By Alok Samantaray, MD, RAYRC
Most of what we understand today about scaling up social change has been borrowed from 19th-century industrial expansion, 20thcentury pharmaceutical regulation, and 21st-century technology startups. We refer to these as the industrial, pharmaceutical, and lean scaling paradigms. While there is much that we can learn from these paradigms, they are insufficient for contemporary social innovation.
Scaling is not an imperative. In fact, sometimes it is better not to scale. The first principle, “moral justification,” balances the pressure to grow against a responsibility to others. Researchers may feel pressure from government, investors, funders, and peers to increase the use of their innovation or to grow their organization. But in making that decision, innovators also have a responsibility to the people affected by their innovation. And part of that responsibility is met by the way in which scaling is justified.
The people who are affected by an innovation are the ones who bear impact risk. They will suffer if an innovation fails to produce its intended positive impacts or unintentionally produces negative ones. If social innovators scale before they are adequately certain about the impacts of their solution, they impose too much impact risk. If they scale too cautiously, they impose too little. Social innovators should search for an intermediate, acceptable level of risk. (See “Three Levels of Impact Risk” below.) Among the factors that help determine what may be an acceptable level of risk are the urgency of the problem, cost of failure, diversity of perspectives, availability of competing solutions, and likelihood of negative impacts.
The pharmaceutical scaling paradigm is premised on the need to capture the sole rights to an approved innovation. The keys are “authority to scale,” in which the government grants an innovator permission to scale up a drug based on phased clinical trials, and “exclusivity of scale,” in which the innovator is empowered through patents and trade secrets to deny others the right to scale up the innovation. The subsequent challenges of operational scale—the manufacture and distribution of a pill, for example—are often trivial in comparison. Current approaches to evidence-based programming, favored by many governments and foundations, draw heavily on this paradigm.
The lean scaling paradigm is premised on the need to grow fast in a competitive market. The keys are “rapid learning,” quickly iterating product designs to understand what markets value, and “resource scale,” securing timely funds to exploit what has been learned and grow market share. The lean development process—build a minimum viable product, bring it to market, learn rapidly from customer behavior, modify the product or pivot, and repeat—drives many of today’s leading tech startups. Unlike pharmaceutical companies, these innovators do not require authorization to scale, only the support of customers and investors, and they often find exclusivity difficult to enforce. As with pharmaceuticals, the problems of operational scale are usually negligible, especially if the innovators are selling intangible goods, such as software as a service. This is the paradigm that social entrepreneurs and impact investors are often encouraged to follow.
A traditional theory of change, which we call a “program theory of change,” presents a plausible explanation of how a program is expected to achieve impact at a given level of scale. This level of impact is expressed as a static construct, often with graduated levels of similarly static activities, outputs, and outcomes to demonstrate a linear process of change that an innovation will travel to arrive at its eventual impact. A scaling theory of change, by contrast, presents a plausible explanation of how scaling is expected to change the way a program achieves impact as it scales. This is the key feature, and what makes a scaling theory of change different. In essence, it aims to capture the dynamism of innovation. It is intended to complement, not replace, a program theory of change. A scaling theory of change has three basic components: a path to scale, a response to scale, and partners for scale.
A “path to scale” is the sequence of stages through which an innovation is expected to pass as it scales. Any number of stages may be specified and named in a way that is most useful for the context. For example, a path may start with generating a promising idea that may produce a solution, followed by building the know-how to implement the idea, then applying the know-how to take action, and lastly expanding action to achieve impact at scale. This general path can be adapted to any type of innovation being scaled—for example, a policy, product, program, or practice.
Although the stages are sequential, an innovator’s path through them rarely is. Advancing from one level to the next requires justification. Assessments of acceptable risk may result in a decision to move up or down one or more levels, or stay at the current level. Identifying the critical points where scaling should be justified helps ensure that scaling decisions are transparent, are based on relevant evidence, and include the people affected by the decisions.