In my last installment, I discussed research supporting the importance of leveraging informal influencers during change. Before we can engage and leverage informal influencers, we must first identify who and where they are. The traditional method is to ask managers to nominate these individuals, in much the same worn-out way we too typically use to create champion networks. This approach, however, often proves more of a popularity contest. And the most outspoken and visible are not always the best or most influential choice.

Snowball sampling has proved an effective and commonly used method for identifying informal influencers in organizational network analysis (ONA). The idea behind snowball sampling is that it’s a way to grow your sample of participants from a small beginning. It works by starting with a few initial participants, usually selected for specific attributes or roles, who then refer other influential individuals within their network. This process builds upon itself, expanding the pool of respondents as each new set of referrals brings more people into the study.

Research has shown that snowball sampling is especially useful in complex or decentralized networks where traditional sampling typically misses out on informal connections. This approach has been used to understand knowledge flow in professional networks, such as in technology sectors or academia, where knowledge-sharing practices rely heavily on informal ties and peer recommendations. Snowball sampling in these contexts has revealed how certain individuals act as hubs or connectors, aiding in the diffusion of knowledge and attitudes.

This method is particularly beneficial in ONA when targeting informal influencers who may not hold formal titles but organically exercise significant influence over their peers. Snowball sampling can help uncover these hidden opinion leaders and uncover insights that might otherwise remain obscured. This is particularly important in networks where influence is dispersed across less visible nodes rather than through official channels. In practice, simple surveys or informal interviews asking participants to nominate individuals they consult on work-related matters can reveal recurring names, which then highlight key influencers in the network after a few rounds of sampling.

To identify informal influencers in an organization using surveys, it’s essential to ask targeted questions that uncover key relationships, trust levels, and communication patterns. To initiate this process, I recommend using free or low-cost survey tools like Microsoft Forms, Google Forms, or SurveyMonkey to gather information on relationships within the organization. Keep the survey as short and concise as possible by only asking questions that map out who individuals collaborate with, seek advice from, or go to for support. As an example, questions might include:

These questions address communication patterns and expertise seeking. Below are some additional questions that could be used, inspired by best practices in ONA. Select (or modify) these questions to fit your particular environment and purpose.

In this way, respondents point you to others in their own network who then become respondents in the next round of surveying. Those new respondents then refer even more influencers in their respective networks. Continue with this expanded process until you run out of newly identified participants.

While the mere identification of individuals who are repeatedly named is itself illuminating, visually representing these relationships will position you to draw additional conclusions. For smaller, simpler networks tools such as Visio or even Powerpoint are usually adequate for this task. Larger more complex networks may benefit from the use of tools designed specifically for social network analysis. My favorite tool, that also has a free tier, is Polinode.

Polinode is a platform for network mapping and analysis that offers various tools for conducting organizational network analysis (ONA), making it a good option even for those looking for cost-effective solutions. While not entirely free, Polinode has flexible pricing and offers a trial that may be sufficient, particularly when organizations want to test the effectiveness of network visualization without making a major investment. It provides intuitive, browser-based tools that allow users to upload data or collect network data using customizable surveys. This enables users to map relationships, analyze these networks, and identify important influencers.

Used together, snowball sampling and ONA provide a powerful mechanism for understanding who are the key influencers in an organization. Armed with this information, change agents can craft interventions that enhance engagement and increase commitment. Who is central in given network? Centrality indicates that people trust and respect that person’s competence, wisdom, and influence. Where are the bridges? Who connects other groups? These are the connectors who can help you promote cross-departmental collaboration and commitment. And where are the energizers? Energizers can create enthusiasm for a change effort.

In my next installment, I will delve into ways the output from a simple ONA can be leveraged in this way.

Sources:

Biernacki, P., & Waldorf, D. (1981). Snowball Sampling: Problems and Techniques of Chain Referral Sampling. Sociological Methods & Research, 10(2), 141-163

Bryan, L.L. Matson, E. & Weiss, L.M. (2007) “Harnessing the Power of Informal Employee Networks”. McKinsey & Company, 11/1/2007.

Cross, R. et. al. (2010), “The Organizational Network Fieldbook: Best Practices, Techniques and Exercises to Drive Organizational Innovation and Performance”, Jossey-Bass, San Francisco.

de Toni, A.F. and Nonino, F. (2010), “The key roles in the informal organization: a network analysis perspective”, The Learning Organization, 17(1), 86-103.

Jewels, T. Underwood, A. and Heredero, C. (2003). “The Role of Informal Networks in Knowledge Sharing”. ECIS 2003 Proceedings. 57

Waters, J. (2014). Snowball sampling: a cautionary tale involving a study of older drug users. International Journal of Social Research Methodology, 18(4), 367–380