Arizona State University Earl and Gladys Davis Distinguished Professor
The Wharton School PhD, Operations and Information Management
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Where are you originally from? Have you lived in other places? What kind of things do you enjoy (eg sports, dance, music, food, art, movies, reading etc)?

Summarized By: Jeff Musk on Thu Sep 12 2019
Thanks Mister Rohit. It's a great pleasure to have this opportunity talking to your audience. And so, I originally came from Shanghai, China and I actually spend almost all my time there before I came to the US in 1997. And in the US, I first lived in Philadelphia, during my PhD years at the University of Pennsylvania and then I lived in various cities in Dallas, Texas after graduation. Unlike some of my colleagues I haven't moved around a lot. And in terms of my interest, I will say I enjoy watching documentaries and reading articles and papers on Science and Technology. And then they are not necessary in my research field but generally I like understanding new advances in science and technology. And the reason I like watching documentaries is that documentaries always enrich my knowledge and which I think it's fascinating in this world that there’s so many things that I don't know.

What do students learn in your bachelor program, and jobs students get afterwards? Please also discuss about your graduate program(s), if you offer any.

Summarized By: Jeff Musk on Thu Sep 12 2019
So, I think our department. I think you're asking about the bachelor program in our department right. So, our department offers two bachelors program, one is the Bachelor of Science in Business Data Analytics and the second is the Bachelor of Science in Computer Information Systems. The first one, we call the BSBDA program and the goal is to help students learn ways to create large scale business data solutions and for in addition, large or small. And in today’s age, in any organization, asking on really huge amount of data and so the question is that how companies can leverage this data and they need help from students from this program. And then the BSCIS program that’s the second bachelor program, and it focuses on more traditional role of information system that is about designing and building information systems that support business operations and the management or decision making and so, I think that the top programs are quite complimentary. And the type of job that people in the first program, the BSBDA program they typically get after graduation is I would say, the typical work would be Business Data Analysts, Data Quality Analysts, Forecast Analysts, Supply Chain Analysts, Marketing Analysts. So essentially, you will see a lot of these jobs are related to data analysis for different business functions. And the type of job that the student in the second program, the BSCIS program, and that the title they typically receive upon graduation would be System Analysts and huge supports in System Administration, Website and E-commerce Development and System Assurance, Software and Application Development, and Database Administration and etcetera. You also asked about the graduate programs, they’re also very similar to our undergraduate program setting. We also have a graduate program (a specialized graduate program) where in Business Analytics we called MSBA and the other is in Information Management. So, the two areas are very similar to the undergraduate level except of course they are all mostly senior level management and the position that they get upon graduation are more in the managerial. So, they will be managers, Assistant managers and the let say the E-commerce managers and the Database managers for the MSIM students and then they will be the Business Analysis managers and the various type of Marketing managers, Supply Chain managers and Industries’ managers that’s in the MSBA program. Yes, I think this is a really good question that actually we did pay quite some attention at the department in terms of the diversity of the students in the program. And so, I think for the MSIM as well as for the BSCIS program, those programs are a little bit more male dominant compared to female students. But interestingly for the Analytics program, both at the undergraduate level and at the graduate level, I think we are now at a much more balanced level. I will not say equal portion of female versus male students, but I think, it’s close to 50-50. And I think what we have done is actually is we at the department, we had quite a bit of reach out program especially with local high schools and high schools in the State of Arizona. So, we run different summer camps, hackathons to encourage them to participate and also to let them know that it is a good experience and what they learn is definitely very useful. And what also helps is that the tools today for both analytics students as well as for the CIS students the tools had advanced so much that it actually is rightfully easy to use compare let’s say 10 or 20 years ago what kind of program tools or what kind of an analytics tools we have. So once students, they gain some experience, they use the tools and of course including auto-visualization. And we did see that it's not all about the mass of coding and it’s more about the conceptually high understanding the problem, how you design a way to solve the problem and then how to apply the right tool. Once they got the conceptual approach to write and I think they really enjoyed experiments and for both male and female students they realize they can do it. 

What are your research interests? Can you discuss major research projects you have worked on?

Summarized By: Jeff Musk on Thu Sep 12 2019
So that’s a very good question. My research interests obviously had evolved over time. But I would say very broadly that I have always been very interested in ways technology addressed information asymmetry in different settings. So, information asymmetry that could be a little bit of obscure term but essentially, it says that when you have multiple people, they interact strategically which means that each player or each agent had different incentives and that they do not necessary want to reveal all the information to the other parties and that creates what’s called information asymmetry. So, you know something your competitors or your clients, your customers may not know. And so, my general research interest is in understanding how technology can play a role in this process and it could be in organizations, it could be e-commerce side but more recently my interest is more in online platforms. So, these platforms, we actually have seen lots of these platforms in today's date and age and that platform could be related to finance, for example, peer to peer lending is a very typical platform. It could be related to healthcare and actually one program that I’m working, actually multiple projects I’m working on is currently related to online platform between patients and doctors. And these platforms could be traditional E-commerce platforms like eBay and so actually you look around, of course, today we have the sharing economy like Uber and the Lyft, they are both platforms that’s connecting basically drivers to passengers. So, what we see is that increasingly our world is mediated by different kinds of platforms. And then my interest is in understanding how technology as well as the design of this platforms can address information asymmetry. And that so the ultimate goal is that to make sure the matching, to improve the matching to improve the efficiency of the overall platform. 

How did you come across these ideas? How did you decide that these projects would be worth pursuing?

Summarized By: Jeff Musk on Thu Sep 12 2019
I think that’s a really good question. I think it doesn’t have an easy answer but fortunately, I think we are in the field of information systems that our interest is related to essentially how information technology affects every aspect of consumer, of businesses life. And so, I think there are a lot of things happening, nowadays we have all the scenes about blockchain and artificial intelligence. A few years ago, we probably have seen lots of discussion about social media. And also, I think the new technology comes all the time and so that's good news in terms from a research perspective is that you would really see a lot of things changing. And then there is a need, actually there’s a quicker need to understand how these changes affects the business competition, affects the overall competitive landscape, affects consumer and affects workers. So, today there’s a lot of discussion about whether people could be out of job because of the robotics technology. So, I think lots of things going on here. And more importantly, I think, to think about what will be a good idea for research. It’s not just thinking about what's new but also you are really to look at what is fundamental in you. So, what's fundamental in you is to create some new it could bring in the economy, introduce some new dilemma that has not been addressed in the past. And the reason I’m interested in online platforms is essentially related to what I consider fundamentally new in this economy or like in neighboring economy. In the past many traditional businesses, I think we should understand that in traditional business, we have many manufacturers, we have service providers and they produce products, they provide service and they spread around the globe through franchises, through their different branches. But today more and more that the business is done through online platforms, so that is a new phenomenon. And so, my interest is in understanding how this platform economy behave differently from the traditional economy. And so, one article that I will recommend to your audience to read is the well-known economist Hal Varian, he wrote the article/paper in 1994. The title is called How to Build an Economic Model in Your Spare Time. So, it’s very much about like building economic analytical models but actually the message is very broad. Essentially, it’s an article that he wrote for his students telling them how to find good research topics. And I think that is a very useful article eve now it’s more than 20 years since he published that article, I still use it in my PhD class and encourage them to read the article and to see where to get a good idea for research. 

What criteria do you use to evaluate papers while reviewing? What are common reasons for papers getting rejected? How can authors improve the chance of getting their papers accepted?

Summarized By: Jeff Musk on Thu Sep 12 2019
That’s a good question. I have been a reviewer in the industry for many years and of course that I would say, when we evaluate each paper, we evaluate each paper individually. But broadly speaking I will say there’s two dimensions. The first dimension, I will say, is to what degree the paper is innovative. Innovative could be on many different dimensions, that you could be an innovation in methodology, it could be an innovation in theory. And of course, these two are the most important and sometimes, we also appreciate innovation in a different context and especially this new context that challenged the, I will say, established ways that are in the field. But I think for every paper, there’s a need of some kind of innovation. And you probably heard that the number one thing we're looking for is innovation in theoretical contribution, or methodological contribution, so innovation is important. And the second it was about is rigor, so rigor is about, especially also from a reviewer’s perspective so whether the author has presented thorough analysis to support whatever claim he's making in the paper. And I think that is a really an important aspect that differentiates an academic paper from a newspaper article or magazine articles. So what reviewers are looking for is that you really have done your analysis and you have exhausted all the possibilities and you’re really convinced beyond reasonable doubt that what you claim is indeed true. I think that is very important to establish. And I think these two, innovativeness and rigor are probably the most important two criteria from the reviewer perspective and that's also from, generally from editor’s perspective. And I think that's also the most common reason for papers getting rejected. I think for also to improve the chance of getting their papers accepted is I would suggest to think through before you start writing a paper. In my own experience, I realize, sometimes that I haven't thought through what really the data is telling me or what really this model is telling me before I start writing paper. And then and I didn't realize that there are all other possibilities, all the alternative explanations. I think, it takes time to think through, to look at the problem from many different angles, to think about what is really new from my serious perspective or from my studies perspective and once you decide what is really new and then you can start crafting the paper. So, I have seen quite a few cases especially among students they get excited, they just run regression, they get some interesting result then they write a paper and I think that it's important to explore the data but in many cases that's not the best way to start writing papers. So, you really need to understand what your observing, what is the fundamentals of a phenomenon. 

What are some major research gaps that you believe needed to be addressed?

Summarized By: Jeff Musk on Thu Sep 12 2019
 I think that's a pretty difficult question. I think each researcher spots the research gap in his area, his field and I won’t say there is a major research gap that in IS field that has not really been looked at. But I will say that for IS research, I think we need to be closer to the industry. So that's one insight I get from many of the CIO friends as well as we have the what you called the IT executive board and I think they sometimes they realize that some of our research may not be exactly, that it’s actually closely related to what their concern is day to day. So, I think that there is an opportunity to talk to the IT executives in the company to understand their concerns and to see and whether some of these concerns may present a research opportunity. And so that I think on a very high level that‘s the suggestion I would give. 

What approaches have you found to be effective in working with various grant making agencies? What common mistakes researchers do while applying for grants and funding?

Summarized By: Jeff Musk on Thu Sep 12 2019
 I think I have a little bit success and I won’t say I have lots of experience, but I have a little bit experience on that front in both the US and in China. Well, first thing I think it's important to understand what the grant-making agencies are looking for. The grant-making agencies, they often put out, essentially, a call for proposal but it’s important to really understand what they are looking for. And you're not going to write a grant cover all things they're looking for, but I think it's important to understand what’s their main focus and what’s their main interest. So, my recent experience is that with my very capable colleague, Kevin Hong. We got a grant from a Robert Wood Johnson Foundation, last year and our focus will be on the what's called the future of work. And so, during this grant application process we worked closely with the agency. And I think the Robert Wood Johnson Foundation, they are really fantastic in terms of guiding us through the grant writing process. They told us what they really like on grant proposals, what they want us to improve on and what’s their main focus. So, I think through this process I learned quite a bit is that initially we had some assumption about what they are looking for and then later we realized okay rather than making assumptions, it is better to talk to them and to talk to them often and to share your grant proposal with them that it will seek feedback and I think that is a very important aspect that sometimes a researcher didn’t realize. The researchers are used to working on their own research problem and I think not many options, at least in our case, initially we realized that we should have communicated with the grant-making agency more but fortunately that grant making agency was very supportive. They are very communicative, they talked to us multiple times over the phone and that even after granting the funding, they have follow-up communication with us, discussing with us about our programs and also tell us what they would like about our findings. What other areas are they looking for? They hope we can do something more on certain fronts. I think this is a really very productive experience. 

What suggestions would you give to a faculty member who hopes to start a new research center or set up a new lab?

Summarized By: Jeff Musk on Thu Sep 12 2019
 I think this is a good question. One thing I want to mention that I haven't started a research center or started a lab. But our department, interestingly, a few years ago, we closed down a research center and we started two labs. And so, the two labs are now headed by our very productive, tenured track faculty members. And based on what I learned from this experience, what I have noticed is that the first thing is that lab or research center, at least in our setting, I think different people have different purpose for the lab, but in our setting, our goal was to connect the department,the researchers to the industry more. And so that was our goal and it's not easy to get it started but I think it's getting into shape over time. So, generally I think the key is that if the goal is to connect researchers with the industry more closely.Then, I think, the first step is to understand the industry’s needs more carefully. To understand the needs actually is easy to say but it's not easy to do because it requires time, it requires essentially investment and the time is a probably the most scarce resource for many assistant professors right, to new faculty members. So, I think that’s very important to understand that how to find the time and therefore this new lab and I think that’s probably is the number one thing for the faculty member before you even start. So, but of course, you need to have the resource, besides you have the time, you understand the need. And the second is you need to have a resource.Many companies that are interested in some consulting type of projects, working with the researchers. But the researchers may not have all the time, they may not have all that many students to work on these problems and not all these problems can be potentially be a research paper and so that at least the question, where you can get the money or at least resource to work on these projects? And in our setting, quite some of the resource, we collaborate it with our different programs and for both our master program as well as our bachelor program and we have essentially practicums or applied projects, elements. And so that is part of a resource from the student, essentially. Students compete in their applied projects or practicums and they're working on these industry projects.And of course, another important resource is to get funding. So, it will be nice and actually very important in the long run to get funding for these centers and for these research labs. And the funding could come from the industry and the funding could come from the different federal agencies. And I think on that front, actually our department, for our two labs, we have been working hard and that I think we have some funding, but I think we can always get more. But I think overall, I think it's important for the faculty members especially if he or she is a junior faculty member to really think through about the overall investment, overall resource needed for a new research center or a new lab and before he takes on such a position. 

What approaches have you found to be effective in working with industry for funding, getting data, and picking consultancy projects?

Summarized By: Jeff Musk on Thu Sep 12 2019
Okay that’s a good question. I’m mainly working with industry for research projects. So personally, I haven't really pitching any consultancy project. And we did talk to industry a bit for funding but partly related to that what kind of value publishing we can provide to that. So, I think when we work with industry especially if you want to get data for research purpose or any experimental research purpose is that you need to think of something and that you work closely with the sponsors within the industry, within the company to understand how your project can be helpful to him or her. And that is really important, I think. Some company, probably they have more dedicated University liaison group but in vast major cases, the industry partner, they need to get some help from the university, from the researcher as well. So, I think the first step for me is always to find out how we can be in a win-win situation, so I am going to propose something that is useful to them and useful to me, for internal research. And the number one thing is that to make sure the interests are aligned. And the second is that the one thing I discovered in terms of working with the industry is the time line is often misaligned. So, industry of course is often, their focus is more short-termed, they want to see result in moments, in a few moments, definitely within a year. And the interest typically moves on after a year and actually people turn over quite quickly and the sponsor you connect within the last year may no longer in this company anymore. But for research purpose, unfortunately, at least in our field, it seems it takes more than one year, it takes quite a while especially think about the review and the process. So I think it's also important to build an understanding with your corporate sponsors that it will be more to be a project and you may go back to them, you may want get more data, you may want to run an experiment again with respect to a different setting and then of course, we want to make sure that you maintain a very good working relationship with your sponsor for the long run. So, in some of my cases, actually, they co-assert data, they go to the company almost every week. These are students, different set of students working there for a very long period of time. That can both make sure we get the data we want and to make sure we understand their changing needs all the time and this will make sure the end that the research project will be a success. 

What do you look for while accepting PhD students or postdocs? What kind of funding do they get and for how many years?

Summarized By: Jeff Musk on Thu Sep 12 2019
Let me answer the last two questions first. So, I think, I say we get funding, pretty typical for most of research activities in universities is that they got full scholarship that includes all tuition and the living stipend, and they also get some summer support at least at ASU. And they also get a little bit of conference support if they have paper accepted at the end of the major conferences. So, typically from the students for five years and there is a little bit teaching requirement in the in the last year, one or two years, but the teaching is not a major part of the PhD experience but I an important aspect. And for the first question, I would say least in my mind I'm looking for three things in a PhD applicant or postdoc. First, it's important that for the applicant to that what I will call the inquisitive mind so he or she need to be curious about a lot of things and so the way we typically assess whether the applicant has the inquisitive mind is through interviews. So, we ask him or her questions, we talk to them, what interests him, what kind of interesting things he has read recently that have nothing to do with academics or social things, so on so forth. I will always try to understand where’s the individual is eventually curious, 'cause that’s important. Second of course we need someone to really have a solid training, that training could be in a quantitative field, could be a qualitative field but the I think whichever field he was in, he must have a solid training methodology. And the third I think it's really important is to have a passion and the passion to do research and the passion to do a really top-notch research. I think we have lots of people I would say that over my experience as a faculty member, some students, a very curious student, with a very inquisitive mind, with very good training but that just doesn't have the passion or perseverance in pursuing research. So, research often takes effort. It takes lots of effort for a very long period of time, working on a problem on your own with one or two co-authors. And so it’s important that you have a passion and the passion can last otherwise that it will be, it’s very difficult for anyone to be successful in the research area. 

How do you evaluate progress of PhD students or postdocs, and decide if they need to leave your program? What mistakes do you see them making in their initial years in the program?

Summarized By: Jeff Musk on Thu Sep 12 2019
I think the program that I have experienced and have seen at both UT Austin as well as ASU is that we have, of course, we have qualifying exams then we have first year paper and we have second year paper. And so qualifying exams are mainly, the goal of qualifying exam is to assess a student’s essential training and methodology training. And the goal of first year and second year papers essentially is to assess whether the students can identify a good research topic and then apply the training from the first two years and the classes that he or she took at ASU. I think that has been our main evaluation process. Of course, after that you have a proposal defense and you have the final defense. But I think mainly I would say that the evaluation process involves the qualification exam, first-year paper and the second-year paper. And the first-year papers at our school, it can be a collaboration with a faculty member while the second-year paper that’s generally needs to be a project done mainly by the students. And so common mistakes that I have seen in students, I would say, vast majority, I have seen mistakes in terms of the paper. I think the students we got into our program, typically are pretty good in terms of their training and so generally. they do well in the qualifying exam. But in terms of papers, I think, it is probably a difficult experience, I know lots of people before they came into the program they may not really understand, at least they may not fully understand what research is. And so, I think it's important to talk to your adviser about what is research and then think about what research question that you will be interested at a very early stage. So, I think that would be my advice. 

What advice would you give to PhD students, particularly who are searching for dissertation topic, and who are looking to enter the job market?

Summarized By: Jeff Musk on Thu Sep 12 2019
For first group of students typically I will say in the second or third year, when they start searching for dissertation topic, I would say that I would advise against finding a topic that is currently popular. I think that is something I often observe but I think that's often the wrong choice. So, research especially as a new PhD student or I’ll say as a new assistant professor and a few years down the road and you really need to look forward. So, think about what will be really important down the road and realizing what is really hot today? What is real hot today? Vast majority the paper was probably, is actually, people start working on this papers, I will say, twice/three years ago and got trendy, got truckloads of interest but it may not be that interesting that, I will say, on one, or two or three years down the road. And I think what’s really important is to think about what is a really interesting topic. And another thing, generally I can see students pick any kind of trendy topic. I don't think trendy is something research should be looking for but rather to think about what is fundamentally new. And if you can think of something fundamentally new and capture that essence in the research, your dissertation and that'll be really helpful. And of course, another important question is that when you find a dissertation topic typically and the goal is that you can catch a few papers from different angle on this topic and then that becomes dissertation. So, that's also an important thing to think about whether whatever the area you are looking for is broad enough that can give you idea for multiple dissertation papers. And for PhD students who are looking for to enter into the job market, I would say, we are looking for energetic, research productive, research PhD students. And also, we are looking for students that can teach in some of the skill sets that we currently don’t have. And at least in my personal preference, I always look for people who are complementary. Complimentary means that he possesses set of skill set that we, as a group, we do not have. And as a complementary is really important and in both in terms of research and in terms of teaching and I think for students looking to enter into a job market, my general suggestion is that you really need to not just look at the job market as a whole, of course job market is important as a whole, how you position yourself. But for every position you apply to, you need to do your homework, understand what they're looking for and understand how you can collaborate with colleagues in the department and hopefully you can make a better pitching job yourself. 

What courses do you teach? How has your teaching philosophy changed over the years?

Summarized By: Jeff Musk on Thu Sep 12 2019
The course that I’m teaching currently are Emerging Technologies and Business Analytics Strategy and the IT-enabled Business Modeling and IT Strategy and Organization Transformation. I think these are all for the graduate level and these are more strategic, managerial-oriented. Before I have taught Statistics and have taught regression models. At undergraduate level, I’ve taught the core class for Introduction to Information Technology. So, my chance of teaching Philosophy is the best way for people to learn is to learn by doing and for example I will give a very old example when I was teaching Statistics and the students had a hard time understanding inference. So, inference is the concept that’s difficult for undergrad students to grasp. What I did is actually very straight forward and rather than teaching them all the concepts, of course, concepts that’s too important. So, I just gave them a very large data sample and then I teach them about sampling and then once they understand sampling then they can see that every time you get a random sample, let’s say this sample mean will be different. And then you take in many, many samples and you get many, many sample means and your goal is to try to understand what is the population mean. And then they immediately understand what inference is. Inference, essentially is, if you only take one sample and you get the sample mean and then what can you say about the population mean. So, that's just one simple example, I will say, learning by doing is probably the best .

What are some of the memorable things that students said or wrote to you? Feel free to share stories behind these notes.

Summarized By: Jeff Musk on Thu Sep 12 2019
What struck me the most when I was teaching undergrad students was that how sometimes that something seems to be very simple to the instructor and becomes really useful to the students down the road. I remember one student wrote me, like two or three years after graduation, wrote to me, actually I don’t exactly remember, but I remember it’s related to Statistics class. It was something I taught maybe standard deviation or something I forgot. It’s a quite a basic concept in Statistics but he told me that how useful it is today and especially now, he’s working in the insurance company, so they need to really understand not only the mean but also the risk. And the risk because it’s captured not only by standard deviation, but it’s captured by many other different ways to understand the risk. But, it just struck me, sometimes we teach very simple concepts, especially from an instructor’s perspective, it seems to be very basic, very simple and it’s really important for the students to fully grasp, grasping. So, I think that is something I learned over time and I think really, it’s important to make sure students fully understand some of the most basic concepts.