Featured Post from Emory University, Goizueta Business School

Expert Q&A: Should We Permit AI to Determine Gender and Race from Resumes?

Jul 12, 2024

7 min

Prasanna Parasurama



The banner ads on your browser, the route Google maps suggests for you, the song Spotify plays next: algorithms are inescapable in our daily lives.


Some of us are already aware of the mechanisms behind a targeted ad or a dating profile that lights up our phone screen. However, few of us may actually stop to consider how this technology plays out in the hiring sector. As with any major technological advancement, it usually takes society (and legislation) a while to catch up and adjust for unintended consequences. Ultimately, algorithms are powerful tools. Like any tool, they have the potential for societal benefit or harm, depending on how they’re wielded.


Here to weigh in on the matter is Assistant Professor of Information Systems & Operations Management Prasanna Parasurama, who recently joined Emory Goizueta Business School’s faculty in fall of 2023.


This interview has been edited for clarity.


Describe your research interests in six words.


Six words…that’s difficult to do on the spot. How about “the impact of AI and other digital technologies on hiring.” Is that condensed enough?


That works!

What first interested you in the intersection of AI and hiring practices?



Before I did my PhD, I was working as a data scientist in the HR analytics space at a start-up company. That is where my interest in the topic began. But this was a long time ago. People hadn’t started talking much about AI, or algorithmic hiring. The conversation around algorithmic bias and algorithmic fairness picked up steam in the second or third year of my PhD. That had a strong influence on my dissertation focus. And naturally, one of the contexts in which both these matters have large repercussions is in the hiring space.


What demographics does your research focus on (gender identity, race/ethnicity, socioeconomic status, all of the above)? Do you focus on a particular job sector?


My research mostly looks at gender and race for two main reasons. First, prior research has typically looked at race and gender, which gives us a better foundation to build on.

Second, it’s much easier to measure gender and race based on the data that we have available—from resumes, from hiring data, like what we collect from the Equal Employment Opportunity Commission. They typically collect data on gender and race, and our research requires those really large data sets to draw patterns. They don’t ask for socioeconomic status or have an easy way to quantify that information. That’s not to say those are less important factors, or that no one is looking at them.



One of the papers you’re working on examines resumes written by self-identified men and women. It looks at how their resumes differ, and how that influenced their likelihood of being contacted for an interview.


So in this paper, we’re essentially looking at how men and women write their resumes differently and if that impacts hiring outcomes. Take resume screening algorithms, for example. One proposed way to reduce bias in these screening algorithms is to remove names from resumes to blind the applicant’s gender to the algorithm. But just removing names does very little, because there are so many other things that serve as proxies to someone’s gender. While our research is focused on people applying to jobs in the tech sector, this is true across occupations.


"We find it’s easy to train an algorithm to accurately predict gender, even with names redacted."


Prasanna Parasurama


What are some of those gendered “tells” on a resume?


People write down hobbies and extracurricular activities, and some of those are very gendered. Dancing and ballet tend to denote female applicants; you’re more likely to see something like wrestling for male applicants.

Beyond hobbies, which is sort of obvious, is just how people write things, or the language they use. Female applicants tend to use a lot more affective words. Men, on the other hand, use more of what we call agentic words.


Can you explain that a little more?


In social psychology, social role theory argues that men are stereotyped to be more agentic, whereas women are stereotyped to be more communal, and that their communication styles reflect this. There’s essentially a list of agentic words that researchers have come up with that men use a lot more than women. And women are more likely to use affective words, like “warmly” or “closely,” which have to do with emotions or attitudes. 

These communication differences between men and women have been demonstrated in social sciences before, which has helped inform our work. But we’re not just relying on social science tools—our conclusions are driven by our own data. If a word is able to predict that an applicant’s resume belongs to a female versus male applicant, then we assign different weights, depending on how accurately it can predict that. So we’re not just operating on theories.


Were there any gendered patterns that surprised you?


If you were to assign masculinity and femininity to particular words, an algorithm would likely assign “married” to be a feminine term in most contexts. But in this particular case, it’s actually more associated with men. Men are much more likely to use it in resumes, because it signals something different to society than when women use it.


"One of the most predictive terms for men was references to parenthood. It’s much easier for men to reference kids than for women to reveal information about their household status. Women face a penalty where men receive a boost."


Prasanna Parasurama
Studies show that people perceive fathers as being more responsible employees, whereas mothers are regarded as less reliable in the workplace.

We haven’t studied this, but I would speculate that if you go on a platform like LinkedIn, men are more likely to disclose details about fatherhood, marriage, and kids than women are.

There were some other tidbits that I didn’t see coming, like the fact that women are much less likely to put their addresses on their resume.


Can AI predict race from a resume as easily as it can predict gender?


There’s surprisingly very little we know on that front. From existing literature outside of algorithmic literature, we know differences exist in terms of race, not just on the employer side, where there might be bias, but we also on the worker side. People of different races search for jobs differently. The question is, how do we take this into account in the algorithm? From a technical standpoint, it should be feasible to do the same thing we do with gender, but it just becomes a little bit harder to predict race in practice. The cues are so variable.

Gender is also more universal – no matter where you live, there are probably men and women and people who identify as in between or other. Whereas the concept of race can be very specific in different geographic regions. Racial identities in America are very different from racial identities in India, for instance. And in a place like India, religion matters a lot more than it does in the United States. So this conversation around algorithms and bias will look different across the globe.



Beyond screening resumes, how does AI impact people’s access to job opportunities?


A lot of hiring platforms and labor market intermediaries such as LinkedIn use AI. Their task is to match workers to these different jobs. There’s so many jobs and so many workers. No one can manually go through each one. So they have to train algorithms based on existing behavior and existing design decisions on the platform to recommend applicants to particular jobs and vice versa. When we talk about algorithmic hiring, it’s not just hiring per se, but spaces like these which dictate what opportunities you’re exposed to. It has a huge impact on who ends up with what job.


What impact do you want your research to have in the real world? Do you think that we actually should use algorithms to figure out gender or race? Is it even possible to blind AI to gender or race?


Algorithms are here to stay, for better or worse. We need them. When we think about algorithmic hiring, I think people picture an actual robot deciding who to hire. That’s not the case. Algorithms are typically only taking the space of the initial part of hiring.


"I think overall, algorithms make our lives better. They can recommend a job to you based on more sophisticated factors than when the job was chronologically posted. There’s also no reason to believe that a human will be less biased than an algorithm."


Prasanna Parasurama


I think the consensus is that we can’t blind the algorithm to gender or other factors. Instead, we do have to take people’s demographics into account and monitor outcomes to correct for any sort of demonstrable bias.

LinkedIn, for example, does a fairly good job publishing research on how they train their algorithms. It’s better to address the problem head on, to take demographic factors into account upfront and make sure that there aren’t drastic differences in outcomes between different demographics.


What advice would you give to hopeful job candidates navigating these systems?


Years of research have shown that going through a connection or a referral is by far the best way to increase your odds of getting an interview—by a factor of literally 200 to 300 percent. Hiring is still a very personal thing. People typically trust people they know.


Prasanna Parasurama is an Assistant Professor of Information Systems & Operations Management at Emory University’s Goizueta Business School.



Prasanna’s research areas include algorithmic hiring, algorithmic bias and fairness, and human-AI interaction. His research leverages a wide array of quantitative methods including econometrics, machine learning, and natural language processing.


Prasanna is available to talk about this important and developing topic - simply click on his icon now to arrange an interview today.

Connect with:
Prasanna Parasurama

Prasanna Parasurama

Assistant Professor of Information Systems & Operations Management

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
Powered by