Organizations are tenuous phenomena; they can fall apart at any time. To navigate the landscape of organizational culture interaction designers need a set of practical tools, language & knowledge drawn from the world of cultural anthropology.

It’s happened to all of us. We walk into what we think is a Web redesign project, only to find we have unwittingly ignited the fires of WW III in our client’s organization. What begins as a simple design project descends – quickly – into an intra-organizational battle, with the unprepared interaction designer caught in the crossfire.

What is it about design projects that seem to attract such power struggles? Contrary to what you might think, being stuck in the middle of an internecine battle is actually an opportunity to effect meaningful change on your client’s organization. But it requires a set of practical tools to negotiate these battles and a more sophisticated language and knowledge to exploit these events to create meaningful change.

The Glue Inside Organizations: Taken-for-Granted Assumptions

Organizations are tenuous phenomena; they can fall apart at any time. It’s quite extraordinary, actually, that organizations don’t spontaneously disintegrate regularly. Organizations have no force of law or violence. They cannot force their members to remain within them. Their members are autonomous adults and can leave, reject, or even revolutionize the organization at any time. Yet organizations endure, held together by taken-for-granted patterns of social interaction.

These patterns of interaction are exactly what the organization must have to survive. Everyone must share what sociologists Berger and Luckman call a “common stock of knowledge” – like using “bandwidth” synonymously with “time” – for an organization to function properly. We could not waste time at every meeting explaining what “bandwidth” or “DTC” or “short selling” mean. Instead we rely on this common stock of knowledge; newcomers must be inculcated with this knowledge for the organization to function.

This common stock of knowledge is exactly what keeps an organization together. The sum of this knowledge is often called “organizational culture.” IBM, for example, would not have the same stock of knowledge as, say, Zappos. What is taken for granted at Patagonia is not the same as what is taken for granted at Ford. Organizational culture is key to keeping an organization intact. Without this sum of knowledge, members of the organization would spend too much time trying to figure out what to do or say in given situations.

An organization simply could not function without its members knowing what to do, say and wear day-to-day. Interestingly, organizations that send out the “dress code memo” are actually reinforcing this argument. If its members are routinely wearing shorts and flip-flops to work, this suggests that the “common stock of knowledge” supports this kind of clothing. The “dress code memo” usually causes tension, derision, and – paradoxically – greater social glue among the casually dressed members of the organization.

Knowing what to do or say in any given moment gives us comfort. It is less stressful. It is efficient. It is even pleasant. Organizations must have a critical mass of its members in this comfort zone to continue to function. In other words, organizational culture must be firmly cemented with a large enough number of its members for an organization to endure.

The downside of culture: change is hard

Culture is, by definition, resistant to change. We like to know what is “appropriate” or normal in everyday situations and the more we engage in these behaviours, the more “normal” they become. The French father of sociology Emile Durkheim even had a word for not knowing what to do: “anomie,” or the lack of norms. Modern life was plagued with anomie, Durkheim argued, causing alienation and confusion.

Organizational cultures like to stay the same, even when it seems irrational. In her painstakingly detailed analysis of the Challenger crash, Diane Vaughan showed that NASA’s major failure was not technical at all. Rather, it was systematic denial which lead to a distortion of the real risks involved in using rubber O-rings on a cold morning. NASA’s organizational culture systematically downplayed certain kinds of information because it threatened the way the organization currently functioned. In a sense, it was the organization’s attempt to endure which paradoxically sowed the seeds of its own demise.

Understanding organizational cultures

Interaction designers are accustomed to discerning individual preferences, particularly for interactivity. But they may not be as well versed in understanding cultural preferences. Anthropology offers us a very clear framework for mapping cultures against key values.

Florence Kluckhohn’s “value orientation model” describes culture as a set of values. To that end, she suggested that cultures have five key elements to them.

Figure 1: F.R. Kluckhohn’s Value Orientation Model

A culture’s value orientation to nature, for example, can be one which values being in harmony with nature. Japan’s penchant for miniaturist design, for example, may be explained by its value of living in harmony with nature.

An organization’s orientation to time may hold the key to understanding why your design project is mired in the mud. How the organization collectively values the future has significant implications for interaction designers, which after all are the harbingers of a thing called “change.” Toyota, for example, exemplifies an organization that values “becoming” or ever aspiring. Such an organization values “new-ness” as inherently worthy.  The French wine-making industry, by contrast, values the past (to a fault, if recent market-share figures are an indicator of success). Reverence for tradition, celebrating its members’ long tenures, and elaborate discussions of past examples are key indicators of a past-oriented organization.

Interaction designers should pay close attention to how their client’s organization values activity.

An organization’s orientation to social relations is also important for interaction designers. The typical interactive agency (or management consultancy for that matter) values individualism. Sociologist Alice Lam calls this kind of organization an “ad hocracy,” which is highly innovative and creative. But such organizations are also very vulnerable to its knowledge walking out the door when the latest “rising star” takes her talent and knowledge with her to the next organization.

By contrast, Lam argues, banks are collective in their orientation. They value consensus above all. This may make decision-making maddeningly slow but it also preserves the organization. It ensures that all members of the organization share in the common stock of knowledge and thereby maintains the organization itself. Interactive agencies may be highly innovative, but they are also highly volatile. Banks may not be innovative but they are stable.

Interaction designers are accustomed to discerning individual preferences, particularly for interactivity. But they may not be as well versed in understanding cultural preferences.

Interaction designers should also pay close attention to how their client’s organization values activity. Some organizations care nothing for what may become of a certain project, if only that something is happening. This kind of organization values “doing.” But perhaps more common to Western organizations is the implicit value on “becoming.” German sociologist Max Weber became famous for his analysis of “the protestant work ethic,” which is really an exposition on the Calvinist value of “becoming.” To be a competent Protestant, Weber argued, one must always be aspiring to work harder, accumulate more and above all, delay gratification. This spirit is so key to capitalism that Western, capitalist organizations may all implicitly value this notion. In such cases, interaction designers may find themselves in the midst of an ever-aspiring organization that wants “best in class” wireframes and project plans but seems strangely unconcerned with a relative lack of actual progress.

Interaction designers finding themselves in the middle or an intra-organizational war may be working with an individualistic organization that values competition above all else. This is common to sales-driven organizations, that offer individual incentives and bonuses to their members.

Organization Design: How To Incorporate It Into Interaction Design Projects

Before you start a design project, evaluate your client’s organization. This need not be a detailed research project but an intuitive ethnographic craft. Stakeholder interviews provide a great opportunity to gather insight about the values of your client’s organization. A colleague and I used to call this stage “business ethnographic gathering,” or BEGging. Do ask about business requirements and goals. But pay attention also to what is valued.

Past-orientation

Does your client spend an inordinate amount of time talking about the ways things have been done in the past? This may reveal a past-orientation. Honour that client’s value of the past. Celebrate past successes. Build “rituals of reverence” into your design process, where the organization’s members can savour the beauty and grandeur of their organization’s venerated history. Carefully frame innovation as putting icing on an already delicious cake. Above all, do not attack foundational elements directly. This is akin to throwing away the Mona Lisa.

Individualistic

Does your client take you into “confidence” frequently and reveal that everyone else in his organization “doesn’t get it”? Does he discuss how he would like to best his rival department? This client’s organization is individualistic and competitive. Consensus is still needed for collaborative design but this kind of organization makes collaboration difficult. For such a client, frame collaboration as an opportunity for him to be a “champion of innovation,” which all the individual glory thereto. Encourage him to be a “hero” for the organization and construct a narrative that allows him to be “victorious” but only if he becomes a collaborative, transformative leader.

Becoming

Does your client’s organization have many symbols of future plans? Perhaps the walls are plastered with posters of its “next generation” goods or services. Perhaps your client spends much time talking about her future “vision” and how she wants to “inspire” her colleagues and her customers. The value here is placed on “becoming,” and not necessarily on “doing.” Your challenge as an interaction designer is actualizing her vision. The idea with this kind of culture is to create some material reality out of the constant visioning, but at the same time allowing the client organization to engage in this imagination process continually. Allow them to savour future “dreams” in concrete form.

In this case, frame your need for concrete deliverables and milestones as “best in class project methodology,” or an “innovative service delivery process.” Convince her that being a “next-generation organization” is about constantly materializing her visions. Show her prototypes. Bring her evidence of the first-mover’s advantage. And above all, show her current, existing evidence of how innovative companies continue to produce innovations instead of simply thinking about it.

When Culture Isn’t Enough

Even sociologists tire of organizations that are continually dysfunctional. As an interaction designer, you can learn to recognize when these organizations are beyond the power of one designer. If the organization has a critical mass of values that are antithetical to your own, then it may be time to move on. Even better, however, is to use the value-orientation model to pre-screen potential clients. We may not all have the luxury of picking and choosing only the clients we want but using a systematic approach beforehand will surely reduce the number of wars we unwittingly ignite within our client organizations.

Deconstruction is one of the most frequently used and fundamental analysis techniques in our toolkit. It is used as both a preparatory technique to get research data ready for use in other ways; and a powerful technique in its own right as a method of isolating, exposing, and testing assumptions deeply embedded in our mental models.

One example of deconstruction is turning an interview transcript into a series of separate comments or answers to questions. Deconstruction is often used simply to prepare data for other analytic processes such as manipulation or summarization, or even abstraction.

Note: this article builds on the first part of the series: Deconstructing Analysis Techniques published in February.

Examples of Deconstruction

• Chemical analysis – mass spectrometry: a technique for determining the elemental makeup of a substance or molecule.
• Philosophy/literary criticism: a technique of isolating and testing ideas contained within a work of philosophy or literature
• Systems analysis: identifying root causes through the identification of individual system ‘actors’ and their interactions
• Quality control: unit testing functional components of an application, requires first identifying those components (typically by recourse to the specification)
• User interviews: identifying individual concepts or ideas
• Card-sorting: working with card-pairs
• Task analysis: breaking down complex activities into individual tasks and their components.

There are a wide range of examples of the way in which deconstruction occurs, but our aim is always to reach a definite ‘atomic’ state (where the atom is defined by our research objectives). It should be noted that there are typically more things going on than merely breaking down the data. In the case of chemical analysis, level of elements or compounds are measured; in the case of a stakeholder or user interview, the individual words, phrases or ideas may be tallied, grouped, manipulated or otherwise worked with to form some new insight.

Deconstruction can – and often is – built into the design of the research. We see this in online card-sorting, for example, where data is stored from the outset as card-pairs. Survey results are another example of data where pre-deconstruction is built into the research.

Why, though, this urge to break data down into smaller and smaller pieces?

Smaller, more granular data provides for greater flexibility in the other analysis techniques we need to undertake. By separating ideas or objects out into their own data elements we can have greater control over how elements are treated and positioned with respect to other elements.

For example, splitting a Name element into separate First Name and Surname elements allows us to treat these two components independently, and ask a broader range of questions – such as: “What are the most common first names?”

Smaller, more granular data provides for greater flexibility in the other analysis techniques we need to undertake.

It requires extra effort to break data down and then to store it in more granular form. It also takes effort to request and record extra data during the research process itself. So, whatever level of data granularity we use should be for specific reasons, and to address specific research questions.

Deconstruction represents a powerful analytic technique in its own right. By isolating concepts and ideas, and exposing them to scrutiny on their own, deconstruction highlights the existence of untested assumptions and ‘sacred cows’.

In this sense, deconstruction is often used to analyze problems or situations to which we need to formulate a response. This use of deconstruction allows us to test the reality of perceived constraints: by isolating each constraint to the design, and looking at the conditions under which they may hold true, new possibilities can open up that may otherwise not have been possible or feasible.

Dangers in Deconstruction

There are dangers in deconstruction that are worth mentioning here. At the end of the day our work should lead to something substantively new. This can be difficult if we lose sight of the macro-level problem in pursuit of an understanding of the data in finer and finer detail.

Secondly, in studying the fine detail of our data we can miss seeing the patterns in our data that help drive insights and accelerate the transition to design concepts. At the same time, some patterns only become visible or apparent when we reach a level of granularity appropriate for the data.

Deconstruction can also generate noise in our data which obscures our sense-making abilities. This noise may be the result of data overload – simply having too much information to allow for processing; or it may be that small-scale, natural random variations are masking higher-level trends or patterns. In these cases, the use of summation and aggregation techniques might be an appropriate contrast to the deconstruction technique.

Deconstruction in practice

Deconstruction can often be used in very close association with other analytic techniques. For example, we may break data down into more granular form to facilitate manipulation of that data as a means of inspection or ‘eye-balling’.

In the same vein, we may manipulate or transform our data to allow us to zero in on a particular characteristic – deconstruction in the critique sense of the term.

It may help at this point to look at some examples to help illustrate the different uses of deconstruction as an analytic technique:

User Interviews
A typical interview scenario involves asking participants a series of questions (usually open-ended; sometimes based around topics rather than using a strict question set) and recording the responses. Recording may be through the use of written notes, audio recording, video taping, interviewer/observer notes; and may include a combination of the above.

To begin drawing connections and identifying themes between interviews we need to break down – or deconstruct – the interviews to the level of individual ideas or concepts, feelings, thoughts etc. The medium we use to record each of these ‘objects’ is not important: a spreadsheet might be used just as effectively as Post-It notes or index cards.

Once the data is in this more granular form we can carry out further analysis on the interviews. We may, for example, want to look at the prevalence of positive versus negative feedback.

Note, however, that the need for deconstruction is entirely dependent on the questions we are trying to answer through our research. For example, if our intent was to formulate an impression of the overall level of satisfaction for each interview subject, the deconstruction would be an entirely unnecessary task.

Diagnosing Causes
When faced with a failure in a complex system – such as the inability of users to complete a multi-step process, or the appearance of a previously unplanned-for edge case – it is typically quite difficult to diagnose the cause of the failure (in the absence of error handling designed specifically with this in mind). In order to identify the root cause of the failure we undertake a deconstruction exercise to help isolate the components of the systems.

Designing a Car: Highlighting untested assumptions
If we were to begin designing a car we might begin with a brain-storming session and list out all of the components or features that are needed. That list might include items such as “wheels”, “engine”, “fuel”, “doors”, “seats” and a whole range of others. We can now look at each of these features and ask why it’s there, and what it says about our notion of the solution.

For example, ‘fuel’ presupposes a form of combustion engine which, increasingly, may not be relevant. More importantly, ‘fuel’ highlights a range of assumptions – mostly tacit – derived from our mental model of the object ‘car’.

Once these assumptions are exposed we can begin to question their validity in the context of the problem – instead of pre-defining a solution in the statement of the problem. Such questioning, enabled through deconstruction, opens up a broader perspective on the design of a solution.

Conclusion

Deconstruction serves a dual role in our analysis work: as both a preparatory technique to get research data ready for use in other ways; and a method of isolating, exposing, and testing assumptions deeply embedded in our mental models.

The technique is not without its drawbacks: more granular data requires effort to gather and record, store, and analyze. It can also generate ‘noise’ in the data, which can obscure instead of illuminate.

Understanding the role of deconstruction in analysis can help us to better target it’s application to the solution of specific research questions.

Photos by s1mone (card sorting), andercismo (magnifying glass), smiling da vinci (interview)