Designing Complex Adaptive Systems


A complex adaptive system is defined as an entity consisting of autonomous and diverse components that are linked, interdependent, interrelated with dense interconnections (Gharajedaghi, 2011). Generally, a complex adaptive system is formed of many individual agents following simple rules, generating emergent patterns with no coordination from leaders and in case of alterations, the system reacts and adapts. This assignment will explore the impact of complex theory in the innovation process. The work will also explore the relation of network theory and collective/pluralistic leadership in complex adaptive organizations. Also, the assignment provides that system thinking principles play a significant role in adaptive and agile teams. 

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Complexity Theory and Innovation Process

Complexity theory influences social science promoting new technologies and innovations which promote technology diffusion and technology adoption in organizations. In the 21st century, organizations are more intertwined with innovations. However, the link is challenged by the complex innovation systems both economically and socially (Dougherty, 2016). While attempting to leverage the 21st-century technologies and sciences, organizations face challenges like economic revitalization, poverty, climate management, water scarcity, alternate energy, and healthcare issues that drive organizational social technologies resulting in innovations (Dougherty, 2016b). 

Dalsgaard (2014) explains that contemporary businesses are faced with complex challenges. Innovation processes are complex as they combine interactions of heterogeneous components resulting in relational complexity. Thus, the innovation process is a distributed process combining differing organization elements. According to Garud, Gehman, and Kumaraswamy (2011), the innovation process faces varying complexities including regulative, manifest, temporal and relational complexities. It is challenging for an organization to deal with some complexities based on their designs which tend to suppress or reduce complexities. For example, some companies follow short-term performance metrics rather than following a long-term approach that boosts innovations over a given time.

Dalsgaard (2014) explains that there are different reasons which dampen the innovation processes due to the challenges relating to forming relational processes reducing the emergence of new ideas. Worse, the emergence of new ideas may not be embraced due to irregularities. Dougherty (2016) argues that most organizations fail to appreciate temporal synchronizes and diachronies which cause intermediary innovation outcomes which are considered as illegitimate thus limiting innovation processes. Innovation processes may boost employee performance or they can obstruct innovation processes through the reduction of efficiency. Besides, Garud, Gehman, and Kumaraswamy (2016) argue that some innovation processes are unrelated resulting in distractions among analysts, employees and top management. 

Nonetheless, Garud, Gehman, and Kumaraswamy (2016) argue that organizations may benefit from complexities through the reconciliation of innovation with current performances. This can be attained by organizations through the infusion of organizational processes with resources and energy to attain the critical threshold above the emerging point of cascading changes. Organizational successes are promoted through the incorporation of temporal rhythm. Garud, Gehman, and Kumaraswamy (2016) argue there is a link between exploitation and exploration of organization processes through generative memory which overcomes illegitimacy challenges. 

Network Theory and Pluralistic or Collective Leadership in Complex Adaptive Organizations

White, Currie, and Lockett (2016) argue that complexity leadership eliminates the aspect of top-down leadership paradigm. This eliminates the aspect of designated leaders. This has led to the emergence of designated leaders through complex interactive dynamics through the adoption of a learning component resulting in desired and specified outcomes. The present world is networked as people tend to be more interdependent and emerging problems and issues due to the connections result in ambiguity, complexity, uncertainty, and volatility. The formation of natural networks in organizations calls for a need to employ a pluralistic form of leadership that employs network theory.

Based on network theory, the effectiveness of collective leadership is not built on future controls but rather depends on the formation of an interactive environment that promotes a productive future (White, Currie and Lockett, 2016). Also, collective leadership does not rely on human interactions that highly focus on the ability of leaders to create relations with employees, but rather follows undirected interactions among employees by viewing the future as uncontrollable. According to Marchi, Erdmann & Rodriguez (2014) network theory on collective leadership views organizational behaviors as global interactions instead of controlling the local events within an organization. 

Following the network theory, complex leaders employ the most appropriate solutions, structures, and innovations without using their wisdom but employ the knowledge learned while interacting with others. White, Currie, and Lockett (2016) explain that collective leadership may employ a relationship-oriented approach to initiate effective networks rather than following a top-down approach. However, this approach differs from the traditional view of leaders of providing answers and directing the behaviors of employees to initiate innovation and structure which is risky and prone to fail. 

Therefore, network theory initiates integration where partners are selected within an organization where complex adaptive systems solve these issues through the multiple, interactions. Some of the challenges that complex adaptive systems solve in collective leadership include preserving ecosystems, anticipating changes in global trade, encouraging economic innovation, anticipating changes in global trade, understanding markets and preserving ecosystems and understanding market (Marchi, Erdmann & Rodriguez, 2014). 

How Organizational Structures and Processes Create and Manage Innovation

Innovation related structures increase the innovative capacity of organizations. Liedtka (2014) illustrates the significance of the organization process in innovation stating that there is a direct link of innovation with marketing, organizational methods, process, and products following a multidisciplinary approach. To maintain innovative competitiveness, companies require static organizational frameworks with labor specialization and rigid division to provide agility and flexibility in uncertain, complex and turbulent business environments. 

According to Werder and Maedche (2018), communication and organization structures that apply interactions, knowledge sharing and experience-based learning increase the performance of innovative activities. Some of the organizational structures that promote innovation include coordination and decision making structures. However, following classical theory, these elements may reduce the efficiency and effectiveness of innovations. This is because many organizations form their methods and structures based on the issues faced by other organizations due to similarity assumptions that what worked in one organization will work on another. 

Organization structure and processes can be explored following the” innovation value chain” model where managers follow an integrated analysis from the inception to the diffusion of components in the process (Liedtka, 2014). Herrera (2015) explains that managers should aim towards structured organizations to maximize efficiency through high volume production, increased economies of scale and increased resources to increase stable environments and reduce competitiveness. There is a need for a business to attain a sustainable competitive edge where either it can offer a wide range of products following a process or product innovation criteria. This opens a new competitive paradigm where a business will follow an innovative directed program requiring agility and flexibility. 

Systems Thinking Principles in Adaptive and Agile Teams

  Herrera (2017) explains that companies are following complex adaptive systems with evolvement towards agile teams following a complicated process. The complex adaptive systems do not apply a fixed change plan but rather they follow flexible approaches to the organizational process. Arnold and Wade (2015) define system thinking stating that it is a discipline that helps to analyze systems to effectively attain desired changes. System thinking rather than viewing static snapshots sees patterns of change, instead of things, it sees interrelationship. 

Contemporary business is adopting an adaptive agile approach where they scale business processes through the incorporation of advanced agile processes that help attain the market speed which is essential in the management of complex and large projects. Herrera (2015) self-organizing teams who are autonomous successfully deliver projects increasing the probability of success in an organization. This is in line with the system thinking principle of change which is essential in the adaptation and growth of innovation strategies. 

Agile teams engage stakeholders and developers during the development process initiating the exchange of ideas through a collaborative approach. Adaptation and agile in system thinking are thus essential as every member of a team understand s the organization’s vision and produces products that meet the clients’ needs hence building trust and increases sales and loyalty from customers. The system thinking model applies the concept of holism and transparency between the development team and the client which increases productivity and increases the predictability of timeframes.  Werder and Maedche (2018) explain that adaptive and agile teams value the components of feedback which is attained through individual leadership style which handles team dynamics. The managers understand the significance of diversity and interactive developments where managers split bigger projects attained through continuous test through more flexible product development.


Arnold, R. D., & Wade, J. P. (2015). A definition of systems thinking: A systems approach. Procedia Computer Science44, 669-678.

Dougherty, D. (2016). Organizing for innovation in complex innovation systems. Innovation19(1), 11-15. doi: 10.1080/14479338.2016.1245109

Dougherty, D. (2016). Taking Advantage of Emergence. Productively Innovating in Complex Innovation Systems. doi: 10.1093/acprof: oso/9780198725299.001.0001

Dalsgaard, P. (2014). Pragmatism and design thinking. International Journal of Design, 8(1), 143–155.

Garud, R., Gehman, J., & Kumaraswamy, A. (2016). Complexity Arrangements for Sustained Innovation: Lessons from 3M Corporation. Organization Studies32(6), 737-767. doi: 10.1177/0170840611410810

Gharajedaghi, J. (2011). Systems Thinking: Managing Chaos and Complexity: A Platform for Designing Business Architecture.
Chapter 7, “Design Thinking,” pages 133–157.

Herrera, M. E. B. (2015). Creating a competitive advantage by institutionalizing corporate social innovation. Journal of Business Research68(7), 1468-1474.

Liedtka, J. (2014). Innovative ways companies are using design thinking. Strategy & Leadership, 42(2), 40–45.

Marchi, J., Erdmann, R., & Rodriguez, C. (2014). Understanding Supply Networks from Complex Adaptive Systems. BAR – Brazilian Administration Review11(4), 441-454. doi: 10.1590/1807-7692bar2014130002

Werder, K., & Maedche, A. (2018). Explaining the emergence of team agility: a complex adaptive systems perspective. Information Technology & People31(3), 819-844. doi: 10.1108/itp-04-2017-0125White, L., Currie, G., & Lockett, A. (2016). Pluralized leadership in complex organizations: Exploring the cross-network effects between formal and informal leadership relations. The Leadership Quarterly27(2), 280-297. doi: 10.1016/j.leaqua.2016.01.004

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