The first part of this series gave an overview how qualitative data and customer development go hand in hand. There are different methods to gather qualitative data. In most books and articles about customer development, the authors (implicitly) describe the method of conducting semi-structured interviews – as I did in the first part of this series. Another possibility to gain qualitative data is through ethnography. Ethnography is not merely an additional option. It is considered to be the hour of birth of qualitative research. Originally stemming from anthropology, Conklin (1968: 172) defined ethnography nearly a decade ago as:

            “[A] long period of intimate study and residence in a well-defined community employing a wide range of observational techniques including prolonged face-to-face contact with members of local groups, direct participation in some of the group’s activities, and a greater emphasis on intensive work with informants than on the use of documentary or survey data.”

This intimate study and intense work with informants is conducted in the so-called field – hence called field study. Field study means data gathered from conversations, behaviors and interactivities happen in a natural context. In other words, an ethnographer captures data in genuine situations (e.g. the natural setting where your customers use your product). Thus, the data is more authentic than in surveys, interviews or study groups. This is the huge advantage of ethnography. The contextual data is also called thick descriptions (Geertz, 1973) as it enables you to capture rich details of the observed processes.

So, you might wonder what it has to do with practice or how it helps to run your startup? Here are the facts: more and more companies are starting to grasp the value of ethnography and ethnographers for customer development, product development and user experience (UX) design. Some examples:

• Facebook was looking for an ethnographer to understand what Facebook actually means to its users.
• High-tech company PARC (orig. Xerox Palo Alto Research Center), which developed laser printing, Ethernet and an awful lot more lists ethnography as a core competencies.
• Ethnography is also used to ethnography to improve the user experience (UX) of customers

The data gained through ethnography can be seen as complementary to Big Data. “Big Data produces so much information that it needs something more to bridge and/or reveal knowledge gaps. That’s why ethnographic work holds such enormous value in the era of Big Data” as Tricia Wang aptly points out. She coined the term Thick Data which she defines as “ethnographic approaches that uncover the meaning behind Big Data visualization and analysis.”

The main reason for ethnography’s usefulness lies in the participant-as-observant role in the field. By just hanging around, you immediately see how people use your product. The data you gain is especially insightful once the observed got used to you for “hanging around”. If people don’t mind your appearance very much, you gain operational data (i.e., natural flow of conversations and activities) instead of presentational data (i.e., trying to maintain a certain appearance in front of you). Gaining thick and operationaldata is the true advantage of ethnography in comparison to other methods. You gather data while it is happening (in contrast to surveys and interviews, for example).

However, as with any method there are also some disadvantages. Ethnography is very time-consuming. This is because the observed (a.k.a. informants) have to get used to you – otherwise no operational data for you! Depending on your ethnographic research design and research question(s), it can take 50, 60 to several hundred of hours as you also have to analyze your newly gained Thick Data. And there’s another problem. It won’t be easy to find users or companies who agree that an outsider just hangs around in their office for a few hours or recording some videos.

There are some tricks, I used during my PhD to mitigate the disadvantages. One is to become friends and help others with daily task. Some researchers reported this has helped to gain acceptance in the field. Another advice is, once you reflected on some data, to ask longer (follow-up) questions naturally while having lunch together.

The good thing about ethnography is that you can almost immediately start some small pilot studies for practicing. Go somewhere and observe what people are doing (e.g. how they approach, use and choose tickets on ticket machine). After some pilot testing, you might start with just one single user and build a user persona around him. Expect to gain unexpected results.

This is a two-part series why qualitative data is fundamental for understanding entrepreneurship and startups. Part one  discusses qualitative data in general.

References:

Conklin, H. 1968. Ethnography. In D. Sills (Ed.), International Encyclopedia of Social Sciences, Vol. 5: 115–208. New York: Free Press.

Geertz, C. 1973. The Interpretation of Cultures. New York: Basic Books.

Image Credit: Sprout Labs

You probably know the importance of early customer development if you’re acquainted with entrepreneurship and startup blogs. If not, read customer development by Ash Maurya. I’ll wait. Often early customer development is summarized with the expression “get out of the building” which is popularized amongst others by Steve Blank. The idea is to gather data – qualitative data – about customer problems and potential solutions.

Qualitative data is about describing something whereas quantitative data is about measuring or counting information (i.e., quantifying). There’s already tons of great material how and what to ask your potential customers. So, I won’t start from scratch. I want to refine what is already out there with asking methods used in academic research. This will help enhance your understanding of what your customers want – the best starting point for building a great product.

There are different ways to gather qualitative data. In this part we look at one of the most common methods – interviews. A good qualitative interviewer is flexible and wants to fully grasp the worldviews of research participants (i.e., your potential customers). That’s why qualitative interviews help you build a great product. Grasping the worldview of your potential customers will de-risk building the wrong product and minimize headaches (any person who has build the wrong stuff like I’ve done knows what I’m talking about).

Interviews in qualitative research are either semi-structured or completely open. In customer development semi-structured interviews might be more useful, as it is easier to compare and contrast the answers. Let’s start with the different kinds of questions used by researchers.  Spradley (1980) categorizes questions into:

• Descriptive: Easiest to ask. Example: “What you do when you want to learn something new?”
• Structural: This kind of question helps you understand HOW informants organize their knowledge. Some structural questions might be:
  • Strict Inclusion (x is a kind of y): “What are the different kind of apps you have installed on your phone?”
  • Means End (x is a way to do y): “What do you do to train yourself on the job?”
  • Cause-Effect (x is the result of y): “What are the causes that you don’t educate yourself more often?”
• Contrast: Gives the interviewer an idea through which dimensions informants distinguish objects.
  • Dyadic: “What’s the difference between online and offline learning?”
  • Triadic: ”Thinking about doing an online course (1), assembling your own ‘course’ through video tutorials (2) or reading an eBook (3), which of these options seems to be most promising regarding learning efficacy?”
  • Rating: ”Which of these options seems to be the most difficult?” Note: Contrast-rating questions can help you rank the most important problems of your potential customers.

Then, there are the different scopes of questions:

• Grand tour questions: The goal is to find out the names of places and objects, activities, etc. and to understand how all of these elements are interrelated (“Tell me, who decides what kind of on-the-job-education your employees receive? I mean, how and how often is the process? Are there different learning options to choose from?”). Grand tour questions are often expanded and repeated with other words to give the interviewee more time to reflect about the question. For customer development, I wouldn’t suggest to use grand tour questions if you only have 15 to 20 minutes per interviewee. However, in some cases it makes sense. First, if you have more time (let’s say 45 to 60 minutes) AND if you quickly lead the next question to a more focused mini tour question.
• Mini tour questions: these questions deal with a much smaller unit of the whole experience. (“How are the learning options chosen?”).
• Specific mini tour questions: Concrete examples (“So, how were the learning options chosen for ‘Ms. X’ last week?”).

Johnstone (2007) gives great and concise suggestions how to structure qualitative interviews. The summary of the summary:

1. Plan topics in advance
2. Set the stage by explaining the purpose and how you will protect confidentiality and the interviewee’s identity.
3. Start with a quick but broad background question (who the person is and how she came to the position) to put the interviewee in a right frame of mind.
4. Questions should be open-ended (“tell me about…”) and NOT leading (“So, you mean x?”)
5. Probing questions(“Can you please elaborate?”/ “Do you have an example”). Don’t over do it. Only keep digging if you are onto something.
6. Silence: Gives interviewees time to reflect and elaborate.
7.  Be grateful for the interview and just before you end it and ask: “Is there anything further you want to tell me?”

And don’t forget to keep in touch for doing follow-up interviews when you have your first Minimum Viable Product (first product release only with key features) ready. Qualitative interviews have opened up the doors for many new solutions that you usually would never have thought of.   That’s enough for now.

Analyzing qualitative data is often very messy. For early customer development categorizing similar answers with the same “tag” (a.k.a. “coding”) should be enough. Often you will discover causal relationships between the tags, which enhance your understanding of your customers’ problems – a great foundation for building a great product.

This is a two-part series why qualitative data is fundamental for understanding entrepreneurship and startups. Part two discusses ethnography.

References:

Johnstone, B. 2007. Ethnographic Methods in Entrepreneurship Research. In H. Neergaard & J. P. Ulhøi (Ed.), Handbook of Qualitative Research Methods in Entrepreneurship: 97–121. Cheltenham: Edward Elgar Publishing.

Spradley, J. 1980. Participant Observation. New York: Holt, Rinehart and Winston.

Image credit: Interviewed by Jack M.

In the last blog post we talked about discovery vs. creation of opportunities. As I promised in my first blog post, I’m going to use my own entrepreneurial journey as an example or my own entrepreneurial journeys as examples for the academic concepts, which I’ve introduced here. So you might one to read the previous post before you continue. So let’s have a look at the open education platform Crowducate. Btw, for the tech-savvy: Manuel Schoebel (@DerMambo) gives you a great introduction to how we developed the prototype of Crowducate.

Crowducate is a brand new startup project. I seriously believe that the best quality of education emerges when the crowd produces and monitors (i.e. updates) the course. I am radically democratic in this sense. At Crowducate people can create courses, which are divided into sections and, on the next level, divided into “bite-sized” lectures. You consume the courses by reading/watching the material and afterwards you could check your understanding via some quizzes. The key features are that learners can send concrete change requests for the lectures and quizzes and furthermore COPY the course to their own profile in order to develop it into a different course (e.g. change the language or just take some of the sections/lectures as basis for a new course). The key features, namely change request and copy course, are inspired by open source software and GitHub (pull requests and forks). Read more about the vision of open education at Crowducate.

But here comes the big question: Did I discover or create this opportunity? After having heard the story how the idea came into being, you might have guessed the answer. Ok, let’s begin. Some of you might have encountered the new phenomena called MOOC (massive open online courses). Most of the greatest universities and many others are offering their courses for free on education platforms (check out edX and iVersity for starters). In some of these courses more than 100,000 online students participate. Yes, hundred thousand! An example from my own experience in this area might give you a clearer picture about the workings of MOOC. In the course of  my “PhD-years”, I decided to  learn more about programming and after my first MOOCs, I stumbled upon one of the greatest MOOCs out there: Startup Engineering by Balaji Srinivasan and Vijay Pande from Stanford. I loved this course. I couldn’t wait until it started. And I learnt a lot.

However, as Balaji mentioned during the MOOC, delivering an offline course online is not trivial. The lecturers in the course were obviously very busy, having full-time jobs outside the university. It was plainly impossible for them to handle all the students. Some parts of the script or quizzes needed further elaboration. The course’s discussion forum was a tremendous help as loads of knowledgeable people voluntarily helped others out whenever they got stuck. What struck me was their intrinsic motivation. It was the sheer number of volunteers in every course and platform that suddenly made me ask myself: What if we open up the education model in a way that not only the lecturers create a course but also the students? Sure, a discussion forum is a first step but somehow it often seems not enough. It also seems inefficient in some cases as many repetitive questions are answered over and over again. A quick fix of the course’s content could save headaches for future learners. In other words, everybody can copy or request changes and so courses become a little better all the time.

I didn’t search for this opportunity. I stumbled upon it. I had the same problem with many other MOOCs where I wondered if it weren’t more efficient if we used mechanisms of social open source software (Github) to enhance education. To make my concept clearer, I started asking people around what they thought about this and a lot of people could grasp the concept and vision behind it. Thus was the initial idea of Crowducate born. I have a very clear vision for Crowducate but Start-ups have to pivot quite often (i.e. repositioning themselves according to user feedback). So coming back to the question: Was the idea of Crowducate a creation or a discovery of an opportunity? Because I was not actively searching, and uncertainty is more immanent than risk in the narrow sense of the word, it was more of creating an opportunity rather than discovering one.

Image credit: Frederic on Flickr

I’m glad you asked. This simple question has sparked many discussions for decades. Should entrepreneurship be a discipline of its own? Is it only about new ventures? If yes, would that mean that larger organizations couldn’t be entrepreneurial? And if larger organization can be entrepreneurial, why should entrepreneurship be a discipline of its own? If that is the case, then entrepreneurship should be a part of strategy management or organizations science. After decades of discussions, Shane and Venkataraman (2000: p. 218) suggest the following definition, which in the meantime has become highly influential, for the scholarly field:

“[T]he scholarly examination of how, by whom, and with what effects opportunities to create future goods and services are discovered, evaluated, and exploited.” (emphasis: A.R.)

Opportunities … this is what makes the scholarly field of entrepreneurship distinct from its neighboring fields. In a nutshell, entrepreneurship is thus about the discovery (1), evaluation (2) and exploitation (3) of opportunities. Finally. Phew!

But wait! Are opportunities always discovered? Or is that they are created? You might think this is some useless academic debate – but hold on to your hat – grasping the difference can be important for your entrepreneurial journey and which paths you should take. So bear with me for a moment.

Alvarez and Barney (2007: 13) explain that the concept of opportunity discovery assumes that “[o]portunities exist, independent of entrepreneurs”. In other words, opportunities have an objective essence – such as the Mount Everest, for example, does. Being the first person to climb (exploit) the Mount Everest (opportunity) fits in the discovery approach. So, the first step is to be an alert entrepreneurs (Kirzner, 1973) and actually see opportunities to climb that mountain. Being alert and seeing opportunities due to exogenous shocks (e. g. political or technological shifts). Thus, alert entrepreneurs search for opportunities that are out there. A further key concept that plays a crucial role in the opportunity discovery approach is the concept of risk. A decision is risky, when decision makers can define different possible outcomes and calculate the probability of each outcome (through information gathering).

The concept of opportunity creation does not assume that opportunities exist objectively. However, that is not to say that nothing is real and there’s nothing to get hung about. But rather, that entrepreneurs develop opportunities through their actions. As Alvarez and Barney (2007) put it, in this context, a mountain is built – not discovered. Searching for opportunities is irrelevant here as each (would-be) entrepreneur travels his own subjective path through action and experience and somehow stumbles upon very specific opportunities that suit her or him. As Alvarez and Barney (2007: 14) sum it up, the concept of opportunity creation assumes “[o]portunities do not exist, independent of entrepreneurs”. Bear in mind that risk is substituted with uncertainty. Uncertainty is the crazy brother of risk. As opposed to risky decisions, it’s impossible to gather information for predicting the probability of future outcomes. Therefore highly sophisticated strategic and financial planning is seen as over-engineering.

Now let’s talk about discovery and creation in practice. The concept of Start-up Weekend serves as a good example. At Start-up Weekends people pitch a rough idea in 60 seconds in front of an audience of around 100 people. The best ideas are democratically chosen (ca. 12-13). After the voting process, people team up and work on their favorite idea for the weekend. You should definitely have this experience under your belt – it’s fun. It’s even more fun, if you play with the concepts of discovery and creation of opportunities. You often sense if the pitcher were deliberately searching for an idea before pitching or if she or he has stumbled upon an idea because of her or his actions and thought: “This is not how x should work. There must be a better way”.

In the internet/software world it’s probably easier to create opportunities than being preoccupied with the search for opportunity discovery. The latter “opportunities” often end up being what Paul Graham calls sitcom or made-up ideas. Bear in mind, that in practice most ideas are a mix of the two. But understanding the different concepts of opportunities can help you understand much more why you follow a specific opportunity. If you have questions or other comments, shoot! I know this is a bit hard to grasp in the beginning. :)

In the next post, I’m going to describe how I came in touch with my very own opportunity.

References:
Alvarez, S. & Barney, J. 2007. Discovery and Creation: Alternative Theories of Entrepreneurial Action. Strategic Entrepreneurship Journal, 1: 11–26.

Kirzner, I. 1973. Competition and Entrepreneurship. Chicago: University of Chicago Press.

Shane, S. & Venkataraman, S. 2000. The Promise of Entrepreneurship as a Field of Research. Academy of Management Review, 25: 217–236.

Image credit: clappstar on Flickr

So, here we go: Another blog about entrepreneurship, startups and strategy (well … at least, the last point is not always used in combination with the other two). Why do we need another blog about such a crowded topic you might ask?

One reason is that most (nascent) entrepreneurs underestimate the benefits academic knowledge and research in the field of entrepreneurship and strategy can provide them in their entrepreneurial journey. Practitioners often dismiss the academic discourse as too theoretical and inapplicable. On the one hand, quantitative research is seen as not detailed and deep enough. On the other hand, qualitative research is often criticized for its lack of generalization.

I can help. Pleased to meet you! My name is Amir. You can read more about me. For this introductory post it’s enough to know that I wrote my master thesis on the subject knowledge transfer from management science to practice and that I just finished my PhD in – you guessed it – entrepreneurship.

Granted, management studies have an issue with being simultaneously relevant (for practitioners) and (scientifically) rigorous. If you are interested in the “rigor vs. relevance” debate, I recommend reading the work of Nicolai and Seidl (2000) for starters. You don’t have to do it now. We’ll get to rigor vs. relevance another time. It’s a truly fascinating topic, which we are going to discuss in easily digestible chunks. But let’s not get ahead of ourselves.

Anyhow, during my studies, I found several amazing concepts that can definitely help you in your entrepreneurial endeavors. However, one caveat: Don’t expect to read an article and immediately implement its key findings. Some of these concepts will help you to understand entrepreneurial decision-making and behavior on a conceptual level and consequently show you how to improve your own entrepreneurial action.

And the best part? I’m going to use my own entrepreneurial endeavor(s) as an example. So, it’s not going to be too abstract. I will tell you more about the current project I’m working on in the next blog post. Just some quick info: It’s going to be an edtech (startups using technology to improve education) and open source – at all levels. That’s it for now. Share and subscribe via RSS, email or twitter for more awesomeness to come. Don’t worry I won’t post too much ;) Oh, and please give feedback in the comments section. Is there any specific topic you would like to read and discuss here?

Reference:

Nicolai, A. & Seidl. D. 2010. That’s Relevant! Different Forms of Practical Relevance in Management Science. Organization Studies, 31: 1257-1285.

Image credit: Sam Klein on Flickr