Stories related to "Faculty".


The Next Generation of Enterprise

On the day of the NVC finals in May, David Rabie, ’15, and his team stood in front of a panel of judges in a Booth classroom and passed out samples of Thai chicken curry, quinoa, and ginger soy broccoli. The meals had been cooked in a futuristic countertop device—similar to a crockpot—called Maestro. Rabie’s vision of simple, healthy meals calls for customers to pop pods of raw vacuum-sealed vegetables, grains, and proteins into the machine and scan the QR-coded cooking instructions on the package. In a half hour, a well-rounded meal is ready to go. The judges tossed out plenty of questions: How did Rabie plan to grow the company? Who would develop the recipes? Rabie had answers, which is why the judges awarded Maestro first place in the Edward L. Kaplan, ’71, New Venture Challenge (NVC), Booth’s signature startup program. With a cash prize of $70,000, business services, and enviable industry connections, Maestro has a good start in life. Four months later, the start up is fine-tuning the product, building a


A Bowl of Cashews

Sometimes you feel like a nut; sometimes you don’t. And sometimes you wish you didn’t have to decide. A quiet revolution in economic thinking instigated by Richard H. Thaler traces its beginnings to a dinner party he hosted in the 1970s. As Thaler explains in his latest book, Misbehaving: The Making of Behavioral Economics, the guests while waiting with cocktails for the meal, were devouring the cashews—the entire bowl half-eaten in minutes. So Thaler, worried that his guests would fill up on the salty snacks, whisked the bowl away. He recalled that when he came back, his friends thanked him for it (and found themselves with room to enjoy a big dinner). “But then, since we were economics graduate students,” Thaler recalled, “we immediately started analyzing this. Because that’s what economists do.” Even cashews could hold the key to unlocking insights about our idiosyncratic behaviors. Without the temptation of the nuts, he said, “We realized that a.) we were happy, and b.) we weren’t allowed to be happy, because a first principle of economics is more choices are better than fewer choices.”


Where’s the Optimal Place to Park a Food Truck?

Pursuing a love of food and cooking, I completed the basic pastry certificate at Le Cordon Bleu in Paris the summer before starting the PhD program. So I was thrilled to see a crêpe truck, Paris Ouh La La, serving lunch during the school year. After several good meals at the food trucks on Ellis Avenue, and observing the variation in the trucks parked each day, I started thinking about how the trucks decide where to park. Where you choose to locate a business is a fundamental economic question—one that food trucks must re-answer every day. The classic location choice model was offered by the mathematician and economist Harold Hotelling in 1929. Consider two ice cream vendors who parked their carts on a one-mile stretch of beach. Assuming the venders offer roughly the same treats, beachgoers will naturally choose to walk to the closest cart. The vendor on the left will serve all the beachgoers to its left, and the vendor on the right will serve all the beachgoers to its right.


I'm No Dr. Love

Marketing is often perceived as being about slick advertising campaigns. To me, marketing is about running a business, a profit and loss account. I start the course by asking students, “If you are running a company and your market share drops, what will you do to fix it?” Students give all kinds of answers to my introductory market share question—they’ll cut prices, innovate, run a sales promotion. I wait until someone says, “We need to figure out what happened.” Unless you get at the underlying cause, you can’t find the solution. I teach from the perspective of presenting the strategic aspects of decision making that are intrinsically linked with marketing. This includes setting an objective for a brand, understanding where customer opportunities lie, and positioning yourself to give your target a reason to buy your product. I call that “the right to win.”<br/>The Framework<br/>I give my students a robust tool kit that enables them to look at any business problem and dissect it. I want my students to be the ones people turn to in meetings because they have something of value to


Meet the Dean

Incoming dean Madhav V. Rajan shares his personal story and lays out his vision for Chicago Booth’s future—including the critical role that Booth’s global alumni network plays in building on the school’s successes. Chicago Booth Magazine: You’ve called yourself a “lifelong learner.” Can you take us back and share an anecdote about a moment in your childhood or school years that sparked your interest in business and/or academia? How can Booth instill a similar love for learning in future generations? Dean Rajan: Steve Jobs famously noted that you can only connect the dots looking backward, and that is certainly true in my case. I did not think through or plan out my career. My decision to study business for my undergraduate degree was based purely on the fact that my older brothers were engineers and I wanted to learn something different. I then moved to pursue a master’s degree at Carnegie Mellon University, for the simple reason that my father worked in Pittsburgh. I did well in my first-year courses and was approached by a faculty member, who asked whether I had considered doing a PhD. I had not, but he persuaded me by noting that I would get paid to study, which seemed an amazing concept! This particular professor was in accounting, and that’s how I ended up in that field. However, Carnegie was unique in not having an economics department separate from the business school. Every student in accounting, economics, and finance did virtually the same coursework. Looking back, I have benefited immensely from the breadth of study and interdisciplinary training I received at Carnegie. Even then I wasn’t sure I would become an academic. Many of my PhD friends ended up in consulting, and I always thought the same would happen to me. But I liked academic research and teaching and was successful at it, so when I got a job offer from Wharton, it was an opportunity to keep going. Coming to Booth, I am firmly of the view that the school should support lifelong learning for its alumni. Two years ago, the school launched Back to Booth, which are short, nondegree classes for alumni. These courses provide opportunities to relive the Booth classroom experience with fellow alumni, and to learn about the latest ideas from faculty across the school. I cannot imagine a better way for alumni to keep connected with the school and to continue to learn from our great instructors and the latest ideas they are working on.


Why Is Productivity Stuck in Neutral?

When we talk about the global economy, we tend to turn to automotive metaphors. A recession brings things to a “screeching halt.” Boom times are said to be “in overdrive,” to have “found a higher gear.” And since the recession, one of the major components of the economy has been stuck in neutral. According to the Conference Board, productivity has barely budged since 2007, was flat in 2014 and 2015, and fell last year. We asked professor Chad Syverson, alumnus Matt Tracey, and Executive MBA student Crystal Lam to tell us why it’s stuck and what might kick it into gear. Chad Syverson, J. Baum Harris Professor of Economics, is the author of “Challenges to Mismeasurement Explanations for the US Productivity Slowdown,” published in the spring 2017 issue of the Journal of Economic Perspectives: Is productivity stuck in neutral? The short answer is yes. It’s been slow for the last decade—truly slow, not mismeasuredly slow or illusorily slow. The mismeasurement hypothesis says that although productivity has been slow since the mid-2000s, that isn’t real. The hypothesis argues that what’s actually going on is that our ability to measure economic growth has gotten worse. New things that people like and use a lot—Google, Facebook, Snapchat—are all free. We calculate GDP by adding up what people spend money on. Those things don’t show up because they’re free, so it looks like output per worker hour isn’t going up much. In my recent paper I asked, if that’s true, what else would it be true of? The patterns I found were consistent with an actual productivity slowdown rather than with mismeasurement.


Judgment Call

In many disciplines—financial accounting, for example—if you try to practice without any sort of formal education, you could very well end up in jail, says Jane L. Risen, professor of behavioral science. But when it comes to decision making, everybody is making personal and professional decisions all of the time without any formal guidance. Risen's class Managerial Decision Making is designed to provide that: a framework to actively recognize when decisions are likely to go wrong so that you can identify what you might be able to do to make them better.


Experimenting with Failure

When students take Booth’s Strategy Lab, an experiential learning course, they are sure to encounter failure. That’s because reconciling setbacks is one of the goals, according to professor Harry L. Davis. Davis teaches the MBA course in partnership with consulting firm A. T. Kearney, and he also presents the curriculum as a semester-long exercise in Executive Education leadership courses. “Most people overestimate the downsides of failure,” said Davis. Students participating in the course use a 20-cell board with experiential commands that allow them to practice basic skills, such as seeking input from a stranger, practicing active listening, and playing devil’s advocate when they’re part of a consulting team. Students take turns rolling dice to determine which approach to experiment with that week. Results are written down and used to track progress—or setbacks. Often, only a small portion of the experiments turns out well; other portions get chalked up as learning experiences. This kind of personal experimentation is critical when building the soft skills required for leaders, added Davis.<br/>


Six Days to Pitch

The History: The Global New Venture Challenge (GNVC) is the Executive MBA track in the New Venture Challenge process, which began 21 years ago. We kick off the GNVC in August when all of the Executive MBA students are in Chicago for their electives. The entrepreneurs put together a feasibility study about their businesses, encourage others to join their teams, and submit an application in October. We choose about six teams from each cohort—Chicago, Hong Kong, and London—to participate in the class. The Preparation: Because the course is so short, it actually starts as soon as we choose the teams. I have a kick-off WebEx call in the fall with the teams, who are located all over the world, to start working on their business models. I then host webinars for all of the teams on business plans and presentations. Finally, I have a second, one-on-one call with each team in the winter. Business plans are due a week before class starts, because I want the teams to have written their story and really gotten it down. The Curriculum: The weeklong class is stressful. It’s intensive, and it doesn’t look like a normal class. On the first day, students present to a group of coaches, judges, and outside mentors, and they get a lot of feedback. We handpick mentors for each team based on industry, business model, and startup experience to get them started working with outside people on their model and story. <br/>


How Do You Avoid Paralysis by Analysis?

Knowing when to stop looking at data comes up constantly in my Algorithmic Marketing class. In this class, one of the main goals is to be able to develop tools that would help someone make better decisions. Building these tools relies on knowing what exactly the decision is or what the question is. Very often people don’t specify their question in a precise enough form. You need to write down a specific question—it can’t be a vague goal or a vague statement. It’s important to thoroughly articulate your question and your research plan. The more precise your question, the easier time you will have looking for an answer. The question in itself isn’t enough, though. We also need to specify the exact parameters of an acceptable answer. It doesn’t occur to people to write down specs of an answer, but that’s another thing that needs to be done before you get started. You need to give yourself some set of parameters to help you understand when you’re going to stop even before you start. These parameters could be a set of rules you have to satisfy. For example, if I’m looking at how advertising impacts sales, it might be that I am looking for a set of parameters in the context of a particular model. Knowing that helps you look in the right direction. You have to chart out what the ideal answer would be, and you have to chart out what you’re going to be satisfied with in the findings<br/>