Category: research

evaluationsocial systems

Baby, It’s Cold Outside (and Other Evaluation Lessons)

Competing desires or imposing demands?

The recent decision by many radio stations to remove the song “Baby, It’s Cold Outside” from their rotation this holiday season provides lessons on culture, time, perspective, and ethics beyond the musical score for those interested in evaluation. The implications of these lessons extend far beyond any wintery musical playlist. 

As the holiday season approaches, the airwaves, content streams, and in-store music playlists get filled with their annual turn toward songs of Christmas, the New Year, Hanukkah, and the romance of cozy nights inside and snowfall. One of those songs has recently been given the ‘bah humbug’ treatment and voluntarily removed from playlists, initiating a fresh round of debates (which have been around for years) about the song and its place within pop culture art. The song, “Baby, It’s Cold Outside” was written in 1944 and has been performed and recorded by dozens of duets ever since. 

It’s not hard for anyone sensitive to gender relations to find some problematic issues with the song and the defense of it on the surface, but it’s once we get beneath that surface that the arguments become more interesting and complicated. 

One Song, Many Meanings

One of these arguments has come from jazz vocalist Sophie Millman, whose take on the song on the CBC morning radio show Metro Morning was that the lyrics are actually about competing desires within the times, not a work about predatory advances.

Others, like feminist author Cammila Collar, have gone so far to describe the opposition to the song as ‘slut shaming‘. 

Despite those points (and acknowledging some of them), others suggest that the manipulative nature of the dialogue attributed to the male singer is a problem no matter what year the song was written. For some, the idea that this was just harmless banter overlooks the enormous power imbalance between genders then and now when men could impose demands on women with fewer implications. 

Lacking a certain Delorean to go back in time to fully understand the intent and context of the song when it was written and released, I came to appreciate that this is a great example of some of the many challenges that evaluators encounter in their work. Is “Baby, It’s Cold Outside” good or bad for us? Like with many situations evaluators encounter: it depends (and depends on what questions we ask). 

Take (and Use) the Fork

Yogi Berra famously suggested (or didn’t) that “when you come across a fork in the road, take it.” For evaluators, we often have to take the fork in our work and the case of this song provides us with a means to consider why.

A close read of the lyrics and a cursory knowledge of the social context of the 1940s suggests that the arguments put forth by Sophie Millman and Cammila Collar have some merit and at least warrant plausible consideration. This might just be a period piece highlighting playful, slightly romantic banter between a man and woman on a cold winter night. 

At the same time, what we can say with much more certainty is that the song agitates many people now. Lydia Liza and Josiah Lemanski revised the lyrics to create a modern, consensual take on the song, which has a feel that is far more in keeping with the times. This doesn’t negate the original intent and interpretation of the lyrics, rather it places the song in the current context (not a historical one) and that is important from an evaluative standpoint.

If the intent of the song is to delight and entertain then what once worked well now might not. In evaluation terms, we might say the original merit of the song may hold based on historical context, its worth has changed considerably within the current context.

We may, as Berra might have said, have to take the fork and accept two very different understandings within the same context. We can do this by asking some specific questions. 

Understanding Contexts

Evaluators typically ask of programs (at least) three questions: What is going on? What’s new? and What does it mean? In the case of Baby, It’s Cold Outside, we can see that the context has shifted over the years, meaning that no matter how benign the original intent, the potential for misinterpretation or re-visioning of the intent in light of current times is worth considering.

What is going on is that we are seeing a lot of discussion about the subject matter of a song and what it means in our modern society. This issue is an attractor for a bigger discussion of historical treatment, inequalities, and the language and lived experience of gender.

The fact that the song is still being re-recorded and re-imagined by artists illustrates the tension between a historical version and a modern interpretation. It hasn’t disappeared and it may be more known now than ever given the press it receives.

What’s new is that society is far more aware of the scope and implications of gender-based discrimination, violence, and misogyny in our world than before. It’s hard to look at many historical works of art or expression without referencing the current situation in the world. 

When we ask about what it means, that’s a different story. The myriad versions of the song are out there on records, CD’s, and through a variety of streaming sources. While it might not be included in a few major outlets, it is still available. It is also possible to be a feminist and challenge gender-based violence and discrimination and love or leave the song. 

The two perspectives may not be aligned explicitly, but they can be with a larger, higher-level purpose of seeking empowerment and respect for women. It is this context of tension that we can best understand where works like this live. 

This is the tension in which many evaluations live when dealing with human services and systems. There are many contexts and we can see competing visions and accept them both, yet still work to create a greater understanding of a program, service, or product. Like technology, evaluations aren’t good or bad, but nor are they neutral. 

Image credit MGM/YouTube via CBC.ca

Note: The writing article happened to coincide with the anniversary of the horrific murder of 14 women at L’Ecole Polytechnique de Montreal. It shows that, no matter how we interpret works of art, we all need to be concerned with misogyny and gender-based violence. It’s not going away.  

education & learningevaluation

Learning: The Innovators’ Guaranteed Outcome

Innovation involves bringing something new into the world and that often means a lot of uncertainty with respect to outcomes. Learning is the one outcome that any innovation initiative can promise if the right conditions are put into place. 

Innovation — the act of doing something new to produce value — in human systems is wrought with complications from the standpoint of evaluation given that the outcomes are not always certain, the processes aren’t standardized (or even set), and the relationship between the two are often in an ongoing state of flux. And yet, evaluation is of enormous importance to innovators looking to maximize benefit, minimize harm, and seek solutions that can potentially scale beyond their local implementation. 

Non-profits and social innovators are particularly vexed by evaluation because there is an often unfair expectation that their products, services, and programs make a substantial change to social issues such as poverty, hunger, employment, chronic disease, and the environment (to name a few). These are issues that are large, complex, and for which no actor has complete ownership or control over, yet require some form of action, individually and collectively. 

What is an organization to do or expect? What can they promise to funders, partners, and their stakeholders? Apart from what might be behavioural or organizational outcomes, the one outcome that an innovator can guarantee — if they manage themselves right — is learning

Learning as an Outcome

For learning to take place, there need to be a few things included in any innovation plan. The first is that there needs to be some form of data capture of the activities that are undertaken in the design of the innovation. This is often the first hurdle that many organizations face because designers are notoriously bad at showing their work. Innovators (designers) need to capture what they do and what they produce along the way. This might include false starts, stops, ‘failures’, and half-successes, which are all part of the innovation process. Documenting what happens between idea and creation is critical.

Secondly, there needs to be some mechanism to attribute activities and actions to indicators of progress. Change only can be detected in relation to something else so, in the process of innovation, we need to be able to compare events, processes, activities, and products at different stages. Some of the selection of these indicators might be arbitrary at first, but as time moves along it becomes easier to know whether things like a stop or start are really just ‘pauses’ or whether they really are pivots or changes in direction. 

Learning as organization

Andrew Taylor and Ben Liadsky from Taylor Newberry Consulting recently wrote a great piece on the American Evaluation Association’s AEA 365 blog outlining a simple approach to asking questions about learning outcomes. Writing about their experience working with non-profits and grantmakers, they comment on how evaluation and learning require creating a culture that supports the two in tandem:

Given that organizational culture is the soil into which evaluators hope to plant seeds, it may be important for us to develop a deeper understanding of how learning culture works and what can be done to cultivate it.

What Andrew and Ben speak of is the need to create the environment for which learning can occur at the start. Some of that is stirred by asking some critical questions as they point out in their article. These include identifying whether there are goals for learning in the organization and what kind of time and resources are invested to regularly gathering people together to talk about the work that is done. This is the third big part of evaluating for learning: create the culture for it to thrive. 

Creating Consciousness

It’s often said that learning is a natural as breathing, but if that were true much more would be gained from innovation than there is. Just like breathing, learning can take place passively and can be manipulated or controlled. In both cases, there is a need to create a consciousness around what ‘lessons’ abound. 

Evaluation serves to make the unconscious, conscious. By paying attention — being mindful — of what is taking place and linking that to innovation at the level of the organization (not just the individual) evaluation can be a powerful tool to aid the process of taking new ideas forward. While we cannot always guarantee that a new idea will transform a problem into a solution, we can ensure that we learn in our effort to make change happen. 

The benefit of learning is that it can scale. Many innovations can’t, but learning is something that can readily be added to, built on, and transforms the learner. In many ways, learning is the ultimate outcome. So next time you look to undertake an innovation, make sure to evaluate it and build in the kind of questions that help ensure that, no matter what the risks are, you can assure yourself a positive outcome. 

Image Credit: Rachel on Unsplash

education & learningevaluation

The Quality Conundrum in Evaluation

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One of the central pillars of evaluation is assessing the quality of something, often described as its merit. Along with worth (value) and significance (importance), assessing the merit of a program, product or service is one of the principal areas that evaluators focus their energy.

However, if you think that would be something that’s relatively simple to do, you would be wrong.

This was brought home clearly in a discussion I took part in as part of a session on quality and evaluation at the recent conference of the American Evaluation Association entitled: Who decides if it’s good? How? Balancing rigor, relevance, and power when measuring program quality. The conversation session was hosted by Madeline Brandt and Kim Leonard from the Oregon Community Foundation, who presented on some of their work in evaluating quality within the school system in that state.

In describing the context of their work in schools, I was struck by some of the situational variables that came into play such as high staff turnover (and a resulting shortage among those staff that remain) and the decision to operate some schools on a four-day workweek instead of five as a means of addressing shortfalls in funding. I’ve since learned that Oregon is not alone in adopting the 4-day school week; many states have begun experimenting with it to curb costs. The argument is, presumably, that schools can and must do more with less time.

This means that students are receiving up to one fifth less classroom time each week, yet expecting to perform at the same level as those with five days. What does that mean for quality? Like much of evaluation work, it all depends on the context.

Quality in context

The United States has a long history of standardized testing, which was instituted partly as a means of ensuring quality in education. The thinking was that, with such diversity in schools, school types, and populations there needed to be some means to compare the capabilities and achievement across these contexts. A standardized test was presumed to serve as a means of assessing these attributes by creating a benchmark (standard) to which student performance could be measured and compared.

While there is a certain logic to this, standardized testing has a series of flaws embedded in its core assumptions about how education works. For starters, it assumes a standard curriculum and model of instruction that is largely one-size-fits-all. Anyone who has been in a classroom knows this is simply not realistic or appropriate. Teachers may teach the same material, but the manner in which it is introduced and engaged with is meant to reflect the state of the classroom — it’s students, physical space, availability of materials, and place within the curriculum (among others).

If we put aside the ridiculous assumption that all students are alike in their ability and preparedness to learn each day for a minute and just focus on the classroom itself, we already see the problem with evaluating quality by looking back at the 4-day school week. Four-day weeks mean either that teachers are creating short-cuts in how they introduce subjects and are not teaching all of the material they have or they are teaching the same material in a compressed amount of time, giving students less opportunity to ask questions and engage with the content. This means the intervention (i.e., classroom instruction) is not consistent across settings and thus, how could one expect things like standardized tests to reflect a common attribute? What quality education means in this context is different than others.

And that’s just the variable of time. Consider the teachers themselves. If we have high staff turnover, it is likely an indicator that there are some fundamental problems with the job. It may be low pay, poor working conditions, unreasonable demands, insufficient support or recognition, or little opportunity for advancement to name a few. How motivated, supported, or prepared do you think these teachers are?

With all due respect to those teachers, they may be incompetent to facilitate high-quality education in this kind of classroom environment. By incompetent, I mean not being prepared to manage compressed schedules, lack of classroom resources, demands from standardized tests (and parents), high student-teacher ratios, individual student learning needs, plus fitting in the other social activities that teachers participate in around school such as clubs, sports, and the arts. Probably no teachers have the competency for that. Those teachers — at least the ones that don’t quit their job — do what they can with what they have.

Context in Quality

This situation then demands new thinking about what quality means in the context of teaching. Is a high-quality teaching performance one where teachers are better able to adapt, respond to the changes, and manage to simply get through the material without losing their students? It might be.

Exemplary teaching in the context of depleted or scarce resources (time, funding, materials, attention) might look far different than if conducted under conditions of plenty. The learning outcomes might also be considerably different, too. So the link between the quality of teaching and learning outcomes is highly dependent on many contextual variables that, if we fail to account for them, will misattribute causes and effects.

What does this mean for quality? Is it an objective standard or a negotiated, relative one? Can it be both?

This is the conundrum that we face when evaluating something like the education system and its outcomes. Are we ‘lowering the bar’ for our students and society by recognizing outstanding effort in the face of unreasonable constraints or showing quality can exist in even the most challenging of conditions? We risk accepting something that under many conditions is unacceptable with one definition and blaming others for outcomes they can’t possibly achieve with the other.

From the perspective of standardized tests, the entire system is flawed to the point where the measurement is designed to capture outcomes that schools aren’t equipped to generate (even if one assumes that standardized tests measure the ‘right’ things in the ‘right’ way, which is another argument for another day).

Speaking truth to power

This years’ AEA conference theme was speaking truth to power and this situation provides a strong illustration of that. While evaluators may not be able to resolve this conundrum, what they can do is illuminate the issue through their work. By drawing attention to the standards of quality, their application, and the conditions that are associated with their realization in practice, not just theory, evaluation can serve to point to areas where there are injustices, unreasonable demands, and areas for improvement.

Rather than assert blame or unfairly label something as good or bad, evaluation, when done with an eye to speaking truth to power, can play a role in fostering quality and promoting the kind of outcomes we desire, not just the ones we get. In this way, perhaps the real measure of quality is the degree to which our evaluations do this. That is a standard that, as a profession, we can live up to and that our clients — students, teachers, parents, and society — deserve.

Image credit:  Lex Sirikiat

evaluation

Meaning and metrics for innovation

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Metrics are at the heart of evaluation of impact and value in products and services although they are rarely straightforward. What makes a good metric requires some thinking about what the meaning of a metric is, first. 

I recently read a story on what makes a good metric from Chris Moran, Editor of Strategic Projects at The Guardian. Chris’s work is about building, engaging, and retaining audiences online so he spends a lot of time thinking about metrics and what they mean.

Chris – with support from many others — outlines the five characteristics of a good metric as being:

  1. Relevant
  2. Measurable
  3. Actionable
  4. Reliable
  5. Readable (less likely to be misunderstood)

(What I liked was that he also pointed to additional criteria that didn’t quite make the cut but, as he suggests, could).

This list was developed in the context of communications initiatives, which is exactly the point we need to consider: context matters when it comes to metrics. Context also is holistic, thus we need to consider these five (plus the others?) criteria as a whole if we’re to develop, deploy, and interpret data from these metrics.

As John Hagel puts it: we are moving from the industrial age where standardized metrics and scale dominated to the contextual age.

Sensemaking and metrics

Innovation is entirely context-dependent. A new iPhone might not mean much to someone who has had one but could be transformative to someone who’s never had that computing power in their hand. Home visits by a doctor or healer were once the only way people were treated for sickness (and is still the case in some parts of the world) and now home visits are novel and represent an innovation in many areas of Western healthcare.

Demographic characteristics are one area where sensemaking is critical when it comes to metrics and measures. Sensemaking is a process of literally making sense of something within a specific context. It’s used when there are no standard or obvious means to understand the meaning of something at the outset, rather meaning is made through investigation, reflection, and other data. It is a process that involves asking questions about value — and value is at the core of innovation.

For example, identity questions on race, sexual orientation, gender, and place of origin all require intense sensemaking before, during, and after use. Asking these questions gets us to consider: what value is it to know any of this?

How is a metric useful without an understanding of the value in which it is meant to reflect?

What we’ve seen from population research is that failure to ask these questions has left many at the margins without a voice — their experience isn’t captured in the data used to make policy decisions. We’ve seen the opposite when we do ask these questions — unwisely — such as strange claims made on associations, over-generalizations, and stereotypes formed from data that somehow ‘links’ certain characteristics to behaviours without critical thought: we create policies that exclude because we have data.

The lesson we learn from behavioural science is that, if you have enough data, you can pretty much connect anything to anything. Therefore, we need to be very careful about what we collect data on and what metrics we use.

The role of theory of change and theory of stage

One reason for these strange associations (or absence) is the lack of a theory of change to explain why any of these variables ought to play a role in explaining what happens. A good, proper theory of change provides a rationale for why something should lead to something else and what might come from it all. It is anchored in data, evidence, theory, and design (which ties it together).

Metrics are the means by which we can assess the fit of a theory of change. What often gets missed is that fit is also context-based by time. Some metrics have a better fit at different times during an innovation’s development.

For example, a particular metric might be more useful in later-stage research where there is an established base of knowledge (e.g., when an innovation is mature) versus when we are looking at the early formation of an idea. The proof-of-concept stage (i.e., ‘can this idea work?’) is very different than if something is in the ‘can this scale’? stage. To that end, metrics need to be fit with something akin to a theory of stage. This would help explain how an innovation might develop at the early stage versus later ones.

Metrics are useful. Blindly using metrics — or using the wrong ones — can be harmful in ways that might be unmeasurable without the proper thinking about what they do, what they represent, and which ones to use.

Choose wisely.

Photo by Miguel A. Amutio on Unsplash

evaluationinnovation

Understanding Value in Evaluation & Innovation

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Value is literally at the root of the word evaluation yet is scarcely mentioned in the conversation about innovation and evaluation. It’s time to consider what value really means for innovation and how evaluation provides answers.

Design can be thought of as the discipline — the theory, science, and practice — of innovation. Thus, understanding the value of design is partly about the understanding of valuation of innovation. At the root of evaluation is the concept of value. One of the most widely used definitions of evaluation (pdf) is that it is about merit, worth, and significance — with worth being a stand-in for value.

The connection between worth and value in design was discussed in a recent article by Jon Kolko from Modernist Studio. He starts from the premise that many designers conceive of value as the price people will pay for something and points to the dominant orthodoxy in SAAS applications  “where customers can choose between a Good, Better, and Best pricing model. The archetypical columns with checkboxes shows that as you increase spending, you “get more stuff.””

Kolko goes on to take a systems perspective of the issue, noting that much value that is created through design is not piecemeal, but aggregated into the experience of whole products and services and not easily divisible into component parts. Value as a factor of cost or price breaks down when we apply a lens to our communities, customers, and clients as mere commodities that can be bought and sold.

Kolko ends his article with this comment on design value:

Design value is a new idea, and we’re still learning what it means. It’s all of these things described here: it’s cost, features, functions, problem solving, and self-expression. Without a framework for creating value in the context of these parameters, we’re shooting in the dark. It’s time for a multi-faceted strategy of strategy: a way to understand value from a multitude of perspectives, and to offer products and services that support emotions, not just utility, across the value chain.

Talking value

It’s strange that the matter of value is so under-discussed in design given that creating value is one of its central tenets. What’s equally as perplexing is how little value is discussed as a process of creating things or in their final designed form. And since design is really the discipline of innovation, which is the intentional creation of value using something new, evaluation is an important concept in understanding design value.

One of the big questions professional designers wrestle with at the start of any engagement with a client is: “What are you hiring [your product, service, or experience] to do?”

What evaluators ask is: “Did your [product, service, or experience (PSE)] do what you hired it to do?”

“To what extent did your PSE do what you hired it to do?”

“Did your PSE operate as it was expected to?”

“What else did your PSE do that was unexpected?”

“What lessons can we learn from your PSE development that can inform other initiatives and build your capacity for innovation as an organization?”

In short, evaluation is about asking: “What value does your PSE provide and for whom and under what context?”

Value creation, redefined

Without asking the questions above how do we know value was created at all? Without evaluation, there is no means of being able to claim that value was generated with a PSE, whether expectations were met, and whether what was designed was implemented at all.

By asking the questions about value and how we know more about it, innovators are better positioned to design PSE’s that are value-generating for their users, customers, clients, and communities as well as their organizations, shareholders, funders, and leaders. This redefinition of value as an active concept gives the opportunity to see value in new places and not waste it.

Image Credit: Value Unused = Waste by Kevin Krejci adapted under Creative Commons 2.0 License via Flickr

Note: If you’re looking to hire evaluation to better your innovation capacity, contact us at Cense. That’s what we do.

complexityevaluationsocial innovation

Developmental Evaluation’s Traps

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Developmental evaluation holds promise for product and service designers looking to understand the process, outcomes, and strategies of innovation and link them to effects. It’s the great promise of DE that is also the reason to be most wary of it and beware the traps that are set for those unaware.  

Developmental evaluation (DE), when used to support innovation, is about weaving design with data and strategy. It’s about taking a systematic, structured approach to paying attention to what you’re doing, what is being produced (and how), and anchoring it to why you’re doing it by using monitoring and evaluation data. DE helps to identify potentially promising practices or products and guide the strategic decision-making process that comes with innovation. When embedded within a design process, DE provides evidence to support the innovation process from ideation through to business model execution and product delivery.

This evidence might include the kind of information that helps an organization know when to scale up effort, change direction (“pivot”), or abandon a strategy altogether.

Powerful stuff.

Except, it can also be a trap.

It’s a Trap!

Star Wars fans will recognize the phrase “It’s a Trap!” as one of special — and much parodied — significance. Much like the Rebel fleet’s jeopardized quest to destroy the Death Star in Return of the Jedi, embarking on a DE is no easy or simple task.

DE was developed by Michael Quinn Patton and others working in the social innovation sector in response to the needs of programs operating in areas of high volatility, uncertainty, complexity, and ambiguity in helping them function better within this environment through evaluation. This meant providing the kind of useful data that recognized the context, allowed for strategic decision making with rigorous evaluation and not using tools that are ill-suited for complexity to simply do the ‘wrong thing righter‘.

The following are some of ‘traps’ that I’ve seen organizations fall into when approaching DE. A parallel set of posts exploring the practicalities of these traps are going up on the Cense site along with tips and tools to use to avoid and navigate them.

A trap is something that is usually camouflaged and employs some type of lure to draw people into it. It is, by its nature, deceptive and intended to ensnare those that come into it. By knowing what the traps are and what to look for, you might just avoid falling into them.

A different approach, same resourcing

A major trap is going into a DE is thinking that it is just another type of evaluation and thus requires the same resources as one might put toward a standard evaluation. Wrong.

DE most often requires more resources to design and manage than a standard program evaluation for many reasons. One the most important is that DE is about evaluation + strategy + design (the emphasis is on the ‘+’s). In a DE budget, one needs to account for the fact that three activities that were normally treated separately are now coming together. It may not mean that the costs are necessarily more (they often are), but that the work required will span multiple budget lines.

This also means that operationally one cannot simply have an evaluator, a strategist, and a program designer work separately. There must be some collaboration and time spent interacting for DE to be useful. That requires coordination costs.

Another big issue is that DE data can be ‘fuzzy’ or ambiguous — even if collected with a strong design and method — because the innovation activity usually has to be contextualized. Further complicating things is that the DE datastream is bidirectional. DE data comes from the program products and process as well as the strategic decision-making and design choices. This mutually influencing process generates more data, but also requires sensemaking to sort through and understand what the data means in the context of its use.

The biggest resource that gets missed? Time. This means not giving enough time to have the conversations about the data to make sense of its meaning. Setting aside regular time at intervals appropriate to the problem context is a must and too often organizations don’t budget this in.

The second? Focus. While a DE approach can capture an enormous wealth of data about the process, outcomes, strategic choices, and design innovations there is a need to temper the amount collected. More is not always better. More can be a sign of a lack of focus and lead organizations to collect data for data’s sake, not for a strategic purpose. If you don’t have a strategic intent, more data isn’t going to help.

The pivot problem

The term pivot comes from the Lean Startup approach and is found in Agile and other product development systems that rely on short-burst, iterative cycles with accompanying feedback. A pivot is a change of direction based on feedback. Collect the data, see the results, and if the results don’t yield what you want, make a change and adapt. Sounds good, right?

It is, except when the results aren’t well-grounded in data. DE has given cover to organizations for making arbitrary decisions based on the idea of pivoting when they really haven’t executed well or given things enough time to determine if a change of direction is warranted. I once heard the explanation given by an educator about how his team was so good at pivoting their strategy for how they were training their clients and students. They were taking a developmental approach to the course (because it was on complexity and social innovation). Yet, I knew that the team — a group of highly skilled educators — hadn’t spent nearly enough time coordinating and planning the course.

There are times when a presenter is putting things last minute into a presentation to capitalize on something that emerged from the situation to add to the quality of the presentation and then there is someone who has not put the time and thought into what they are doing and rushing at the last minute. One is about a pivot to contribute to excellence, the other is not executing properly. The trap is confusing the two.

Fearing success

“If you can’t get over your fear of the stuff that’s working, then I think you need to give up and do something else” – Seth Godin

A truly successful innovation changes things — mindsets, workflows, systems, and outcomes. Innovation affects the things it touches in ways that might not be foreseen. It also means recognizing that things will have to change in order to accommodate the success of whatever innovation you develop. But change can be hard to adjust to even when it is what you wanted.

It’s a strange truth that many non-profits are designed to put themselves out of business. If there were no more political injustices or human rights violations around the world there would be no Amnesty International. The World Wildlife Fund or Greenpeace wouldn’t exist if the natural world were deemed safe and protected. Conversely, there are no prominent NGO’s developed to eradicate polio anymore because pretty much have….or did we?

Self-sabotage exists for many reasons including a discomfort with change (staying the same is easier than changing), preservation of status, and a variety of inter-personal, relational reasons as psychologist Ellen Hendrikson explains.

Seth Godin suggests you need to find something else if you’re afraid of success and that might work. I’d prefer that organizations do the kind of innovation therapy with themselves, engage in organizational mindfulness, and do the emotional, strategic, and reflective work to ensure they are prepared for success — as well as failure, which is a big part of the innovation journey.

DE is a strong tool for capturing success (in whatever form that takes) within the complexity of a situation and the trap is when the focus is on too many parts or ones that aren’t providing useful information. It’s not always possible to know this at the start, but there are things that can be done to hone things over time. As the saying goes: when everything is in focus, nothing is in focus.

Keeping the parking brake on

And you may win this war that’s coming
But would you tolerate the peace? – “This War” by Sting

You can’t drive far or well with your parking brake on. However, if innovation is meant to change the systems. You can’t keep the same thinking and structures in place and still expect to move forward. Developmental evaluation is not just for understanding your product or service, it’s also meant to inform the ways in which that entire process influences your organization. They are symbiotic: one affects the other.

Just as we might fear success, we may also not prepare (or tolerate) it when it comes. Success with one goal means having to set new goals. It changes the goal posts. It also means that one needs to reframe what success means going ahead. Sports teams face this problem in reframing their mission after winning a championship. The same thing is true for organizations.

This is why building a culture of innovation is so important with DE embedded within that culture. Innovation can’t be considered a ‘one-off’, rather it needs to be part of the fabric of the organization. If you set yourself up for change, real change, as a developmental organization, you’re more likely to be ready for the peace after the war is over as the lyric above asks.

Sealing the trap door

Learning — which is at the heart of DE — fails in bad systems. Preventing the traps discussed above requires building a developmental mindset within an organization along with doing a DE. Without the mindset, its unlikely anyone will avoid falling through the traps described above. Change your mind, and you can change the world.

It’s a reminder of the needs to put in the work to make change real and that DE is not just plug-and-play. To quote Martin Luther King Jr:

“Change does not roll in on the wheels of inevitability, but comes through continuous struggle. And so we must straighten our backs and work for our freedom. A man can’t ride you unless your back is bent.”

 

For more on how Developmental Evaluation can help you to innovate, contact Cense Ltd and let them show you what’s possible.  

Image credit: Author

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Elevating Design & Design Thinking

 

ElevateDesignLoft2.jpgDesign thinking has brought the language of design into popular discourse across different fields, but it’s failings threaten to undermine the benefits it brings if they aren’t addressed. In this third post in a series, we look at how Design (and Design Thinking) can elevate themselves above their failings and match the hype with real impact. 

In two previous posts, I called out ‘design thinkers’ to get the practice out of it’s ‘bullshit’ phase, characterized by high levels of enthusiastic banter, hype, and promotion and low evidence, evaluation or systematic practice.

Despite the criticism, it’s time for Design Thinking (and the field of Design more specifically) to be elevated beyond its current station. I’ve been critical of Design Thinking for years: its popularity has been helpful in some ways, problematic in others.  Others have, too. Bill Storage, writing in 2012 (now unavailable), said:

Design Thinking is hopelessly contaminated. There’s too much sleaze in the field. Let’s bury it and get back to basics like good design.

Bruce Nussbaum, who helped popularize Design Thinking in the early 2000’s called it a ‘failed experiment’, seeking to promote the concept of Creative Intelligence instead. While many have called for Design Thinking to die, it’s not going to happen anytime soon. Since first publishing a piece on Design Thinking’s problems five years ago the practice has only grown. Design Thinking is going to continue to grow, despite its failings and that’s why it matters that we pay attention to it — and seek to make it better.

Lack of quality control, standardization or documentation of methods, and evidence of impact are among the biggest problems facing Design Thinking if it is to achieve anything substantive beyond generating money for those promoting it.

Giving design away, better

It’s hard to imagine that the concepts of personality, psychosis, motivation, and performance measurement from psychology were once unknown to most people. Yet, before the 1980’s, much of the public’s understanding of psychology was confined to largely distorted beliefs about Freudian psychoanalysis, mental illness, and rat mazes. Psychology is now firmly ensconced in business, education, marketing, public policy, and many other professions and fields. Daniel Kahneman, a psychologist, won the Nobel Prize in Economics in 2002 for his work applying psychological and cognitive science to economic decision making.

The reason for this has much to do with George Miller who, as President of the American Psychological Association, used his position to advocate that professional psychology ‘give away’ its knowledge to ensure its benefits were more widespread. This included creating better means of communicating psychological concepts to non-psychologists and generating the kind of evidence that could show its benefits.

Design Thinking is at a stage where we are seeing similar broad adoption beyond professional design to these same fields of business, education, the military and beyond. While there has been much debate about whether design thinking as practiced by non-designers (like MBA’s) is good for the field as a whole, there is little debate that its become popular just as psychology has.

What psychology did poorly is that it gave so much away that it failed to engage other disciplines enough to support quality adoption and promotion and, simultaneously, managed to weaken itself as newfound enthusiasts pursued training in these other disciplines. Now, some of the best psychological practice is done by social workers and the most relevant research comes from areas like organizational science and new ‘sub-disciplines’ like behavioural economics, for example.

Design Thinking is already being taught, promoted, and practiced by non-designers. What these non-designers often lack is the ‘crit’ and craft of design to elevate their designs. And what Design lacks is the evaluation, evidence, and transparency to elevate its work beyond itself.

So what next?

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Elevating Design

As Design moves beyond its traditional realms of products and structures to services and systems (enabled partly by Design Thinking’s popularity) the implications are enormous — as are the dangers. Poorly thought-through designs have the potential to exacerbate problems rather than solve them.

Charles Eames knew this. He argued that innovation (which is what design is all about) should be a last resort and that it is the quality of the connections (ideas, people, disciplines and more) we create that determine what we produce and their impact on the world. Eames and his wife Ray deserve credit for contributing to the elevation of the practice of design through their myriad creations and their steadfast documentation of their work. The Eames’ did not allow themselves to be confined by labels such as product designer, interior designer, or artist. They stretched their profession by applying craft, learning with others, and practicing what they preached in terms of interdisciplinarity.

It’s now time for another elevation moment. Designers can no longer be satisfied with client approval as the key criteria for success. Sustainability, social impact, and learning and adaptation through behaviour change are now criteria that many designers will need to embrace if they are to operate beyond the fields’ traditional domains (as we are now seeing more often). This requires that designers know how to evaluate and study their work. They need to communicate with their clients better on these issues and they must make what they do more transparent. In short: designers need to give away design (and not just through a weekend design thinking seminar).

Not every designer must get a Ph.D. in behavioural science, but they will need to know something about that domain if they are to work on matters of social and service design, for example. Designers don’t have to become professional evaluators, but they will need to know how to document and measure what they do and what impact it has on those touched by their designs. Understanding research — that includes a basic understanding of statistics, quantitative and qualitative methods — is another area that requires shoring up.

Designers don’t need to become researchers, but they must have research or evaluation literacy. Just as it is becoming increasingly unacceptable that program designers from fields like public policy and administration, public health, social services, and medicine lack understanding of design principles, so is it no longer feasible for designers to be ignorant of proper research methods.

It’s not impossible. Clinical psychologists went from being mostly practitioners to scientist-practitioners. Professional social workers are now well-versed in research even if they typically focus on policy and practice. Elevating the field of Design means accepting that being an effective professional requires certain skills and research and evaluation are now part of that skill set.

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Designing for elevated design

This doesn’t have to fall on designers to take up research — it can come from the very people who are attracted to Design Thinking. Psychologists, physicians, and organizational scientists (among others) all can provide the means to support designers in building their literacy in this area.

Adding research courses that go beyond ethnography and observation to give design students exposure to survey methods, secondary data analysis, ‘big data’, Grounded Theory approaches, and blended models for data collection are all options. Bring the behavioural and data scientists into the curriculum (and get designers into the curriculum training those professionals).

Create opportunities for designers to do research, publish, and present their research using the same ‘crit’ that they bring to their designs. Just as behavioural scientists expose themselves to peer review of their research, designers can do the same with their research. This is a golden opportunity for an exchange of ideas and skills between the design community and those in the program evaluation and research domains.

This last point is what the Design Loft initiative has sought to do. Now in its second year, the Design Loft is a training program aimed at exposing professional evaluators to design methods and tools. It’s not to train them as designers, but to increase their literacy and confidence in engaging with Design. The Design Loft can do the same thing with designers, training them in the methods and tools of evaluation. It’s but one example.

In an age where interdisciplinarity is spoken of frequently this provides the means to practically do it and in a way that offers a chance to elevate design much like the Eames’ did, Milton Glaser did, and how George Miller did for psychology. The time is now.

If you are interested in learning more about the Design Loft initiative, connect with Cense. If you’re a professional evaluator attending the 2017 American Evaluation Association conference in Washington, the Design Loft will be held on Friday, November 10th

Image Credit: Author