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Diversity training is more effective when its personalized, according to my new research in the peer-reviewed journal Applied Psychology. As a professor of management, I partnered with Andrew Bryant, who studies social marketing, to develop an algorithm that identifies peoples personas, or psychological profiles, as they participate in diversity training in real time. We embedded this algorithm into a training system that dynamically assigned participants to tailored versions of the training based on their personas. We found that this personalized approach worked especially well for one particular group: the skeptics. When skeptics received training tailored to them, they responded more positivelyand expressed a stronger desire to support their organizations diversity effortsthan those who received the same training as everyone else. In the age of social media, where just about everything is customized and personalized, this sounds like a no-brainer. But with diversity training, where the one-size-fits-all approach still rules, this is radical. In most diversity trainings, all participants hear the same message, regardless of their preexisting beliefs and attitudes toward diversity. Why would we assume that this would work? Thankfully, the field is realizing the importance of a learner-centric approach. Researchers have theorized that several diversity trainee personas exist. These include the resistant trainee, who feels defensive; the overzealous trainee, who is hyper-engaged; and the anxious trainee, who is uncomfortable with diversity topics. Our algorithm, based on real-world data, identified two personas with empirical backing: skeptics and believers. This is proof of concept that trainee personas arent just theoreticaltheyre real, and we can detect them in real time. But identifying personas is just the beginning. What comes next is tailoring the message. To learn more about tailoring, we looked to the theory of jujitsu persuasion. In jujitsu, fighters dont strike. They use their opponents energy to win. Similarly, in jujitsu persuasion, you yield to the audience, not challenge it. You use the audiences beliefs, knowledge, and values as leverage to make change. In terms of diversity training, this doesnt mean changing what the message is. It means changing how the message is framed. For example, the skeptics in our study still learned about the devastating harms of workplace bias. But they were more persuaded when the message was framed as a business case for diversity rather than a moral justice message. The business case message is tailored to skeptics practical orientation. If diversity training researchers and practitioners embrace tailoring diversity training to different trainee personas, more creative approaches to tailoring will surely be designed. Why it matters The Trump administration is leading a backlash against diversity initiatives, and a backlash to that backlash is emerging. This isnt entirely new: Diversity has long been a contentious issue. Organizations like the Pew Research Center, the United Nations, and others have consistently reported a conservative-liberal split, as well as a male-female split, around diversity. Diversity training has done little to bridge these gaps. For one, diversity training is often ineffective at reducing bias and improving diversity metrics in organizations. Many organizations treat diversity training efforts as a box-checking exercise. Worse, its not unusual for such efforts to backfire. Our research offers a solution: Identify the trainee personas represented in your audience and customize your training accordingly. This is what social media platforms like Facebook do: They learn about people in real time and then tailor the content they see. To illustrate the importance of tailoring diversity training specifically, consider how differently skeptics and believers think. One skeptic in our studywhich focused on gender diversity trainingsaid: The issue isnt as great as feminists try to force us to believe. Women simply focus on other things in life; men focus on career first. In contrast, a believer said: In my own organization, all CEOs and managers are men. Women are not respected or promoted very often, if at all. Clearly, trainees are different. Tailoring the training to different personas, jujitsu style, may be how we change hearts and minds. What still isnt known Algorithms are only as good as the data they rely on. Our algorithm identified personas based on information the trainees reported about themselves. More objective data, such as data culled from human resources systems, may identify personas more reliably. Algorithms also improve as they learn over time. As artificial intelligence tools become more widely used in HR, persona-identifying algorithms will get smarter and faster. The training itself needs to get smarter. A onetime training session, even a tailored one, stands less of a chance at long-term change compared with periodic nudges. Nudges are bite-sized interventions that are unobtrusively delivered over time. Now, think about tailored nudges. They could be a game changer. The Research Brief is a short take on interesting academic work. Radostina Purvanova is a professor of management and organizational leadership at Drake University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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The buzz in Silicon Valley around AI agents has many asking: Whats real and whats hype? Box’s cofounder and CEO, Aaron Levie, helps decipher between fact and fiction, breaking down the fast-paced evolution of agents and their impact on the future of enterprise AI. Plus, Levie unpacks how AI is really being adopted in the workplace and what it takes to legitimately build an AI-first organization. This is an abridged transcript of an interview from Rapid Response, hosted by the former editor-in-chief of Fast Company Bob Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with todays top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. I talked with Marc Benioff at Salesforce several months ago about his embrace of AI agents, but the use of his agents hasn’t quite taken off the way he hoped. I know you launched Box AI Studio to help organizations build their own custom AI agents. I’m curious how that’s going. So far, it’s either at or exceeding our expectations on all the use cases that customers are coming up with. So we’re pretty blown away about what we’re starting to see. We’re still very early days to be clear, but the rate of adoption is going fairly exponential, and the imagination that customers now have on this is blowing us away. I’ve rarely been in a customer conversation, either one-on-one or at a dinner, where I’m not hearing about a new idea that the customer has for Box AI that we did not already have on a whiteboard. And what’s excitingand this is counterintuitive, I think, to a lot of folks outside of AIyou initially sort of see AI in sci-fi and sometimes in news headlines, The New York Times or whatever, as like, “Okay, it’s going after jobs. It’s going to replace these types of work.” From my anecdotes, I’ve had at least 100 interactions with customers in the first quarter of this year, the vast majority, 80%, I’m guessing, the bulk of the time of AI use case kind of conversation was spent on things that the company didn’t do before AI. So it wasn’t, “Hey, I want to take this type of work, and I want AI to go replace it.” There’s a type of work that we never get around to in our company. I want AI to go and do that, because finally, it’s affordable for me to deploy AI agents at the kind of work that we could not fund before. It’s opening up people’s imagination to, “Hey, I’m like sitting on 50,000 customer contracts. What if I could have an AI agent go around all those customer contracts, and figure out which customers have the highest propensity to buy this next product from me?” And this is not something that they would have people ever do. So it’s not replacing anybody’s job. They never said, “Oh, let’s have 50 people go read all the contracts again.” It just never happened. But now, if it only costs them $5,000 for an AI agent to go do that, they would do that all day long. And then guess what? When they get those insights, they’re probably going to now have more work for the humans in their business to go and do as a result of this, that hopefully, if it’s effective, drives more growth in their businesswhich then causes even more productivity, and then ultimately hiring and growth. And so it’s not kind of everybody’s first instinct, but most of the use cases that we’re hearing about are things where, “Because it is now affordable to deploy AI at a problem, I’m actually expanding the set of things my company can go do, and then the work that we can now execute on.” And that’s not only very, I think, exciting, but I think it’s going to be the default case for most AI adoption in the enterprise. In some of the conversations that I have, it feels almost like some of the businesses and leaders, they don’t really know what they’re looking for from AI. And hearing you, it sounds a little bit like you have to think about your mindset on it a little differently to open up and find those things that are most valuable to you. Yes. Yeah, every business is going to be different because some of the upside is a virtue of your business model. What are the core parts of your business model that, as a result of access to information, can change or be modified or improved? If I am a law firm, I could either reduce my cost, because now AI is going to do more of the, let’s say, paralegal work, or I could expand my service offerings, because now, all of a sudden, my team can venture into more domains because they can take their expertise and use AI to augment that. The default assumption is, “Oh, no, it’s going to go after the hours of a law firm.” But once this technology hits an individual business, they can actually decide to expand their customer base. They can go after, previously, customers that would’ve been unprofitable for them to serve. So these industries are not as static and zero-sum. The software industry . . . on one hand, everybody says, “Okay, if AI can do coding, then will we hire fewer engineers?” And in general, my argument is that we’ll probably hire as manyif not moreengineers if AI can get really good at coding, because what will happen is the productivity rate of our engineer goes up, which means that we can then ascribe a higher degree of value per engineer in the company. So your ROI is even better on each of those positions? Exactly. And take something like sales. If we can make a sales rep able to sell 5% more, because we give them better data, and they can prepare for a customer meeting that much better, or they can understand exactly the best pitch because they have access to all of Box’s data and they can ask it questions, I’m not going to just bank that as 5% more profit. Because what will happen is we’re going to internally, in some planning session, we’re going to get greedy, and we’re going to say, “Wait a second, that 5% gain that we just got in sales productivity, what if we reinvested that back into the sales team to grow even faster and get that much more market share?” And so you have an entire economy of companies making those individual decisions of, “Do you bank the profit, or do you use it to go and accelerate growth?” And what we tend to know from history is that the companies that get too greedy on the profit side, you just end up leaving yourself vulnerable to being outflanked by competitors. So capitalism has a prety convenient way of almost driving the sort of productivity gains of these types of innovations to get reinvested back into the business. You’ve been talking about running Box in an AI-first way, and encouraging other leaders to do it. Are you like Shopify and Duolingo, who’ve announced that staffers have to justify anything that’s not AI-produced? What does AI-first mean? Yeah. So for us, AI-first means that we want to use AI as a means of driving an acceleration of the customer outcome, an acceleration of decision-making, an acceleration of building new features. So just think about it as mostly a metric of speed. On one hand, you could think about AI as going after like a massive work, and you could say AI is going to remove some part of that massive work and do it instantly, so the massive work goes down, or think about work as a timeline, and not a mass. All we’re doing is trying to get through each step so that way, we can get to the next step and so on. And everything’s faster. And everything’s faster. So I want to have us use AI to move faster down the timeline, not just purely to reduce the total mass of work that we’re doing. There’s probably one pronounced difference versus, let’s say, the Duolingo memo. There’s some emerging idea, which is sort of you have to prove that AI can’t do this thing for you to get then head count, and our general instinct is actually the opposite. If you can prove that you can use AI, then that’s actually when you will get head count, because what we want is we want the dollars of the business to go back into the areas that are the increasing areas of productivity gain, because those areas will then be higher ROI for us over time.
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People often think of disasters as great equalizers. After all, a tornado, wildfire, or hurricane doesnt discriminate against those in its path. But the consequences for those affected are not one-size-fits-all. Thats evident in recent storms, and in the U.S. Census Bureaus national household surveys showing who is displaced by disasters. Overall, the Census Bureau estimates that more than 4.3 million Americans had to leave their homes because of disasters in 2024, whether for a short period or much longer. It was the fourth-costliest year on record for disasters. However, a closer look at demographics in the survey reveals much more about disaster risk in America and who is vulnerable. It suggests, as researchers have also found, that people with the fewest resources, as well as those who have disabilities or have been marginalized, were more likely to be displaced from their homes by disasters than other people. Decades of disaster research, including from our team at the University of Delawares Disaster Research Center, make at least two things crystal clear: First, peoples social circumstancessuch as the resources available to them, how much they can rely on others for help, and challenges they face in their daily lifecan lead them to experience disasters differently compared to others affected by the same event. And second, disasters exacerbate existing vulnerabilities. This research also shows how disaster recovery is a social process. Recovery is not a thing, but rather it is linked to how we talk about recovery, make decisions about recovery, and prioritize some activities over others. Lessons from past disasters Sixty years ago, the recovery period after the destructive 1964 Alaskan earthquake was driven by a range of economic and political interests, not simply technical factors or on need. That kind of influence continues in disaster recovery today. Even disaster buyout programs can be based on economic considerations that burden under-resourced communities. This recovery process is made even more difficult because policymakers often underappreciate the immense difficulties residents face during recovery. Following Hurricane Katrina, sociologist Alexis Merdjanoff found that property ownership status affected psychological distress and displacement, with displaced renters showing higher levels of emotional distress than homeowners. Lack of autonomy in decisions about how to repair or rebuild can play a role, further highlighting disparate experiences during disaster recovery. What the census shows about vulnerability U.S. Census data for 2023 and 2024 consistently showed that socially vulnerable groups reported being displaced from their homes at higher rates than other groups. People with less high school education had a higher rate of displacement than those with more education. So did those with low household incomes or who were struggling with employment, compared to other groups. While the Census Bureau describes the data as experimental and notes that some sample sizes are small, the differences stand out and are consistent with what researchers have found. For example, research has long pointed to how communities composed predominantly of Black, Hispanic, Native American, and Pacific Islander residents have disproportionately worse recovery trajectories after a disaster, often linked to aspects such as housing tenure and land-use policies. Though reporting individual experiences, the Census Bureaus findings are consistent with this research, noting a higher rate of displacement for these groups. Low-income and marginalized communities are often in areas at higher risk of flooding from storms or may lack investment in storm protection measures. The morass of bureaucracy and conflicting information can also be a barrier to a swift recovery. After Hurricane Sandy, people in New Jersey complained about complex paperwork and what felt to them like ever-changing rules. They bemoaned their housing recovery as, in researchers words, a muddled, inconsistent experience that lacked discernible rationale. Residents who dont know how to find information about disaster recovery assistance or cant take time away from work to accumulate the necessary documents and meet with agency representatives can have a harder time getting quick help from federal and state agencies. Disabilities also affect displacement. Of those people who were displaced for some length of time in 2023 and 2024, those with significant difficulty hearing, seeing, or walking reported being displaced at higher rates than those without disabilities. Prolonged loss of electricity or water due to an ice storm, wildfire, or grid overload during a heat emergency can force those with medical conditions to leave even if their neighbors are able to stay. That can also create challenges for their recovery. Displacement can leave vulnerable disaster survivors isolated from their usual support systems and healthcare providers. It can also isolate those with limited mobility from disaster assistance. Helping communities build resilience Crucial research efforts are underway to better help people who may be struggling the most after disasters. For example,our center was part of an interdisciplinary team that developed a framework to predict community resilience after disasters and help identify investments that could be made to bolster resilience. It outlines ways to identify gaps in community functioning, like healthcare and transportation, before disaster strikes. And it helps determine recovery strategies that would have the most impact. Shifts in weather and climate and a mobile population mean that peoples exposure to hazards are constantly shifting and often increasing. The Coastal Hazard, Equity, Economic Prosperity, and Resilience Hub, which our center is also part of, is developing tools to help communities best ensure resilience and strong economic conditions for all residents without shortchanging the need to prioritize equity and well-being. We believe that when communities experience disasters, they should not have to choose among thriving economically, ensuring all residents can recover, and reducing risk of future threats. There must be a way to account for all three. Understanding that disasters affect people in different ways is only a first step toward ensuring that the most vulnerable residents receive the support they need. Involving community members from disproportionately vulnerable groups to identify challenges is another. But those, alone, are not enough. If we as a society care about those who contribute to our communities, we must find the political and organizational will to act to reduce the challenges reflected in the census and disaster research. This article, originally published March 4, 2024, has been updated with latest severe storms and 2024 census data. Tricia Wachtendorf is a professor of sociology and director of the Disaster Research Center at the University of Delaware. James Kendra is the director of the Disaster Research Center and a professor of public policy & administration at the University of Delaware. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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