How to Monetize Happiness
Inspired by research linking happiness and productivity, the Japanese multinational conglomerate Hitachi Ltd. invested in developing “people analytics” technologies like high-tech badges (so-called “happiness sensors”) to help companies monitor and increase employee happiness. Ethan Bernstein discusses Hitachi’s next challenge—how to find the right business model—as well as the ethics of collecting and sharing employee happiness data and whether a happier workplace is truly a more productive one.
Brian Kenny: The Kingdom of Norway has a lot going for it. It has one of the lowest crime rates in the world. It has the world’s longest tunnel and Europe’s deepest lake. It’s the birthplace of skiing and aquavit. And if that’s not enough, it’s the happiest place on Earth, according to the annual United Nations World Happiness Report.
The report, published each year since 2012, looks at the state of world happiness, causes of happiness, and misery, and policy implications highlighted by case studies. Each report contains analysis by economists and psychologists describing how measurements of wellbeing can be used effectively to assess the progress of nations. There’s even a World Happiness Day, March 20th. Happiness is the new key performance indicator and perhaps, the next big idea in business.
Today, we’ll hear from Assistant Professor Ethan Bernstein about his case study entitled Sensing (and Monetizing) Happiness at Hitachi.
I’m your host, Brian Kenny, and you’re listening to Cold Call.
Ethan Bernstein studies the impact of workplace transparency on worker productivity, its implications for leadership, collaboration, organization design, and the new forms for organizing. And he’s pretty much always happy, in my experience with Ethan. Welcome.
Ethan Bernstein: Brian, I’m happy to be here today.
Kenny: The case brings up a lot of really interesting issues, both about technology and wearable technology, but also how we use technology in the workplace. There are some ethical questions that surface as part of this case, too. Start by telling us who’s the protagonist in the case, and what’s on his mind?
Bernstein: The protagonist in this case is Dr. Kazuo Yano. He is the chief corporate scientist at Hitachi in Japan. And he’s an interesting guy. He actually did his original work in physics. So, imagine, everything you just said about happiness, but seeing through the lens of a person used to studying physics. In some respects, he is the scientist behind Sandy Pentland’s work on social physics. The idea being that if you understand enough about the physics of someone being happy, or a group being happy, you might be able to increase their happiness.
Kenny: How did you hear about this work that he’s doing? And what prompted you to write the case?
Bernstein: Well, in part, I give credit to finding out about him to my colleagues at the Harvard Business School Japan Research Center in Tokyo… He did some work with the MIT Media Lab and he knew at the time Ben Waber, who is now the CEO of Humanyze, which does some interesting social interaction work with sociometric badges, which we’ll talk about in a moment. Ben and I were also peers in the doctoral program. Old ties lead to new cases.
Kenny: Hitachi has a happiness unit, I thought that was interesting. What does the happiness unit do?
Bernstein: He has built this service to, first, try to quantify happiness, and then to try to improve it. That’s the simple basis of the business. The quantification is about trying to figure out, well, if we can’t really rely on people’s self report about whether they’re happy or not, and we can’t really rely on facial cues, because facial cues can be very misleading, sometimes unintentionally, sometimes intentionally, then is there some other way in which we might be able to sense from the way that two people interact? If we could just take all of that analog interaction and make it digital, like an email communication, could we then run it through an algorithm and sense happiness in a more physics oriented kind of way? That’s the quantification part. Once we could quantify it at work, then it’s relatively easy to look at what people are doing at that point in time, correlate it with those activities, and improve it by providing more of the activities that prompt happiness.
Kenny: You mentioned the badge. It’s ultra-small computing technology. Can you describe how the badge works?
Bernstein: It’s a badge you wear around your neck.
Kenny: Like an ID badge?
Bernstein: Like an ID badge, and in fact, it looks like an ID badge, but it is the most advanced ID badge that you can find. It’s actually more like your smart phone. It has sensors to record motion, they’re called accelerometers. It has sensors to record interaction. If you and I are facing each other, we might know that through an IR sensor, or an array of IR sensors. And then, aside from movement and co-presence, it’s measuring, literally, are we speaking to each other? So, it’s measuring voice. If you think about all those pieces, it can actually pick up my body motion. It can pick up our co-presence, and it can pick up our interaction.
Hitachi employees wearing happiness sensors, and examples of related data output. Source: Courtesy Hitachi.
Kenny: So, it’s following me around, measuring my interactions with other people, and my response to the work that I’m doing throughout the day.
Kenny: And this is all part of the field of people analytics, that was another term that I saw in the case and hadn’t heard before, but it’s sort of the big data play on people. Can you describe that a little bit?
Bernstein: A number of us here at HBS, including my colleague Jeff Polzer, we’re studying people analytics as a data-driven approach to people-related decisions and practice.
That could be many different things. In this case, it is looking at behaviors. Quantifying behaviors that we might not have been able to quantify before, related to people. And then, once we have a real empirical view of those behaviors, seeing if we can’t improve performance as a result of giving some nudges, some coaching, around how those behaviors might or might not be improved in the workplace.
Kenny: And so, that is kind of the crux of why business cares about this. Talk a little bit more about the relevance of this, as you see it, emerging in the business community?
Bernstein: More broadly, people analytics has been applied to hiring, diversity inclusion, performance management, feedback, engagement, succession planning, all of these pieces, all these things that we previously made by intuition, or some degree of intuition. The idea is that big data can fill it in. Maybe we can finally find a use, Brian, for all that big data we’re not sure how to use. And we can do so through the improvement of the way people interact, the way people work together, and that’s why it’s people analytics, as opposed to any other kind of analytics.
This sort of emerged from initial conversations between HR and a bunch of data scientists about what HR might be able to learn from using the data that they were generating within organizations.
Then it became interesting not just to data scientists, to data-literate people, and to HR people, but ultimately to general managers. And then it became interesting not just to general managers but to the people themselves.
So, each of those communities is now part of this people analytics movement. There are lots and lots of books being written about it. The goal of this case is not to focus on the broader people analytics movement, but rather to focus on this one individual way in which we might be able to sense something we couldn’t before. And then ask the question, is there a business model?
Kenny: That’s what our protagonist is trying to figure out. How was he able to convince people to wear this? Why would an employee want to wear this? Why would they want to have this measured?
Bernstein: Are you wearing a Fitbit, by any chance?
Kenny: I’m not, but I have in the past.
Bernstein: Well, see there. And I am wearing a Fitbit.
Kenny: I have an Apple Watch.
Bernstein: So, it’s recording even more about you than my Fitbit is. We like actually having data on ourselves because it helps us know what we’re doing unconsciously, where we might be biased, where we might be able to act differently and have better results, whether it’s with other people or in our own health, or lives, as a result. We love the idea of having more data on ourselves. So, that’s the key reason people would want to wear it.
Kenny: I want to have data on myself — I’m not sure I want my boss to have data on myself.
Bernstein: It’s a really good question because on the one hand, we all wear Fitbits and other wearables because we want to know our selves. That’s one of the principles of life. On the other hand, we really don’t want others to know us in that way. We’d like them to not know maybe when we’re happy or not happy. It’s not even clear that having everyone know when I’m happy would be helpful to the organization. Every time I write a case, I try to include something in there that is just a little bit fun. And so, you may have noticed in this case I talk about the brooding academic, who might be more productive if he or she were not happy when they’re writing that paper that’s trying to be published in an academic journal.
Kenny: I did notice that.
Bernstein: I think that’s true, though. It may very well be that there are times when happiness is productive, and times when happiness is not productive, and if we suddenly had everybody know how we felt, it’s so biased towards wanting everyone to be happy, that we might actually undermine the work that we do as a team or an organization. So, from all of those perspectives, there’s both a real opportunity and a deep curiosity to know, and then an uncertainty about whether or not we’re prepared to handle knowing.
Kenny: In other words, maybe there are some jobs where happiness really doesn’t matter all that much. And I don’t know if this is something Hitachi has looked at because it would differ, I guess, from industry to industry, job to job.
Bernstein: It’s something they’re still looking at. One of the reasons I talked to my co-author, Stephanie Marton, into doing this with me… Stephanie is a great graduate of Harvard Business School, Baker Scholar. She was at Boston Consulting Group as a project leader in Tokyo at the time, working for their Tech Advantage in People and Organization practices. I convinced her on the side to go and talk to Dr. Yano and write this case with me. Her interest in doing so is exactly to try and answer that question, we were just curious, well, how do you think about this process? How do you think about this discussion? I don’t think they’ve solved that yet, but what’s interesting is how many organizations and, quite frankly, how many employees are willing to engage in the discussion of it because they’re just so curious. Less so about their own happiness because we think we’re good judges of that, and more so about the happiness of the people around them because they ultimately care about the working environment that they’re in.
Kenny: Related to that, we are introducing a whole new generation of people, millennials, into the workforce Millennials are different, and certainly think carefully and hard about where they’re going to go to work. So, I wonder if this isn’t part of ushering in a whole new approach to thinking about how we create a happy work environment because that’s going to give us a competitive edge in the search for talent.
Bernstein: Absolutely. Much of people analytics generally, and certainly what Hitachi and their clients are thinking about, is the way in which this is attractive to talent. Not just attractive to talent in terms of its attraction, but in terms of how they get talent to really come out and see its full blossoming self within a workplace.
That said, even I remain skeptical that happiness is predictive of productivity. It’s unclear that there will ever be a scientific study that shows that connection as clearly as we would like. There’s some evidence on one hand, there’s some evidence on the other. We may just have to take the leap of faith, as some of these organizations are doing, to believe that a happier workplace, whether by encouraging productivity, attracting better talent, keeping better talent, whatever the potential dependent variable is, that improving happiness is indeed a worthwhile goal in and of itself.
Kenny: Have you discussed the case in class?
Bernstein: We have.
Kenny: Did … anything really surprise you about the reaction?
Bernstein: I am always delighted and impressed by the creativity of our students, be it MBAs or executives, to come up with answers I haven’t thought of before. Without giving anything away, one of the most interesting realizations for me is that if you have a conversation about what you might do with data that indicates happiness people will come up with lots of different possibilities. Some of them crazy, and some not so crazy. So, there might actually be some real opportunity in this whole big data people analytics world, where if we knew more, we could actually do more together to improve culture at work. So, that was the key takeaway for me.
Kenny: That’s great. Ethan, thank you for joining us today.
Bernstein: Thank you. This was fun and I am even happier now than I was to begin with.
Kenny: Perhaps next time we talk, we’ll have badges on and we can sense how happy each one of us really is.
Kenny: You can find the Happiness at Hitachi case, along with thousands of others in the HBS case collection at HBR.org. I’m your host, Brian Kenny, and you’ve been listening to Cold Call.
source: Harvard University – Harvard Business School