Key lessons to drive digital transformation of SMEs through the experimental approach

Disclaimer: This blog post has been cross-posted from its original version in Spanish, which can be found at the Inter-American Development Bank blog here.

Micro, small, and medium-sized enterprises (SMEs) have significant potential to improve their productivity and competitiveness while boosting local economies through technological adoption and digital transformation. According to the "Digital Panorama of SMEs in Latin America 2021", SMEs make for a sizable proportion of the total number of enterprises in the region, accounting for 60% of employment; nevertheless, their contribution to GDP is just 25%. In comparison, the share of this business segment in GDP for European Union countries averages 56%, demonstrating the enormous potential that remains untapped in the Latin American region.

So, how can technology adoption and digital transformation be effectively promoted among SMEs? According to a recent evidence review developed by the Innovation Growth Lab (IGL), there are numerous opportunities to apply an experimental approach to answer policy-relevant questions about the effectiveness of technology adoption programs. This can also assist SMEs to overcome some of the barriers they face when beginning their digital transformation process.

Experimentation has been crucial to enable the production of new knowledge. David L. Rogers, author of "The Digital Transformation Playbook", states that experimenting for digital adoption programmes in companies provides for early, rapid, and timely learning, which promotes feedback mechanisms and a rigorous evaluation of their results.

In this blog, we will show you how to use an experimental approach for innovation policies, which allows to identify and evaluate different alternatives in an agile and rigorous manner. This method can lead to the improvement of the design and the effectiveness of public policies and, as a result, improve decision-making and programme prioritisation.

IGL and Inter-American Development Bank for Digital Transformation 

Between September and November 2022, the Inter-American Development Bank (IADB), working with IGL, held a series of online workshops for technical teams in charge of designing and implementing public innovation and entrepreneurship policies in three Latin American and Caribbean countries: Argentina (Undersecretary of Small and Medium Enterprises of the Ministry of Economy of Argentina), Jamaica (Development Bank of Jamaica), and Uruguay (National Development Agency). The goal of the workshop series was to guide policymaker teams in the development of an experimental pilot of digital transformation programmes.

The methodology used in these workshops included an important practical component that facilitated for the three countries' technical teams to quickly integrate concepts such as theory of change, target population, beneficiaries, and measurement, facilitating their learning and use.

Each participating institution, accompanied by one IGL specialist per team, collaboratively proposed and developed the design of the various stages of their respective pilot projects, based on the context of their countries and SMEs. Furthermore, the facilitation of peer-to-peer learning spaces allowed for feedback not only from an expert perspective but also from the participants' own complementary experience.

Despite all the benefits of experimentation, only few SME digital adoption programmes in Latin America and around the world conduct rigorous evaluations of their results and impacts. This is often due to time or resource constraints, or because taking an experimental approach requires an additional effort to the public policy cycle that is not usually considered.

Advantages of experimentation in public policies for innovation and productive development 

Applying an experimental approach can provide a series of advantages when designing, implementing and identifying areas for improvement in public policies for innovation and productive development. 

• The approach is useful in the definition and design phases of interventions because it can encourage innovation in the formulation of new ideas. By developing a clear theory of change, it is possible to identify and design different solutions which can be either marginal modifications of the initial idea or new programs targeting a variety of activities. These, in turn, can then be rigorously evaluated during their implementation to provide policymakers with the most effective options before taking programs to scale. 
• Applying an experimental approach improves results’ robustness. It assists policymakers in properly defining and measuring programme indicators, distinguishing between programme outcomes - dependent on the program's proper implementation on the ground - and programme impacts - which indicate a change in the conditions and characteristics of the policy's beneficiaries.
• Opportunity to identify causal pathways of a program’s impact. Experimental evaluations allow not only measuring whether a programme is effective or not, but also why it has been more or less effective; this is due to the methodology used to measure the impact of public policies through randomised controlled trials (RCTs), which, when properly developed, can provide reliable and relevant results for decision making. By identifying causal relationships between the policy and its outcomes, recommendations can be made to improve the policy in the future. This allows for continuous monitoring and adjustment of the policy as more information is acquired. 
• Experimentation enables public programmes to be replicated and scaled in other similar contexts and scenarios. Policymakers who use the experimental approach can both aid their peers by generating evidence through experimentation and benefit from the evidence generated by others in comparable circumstances. 

Achieving the potential of these benefits requires substantial government capacities not just for the effective application of experimentation, but also for the interpretation of its outcomes. As a result, providing policymakers involved with programme design and execution with the necessary skills and understanding of experimentation is critical. 

Practical recommendations

Training workshops on the experimental method, such as those we held in Argentina, Jamaica, and Uruguay, provide theoretical and practical preparation of important elements to government agency teams while also increasing interest and receptivity to the experimental approach's use. 

When designing similar projects, it is important to consider the following four key factors:

1. Train the technical teams participating in the various stages of policy creation for innovation. This guarantees that all relevant teams have ownership of the ideation and experimentation process and may contribute their viewpoints, learning, and experiences, in addition to favouring a transversal institutionalisation of the experimental method. 
2. Leverage these experiences to connect work teams from diverse settings and at similar phases of their experimentation process allows for sharing  experiences, difficulties, opportunities, and learning across realities, resulting in synergies that would otherwise be difficult to create. 
3. Adapt the workshop topic based on the stage of development of the public policy to be evaluated. Content on ideation, theory of change, and policy design will be more appropriate for teams in the early stages of developing or improving interventions, whereas more technical content on data collection, experimental design, and measurement strategies will be more appropriate for institutions that are already well into the design of their pilots. 
4. Ensure bespoke and expert support throughout the capacity building and implementation of the first experiments. This is critical to the effectiveness of experimentation and continuous learning. 

Incorporating an experimental approach in public policies for innovation and entrepreneurship can offer numerous advantages, and as policymakers continue to design and implement innovation policies, it is essential to promote the value of experimentation and provide them with the right tools towards evidence-based decision-making.