Open Innovation and Economic Development (2020)

This is an independent study I completed with my partner Rocio Chavez Telleria in the summer of 2020. The result was the investigation of the use of business intelligence platform co-ops for accelerating economic development.

HOW MIGHT COLLECTIVE INTELLIGENCE ENABLE US
TO BUILD BACK BETTER FROM COVID-19?
OPPORTUNITIES FOR SMALL AND MEDIUM ENTERPRISES IN CANADA

Rocío Chávez Tellería1, Geoffrey Evamy Hill1

1OCAD University

August 14th, 2020 – Revised December 23, 2020

Principal Advisor: Greg Van Alstyne

Independent Studies Report

1- ABSTRACT

This paper examines the disruptions caused to small and medium enterprises in Canada by the COVID-19 pandemic. In response to this crisis, collective intelligence design, economic gardening and the emerging platform cooperative model are examined as methods to improve business prospects through the integration and dissemination of strategic business intelligence. This paper proposes a framework for a potential solution based on eleven points of design criteria. Early prototypes are introduced as are next steps to continue the development of the proposal. 

The paper includes a discussion of the initial spark that inspired this research topic, a primer on collective intelligence and a divergent exploration of collective intelligence initiatives for inspiration and insights. It then presents a convergent problem finding with design criteria and a solution proposal: collective intelligence platform cooperative for strategic business intelligence. Finally it includes a conclusion and discussion of next steps.

 2- INTRODUCTION: THE INITIAL SPARK AND PROBLEM FINDING

The coronavirus disease COVID-19 is a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as officially named by the International Committee on Taxonomy of Viruses (Lin, Kuo, and Shih, 2020). Coronaviruses comprise a large family of viruses that originate in animals but later develop the ability to cause respiratory illness in humans (Public Health Ontario, 2020). COVID-19 was first reported in Wuhan, China in late December 2019, and subsequently spread worldwide (Lin, Kuo, and Shih, 2020). When the World Health Organization (WHO) classified COVID-19 as a pandemic on March 11, 2020 (WHO, 2020), it became the fifth documented pandemic since the 1918 flu pandemic (Lin, Kuo, and Shih, 2020).

As the COVID-19 pandemic quickly stressed public health systems and disrupted contemporary ways of living all over the world, many institutions turned to the ‘wisdom and power of the crowds’ to help deal with the situation (Bowser et al., 2020). Such is the case of the province of Ontario in Canada, which launched the Ontario Together initiative to generate a mass of ideas and proposals to deal with the immediate emergency response to the crisis (Government of Ontario, 2020). Through conversations with close contacts working on the initiative or in government, we learned that an effective and efficient strategy to sort through the submitted ideas, evaluate their impact and assess the correct timeline to deploy them had yet to be developed. 

That was the opportunity that sparked interest in this independent study with the initial purpose to create the basis of a new assessment and priority “crowdranking” system for the Request for Proposals by combining open innovation, crowdsourcing, collective intelligence, big data, and complexity economics. Might this offer promise to benefit government organizations in sorting the wide range of ideas being submitted through open innovation and collective intelligence initiatives? The initial research questions were:

  • How can Open Innovation systems be improved to effectively and efficiently assess the value and impact of ideas submitted through open innovation platforms and identify a timeline for strategic deployment?
  • How can Open Innovation be governed to ensure the best results in terms of impact and value over noise and popularity?

However, these open innovation initiatives advanced quickly, and helped with an initial emergency pivoting of the economy towards immediate response with the production of protective equipment such as masks and hand sanitizer. As we started our research on the impacts of COVID-19, and as the pandemic advanced and quarantine measures continued to be extended, we realized that Canada’s Small and Medium Sized Enterprises (SMEs) represented a stakeholder group  facing serious struggles as a consequence of the pandemic. 

To understand the impact of SMEs in Canadian economy, let us look at the following statistics. As of December 2018, there were 1.2 million employer businesses in Canada (See Figure 1). 

Canada’s private sector economy is made up of large (500+ employees), medium (100-499 employees) and small (1-99 employees) businesses. Of these, 97.9% were small businesses that employed 8.4 million individuals in Canada, or 69.9% of the total private labour force; 1.9% were medium-sized businesses, which employed 2.4 million individuals (19.6% of the private labour force); and 0.2% were large businesses, which employed 1.3 million individuals, or 10.5% of the private labour force (Innovation, Science, and Economic Development Canada, 2019). Moreover, in 2015, small businesses contributed 41.7% to gross domestic product (GDP) generated by the private sector, while the contribution of medium-sized businesses was 13.2% and the contribution of large businesses was 45.1% (Innovation, Science, and Economic Development Canada, 2019). 

Figure 1. The outer ring in the figure represents how many SMEs are there in Canada. The middle ring reflects how many people SMEs employ in Canada. Finally, the inner ring reflects how much SMEs contribute to Canadian GDP.  Source: Innovation, Science, and Economic Development Canada, 2019.

The consequences of the pandemic have been particularly damaging for SMEs and, as established by the statistics above, have had a considerable impact on the Canadian economy overall. Statistics Canada reported in May 2020 that small businesses were more likely than those with 100 employees or more to report that their revenue from the first quarter of 2020 were down by 20% or more as compared to the first quarter of 2019 (Statistics Canada, 2020). Additionally, 32.0% of businesses with 500 or more employees reported declines in revenue of 20% or more. This figure almost doubles for smaller businesses where nearly 60% of those with 1 to 4 employees and nearly 56% of those with 5 to 19 employees reported declines in revenue 20% or more (Statistics Canada, 2020). 

The negative impact of COVID-19 in SMEs revenue causes an increase in layoffs. Nationally, nearly 41% of businesses reported that they laid off staff. Of those businesses that laid off at least one employee, 45% laid off 80% or more of their workforce. 47% of small businesses with 5 to 19 employees that laid off at least one employee, laid off 80% or more of their staff.  In comparison, 18% of businesses with 500 or more employees and nearly 30% of businesses with 100 or more employees that laid off at least one employee, laid off 80% or more of their staff (Statistics Canada, 2020). 

The negative impacts of COVID-19 in SMEs and unemployment rates are clear, and the pressure is felt by business owners. According to a CIBC study, 81% of Canadians small business owners say COVID-19 has negatively impacted their operations, and many (32%) even worry about the viability of their business over the next year. However, optimism for the longer term remains strong with most business owners (76%) confident they can rebound after the crisis (CIBC, 2020). These last statistics summarize the challenge for SMEs with regards to COVID-19: finding innovative solutions to stay afloat during the pandemic, and then keep innovating to flourish after the crisis. 

Therefore, exploring the possibilities of the collective intelligence for SMEs to deal with the disruption of COVID-19 seemed like an opportunity for a more impactful contribution of this independent study and aligned with prior interests that were anticipated even in the proposal. With that new purpose in mind, the reframed research questions were:

How might we leverage the power of collective intelligence1 to foster economic flourishing2 during and after COVID-19 for Canadian SMEs?

  1. What benefits and opportunities does collective intelligence offer to SMEs dealing with crisis and disruption?
  2. What does economic flourishing mean in the context of COVID-19?

In the following sections we explain the literature review conducted to address the previous questions, our insights, and a solution proposal. 

3- UNDERSTANDING COLLECTIVE INTELLIGENCE

Collective intelligence takes early shape in the literature through the key concept ‘the wisdom of the crowds’. In his work bearing this title, James Surowiecki described it as the idea that large groups of people are, under the right circumstances, collectively smarter than individual experts when it comes to problem-solving, decision making, innovating and predicting (2004). The right circumstances, or conditions for the crowd to be wise that Surowiecki (2004) identifies are:

  • Diversity of opinion: each person should have some private information, even if it is just an eccentric interpretation of the known facts. The larger and more diverse the group, the wiser its decisions. 
  • Independence: People’s opinions are not determined by the opinions of those around them. This element is important, as in collective thinking where early choices are made visible and in sequence, an “information cascade” can form in which only the first free decision makers gain anything by contemplating the choices available; once a given pattern of decisions has become sufficiently informative, it pays for later decision makers to simply copy the pattern, which can be understood as herd behavior. In its uniformity, the herd pattern can lead to fragile social outcomes. 
  • Decentralization: People are able to specialize or draw on local knowledge. The hierarchical management and bureaucracy that characterize centralization can totally close the wisdom of the crowds. 
  • Aggregation: Some mechanism exists for turning private judgements into a collective decision. It is essential that information is available to all contributing parts. 

Drawing on the possibilities of these understandings of collective wisdom and the opportunities presented by social media, Marina Gorbis, executive director of the Institute for the Future outlines in her book, The Nature of the Future: Dispatches from the Socialstructed World, the concept of socialstructing. Gorbis describes socialstructing as creating a new economy around social connections and social rewards through a new form of value creation that involves micro-contributions from large networks of people utilizing social tools and technologies to create a new kind of wealth (Gorbis, 2013). The participants of the socialstructured world are amplified individuals, which Gorbis describes as individuals empowered with technologies and the collective intelligence of others in their social network (2013). MIT professor Thomas Malone extends this concept further by pairing it with the potential of smart computing. For Malone, collective intelligence is the combination of groups of humans and computers “thinking together” in such a way that the whole is more than the sum of the parts (Malone, 2018). 

The idea of humans and machines thinking together is well developed, with centres at both MIT and UCL (University College London) studying the phenomenon and interest growing in the past three years with new popular publications. NESTA, the innovation charity in the United Kingdom, has developed a framework for collective intelligence design or the “tools, tactics and methods to harness the power of people, data and technology to solve global challenges” (NESTA, 2019). Former NESTA chief, Geoff Mulgan, refers to these as “assemblies.” Malone sees collective intelligence as forming a type of superorganism that he calls a “supermind.” . There are different species of superminds, including :

  • “Hierarchies, where people with authority make decisions that others are required to follow.
  • Democracies, where decisions are made by voting.
  • Markets, where decisions are made by mutual agreement among trading partners.
  • Communities, where decisions are made by shared norms” (Malone, 2018).

To harness the power of collective intelligence, the interactions of these species must be understood. But the general insight is that the addition of smart computing power to facilitate the interaction of groups ultimately will lead to better outcomes towards whatever problem one is trying to solve. As Gorbis (2003) describes it, we can enter into a new partnership with smart machines that amplifies our ability to deal with complexity and enhance the quality of our decisions. Another critical insight is that intelligence is distributed, and that diversity within these superminds will lead to better outcomes (NESTA, 2019; Surowiecki, 2004). Overall, there is a need for new assemblies or superminds that can “marshall collective intelligence for global tasks, from addressing climate change to avoiding pandemics, solving problems of unemployment to the challenges of aging” (Mulgan, 2017). 

4- DIVERGENT EXPLORATION OF COLLECTIVE INTELLIGENCE INITIATIVES FOR INSPIRATION AND INSIGHTS

Once we understood the theoretical underpinnings of collective intelligence, we searched for successful initiatives to learn from the practice. We now describe briefly the cases analyzed and the insights gathered from them. 

First, Thomas Malone’s theory of superminds led us to look for initiatives that provide smart information systems that allow humans to deal better with complexity and make better decisions. Considering the intended goal of this independent study, which is to help SMEs navigate the disruption of COVID-19 and innovate their business to survive and thrive, we found the Atlas of Economic Complexity (AEC) as a good example. The AEC is a data visualization tool that compiles and mathematically compares global trade data in order to identify new growth opportunities for every country world wide. We were inspired by the use of big data and geographic information systems (GIS) to help SMEs identify new opportunities for their businesses despite the doom and gloom of conventional reality. From this example, we came up with the following question:

  • How might we design a system that allows SMEs to visualize rich and complex data about their context and spot opportunities for their business?

The research about Thomas Malone’s approach to collective intelligence led us to find his Pandemic Response CoLab initiative, an MIT Centre for Collective Intelligence platform to identify problems to be solved in relation to the pandemic, developing solutions for identified problems, and recruiting people and resources to implement the selected solutions. By analyzing this case we realized that, as an extraordinary application of collective intelligence to deal with the pandemic, there was one element missing. COVID-19 represents a crisis situation with high levels of disruption that question every paradigm and understanding we had of how to do business. A crisis situation puts the crowd in a different position, where they first need to navigate a great deal of uncertainty and transform their mental models to think in new and broader spectrums that inspire new paradigms and ways of doing and working. As Gorbis (2013) explains, one of the main challenges to envision the future is overcoming fear of the unknown and uncertainty, But, as psychologists beginning with Bruner and Postman have noted , our ceaselessly-resolving minds have a distaste for incongruity and ambiguity, especially when we are under pressure (Bruner & Postman, 1949; Holmes, 2016). This natural distaste of ambiguity, the negative mental states caused by uncertainty, and how these affect our working theories of the world, have been deeply explored beginning with psychologist Jean Piaget and subsequently expanded on . 

Our mental sensemaking process in the face of ambiguity, Proulx and Inzlicht (2012) found, can be summarized in five A’s: assimilation, affirmation, accomodation, abstraction, and assembly. When a phenomenon disrupts our mental models of how the world works we can cognitively respond through assimilation, which is when we extend our current mental models to try to “fit in” and explain the novel phenomenon. No real transformation of our thinking paradigm happens here. But the authors/ later learned, “assimilation” [in face of uncertainty] is so often incomplete. One way we can respond to these lingering anxieties is by finding comfort in what is known to us, in our social groups, and passionately emphasizing our ideals, even if it seems irrational in the face of new information. Proust and Inzlicht called this reaction affirmation. Affirmation is the intensification of beliefs, whatever those beliefs might be, in response to a perceived threat. As Piaget signaled out, we can also adjust the way we see the world when challenged with an inconsistency. He called this process accomodation. However, Proulx and Inzlicht further identified that when something disturbs our sense of order and consistency, we enter a state of anxious vigilance where we are motivated to seek out new information.  In light of the patterns retrieval characterizing the phase, Proulx and Inzlicht have dubbed this response abstraction. It’s when we’re galvanized to collect clues from our environment. 

The fifth and final reaction that Proulx and Inzlicht found in reaction to contradictory, ambiguous experiences is a type of creativity. In keeping with the other four A’s — assimilation, accommodation, abstraction, and affirmation — the authors label this fifth reaction assembly. When we assemble, we take the uncertainties in our lives and create something out of them. The challenge for SMEs, and everyone, then, when responding to a crisis situation with such a high level of disruption like COVID-19, is to navigate that cognitively painful process to deal with uncertainty and reach a state of assembly. As we can see from the five A’s of sensemaking, reaching a state of assembly involves a level of purposeful, conscious learning. 

After a sudden catastrophe, people experience what psychologist Ronne Janoff-Bulman called a “double dose of anxiety”. The first dose reflects longer-term fear for our well-being: suddenly, the world doesn’t feel as safe. The second dose of uncertainty comes from the challenge to our working models of the world, from the threat to our “conceptual system, which is in a state of upheaval”. The world feels less safe, but the assumptions that provided us with a sense of coherence are also challenged (Holmes, 2016). The challenge with COVID-19 is that it has disrupted our working theories of the world, and we have to make the cognitive effort to navigate the new swath of information to figure out new working theories. To navigate that suddenly new swath of information we need synthesizers. As biologist EO Wilson describes it: “We are drowning in information while starving for wisdom. The world henceforth will be run by synthesizers, people able to put together information at the right time, think critically about it, and make important choices wisely” (Wilson in Gorbis, 2013: 18).

The information synthesis element was definitely present in the Pandemic Response CoLab initiative, but the learning component to manage uncertainty was not. That insight led us to the following question:

  • In a crisis situation, how do we help people navigate high levels of uncertainty and transform their mental models to come up with a broader spectrum of thinking when current paradigms have been highly and quickly disrupted? 

That insight about how effective collective intelligence initiatives might change when dealing with a crisis situation led us to analyze Climate CoLab, another MIT Centre for Collective Intelligence platform. This initiative seeks to involve crowds to generate innovative proposals to respond to global climate threats. This was more relevant to our purpose of using collective intelligence for crisis response, and their approach to crowdsourcing is one of the best ones from the examples we analyzed, but the same transformational learning component identified in the previous case is also missing in this example.

Then, we looked for another example of a collective intelligence initiative. We resorted to our previous experience and analyzed Chaordix,  the company that is behind LEGO Ideas, one of the most prominent examples of collective intelligence on the web that is used by the Danish toy giant as an engine to crowdsource ideas for new products. Chaordix has streamlined the development process for co-creation community platforms for various companies. From their approach we learned that there is a difference between groups and communities. A group is just several people who are geographically (or digitally) close together or have been brought together by some external force. A community is a group of people who share a sense of values, mission, and purpose (Chaordix, n.d.). Another practical lesson from the firm was that designing variety into the activities of a co-creation digital community ensures broader participation. Most importantly, we learned that unstructured “water cooler” spaces for peer-to-peer interactions uncover powerful insights. Another important insight from Chaordix is that multi-phase creativity workflows are more productive than simple idea generation forums. As the firm explains it, “with a strategically planned creativity workflow, there are several touchpoints, a clear explanation of the problem to be solved, and an opportunity for participants to be iterative in their thinking” (Chaordix, n.d.). These insights from Chaordix’s approach sparked the following questions:

  • How can we foster a community of SMEs with shared values, mission, and purposes to deal with the disruptions of COVID-19?
  • How can we design a platform that allows SMEs to interact with peers in “water cooler” spaces to uncover powerful insights?
  • How can we design a system that allows SMEs to be iterative in their thinking when looking for solutions to help their business survive and thrive within the disruptions of COVID-19?

Another example that we analyzed was the Global Village Construction Set. It is an open source blueprint toolbox of the essential tools that humans need to meet material wants. The project is about designing low cost, DIY tools ranging from brick presses to bread ovens so that they can be replicated by makers and entrepreneurs around the world at a very low cost. This example led us to the following questions:

  • How can SMEs share and access open source solutions, not necessarily just about hardware, in a replicable way?
  • What is it about business intelligence that can be effectively shared as open source and replicated by varied businesses in diverse contexts? 

The previous questions led us to search for an example that addressed how to do open source problem solving, and we found Seenapse. It is a platform, or “inspiration engine,” that helps to connect new ideas and see the connections of others. Basically, it allows people to share their mental connections. We realized that it could be a model for how to share new and novel connections and how they lead to new types of innovations. The most important insight that we got from Seenapse was that, in open source and collective intelligence endeavors, probably sharing the thought process is more valuable than sharing just the ideas, since the thought processes can be more effectively adjusted or replicated to achieve better results in diverse contexts. This insight led to us to the following question:

  • How can SMEs share the thought process and mental connections that sparked ideas to solve their challenges? 

Through the analysis of Chaordix, Global Village Construction Set, and Seenapse, peer to peer learning emerged as another important conceptual element that we need to consider if we seek to develop a proposal to help SMEs navigate the disruption of COVID-19 to flourish within the crisis. Peer to peer (P2P) learning aims to provide opportunities to exchange knowledge and experience on a particular topic. This learning is primarily facilitated by bringing individuals from different backgrounds together as “peers,” who through sustained engagement exchange knowledge and experience learning to mutual learning on how to deal with the challenge or issue in question. Then these individuals feed the learning back to their organizations and work towards an application at scale in their organization or context (Effective Institutions Platform, 2017)

Next, as a second stage of case analysis, we looked for collective intelligence initiatives that dealt more directly with economic development situations, which addresses the purpose of fostering economic flourishing as stated in our research question. A frequently cited effort related to this purpose is the project of JP Morgan Advancing Cities. This is an ambitious undertaking by one of the largest banks in the world, originally focused on Detroit, to help spur economic development and urban revitalization. What stood out for the purposes of our independent study was how a ‘big data’ approach was used to help minority entrepreneurs to identify optimal places to start their businesses in the city. Further research about this example led us to the discovery of the established practice of ‘economic gardening’.

Economic gardening is an entrepreneurial approach to growing communities from within. (Gibbons, 2010) It emerged in 1990s Colorado as a response to massive layoffs in the community of Littleton. It has helped to double the job base in Littleton from 15,000 to 30,000 in the first 20 years of its use. (Gibbons, 2010). Economic gardening is an alternative to ‘business attraction’ policies which seek to promote economic growth by bringing new businesses in. We view the approach as more sophisticated and a different mental model than not only business attraction, but also the common economic development practice of business retention and expansion (BR&E). The practice has been adopted in jurisdictions across the United States, including the large GrowFL initiative, but has not been consistently adopted in Canada.

Economic gardening (EG) is about creating an information advantage for local companies: not by financial subsidies, but through improved knowledge. According to the proponent of the model, Christian Gibbons (2010), economic gardening uses corporate tools such as database searching, geographic information systems (GIS), search engine optimization (SEO), web marketing, social media and research tools, and networking mapping to gather systemic information that entrepreneurs and businesses within the community can use to grow their endeavors. As Gibbons (2010) furtherly explains, EG focuses on front end, strategic issues of business such as core strategy, market dynamics, marketing, teams, and finance. As per current practice, EG depends on a highly skilled staff working in an iterative manner with business owners to use this framework of business intelligence to enhance business growth strategies. Nevertheless, we believe that as long as conditions are met for the wisdom of crowds (Surowiecki, 2004), and the community’s wisdom is amplified with smart computing, as pointed out by Gorbis (2013) and Malone (2018), this framework can be adapted as a collective intelligence practice. 

Following that new insight of economic gardening, we then researched if there were any collective intelligence initiatives that addressed economic development with a more local focus and resources as compared to the MIT initiatives. aLocal Solutions appeared as an example. This is a tool from a professor at Evergreen College in Washington State, USA, that allows free demand mapping of the United States down to the census block level. It provides its users not just with complex data visualization but also the possibility of data analysis to develop plans for economic development. This example sparked the following question:

  • How can we design a system that allows SME owners to easily synthesize and analyze data, and then apply it in practical, iterative plans to innovate at low-or-no cost? 

Finally, we looked once more for an example that would combine elements of collective intelligence, economic development and a local approach, now in Canada. We encountered the case of Community Economic Development in Winnipeg. Policy Alternatives Canada analyzed the work being done in Winnipeg, Manitoba to develop an inclusive and environmentally-friendly economy. This project highlighted circular economies built on the Neechi Principles, which helped to carry up the entire populace in terms of a more comprehensive view on development and human flourishing. This introduced us to the support and planning mechanisms to impactful social enterprises, and grounded our approach. The Neechi Principles refer to the guiding principles of the Neechi Foods Worker Co-op in Winnipeg (Canadian CED Network, 2020): 

1. Use of locally produced goods and services

2. Production of goods and services for local use

3. Local reinvestment of profits

4. Long-term employment of local residents

5. Local skill development

6. Local decision-making

7. Public health

8. Physical environment

9. Neighbourhood stability

10. Human dignity

11. Support for other community economic development initiatives

Neechi is the Ojibway word for friend and, as we can see from the list of principles, it complements the focus of economic development with a human-centered, community-based approach. The analysis of this case pivoted our framing of the problem and steered us into a direction that is more focused on community, ownership, and overall sustainability. That insight sparked the following questions:

  • What economic development frameworks would be necessary to propose a solution based on human and environmental flourishing? 
  • How can we explore models of equitable and fair ownership that allow SMEs to gain even more benefit from their participation in a collective intelligence initiative that forms a true community?

Building on the first question about economic development frameworks aligned with human and environmental flourishing, we chose Doughnut Economics as the more comprehensive approach to build on. Doughnut Economics is a model for an alternative metric to gross domestic product (GDP). It places human needs and environmental boundaries in concentric circles, and challenges economies to find a sweet spot within the donut to flourish (Raworth, 2012). This is a visual framework (inset) that helps as a new indicator for economic development. It is interesting to point out that, in April 2020, Kate Raworth, the creator of this framework, was recruited by the City of Amsterdam to help make the Doughnut the model for the city in recovering from COVID-19 (WEF, 2020). 

Then, exploring the second question about models of equitable and fair ownership, we came across the model of platform cooperatives. A platform cooperative is a 10 year old concept for reorganizing digital technologies to meet human needs collectively. To break the term down into its constituent parts, a cooperative is a “legally incorporated organization that is owned by its members, who use the cooperatives services or purchase their products” (Ontario.coop, 2020). A platform is “a digital environment characterized by near-zero marginal cost of access, reproduction, and distribution” (McAfee & Brynjolfsson, 2017). The combined definition, from the platform.coop website is “businesses that use a website, mobile app, or protocol to sell goods or services. They rely on democratic decision-making and shared ownership of the platform by workers and users” (platform.coop, 2020).

While there are many platform cooperatives sprouting up around the world, the simplest explanation of the platform co-op idea uses the well-known taxi-replacement service as a hypothetical example: “What if Uber drivers owned the Uber app?” A notable feature of this model is its well-established foundation: cooperatives have existed for almost 200 years, so there are many instances in which coop organizational best practices are being integrated with technology (platform.coop, 2020). Platform co-ops are emerging around the world across a variety of industries, which also makes exciting the ability to collaborate and share best practices. 

5- CONVERGENT PROBLEM FRAMING 

Let’s review the key theoretical principles of collective intelligence that we identified through the literature review: 

  • cognitive diversity
  • independence
  • decentralization
  • aggregation
  • amplification by smart computing to manage complexity.

Furthermore we can reiterate key insights, which we’ve summarized in the form of questions from our divergent exploration of collective intelligence and economic recovery initiatives. When looking for solutions to help business survive and thrive within the disruptions of COVID-19: 

  • How might we design a system that allows SMEs to visualize rich and complex data about their context and spot opportunities for their business?
  • In a crisis situation, how do we help people navigate high levels of uncertainty and transform their mental models to come up with a broader spectrum of thinking when current paradigms have been highly and quickly disrupted? 
  • How might we foster a community of SMEs with shared values, mission, and purposes to deal with the disruptions of COVID-19?
  • How might we design a platform that allows SMEs to interact with peers in “water cooler” spaces to uncover powerful insights?
  • How might we design a system that allows SMEs to be iterative in their thinking?
  • How might SMEs share and access open source solutions, not necessarily just about hardware, in a replicable way?
  • What is it about business intelligence that can be effectively shared as open source and replicated by varied businesses in diverse contexts? 
  • How might SMEs share the thought process and mental connections that sparked ideas to solve their challenges? 
  • How might we design a system that allows SMEs owners to synthesize and analyze data, and then apply it in practical, iterative plans to innovate? 
  • What economic development frameworks would be necessary to propose a solution based on human and environmental flourishing? 
  • How might we explore models of equitable and fair ownership that allow SMEs to gain benefit from their participation in a collective intelligence initiative that forms a true community? 

Synthesizing this, we arrive at the problem statement and design criteria which follow. 

Problem statement:

SMEs in Canada are in need of emergency innovation to survive and thrive in the disruption presented by the COVID-19 pandemic.

Design criteria:

  • Ensure cognitive diversity within the community
  • Ensure certain level of independence to avoid information cascades and herd behavior to keep the contributions divergent
  • Amplify individuals with smart computing tools and data visualizations that help manage the complexity of information
  • Provide a decentralized organization, but complement it with an aggregation tool 
  • Provide a learning element to help strengthen skills to manage uncertainty and achieve a creative state
  • Provide an opportunity for peer to peer learning and interaction
  • Provide an opportunity to share and consult thought processes and mental connections
  • Provide an opportunity to find creative solutions in an iterative way
  • Provide a human-centered, community-centered an environmentally sustainable framework to steer solutions into a flourishing direction
  • Provide an equitable and fair model of participation and ownership.

The uncertain, highly connected, muti-agent nature of this situation calls on us to tap into the systemic design approach known as designing for emergence (Van Alstyne & Logan, 2006). We recognize that the role of community economic development, and perhaps any type of economic development practice, is of setting initial conditions for the complexification of an economy. We cannot anticipate specific or precise emergent outcomes, but we can help set the conditions for their development. 

Both the problem statement and design criteria that we arrive to point to a direction to help SMEs build back better in the midst of the COVID-19 pandemic. ‘Build back better’ is an approach to post-disaster recovery aimed at increasing the resilience of nations and communities to future disasters and shocks. The approach was first defined and officially used in the United Nations Sendai Framework for Disaster Risk Reduction and is now coined again in the latest Future Possibilities Report 2020 (United Nations, 2020).  

Building on the build back better approach and in the nature of this current disruption, as a health crisis, we decided to use a “multi-vitamins for immune system” metaphor to build and communicate our solution to the problem. This is a first iteration communications approach that would need to be tested with key stakeholders.

6- SOLUTION PROPOSAL: ‘COLLECTIVE INTELLIGENCE PLATFORM COOPERATIVE’ FOR STRATEGIC BUSINESS INTELLIGENCE 

Solution statement: #buildbackbetter

To answer the main question that shaped this independent study, “How might we leverage the power of collective intelligence1 to foster economic flourishing2 during and after COVID-19 for Canadian SMEs?”, we see the opportunity of integrating the concepts explained in the previous sections. These were collective intelligence, P2P learning, economic gardening, doughnut economics, and platform cooperativism. 

To achieve connection and scale in this time of social distancing, and to facilitate the participation of a large, diverse group of people, we chose a digital platform as the best way to address the purposes of this project. The initial approach we are taking is one of creating “digipills”, or bite-sized boosters to the emergency “immune system” of various SMEs, that will help them quickly adapt to the new conditions on the ground of COVID-19 and its fallout. To do this, we are suggesting the formation of a platform cooperative, growtogether.coop. 

Image 1. Platform concept

The platform would carry out the following functions (see Image 2) for its members:

Image 2. Platform system map

The growtogether.coop is an open membership, multi-stakeholder co-op that focuses on providing strategic information support and access to an eco to its members and, where appropriate, their stakeholders. The services offered by the co-op are firstly general toolboxes to promote new thinking in order to encourage members to embrace all features of the co-op, and a triage system to identify how to prioritize the provision of more intensive services. The mainline of services is openeconomicgardening.ai, which is a collective intelligence powered version of the type of strategic business intelligence provision of traditional economic gardening. The second lines (in order of implementation)  are open foresight and open systems. Open foresight is an apporach to understanding possible and preferable future scenarios through a non-proprietary, public, crowdsourced process. Open systems allow companies and jurisdictions to share and view maps of economies to find gaps, and also share blueprints of products and services that can be replicated elsewhere. 

A key value proposition of this platform is its open foresight function. This refers to a foresight process that involves many people in its process (Wiener, 2017). The purpose is to generate better insights through diverse exploration of the cone of possibilities that the future holds. The basis of the signals and trends will be procured by the variety of business staff and members as part of the platform co-op through a simple training exercise. A small group of core co-op members will then turn these signals into trends of different industries, that will provoke new thinking in the business planning and operations of the members. This fills a sensemaking and problem framing purpose.

The next value proposition is strategic information that can be provided to SME members or the indirect members of various municipality members. For this element, we have come up with a couple ways that the emerging economic development practice of economic gardening can be scaled up. The collection and provision of strategic information may be crowdsourced amongst community and national experts in a manner similar to Innocentive or WikiStrat. For instance, co-op members might be municipalities who will facilitate the Economic Gardening provision of information to local businesses. This could even be in the form of prediction markets to determine when a business could be ready to expand. This might be supplemented by enlisting the help of business students or other students at local colleges and universities as co-op members. It might be augmented by a larger open crowdsourcing of business strategy advice from around the world. And it might be connected to other communities, with one municipality providing peer-to-peer (p2p) support to another. Essentially, this co-op is built on the idea of using extra processing capacity to help others in mutual aid and uncovering hidden patterns.

As part of the provision of strategic information, big data is a critical component. We see the opportunity to support the provision of big data analysis and synthesis through a mixture of crowdsourcing and machine learning. This is made possible by the structure of the platform co-op. There are various AI tools already on the market for both market and prospect research. However, it appears that the practice of economic gardening is done manually. It is conceivable to imagine larger benefits and lower costs if we can automate parts of this process, including GIS analysis that is typically quite labour intensive. By automating large aspects of the data analysis done for economic gardening, and crowdsourcing the rest, we can scale up a promising approach that is held back by its labour intensivity despite low barriers to entry.

There are two other more experimental approaches that we would like to put forward include an open innovation adaptation repository, and an open value flow system. Firstly, on the less experimental side, we see an opportunity for this platform to make open source the “blueprints” of economic recovery and building back better. This could be as simple as sharing best practices for social distancing in restaurants, or could be more conventionally unusual such as sharing excellent pizza recipes between restaurants. The possibilities here are significant. Open innovation for SMEs, especially if some larger players can become involved as well, could lead to rapid adaptation opportunities. Secondly, and greater in terms of ambition, would be to set up the platform cooperative to help map the entire material flow of local economies including the recipes used. This idea is inspired by the Valueflo.ws open source software. The purpose would be to make transparent the inefficiencies and gaps in a local economy in a data-equitable manner in order for these to be filled efficiently and effectively. 

Finally, we need to balance all of this bottom-up approach to economic development with a top-down framework that helps to guide the goals and ethics of the systems. We are particularly concerned with social equity and environmental flourishing. For this, we have identified Kate Raworth’s Doughnut Economics as an excellent basis of a framework for an indicator to help drive flourishing as building back better. This should be considered core and critical to our effort. We are also interested in open strategic planning processes, which are not in the scope of this independent study.

Overall, the purpose of this co-op is as a catalyst and information broker that helps to broaden the possibility space for small and medium sized enterprises out of this most difficult time.

We will now explain a more detailed overview of how all those functionalities would look for the platform users. 

Figure 3. Initial features for the platform

The first features for the growtogether.coop platform, as seen in Image 3, would serve as a preparation phase. Participants would find a first “Take your digipills” section that represents the learning component of the system. Participants would encounter gamified activities and tools that would help them strengthen their creativity and innovation skills. The skills offered are centered on what Marina Gorbis (2013) signals as inherently human skills: managing uncertainty, sensemaking, social and emotional intelligence, novel and adaptive thinking, and ethical reasoning. 

Additionally, participants would find a section that allows them to “find their support system”. This would be an Artificial Intelligence powered team-making tool designed to create diverse communities, by combining match-making based on the conditions for the crowds to be wise outlined by James Surowiecki (2004) and serendipity. 

Figure 4. Diagnostic features of the platform

The growtogether.coop platform would also offer its members diagnostic tools. These are an “Economic Gardening Triage”, where the member’s team or community (as assigned by the AI tool) could assess the situation of the business and provide insights for the problem finding, problem framing, and problem solving. Also, members could do a self-diagnosis of their business, by exploring the provided economic and market complexity maps and value flows to identify opportunities for innovation. 

Figure 5. Open foresight and thought process sharing features. 

Furthermore, the platform would offer members a “set up long-term care” section, which would provide the opportunity to navigate an open foresight engine to collectively find and browse signals, trends, and drivers of change in the horizon that could impact their business. The same section would also provide a space to, individually or collectively, distill the possible implications that those horizon changes would have on the members’ business. 

Moreover, a “share the cure” section would be offered where the members would be able to publish, if desired, all their recorded activity in the platform, offering a visualization of the thought process and insights that took place through the connection of information and resources within the platform to iterate a solution. In other words, it will allow users to connect different information generated by the platform to serve their business needs. The platform is about making clear and obvious the value of different connections, and making the connections more often. The connection process will be visible to all others on the platform. This would be called the members’ “medical files” and they would also be able to access those published by others to gather inspiration. They would be able to duplicate and reiterate a “medical file” if the same thought process followed by other members could be usefully replicated by another one, but adapting it to their own situation. 

Figure 6. Platform co-op join function

Finally, the platform would also provide participants with the opportunity to join the platform co-op, which we furtherly outline in the following paragraphs. 

We are in an early, conceptual business model stage, but we imagine for future reference that a multi-stakeholder platform cooperative model would be ideal for growtogether.coop. This is a “‘hybrid’ kind of co-operative whose members represent more than one typical co-op ownership group, such as producers, consumers or workers” (ECOOPE, 2017). There is much to learn from existing co-operative literature, and we will explore this further in the forthcoming paper. We intend to augment, and not reinvent, the wheel.

We are exploring mesh networking (a highly connected, non-hierarchical network) (inset) as a model for the structure of the platform cooperative, with the key stakeholder members being municipalities, regions, SMEs and individuals. The approach may be a partial or complete mesh network. The idea is that by sharing capacity to perform economic gardening activities, the overall membership of the cooperative benefits. Larger cities get access to capital to finance the automation of economic gardening for their larger jurisdictions, and smaller municipalities and regions get access to capacity for economic gardening that would be limited if they set up offices in their jurisdictions. SMEs, both in terms of producers of the IT or consumers of the services, can join regardless of whether they are inside a member jurisdiction or if they are seeking the services from outside. Finally, individuals would be decentrally organized producers and consumers of the intelligence. We see these primarily as the developers and analysts actually producing the infrastructure to provide information. An open question is how do the participants in the open innovation, i.e. crowdsourcing, fit into this membership model. This is overall a business intelligence cooperative, filling the gap (and occasional market failure) of providing information and strategy to firms who cannot afford in-house capabilities. A key question is how do we do this in terms of doughnut economics, which would be a key area for further research and iteration.

7- CONCLUSION AND NEXT STEPS 

We see a rich potential for impact with the concept of “a collective intelligence platform cooperative for strategic business intelligence”. We see the next steps as a paper, conversations or pitches, and possibly an entrepreneurial project. In terms of a paper that advances and formalizes the ideas laid out here, we intend to publish or self-publish our ideas, and do so after expanding on the values, rhetoric, and scaling of this concept. We are also interested in gaining feedback on this idea from various economic development professionals. We are working on a 2-pager for economic development organizations to consider launching an Economic Gardening program, with an eye towards implementing our more advanced ideas. Finally, we are considering doing the business modelling, planning and drumming up of support to turn our idea into a platform cooperative itself. The concept has a lot of promise, and with further development it could possibly be turned into something significant.

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