Continuous change
Continuous change theory can help leaders to address the limitations of episodic change models by providing insight into the informal, continuous, and adaptive processes that dynamically interact and adapt to factors inside and outside the organization (Livne-Tarandach & Bartunek, 2009). Developing sensitivity to the change processes continuously at work within the organization can help leaders to evaluate and influence change readiness and develop organizations that can readily adapt in a turbulent environment.
Continuous change is the incremental change that happens to the organization through the dynamic interactions among its people, processes, and environment. Weick and Quinn (1999) defined the metaphor of continuous change as the emergent and self-organizing organization that constantly evolves and adapts. This metaphor provides a view of “first-order change” (p. 365), which shows an extension and evolution of past practices with current people, knowledge, and skills.
Rather than being an occasional disruption brought on by leadership failures, continuous change involves unending modifications in process and practice. The tempo of episodic change is “infrequent, discontinuous, and intentional” (Weick & Quinn, 1999, p. 365) events that spur the spontaneous evolution of the organization. Alert reaction to inherent instability drives changes, as small changes cumulate and multiply to drive cycles of adaptation and evolution. Rather than throwing out old processes and people through an unfreeze-move-refreeze process, the agent driving continuous change redirects and shapes the change, by identifying clarifying, and reframing current patterns while fostering creativity, transformation, and learning.
Weick and Quinn (1999) proposed that this process is a “freeze, rebalance, unfreeze” (p. 379) sequence. In this sense, freezing means to capture and define emergent processes. Rebalancing means reinterpreting the patterns and reframing issues as opportunities. Unfreezing after rebalancing means to “resume improvisation and learning” (p. 380).
Organizational systems
As Lewin’s linear model— “unfreeze-move-refreeze”—provides the theoretical foundation for episodic change processes, systems theory provides a basis for recognizing non-linear phenomena at work in continuous change processes. Bertalanffy (1972) proposed general systems theory to show the dynamic nature of change. He proposed a science of “wholeness” (p. 37) while providing insight into how mechanistic and organismic models “are not mutually exclusive” (p. 25). Bertalanffy offered the biological organism as a metaphor to explain how organizations are complex open systems. Katz and Kahn (1966) later adapted Bertalanffy’s process for organizational development practice, providing an “anchor” for the field of organizational behavior (McShane & Von Glinow, 2005; Jex, 2002).
The organization-as-organism metaphor offers insights into the dynamic process of input, process, and output that drive organizational adaptability. The organization imports sustenance from the environment while influencing the environment through its output. An organizational system consists of interrelated subsystems composed of individuals, groups, and processes dynamically interacting to maintain the organization within its competitive environment. These subsystems dynamically interact within organizational boundaries to maintain the organization and assure survivability, while the organization continuously exchanges resources with and adapts to its environment.
If the organization exists as a monopoly in a static competitive environment, leaders can maintain internal processes at a relatively steady state. However, as the competitive environment becomes increasingly dynamic, the internal people and processes must increasingly enhance productivity, innovation, and adaptation so the organization can survive. As an interrelated web of relationships, change that happens to an individual, group, or subsystem within the organizational system or to any variable outside of the organization can have unplanned and unintended consequences throughout the organization (Bertalanffy, 1972; Jex, 2002; Katz & Kahn, 1966; Gleick, 2008; Duncan, 2009).
Complex organizations
Complexity science provides a perspective for understanding the complex web of micro-level connections among interacting variables that macro-level analysis overlooks. Complexity science integrates general systems theory with living systems to offer a framework for understanding organizations as complex open systems in a dynamic context. Dooley (2004) argued that complexity theory offers “superior explanatory power” (p. 374) because they account for organizations as living systems with characteristics of self-renewal, generating order from energy, self-organization, co-evolution with the environment, non-linearity, and emergence. Complexity science models also account for human factors, like autonomy, desires, norms, and preference; whereas episodic perspectives focus on macro-level processes.
In addition, complexity theory accounts for time, cause, and links. Regarding time, episodic change models tend to see change as a sequence of events—unfreeze-move-refreeze—whereas complexity models specifically show events as they happen in time. Regarding cause, other models see change as a sequence of activities—state vision, develop change coalition, eliminate obstacles, celebrate wins, etc. In comparison, complexity models specifically address the underlying mechanisms of change.
Finally, complexity theory models explain how change occurs over time by specifically addressing the links between variables. Understanding the dynamically interacting links between variables can help leaders better understand how small changes in one area can have a large impact on how events unfold during change processes. This “sensitive dependence on initial conditions” (Gleick, 2008, p. 23) would become known as the Butterfly Effect and served as the foundation of chaos theory; but helps leaders understand how chaos can find its way into even the most controlled processes. This suggests a counter-intuitive business strategy that de-emphasizes long-term change planning while refocusing resources on developing flexible organizations and operations with redundant processes that allow the organizational system better adapt in a turbulent environment.
Dooley (2004) proposed that viewing organizations as “complex adaptive systems” (CAS) (p. 374) provide insights into how system components interact in dynamical, non-linear, or self-organizing patterns. Complexity means that the behavior of the CAS depends on its structure and that they are not predictable. Economics, traffic patterns, and cultures serve as examples of CAS. In such systems, individuals, “semi-autonomous agents” evolve over time through interaction and mutual adaptation. The goals of individual agents drive changes with mediating factors, including rewards, the pace of intervention, and system structure.
Limitations
Although the continuous change perspective helps to address some of the limitations of episodic change approaches, it has weaknesses. A key limitation of the continuous change perspective is that its models lack definition, method, and measurement. For example, Orlikowski (1996) found that continuous change processes are difficult to observe, making it difficult to determine when a change has occurred and if it is a desirable change. Being difficult to define also makes continuous change processes difficult to predict, control, or explain. In addition, little research exists to support emergent change as a practical model in real-world environments. Finally, cultivating dynamic processes to encourage organizational evolution can be a timely process with unintended consequences, which can require a planned intervention to correct.