Hila Lifshitz-Assaf – NYU Stern School of Business
Listen below to the January installment of the ASQ Blog Podcast Series:
Transcript of Podcast:
Heather Altman: Hello ASQ Bloggers. My name is Heather Altman, and I’m a doctoral student at the Center for Work, Technology, and Organization in the Department of Management, Science, and Engineering at Stanford. Today I’m excited to talk with Hila Lifshitz-Assaf about her 2018 ASQ article titled “Dismantling Knowledge Boundaries at NASA: The Critical Role of Professional Identity in Open Innovation.” One of the purposes of the ASQ blog is to get insights into the processes that go into the final papers that we see in ASQ. So thank you very much for joining us today, Hila. Today we’re really interested in diving into all aspects of your research and also the research process. So maybe you can start by telling us a little bit about yourself and also your current research focus.
Hila Lifshitz-Assaf: Sure. So, I’m Hila, and I work at Stern today as a professor. I did my Ph.D. at HBS under the amazing Michael Tushman, as my main advisor, and Karim Lakhani, as well as Michel Anteby. So, three—a really great combination of mentorship. I warmly recommend for students to think about, kind of how to combine different people. So I would say that really shaped me and my process, and I was always curious about innovation.
I came into the Ph.D. as kind of a second/third career, knowing that I wanted to do field research. I was a consultant, I enjoyed working with companies and organizations. I knew that I want[ed] to do something related to innovation. I did not know which level of analysis, whether it’s open innovation, what is open innovation.
It just happened that at that period, that was the buzz—that was what everyone talked … When I went to organizations and companies, that’s what they talked about. And I actually did not think so highly about this whole open innovation thing. But I just, I’ll listen to them. I kind of know how hard innovation is and this open innovation, in the beginning seemed very light, online. You just go somewhere and you click your problem and things kind of get solved. It didn’t seem serious enough to me. But I learned about it, and I kind of, I would say “dated” almost (quote unquote) different organizations until I realized what is my fieldsite. And it helped me learn about the stage of the field at that time. So that was also something very helpful.
Heather: So this was your dissertation work, then?
Hila: Yes, definitely. This was my everything. This was my dissertation work, my life’s work, for those years. This was kind of a very significant chunk of my life. It was almost three years of field work, just collecting the data. So it was a forming period of my life, of my intellectual being. I’m so happy that I was able to do it. But it wasn’t as easy as it seems, always, in the paper, in the outcome. It seems like, yeah, I knew what I wanted to do and how it seemed. So you know, the behind-the-curtain perspective I think is very helpful ’cause still until today when I read papers, it’s much more interesting—I’m always curious what actually happened. How did they get to this fieldsite? You know, how did they get to understand that this is the thing to focus on? So I think it’s fun to talk about the behind the curtains.
Heather: Yeah. Well, hopefully we can get to a lot of that today. First, I’m wondering, you mentioned that in the beginning, you didn’t think so highly about open innovation, or you didn’t take it so seriously. So what about it then motivated you to take on this project?
Hila: To be honest, the thing was that it’s NASA. You know, I had a dream all my life to visit, to do something related to NASA. I was kind of into space when I was a teenager, and I always dreamt about maybe one day I can be an astronaut or something like that. I don’t know. It was always kind of one of those farfetched dreams. And then there were other companies that were much closer to what I knew to do, back then from consulting, it was much easier for me, and I did talk to P&G and General Mills and Starbucks, and other companies were doing interesting things, open distributed innovation. So, to be honest, that’s what everyone [was] trying to do at the period, they were trying to experiment with these tools and see as well if this is serious, if this is significant, how to go about it.
So that’s why I got into it, but the first thing that, you know, I think it started this in executive education program. A group of the executives from NASA came to HBS and did this program and heard about one of these approaches. It was even I think a year or two before I arrived. I can’t remember which program he took, but he basically at some point told that they’re thinking of doing it. And then as my advisors knew that I’d been searching and knocking on many doors of organizations, and trying to see that, they shared it with me. And I was excited at the possibility that maybe I can go, and we asked them if I could just go to the first workshop and listen. And it was amazing. I didn’t know immediately that that’s going to be my fieldsite, and that’s gonna be my dissertation. Feverishly, I took notes, field notes, but I couldn’t understand anything they were saying. This was NASA. I came from a business school background. It was exciting, yet I lacked a lot of knowledge to truly understand the conversation and the nuances, which are very important as a field researcher.
So, after a few months, I told my advisor, “This is exciting, but I’m not sure. Let me keep on checking with other companies that I can actually understand what they’re talking about.” But what happened is that I realized that the NASA folks, the management and the professionals, were actually doing it in the most significant way. Where other companies were experimenting with open innovation, crowd sourcing, different ways, but maybe two/three people were trying that in the organization. Or maybe the chief innovation officer was doing it, but not the real R&D folks. There was some disconnect between what they would tell professors on HBR, what they would say about open innovation, and what they actually did.
It was more of a marketing thing, I would say, for many of these companies; whereas at NASA, I saw that they were very serious about it. They really ran an experiment that was very brave. Until today, many companies are not doing it to really try open innovation simultaneously on the same strategic projects that your top professionals are doing. That is different. That is core. That I knew would have an impact on the core of R&D, and if it would have, then the scientists and the professionals or the top experts that are working on this will have to do something about it. If it’s one of your top strategic challenges and on your strategy plan gets an input that may be important from a crowdsourcing platform, you will have to do something about it.
Whereas, let’s say if GE or Starbucks and some of the projects that I was looking at, at the time, were using crowd sourcing for something that is not core, not core to their strategy. Then, until today, I always say to organizations also, that means it will have less of an impact—of course, less attention—so usually it’s less interesting, research-wise. Of course as a researcher, one always wants to go where there is impact. And that’s what happened. So that’s why after a while, I stopped meeting and collecting data on other organizations, and decided to dive deeply and jump ahead and spend time in the field to understand what they’re actually talking about, so I can really truly understand the traditional regular way of innovating, and then this new way of innovating, and how truly or not different they are. And I was surprised by the astonishing results of their experiments, as they were as well.
So I really think that following where you think there is more impact, where you think you will see change, that’s something I think is very important when you choose a fieldsite. If it’s core and strategic to your fieldsite, it will be interesting. If it’s something kind of in addition, on the side, something cool that we’re trying out, less chances that it’s worth a field research dissertation.
Heather: So you mention following where there’s more impact and where there’s more change. How did you think about your study design during the process? For example, you mentioned initially sampling or dating different fieldsites, and over time, learning to focus more on NASA for various reasons. How, in this process, did you think about your study design? And then once you committed to NASA, how did you approach the research framework at that point and onwards?
Hila: Yes. So I have to say that I tried different approaches. In my Ph.D. program, we were supposed to do a first-year project already, and I did one that did survey with another organization that I cannot mention, but a big known company—survey and a little bit of interview on something related more to social innovation. That has really taught me about the methods that I trust more. A lot of the responses from the survey were really different from the responses in the interviews. And that led me to believe that research-design-wise, I need to make sure that I have in-depth access—that surveys and interviews are not enough. I actually need to go to the field, to have multiple interviews, to gain trust of people. I cannot do it remotely or just flying in and out or something like that. I need to get that in-depth immersive ethnography-like experience.
And then when I sampled or tried different organizations, with each of them I tried to test and to see what will it mean. Who will I need to get closer to? Where is it located in the organization? And my dream and passion was to pursue something related to innovation, which means it’s R&D. So the closer it was to the labs, the more exciting it was for me. ‘Cause I really wanted to see the work itself.
And I basically learned through that first- and second-year project that there’s a difference between what people say, what people think, and what people do. And I need to be able to try to capture all three. So what they say, I can capture through, you know—there’s the difference between the survey and interview, but I can try at least. What they think, I can maybe try to access, but if I can listen to how they talk between their friends and not just to me, that would be much more reliable. And what they do is something that is missing if you only do interviews and survey, and that’s why I need the participant observation. I need to be there, in the meetings, in the project work, to be able to keep track of the project work, to get the access to their documents of the project work, and to see how things are really making progress, or not, on their R&D project.
So that’s something that I think is not done enough in the business world in general. We don’t do it as researchers of management. We don’t pay enough attention to the actual work of people. We talk about it, around it, the budgets, incentives, like not enough work on the work process itself. And I’ve gained a lot of respect to it. As I was doing it, the more I did it the more I got convinced by how important it is to have it as a source of data. It is harder, it is messier, but it does enhance the level of reliability, of internal validity of whatever you’re trying to say as a field researcher. So, I think it’s very important. And I think it proved itself in the end, that I was able to see specifically in the study which labs, which R&D professionals of those labs were adopting open innovation or not. That was not something that was easy to see even for the managers, initially, because they were doing so much of talking and kind of talking the talk but not walking the walk. And I was able to see both the talk and the walk because I was in the labs. So that made a big difference.
Heather: Great. So just now you were talking about the importance of understanding what people are saying, what they’re thinking, and also what they’re doing. And in the paper, you talk about that too. You talk about triangulating between different sources of information. So can you tell us a bit more about that process for you: bridging the saying, the thinking, and the doing through these different sources of information?
Hila: I’ll tell you, the broad research design was to look at how innovation was done before, throughout, and after the experiment with open innovation. And as I said, the experiment was to try to work on the same strategic challenges that they were working in house, with collaborators, externally, in all their best-known practices, and in the same time, to post it on crowdsourcing platforms for anyone to solve. So the data sources that I used, as I said, the first thing was participant observation and being in the meetings, in the workshops, in the project meetings as well. Then interviews, I had more than 100 interviews, semi-structured interviews, with an interview protocol that I kept on changing based on what I saw that was taking place. But the first year, let’s say almost, what I wanted to understand is just their regular day to day. As Van Maanen kind of talks about, to get yourself immersed in the field, in the beginning you just want to see, to understand their day-to-day reality.
So in the first year, the interviews were not focused on the open innovation, they were just, “Tell me about what is a good day for you?” “What is a good year for you?” “Tell me about your work.” You know, “What does that mean?” I would just ask a lot of clarification questions, trying to understand what do they do and what do they value in what they do? Because it’s innovation, I would ask them about, “Tell me about innovation in your labs.” So I got to hear a lot of innovation stories and the way they narrate and think about innovation. And then there were a lot of internal documents that I asked permission to get on the project work, presentations that they presented, project work documents that showed progress, and what took place there. And they did internal surveys that I also asked access for.
So this was my primary data sources that enabled this triangulation between what they think, they say, and they do. Then, the secondary data sources were what’s happening on the online platform, since the open innovation experiment was taking place online. I requested the data from the online platforms themselves, and also just, you know … today, luckily, many of those things you can just kind of scrape while it’s happening. So while the challenges were there, all the solutions that were posted, people that tried solving them, their demographics, where do they come from, who they are, how do they try to solve them—the platforms themselves, the consultants that came by … any data I could get [from] the platforms.
In addition, I looked in all the patents and publications of all the people that I studied, and because they’re scientists and engineers, and all the public documentation that they had. That helped me really understand their world, to see it in their eyes. I went to their conferences to understand their professional world. So I would say a lot of documentation, a lot of reading. And because it’s NASA, they have a lot of documentation, but I would say in each scientific and technological area—even now that I’m studying other areas—there’s always a wealth of documents out there that’s gonna help researchers understand the background and speak the language of the people that they come and interview. It helps.
Heather: Great. And so you spent a really long time in the field, three years, and you amassed a large amount of data. When did you know that it was enough? How did you know that it was time to exit the field, and what was that process like for you?
Hila: That’s very hard to stop collecting data. I think I am in love with collecting data. I always love collecting data. So I think some people enjoy different phases of the research. I love going to the field. I love being in the field, I love being a part of something. I love learning, and when you’re in the field, the learning is so intense and so much fun. And when you’re going back, and you’re writing all the notes and you’re trying to make sense of them, that’s not as much fun. Then you see the messiness, then you see all the holes, then you have to like iterate and think. So I think I collected a lot of data, and at some point, as Steve Barley says, I was drowning in data.
And then I had the challenge of where do I even start my analysis from? So today, when I advise, I do it differently than the way I did it. I was kind of collecting, collecting, collecting, and analyzing on a very high level. Then at some point, I was like “Oh my goodness, this is a flood of data, I don’t know how I can handle this.” And then I actually had to sample from my own data, according to the thematic analysis of my field notes. And this is what I did, ’cause at some point, I was just really overwhelmed by my own data.
It took me … I don’t want to discourage anyone, but it took me seven to nine months to do just the analysis. That was painful. But I came out of it with a clear analysis, but it’s hard. When you have this bulk of data, that’s the challenge of it. So I would say, today when I advise students, I try to do analysis much more frequently, and not to collect as much before you do it. And I thought I was doing it, but I now know the difference between thinking you’re doing it and really, fully doing it. So, going deeper into things and kind of having, I would say, that second level of analysis, the memo-ing—all kind of things that help you reduce this sensation of this overwhelmed sensation of the data. I think cumulatively, it is hard at some point because you’re doing it alone.
Another thing is no one says we have to do field research alone. I think that’s something that was very accepted for many years, and when I was doing the Ph.D., that’s the methods, that’s the ethos, you know, the Steve Barley, the Van Maanen, Beth Bechky, others … Like, you go into the field, you go alone. And as I was doing my dissertation, one of my advisors did something different, and he started experimenting with doing field work together with someone. This was Michel Anteby, and he was doing it with Curtis Chan, and they were doing a beautiful study, and I watched him, and I started thinking, “Wow, that makes a lot of sense!” And that actually can alleviate some of this emotional burden, I would say, that the wealth of data creates. And also, intellectually, at some point, you get to saturation. The way you see things is the way you see things. It’s hard for you to see them differently.
So having that someone else to kind of juxtapose the way you see it, and then you go and check in your data, you know, which way was it? I love that. And today, I have a project with my Ph.D. student Sarah Lebovitz at NYU on hackathons, and having someone else to kind of brainstorm on the analysis process, I have to say, was much better than doing it fully by myself. So I warmly recommend considering this at some point. It is harder, maybe, to collaborate, and maybe when you are on your own, you have full control. But there are nice advantages if you’re taking a big project on yourself, especially in the analysis part, ’cause I think for qualitative research, that’s the hardest, the least codified, it’s the most mysterious part, and it’s the hardest to see clarity and to have really strength in it. I would say that’s kind of what I took away from that collection and analysis part.
Heather: You mentioned this painstaking process of going through all of your data, all the data you had amassed. Can you help us understand that process a little bit more? I know you mentioned you didn’t collaborate directly with other people, but were you having conversations throughout this process? And how did your thinking change over time, as you were working your way through all of the data you collected?
Hila: So the people I talked to were my advisors, and every two weeks or a month, depends, I would meet especially with Michael Tushman, I met very frequently. And I would share with him either in the form of a memo or a form of slides, the results of the higher-level thematic analysis.
So the two things that helped me, basically, is my advisors and the second thing is Leslie Perlow created this wonderful group of qualitative students from all the Boston universities—BU, BC, HBS, MIT—that we would read each other memos. So a memo is kind of the document you produce as you are trying to sensemake your data. So it’s not something that is done with the kind of qualitative softwares that we have today. It’s something that is usually done manually by the researcher. Different people have different styles for it, but it’s trying to capture, trying to summarize, what have you seen in the field? What are the important themes? What are the important stories that you can see in your data?
And reading it in a small group of trusted people, to each other, and we would meet every month and read someone’s memo, and give very honest and constructive feedback of what we think is interesting. So that, I would say, was the closest to kind of, not collaboration, but having other people that know my work closely and could say, “What about this theme over here? You’re not talking about this, but I think it’s really interesting. Do you have more data, anything else to say?” Or “How come this is different?” That’s the kind of sensemaking that I feel is necessary and hard to do if you’re completely alone. And that’s what I meant, you get this overwhelmed feeling. So once you have someone else that you juxtapose it with, and you kind of throw it over the fence and someone else kind of bounce[s] it back to you, that’s very helpful, intellectually and emotionally, I think, to kind of digest what you’ve written, what you’ve seen.
So we did that, and with my advisor, so I think these are the two processes that helped me. It’s basically the high level of thematic analysis you do share, all the hard work and the dirty work of the much more nuanced qualitative analysis that I did both manually and using ATLAS.ti at the time. Today, you know there are many different programs that you can use. I had to do it, I don’t think that’s something that we shared … When I do it collaboratively today, of course I share everything, like all the stages. But back then, I don’t know, people don’t usually wanna see all these mountains of data. They want to see the end, and even the end, my memos were really long. I have a tendency of writing long memos so my memo was, I’m embarrassed to say, but at times it was between 100 and 120 pages. Just a memo, without framing, without methods, so … Like it started at 40, it went to 60, I remember, 80, 100, 120. Then I had to, afterwards, you know, turn it into kind of dissertation paper-like format, that’s not easy.
Yeah. I didn’t think enough about writing a paper. I thought more about this is my dissertation, so I really kind of went on depth and breadth, and only after my committee and I decided, they’re like, “Okay, how do we chunk this into a paper, or multiple papers?” But initially, I did it more in the, I would say, sociology, ethnography style of writing, this one long, strong dissertation.
Heather: And so, in what way did the framing of this paper change over time? Like, in the final output we see that shifting professional identity and boundary work kind of won out in terms of explaining the behaviors and attitudes of these NASA experts, but were there other runner-up theories that you played with prior to this approach?
Hila: Once this was the focus of the paper, these were the main theories, and I have to say the amazing thing about the ASQ review process is they respect the writer. I think if they see that there is strong validity—internal validity, and interesting external validity—to the paper, they do not try to tell you, “Oh, remove completely …” I’ve seen in other journals, “Remove completely this—all of your framing, reframe it this way, you got it completely wrong.” They may try to go with you, but they challenge you to be much sharper and much clearer.
So I think on the high level, these were the theories, these were the findings, this is what I saw. But on the writing level, of course I wrote and rewrote every word and every sentence of the whole paper in the review process. But it was much more about sharpening what I’m saying, honing it in, focusing, removing what is not necessary, removing things that are more phenomena-related and not theory-related. When you deal with hardcore phenomena such as open innovation and crowd sourcing, often time people want to know more about the phenomena. And ASQ was very clear about, you know, being theory-driven. That was kind of … I didn’t need to have this in the framing, for instance. It was much more the setting is the phenomena, let’s keep the framing only theory-focused.
I also have to say that I was impressed by the ability to have a more rich story, data-wise. That’s one of the things that ASQ qualitative papers are amazing about, and I enjoy reading them, and I’m a big fan of papers that were published there. Even in my own month [the December 2018 issue], there are a few papers that I’ve seen of friends and colleagues over the years, I think the richness that they capture … ‘Cause many times, the review process just cuts all the richness out of field work. So you start with this rich story, and sometimes in different journals, you just have to take the richness out, ’cause they feel “One paper, one message. That’s it. Don’t confuse the reader. Don’t give them the nuances of the reality. Just make it simpler.”
And I never felt that at ASQ. They just wanted me to be clearer and more focused and sharper, but not to remove parts of the truth, the small “t” truth—in the sense, not something that happened in the field and I thought was important. Not, let’s say I had four categories of what I saw people were doing, they never asked me to shrink it into two, to summarize it, to make it easier to digest.
And I could see that happening. I had, in rounds of feedback that I received—this was my job market paper—many people would tell me, “You have two different papers here. No journal will take these together. One paper is on the knowledge boundary work, and another one is the professional identity work. You can’t put it in.” And I kept on insisting that one paper, one co-evolving with the other—the way professionals received and reacted on their identity led to what they did with their knowledge work and co-evolved. I saw the process over two years. And [they’d] say, “Yeah I understand that’s what happened in reality, but—”
And I was a Ph.D. student, kind of very driven by what I saw in the field, and they would say, “Yeah yeah yeah, this is nice. You’re very naïve, but you know, the reality out there is when you write in academia, you have to simplify the field. You can’t just have this richness in a paper.”
And that was very frustrating to me, and I wasn’t sure if it was the right decision to submit the paper as I did, with both stories in that sense, both parts. ‘Cause these are literatures that are not usually talking to each other, professional identity and knowledge work, and knowledge boundary work and innovation. There are not other papers that I could see that were doing this together. And that’s why I think people, they weren’t being mean, they were trying to be helpful. They tried to say, “You know, you’re ruining it for you if you’re trying to do both in one paper.”
But I wanted to stick to what I saw as true in the field. And I tried, and I said, “You know what, I’ll do it, and if not then the reviewers will tell me I have to, you know, I will see what I do,” but they never did. They really just tried to sharpen me and make the story clearer and stronger, theoretically. But they never tried to simplify it. They wanted the richness, they wanted the nuances, and I think that’s something that I would love more journals to do for field work. Because I think the reality is more complex and rich, and if we’re trying to simplify it into an abstract … Like, you know, it has to be clear when you read the abstract, and that’s it, you don’t need to read the paper. Then what’s the point of spending all this time of working and writing a whole paper if you want it to be something that in one paragraph can be fully understood?
But that’s what I think, unfortunately, many times I feel that’s where things are going. So I think there are these two contradicting forces, and I’m definitely pushing on the force of having—as a reviewer today, and you know, as an editor, if I’m on conferences and stuff like that—I’m definitely pushing on having the full, rich, nuanced picture of what one sees in the field that explains all the relevant things and outcomes that we are trying to explain. And not to leave good things outside just because we think it’s too hard for the reader to deal with it all. I think we need to give respect to the reader as well.
Heather: No, that’s wonderful. And it’s that complexity that ultimately makes it also so compelling and attractive to read and learn about, right? So, can you tell us a bit more about that review process for you? How did the review process help you to refine and sharpen your ideas? And what areas, if any, underwent the most change?
Hila: So, you know the review process is such a hard process that I think our memory tends to completely change what really happened from what now I remember. It’s such a hard process. It’s such a hard process, so I will try to say what I feel is completely true. But I’m sure if you would ask me in the middle, I would say, “Oh, it’s so hard!” But I would tell you … ‘Cause I do think a review process is, by its nature, very hard, especially when it’s something that you kind of slave yourself over for years. When it’s your dissertation work, you’re so emotionally attached to every word of it, it defines who you are and whether you’re good enough as a researcher. So I think this emotional attachment doesn’t help in the review process. You can take things too personal.
And luckily, in my review process, and I think that’s thanks to the ASQ editorial board, as I said, they were very respectful, kind of the whole tone of writing … See, it’s not something that should be taken for granted. I see in other places that reviewers are kind of going down on an author, almost, and preaching to them or educating them. And here it was much more in this respectful manner and really trying to help me make it better.
So I have my own mini practice of trying to deal with the constructive feedback of the reviewers, which is I use a lot of color coding in my text analysis in general, so I also do it for the review letter. So I kind of invent my own internal color code for … I literally kind of highlight. So what I mean [by] color coding is I highlight the text in different colors, and I always keep a color for good things. So that something that I developed over the years, like let’s say I put in purple or any color that I like—so every time it gets too hard for me to go back to those comments and not to forget also the good things. ‘Cause I think the first time you read the review letter, you know, if they’re good things you’re smiling and you’re so happy and you’re like, “Wow, this is the biggest compliment ever.” But the second time you read it, the third time you read it, you just feel like, “Oh my goodness, there’s so much work.” “Oh, they’re asking for so much of me. How can I do this?” “Oh, this will take me ages.” And you get stressed.
So I think having kind of the anchor of remembering, “Yeah, they asked you for all of these, but they also respect your work, they also said this is interesting. They also believe in it. They also think there’s potential here for something important”—every time you kind of get frustrated, has helped me. So I would say this kind of color coding the reviewers and their comments helped.
Heather: And I think that’s a great tip that I might take away for myself, to applying the color coding to remember the good points as well as the points of constructive criticism.
Hila: Choose your favorite color and highlight all … Like the first time you read it, highlight everything that they say positive with your favorite color. Don’t even yet color everything else. And then second read, start, you know, I do, for instance, green is everything that is easy to fix. Okay, like easy to fix, I know what they mean, I can do it. And then blue, let’s say is things that I need to think about. This requires some incubation. I need some time. And this you can also choose, sometimes you feel like, “Okay, today I’m not even going to really think deeply. I just wanna do some quick things and feel I can, you know, checkmark something.”
And there are different practices. I know Julia DiBenigno has different practices and we’ve shared each other’s over the years, so I think sharing it with someone else, how to deal with the review letter, and how to work on the response letter, is very helpful. Because it is quite a challenging process that only people in academia can understand. When you tell it to someone outside and you say there are gonna be three people that really decide if my three-year project is gonna make it or break it. You know, it’s like, they can’t understand, it just doesn’t make sense that these three individuals are gonna be so significant in your life, and now for half a year, you’re just gonna work on the comments of these three individuals that you don’t know who they are.
I think you have to be in this profession to really understand and to learn the practices of how to deal with it, so not to get scared for it, but to really see how nice it is that people contribute their time to really help young scholars especially. And ASQ, that’s why I submitted it there also, said that they wanna help Ph.D. students, they wanna get dissertation work. So I think they’re known for this approach of trying to help young scholars and shape their way. So I think that’s wonderful.
Heather: So as we start to come to a close, I’m wondering, is there something else you’d like us to understand, either about the ideas in this paper, or the process and its development?
Hila: On the paper, I think the main point is not to be scared if you see something coming at you from the data that is not related to the literature or to anything that you thought about when you went into the field. So in my case, I was very much an innovation-, process-focused person. I knew everything about knowledge work and technology, and work and technology in organization. But the identity thing, that scientists and engineers all of a sudden started telling me in their interviews about who they are, how they were trained, in their school, why they love their work or not … I didn’t really listen to it initially.
I would ask, “What do you think about the open innovation experiment?” And they would start telling me about their Ph.D. days and their advisors, and I couldn’t get it in the beginning. And I was really, “Let’s focus on what I ask.” Like, inside—I wouldn’t say it to them—but inside I felt like, why are they telling me all these things? But once I saw that there was a pattern, and multiple people started sharing with me this more professional story of theirs, I didn’t even think of identity yet. I started asking questions about it. So instead of being scared of the fact that they’re taking you somewhere else, as a researcher, try to understand that place.
So I asked them, “Okay, tell me more about your Ph.D. days? Uh-huh. And today, how do you think of yourself when you go to conferences? How do you call yourself?” ‘Cause of course they have titles, but the way we perceive ourself is different. And then I went deeper into the narrative analysis of all the stories that they told me, trying to understand who’s the hero figure there. And then it became very clear to me, but it took me at least half the year to see this thing. And then of course it took me a whole year to delve into this literature that I never knew. And that’s one of the blessings and maybe curses for others of doing field research in this inductive way that you don’t know which literature you’re gonna draw upon.
So I had to become familiar with the professional identity literature—today I love, I write in it, but it took me a while as someone coming from more innovation and organizational theory research to read this literature that a lot of it is more micro. Today I see it as a great synergy, and that it is something great to take, but I would remember how this kind of created some resistance inside me initially when I was in the field. So I think I warmly recommend for young scholars to truly listen to what is happening in the field, and if these things are not connected in our theories, that does not mean much. Like, we really need to bring those connections. And then that’s already nice, like in the end of the day you have a contribution by connecting things that were not connected before, and seeing the impact, in this case of the professional identity of scientists and engineers and the process that they went through to their ability to adopt or reject new ways of organizing for innovation such as open innovation.
So I warmly recommend to truly listen, and even if it’s not what you planned for, go deeper until you feel you’ve understood it, and only then decide whether it’s part of your project or not.
Heather: And then, lastly, how has this project influenced your own research agenda, and what questions do you think are interesting to study next?
Hila: Well, I think understanding new ways of organizing for innovation is really fascinating, and this is an area that is broadly open for many other researchers and students to come and pursue. If you think about it, everyone talks about innovation but not many people try to see the process itself and whether it’s different. We have the web now, the internet, and the digital revolution is taking place, still, I think, as we speak.
On the other hand, we have people approaching problems—scientific, medical, technological problems—still by sitting down and brainstorming solutions usually. In most leading organizations and still today, in most private, public organizations. That just doesn’t make sense to me. It’s got to be better than that. This study has really convinced me, once I saw a scientific breakthrough taking place in less than three months—something that the best scientists of the world have been thinking about and working on for a decade. A problem that cost $800 million a year, being solved in $30,000. I was shocked. That really stuck with me. I realized things can be different. We can do R&D better and faster. We just need to do a lot of change for this to happen. So how is it possible?
Heather: Well, thank you very much again, Hila. This has been a very motivating conversation. So thank you very much for giving us this deep dive into your project and also for sharing the research process with us.