Yesterday I finished writing a full paper about the research tool LoopMe that I’ve been working on for six years now (see www.loopme.io). While LoopMe has been briefly described in a number of articles before, this was the first time I wrote a full paper about the research methodology underpinning LoopMe. The resulting paper can be downloaded here.

Disruptive advantages to entrepreneurship researchers

In research you often think by writing, and this was no exception. Writing the paper made me realize that LoopMe is one of the first examples ever of what I ended up labeling “Scientific Social Media” (abbreviated SSM). I defined SSM as social media platforms optimized for social science and used primarily for data collection and analysis. SSM combines important and complementary strengths of established research methods such as surveys and interviews. This facilitates the collection of large amounts of interconnected qualitative and quantitative data. SSM also allows for new possibilities to conduct longitudinal studies, to triangulate data and to analyze data in new and time efficient ways. These benefits imply that SSM could offer significant advantages to entrepreneurship researchers in terms of significantly lowering the cost of high quality data collection efforts, providing new-to-the-world data collection and analysis techniques and also bridging between qualitative and quantitative research. The new possibilities could be employed in many entrepreneurship related environments such as entrepreneurship and enterprise education, incubators, accelerators and other business start-up related environments. It could also be used to advance research in subfields such as venture capital, social entrepreneurship and intrapreneurship. Scholarly fields outside entrepreneurship could also use SSM to advance sociological research in diverse areas such as health, parenting, dieting, leadership and sustainability.

The writing process leading up to the article

The writing process started in late 2016 when I was asked by professor William Gartner to write a chapter about LoopMe in an upcoming Research Handbook he is working on. He wrote to me in December 2016, asking me to write a chapter to the method section, containing essentially what I wanted to say around LoopMe. The writing process started with an abstract to the 3E research conference, pitching a workshop around LoopMe for interested researchers. The workshop was delivered in May 2017 in Cork, spurring some enthusiasm among research colleagues. Next step in the writing process was in February 2017 when we were asked to describe LoopMe for a funding application to EU (which was later rejected). Eight pages were thrown together quite quickly. The bulk of the writing process happened in April to June. I then realized that LoopMe was an example of using social media for research in social science.

An emerging new research field applying digital methods

Now an entire new field of literature had to be reviewed, the emerging field of “Computational Social Science”, or “Digital Sociology”, or “Virtual Ethnography”. An emerging field with many names. But most studies that they wrote about had so far been focused on using data from established social media platforms such as Facebook and Twitter. This technique indeed has a number of shortcomings. The most important one is perhaps that you need to go on a fishing trip in a sea of data and see what research questions pop up. This is contrary to what has been recommended by many method scholars.

One of the first attempts at not doing social media research backwards

I then realized that our group of researchers and programmers behind LoopMe were among the first ones to start with a research question, then building a social media platform tailored for generating answers to that question. The question was: “How do people develop their entrepreneurial competencies?”. The work has so far generated a lot of articles that you can find here. From a method point of view, LoopMe thus turned out to be perhaps the first social media platform in the world built around a research question (or research program). Most social media platforms are instead built for the purpose of making money. If you want to do research around the data that such a platform generates, you will be quite limited in what questions you can answer. This can be seen in much of the work out there. Fantastic and exciting new methods are applied, but in order to answer rather dull research questions. There are of course exceptions. But still, I could not get away the feeling that most research in this field was done backwards. First a sea of data was picked, then people started fishing relentlessly. I’ve been taught to avoid that approach in my PhD studies. But perhaps it is natural in an emerging methodological field to experiment with what is out there.

Combining strengths of interviews and surveys

One of the most intriguing things that came out of the writing process was a table contrasting SSM to the two most common data collection methods in my field – interviews and surveys. I think it is quite interesting to see how SSM manages to combine most of the strengths of both interviews and surveys, and at the same time mitigate many of the weaknesses of both methods. Now that is quite cool, isn’t it? See the table below here:

Your feedback, please!

While I now have a full paper on LoopMe for the first time since the journey started, it is not fully ready. It will be revised in autumn after I have received feedback from the editors. I would also love to get your feedback on it. I have until December 2017 to improve the paper. So download my paper and see what you think, and then let me know by dropping me an e-mail. Thanks!

It is a well established fact that IT is reshaping just about every single field of human activity. But one thing that has remained surprisingly stable is research methodology in social sciences. Social science scholars still today focus on doing interviews and distributing surveys, as well as conducting the occasional observation study. Collecting primary data has not yet been disrupted in any way, despite its tremendously labour-intensive and fuzzy characteristics. Quantitative researchers still struggle with low response rates on surveys, meticulous item development and non-respondent analysis. Qualitative researchers still struggle with tedious interviews, labor-intensive transcriptions and subjective coding of interview data. Shouldn’t there be a better way to conduct rigorous data collection in our modern digitized society?

Reinvention of social science?

Digitalization in any human activity often follows a pattern of going from small adaptations, to larger transformations and finally to end up in complete reinvention. If we look at how research is conducted, we are arguably still at the adaptation stage. When distributing surveys we use a web-based survey engine sent through email. When conducting interviews we record what is being said by using a convenient recording app on our tablet or smartphone. When observing human activity we record it on video using a camera or perhaps a smartphone. But we are not even near a transformation of how data is collected, and the reinvention of social science research is nowhere in sight.

In my work I have stumbled upon a completely new way to collect primary data for research purposes. It’s not an interview method, but you can still ask follow-up questions to respondents in a synchronous way and take context into account. It’s not an observation method, but you can still observe what is going on between key social actors such as between a teacher and her students. It’s not a survey method, but you can still distribute it easily on large amounts of people and readily quantify their experiences. So, what is it? Well, that is the question I am currently struggling with. I am now writing an article on the topic, and this blog post is a way to get started writing text.

The data comes to me

What we are talking about here is an IT system that is possible to deploy at a large number of people in a context I want to study, such as an entire school or an entire incubator for entrepreneurs. It resembles social media, but it’s more private. It resembles learning platforms, but it’s way less administrative. It is similar to a messaging app, but it has more structure and also a few mandatory quantification steps in each transmission of text. So, while it is similar to many things, it is equal to none I have seen so far. The system we have built goes under the name LoopMe (see here), and we have tried to categorize it as “Social Learning Media”. We believe this is a new category of IT systems. I’ve done a video about it that you can watch here.

For me as a social science researcher it is really a dream come true. Once the system is deployed, the data comes to me. I can sit at a distance and observe what’s going on, and whenever I see something of interest I can intervene and ask follow-up questions in real-time. All the generated data is saved on a central database. I can download the entire dataset in an Excel sheet, and analyze it however I see fit. I can make really nice Pivot diagrams showing causation in terms of which activity led to which kind of perceived outcome. This does not preclude offline real-life encounters, but can actually empower such meetings. I have used Loopme as a sampling strategy by choosing who to interview based on information online. When I meet a respondent, I often prepare a semi-structured interview template where questions are tailored to the particular respondent. I can skip contextual questions, and go directly to the core issues relating to the research question I am investigating.

Martin’s 1000 eyes or just unrealistic hype?

The real challenge comes when I want to explain to other researchers what this method is about, and why it’s good. A research colleague commended the method and called it “Martin’s 1000 eyes”, since each user of the IT system becomes a participant observer working for me as a researcher, telling me whenever something significant happens in their natural setting. Another research colleague was more hostile, saying “Here you come with your little app, and everything will solve itself – that is an arrogant rejection of established educational research methods”.

While I have been using it now in my own research since 2012, I have not been able to get other researchers to use it in their data collection procedures. I really wonder what is stopping them, and I suspect it is my low ability to explain what is going on here, coupled with new unorthodox ways of working that scare people off. So I hope this text will turn into an article that makes sense and clarifies some things (also to me). A first step I’ve done is to try to categorize common methods, see image below. As far as I can see, LoopMe spans the entire field here, from the subjective nonverbal social communication you would capture in observation studies, through written accounts you get from transcribed interviews, to the objective individual quantified responses you would expect from a survey.

Win-win for all involved

The main reason it works so well is because it delivers a very tangible and appreciated value for the practitioners using it, which contrasts to surveys and many interviews that are done primarily for the researcher’s benefit. The Loopme system connects people in ways they have not experienced before, and this helps them a lot in their daily life. This is what gives the new method its observational quality – participants use it not for me as a researcher, but for their own benefit in their natural setting. This makes it even more odd that it is new – how could we come up with a new social media tool at the same time as we constructed a new kind of research tool? Well, maybe precisely because of this. When optimizing social media for research purposes, we ended up with some truly novel functionality. There is no equal solution for practice, resulting in the obvious conclusion that using such a solution for research purposes is something that most likely has not happened before.

Capturing the “flow” of experience

The most similar development I’ve seen within research is a couple of IT systems that try to facilitate for researchers working with a method called ‘experience sampling’. Experience sampling is when short  surveys are used to capture respondents’ experiences directly in their natural environment, attempting to capture the “flow” of everyday experience. There are a few companies out there developing such tools, such as Metricwire and MovisensXS. But there seems to be no thriving community of MethodTech companies, pushing the boundaries of data collection, and redefining what it means to be a researcher. That is a missed opportunity in my opinion.

I’ve also found a small community of ethnographic researchers discussing how to use Social Networking Sites (SNSs) for research purposes (see for example here, here and here). Facebook is the most common platform. Some ethnographers have viewed Facebook posts as empirical data. Others have used Facebook for snowball sampling in order to find respondents that otherwise would have been very difficult to find. I have however not found any example of an ethnographic research team building their own SNS tailored to the needs of such research. This leaves ethnographers with a number of unsolved dilemmas, such as how to protect participants’ privacy, and other fully legitimate requirements from ethical committees.

Towards a MethodTech community?

In our increasingly digital world there are endless opportunities to collect data in novel ways. But established solutions used by practitioners for communication purposes, such as Facebook and others, are currently almost never optimized for research purposes. While we have EdTech, FinTech, AdTech, GreenTech and WhateverTech, we do not yet seem to have MethodTech. We scholars will have to create it ourselves. Who wants to join me here? The first step is to be able to explain myself. So if you get what I’m writing about here, let me know. And if you don’t get it, let me know too! I write this blog post in hope of getting some help in explaining myself and growing this non-existent MethodTech community from one (?) single researcher. Anyone else out there?

I just finished an article summarizing five years of work with six in-depth empirical studies on entrepreneurship education, enterprise education and value creation education. The six studies involved 928 participating students who in total made 10855 app reports through our smartphone app based data collection instrument Loopme. We interviewed 300 of the participants. I will here try to summarize what we found out. If you want to read the full paper, download it here. If you want to hear me present this paper, come to Cork, Ireland, in May, to this conference.

The article is basically about the dilemma most teachers are faced with when having to choose between two established but problematic approaches in entrepreneurial education. On one hand, entrepreneurship education based on an organization creation focus remains marginalized due to its connotations with egoistic capitalism, making it difficult to integrate with most kinds of non-business education. On the other hand, enterprise education based on an opportunity recognition focus remains largely irrelevant due to its weak effects and vague state of being indistinguishable from the centuries-old progressive education movement.

Through a comparison and contrasting of six different impact studies, an escape from this dilemma was generated and evaluated. The six studies contrasted the two established kinds of entrepreneurial education with a third kind building on a value creation based view of entrepreneurship, here termed ‘value creation education’. See table here:

Value creation education was shown to be widely applicable by integrating well on all levels of education and giving strong positive effects on entrepreneurial competencies, student engagement and subject matter knowledge. It removes much of the complexity associated with entrepreneurship education and also the definitional fuzziness associated with enterprise education. Value creation education was thus found to open up a new solution space for entrepreneurial education theory and practice. It remains to be seen how large this new solution space is. Based on this, time and effort invested by teachers and other practitioners into value creation education is most likely well spent. This is particularly so for practitioners in enterprise education where the step needed to take in order to reach a much stronger effect is small.

Policymakers now need to reconsider many of the currently on-going initiatives to infuse entrepreneurship into education. Value creation education is arguably a more effective and efficient practice than both entrepreneurship and enterprise education in many situations. Entrepreneurial education also no longer needs to rely on problematic economic policy objectives causing a value clash for teachers, but can instead be connected directly to educational policy objectives of improving student learning and achievement.

While potentially a breakthrough for the field of entrepreneurial education, these results give such a positive image of value creation education that one needs to question whether the findings are too good to be true. Other research teams now need to corroborate the results presented in this article and see if they can be reproduced in other settings and with other methodologies. The emergence of value creation education also poses new semantic challenges that need to be discussed. Finally, there could be other definitional starting points out there that have not been explored and that could be useful for educational practice.

Late last year Yashar Mansoori defended his licentiate thesis at Chalmers University in Sweden. The focus was on entrepreneurial methods. Opponent was Steffen Korsgaard at Aarhus University. The interesting thing about this thesis is that it is perhaps one of the first comprehensive overviews of different methods in entrepreneurship. Methods explored and contrasted / compared in the thesis include effectuation, lean startup, customer development and bricolage, and also some more old-style methods such as business planning and discovery-driven planning. Some rather interesting similarities and differences between them were discovered!

Mansoori concluded his thesis with a three-tier framework useful if we want to understand and improve entrepreneurial methods. The three levels were logic (highest thought oriented level), model (middle process oriented level) and tactics (lower behaviour oriented level). Some entrepreneurial methods give a lot of advice on how entrepreneurs should think, but less advice on how to act. Other methods are strong on action-taking, but weaker on the philosophical underpinnings. Here Mansoori points out some work that needs to be done.

The thesis can be downloaded here. Mansoori will now continue towards his doctoral thesis completion, due in 2018.