# 2. The Fundamentals of Collective R\&D *With its many ambiguities and unknowns, a research and development initiative can be daunting to navigate. In this chapter, we present the three fundamentals of our approach to R\&D: principles that define the logic of our collective approach, modes that shape how we engage with the world and practices that enable us to make big steps forward. These fundamentals will help you, your colleagues and partners to successfully navigate, plan and reflect upon your collective R\&D journey.* ## Five principles for making change Principles define the [logic](/undp-accelerator-labs/references/glossary.md#logic) underlying our approach. They enable us to coherently structure our perceptions (how we see and understand the world), thinking (how we analyze and conceptualize the world) and actions (how we try to transform the world). They express core beliefs that form our intentions, the ways we relate to others and how we go about pushing sustainable development forward. Figure 6: R&D Principles Five principles (see Figure 6) serve as overarching guidelines that help decision-making, create alignment among key ecosystem [actors](/undp-accelerator-labs/references/glossary.md#actor) and prioritize collective learning to shape action: {% hint style="info" %} R\&D Principles: ### **Oriented towards action and impact** We have a bias towards action: We learn by doing and by making ideas real,[\[1\]](#footnote-0) In our attempt to create change, we learn what to change. Our actions generate new information, helping us better understand the problem, solution or opportunity [space](/undp-accelerator-labs/references/glossary.md#space). We embrace exploration and experimentation in short [learning cycles](/undp-accelerator-labs/references/glossary.md#learning-cycle) of planning, acting and reflecting. We adjust when new insights emerge and double down when we find momentum or new pathways to impact. ### **Driven by learning and evidence** We approach the world with [curiosity](/undp-accelerator-labs/references/glossary.md#curiosity), seeking to understand both how it works and how to change it. We develop learning questions that form the foundations of our R\&D efforts. They coordinate curiosity among actors and direct our attention to what we need to learn.Through deliberate exploration of the [adjacent possible](/undp-accelerator-labs/references/glossary.md#adjacent-possible),[\[2\]](#footnote-1) we systematically scan for signals of change, unmet needs, emerging technologies and untapped potential, creating space for [serendipity](/undp-accelerator-labs/references/glossary.md#serendipity) and new opportunities. We gather and create diverse forms of evidence, from data patterns and experimental results to ethnographic insights, to demonstrate the magnitude of emerging issues or momentum for change. ### **Working from the bottom up** We start with the local nuances and knowledge that emerges from community groundwork. We listen deeply to gain an understanding of development challenges through the perspectives of those who are concerned,[\[3\]](#footnote-2) unheard, or most affected – those who often have the deepest understanding of the problem or opportunities to create change. We have a particular interest in [grassroot innovations](/undp-accelerator-labs/references/glossary.md#grassroot-innovation) – solutions that are found where problems are[\[4\]](#footnote-3) – to identify unmet[\[5\]](#footnote-4) or unspoken needs as well as to elevate the voices, knowledge, experience and ingenuity of creators. ### **Empowering collectives** We combine collective brainpower with diverse data, knowledge and perspectives through [participatory](/undp-accelerator-labs/references/glossary.md#participatory) methods and platforms to deepen understanding of complex challenges and uncover solutions. We help the [collective](/undp-accelerator-labs/references/glossary.md#collective) see itself. We build and strengthen relationships between diverse ecosystem actors, enabling them to find common ground; collaborate, learn, decide together, and mobilize action. We usually do this with the help of technology to build more accountable, resilient and equitable systems that serve as the backbone of communities and societies. ### **Promoting and enabling open sharing** We promote “[working out loud](/undp-accelerator-labs/references/glossary.md#working-out-loud),” sharing what we learn – both successes and [failures](/undp-accelerator-labs/references/glossary.md#failure).[\[6\]](#footnote-5) We pool and openly share our data, knowledge, solutions, practices and tools to accelerate collective learning and innovation diffusion. We make our work [openly](/undp-accelerator-labs/references/glossary.md#open) accessible and adaptable to enable others to process, analyze, reinterpret, recombine and build upon it, sparking new possibilities. {% endhint %} ## Three modes of engaging and interacting Modes are specific ways of *doing* and of *being* that set the focus of our attention and action. Our approach involves three modes: Sense & Explore, Develop & Test, and Diffuse & Catalyse (Figure 7). These are like postures[\[7\]](#footnote-6) that determine how we engage with, experience and perceive development [ecosystems](/undp-accelerator-labs/references/glossary.md#ecosystem), and how we navigate their complexities and [uncertainty](/undp-accelerator-labs/references/glossary.md#uncertainty). ![Figure 7: R\&D Modes](/files/bb52189b04897376af725bcb32ac723ab43ced19) You may already use these modes unconsciously or unintentionally. We argue for being more intentional in using them. This raises your awareness of what and who is overlooked or ignored. It also allows you to determine the best course of action and select the most appropriate tools, while creating the conditions for greater [inclusivity](/undp-accelerator-labs/references/glossary.md#inclusivity), continuous learning and strengthening collective [agency](/undp-accelerator-labs/references/glossary.md#agency). These modes might appear as sequential steps, but they are dynamic and non-linear[\[8\]](#footnote-7) in nature. We actually shift and transition between these different styles of engaging and interacting as opportunities and insights emerge.[\[9\]](#footnote-8) As we move between modes, information starts to flow, informing new learning questions and activities. In [Chapter 4](/undp-accelerator-labs/doing-r-and-d/4.-r-and-d-journeys.md), we discuss three real-life R\&D journeys that illustrate the dynamics of these interacting modes. {% hint style="info" %} R\&D Modes: ### **Sense and Explore** This mode focuses on sensing what's happening and exploring the [adjacent possible](/undp-accelerator-labs/references/glossary.md#adjacent-possible): identifying what's already there, what's [emerging](/undp-accelerator-labs/references/glossary.md#emergence) or what is (gradually) disappearing. This mode activates purpose, putting us and the broader ecosystem in tune with the situation at hand. Through this mode, we address questions to learn: *What’s going on? Where to focus our attention? Where do we need to build knowledge more rapidly? Who do we work with?* When we engage with local development ecosystems… * We continuously scan for signals of emerging [futures](/undp-accelerator-labs/references/glossary.md#future), reflect on their implications for sustainable development and explore what others have learned on the issues we are addressing to enrich our collective understanding. * We map potential changemakers (startups, grassroots innovators and [positive deviants](/undp-accelerator-labs/references/glossary.md#positive-deviance)[\[10\]](#footnote-9)) and discover existing local solutions, data sources and emerging technologies that can drive positive change and help shape more sustainable futures. * We learn with key stakeholders to understand their most pressing challenges, what's driving these issues, and the impact they have on whom. We explore areas for potential change and where we need to accelerate learning. * We immerse ourselves in communities to understand their experiences, behaviors, traditions, knowledge, needs, capabilities and relationships. These immersions build trust, laying foundations for sharing [collective intelligence](/undp-accelerator-labs/references/glossary.md#collective-intelligence) about sustainable development. * We map knowledge that is already available within the [ecosystem](/undp-accelerator-labs/references/glossary.md#ecosystem). We identify existing knowledge sources, assets and holders that can help us quickly develop a real-time and comprehensive understanding of a situation. We prioritize local knowledge, while also drawing on policy expertise and theoretical knowledge from scientific sources to better understand what is going on. When engaging with ecosystems in this mode, it's important to create conditions that encourage open-ended discovery, adopting new perspectives and partnering with *unusual suspects*, while maintaining space for serendipity. ### **Develop and Test** This mode focuses on developing, testing and improving ideas with others, and probing the system to discover where there is momentum for change. Through this mode, we address questions to learn: *Where to intervene? What works and what does not? Which configurations generate unexpectedly or unintentionally desirable* [*effects*](/undp-accelerator-labs/references/glossary.md#effect)*?*[*\[11\]*](#footnote-10) When we engage with local development ecosystems… * We define learning [questions](/undp-accelerator-labs/references/glossary.md#question) and [reframe](/undp-accelerator-labs/references/glossary.md#reframing) issues based on how systems operate, helping key stakeholders to see an issue or system in a new way, inspiring action and learning. * We explore and develop possible futures with communities, creating a shared vision of \_what could or should b\_e. * We co-create, prototype and test solutions with communities, end-users and citizens to find what works, building ownership through participation and generating evidence to make the case for change and further investment. * We codesign and coordinate [portfolios](/undp-accelerator-labs/references/glossary.md#portfolio) of interconnected interventions, addressing complex issues, facilitating collective learning and orchestrating system transformation. * We monitor system responses to our interventions, observing and capturing any changes so we can capitalize on momentum when we see positive outcomes or adjust our approach based on emerging insights, situations and opportunities. When engaging with ecosystems in this mode, it's important to create conditions for co-creating with others, agility and improvisation to embrace [emergence](/undp-accelerator-labs/references/glossary.md#emergence) and unforeseen system responses to your interventions, and maintain a safe space to fail[\[12\]](#footnote-11) – because good experiments are designed to fail.[\[13\]](#footnote-12) ### **Diffuse and Catalyze** This mode focuses on spreading and socialization of insights, innovations and knowledge, and triggering acceleration in R\&D processes and ecosystem uptake. Through this mode, we address questions to learn:\_ How to scale through the ecosystem? How do we ensure open sharing by design? How can we make what we know useful and usable to others? Who needs to know? How do we facilitate ownership?\_ When we engage with local development ecosystems… * We share knowledge, insights, solutions and innovations continuously[\[14\]](#footnote-13) in ways that make them accessible, useful and usable for others.[\[15\]](#footnote-14) We pay particular attention to recognizing innovators when sharing grassroots solutions and local knowledge. We feed back what we've learned to those who have contributed, in order to empower the collective.[\[16\]](#footnote-15) * We engineer flows of ideas, data, information, knowledge and [value](/undp-accelerator-labs/references/glossary.md#value) with two aims: boosting collective intelligence, and generating network effects that accelerate learning and innovation across the ecosystem. * We promote ownership through co-creation and enable ecosystem scaling by making our work openly available. Through innovation commons,[\[17\]](#footnote-16) open licensing[\[18\]](#footnote-17) and [open-ended design](/undp-accelerator-labs/references/glossary.md#open-ended-design),[\[19\]](#footnote-18) others can freely use, adapt and build upon ideas, innovations, data and knowledge. This encourages others to create new [combinations](/undp-accelerator-labs/references/glossary.md#combination), configurations[\[20\]](#footnote-19) that form a “[bricolage](/undp-accelerator-labs/references/glossary.md#bricolage)”[\[21\]](#footnote-20) of solutions, technologies and capabilities that fit their needs and often resource-constrained contexts. * We identify opportunities, leverage points, technologies, relationships, ambassadors and intermediaries[\[22\]](#footnote-21) within the ecosystem that can help create legitimacy, increase uptake, or set things in motion, taking insights and innovations further. We do this either through active seeking or by creating spaces for serendipitous discovery. When engaging with ecosystems in this mode, it's important to create conditions for open sharing of data, knowledge and innovations, to enable actors to reconfigure or recombine ideas, solutions and technologies, and support the ecosystem to leverage its own relational infrastructure. {% endhint %} ## Twelve practices for making big steps forward Figure 8: R&D Practices Practices are the most crucial “jobs to be done” and involve activities that move the R\&D process big steps forward. There are twelve practices[\[23\]](#footnote-22) (see Figure 8) that can be employed flexibly in different configurations throughout the entire R\&D journey. {% hint style="info" %} R\&D Practices: ### **Systems, collectives and strategies** Practices to enable collective learning and action: * **Thinking in systems**: We look at systems from different perspectives, map them to reveal hidden dynamics and probe through experimentation to identify leverage points and trigger larger transformations. * **Making collectives smarter**: We diversify data sources, inputs and perspectives, orchestrating information flows and feedback loops to help collectives understand situations more deeply and in real time to act more effectively. * **Forming collectives**: We identify key actors and existing collectives, and build and strengthen relationships that bring together distributed wisdom, resources and capabilities. ### **Experimentation and technology** Practices to accelerate and scale initiatives: * **Finding out what works**: We probe systems through experimentation to identify momentum for change, test solutions to create evidence and legitimacy, use [prototypes](/undp-accelerator-labs/references/glossary.md#prototyping) to reveal hidden needs and co-design with stakeholders to build ownership. * **Leveraging technologies**: We identify and explore new technologies, experiment with different combinations and promote open licensing models to amplify our collective potential. ### **Ideas, innovations and visions** Practices to discover or co-create new possibilities: * **Seeking existing solutions**: We look for grassroots innovations from people facing challenges firsthand, we document and share them to help them gain recognition and access to resources while enabling others to build on their work. * **Exploring new options and alternatives**: We explore what already exists but lies just beyond our current awareness, reframe issues to understand them differently, and imagine alternative futures together to identify new potential pathways to sustainable development. ### **People, communities and culture** Practices to build empathy and unlock people's potential: * **Empowering communities and individuals**: We help communities see the ecosystems they're part of, build ownership through participation and co-creation, and support them in generating their own data to mobilize their capabilities and resources for collective action. * **Understanding culture and everyday experiences**: We deeply immerse ourselves in communities and work alongside them to map cultural practices and local knowledge, uncovering the beliefs, behaviors and practices that shape how people navigate uncertainty and adapt to change. * **Engaging with people and communities**: We meet people where they are and recognize their knowledge, realities and achievements to build trust and relationships for learning and working together on critical development challenges. ### **Data, insights, evidence** Practices to find patterns, inform decisions, and spark new questions: * **Analyzing data and generating insights**: We use technology and build community skills to make data analysis participatory, transforming raw data into visual narratives that help communities spot [patterns](/undp-accelerator-labs/references/glossary.md#pattern) and generate actionable insights. * **Capturing data**: We seek out real-time, local and often overlooked data streams, build data partnerships, and diversify inputs to reveal hidden patterns, enabling learning and timely responses to changing conditions. {% endhint %} [Chapter 5, R\&D Practices](/undp-accelerator-labs/doing-r-and-d/5.-r-and-d-practices.md), provides detailed descriptions of how to put these practices to work, walking through the methods and technologies that make them possible, with real examples from the Labs to show what they look like in action. ## Balancing flexibility with the fundamentals In this chapter, we have outlined our principles, modes and practices that define the logic of our approach to R\&D. You may have noticed that there is flexibility in how these fundamentals can be applied, allowing you to adapt them to your specific context, respond to emerging situations and discover your own path forward. However, we want to be clear about what remains at the very core of this approach, regardless of how you adapt it. The collective dimension is essential and distinguishes this approach from conventional R\&D. Institutional R\&D is usually driven by researchers and institutions, whereas collective R\&D operates more like a distributed model across communities and ecosystems. This changes where R\&D happens, how knowledge is created and who benefits from it (Table 2). | | **Institutional R\&D** | **Collective R\&D** | | ----------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------------------------------------- | | *Where does R\&D happen?* | In labs or institutional settings studying communities from the outside | Embedded in and with communities, networks and ecosystems | | *What's the relationship to communities?* | Communities as research subjects or beneficiaries | Communities as co-researchers and knowledge holders | | *What counts as valid knowledge?* | Primarily expert and scientific knowledge | Local knowledge and collective insights bridge gaps with expert knowledge | *Table 2: How collective R\&D shifts where, who and what in the R\&D process* The approach described in this guide builds on collectiveness: actively involving the broader ecosystem,[\[24\]](#footnote-23) enabling collective intelligence[\[25\]](#footnote-24) and generating collective impact. *** ## Notes 1. Building prototypes and visualizing our thoughts through tangible materials (e.g. Lego Serious Play) plays an important role in making ideas real. This “thinking with our hands” is a form of embodied cognition (Power, 2023; also see Rayner, 2020); it stimulates reflection, helps us consider overlooked aspects of problems or solutions, and facilitates sharing our tacit mental models with others (Heracleous & Jacobs, 2008, 2011), creating opportunities for collective intelligence. [↑](#footnote-ref-0) 2. Johnson (2010a). [↑](#footnote-ref-1) 3. The term “concerned” refers to the Spanish expression “nada sin los concernidos” (nothing without those concerned), acknowledging all stakeholders with interest in an issue, including both marginalized communities and those who hold power. [↑](#footnote-ref-2) 4. Saeed (2020). [↑](#footnote-ref-3) 5. Gupta, Anil K. (2019) Gupta’s central thesis is that grassroots innovations serve as diagnostic tools for identifying gaps in formal systems and revealing where communities have unaddressed needs that neither markets nor governments are meeting effectively. [↑](#footnote-ref-4) 6. There is no failure as long as we learn. [↑](#footnote-ref-5) 7. In their book “About Face,” Cooper et al. (2007) introduce the concept of “posture” in interaction design, describing it as a “behavioral stance” that digital products embody toward users. We interpret this concept similarly as a stance towards a development ecosystem and the world in general: an orientation that shapes how we approach and interact with our environment. [↑](#footnote-ref-6) 8. According to VanPatter and Pastor (2018, p. 42), innovation process models can be categorized as either step models or zone models. Step models specify a sequential order of procedures, behaviors and activities where one step builds on another. Zone models offer a flexible framework where clusters (i.e. zones) of related procedures, behaviors and activities can be applied in any sequence depending on the situation’s demands, insights or knowledge needs.We consider the modes more as zones, instead of steps, but they can be used as steps in a process of course. [↑](#footnote-ref-7) 9. This reflects the co-evolution of problem and solution spaces. As we progress, we simultaneously deepen our understanding of the problem while expanding our knowledge of potential solutions and opportunities through exploration and experimentation. [↑](#footnote-ref-8) 10. Positive deviants are individuals or groups who achieve significantly better outcomes than their peers through unique behaviors and strategies despite facing the same constraints and resources (Sternin & Choo, 2000). We actively search for these outliers within affected communities using data-driven approaches to learn from their effective approaches to common challenges (UNDP, GIZ Data Lab, & University of Manchester, 2021). [↑](#footnote-ref-9) 11. We should remain receptive to unexpected desirable effects, thinking of change in terms of multiple possibilities (some favorable, some not; some anticipated, some unexpected) rather than fixating solely on predefined outcomes in project plans and log frames (also see Lucarelli, 2019). [↑](#footnote-ref-10) 12. It's worth looking at the work of Amy Edmondson (2011, 2018, 2023), whose research focuses on creating conditions for learning from failures. There are good and bad failures, according to Edmondson. Bad failures are blameworthy as they are preventable and occur in predictable conditions when standard procedures are not followed. Good failures, on the other hand, are praiseworthy. They are either unavoidable failures that result from uncertainty and complexity, or they are intentional failures through experiments – an intelligent way to learn. A more radical format, to create the conditions to learn from failure, is FuckUp Nights, where people share and celebrate stories of failure (FuckUp Nights, n.d.). The UNDP Accelerator Lab in Mexico, for example, has experimented with this format. [↑](#footnote-ref-11) 13. See Snowden’s musings on safe-fail probes (2007) for more details. [↑](#footnote-ref-12) 14. We regularly share our learnings through “Working Out Loud” by openly documenting our work in progress. This approach stimulates reflection, boosts knowledge sharing and creates opportunities for unexpected collaborations. [↑](#footnote-ref-13) 15. It’s important to recognize that several factors influence whether solutions, knowledge, insights and innovations are adopted by a social system or group, and this process can often take a significant amount of time (see Rogers, 2003). Adoption largely depends on the group’s or organization’s absorptive capacity (Cohen & Levinthal, 1990; Zahra & George, 2000), which is their ability to recognize the value of new information, integrate it and apply it effectively. [↑](#footnote-ref-14) 16. Helping a collective see what it actually knows and what it is learning helps it to become smarter. Feeding back knowledge to empower – not extraction – is an important principle of collective intelligence (see Peach et al., 2020, p. 40). [↑](#footnote-ref-15) 17. See for example our SDG Commons, a resource hub with data, insights, solutions and next practices for the Sustainable Development Goals (). [↑](#footnote-ref-16) 18. See for example Digital Public Goods (). [↑](#footnote-ref-17) 19. Open-ended design allows end users to finish, alter or amplify a solution to better align with their specific needs, goals and contextual requirements. See Redström (2008) for more on this, or see Grint (2008) who refers to open-ended solutions as “clumsy solutions.” [↑](#footnote-ref-18) 20. By new configurations we mean a change in architecture (Henderson & Clark, 1990) or in the arrangement (Lobenstine et al., 2020) of how elements of solutions are related. Such new configurations can, when successful, generate new value through different effects. [↑](#footnote-ref-19) 21. “Bricolage” describes how innovators in resource-constrained environments creatively combine and repurpose existing solutions, technologies and capabilities to address local challenges (see Mateusa & Sarkar, 2024). [↑](#footnote-ref-20) 22. These intermediaries often operate as knowledge brokers (see Meyer, 2010) who translate, interpret and transfer information across different contexts, or as “boundary spanners” (Tuschman, 1977), who enable interaction between different social groups (Star & Griesemer, 1989; Akkerman & Bakker, 2011), or gatekeepers who can provide access to hard-to-reach communities. [↑](#footnote-ref-21) 23. While these twelve practices emerged from the learnings of a network of labs devoted to reimagining development for the 21st century, we see them as relevant for anyone trying to create change in uncertain environments. It is not necessary to have specialized roles with one person assigned to each practice; in fact, these practices are most effective when approached collectively. [↑](#footnote-ref-22) 24. The broader ecosystem includes communities, local networks, existing collectives and both usual and unusual actors – those who are typically engaged as well as those who are often overlooked. [↑](#footnote-ref-23) 25. Enabling collective intelligence involves developing the infrastructure, conditions and capabilities that help ecosystems see themselves, coordinate learning, make decisions together and sustain this intelligence over time. [↑](#footnote-ref-24) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://bas-leurss-personal-organization.gitbook.io/undp-accelerator-labs/understanding-r-and-d/2.-the-fundamentals-of-collective-r-and-d.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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