilizing it can run into trouble with the law. In these cases, human reviews are well worth the extra effort for all stakeholders.

The paper grew from Dean James’ ongoing work as a data scientist when time allows. “Many of us data scientists are worried about bias in AI and we’re trying to improve the output,” he notes. And as new versions of ChatGPT continue to roll out, “new guardrails are being added – some better than others.”  “I’m optimistic about AI,” Dean James says. “And one thing that makes me optimistic is the fact that AI will learn and learn – there’s no going back. In education, we think a lot about formal training and lifelong learning. But then that learning journey has to end,” Dean James notes. “With AI, it never ends.” Gareth James is the John H. Harland Dean of Goizueta Business School. If you're looking to connect with him - simply click on his icon now to arrange an interview today.

  


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#Expert Insight: Decoding Hierarchies in Business: When is Having a Boss a Benefit for an Organization?

Associate Professor of Organization & Management

  Most companies around the world have a leader, whether that title is a President, CEO, or Founder. There’s almost always someone at the very top of a corporate food chain, and from that position down, the company is structured hierarchically, with multiple levels of leadership supervising other employees.

It’s a structure with which most people in the working world are familiar, and it dates back as long as one can remember. The word itself—leader—dates back to as far as the 12th Century and is derived from the Old English word “laedere,” or one who leads. But in 2001, a group of software engineers developed the Agile Workflow Methodology, a project development process that puts a priority on egalitarian teamwork and individual independence in searching for solutions.

A number of businesses are trying to embrace a flatter internal structure, like the agile workflow. But is it necessarily the best way to develop business processes? That’s the question posed by researchers, including Goizueta Business School’s Özgecan Koçak, associate professor of organization and management, and fellow researchers Daniel A. Levinthal and Phanish Puranam in their recently published paper on organizational hierarchies. 

“Realistically, we don’t see a lot of non-hierarchical organizations,” says Koçak. “But there is actually a big push to have less hierarchy in organizations.” Part of it is due to the demotivating effects of working in authoritarian workplaces. People don’t necessarily like to have a boss. We place value in being more egalitarian, more participatory. Özgecan Koçak, Associate Professor of Organization & Management  “So there is some push to try and design organizations with flatter hierarchies. That is specifically so in the context of knowledge-based work, and especially in the context of discovery and search.” Decoding Organizational Dynamics  While the idea of an egalitarian workplace is attractive to many people, Koçak and her colleagues wanted to know if, or when, hierarchies were actually beneficial to the health of organizations. They developed a computational agent-based model, or simulation, to explore the relationships between structures of influence and organizational adaptation. The groups in the simulation mimicked real business team structures and consisted of two types of teams. In the first type, one agent had influence over the beliefs of rest of the team. For the second type, no one individual had any influence over the beliefs of the team. The hierarchical team vs. the flat structured team.

“When you do simulations, you want to make sure that your findings are robust to those kinds of things like the scale of the group, or the how fast the agents are learning and so forth,” says Koçak. What’s innovative about this particular simulation is that all the agents are learning from their environment. They are learning through trial and error. They are trying out different alternatives and finding out their value. Özgecan Koçak  Koçak is very clear that the hierarchies in the simulation are not exactly like hierarchies in a business organization. Every agent was purposefully made to be the same without any difference in wisdom or knowledge. “It’s really nothing like the kinds of hierarchies you would see in organizations where there is somebody who has a corner office, or somebody who is has a management title, or somebody’s making more than the others. In the simulation, it’s nothing to do with those distributional aspects or control, and nobody has the ability to control what others do in (the simulation). All control comes through influence of beliefs.” Speed vs. Optimal Solutions  What they found in the simulation was that while both teams solved the same problems presented to them, they achieved different results at different speeds.

We find that hierarchical teams don’t necessarily find the best solution, but they find the good enough solution in the shorter term. So if you are looking at the really long term, crowds do better. The crowds where individuals are all learning separately, they find the best solution in the long run, even though they are not learning from each other. Özgecan Koçak   Özgecan Koçak (pronounced as ohz-gay-john ko-chuck) is associate professor of Organization & Management at Emory University’s Goizueta Business School. She holds a Ph.D. in organizational behavior from the Graduate School of Business at Stanford University. For example, teams of scientists looking for cures or innovative treatments for diseases work best with a flat structure. Each individual works on their own timeline, with their own search methodologies. The team only comes together for status updates or to discuss their projects without necessarily getting influence or direction from colleagues. The long-term success of the result is more important in some cases than the speed at which they arrive to their conclusion.

That won’t work for an organization that answers to a board of directors or shareholders. Such parties want to see rapid results that will quickly impact the bottom line of the company. This is why the agile methodology is not beneficial to large-scale corporations. Koçak says, “When you try to think about an entire organization, not just teams, it gets more complicated. If you have many people in an organization, you can’t have everybody just be on the same team. And then you have to worry about how to coordinate the efforts of multiple teams.

That’s the big question for scaling up agile. We know that the agile methodology works pretty well at the team level. However, when firms try to scale it up applied to the entire organization, then you have more coordination problems. Özgecan Koçak  “You need some way to coordinate the efforts with multiple teams.” The Catch: Compensation Makes a Difference  The simulation did not take into account one of the biggest parts of a corporate hierarchical structure—incentives and reward. The teams in the simulation received no monetary compensation for their leadership or influence. That is not something that happens in real life.

Koçak says, “If you built up an organization with just influence, you just say we’re not going to have any authority, and we’re not going to give anybody the right to control anybody else’s actions. If we’re not going to be rewarding anyone more than the other, there’s not going to be any marks of status, etc. We’re just going to have some people influence others more. I would guess that would automatically lead to a prestige hierarchy right away. The person with more influence, you would start respecting more.” It’s almost like we’re incapable of working in a flat society, because somebody always wants to be or naturally becomes a leader and an influencer whether they planned on it or not. Özgecan Koçak  The paper concludes that both methodologies, with either hierarchical and flat organization of teams, reach their goals. They just arrive at different times with different end results. If an organization has the luxury of time and money, a flat, agile methodology organization might be the right structure for that company. However, even agile workflow needs some coordination, according to Koçak.

“There are also some search tasks that require coordination. You can’t always be searching on your own independently of others. There are some situations in which search needs to be done in a coordinated fashion by more than one person in teams. That’s because many of the knowledge-based settings where we do discovery require some division of labor, some specialization by expertise.”  Communication is Key  The key to any successful workflow, whether it be agile or hierarchical, is coordination and communication. Looking back to the example of scientific researchers, Koçak said, “You have scientific teams working independently of one another without a common boss dictating what they do research on or how they do it. Instead, they explore and experiment on their own. They write up their results, share their results, and learn from each other, because they are in the long-term game. The goal is to find the truth, however long it takes. 

“But when you look closely at a scientific team where everybody’s exploring, there is still some need for coordination. A lot of that happens through communication, and a lot of times projects will have a lead. Not necessarily somebody who knows better than the others, but somebody who’s going to help with coordination.” The leaner, flatter organizational structures in businesses might be gaining popularity. This simulation done by Koçak and colleagues, however, shows that it isn’t a perfect fit for every company, Further, some form of hierarchical workflow is necessary to maintain communication and coordination. Hierarchical structures don’t always find the best solution to a problem, but it’s almost always a good solution in a timelier fashion.

Looking to know more?  Özgecan Koçak  is associate professor of Organization & Management at Emory University’s Goizueta Business School. She is available to speak with media about this topic - simply click on her icon now to arrange an interview today.



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