Can research be agile? (Part 1)

Three weeks ago I took a leave from my day job as a Lean-Agile consultant to finish my doctoral dissertation in three months. The last time I did research full-time I was working at Aalto University SimLab from May 2013 to December 2015. Based on this, you might think I had this whole “how to do research” under wraps, but I was always unsatisfied about how use my time effectively and actually write the dissertation. But now after a year as a Lean-Agile coach I felt more prepared to tackle the challenge than ever before.

This is a retroperspective on how I’ve managed to make myself a better researcher and enhance my probability of getting the dissertation done by July. In this part 1, I will give you an overview into some techniques and tools that I’ve created in the last three months leading up to and the beginning of my writing leave. In part 2, I will explain more about how I actually use the system formed by these tools.

Thinking about work items

When thinking about a big project, even one where a lot of thinking and creativity are needed, it helps me to accept that everything is just work that needs to be done. This comes across in both Lean and Agile and it applies to assembly lines where the machines are literally just waiting for you to assemble them, but also software engineering where we track our progress based on the system functionalities we have delivered. You can have work items of different types and sizes, but each item of work is always either not started, in some state of work-in-progress (WIP), or done. Sometimes pieces of work need to be iterated, but the new iteration is thought of as a new item of work.

Breaking down large chunks of work

The biggest lesson I learned from working in an agile team was that the size of the work items you handle matters. Agile teams work with work items called Stories that can be completed within two weeks, i.e. a Sprint, and Lean teams just want to keep item sizes small and consistent. By the time it is finished, my doctoral dissertation will have taken me 3½ years, over 10% of my life. If that is not a work item that needs breaking down, I don’t know what is.

When I look at my work as an individual, my biggest item of work should be roughly half a day. This means that if I work on an item for half a day (e.g. the whole morning), I am reasonably confident that I will get it done. If a job has multiple phases, I might try to break it down as small pieces as I can just so I have more focus and flexibility during the day. Work item size might be the most important thing on this list, which will make even more sense when we start talking about iterations and limiting work-in-progress.

Different areas of work

I started by creating categories of work based on the end result – the dissertation text itself and its chapters. Each chapter. Writing a dissertation or any kind of thesis is hard because you can’t just sit down and write it, you need to write multiple chapters in parallel because your view on the literature changes how you analyze your data and the conclusions will place requirements on how you frame your research in the introduction and so on. Each chapter will also need a different kind of mind set, whether it’s talking to a large audience in the introduction, reading and commenting on literature or working with data in analysis.

Now if I plan to do a revision that will impact multiple chapters, I can write down work items to each chapter and then focus on one chapter at a time instead of jumping wildly from one chapter to another. Having a system in place for tracking which chapter each work item will impact also helped me talk with my supervisors. When talking about the dissertation and giving feedback, we are always talking about the state of this or that chapter, so this system helps me to both describe how I have developed each chapter and create work items based on the feedback of that chapter.

Focus on the desired state

When starting an agile way of working, a lot of people have problems internalizing that the best way to write down work items is not “do thing X” but rather “X is done”, or even better, “Y can X”. For example, my work item for analysis reads “Episodes have been searched and listed from the transcription”. The difference from “Search and list episodes from the transcription” is subtle at first, but reading the former automatically triggers a response of “What still remains to do?” which helps me get that work item into Done and on to the next instead of evading a vague work item because I’m not really sure what to do next.

Visualizations for everything

Most of my work revolves around three objects I have on the wall next to my monitor. My Kanban wall, my weekly calendar and my weekly objectives:

The big mess of tape and magnetic notes is my Kanban wall. The columns tell which phase each work item is in. I often have just a mess of things I might do on the left – called a Backlog – which is then refined into things waiting to be done under “To Do”. I only allow one item of work to be “In Progress” at a time so I know what I’m currently doing – this is called a WIP limit and it helps me finish work items before I move on to the next. The rows are the different chapters: Introduction, Theory, Method, Analysis, Results & Discussion, and then one for planning and other general work like “remember to tweet a lot”.

The brown framed whiteboard is my weekly calendar that I update at the start of every week. I plan my daily work time in two chunks - morning and afternoon – and mark down what I expect to finish during that time. I also mark down everything else that impacts my available work time e.g. meetings, gym, rest days or the time I must leave to catch a bus to meet my friends. During the week, I tally the number of 25-minute timeboxes that I get done, with breaks between them for stretching and snacking. This gives me a sense of how well I’m able to keep to my writing routines and creates a bit of accountability so I have a better chance of reaching my goals.

The two big magnetic notes above the weekly calendar are weekly objectives. I initially did my objectives for two weeks i.e. Sprints, but quickly realized that I had to have objectives that fit my cadence (more on that later). They’re not (always) replications of the work items, but instead they’re usually things I’ve agreed with my supervisor to do in the following weeks. After settling down with the objectives, I can then identify which chapters need work items and how to write them in an actionable way.

Finally, I use wall space for all kinds of notes and data analysis. Because I don’t have a whiteboard at home, I use Magic Chart, a roll of whiteboard paper that sticks to your walls. They are handy for theory formulation and data analysis, and they can easily be moved around or trashed after they’ve been incorporated into the text.

That’s all for today, follow me on Twitter to get a heads-up on Part 2: Making it all come together.

 

 

Theoretical perspectives in studying design games as interaction

The objective of conversation analysis is to marvel at our ability as humans to make sense of each other, and occasionally transfer meaning across the vast void that separates us. In my research, then, the point is to marvel at how we can engage in such overwhelmingly complex activities such as playing games and coming up with new ideas. To be truly frank, I have skipped at least two dissertations' worth of groundwork and analysis to properly understand the phenomenon I am studying, and am therefore walking carefully in the cross-section of so many intertwining discourses.

But that, I'm afraid, is the role of applied science: to attempt solving the problems that arise in the world in their natural, messy form, trying and failing to learn all the lessons that basic researchers have made available. Here are the three synthetic perspectives that I'm trying to outline, utilize and develop in my dissertation.

1. Dialogical knowedge co-creation

The motivation for studying service design games in the field of industrial engineering and management lies understanding and exploiting the ability of service design games (SDGs) to facilitate knowledge creation in organizations. In my work I have chosen to utilize an interaction analytical approach to studying SDGs and as such will utilize a theoretical framework of dialogical knowledge co-creation, following Tsoukas (2009). This focus on the interpersonal creation of knowledge is reflected in the use of the term knowledge co-creation to delineate the phenomenon of dialogical knowledge co-creation from other perspectives such as knowledge management.

My contribution to this perspective will be a continuation of dialogical knowledge co-creation using conversation analysis in a setting of intentional knowledge co-creation. The conversation analytical approach will provide further understanding and examples of the structures employed in conversation of knowledge co-creation.

2. Scaffolding knowledge co-creation

The ability of SDGs to support knowledge creation is conceptualized in my work using the scaffolding metaphor which originates from learning science and sociology. This perspective, after Wanda Orlikowski, posits that all knowing is made possible in interaction with material infrastructure which both enables and restricts us. In this work scaffolding is extended into the realm of practice knowledge, the primary definition of knowledge in this work, to propose that action is scaffolded not only by material artefacts but also conceptual artefacts such as game rules and institutions.

My work will provide a process-oriented view into the role material and conceptual scaffolds play in a game setting. The analysis explores the different use of scaffolds in different phases of the game and as a part of different interaction structures.

3. Service design games as interaction

A study of the SDGs to scaffold knowledge co-creation will, finally, require a way of analyzing SDGs as interaction. This work is informed by game studies where games and play are studied as socially constructed activities, but the primary perspective for studying games in this work is institutional interaction.

According to Drew and Heritage (1992, 22), institutional interaction has three typical features:

  1. at least one participant is oriented to a particular institutional task or identity
  2. the interaction is restricted
  3. the interaction involves interpretation frames that are typical for that context

The contribution of my work for the study of service design games in particular and of games in genera is to provide an example of studying gameplay as institutional interaction constructed by the players and afforded by the game material, rules and the larger institution of games. 

Public beta of my dissertation is out: PLAY WITH ME HERE! Design games as scaffolds for knowledge co-creation

Go directly to the public beta release of my dissertation from this link

 

PLAY WITH ME HERE!
Design games as scaffolds for knowledge co-creation

Science is the pursuit of knowledge and as I have described over one master’s thesis, three conference papers and one journal article, knowledge is created in dialogue. With that insight and the ever-growing anxiousness to make headway with my dissertation while simultaneously working full time in the industry, I am making a step I haven’t seen anyone else do and would not have thought about just a year ago.

Today I’m releasing the up-to-date work-in-progress version of my dissertation on my website at http://otsohannula.com/blog/dissertation-public-beta. I do this to open myself to criticism and feedback from the largest imaginable audience: The Internet. If you end up reading this document, I invite you to give your piece in any form you see fit, such as the following:

  • Commenting on the document: I have opened the document for anyone to comment with a username or anonymously. This allows you to ask questions or make suggestions directly to the text. This also helps me get a “heat map” of the text about which sections provoke most responses.

  • Commenting on the blog: If you would rather give feedback in long form or provide a piece in response, my blog provides a great platform to kick off a discussion by making your comment available to others.

  • Email/IM: If you would rather give private notes you can of course send me an email to otso.hannula (at) aalto.fi. You can also just give me the highlights on any social media you see fit. ;)

To put it bluntly in the spirit of Ed Catmull in Creativity Inc., all dissertations start by sucking. In the document you will find a lot of (almost) empty headings and bullet points standing in for interesting and well-thought ideas, as well as passages lifted verbatim from my previous publications (with apologies to co-authors). If you feel like a section is just going nowhere, skip to the next one.

So my work sucks, and it is by opening my work to the world before it is too refined that I try to take full advantage of your feedback so that one day my work might not suck. It is in this moment that I feel more scared and less afraid than ever before.

I love you.

In Espoo, September 30th 2016,

Otso Hannula

Impotent Experts and the Idea of the University

I recently came across a third person blog piece (in Finnish) about the failure of a university to meet the writer's expectations as an employer. Even though most of the post was devoted to arguing against bringing practices of corporate management into Finnish universities, my mind immediately stuck to a rhetorical device at the beginning of the post (freely translated, formatting per original):

When going to work for a university Julia thought that a place in which people do creative work, building new knowledge, is surely different from companies which still apply principles of the industrial age. Julia also though that out of all places universities have a lot of research on how to best support creative thinking, problem-solving, team work [and] workplace well being. Surely the structure and practices of the university itself reflect this knowledge that the organization holds?

As a fellow second-year doctoral researcher and the employee of a university I fullheartedly agree with the sentiment of the post, but I believe that by expanding on where problems arise we can elevate the discussion surrounding them. From the point of view of organization research - a social science - the fact that universities continue to lag behind best practice in terms of supporting knowledge work makes perfect sense. 

1. Explicit knowledge is not practice knowledge

A plethora of knowledge management literature, including the often-cited SECI model by Nonaka and Takeuchi, differentiate between explicit knowledge (knowledge what) and practice knowledge (knowledge how). This is especially pronounced in the field of practice studies in which knowing is inseparable from acting. However, the ability to act is always constrained by practice: practice of being a doctor, practice of management, and the practice of being a researcher. According to practice theory, information about how to best manage creative organizations exists as the object of inquiry within the practice of being an organization researcher and separate from the practices of developing organizations. Merely having access to explicit scientific knowledge about managing creative organizations does not imply anything about the ability to change the surrounding world.

2. Converting explicit knowledge into practice is hard work

The utility of scientific knowledge means that it can be applied in real life but doing so literally means developing a new skill. Even getting to the point where a theory explains one's surroundings takes effort, but then coming up with a change proposal and negotiating with coworkers on how to implement it requires the creation of organizational development capabilities. 

3. Universities are old, rigid and have a lot of moving parts

All organizations have a history and universities have long histories as public institutions which makes them prone to formal hierarchies and bureaucracy. What's more, universities rarely act as single entities, but are instead divided into dozens of semi-autonomous units such as faculties or schools, which in term house semi-independent departments which are home to research groups headed by academic staff - who, according to academic tradition, hold significant autonomy on how to run their groups. While as a rule each level reports to the level above it and grants funding to the level below it, academic employees have free reign to judge whether they want to follow some particular best practice.

4. Researchers don't really care

The first rule of every researcher, at least in my field, is that every researcher tries to maximize the time they are able to spend on their research. Joint papers, shared projects and all teaching are all commitments that eat up the time the researcher is able to freely follow their particular passion, the mastery of which is the defining feature of their career. Add to this that 1) they have no sense how precisely to change their surroundings, 2) figuring it out would require a lot of effort, and 3) changing practices requires voluntary participation from everyone else, you have a prisoner's dilemma where no one makes the choice to keep investing their energy into creating a supportive organizational culture. Curiosity requires autonomy and the freedom to delve deep into subjects and explore a lot of dead ends.

Solutions?

The prevalence of IT in all areas of life often blinds us to the fact that social systems don't scale well if at all. Organizations change through slow evolution and a complete overhaul by frustrated managers will evoke even more hostility from people whose autonomy is both prized and always under threat. I think management practices from the private sector may hold the key for renewing universities, but they have more to do with servant leadership and autonomous teams than centralizing power to a chain of command. True change always starts at a community level, and research groups would do well to make clear for themselves if they expect to act as a creative team or a loose assembly of autonomous researchers. A mismatch of expectation will always end up with doctoral students who wonder why they ended up in academia.