Capacity for Change

Written By: George Miyata , SPC, Director of Innovation and Technology

Introduction

Anyone responsible for driving organizational change to adopt a new practice, behavior, or style inevitably comes up against organizational friction to the changes trying to be introduced. That resistance often manifests itself in several forms. Key identifiers of this friction are:

• An Us (the Org) versus Them (change agents) attitude
• Statements like “They (change agents) just don’t understand all of our requirements.”
• Low-velocity approval boards
• Resisting anything new because “it’s not how we do things” (Why fix it if it isn’t broke attitude)

While it’s often frustrating as the change agent to experience this resistance, we must understand and adapt to the friction as it appears.

At DeNOVO Solutions, we refer to friction or resistance as a manifestation of exceeding an organization’s Capacity for Change (C4C). We can approximate the capacity for change through qualitative analysis of monitoring individual behaviors, reactions, and emotions.

Every individual and every organization has an upper limit for the change they can bear at a given moment. An organization can have one C4C, while the individuals within can have various levels of C4C. Change agents must be mindful of the variability of C4C across individuals and the organization itself. Change agents do not always need to adjust planning to the lowest common denominator but should aim to satisfy a majority or critical mass. Satisfying the critical mass enables individuals with lower C4C to witness the success of others and gain trust that the change may also work for them, thus increasing their C4C. There will always be holdouts; change agents should only expect to satisfy some. By building recognition of C4C, change agents can prepare themselves for resistance from minority opinions while ensuring they meet the majority’s expectations.

How does C4C develop, and why is it different? It is learned for better or worse. Generally, a C4C is comprised of the following factors that we will describe further below:

• Risk tolerance
• Scale of change
• Attitude

An individual’s or organization’s risk tolerance plays a significant factor in their C4C. Organizations that encourage and promote calculated risk-taking often enjoy a greater C4C. They have formulated a culture or persona willing to try something new and adjust to the outcomes. Acceptance that failure is part of the process is a strong corollary to risk tolerance; organizations that respond harshly to failure are generally less risk tolerant, as are individuals who have experienced negative responses to taking risks. Past experiences with risks and trying new things shape how organizations and individuals will accept novel ideas. If past experiences have been generally positive, they act as a bank of goodwill, contributing to being more open with the next novel attempt. If past experiences have been negative, especially if they have resulted in poor performance reviews, negative emotions or reputations, or ill will, C4C will be greatly diminished and reduced.

The scale of change shapes C4C because complexity (the number of moving parts) affects perceptions of the achievability of the change. Individuals will tend to focus on the nuances they are familiar with. Thus, large complex changes instinctively drive deep-rooted questioning in individuals who wonder if the change agents have considered these nuances. Creating smaller changes helps alleviate those fears, as it is easier to see and forecast the nuances and how they will develop in response to the proposed change.

Attitude is the third leg in the triangle that comprises C4C. Individuals or Organizations that carry themselves and promote a growth mindset and thus have developed a continuous learning personality embrace change. They enjoy a greater C4C. On the other hand, C4C is reduced when culturally, they have established a fixed mindset.

Capacity for Change (C4C) Definition

Let us depict the Capacity for Change mathematically. Since we are using capacity, let’s evaluate the mathematical definition of a capacitor and extrapolate to C4C.

Variable Definition:

• C = The organization/person’s Capacity for Change (C4C)
• ε = The permittivity (or filters) change must go through for an organization/person
• For an org: The organization’s learning process, its agency (as allowed by industry, law, customers, and upper management), its past experiences, and the personalities comprising the organization
• For a person: their learning process, their agency, their experience, and their attitude toward new concepts
• A = the area or size
• For an org: it is inversely proportional to the size and breadth of the org. It measures the number of people, disciplines, and geographic dispersion of the org.
• For a person: it is inversely proportional to the size of their role. As the person moves up the hierarchy in an organization, they have competing responsibilities that inversely affect their ability to accept change
• d = the relationship distance between the change agents and the organization/people
• The relationship “distance” becomes “trust” that is established between the change agents and the organization/people
• Change agents kept separate from the greater organization have a harder time engaging with stakeholders and shaping the path to change. They are seen as outsiders; thus, the organization/people have low trust and quickly establish an Us versus Them view. The closer change agents interact with the organization, the higher the C4C, as people feel heard and can better communicate their needs and concerns. This creates an atmosphere where trust increases, improving risk tolerance and attitudes toward change.

The flow of Change Modeling

Why is this important? The Capacity for Change drives the total flow of change that can be introduced in elevating the performance of an organization. To model flow, we use the capacitor charging equation:

• I(t) = The flow of change that can be adopted now within the organization
• V? = the effort or energy put into driving change by the change agents
• C = the Capacity for Change
• Leveraging the Capacitance equation earlier, we approximate the result to a number in the Fibonacci sequence
• Thus, a C value of 1 represents few changes with little to no complexity
• R = The organization/individual’s resistance to change
• R modeled with a Fibonacci sequence, with 1 being almost no inherent organizational resistance
• Since R appears in both τ and as a divisor to the change agents’ effort, it is inherent for change agents to minimize R while increasing C to efficiently deploy a change to the organization.
• The resistance to change is not directly related to the organization’s C4C. While an organization and/or its people may have total trust in the change agents (distance [d] ~0), the organization may have inherent processes or outside forces that further constrain and thus naturally inhibit or resist change. Therefore, the amount of change that can be made in a moment is inversely proportional to the organization’s resistance.
• An example of external processes or forces is the number of boards and/or approvals an organization must go through to make changes. Often these approval boards are mandated by corporate reporting requirements, transparency requirements from their customer, or regulations/laws.
• R is the processes, practices, and tools an organization uses to manage change

Introducing the “Right” Amount of Change

With C4C as a model for understanding the amount of change the organization and its people can handle, it’s imperative that change agents refrain from introducing change greater than the capacity the org can receive it, or else they risk overwhelming the system.

This means introducing changes and letting them simmer and boil off (charge/discharge) before introducing more changes; otherwise, you risk constantly pegging capacity and creating an inefficient network for introducing change.

In practice, this requires change agents to monitor, evaluate and understand the natural rhythms of the organization before introducing change. Every organization has rhythms for its work. These rhythms take many forms, such as monthly financial reporting, taking the status of the work/progress activities, planning activities, product delivery cadence (material goods or SW releases), and most of all, meeting cadence. By understanding these rhythms, change agents can discover two critical things: 1) within these natural rhythms, when is the organization most stressed and thus less likely to adopt change; 2) the cycle time of the high to low stress within the organization. By understanding this cycle, change agents can target their changes to be introduced at the appropriate moments to allow the organization to cool off from the last change.

C4C Over Time

With these definitions modeling an organization’s ability to adopt change, we can revisit our first equation and now model the capacitance of an organization as a variable over time, C(t).

Thus C4C [C(t)] is dependent on the relationship of trust [d(t)] between those going through change and the change agents driving it. d(t) itself is modeled as a summation of all past experiences, positive and negative, and acts as a step function based on the perceptions and attitudes of those going through the change. d(t) is a “distance” of the perceived trust, where high trust is represented as a small number because the change agents have established a “close” relationship between them and those being changed. Likewise, a lack of trust creates a large distance between the change agents and those being changed, thus driving the C4C smaller.

Organizations with individuals with negative risk-taking experiences or cultures that punish risks that didn’t work out create low capacitance and an unwillingness to try change and be burned again.

Capacitance increases as trust in a new system and culture is created. Small wins built upon small risks supplant the negative experiences and enable capacitance increases. This creates a recursive loop that, with small changes creating small wins, trust is increased, thus minimizing the d(t) term over time. This increases capacitance, allowing larger changes to be tried and fed back into the system, creating larger capacitance again and again. With each incremental success, the positive results compound, enabling greater productivity and efficiency for introducing more changes. This results in a net benefit to the overall system, be it individual or organization, as productivity and efficiencies, are realized due to the introduced changes.

Conclusion

Why are these mathematical models for Capacity for Change Important? They help define the relationships between the change introduced and the results we observe when introducing change. These models allow us to predict what changes are acceptable and which should be tabled later when greater capacity is earned. The models also identify the work change agents must do to enable desired changes and outcomes. At its core, these models establish trust as the cornerstone upon which change must be delivered. It also defines that the level of trust ultimately decides how much change can be introduced to the system.

With these models, we can establish a basis for the behaviors we observe and create an empathic understanding of the individuals and organizations we serve. From this foundation, we can create a shared experience and foster collaboration that enables the outcomes and performance we all want to achieve.

About the Author

George Miyata , Director of Innovation and Technology, has over 11 years of business and engineering experience supporting Defense and Intelligence customers with engineering and management solutions.

Mr. Miyata is an experienced systems engineer, system architect and project manager leading cutting edge technical and organizational performance initiatives in addition to defining and executing strategic innovation adoption.

Mr. Miyata is a certified SAFe? Program Consultant (SPC), has a BS from Gonzaga University in Mechanical Engineering and a MS in Aerospace Engineering Sciences from the University of Colorado, Boulder.