In recent studies on the ability of informal influencers to drive organizational change, evidence reinforces the critical role these informal networks play in the success of change initiatives. Research employing Organizational Network Analysis (ONA) has illustrated that informal influencers, although sometimes lacking formal authority, are often well-connected within an organization. This connectivity allows them to influence and expedite change by building trust and spreading innovations effectively.

Such influencers typically occupy unique positions within social networks, enabling them to bridge departmental divides and shape perceptions of change across various areas. By leveraging ONA to pinpoint and involve these influencers, organizations can enhance strategies like cross-departmental collaboration and role adjustments to support change more efficiently.

Studies indicate that using ONA for identifying informal influencers can double the reach of change efforts compared to conventional hierarchical or manager-nominated approaches. Central connectors, for instance, align teams and distribute ideas naturally, while brokers link different organizational groups, facilitating essential cross-functional collaboration. Additionally, energizers promote enthusiasm and engage peers, helping to avoid potential resistance and maintain momentum by exemplifying a constructive attitude towards change.

Implementing ONA in change efforts has shown high effectiveness, as demonstrated by a petrochemical company case where ONA optimized knowledge sharing and encouraged collaboration. By actively involving informal influencers in strategy execution, the company achieved greater efficiency in adopting and integrating changes across teams. Engaging informal networks provides a more adaptable and responsive method for change management, enabling broader and faster influence than formal communications.

A recent MDPI study also indicates that informal influencers, when combined with leadership styles such as “humble leadership,” significantly enhance change outcomes. This study, focused on the telecommunications and refinery industries, found that engagement through informal networks and participative leadership strongly correlates with successful change, highlighting the necessity for leaders to facilitate rather than direct change processes.

Additional findings highlight that the effectiveness of informal influencers arises from their deep integration within organizational networks. Their strong connectivity across various parts of the organization allows them to understand team dynamics and subtle resistance points, positioning them as effective champions of change in ways that formal leaders may struggle to achieve. Research on informal organizational structures reveals that these influencers skillfully manage the relational and emotional facets of change, often neglected by formal hierarchies (Molina, 1995).

By harnessing informal networks and engaging key influencers, change agents can adopt a more adaptable, responsive, and trustworthy approach to implementing change, fostering organizational resilience and alignment throughout transitions. In my next piece, I will explore specific methods for using ONA to identify and gather insights about these informal influencers.

Sources:

Battilana, J. Casciaro, T. (2013) “The Network Secrets of Great Change Agents”. Harvard Business Review, July-August 2013, 62-68.

Burt, R.S. Ronchi, D. (1994) “Measuring a Large Network Quickly” Social Networks, 16(2): 91-135.

Horak, S. Afiouni, F. Bian, Yanjie Ledeneva, A. Muratbekova-Touraon, M. & Fey, C.F. (2020) “Informal Networks: Dark Sides, Bright Sides, and Unexplored Dimensions”. Management and Organization Review, 16(3): 511-542.

Jian, X. Du, J. Zho, J & Cui, Y. (2020), “The Impact of Negative Informal Information Before a Change on Performance: A Within-Person Approach”, International Journal of Environmental Research & Public Health 17(2): 670.

Molina, J.L. (2001) “The informal organizational chart in organizations: An approach from social network analysis. CONNECTIONS, 24(1): 78-91.