Coordination, Cooperation, and Collaboration

I was musing about the differences between these three concepts. They are not explained clearly in any resource I could find (although many people take a stab at this), so I thought I’d try bending my brain around the problem. The three types of collectivity appear goal-oriented (as in, sharing a common purpose), but there are big differences between the ways in which group members interact – and the reasons for these types of interaction.

Cooperation is when people share ideas about how to work, or share effort to complete the work towards a shared goal, which is understood in common. People work together to complete a task that would be much more difficult to complete individually. Cooperation often involves deciding how to divide the work between individuals in a group for an optimal outcome – for example, in software or organizational change projects. Work may be divided laterally (each person takes a separate slice of the work towards a deliverable), vertically (each person takes a separate deliverable), or performed collectively, where people share the effort required to achieve a goal (for example, analyzing a business process that is too diverse – involving too many stakeholders – for one person to explore in a reasonable amount of time).

Coordination is the organization of work-tasks across individuals to achieve a complex goal that requires analysis (breakdown into subtasks) before it can be addressed. People work together towards a common goal within an agreed timeframe, even if they don’t understand all the tasks required at the start. They organize their activities around a schema, which provides a model of the parts of the work to be done. They divide their labor on the basis of this schema, with individuals or sub-groups completing each part, which is assembled into a whole once all relevant parts have been completed. They may collaborate to perform shared subtasks.

CCC2A Work Coordination Schema

Coordination may be organized around interim deliverables, which are completed individually from subsets of the work-schema, then assembled once all the parts are complete. The underpinning concept to coordinated work activity is that of a plan – a plan of work, or a plan of how the parts of the whole are organized. This is used to guide the coordination of work, across individuals and across groups. For example, in traditional software project management, work is coordinated around a work breakdown structure (WBS).

Collaboration is the pooling of effort, to achieve a joint goal, which everyone in the group of coordinated workers may not understand in the same way (so this is not a shared goal – subgoals may emerge through the processes of discussion and experimentation over how to perform the work). People work together, taking different parts of a task, to achieve a goal that, if not understood in common at the start of the process, will probably be understood in the same way by the end. Collaboration requires trust (that other people will work towards a common goal), but it is more adaptive than coordinated work – instead of agreeing a model of the task in advance, collaborators develop a shared model of the task deliverables as they collaborate on the task. Working together increases the amount of shared understanding between people, which allows them to improvise and adapt the plan of work to contingencies that arise. So both goals and work-practices evolve as shared practice increases shared understanding between collaborators. Software developers, working on agile software projects, collaborate in analyzing how to coordinate their team’s work around a feature-breakdown then coordinate team work around each person implementing the next feature in the backlog. Finally, they collaborate around integrating the feature components into a coherent prototype system.

Improvising Design

Why is design improvisational?  We talk about design as if it were fixed: as if there were one best way to design everything. We celebrate designers who produce especially elegant or usable artifacts as if they were possessed of supernatural powers. Yet design should be easy. It is the application of “best practice” principles to a specific situation. We can observe how the users of a designed artifact or system work, then design the artifact or system accordingly. Why does that approach fail so often?

The key issue is the problem of “the problem.” Designers are taught a repertoire of designs-that-works: patterns that fit specific circumstances and uses. Experienced designers are capable of building up a deep understanding over time, of which problem-elements each of these patterns resolves. So they can assess a situation, recognise familiar problem-elements, then fit these with design patterns that will work in these circumstances. The problem comes when a designer is faced with a novel or unusual situation that they have not encountered before. Novice designers encounter this situation a great deal. As designers succeed or fail at successive designs, they accumulate experiential knowledge, that allows them to assess new situations quickly and to understand which design elements will work or fail in that situation. The problem with this is that (as the Princess said) you have to kiss an awful lot of frogs to get a Prince. An awful lot of people end up with really bad designs, because their designer did not recognize elements of the situation well enough to understand which pattern-elements to implement. If you are really unlucky, you will also end up with one of those designers who feel it is their mission in life to prevent the end-user “mucking about with” their design. If you are lucky, your designer will recognize that it is your design, not theirs. They design artifacts and systems in ways that allow people to improvise how they are used — and the role that they play in the work that people do.

Improvisation takes a multitude of forms. It might be that you customize the color of your screen (often because the designer thought that a good interface should look like a play-school). This may not do much for the function of your work-system, but it does mean that your disposition towards work is a heck of a lot sunnier as you use it. Or it might be that the information system which you use expects you to enter data on one step of your work before another. You might be able to enter data into a separate screen for each step, reordering the steps as you wish. More usually, you have to enter fake data into the first step, then go back later to change this, once you have the real data. This is because IT systems designers treat software design as a well-structured problem. A well-structured problem is one that contains the solution within its definition. Defining the problem as a tic-tac-toe game application means that you have a set of rules for how the game is played which absolutely define how it should work. The only discretion left to the designer is whether to support one or two players and how to present the functions in a usable screen interface. This is not rocket science: most designers can manage this level of design without making the game unusable.

But information systems applications tend to present wicked problems. A wicked problem is a problem that cannot be defined objectively, but needs the people involved (the stakeholders) to agree on what the problems that they face are, what are their priorities in resolving these, and what they want to achieve in changing things in the first place. A wicked problem can be understood as a web of interrelated problems. It is not always clear what the consequences will be, of solving any part of this mess. Some of the problems may have “obvious” solutions. But implementing these solutions may make other, related problems worse or better. For example, consider the problem of providing State-based unemployment benefit in the USA (see the diagram on the “systems thinking” page). If one State offers such benefits and a neighboring State does not, unemployed people will move to the State which does offer benefit payments. This will place a greater tax burden on that State, causing the more affluent residents and businesses to move out. This increases unemployment, raising the tax burden, causing more people and businesses to move out. The act of offering State-based unemployment benefits leads that State into a downward spiral in which their budget becomes unmaintainable and employment opportunities are significantly reduced. For wicked problems, a wider perspective is needed, that examines interactions between problem elements and which analyzes the impact of one problem-solution on other problems. It is not always possible to foresee all unintended consequences. So solutions must be designed flexibly, for changes to be implemented as the consequences are realized and to permit customization by stakeholders and users.

People are infinitely improvisational. They develop work-arounds and strategies to manage poor design. But I constantly ask myself why should they have to develop work-arounds for poor design? What is it, about the design process, that leads us to such constraining IT systems, interfaces, and work procedures that are based on the system design, rather than system designs that are based on flexible work-procedures? This website reflects findings from my research studies and reflections from my own experience in design, to discuss some key underlying principles of design, to explore how the design process works in practice (rather than how we manage it now, which is based on unsupported theoretical models), and to present a way of managing design differently.  Improvisationally.

The Co-Design of Business & IT Systems

Business analysts, change managers, and IT systems analysts are in a no-win situation. They are expected to understand myriad interpretations of the business strategy, reconcile conflicting viewpoints on how business processes work, and – somehow – define a coherent set of change objectives that pleases everyone. While the stakeholders of change each understand only a fraction of what the business does.

The co-design of business & IT systems is like piecing together a jigsaw puzzle without the picture. You get an edge here and there, part of a building outline, or a connecting feature, but mainly you are assembling bits and pieces that are tacked together in whatever way makes sense at the time. Most people fudge this by kludging viewpoints together under a single goal, with multiple objectives that reflect the main things that stakeholders seem to value. Objectives move in and out of the picture, as the focus shifts. Analysts have to understand multiple business domains, as stakeholders pull in different directions like the wild horses in the site header.  Even business managers don’t really understand their processes – and know very little of the processes they interface with. Conflicts, priorities, and omissions in change objectives are seldom realized as current analysis methods don’t provide ways to map out the full scope of change, or to present this to business managers for input.

Systemic analysis uses a divide-and-conquer strategy. The parts of the jigsaw puzzle are assembled separately, then the analyst can piece together the whole. Conflicts, priorities, and omissions from the change requirements become obvious because of the way in which the whole picture is explored. This allows the change analyst and — more importantly — the managers, system users, victims, and beneficiaries of change to understand the scope and priorities of what will change.

This website provides a tour of how to perform a systemic analysis of requirements for change in business organizations (nonprofit and for-profit). It deals with how to get groups of people, who come from very different backgrounds, on the same page – talking a common language for the co-design of business and IT systems.

Design Methods as Performative Objects

Brown and Duguid’s (2001) concept of a “network of practice” has been niggling away at my consciousness. The idea is that a collection of people are enabled to understand each others’ work because of commonalities in practice, but not to the extent that a Community of Practice creates shared ways of framing and performing work:

“we include under the rubric … groups whose members, to the extent that they have common practices, are able to read and understand one another’s work. Disciplinary networks of practice cut across heterogeneous organizations, including, for example, universities, think tanks, or research labs. Professions make up yet other such networks of practice, where again similar practitioners, by virtue of their practice, are able to share professional knowledge through conferences, workshops, newsletters, listservs, Web pages and the like. … different networks of practice cut horizontally across vertically integrated organizations and extend far beyond the boundaries of the latter. Along these networks, knowledge can flow.” (Brown and Duguid 2001, p. 206)

So create closer bonds than organizational membership, spanning organizational boundaries. If the type of intersubjectivity that derives from shared practice (i.e. what Polanyi calls tacit knowledge) does not underpin a network of practice, what does? This rings true, given the observation that IT professionals identify more with the interests of their profession than with their organization (Chou and Pearson 2012). Which brings me to the second property of networks of practice:

“it is important to note that networks of practice may also inhibit the flow of knowledge. As Lynn et al (1996) show, professional networks will occasionally work to resist the spread of ideas felt to be inimical to the interests of the network’s members.” (Brown and Duguid 2001, p. 207).

So how do networks of practice share knowledge? Brown and Duguid have an explanation:

“We have used the notion of networks of practice to explain leakiness. This is not, we have suggested, simply an inherent property of some kinds of knowledge. It does not result from making knowledge explicit and so tradable. It is, rather, a function of the common underlying practice, which creates social-epistemic bonds. Where practice doesn’t prepare the ground, knowledge is unlikely to flow.” (Brown and Duguid 2001, p. 207)

But this is not very satisfying when members of the network are not co-located. Surely, “common underlying practice” includes some form of shared framing as the basis of those social-epistemic bonds? I thought back to the work of Howard Rosenbrock (1981), who explains that IT professionals’ paradigm of system design with the aim of making users interchangeable results in deskilled, repetitive, and unfulfilling jobs for those who use these systems. He explains:

“The paradigm is transmitted from one generation to another, not by explicit teaching but by shared problem-solving. Young engineers take part in design exercises, and later in real design projects as members of a team. In doing so, they learn to see the world in a special way: the way in fact which makes it amenable to the professional techniques which they have available.” Rosenbrock (1981, p.6),

So we have design methods as a form of performativity, embedding ways of framing job design, as well as creating a shared design practice that ignores users’ psychological and motivation needs. But surely, IT professionals are continually learning, acquiring new skills and approaches to system design? It would appear not:

“The fact that most IS professionals learn the bulk of their technical skills during college or immediately afterward encourages recruiters to focus on technical skills for new hires. IS professionals generally learn non-technical skills in the workplace.” (Lee et al. 2001, p.28).

All is not lost. Lee et al. (2001) go on to observe

“IS professionals generally learn non-technical skills in the workplace. And because these non-technical skills are so valuable in the long term, new hires need to possess the aptitude to learn these skills. This may help explain why recruiters prefer graduates who took more MIS classes than those who concentrated strictly on computer science courses.” (Lee et al. 2001, p.28).

How can we remedy the perspective that leads to such impoverished outcomes? As Rosenbrock observes, IT systems can be seen as a replacement for human ingenuity and skill, or as a way of supporting these. We have a choice to automate or to informate work (Zuboff 1988). We also have two chances to undermine the automation-on-rails approach taught in so many methods classes. Back to the network of practice idea. IT professionals have a network of practice with really strong bonds. We can teach IS methods more thoughtfully to those who return – for ongoing education in Masters degrees, etc.  Finally, we can mobilize the network of practice, on LinkedIn and elsewhere, to ensure that IT professionals are aware of the types of skill and knowledge-preserving approaches to organizational system design that we would want to see used in our own organizations.


Brown, J.S. and Duguid, P. 2001. “Knowledge and Organization: A Social-Practice Perspective,” Organization Science (12:2), pp. 198-213.

Chou, S.Y. and Pearson, J.M. 2012. “Organizational Citizenship Behaviour in It Professionals: An Expectancy Theory Approach,” Management Research Review (35:12), pp. 1170-1186.

Lee, S., Yen, D., Havelka, D., and Koh, S. 2001. “Evolution of Is Professionals’ Competency: An Exploratory Study,” The Journal of Computer Information Systems (41:4), pp. 21-30.

Rosenbrock, H.H. 1981. “Engineers and the Work That People Do,” IEEE Control Systems Magazine (1:3), pp. 4-8.

Zuboff, S. 1988. In the Age of the Smart Machine. New York NY: Basic Books.

Responsive Web Design

I manage the website for an Animal Rescue shelter. I have been struggling with the design of the site for some time now, as I have some users who are still using IE6 under windows XP (on an SVGA screen), some who want to view the site on their mobile phones, and some who have really wide displays and think my two column design looks outdated (it does). While looking for a solution, I came across the concept of responsive web design. Because the reference I just provided is stuffed with code snippets (and I personally think it is obscure), I will point you instead to some really great examples that demonstrate how a website design can be responsive.

There is a neat concept at play in most of these designs, where a webpage layout is segmented into multi-device layout patterns, that simply “flow” differently, depending on the screen size that the user will display the site on. But screen size is not the only consideration – images have to be resized to scale with the device and the performance of the device must be considered (it is painful to load a large, graphics-intensive page on a slooow tablet!). I was also musing that – most relevantly to this course – site menus and navigation toolbar interfaces have to be designed so that they will work on any device or layout. Which is harder than you’d think, simply because of the layout conventions that we use on a typical web-page.

Off to experiment with scripts and pageflow layouts …

On Realizing The Relevance of Actor-Network Theory

A recent emphasis on sociomateriality appears to have entered the IS literature because of discussions by Orlikowski (2010) and the excellent empirical study of Volkoff et al. (2007). Now that people have been sensitized to the literature on material practice, actor-network theory is classified as “tired and uninformative” [1]. Which leads me to wonder just how many IS academics have actually read the actor-network theorists? Or pondered how this applies to technology design?

Long before people started discussing socio-material “assemblages,” Bruno Latour (1987)and John Law (1987) were discussing how technology developed by means of “heterogeneous networks” of material and human actants, the combination of which directs the trajectory of technology design and form. Latour (1999) suggests that he should recall the term “actor-network,” as this is too easily confused with the world-wide web. Yet actor-networking – in the sense of a web of connectivity, where heterogeneous interactions between diverse individuals, between virtually-mediated groups, and between individuals and material forms of embedded intentionality – is exactly what is going on in today’s organizations.

In addition, Michel Callon’s (1986) work on how the “problematization” of a situation in ways that aligns the interests of others leads to their enrolment in a network of support for a specific technological frame. Once support has been enrolled, such networks endow irreversibility, which makes changes to the accepted form of a technology solution incredibly difficult. So we have paradigms that are embedded in a specific design. Akrich coined the term “script” to define the performativity of technology and the term was adopted by the other leading actor-network theorists [2]. This thread of work articulates incredibly deeply the ways in which technology design directs its users (and maintainers) into a set of roles and worldviews that are difficult to escape. We must “de-script” technology to repurpose it to other networks and other applications – which is much more difficult than one would suppose, given the embedded social worlds that are carried across networks of practice with the use of common technologies (Akrich 1992).
So what does actor-network theory give us? It provides a conceptual and practical approach to understanding and modeling why design takes specific forms – and what needs to be “undone” for a design to be conceived differently than in the past [3]. It provides a rationale for understanding technology as a network actor in its own right, influencing behavior and constraining discovery. The assumptional frameworks for action embedded in – for example – a software book-pricing application will direct the evaluation of price alternatives in ways that reflects the model of decision-making adopted by the software’s author. This results in the type of stupid automaticity that recently saw an Amazon book priced at $23,698,655.93 (plus $3.99 shipping). The cause of this pricing glitch was traced back to an actor-network of two competing sellers, unknowingly connected via their use of the same automated pricing software [4].

Finally, I want to observe that a lot of the recent “materiality of practice” literature has identified new phenomena and new mechanisms of actor-networks. For example Knorr Cetina (1999) has sensitized us to how epistemology is embedded in socio-technical assemblages, Rheinberger (1997) has demonstrated how some technical objects are associated with emergence while others enforce standardization and Henderson (1999) demonstrates how the use of specific representations can conscript others around an organizational power-base. But I would argue that these effects can be understood by using Actor-Network Theory as one’s underpinning epistemology – and that exploring actor-network interactions continues to reveal ever newer mechanisms that are relevant to how we work today. I would strongly recommend Bruno Latour’s latest book, Reassembling The Social.

[1] I have to declare an interest here – this comment was contained in a review of one of my papers … 🙂
[2] As Latour (1992) argues: “Following Madeleine Akrich’s lead (Akrich 1992), we will speak only in terms of scripts or scenes or scenarios … played by human or nonhuman actants, which may be either figurative or nonfigurative.”
[3] One of my favorite papers on the topic of irreversibility in design is ‘How The Refrigerator Got Its Hum,’ by Ruth Cowan (1995). Another good read is the introduction to the same book by MacKenzie and Wajcman (1999).
[4] The amusing outcome is recounted by Michael Eisen, at

Akrich, M. 1992. The De-Scription Of Technical Objects. W.E. Bijker, J. Law, eds. Shaping Technology/Building Society: Studies In Sociotechnical Change. MIT Press, Cambridge, MA, 205-224.
Callon, M. 1986. “Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay.” J. Law, ed. Power, Action, and Belief: a New Sociology of Knowledge? Socioogical Review Monograph 32. Routledge and Kegan Paul, London, 196-233.
Cowan, R.S. 1995. “How the Refrigerator Got its Hum.” D. Mackenzie, J. Wajcman, eds. The Social Shaping of Technology. Open University Press, Buckingham UK, 281-300.
Henderson, K. 1999. On Line and on Paper: Visual Representations, Visual Culture,and Computer Graphics in Design Engineering. MIT Press, Harvard MA.
Knorr Cetina, K.D. 1999. Epistemic Cultures: How the Sciences Make Knowledge. Harvard Univ. Press, Cambridge, MA.
Latour, B. 1987. Science in Action. Harvard University Press, Cambridge MA.
Latour, B. 1992. “Where Are the Missing Masses? The Sociology of a Few Mundane Artifacts.” W.E. Bijker, J. Law, eds. Shaping Technology/Building Society: Studies In Sociotechnical Change. MIT Press, Cambridge MA.
Latour, B. 1999. “On Recalling ANT.” J. Law, J. Hassard, eds. Actor Network and After. Blackwell, Oxford, UK 15-25.
Law, J. 1987. “Technology and Heterogeneous Engineering – The Case Of Portugese Expansion.” W.E. Bijker, T.P. Hughes, T.J. Pinch, eds. The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology. MIT Press, Cambridge MA.
MacKenzie, D.A., J. Wajcman. 1999. Introductory Essay. D.A. Mackenzie, J. Wajcman, eds. The Social Shaping Of Technology, 2nd. ed. Open University Press, Milton Keynes UK, 3-27.
Orlikowski, W. 2010. “The sociomateriality of organisational life: considering technology in management research.” Cambridge Journal of Economics 34(1) 125-141.
Rheinberger, H.-J. 1997. Experimental Systems and Epistemic Things Toward a History of Epistemic Things: Synthesizing Proteins in the Test Tube. Stanford University Press, Stanford CA, 24-37.
Volkoff, O., D.M. Strong, M.B. Elmes. 2007. “Technological Embeddedness and Organizational Change.” Organization Science 18(5) 832-848.

Designing Social Media Platforms For Online Learning

Recently, I have been using a new social media platform to run one of my classes. The idea was, that as we are studying social informatics, we could study the effect of using social media on our own workflows first hand. I also thought that – in these days of daily Facebook and Twitter use – a social media site would add some relevance to the class. My thinking was that the “right-brain” expression that Daniel Pink  extolls as critical to motivation in the 21st Century – the design, narrative, synthesis, empathy, play and sensemaking skills – would be enabled by the use of social media (Pink, 2005). The site has a WIKI, blogs, discussion forums, and an interactive chat facility. I was proposing that we used Google+ hangout for short class discussions by video. For the first week, I set students the task to post to the WIKI, to post to their own blog, to locate some web readings, and to join Google+ if they had not already done so.

By Thursday (from a Monday start), almost all of the students had posted to the discussion forum. Several had asked me questions by email. But no-one had posted to the Blog or the WIKI. By Friday, two of the more technologically-literate students had made blog posts. But most of the activity was still on the discussion forums – and only three students had provided me with Google+ contact details. Then I started to question my own assumptions. All of the students had used Blackboard for their online course access, which revolves around an asynchronous discussion board. So they were used to interacting via an asynchronous forum. I had assumed that they would be excited to use more “social” media for class interactions or for sharing what they had discovered about the topic. But how did this fit into their idea of how they would behave in an online class? Very badly. Most students sign up for online courses because this provides them with choices about what to do, when. They have a low learning-curve for using a discussion forum. Anything else is hard work.

Clay Shirky talks about the cognitive surplus that is available from zillions of digitally-literate people with mundane jobs and untapped creativity. He argues that this expresses itself in the groundswell of free, open source software initiatives and in the crowdsourcing phenomenon (Shirky, 2010). But graduate students with a full-time job are already using their cognitive surplus in grappling with new areas of learning. My assumption that they may have some left over for experimenting with social media may be false. The problem is that the learning curve gets in the way of the “right-brain” expression that I wanted to encourage. I may need to rethink how far experimenting with social media is constraining people’s’ ability to express themselves.

Daniel Pink  (2005) A Whole New Mind: Why Right-Brainers Will Rule the Future. Berkely Publishing: New York.
Pink (2005) Revenge Of The Right Brain, Wired Magazine, Feb. 2005.
Clay Shirky (2010) Cognitive Surplus: Creativity and Generosity in a Connected Age, Penguin Press: New York.
Clay SHirky (2010) An Extract From Cognitive Surplus. Wired Magazine, Business Video, June 16, 2010.
Clay Shirky and Daniel Pink  (2010) Cognitive Surplus: The Great Spare-Time Revolution. Wired Magazine, June 2010.

Organizational Forms Of Coordination

I have been working for a while on comparing the results from some very complex research studies of collaborative design in groups that span disciplines or knowledge domains. I was stunned to realize that I had different types of group activity depending on the sort of organization.

By “organization,” I mean the way in which work is organized, not the sort of business they are in. I noted three types or organization, that seem to respond to collaboration in different ways:

  • Tightly-coupled work organizations rely on well-defined work roles and responsibilities to coordinate tasks across group members. When people in this sort of group have to make decisions, they partition these decisions, based on expertise. Because they all know each others’ capabilities and roles, they don’t have to think about who-knows-what: this is just obvious. This type of organization falls down when people don’t perform their role reliably. For example, if the whole system relies on accurate information coming into the group, someone who misinterprets what they observed can undermine the whole group system.
  • Event-driven organizations rely on external crises and pressures to coordinate group action. People in this sort of group have strongly-defined roles in the wider organization that take precedence over their role in the group — for example in management taskforce groups, business managers tend to prioritize their other work over problems that the group needs to fix. When people in this sort of group make decisions, they partition these decisions according to who-claims-to-know-what, who has time to do the work, and who knows people connected to the problem. They get to know each others’ capabilities over time, but this is a slow process as priorities and decisions are driven by external events, rather than a shared perception of what needs to be done. This type of organization falls down when decisions or actions that were put on a back burner because of another crisis inevitably become a crisis themselves because they were not followed through.
  • Loosely-coupled organizations rely on ad hoc work roles and cooperation among group members. This type of group is commonest in business process change groups, professional work-groups, and community groups, where people are there because they share an interest in the outcome.  When people in this sort of group make decisions, they partition these decisions according to who can leverage external connections to find things out and who has an interest in exploring what is involved. People often share responsibilities in these groups, comparing notes to learn about the situation. This type of organization falls down because it is hard to coordinate. So shared tasks are performed badly because someone knew something vital that they failed to communicate back to the group.
Wild Horses
Managing group collaboration can be like taming wild horses

Why would we care about these different types of organization? Well these structures affect how we approach problem-solving and design. If we (process and IS analysts) need to work with one of the tightly-coupled work-groups, we need to identify who has the decision-making capability for what. It would not occur to a tightly-coupled group member that anyone would not realize who to go to for what. If we need to work with an event-driven group, we have to realize that our work will not be a priority for them — it must be made a priority by gaining an influential sponsor who can kick a$$ within the group(!).  If we work with a loosely-coupled group, we need to engage the interest of the group as a whole. Working with individuals can lead to failure, as this type of group makes decisions collaboratively, not on the basis of knowledge or expertise.

I have a fair amount of evidence for this line of thought and I am pursuing other factors that make these groups different. More to follow …

The Potential of Interaction Design

While browsing and working on a recent paper, I mused on the missed opportunity of interaction design. Reading Terry Winograd’s (1997) From Computing Machinery to Interaction Design, I was stunned to see how visionary this was, in the context of contemporary HCI thinking which focused on interactions with computer screen interfaces (still, sadly, the main focus of much HCI work).  Winograd saw computing as a “social and commercial enterprise” and saw the role of interaction design as situating technology within social and commercial processes. This thinking is related to Suchman’s (1987) Plans and Situated Actions: The Problem of Human-machine Communication, which saw human-computer-interaction as part of a stream of activity, located in the rationale of a wider sequence of tasks. While HCI theorists were fixated on task-analysis and screen-interface design, Suchman argued that we should see tasks as related to what had gone before and what was to follow.  Winograd argued that we should design technical artifacts to be useful in the larger context of social networks and the complexities of interactive spaces.

I was reminded of this when reading a discussion of Don Norman’s (2005) Human-Centered Design Considered Harmful. In this essay, Norman argues that HCI designers focus on “human-centered design,” which he relates to support for tasks and artifact-interactions, when they should focus on “activity-centered design,”  related to the larger context of what people do. While I agree wholeheartedly with the sentiment (and applaud the fact that the idea will at last get an audience if Don Norman has taken it up), the concept of activity-centered design still misses the point that we need to understand how actors perceive their stream-of-reality, situated within both a social and a cognitive-processual context, for interaction design to fulfill its potential.

In my 2003 paper, Human-Centered vs. User-Centered Approaches To Information System Design, I argued that human-centered design is not the same as user-centered design. User-centered design sees the human-being as a consumer of technology, whose reality is – somehow, magically – represented by the set of functions accessed via the computer artifact. This tends to be the focus of “traditional” HCI research. Human-centered design, on the other hand, sees the human-being as an autonomous individual, who may want to perform tasks in a different way, or a different order, to other computer “users.” They see the logic of what they do – and therefore the manner of its execution – as part of a socially-situated stream of activity that is meaningful to their understanding of work-processes and not some engineer’s idea of “best practice.” This means that design methods need to deal explicitly with problem inquiry, rather than just focusing on problem closure.

In a new paper (hopefully to be accepted soon!), I have argued that situated interaction-design needs an analysis of two dimensions of the work that people do:

  • the formal vs. informal translations that need to take place, to locate work practice in both the social (unstructured-interaction)  and organizational (structured-interaction) worlds, and
  • the global vs. local translations that need to take place to locate work practice in both the situated and generically subjective worlds.

Most of our design methods focus only on one quartile of this reality: the formal, structured world of data-processing. To really support interaction design, both education and practice need to take on a much wider scope.

Thank you, NSF!

I just filed the final project report for my Career Award yesterday, so I’d like to give my personal thanks to the good folks of the Human-Centered Computing group at the Computing, Information Systems & Engineering (CISE) Directorate of the National Science Foundation. The materials in my book and my ongoing research agenda are possible thanks to their support under Grant No. IIS-0347595. (Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.) Many thanks, NSF!