Increasing the use of bikes in urban centers can help reduce car trips – providing health benefits and reducing fossil fuel use

With this perspective in mind, those working to support practice change for sustainability can work to provide: i) an enabling environment for the adoption of desired practices, and ii) offer opportunities for people to be inspired to change – by their peers, by the idea of new possibilities, etc. Fortunately, experiences from decades of social marketing and research in fields such as social psychology do offer a range of techniques that can help sustainability advocates with the latter by supporting the development of more effective communication and social marketing campaigns. One selection of these lessons set out in a popular book, Influence: The psychology of persuasion, by Robert Cialdini puts forward six principles: liking, reciprocity, authority, commitment, social proof and scarcity.

While these principles will have to be integrated within any individual social marketing campaign on an individual basis, it is vital to approach the audience like the individuals they are. Different groups will respond better to different influence principles, so it is important to undertake some simple stakeholder analysis as a starting point. Thinking about how to use these six principles reminds us that while it is important to understand the technical content of the message, understanding the people you are communicating with is equally as important.

Liking
People tend to agree with people they like. They also like people who like them and who they see as being like them. This is why companies often take care to use sales agents from within the community they are selling to. We are more likely to listen to people like ourselves, from friends, and from others that we know and respect.

Taking time to build a rapport can also be useful in this regard. Cialdini suggests, however, that letting another party feel that they are genuinely liked by someone trying to persuade them is actually more important than them liking the persuader. The important work here is ‘genuine’! However, spending time learning about similarities can provide the means for this genuine liking to occur and, by taking the time to do this, it is possible to build constructive relationships.

Reciprocity
People like to give – and take. Helping someone means an individual is more likely to receive help in return, because there is a tacit obligation to repay the favor. Providing assistance in this way will increase the likelihood of people helping out in return. Helping in this way also provides a good basis for building on-going, trust-based relationships. Start the communication process by offering something. Examples include offering free information, a discounted sample, or even delivering a positive experience in a first visit.

Reciprocity also affects negotiation processes. Thus it is possible to increase the chance another party will be collaborative by being collaborative first. Being competitive and uncooperative during negotiations will most likely result in reciprocal behavior by the other party. Giving concessions and sharing information first will encourage the other party to do the same.

Authority
People like to follow legitimate experts. This means that it can be good to highlight a high level of expertise. However, it is equally important for a person to show that they are a trusted source of information, which can be difficult, particularly in situations where they are advocating for an unpopular decision already made. No amount of expertise and authority is going to persuade people that an unpopular decision that has been forced on them is a good idea. In public campaigns, however, it may be useful to point to experts that people have reason to trust.

In the end, this principle is less about authority than it is about building and maintaining trust. In fact, it is clear that if authority is used as a tool to coerce rather than inform or encourage, it can create resistance rather than cooperation. In some situations it is possible that trustworthiness can be demonstrated by admitting weaknesses – provided of course there is also evidence of strengths and expertise. In the right situations, admitting weaknesses can highlight a person’s credibility and show them to be honest and trustworthy. Remember – higher credibility, lower barriers!

Commitment and consistency
Most people want to look consistent through their words, beliefs, attitudes and deeds, because personal consistency is highly valued by society. People have a deep need to be seen as consistent. Moreover, being consistent offers a valuable shortcut through the complexity of modern existence. Instead of processing all information in similar situations, it is easier to recall earlier decision and respond consistently.

To use this effectively it is important to ask people to commit to something. Often this will just be something small such as a trial of a product or service. This means that the other person can lock in an initial commitment without having to feel trapped in a long-term relationship with a product or service that they are unfamiliar with.

Social proof
In the end, most people are happy to be followers. When it comes to decision making, or deciding what is important in a given situation or in times of uncertainty, people look to what people similar to them have done. Thus providing evidence of what other people have done and how they have benefited from that action can help persuade a doubtful audience.

In negotiations around sustainability issues, the situation can often be ambiguous and the issues being discussed can be very complex. In such situations, parties will look to the experiences of other similar groups and perhaps the views of unbiased experts to guide their decision In the book Cialdini and his co-authors outline a series of experiments with the cards we often see in hotel bathrooms attempting to persuade us to reuse the towels. The standard appeal on these cards is to our concern for the environment. The researchers experimented with changes to the wording on these cards and found that people tend to do what they perceive the majority of others do. For example, guests who were told that most other guests reused their towels, were up to almost 30 percent more likely to reuse their towels than those who saw the standard message.

Scarcity
People assign more value to opportunities when they are seen as scarce. The use of this principle can be seen in sales techniques that suggest an offer is limited either in number or in time. The scarcity principle works for two reasons: First, things difficult to attain are typically more valuable and then when something becomes less accessible, the freedom to have it may be lost. In a sustainability context, this principle means that saying what benefits stand to be lost might be more important than saying what stands to be gained. This holds as true for face-to-face negotiations with individual groups or industry sectors, as it does for developing a television-based awareness campaign.

Using frameworks for influence
Hopefully, this overview of Cialdini’s influence principles provides some ideas that you can apply to your own communication and social marketing campaigns. They don’t just apply to product and service sales and uptake either, but also to your efforts in creating engagement and building relationships. Its important to use these principles with integrity – to use them honestly, with a sense of public good, and with good intentions. In the end the key to these principles is that using them wisely can build your reputation, but using them for selfish reasons can just as easily adversely affect your reputation.

Remember that these are just six ways that you can influence others. More information on these principles and other frameworks can be found from the Learning for Sustainability resource pages on Social Marketing and Communicating for change.

Birds flocking provide a good example of a complex adaptive system

A major breakthrough in understanding the complex world of organizations and socio-ecological environments is the field of systems theory.  ‘Systems thinking’ is a way of helping people to see the overall structures, patterns and cycles in systems, rather than seeing only specific events or elements. It allows the identification of solutions that simultaneously address different problem areas and leverage improvement throughout the system. It is useful, however, to distinguish between ‘simple’, ‘complicated’ and ‘complex adaptive’ systems.

According to a classic report in healthcare by Sholom Glouberman and Brenda Zimmerman systems can be understood as being simple, complicated, and complex. Simple problems, such as following a recipe or protocol, may encompass some basic issues of technique and terminology, but once these are mastered, following the “recipe” carries with it a very high assurance of success. Complicated problems, like sending a rocket to the moon, are different.  Their complicated nature is often related not only to the scale of a problem (cf. simple systems), but also to issues of coordination or specialised expertise. However, rockets are similar to each other and because of this following one success there can be a relatively high degree of certainty of outcome repetition. In contrast complex systems are based on relationships, and their properties of self-organisation, interconnectedness and evolution. Research into complex systems demonstrates that they cannot be understood solely by simple or complicated approaches to evidence, policy, planning and management. The metaphor that Glouberman and Zimmerman use for complex systems is like raising a child. Formulae have limited application.  Raising one child provides experience but no assurance of success with the next. Expertise can contribute but is neither necessary nor sufficient to assure success.  Every child is unique and must be understood as an individual. A number of interventions can be expected to fail as a matter of course.  Uncertainty of the outcome remains. You cannot separate the parts from the whole. The most useful solutions to problems usually emerge from within the family and involve values. An outline of the management differences between complicated and complex systems can be seen below in  Table 1.

Table 1 Managing complicated and complex systems

Complicated systems are all fully predictable. These systems are often engineered. We can understand these systems by taking them apart and analyzing the details. From a management point of view we can create these systems by first designing the parts, and then putting them together. However, we cannot build a complex adaptive system (CAS) from scratch and expect it to turn out exactly in the way that we intended. CAS are made up of multiple interconnected elements, and adaptive in that they have the capacity to change and learn from experience. Examples of CAS include ourselves (human beings), the stock market, ecosystems, immune systems, and any human social-group-based endeavor  in a cultural and social system. CAS defy attempts to be created in an engineering effort, and the components in the system co-evolve through their relationships with other components. But we can achieve some understanding by studying how the whole system operates, and we can influence the system by implementing a range of well-thought-out and constructive interventions.

Getting people to work collectively in a coordinated fashion in areas such as poverty alleviation or catchment management is therefore better seen by agencies as a complex problem, rather than a complicated problem – a fact many managers are happy to acknowledge …. but somehow this acknowledgement often does not translate  into different management and leadership practice. Of course, many issues will have all system types present (simple, complicated and complex), and there may well be multiple systems involved. What is important is distinguishing between system types, and managing each in the appropriate way.

Indicators of progress in managing a complicated system are directly linked through cause and effect. However, indicators  of progress in a complex system are better seen as providing a focus around which different stakeholders can come together and discuss, with a view to potentially changing their practices to improve the way the wider system is trending. Understanding this difference has important implications for management action as Table 2 below highlights. In many cases people continue to refer to the system they are trying to influence as if it were complicated rather than complex, perhaps because this is a familiar approach, and there is a sense of security in having a blueprint, and fixed milestones. Furthermore, it is easier to spend time refining a blueprint than it is to accept that there is much uncertainty about what action is required and what outcomes will be achieved. When dealing with a complex system, it is better to conduct a range of smaller innovations and find ways to constantly evaluate and learn from the results and adjust the next steps rather than to work to a set plan. The art of management and leadership is having an array of approaches and being aware of when to use which approach. Most issues will have simple, complicated and complex system types present, and there may well be multiple systems involved.

Table 2 Different leadership tasks for different systems (from Anderson & McDaniel 2000; Snowden & Moone 2007)

As Irene Ng points out in her Complicated vs Complex Outcomes post we have spent the last 100 years doing complicated rather well. “We can pat our backs on putting the man on the moon, doing brain surgeries etc. We are now moving to a world where complex outcomes matter and this is a new capability. This capability uses different words. We can determine complicated outcomes. We can only enable complex outcomes. We can specify complicated systems. We can only intervene in complex systems. Often, the best way to think about whether a system is complex or complicated is to ask – ‘what is the outcome’; ‘is it achievable through a command and control structure’ and if the latter is no, then it’s usually complex.”

In complex situations it is useful to move beyond thinking of ‘a change’ that will fix the system, and instead look for a number of leverage points that may be changed to improve the system. Changing what people do, for example, may require changes in rules (e.g. laws, protocols and tacit norms), changes in relationships, networks and patterns of behavior (e.g. how conflict is handled, how mistakes are managed, how power is used), and tools (e.g. databases, checklists, guidelines) for this change to ‘stick’. One-sized fits all approaches are unlikely to work in complex adaptive systems. The way solutions are visioned and delivered locally must reflect the values, contexts and cultures of each different community.

Finally, as with raising a child, people working in these complex adaptive situations need to keep learning about that situation, and to keep talking and working together in an ongoing way. Future visions and common goals need to be openly discussed and negotiated, and tentative pathways forward charted. While some actions will be taken by individual agencies working alone, new layers of creative collaborative and partnering arrangements will need to emerge. In these situations agencies should look to theories of change, to go beyond linear paths of cause and effect, to explore how change happens more broadly and then analyze what that means for the part that their particular agency or program can play.

Related resources

The previous blog post provides an introduction to theories of change, and a set of annotated links to key resources in this area can be found from the LfS web page – Theory of change. The BetterEvaluation Blog also has a very useful and related posting Addressing complexity which discusses the growing topic of how to address complexity in evaluation.

Community-based change initiatives often have ambitious goals, and so planning specific on-the-ground strategies to those goals is difficult. Likewise, the task of planning and carrying out evaluation research that can inform practice and surface broader lessons for the field in general is a challenge. Theories of change are vital to evaluation success for a number of reasons. Programs need to be grounded in good theory. By developing a theory of change based on good theory, managers can be better assured that their programs are delivering the right activities for the desired outcomes. And by creating a theory of change programs are easier to sustain, bring to scale, and evaluate, since each step – from the ideas behind it, to the outcomes it hopes to provide, to the resources needed – are clearly defined within the theory. Program theory is another common term with a similar meaning. Within this wider framework logic or outcome models are very closely related, often being used to take a more narrowly practical look at the relationship between inputs and results.

Moves by the evaluation community to tackle this challenge were well illustrated in a 1995 publication, New Approaches to Evaluating Comprehensive Community Initiatives put out by the Aspen Institute Roundtable on Community Change. In that book, Carol Weiss, a member of the Roundtable’s steering committee on evaluation, hypothesized that a key reason complex programs are so difficult to evaluate is that the assumptions that inspire them are poorly articulated. She argued that stakeholders of complex community initiatives typically are unclear about how the change process will unfold and therefore place little attention to the early and mid-term changes that need to happen in order for a longer term goal to be reached. The lack of clarity about the ‘mini-steps’ that must be taken to reach a long term outcome not only makes the task of evaluating a complex initiative challenging, but reduces the likelihood that all of the important factors related to the long term goal will be addressed. Weiss popularized the term ‘Theory of Change’ as a way to describe the set of assumptions that explain both the mini-steps that lead to the long term goal of interest and the connections between program activities and outcomes that occur at each step of the way. She challenged designers of complex community-based initiatives to be specific about the theories of change guiding their work and suggested that doing so would improve their overall evaluation plans and would strengthen their ability to claim credit for outcomes that were predicted in their theory. Over subsequent years a number of evaluations have been developed around this approach, fueling more interest in the field about its value.

A theory of change is usually presented in a visual diagram that allows the reader to see the big picture quickly. It does not usually provide a specific implementation plan.  The purpose of the process is to allow people to think about what must be changed before doing it.

Theory of change is both a process and a product (Vogel 2012). At its simplest, theory of change is a dialogue-based process intended to generate a ‘description of a sequence of events that is expected to lead to a particular desired outcome.’  This description is captured in a diagram and narrative to provide a guiding framework for the project team and stakeholders.

In brief, a theory of change starts by identifying a clear ultimate goal and working backwards to establish preconditions for reaching that goal. At each step any assumptions are examined. The next step is to identify indicators. Only when these steps have been completed are the activities or interventions identified. Finally a narrative is drafted to explain the theory of change in everyday language.  As Vogel (2012) points out, developing a theory of change requires discussion between the different stakeholders groups of the following elements (in order):

•  the context for the initiative, including social, political and environmental conditions, the current state of the problem the project is seeking to influence and other actors able to influence change;
• the long-term outcomes that the initiative seeks to support and for whose ultimate benefit;
• the broad sequence of events anticipated (or required) to lead to the desired long-term outcome;
• the assumptions about how these changes might happen, and about contextual drivers that may affect whether the activities and outputs are appropriate for influencing the desired changes in this context;
• a diagram and narrative summary that represents the sequence and captures the discussion.

The main benefit of theory of change comes from making different views and assumptions about the change process explicit, especially seemingly obvious ones. A good theory of change can specify how to create a range of conditions that help programs deliver on the desired outcomes. These can include setting out the right kinds of partnerships, types of forums, particular kinds of technical assistance, and tools and processes that help people operate more collaboratively and be more results focused. The purpose of doing so is to check that programs are appropriate, debate them and enrich them to strengthen project design and implementation.   For this reason, theory of change as a process emphasizes the importance of dialogue with stakeholders, acknowledging multiple viewpoints and recognition of power relations, as well as political, social and environmental realities in the context.

Theories of change are vital to evaluation success for a number of reasons. Programs need to be grounded in good theory. By developing a theory of change based on good theory, managers can be better assured that their programs are delivering the right activities for the desired outcomes. And by creating a theory of change programs are easier to sustain, bring to scale, and evaluate, since each step – from the ideas behind it, to the outcomes it hopes to provide, to the resources needed – are clearly defined within the theory.

Often people talk about logic models and theory of change processes interchangeably, Logic models connect programmatic activities and outputs to client or stakeholder outcomes. But a theory of change goes further, specifying how to create a range of conditions that help programs deliver on the desired outcomes. These can include setting out the right kinds of partnerships, types of forums, particular kinds of technical assistance, and tools and processes that help people operate more collaboratively and be more results focused.

This post provides a short introduction to the language and concepts of Theories of change or program theory. Links to a wealth of information, tips and guides from around the world can be found from the LfS Theory of change webpage. Another LfS section – Frameworks, guides and tools for change – provides links to a range of resources that include guides, tools, papers and web pages on how change occurs, and how it can be facilitated.

Comments on this post (or set of posts) are welcomed, as are suggestions for additional on-line resources to link to. The other ‘Managing for outcomes’ posts are:

• Part 1: Managing for outcomes: Linking project planning with monitoring & evaluation
• Part 2: An introduction to developing logic models – eight key elements to include
• Part 3: More about outcomes – why they are important … and elusive!

The next post – Complicated or complex? – follows on from this post. It recognizes that   theories of change are used in different ways depending on the ways that agencies see themselves and their programs operating.

Outputs are the goods and services that result from activities. Outcomes are the constructive impacts on people or environments. In the past planning and evaluation has tended to focus on program outputs, or how we keep ourselves busy – the ‘what we do’ and ‘who we do it with’ (Fig. 1). This enables us to tell our partners, funders and stakeholders about what the program does, the services it provides, how it is unique, and who it serves. We can describe and count our activities and the different goods and services we produce. Now, however, we are being asked what difference it makes! This is a question about outcomes (Fig.1). Outcomes are the changes, benefits, learning or other effects that happen as a result of what the program offers or provides. Outcomes are usually specified in terms of either: i) social and organizational capacities (social outcomes – e.g. learning, understanding, perceptions, attitudes and behaviors), or ii) state conditions (the bio-physical, ecological, social or economic changes in a system).

Fig. 1. Logic models highlight the need to consider both program efficiency and effectiveness

While most people intuitively appreciate this distinction between outputs and outcomes, experience in results-oriented training sessions suggests that for many program staff, turning that appreciation into practice takes time. As the Keystone (2009) guide points out it takes most people quite a lot of conscious practice before they start thinking in terms of outcomes, rather than outputs or needs or activities. An outcome statement describes a result – a change that has taken place. It is not a needs statement, or an activity that is still in progress. Outputs comprise the products and activities that you do, while outcomes are what we see as a result of our outputs.) One simple test is to ask two questions of each statement: i) is it written as an outcome? and ii) does it describe changes that we can plausibly enable or facilitate in people, groups, institutions or environments?

Outcomes may be specified in different ways. Often a distinction is made between short-term, intermediate and long-term, or just intermediate and long-term. Short-term outcomes can be seen as the immediate difference that your program makes in the wider environment. A long-term outcome often has a number of short-term and intermediate outcomes that together contribute to the ultimate achievement of the long-term outcome. Collectively these outcomes should contribute explicitly to the wider vision underpinning program development. An intermediate outcome is a specified intermediate state that contributes to the desired long-term outcome – a step along the way. Intermediate outcomes are especially useful when time lags in measurable state outcomes are significant or limit timely response.

The program outcomes and intermediate outcomes should be structured in a logical hierarchy reflecting how each leads to another and/or contributes to the long-term community outcome(s). A useful way of doing this is to take each outcome and ask the question, ‘If we achieve this, what will it lead to and how will it contribute to the long-term outcome?’ Look for gaps – starting from the highest level outcome and working down the outcomes model. A test is being able to read an outcome and say, ‘Yes, this will likely be achieved if all of these initial (contributing intermediate) outcomes (and corresponding outputs) are achieved.’ The answers to these questions will enable you to draft a succinct statement of each outcome.

Each outcome statement should therefore define what will change as a result of an intervention and by how much (or, at the very least, in what direction the change will occur). This then allows the means of performance measurement to be defined. The more clearly an outcome statement specifies a desired change, the easier it is to define an appropriate indicator or indicator set.

It is not always easy to identify outcomes, and harder still to clarify them, but there are a number of key questions that can help. For example, begin by asking what is/will be different as a result of the initiative? For whom? What will be changed/improved? What do/will beneficiaries and other stakeholders say is the value of the program? For an existing program, look at the major activities. For each activity, ask yourself, ‘Why are we doing that?’ Usually, the answer to the ‘Why?’ question is an outcome. Most importantly, seek ideas and input from others. Their perspectives will help provide a broader understanding of the program and its benefits. This activity will also help build consensus among key program stakeholders.

When writing outcomes be sure to describe the desired change. Keep your outcomes SMART: Specific, Measurable, Achievable, Relevant, Time-limited. Say ‘what’, not ‘how’ – Establishing the means and plausibility of the ‘how’ is a later step. Consider whether outcomes are likely to be achieved in the program time frame.

Table 1 Examples of outcome statement structure from a range of sectors

This post provides a short introduction to the language and concepts of outcomes. Links to a wealth of information, tips and guides from around the world can be found from the LfS Managing for outcomes: using logic modeling webpage. These include a starting guide to outcome model development that some colleagues and I wrote in the context of results-based biodiversity/biosecurity management – Setting outcomes, and measuring and reporting performance …. Comments on this post (or set of posts) are welcomed, as are suggestions for additional on-line resources to link to. Other posts in the ‘Managing for outcomes’ series are:

• Part 1: Managing for outcomes: Linking project planning with monitoring & evaluation
• Part 2: An introduction to developing logic models – eight key elements to include
• Part 3: More about outcomes – why they are important … and elusive! (this post)
• Part 4: Using a Theory of Change to better understand your program

Successful development programs (be they urban- or rural-focused) are often dependent on the coordinated actions of of a range of decision-makers, each operating at different scales. Gaining effective buy-in, and subsequent co-ordination, is not always easy as different stakeholders may see the world – and your program activities – in very different ways.  Managers need to keep a range of activities on-time and on-track, be adaptable to changing conditions; encourage best practice, and foster collaboration between institutions and user groups. More often than not it is also important to be able to keep stakeholders focused on a long-term view of change – much longer than the common one- to three-year funding time-frames of many research and policy initiatives.

In these contexts logic or outcome models are useful management tools to help plan complex programs (or policies), and maintain a focus on the longer-term outcomes and impacts.  They enable program staff and stakeholders to envision the ‘big picture’, view the scope of the work and the potential significance to various target communities, and to their wider economic and environmental systems. They provide a tool  to think about how multiple short-term investments fit together to underpin the longer-term trajectory towards desired social and environmental outcomes. These approaches to modeling program logic go by different names  – e.g. intervention logics, program logics, program theories, theories of change,or  causal maps  – but they are all trying to do a similar job. In development terms they set out the key steps that are needed to help a program move a target community from their present situation to achieve a set of desired outcomes.

Creating a clear, graphical representation of your program

Logic models are narrative or graphical depictions of processes in real life that communicate the underlying assumptions upon which an activity is expected to lead to a specific result.  There are eight essential components of a logic model (Fig. 1). These are the four primary components of the logic model itself – inputs, activities, outputs and outcomes – and four key supporting activities – stakeholder analysis, to identify which stakeholders should be involved in model development; the scoping and planning exercise that underpins any model development; ensuring that underpinning assumptions are documented; and noting internal and external factors that may influence outcomes.

Fig. 1. How the eight essential components of a logic or outcomes model (colored boxes) fit together

There is no single or correct way to draw a logic model. It can be drawn horizontally (as in Fig. 1) vertically, or even in a more free-form fashion. Ideally, a logic model should be able to be displayed on a single page with sufficient detail that it can be explained fairly easily and understood by other people. Much of the value of a logic model is that it provides a visual expression of our underlying beliefs about why the program is likely to succeed through one step leading to another. Thus, each step between an activity and an output or between an output and an outcome can be thought of as an ‘if this happens … then that is likely to happen’ statement. For large or complex programs, the logic model may be divided into more detailed sections or sub-models. These may be summarized by a less detailed ‘overview’ model, often given on the first page, that shows how the component sub-models fit together into a whole.

As an example, Fig. 1 illustrates the eight program logic elements in a horizontal fashion. The inputs are the resources used to resource the activities, produce the program outputs, and ultimately contribute towards desired outcomes. Inputs typically include such things as money, staff, and equipment/infrastructure. Inputs are usually measured as counts, such as hours of staff time, dollars spent, etc. Activities are the actual interventions and actions undertaken by program stakeholders, staff and agencies to achieve specified outputs. Activities can range from writing a memo, to holding workshops, to creating infrastructure. Activities are usually measured in terms of number of things done – e.g. x meetings held with communities. Outputs are the tangible results of the major activities in the program (the goods and services produced). They are usually measured by their number – e.g. reports produced, newsletters published, numbers of field days held. Collectively the inputs, activities and outputs define what the program does, and how efficient it is in managing those elements. Outcomes represent the effectiveness of the program – are the desired states of the community, biological system or production sector achieved by the program.  Outcomes may be specified in terms of short-term, intermediate and long-term, or just intermediate and long-term. A long-term outcome will usually have a number of intermediate outcomes that together contribute to its ultimate achievement.

Beyond the program elements themselves a logic model requires four key sets of supporting information and activities so that the model and the intended program can be understood in their wider context. The first step in creating a logic model involves a planning and scoping phase to define clearly the problem or need, and the desired outcome. An ‘issue’ statement should explain briefly the current situation: what needs to change; why is there is a need for intervention; and, what problem/issue does my program aim to solve?’ This requires that ‘who, what, why, where, when, and how’ are all considered in relation to the problem/issue. Then, the overall purpose of the program needs to be defined. What are you trying to accomplish over the life of the program and beyond? The answer to this question is the solution to the issue statement, and will serve as the program’s vision. The program vision serves as a reference frame for all elements of the logic model that follow. Involving your key stakeholders in the process of developing an outcomes model provides an opportunity to engage them in a discussion about the program and to get their input to the process.

The link between a program’s activities and outputs and its desired outcome is based on the assumptions that explicitly, or implicitly, are built into your program theory. Your program theory (or theory of change) sets out why you believe that the successful delivery of the program’s activities and outputs is expected to lead to the desired change (the predicted outcomes). The first step is to document the program rationale – the beliefs about how change occurs in your field, based on research, experience, or best practice. The next step is to identify the corresponding assumptions that are built into your program rationale and to acknowledge and document where uncertainties exist.  The last element takes account of the risks and opportunities facing the program. These can derive from internal and external factors. Programs that are operating in complex environments cannot control all the factors that will influence how, when or even if they reach their goals .  Therefore it is also important to be aware of similar or related external initiatives that will impact on the final outcomes. This is important in terms of attribution – how to ascertain how much impact can be attributed to your program. It also provides the opportunity to look for other initiatives to link and integrate with, to develop useful synergy and maximize the overall influence of the program.  Internal factors might relate, for example, to staff and stakeholder capacities.

Three key reasons for using logic models are that they: i) helps you understand why and how something works; ii) provide a guide for implementing useful monitoring and evaluation systems; and iii) help you tell the story of your program quickly and visually. Logic models are most useful when they are developed at the beginning of a program.  In this way they can be used to plan how resources can be coordinated and even inspire particular  project strategies.  They can also at this stage help set  realistic expectations for outcomes, bearing in mind that the ultimate desired end-state outcomes of an initiative can often take many years to emerge.  Their initial development helps subsequent evaluation as once a program has been described in terms of a logic model, it is then possible to identify meaningful and easily measurable performance indicators. Finally, the simple, clear graphical representation that a logic model provides helps with program communication.

Finally ––Some tips for working with logic models

• Start with a vision:   It’s important to know where you are trying to get to. This has often been expressed in already published documents, mission statements, etc. The important thing is to ensure that the outcome has been agreed to by all those that you are trying to work with on your journey.
• Involve stakeholders:   A strong focus on the process of developing a logic or outcomes model (rather than seeing it as just a task to complete) can increase engagement in the program. Building a logic model provides an opportunity, often rare in the everyday provision of services, to discuss what it is about your program that is most meaningful to constituents.
• Keep the model simple:  Concentrate on the most important activities and outcomes, and cut back on detail. Describe your activities and outcomes in language that is understood by a wide range of stakeholders.  This lets your logic model provide a common picture of your project that is easily understood. It’s important to get an  overview of the model on one page that can be used as a communication aid, and more detail can be added behind it if necessary.
• Avoid siloed thinking:   Don’t just include steps and outcomes that are measurable or which you can absolutely prove you changed (attributable to you) – these may not end up being the most important part of the programme. Similarly don’t force lower steps to only contribute or influence a single higher-level step or outcome. Most elements influence a number of things in the real world.
• Work constructively with disagreement:  Although it might be difficult, keep key stakeholders involved, including staff, program participants, collaborators, or funders. Take time to explore the reasons for disagreement about what should be captured in the logic model. Look for the assumptions, identify and resolve disagreements, and build consensus.

This post provides a short introduction to logic models. Links to a wealth of information, tips and guides from around the world can be found from the LfS Managing for outcomes: using logic modeling webpage. A starting guide to model development that some colleagues and I wrote in the context of results-based biodiversity/biosecurity management – Setting outcomes, and measuring and reporting performance … usefully expands on many of the concepts introduced in this post. Comments on this post (or set of posts) are welcomed, as are suggestions for additional on-line resources to link to. The other ‘Managing for outcomes’ posts in this series are:

• Part 1: Managing for outcomes: Linking project planning with monitoring & evaluation
• Part 3: More about outcomes – why they are important … and elusive!
• Part 4: Theory of change or program theory

• Part 2: An introduction to developing logic models – eight key elements to include
• Part 3: More about outcomes – why they are important … and elusive
• Part 4: Using a Theory of Change to better understand your program

The range of current efforts towards results-based management in policy, management and development circles highlight the importance of more closely integrating these two functions. Planning, monitoring and evaluation are at the heart of a results and learning-based approach. Achieving collaborative, business/environmental or personal goals requires effective planning and follow-through. The plan is effectively a “route-map” from the present to the future. To plan a suitable route you must know where you are (situation analysis) and where you want to go (establish goals and identify outcomes). Only then can appropriate action plans be developed to help achieve the desired future.

Fig. 1. Project planning: How monitoring plans support planning

Both sets of plans are best developed in conjunction with the people who will carry them out, as they are then more likely to actually do so. As the accompanying diagram  on the left (Fig. 1) illustrates, monitoring plans need to account for what is being done and how it is being done. Results monitoring focuses on whether you are getting where you want to go, while process monitoring focuses on how efficiently you are getting there. Indicators in this regard may be either qualitative or quantitative, and a combination of the two is often best. An evaluation is like a good story, it needs some (qualitative) anecdotal evidence to put the lesson in context, and some (quantitative) facts and figures that reinforce the message.

The starting point for introducing challenging programs that cut across many work groups and departments is to find ways to articulate and guide planned project activities, especially those intended to produce dissemination and utilization outcomes that provide for scaling up and out. Many managers do not have the tools to easily set out, document, and communicate complex program goals, activity strategies, and intended outcomes. Logic models can assist here by encouraging project staff to plan for results by envisioning a ‘big picture’ view of a project’s scope of work and potential significance to various target systems. Other names for logic models include ‘outcomes models’, ‘causal chains’, or ‘intervention logic models’ (ILMs). Logic models are narrative or graphical depictions of processes in real life that communicate the underlying assumptions upon which an activity is expected to lead to a specific result. They generally illustrate a sequence of cause-and-effect relationships, i.e. a systems approach to communicate the path toward a desired result. As the top half of the below diagram (Fig.2)  illustrates, the model describes logical linkages among program resources, activities, outputs, and audiences, and highlights different orders of outcomes related to a specific problem or situation. Importantly, once a program has been described in terms of a logic model, critical measures of performance can be identified. In this way logic models can be seen to support both planning and evaluation.

A project logic model showing how different evaluation types and approaches can be used to measure progress through different stages of implementation

Logic models help by encouraging those involved to look at the bigger picture, to uncover underlying assumptions and, in particular, to consider how or why a particular action (intervention) or set of activities is expected to lead to a particular result. Often the process of developing a logic model – and underpinning theory of change -  is as valuable to program planning and evaluation teams as the logic model itself. Use of this approach can also lead managers to acknowledge uncertainties in the logic on which a program is designed. This can help to identify where more information or research is needed and can lead to the application of data from monitoring to guide improvements in program design.

Often people talk about logic models and theory of change processes interchangeably, Logic models – such as the one above – connect programmatic activities to client or stakeholder outcomes. But a theory of change goes further, specifying how to create a range of conditions that help programs deliver on the desired outcomes. These can include setting out the right kinds of partnerships, types of forums, particular kinds of technical assistance, and tools and processes that help people operate more collaboratively and be more results focused.

As the lower part of the diagram shows different evaluation approaches can be used to measure different parts of the overall project or change initiative. With the broader monitoring and evaluation context there are a number of framings that will influence the final approaches chosen. These are highlighted on different pages here. Different types of evaluations can be seen to answer different questions, each relevant to a different part of the bigger project management/policy cycle.

Worldwide there is a trend towards an increased use of indicators to monitor development and track progress. Indicators lie at the heart of the evaluation process. They quantify and simplify phenomena, and help us understand and make sense of complex realities. To be most meaningful, a monitoring and evaluation programme should provide insights into cause-and-effect relationships between environmental or socio-economic stressors and the anticipated system responses and subsequent environmental, social and economic outcomes.

There are also other frameworks which can provide ideas as to the appropriate scale and levels of programme intensity to be considered by project planners, managers and evaluators. In the application of these general frameworks a range of contemporary planning, monitoring and  evaluation approaches routinely promote the involvement of a wide range of stakeholders, employing methods that allow a more equal opportunity for the expression of views and sharing of lessons. In turn this supports social learning, incremental project improvement and the empowerment of stakeholders – on the way to achieving desired and feasible sets of outcomes.  Future posts will look at each of the topics raised here in more detail.

• Part 1: Managing for outcomes: Linking project planning with monitoring & evaluation (this post)
• Part 2: An introduction to developing logic models – eight key elements to include
• Part 3: More about outcomes – why they are important … and elusive
• Part 4: Using a Theory of Change to better understand your program

p.s. (7 March update) As Paul Duignan highlights in a recent post – Putting the Planning back into M&E – PME or PM&E what’s the acronym going to be? – there is a real need for evaluation skills to also be used to support good program planning.
 

The major structural change that readers will note is that the LfS ‘evaluation’ section has been expanded to include ‘Planning and Evaluation‘. This reflects that planning and evaluation are inextricably linked within effective and learning-based adaptive management. The plan is effectively a “route-map” from the present to the future. To plan a suitable route you must know where you are (situation analysis) and where you want to go (establish goals and identify outcomes). Only then can appropriate action plans be developed to help achieve the desired future.However, because the future is uncertain, our action plans must be adaptive and allow continually for “learning by doing”. To do this we need appropriate evaluation tools and processes, and information flows that help the different stakeholders involved check that their efforts are proceeding as planned, and to refine and guide their responses if changes are needed. Within this section a number of links are provided to each of the following topic pages on the LfS site.

• Theory of Change: By developing a theory of change based on good theory, managers can be better assured that their programmes are delivering the right activities for the desired outcomes. And by creating a theory of change programmes are easier to sustain, bring to scale, and evaluate, since each step – from the ideas behind it, to the outcomes it hopes to provide, to the resources needed – are clearly defined within the theory.
• Logic or Outcomes Modeling: These generally illustrate a sequence of cause-and-effect relationships, i.e. a systems approach to communicate the path toward a desired result. Models describe logical linkages among programme resources, activities, outputs, and audiences, and highlights different orders of outcomes related to a specific problem or situation.
• Developing Indicators:. Indicators quantify and simplify phenomena, and help us understand and make sense of complex realities. Within natural resource management their greatest strength is in the way they can help us assess resource status and monitor performance effectiveness. As the linked resources on this page highlight reviewers of effective indicator reporting processes highlight the importance of using a conceptual framework and models to guide the development of a set of indicators. These frameworks and models provide a formal way of thinking about a topic area and help us build a coherent set of indicators for any particular system.

This update is posted on a (very) occasional basis – please feel free to forward to interested colleagues. Feedback is welcomed, and visitors are encouraged to suggest papers, reports and other material to add. Thanks to those of you who have pointed to papers and other links for inclusion and sharing among the wider global community of practice in this area. The site has now been operating for more than 10 years. In the 2011 year the site received around 750 visitors each day. You can visit the Learning for Sustainability (LfS) site directly at http://learningforsustainability.net/ and your ideas and suggestions for links are welcomed.

Improvisation as art is, of course, a collaborative activity. Seen in this context, the skills Dave spells out are all very relevant to collective activities and initiatives – co-management, collaborative adaptive management, participatory planning, etc. Listening, for example, is one of these key skills. However, as Dave says, we often only listen just enough, so we can respond (with what we are already thinking). Whereas the underlying ethic of listening in collaboration is “the willingness to change”. If we are not willing to change, then we are not really listening. He points out that this doesn’t mean we “have to change”, but it does mean that we must enter the process being prepared to change. This highlights the importance of intent in collaborative initiatives, and reminds us that as facilitators and proponents of collective exercises we have to really concentrate on fostering a constructive climate for change, before bringing people together ‘for the big meeting”.

Other skills that are important for collective improvisation include the importance of being in the moment, of saying “yes” to others “and” adding suggestions. Even reminding us to enjoy the process is still relevant for those of us that would consider our efforts are far from a game. The full TEDx talk is embedded here.

If you that would like to see the lessons set out on one page, you can see an illustrated summary by Lynne Cazaly posted on Dave’s blog, an illustrated talk. If you were interested in this talk, then you may also be interested in getting access to related online papers and reports around collaboration and innovation in health, community development and natural resource management areas through the Learning for Sustainability (LfS) portal site. Key LfS topics areas around real life improvisation include adaptation and adaptive governance. Similar pages focusing on collaboration include links to resources with tips and ideas for managing participation and engagement and approaches to supporting negotiation and dialogue.

The LfS site highlights the wide range of social skills and processes that are needed to support constructive collaboration, and indicates how these skills and processes can be interwoven to achieve more integrated and effective outcomes. It brings links to several hundred annotated on-line resources from different sectors and geographic areas together in one easy to access place, and it concentrates on providing links to open access materials. The featured links in the site newsletter are drawn from some of the new sections updated recently. Direct links to these papers are provided through the on-line update at http://learningforsustainability.net/newsletters/sep11.php – and they include the following:

• “Taking responsibility for complexity”- This ODI briefing paper aims: to give readers the tools to decide when a problem is complex, outline why this matters, and provide guidance on how to achieve results in the face of complexity. [go to newsletter]
• “Liberating structures: A new pattern language for engagement”- This recent OD Practitioner paper by Lisa Kimball looks at how managers can generate meaningful engagement that constructively transforms
  work and organizations. It shows that to facilitate significant, transformative changes in organizations we need to make a profound change in how people interact, not just at off sites and other special occasion meetings, but in all the get togethers that make up daily life in organizations. [go to newsletter]
• “Forests and Climate Change: Linking Adaptation and Mitigation”- Climate change can be addressed by mitigation and adaptation. However, there is a need to explore the linkages between these two options in order to understand their trade-offs and synergies. This paper explores this issue using Latin American examples. [go to newsletter]
• “The Role of ICT in Building Communities and Social Capital”- This paper examines the debate around the impact of ICT and argues that ICT supplements and transforms social capital rather than diminishing it. [go to newsletter]

This update is posted on an occasional basis (around 3-4 times/year) – please feel free to forward to interested colleagues. Feedback is welcomed, and visitors are encouraged to suggest papers, reports and other material to add. Thanks to those of you who have pointed to papers and other links for inclusion and sharing among the wider global community of practice in this area. The site has now been operating for more than 10 years and receives more than 600 visitors each day, with the highest number of visits in any one day being in excess of 1500. You can visit the Learning for Sustainability (LfS) site directly at http://learningforsustainability.net and your ideas and suggestions
for links are welcomed.

Tomorrow will see the start of the 2nd International Conference on Crisis Mapping – Haiti and beyond -   (ICCM 2010) being held in Boston. ICCM 2010 aims to bring together the most engaged practitioners, scholars, software developers and policymakers at the cutting edge of crisis mapping to address and assess the role of crisis mapping and humanitarian technology in the disaster response to Haiti and beyond. As Secretary of State Hillary Clinton recently noted in a policy speech in Washington DC, “The technology community has set up interactive maps to help us identify needs and target resources” in Haiti. The question is, how did we do and how can we improve?

Key emerging platforms for bringing together a structuring the sharing of information for development or disaster management include the following:

SwiftRiver: This is a free and open source platform that helps people make sense of a lot of information in a short amount of time. The SwiftRiver platform was born out of the need to understand and act upon a wave of massive amounts of crisis data that tends to overwhelm in the first 24 hours of a disaster. Since then, there has been a great deal of interest in this tool for other industries, such as news rooms and brand monitoring groups. In practice, SwiftRiver enables the filtering and verification of real-time data from channels such as Twitter, SMS, Email and RSS feeds. This free tool is especially useful for organizations who need to sort their data by authority and accuracy, as opposed to popularity. These organizations include the media, emergency response groups, election monitors and more. This might include journalists and other media institutions, emergency response groups, election monitors and more.

Open Street Map:  This  is a free editable map of the whole world. OpenStreetMap allows you to view, edit and use geographical data in a collaborative way from anywhere on Earth. Anyone can map, code, bring your expertise, translate, create user manuals, contribute and collect data using walking-papers.org. Go ahead and add missing information to the map.

Sahana: Sahana is a Free and Open Source Disaster Management system. It is a web based collaboration tool that addresses the common coordination problems during a disaster from finding missing people, managing aid, managing volunteers, tracking camps effectively between Government groups, the civil society (NGOs) and the victims themselves.

Ushahidi/Crowdmap: The Ushahidi Platform allows anyone to gather distributed data via SMS, email or web and visualize it on a map or timeline. Their goal is to create the simplest way of aggregating information from the public for use in crisis response. Crowdmap is designed and built by the people behind Ushahidi, a platform that was originally built to crowdsource crisis information. As the platform has evolved, so have its uses. Crowdmap allows you to set up your own deployment of Ushahidi without having to install it on your own web server.

Frontline SMS: This application allows you to text message with large groups of people anywhere there is a mobile signal.