#Expert Insight: Political Fandom

Analyzing Trump's and Harris' Core Support and Future Negative Campaigns

Jul 30, 2024

13 min

Michael Lewis

The 2024 Presidential campaign has been a roller coaster ride this summer. The upheavals are so fast and unprecedented that the reaction to each event often seems too muted. An assassination attempt and sudden pre-convention withdrawal? In a past generation, these events would be decisive, but in 2024, they seem like just the latest blip in the news cycle. The polls never seem to move more than a couple of points. In such an oddly volatile but also stable environment, our best bet to understanding what is going to transpire during the last 100 days of the election cycle is to look at data that gets to the heart of how voters view the candidates. My choice of fundamental data or essential metric is candidate fandom.


Fandom is an unusual metric in politics, but it should be more common. Fandom is about passion for and loyalty to a cultural entity, be it a team, singer, university, or even politician. In fact, MAGA Trump supporters and Bernie Bros share many characteristics with Swifties and Lakers fans. Fans of all these things show up, spend, wear branded apparel, and fiercely defend the object of their fandom. The politicians who inspire fandom, such as AOC, Donald Trump, Barack Obama, and Marjorie Taylor Green, enjoy many advantages and are the celebrities of the political world.



Fandom is critical in politics because fans are loyal, engaged, and resilient. Fans are not casual potential voters who may change preferences and are unlikely to make an effort to stand in line to vote. Fans are the voters who will show up rain or shine and who can’t be swayed. In 2024, a fan will interpret a conviction of their candidate as political “lawfare” rather than evidence of criminality. Also, in 2024, a fan will make excuses for signs of aging that would result in children taking a senior’s car keys.


The flip side of fandom, anti-fandom, is also a powerful political force. Indeed, politics may be the cultural context in which anti-fandom has the most impact. Taylor Swift may have haters, but these anti-Swifties are not buying tickets to see Katy Perry in protest. But in politics, hatred of a candidate might be as powerful a tool for generating a vote as fandom. Joe Biden’s 2020 campaign was notoriously bad at drawing crowds, suggesting he inspired little passion. In contrast, Trump’s rallies looked like rabid sports crowds complete with matching hats. However, the hatred and fear of Donald Trump inspired sufficient anti-fandom to make Biden competitive.


Of course, fandom doesn’t entirely decide elections. In most elections, there isn’t all that much fandom or passion. Beyond the presidency and senatorial contests, most candidates are barely known, and identity factors (race, gender, party affiliation) and candidate awareness are the determining factors. Even in presidential elections, get-out-the-vote efforts (ballot harvesting) and election regulations (voter suppression) combined with effective marketing to the few percent of swing (low information) voters are often the determining factors.


Looking toward the future, fandom may be an increasingly salient political metric for multiple reasons. First, the last two decades have witnessed many candidates raised quickly from obscurity with somehow Hollywood-worthy origin stories (Barack Obama, AOC, JD Vance, etc.). In the modern media environment, candidates’ reputations (brands) are increasingly the product of marketing narratives rather than a lifetime of real-world accomplishments. In this new world of politics, fandom will be a critical metric.


Second, with the increasing diversity of the American electorate, voting will be increasingly based on identity rather than ideology. Identity-based voting segments are likely to be driven by fandom (and anti-fandom) rather than policy. We see a form of this in 2024, as high inflation has barely made a dent in voters’ preferences for the two parties. A fragmented electorate comprised of racial and gender segments whose preferences are driven by fandom and anti-fandom will lead to increasingly negative campaigns featuring ads highlighting the threat of the non-preferred party’s candidates. When voters are focused on identity, negative advertising becomes the ideal method to use fear to create anti-fandom (hate) to motivate turnout.



Kamala Harris versus Donald Trump

Barring further disruptions, the matchup is set for the 2024 presidential contest (as of this writing, we do not know the Democratic VP). We do know the matchup between Donald Trump and Kamala Harris is a contest between polarizing figures. Donald Trump is a movement candidate who has redefined the Republican party. He inspires passionate fandom from his followers and amazing antipathy from major media and cultural outlets. Harris is also polarizing. In the immediate aftermath of Biden’s withdrawal, Harris received massive media and donor support. However, Harris has not demonstrated any significant national voter appeal, and her time as VP has generated ample blooper real material.


My approach to assessing the race is to examine each candidate's fandom and anti-fandom. Fandom is the candidate’s core, resilient support, while anti-fandom is about antipathy. Fandom and anti-fandom are especially powerful metrics for a candidate because they are relatively fixed after a candidate gains high awareness. Once an individual identifies with the candidate (e.g., they are on the same team), an attack on the candidate is an attack on the individual. This means attack ads do not work because fans feel they are being attacked. Anti-fans are also important because they constrain a candidate’s support. A Trump anti-fan is unpersuadable by efforts from the Trump campaign because their identity is steeped in opposition to him. Fans and anti-fans are trapped in a cycle of confirmation bias where all information is processed to fit their fandom.


I use data from the Next Generation Fandom Survey to assess candidate fandom and anti-fandom. The Next Generation Fandom Survey involves a nationwide sample of the U.S. population regarding fandom for sports and other cultural entities. In the 2024 edition, political figures such as Donald Trump, Joe Biden, Kamala Harris, and RFK Jr were included. The survey captured responses from 2053 subjects split evenly across the four primary generations (Gen Z, Millennials, Gen X, and Baby Boomers), and the sample is representative in terms of racial background. The survey does not focus on likely or registered voters, so the results reflect overall societal sentiments rather than the electorate's opinions. The critical survey question asks subjects to rate how much of a fan they are of a celebrity on a 1 to 7 scale. In the following discussion, individuals who rated their fandom a 6 or 7 on the 7-point scale are categorized as Fans, while those who rate their fandom a 1 or 2 are classified as Anti-Fans.


Table 1 shows the Fandom and Anti-Fandom rates for the entire sample. Donald Trump has a 27% fandom rate compared to Harris's 21%. The fandom rate is crucial because it identifies the candidate's core support. It also indicates something important about the candidate’s potential likability. In terms of anti-fandom, Harris has a slightly higher Anti-Fandom rate. Anti-Fandom is also critical as it shows the percentage of people who hate a candidate. The data suggests that Americans find Harris to be more dislikable than Trump. Notably, the anti-fandom rates are significantly higher than the fandom rates. The American public has significant disdain for politicians. The high anti-fandom rates are both the product of past negative advertising and the cause of future negative campaign strategies.


Table 1: Candidate Fandom and Anti-Fandom



Table 2 reports fandom rates based of the two gender segments. Trump has a 7%-point advantage with men and a surprising 4% advantage with women. This is a stunning result as Trump is generally regarded as having weakness with female voters. However, this weakness shows up in the anti-fandom rates. In the male segment, Trump has a 5%-point advantage in anti-fandom (fewer anti-fans), but a 3% disadvantage in the female segment. This reveals that Trump is polarizing to women, and almost half of women find Trump to be highly dislikable. This finding is why the Harris campaign is likely to use advertising that casts Trump as misogynistic or a threat to women to motivate turnout by female voters.


Table 2: Candidate Fandom by Gender



Table 3 shows the fandom rates for the two younger demographic segments: Gen Z and Millennials. This Table also shows Trump’s relative performance versus Biden (in parentheses in the last column). Trump enjoys higher fandom and lower anti-fandom than Harris in both the Gen Z and Millennial segments. In terms of fandom, Trump is plus 6% in Gen Z and plus 11% with Millennials. Critically, Harris outperforms Biden. The Gen Z anti-fandom gap between Trump and Biden favored Trump by 6% points. However, this gap shrinks to just 1% point when Harris is the comparison. The data suggests that Harris is stronger with Gen Z than Biden.


Table 3: Candidate Fandom in Younger Generations



Table 4 reports the fandom rates based on a racial segmentation scheme. Specifically, the sample is divided into White and Non-White categories. This is a crude segmentation, but it illustrates some essential points. Trump enjoys a significant 14% positive fandom advantage in the White demographic. He also enjoys a 10-point edge in (lower) anti-fandom. The pattern essentially reverses in the Non-White segment, as Harris has a 10-point advantage in fandom and a 17-point edge in anti-fandom. Trump’s anti-fandom in the Non-White segment is critical to the campaign. Nearly half of this segment has antipathy or hate for Trump. This high anti-fandom suggests an opportunity for the Harris campaign to emphasize racial angles in their attacks on Trump.


Table 4: Candidate Fandom by Race



In addition to fandom and anti-fandom rates across demographic categories, insights can be gleaned by looking at segmentation variables that reflect cultural values or personality. Table 5 shows fandom and anti-fandom rates for Trump and Harris for segments defined by fandom for Taylor Swift (Swifties) and Baseball.


The Swifties skew towards Harris. The implication is that young women engaged in popular culture have more positive fandom for Harris and more negativity toward Trump. This is unsurprising given the content of the popular culture and Swift’s personal liberalism. The Swiftie segment shows a much stronger skew for Harris than all but the Non-White segment. Examining the data at a cultural level is vital as it indicates that it isn’t necessarily youth or gender where Harris has an advantage but a combination of youth, gender, and a specific type of cultural engagement.


The table also includes fandom rates for baseball fans. In the Baseball Fan segment, Trump enjoys an 8% point fandom advantage and a 7% anti-fandom advantage (lower anti-fandom). Like the case of the Swifties, the fandom rates of Baseball Fans reveal something about Trump’s core support. Baseball is a very traditional game with an older fan base, and traditionalism is probably the core value of Trump fans. Trump’s negative advertising is likely to focus on the threats to traditional values (i.e., Harris is a San Francisco liberal).


Table 5: Candidate Fandom and Cultural Segments



Commentary and Prediction

Fandom is a powerful metric for predicting political success, but like most data points, it doesn’t tell the whole story. Fandom is a measure of unwavering core support while anti-fandom measures the group that will never support and is likely to show up to vote against a candidate. Examining fandom rates across multiple segments reveals that Harris’ core support is concentrated in specific cultural and racial segments. The analysis also suggests that Trump's core support is broader than is usually acknowledged and that his main problem is significant anti-fandom with women and minorities. Harris’ problem is a lack of love, while Trump’s is too much hate.


Notably, I am not paying too much attention to the current wave of excitement and enthusiasm surrounding Harris. The recent enthusiasm is likely more a manifestation of the Democratic base’s hopes and a relentless media onslaught than an actual increase in passion for Harris. Maybe there will be a permanent shift upward in Harris’s fandom, but I don’t see any logic for why this would occur. Harris isn’t suddenly more likable or aspirational than she was last month. The argument that the American people are becoming more acquainted with her is dubious, given that she has been the Vice President or a major presidential candidate for almost five years.


What are the implications for the upcoming election? Voting is not only about fandom or hate, so we must consider some additional factors. For instance, many potential voters lack passion and knowledge and are more prone to vote based on identity rather than ideology. If a region or demographic segment consistently votes for a party 75% of the time, that’s voting more based on fixed identities than current societal conditions. The American electorate has many of these types of fixed-preference voter segments. Furthermore, as the American electorate becomes more diverse, identity-based voting seems to be making presidential contests more predictable. The baseline seems to be that the Democratic candidate will win the popular vote by a few percentage points, and the Electoral College will come down to a few states, such as Michigan, Pennsylvania, and Wisconsin. Examining past electoral maps shows far more shifting of states across elections. Now, all but a handful of states are regarded as non-competitive.


The Figure below shows the presidential popular vote margins for the last 50 years. It shows a trend towards smaller margins for the winning candidate, which is at least partly due to growing ethnic diversity and more fixed (at least in the near and medium terms) identity-based voting. Over the last 13 cycles, the margin of victory has dropped by about 1% every four years. Demographic change has also locked in a high baseline level of support for Democratic candidates. The last time a Republican won the popular vote was in 2004, with George Bush as the incumbent.


Figure 1: Presidential Vote Margin 1972 to 2020



In addition to shrinking election margins, demographic change promises to change future campaign tones. The increasing relevance of fandom and anti-fandom, combined with the growing diversity of the electorate, will make 2024 an extremely negative campaign.


The 2024 election will be determined by identity-based demographic trends and negative (anti-fandom) marketing campaigns. Demographics are destiny, and America is changing rapidly in ways that make it increasingly difficult for the Republicans to win the popular vote. It doesn’t matter if the Democrat is Harris, Newsom, Clinton, or Whitmer while the Republican is Rubio, Haley, Cruz, or Burgum. The baseline is probably 52% to 48%, D to R. Candidate fandom and anti-fandom probably shift the vote 2 or 3 percent in either direction.


The correlation of demographic traits with voting behaviors creates incentives for campaign strategies that focus on identity. Republicans are eager to shift some percentage of Black or Hispanic voters to their cause because it simultaneously reduces the Democrats' base and grows Republican totals. In contrast, Democrats need to motivate marginal voters in the female, Black, and Hispanic segments to turn out. Fear-based appeals are the most effective tool for both parties' goals.


Negative messaging is also prevalent because of the general view of politicians. Politicians tend to inspire more antipathy (anti-fandom) than admiration (fandom). The fandom data shows this, as both candidates have far more anti-fans than fans (this holds with other politicians) . The modern election calculus is, therefore, focused on aggressive negative ads that inspire marginal voters to take the initiative to vote against a hated candidate. Passion drives behavior, and it's far easier to drive fear and hatred of a candidate than to inspire passion and admiration.


Considering the fandom data and the current electorate, I have two predictions. First, we will witness an incredibly nasty race. Harris’s best bet is to demonize Trump to motivate the anti-Trump voters to turn out. The American culture of 2024 includes constant repetition that many Democratic voting constituencies are marginalized and threatened. These segments are best motivated by using messages that cast the Republicans as the danger or oppressor. Women will fear losing reproductive rights, and African Americans will be primed with threats to voting rights.


Trump will also employ negative messaging, but Trump’s adoption of a negative campaign comes from a slightly different motivation. Trump’s core support consists of conservatives who are frustrated by a lack of cultural power and representation. This group is looking for someone who will fight for their values. This desire for a “fighting advocate” explains much of Trump’s appeal, as his supporters are enthusiastic about his “mean tweets and nicknames.” There will also be fear-based advertising as Harris will be positioned as wanting to defund police and open the border.


Second, Trump wins in a close contest. Comparing Trump’s and Harris’ fandom and anti-fandom suggests the Harris campaign faces an uphill challenge. Despite the current blitz of enthusiasm for Harris as a replacement for a failing Joe Biden, her “brand” has not shown an ability to stimulate passion, and her dislike levels exceed Trump's. It seems unlikely that she will be able to inspire fans. While Trump has a significant fanbase and weaknesses in terms of strong anti-fandom levels in minority and cultural segments, he probably beat Clinton in 2016 because her anti-fandom was equivalent to his. In contrast, he lost to Biden because Biden had less anti-fandom (in 2020). Kamala Harris seems more like Clinton than Biden, so look for a similar outcome as in 2016.


The bottom-line prediction: An exceptionally negative campaign, with Trump’s greater baseline fandom and Harris’s charisma deficit leading to a narrow Trump victory. As in 2016,Trump wins the Electoral College while losing the popular vote.


Addendum: Future Fandom Lesson

The structure of the American electorate and the propensity of people to vote based on identity rather than ideology mean that negative campaigns are the standard in the near future. The essential observation is that demographic trends create an electorate that is more a collection of identity segments than a homogeneous population that varies in ideology. An increasingly diverse electorate likely means increasingly negative presidential campaigns as negative or fear-based appeals are especially effective when elections focus on threats to identity groups. The tragedy of this situation is that the negative messages of campaigns amplify racial division and acrimony. When the next election occurs, the electorate is even more polarized, and negative or fear-based appeals are again the most effective.



Mike Lewis is an expert in the areas of analytics and marketing. This approach makes Professor Lewis a unique expert on fandom as his work addresses the complete process from success on the field to success at the box office and the campaign trail.


Michael is available to speak with media - simply click on his icon now to arrange an interview today.


 

Interested in following Future Fandom! Subscribe for free to receive new posts.


Connect with:
Michael Lewis

Michael Lewis

Professor of Marketing

www.fandomanalytics.com All Things Fandom and Sports Analytics

Revenue Management & Dynamic PricingCustomer Relationship ManagementSports AnalyticsSports MarketingFandom

You might also like...

Check out some other posts from Emory University, Goizueta Business School

8 min

#Expert Research: Incentives Speed Up Operating Room Turnover Procedures

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.

5 min

Why Simultaneous Voting Makes for Good Decisions

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.

4 min

Expert Perspective: The Hidden Costs of Cultural Appropriation

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.

View all posts