Overcoming Resistance to Change Using Organizational Network Analysis
A good example of how organizational network analysis (ONA) can break down resistance to change would be Dr. Cross’ example of the M&M business unit of Master Foods Inc. In this case a research and development team came up with an innovative new way of producing M&Ms with customized print and logos. Through the use of an organizational network analysis, they put together a team which was multidisciplinary in nature, and utilized the knowledge of multiple individuals/sources in the network.
They also created an external partnership network to help them execute the new idea. However, much resistance was faced from the executive team of the organization. The organizational network analysis helped the executive sponsor of this project reach out to important decision makers and change agents, while connecting with middle managers who had high resistance to the project. The sponsor “specifically asked them to leave baby teams throughout the enterprise in testing the new business model internally” as he explains in his book.
This use of organizational network analysis is credited with breaking down the internal resistance of the executive team and allowing the new business model to be implemented and executed within 60 days of the final idea inception. It is important to note that Master Foods had a typical turnaround on new projects over 2.5 years.
one component of resistance to change in organizations is cynicism. It is important to note that cognitive, emotional, and behavioral factors almost always result in change-specific cynicism and consequently resistance to anything new (See Qian & Daniels, 2008.) Cynicism and resistance are two distinct concepts though. Cynicism is considered more to be a passive reaction to change while resistance implies that a given network actor intends to or is in the process of actively resisting the change.
This is relevant because in semi-closed settings such as an organization, influence can become “contagious” as rumors dissipate throughout an organization from network members who are highly trusted or influential. In a work related setting, attitudes and change-specific dispositions are highly influenced by verbal and non-verbal communication between co-workers, supervisors and managers, so behavior (especially in extreme situations) tends to be highly contagious. Think back on the last time there were changes to your organization: Did you talk about it with co-workers, and did you feel closer to the ones that agreed with your view? perhaps even adopting some of their views?
Let us think about resistance and cynicism toward change as a socially constructed dynamic which emerges when change is impending in organizational contexts. For our benefit, we will not model it as a trait inherent in an actor’s personality or a mental model (people are not resistant by nature, but just resist things they don’t like or agree with), but as an event that occurs when change is coming for any social network or organizational system. This definition is somewhat supported by research (See Wanous et al, 2000) when researchers found that cynicism, and thus the likelihood for resistance to change, “results from situational factors” and not from the stable point of view of any member of a network.
In Qian and Daniels’ (2008) study of a major academic institution’s change initiative through the use of network analysis, their statistically significant model proposed that quality of communication, peer cynicism, and trust-based relationships accounted for over 74% of employee cynicism toward change and 79% of that cynicism resulted in active resistance to change. More importantly, two network-specific variables (i.e. peer cynicism and trust relationships) accounted for 55% of the reason for employee cynicism alone. This demonstrates the insufficient macro-based perspectives of traditional change methods, due to their lack of focus on resistance that is caused by peer/social networks and can probably account for a significant percentage of change failure.
Resistance to change is often created by network biases as well. Network biases are essentially caused when one person relies too heavily on the advice, opinions, and views of one person or a groups of people in the organization. In the field of organizational network analysis we traditionally call those groups, cliques. The traditional literature of organizational network analysis argues that in order to create a cohesive and collaborative culture in any organization, these cliques must be minimized and members of the cliques should be encouraged to contact members of the organization that are outside of any given in-group. This ensures that groupthink (and thus resistance) is minimized, innovation is increased, and bureaucracy is limited. Think of it as an attempt to limit power structures. When these structures are reduced in size and quantity, the internal culture of those groups are weaker, and members of those groups are probably more open to change.
In the final years of Enron’s collapse founder and CEO Kenneth Lay began to rely heavily on the advice and consultation of the very limited few of his top executives, namely Jeff Skilling and Andrew Fastow. This, be definition is the process of creating a clique or an in-group. When he received reasonable warnings from someone that was outside of his local clique, i.e. Sharon Watkins, about some of the challenges Enron would be facing, he failed to take into account her valuable expertise on the subject of Enron’s survivability. He opted to not be influenced by someone outside his own clique, regardless of their expertise on the subject. Better yet, he probably failed to see her expertise at all, since there wasn’t a strong relationship that allowed him to trust her expertise. In the sense of ensuring that any organization limits its susceptibility to group-think, unethical practices, and bureaucracy, the arguments made by the large majority of social network analysis academic and business articles is sound.
However, it is part of human nature to form communities. People are in constant need to be part of groups bigger than any individual. They seek to be part of a group, a team, and an organization, and most commonly, a family—any of which can be labeled as in-group by standard methods. The result then is not to build organizations to be homogeneous blobs without any structure, but engineer and then energize the structure to align with organizational objectives.
Of course, cliques are not prevalent in all circumstances and context – sometimes they don’t exist in the network structure at all. A Red Sox fan might be willing to consider the opinions of a Red Sox player as it pertains to baseball, but he might not necessarily be willing to take similar advice on financial issues from the same player or fans of that player, which can be defined as a clique from a certain perspective.
Resistance can be lessened through the right application of network measurement and analysis because it enables us to see and trust networks in ways that traditional change practices do not.