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The ever-growing complexities of business environment in today's era has connected products to market opportunities in unlimited scales. The expansion of technology creates significant improvement and digitally transform how business works. In order to manage the complexity effectively, a holistic and systematic approach is needed to capture the broader vision of each system in the business environment. Therefore, business leaders should make benefit of System Thinking approach to continuously evolve their business in Digital Transformation era and gain feedbacks through connected system and processes. However, before studying about System Thinking, we need to understand the elements that construct the approach itself.
What is a System?
A system is a network of multiple variables that are connected to each other through causal relationships and expresses some sort of behavior, which can only be characterized through observation as a whole (Rosnay, 1979; Dörner, 1996; Sterman, 2000). When we define and restrict a system, we must comprehend its basic behavior and what is going into and out of the system, whether it be actual objects or just information. In addition, we need to understand the system level we are detecting, whether it is on a molecular level or on a global level. Identifying the system level and its boundaries requires great consideration in defining the problem and asking the basic question we want answers to (Harladsson and Sverdrup, 2003). This is where a framework that provides holistic thinking can evoke a certain understanding to generate deeper meaning in a system.
Introduction to System Thinking
The concept of “Systems Thinking” originated in 1956, when the Systems Dynamic Group was created by Professor Jay W. Forrester at the Sloan School of Management at MIT. It utilizes computer simulations and different graphs and diagrams to illustrate and predict system behavior. Some of the popular graphics used in the analysis include the causal loop diagram, the behavior over time graph, the management flight simulator, and the simulation model. The principle of Systems Thinking is that all behavior in a system is a consequence of its structure (Haraldsson, Hördur V, 2004).
System Analysis is about discovering organizational structures in systems and creating insights into the organization of causalities (Haraldsson, Hördur V, 2004). System Analysis involves group modeling, where we state the initial question of the problem and create a mental model structure, using the Causal Loop Diagram, to reflect that problem. System Dynamics, on the other hand, aims at investigating dynamic responses to changes inside or from outside the system (Haraldsson, Hördur V, 2004). All models, from conceptual, mathematical, or written, have a "system thinking" structure built into them since they are constructed using particular logic and reasoning. Therefore, the combination of holistic modeling and logical thinking is effective in developing Systems Thinking by using the Causal Loop Diagram concept.
Systems Thinking: System Dynamic
Systems Thinking is a method of studying the dynamic behavior of a complex system considering the system approach, i.e. considering the entire system rather than in isolation and system dynamics is a tool or a field of knowledge for understanding the change and complexity over time of a dynamic system (Bala, Bilash Kanti, et. al. 2014). Systems Dynamics refers to the co-creation of the understanding of a system and its feedback. Furthermore, System Dynamics deals with a mathematical representation of our mental models and is a secondary step after we have deployed our mental model (Haraldsson, Hördur V, 2004). Systems Thinking is a causality-driven method for describing how components and systems interact. Meanwhile, System Dynamics presents the impact on the interactions between parts and systems and quantified them.
Emerging Era of System Thinking
Systems Thinking provides a holistic approach for businesses in seeing the greater picture of their business environment and connecting feedback in order to create conclusions and values. Globalisation and digital technology has deliver complex global-supply chains, real-time manufacturing, digital marketplace, as well as innovative growth in mass communication and social media. These circumstances demand optimization in speed, scale, and efficiency. On the other hand, several unexpected shocks such as COVID-19 Pandemic has led disruptive business environment and uncertainty. With greater challenges and complexity of the Digital Transformation era, System Thinking arise to convey the holistic approach of each systems in business model and its relationship over time.
Causal Loop Diagram (CLD)
The Causal Loop Diagrams concept was first discussed in the sixties by Jay Forester (1961) and further elaborated by researchers such as Rosnay (1979), Richardson and Puch (1981), Senge (1990) and Sterman (2000). CLDs are used to map out a system's structure and feedback in order to comprehend its feedback mechanisms. The CLDs are utilized to comprehend how behavior has been acting out in a system so that we can create plans to either work with or against the behavior. When we use CLD language, we use feedback to explain the process. CLDs allow us to identify the activity in great detail and read the feedback like a story.
CLD is able to display reinforcing feedback loop and balancing feedback loop. A reinforcing system is an escalating effect due to equivalent influence between the components, which can be either a downward spiral or an upward (Haraldsson, Hördur V, 2004). Meanwhile, a balancing feedback loop is a resisting effect that withold further changes in one direction, rather it counters it with a change in opposite direction to stabilize the system. In a balancing system, there is a variable which hampers the exponential growth or is limiting factor to the growth of the loop (Haraldsson, Hördur V, 2004).
There are several steps in creating a CLD, which are define the problem, state the question, sort the main elements, start a simple CLD, create a Reference Behavior Pattern (RBP), test the CLD model, conduct learning and Revising, and finally make conclusion.
Advantages and Disadvantages of System Thinking
Linda Sweeney and John Sterman have listed specific system thinking skills as including the ability to:
1. Understand how the behavior of a system arises from the interaction of its agents over time (i.e., dynamic complexity);
2. Discover and represent feedback processes (both positive and negative) hypothesized to underlie observed patterns of system behavior;
3. Identify stock and flow relationship;
4. Recognize delays and understand their impact;
5. Identify nonlinearities; and
6. Recognize and challenge the boundaries of mental (and formal) models.
Based on these abilities, Systems Thinking delivers several advantages in enhancing the business model in today's era which is increasing the value and effectiveness of Digital Transformation, delivering early identification and mitigation of risks that can lead to a destructive failure, eliminating purposeful overdesign, delivering better processes for managing new technology fitting, and reduce time, resources, and costs effectively.
However, concept of systems thinking totally ignores or much worse destroys the most important aspects of human systems, for e.g. the interconnections or inter-relationships amongst and between the constituent sub-systems (Morgan, 2005). Reductionism can’t be implemented in every project. It tries to deal with the issues of the project one at a time, which leads to the problem of backing up which make things much worse. Also it is not helpful in dealing with multiple or delayed causality, as it is leading us to the simplistic way of thinking where individuals instead of focusing on the core problem focus on ‘either-or’ choices and blame mentality (Morgan, 2005). Systems thinking requires a significant investment in terms of skills, organisational structure where people are trained across a series of interrelated issues to make systems thinking work, because if they give up on the practise of systems views they will probably get back to much easier conventional approaches (Morgan, 2005).
Utilizing System Thinking in Digital Transformation Era
Companies initiate digital transformation programs in order to optimize their existing business model, but often do not follow through, leaving the innovative potential of information technologies untouched (KUTZSCHENBACH, VON Michael and Carl BRØNN, 2017). Barnett states that "disruption is not just about changing technology; it is about changing the logic of a business." Most transformation initiatives fail due to their fragmented view and outdated theories of change that ignore the dynamic relationship aspects of the organization (KUTZSCHENBACH, VON Michael, and Carl BRØNN, 2017).
Dynamic Decision-making in Technology Markets
Several complex systems examples that apply in current technology markets are Direct Network externalities adopted by Facebook, Indirect Network Externalities adopted by Android, High Infrastructure & Capital Investments applied in 4G LTE technology, and Disruptive business models projected by Software as a Service (SaaS) business model. Technology markets are dynamically complex due to the dynamics of adoption and substitution and delays in infrastructure and Research and Development (R&D) spending. On the other hand, the human mind is much better at dealing with detail complexity than with dynamic complexity.
The implementation of Systems Thinking in business modeling in the Digital Transformation era will support businesses to understand the dynamic complexity of the market that include cause-and-effect relationships, focuses on the feedback linkages among components of a system, and determine the appropriate boundaries for defining what is to be included within a system.
CLD in Uber: The On-Demand Transportation Services
Uber becomes one of the company's pioneers in modernizing transportation services. Despite the market's initial acceptance of the business model, "Uber should feel magical to the customer. They push the button and the car comes. But there's a lot going on under the hood make it happen." -CEO Travis Kalanic . Thus, Uber provides a unique opportunity to illustrate the use of feedback systems view to operationalize the theory of success of a platform business (KUTZSCHENBACH, VON Michael and Carl BRØNN, 2017).
The effects of Uber's breakthrough against the traditional taxi concepts continue to emerge as market and competitors respond to disruptive challenges. Uber's business model reveals that the company relies on a series of reinforcing feedback loops that reinforce the power of the system from one side of the market to the other, thereby creating a growth engine. The important component of this growth engine is also known as 'get-big-fast' (GBF) strategy (Olivia, R. et. al., 2003). GBF strategy promotes a strong focus on reinforcing feedbacks that create a large customer base and the acquisition of capital for rapid growth (KUTZSCHENBACH, VON Michael and Carl BRØNN, 2017).
Based on the aforementioned strategy, Uber's business model is analyzed using CLD and shows the reinforcing feedback loop that signify reinforcement of invovled behaviour in the diagram. This can be found in the relationship between the rapid availability of a car and Uber user satisfaction, as well as rapid revenue growth and high stock valuations. The more cars available in the city, the higher Uber user satisfaction will increase. The incremental revenue growth also complement the stock valuations to higher value. With a great capital amount gained, Uber later able to spending more money to win markets and achieve scale. In addition, the satisfactory IT Infrastructure (speed, data analysis and security, etc) and brand equity (awareness, reputation, etc.) improves Uber's attractiveness towards markets, which later drives organizational growth.
On the other hand, Uber's business model also contains balancing feedback loop which underline the resistance of involved behaviour in the diagram in order to create balance. This can be found in the relationship between insufficient working conditions and poor customer experience which decreasing Uber's attractiveness. Uber's success also illustrates the dramatically behavioral changes of its customers, resulting in different customer demands. Several balancing systems that included in Uber's business model are risks of cyber threats, service delivery infrastructure adjustment, delays in IT improvements, and dynamic marketing adjustment over surge pricing.
The model captures the interplay of the powerful reinforcing feedbacks that drive Uber's rapid growth and their interaction with limits to growth arising from the behavioral changes of major stakeholders, potential decline of the customer base resulting from limited availability of capital and the delays in deploying the capabilities and competencies needed to provide an attractive Uber app (KUTZSCHENBACH, VON Michael and Carl BRØNN, 2017).
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