It is logical to expect inter-dependencies in any system
I agree that it seems to be logical to consider that all systems are interdependent because of the essence of the very concept. The notion of a system was discussed by numerous researchers, and they did not agreed on one particular definition. However, the majority of the state that it is a set of interdependent components, which presupposes that any system is interdependent. In this way, water pumping depends upon telecommunications that depend upon electricity. Healthcare relies on transportation and electricity, and government services on financial ones. Interdependency exists on different levels and can be functional and operational (Beckford, 2015).
Thus, when a flight is canceled, the roads may be laden with people who had no opportunity to leave. The same can be said about enterprise systems. Still, the fact that one organization can produce cotton and wheat, for example, separately cannot be neglected. But even in this situation, different departments need to interact to share resources. It is claimed that enterprises today tend to become interdependent to replace independent systems and improve business processes.
For instance, all sales processes rely on resource planning (Nordmeyer, 2016). The creation of enterprise is a way to make interdependent systems work harmoniously to reach common goals. It is critical for business as the effectiveness of cooperation between different people and departments determines their success. They are to share information, equipment, and finances so that no issues occur and organizational goals can be successfully achieved.
Just because there are inter-dependencies in a system that does not mean there is risk
I do not agree that because there are interdependencies in a system that does not mean there is a risk. The existence of any system presupposes at least some potential risk. Various elements that interact with each other to meet common goals and create a system may face problems with their operations. In this way, when one element occurs to be not able to cope with its tasks and execute its function, all other components are influenced adversely, and the performance of the system becomes affected (Heal & Kunreuther, 2007).
For example, when the finance department made a mistake when allocating resources, other departments received not enough money to purchase the equipment they needed and to have a training program, which influenced the outcomes of their work. Except for that, there is a possibility of mistakes made during the transfer of information. Some data may be shared later than expected, which may result in inefficient decisions. For instance, when the company was planning to have an expatriate, the HR department should have trained one. But this information did not reach the financial department in time. As a result, the costs needed for training were not obtained when expected, and expatriation was delayed.
The information assurance architecture within an enterprise should best be viewed as a system
Information assurance architecture should be viewed as a system, as it deals with the practices that help to analyze current and future security processes in the organization. It is beneficial for the enterprise to treat it as a system because information security architecture is a complex notion that considers the harmonious interaction of information assurance services and mechanisms. Due to the nature of security and risks, it focuses on people, technology, operations, information, and related infrastructure that constantly interfere with one another.
The task of the information assurance architecture is to organize all these services and mechanisms so that they become structured, which presupposes the creation of the system (Willett, 2008). Being seen as a system that exists within the enterprise, it can define all business risks step by step so that nothing will be omitted.
If the architecture is treated just as separate practices, their interrelation will not be considered. As a result, the evaluation of the current and future situation maybe not efficient and accurate, and potential issues that occur in the framework of interdependence may be neglected. Of course, it does not mean that the enterprise will not be able to operate, but it is much easier to work with an organized set of items than with a random mass. Thus, a systems approach is rather beneficial.
The overall risk of a system is exactly equal to the sum of the individual risks of each of the system components
I agree that the common view exists that the overall risk of a system is equal to the sum of the risks of each of its elements, and such opinion is supported by numerous scientists. For example, when it is considered that each of ten parts of a bicycle can break once, this bicycle can break ten times in total. Still, Hood and Jones (2004) believe that total risks are not equal to the sum of the individual ones and are not greater than they. Such a contradiction occurs because enterprises are open systems but the risks are measured as if they were closed. It is critical to consider various influences, including unexpected human behavior.
For example, employees at the Ekofisk successfully identified the fail-safe equipment and solved the problem (Hood & Jones, 2004). In this way, it is important to take into consideration the type of system when measuring its risks. The quantitative approach may be effective in some situations and ineffective in others. Still, if discussing the situation in general, it can be said that the overall risk of a system is not greater than the sum of the risks of each of its elements and equals it.
A system is a group of interrelated parts that form a unified whole for a specific purpose. The system’s definition underlines the dependence of the parts of a system as its essential characteristic. The collection of elements that are not united by a specific purpose and do not function in an interdependent way cannot be called a system. Integrity holds the elements together and makes the system work. It allows the system to maintain stability and ensure continuous operation. Such intricate work of one single body makes the process of analyzing it much more challenging. Various processes and relationships are needed to be taken into account to maintain the stability of a system. The most effective systemic approach to analyzing the parts of the system and their integration is systems thinking.
Systems Thinking Definition
Systems thinking is an approach to system analysis based on the interconnectedness of the elements in a system. It states that the component of the system acts differently in and out of the system. The connection with other elements determines the work processes of the system. Thus, system thinking requires examining the interactions and linkages between the component parts of a system. It encourages to explore the inter-relationships, perspectives, and boundaries in a system (MacLachlan & Scherer, 2018).
Inter-relationships are perceived through the context and connections between the elements of a system. Perspectives are formed as each actor has one’s own perception of the current situation. The systems thinking approach was created by an MIT professor Jay Forrester in 1956 (Leveson, 2016). He felt the need for an analytical tool to be implemented to analyze the social systems as in engineering. It was especially relevant for testing new ideas and predicting their effectiveness. Professor Forrester wanted to make an understanding of social systems more accessible and explicit to improve the systems’ understanding of social processes just as engineering principles. He established the similarity between social and mechanical systems and based the approach on their connection.
However, it is still fundamentally different from the traditional analysis that focuses on separating the components and analyzing them exclusively. On the contrary, systems thinking deals with connecting the set of elements and expanding from individual parts to the extensive interactions. Systems thinking presents merely the way of seeing and analyzing a system to influence its quality or work. From the layperson’s perspective, systems thinking can be regarded as a type of perspective on a particular concept. It offers a range of tools and techniques for measuring and analyzing a system’s work and overall effectivity (Swanson et al., 2012). The systems thinking approach can be divided into four categories of making distinctions, organizing ideas in part and wholes, identifying the relationships between the parts, and taking perspectives.
The Effectiveness and Drawbacks of Systems Thinking
Systems thinking proves to be useful for analyzing complex systems of many elements, perspectives, and interactions. Today, many problems present an intricate, interconnected knot of elements with multiple actors resulting from previous interaction stages. Moreover, as the systems are continually evolving and the problems reoccur, it becomes increasingly difficult to identify the issue traditionally. Such system becomes incredibly challenging to analyze in a traditional manner as the conventional solutions are barely enough to see the underlying causes of a disfunction. It does not allow the researchers to create any prospect of effectively figuring out the issue. Systems thinking deals with the most complicated problems and finds the most convenient solution on an individual or organizational level (Leveson, 2016). Such devises as causal loops and system archetypes give the possibility to successfully identify the significant number of interactions and elements even in the occasion of the ineffectiveness of apparent solutions marked by complexity.
Systems thinking allows scientists to analyze the different levels of perspective and problems in integration. Causal loop diagrams give a visual representation of how a system needs to work and its missing elements. Systems thinking helps to see the connectivity between different elements in a system. It enables the researcher to see the big picture, identify the leverage points, and systematically consider the situation to bring constructive change when it gets messy (Solin, 2017). Such analysis often brings strikingly different conclusions in comparison to the traditional analysis.
However, social systems often appear to be harder to analyze from the systems thinking perspective as they do not have a defined algorithm, function in a counter-intuitive way, and are more challenging to manage. It creates a significant problem for the system thinking approach because individual systems’ outcomes remain unclear, so it is impossible to determine how to produce better results logically (Farmer, 2009). That is why it is especially important to determine the system’s purpose to fix the recurring mistake. The inability to find the system’s root purpose and design causes the systems thinking failure as the outcomes cannot be predetermined.
Systems thinking in IT projects appears to be especially relevant as these projects usually have multiple interconnected elements in a complex system. The larger the project is, the easier it becomes to analyze how each component affects the outcome in a system. IT sector often faces the issue of the recurring mistake that prevents the code from functioning effectively in complex projects. Systems thinking gives the tool of determining the causes of the outcome failure through the in-depth analysis of the system elements’ relationships.
The purpose of this paper is to critically analyze and evaluate various publications that cover different topics in system thinking. The topics include the challenges in studying complex systems, qualitative mapping, quantitative models, addressing system failures, and the importance of defining the success of IT projects. The analysis shall be based on the perspectives of various scholars concerning these topics.
Complex Systems: The Challenges
Scientists and social researchers face significant challenges when analyzing the behavior of complex systems. Science involves obtaining meaningful insights into the changes and relationships between the elements of a system. This leads to several challenges, which include collecting data in large-scale experiments, moving from data to dynamic models, cause-effect relationships, the relationship between comprehensive and simple models, and developing new methods of prediction.
At the beginning of modern science, scientists studied complex systems by analyzing simple phenomena and physics. The reductionist science was used to understand the complex systems in the society. The advancements in modern science and the society have motivated researchers to understand the principles of complexity and the phenomena associated with complex systems. This involves focusing on synthesis rather than analysis. Thus, FuturICT has been developed to promote synthesis in the study of complex systems. In the social sciences, statistical laws and techniques are used to study complex systems. This trend has been promoted by the emergence of new large databases, new complex social phenomena, and development of simplified models.
In order to overcome the challenges of the science of complex systems, researchers should use the following criteria. To begin with, researchers must have adequate empirical data to explore and to understand the features of complex systems. Accurate and complete data is also necessary when calibrating and validating the models that are used in empirical studies. Complex systems can be studied systematically by following a five-step approach. The first step involves making observations, explorations, and collecting data. The second step involves determining correlations, patterns, and mechanisms in the data. In the third step, modeling techniques are developed to study a phenomenon. In the fourth step, the model is validated, implemented, and used for predictions. Finally, the output of the model is used to construct theory.
The reliability of the findings of studies that focus on complex systems depends on modeling techniques, effective aggregation of information, and ability to understand extreme events. In this regard, simple models have been found to be effective since they lead to a better understanding of the stylized facts about a complex system. Simple models have better forecasting power than complicated ones because of their clarity and tractability. Modeling is often challenging due to limited access to high quality data, use of ineffective mathematical or statistical techniques, and emergence of phenomena that are not reducible.
In addition, selecting the right information to use in modeling is challenging because complex systems are characterized with dynamical properties that are linked to the topology of the network of the relationships among their parts. Effective selection and aggregation of information during system analysis can be achieved through social and individual learning. When studying socio-technical systems, researchers should use theories and methods drawn from the social sciences since ICT networks are comprised of interacting humans. In order to overcome the challenges of collecting data, technologies such as participatory sensing and social computing should be used. Moreover, extreme events can be understood through extreme value and self-organized criticality analyses.
Given the challenges associated with understanding complexity, scientists are yet to agree on the best approach or technique to use when studying complex systems. Generally, some scholars believe that qualitative maps should be used, whereas others consider quantified models to be the most appropriate techniques for studying complex systems. Existing literature presents mixed findings on the use of qualitative and quantitative models to understand the dynamics of various complex systems. Research conducted by Wolstenholme and Coyle indicated that quantitative models were effective in analyzing system dynamics.
Lane supported this view by demonstrating that studying system dynamics without using quantified simulations leads to contradicting findings. Similarly, Richardson argued that quantitative models are superior to their qualitative counterparts. The use of qualitative maps or models, on the other hand, has been supported by researchers who believe that quantitative models have limited ability to explain complex systems.
The use of qualitative maps is mainly justified by the fact that quantitative models are characterized with uncertainties that often lead to misleading conclusions. Uncertainties arise from the use of soft variables, which are often difficult to formulate as equations. Moreover, data for soft variables such as customer satisfaction are not readily available. In this regard, qualitative maps such as causal-loop diagrams are considered to be effective when studying complex systems that are characterized with uncertainties. Specifically, qualitative maps should be used without being supplemented by simulations since they provide insights into the problem being studied through inference rather than calculations.
The use of soft variables in quantitative models leads to generation of parameters whose meanings are uncertain. An effective system dynamics model must use variables that correspond to the real-system variables. In this regard, the decision functions used in modeling have to represent the social factors, concepts, and sources of information that influence the actual decisions. When quantitative models fail to meet these criteria, their output is considered to have no value. For instance, empirical studies have found that quantitative models cannot explain the collapse of the Maya Civilization, whereas qualitative models have provided valuable insights into the collapse. Thus, qualitative models can be used to describe the dynamics of a complex system.
Qualitative models such as influence diagrams enhance our understanding of system dynamics in the following ways. First, they provide a summarized description of the dynamics of the system, thereby enhancing the researcher’s understanding of the causes and effects of the problem. Second, the models provide a clear relationship between the components of the system and the stakeholders who are involved.
This facilitates effective articulation of the problem to identify the most appropriate solution. Third, qualitative models can provide insights that facilitate understanding of the behavior of complex systems. Fourth, a qualitative model provides a holistic view of the nature of the system that is being studied. This enables scientists to understand the nature of the changes in a system in order to develop an appropriate model to analyze it. Finally, qualitative maps enable researchers to develop quantitative models easily.
Quantitative Models/ Simulations
Although quantitative models are associated with uncertainties, they are considered to be effective in the study of complex systems. This perspective developed as a result of the popular belief that system dynamics could not be understood by making inferences from causal-loop diagrams. Thus, quantitative models were considered as the only reliable means of studying complex systems and conducting policy analysis.
In order to demonstrate the credibility of quantified models, several researchers including Homer and Oliva have reviewed the importance of simulation models in the process of analyzing the dynamics of complex systems. Simulation has been found to have great importance in the process of analyzing complex systems. This perspective is based on the fact that reliable inferences cannot be drawn from complex causal maps in the absence of simulation. Furthermore, system dynamics can be used to address the challenges attributed to soft variables and incomplete data.
Simulation using quantitative models is always important in dynamic analysis due to the following reasons. To begin with, a model can only be used as a reliable solution to a problem if its superiority to other alternatives can be demonstrated. Simulation not only helps in demonstrating the superiority of the selected technique or model, but also identifies the core structure of the problem. The uncertainties associated with simulation can be dealt with in two ways. First, mental databases have adequate information that is required during modeling. Thus, they can be used to address the challenges associated with limited access to numerical data in order to avoid generating parameters whose meanings are uncertain. Second, sensitivity testing can be used to demonstrate that behavior models and inferences are independent of the uncertainty of parameter values. Thus, reliable conclusions can still be made using quantitative models.
Several conclusions can be made concerning the choice between qualitative and quantitative models. To begin with, qualitative models are always used in the first two stages of research work. In the first stage, diagrams are used to describe the dynamics of the system or the problem being studied. In the second stage, the diagram is analyzed to establish the connection between the elements of the system. Thus, quantitative models become relevant only from the third stage as the researcher seeks to gain deeper insights into the problem or data. Simulation often provides important insights into the nature of complex systems, especially, in policy analysis.
In addition, simulation is important because it helps in determining whether qualitative maps are misleading or not. This implies that quantitative models are still important despite the uncertainties associated with them. Overall, qualitative models have their weaknesses, which limit their ability to explain the dynamics of a complex system. However, the quantitative models are not always superior to qualitative models. This conclusion is based on the fact that quantitative models also have weaknesses that limit their application in the study of the dynamics of complex systems. Thus, the study of complex systems, especially, policy analysis can be improved by using both qualitative and quantitative techniques to obtain meaningful findings.
Effective System Dynamic Modeling: Resistance to Policy
The effectiveness of dynamic modeling can be illustrated by the society’s reactions to its output. Resistance to policy is one of the outcomes of poor modeling of dynamic systems. The problems associated with policy formulation call for the implementation of three strategies to avoid failure. First, the policy maker has to understand the complex systems in the society in a holistic manner. Second, the major causes of policy resistance must be analyzed and understood clearly. Finally, appropriate methods must be adopted to formulate policies that produce sustainable benefits in order to avoid resistance. Developing an effective policy is often difficult because every solution to a problem has a side effect. Thus, a policy that has the potential to address the problem for which it was formulated to solve can still be resisted if it produces undesirable side effects.
Traditionally, system thinking has been identified as a viable solution to the problem of resistance to policy. System thinking is a problem solving technique in which the world is perceived as a complex system. It enables scholars to view the world in a holistic manner. This improves researchers’ ability to learn quickly and effectively. As a result, the researchers are able to identify the weaknesses in complex systems that have to be addressed to avoid policy resistance. In addition, a systematic view of the world enable researchers and policy makers to make decisions that lead to achievement of individual interests and the interest of the whole society.
The problem of policy resistance is mainly attributed to the limited ability of most researchers to comprehend complexity. Specifically, policy resistance is caused by our inability to understand complex systems. People fail to understand the effects of their decisions because their mental models are limited, inconsistent, and unreliable. As a result, people make decisions that are meaningful in the short-term, but harmful in the long-term.
Policy resistance also arises because we perceive experiences as a series of events in which one event causes another. However, the real world provides feedback by responding to policy actions. This leads to policy resistance because of inadequate understanding of the feedback generated by complex systems. Policy resistance also occurs due to the fact that the effects of decisions can take a prolonged period before they are felt in the society. This delay creates difficulties in studying the causal relationships that are associated with a complex system.
The problem of policy resistance can be addressed by using tools that facilitate understanding of the causes of dynamic complexity. These tools are qualitative maps that show causal relationships and quantitative modeling. The tools facilitate understanding of the feedback in dynamic systems that can make policy decisions to be irrelevant and ineffective.
Addressing System Failures
Information and communication (ICT) systems play a vital role in the study or analysis of complex systems. Specifically, they facilitate important processes such as data collection and modeling. However, effective application of ICT is limited due to recurrent system failures. Generally, similar system failures often occur regularly in organizations. In addition, the quick fixes used to solve system failures have side-effects. For instance, most organizations use safety redundant mechanisms to respond to system failures. However, this technique is ineffective because it does not reduce or eliminate human error.
Researchers have developed various methods that are currently being used to analyze system failures. These include the failure mode effect analysis (FMEA) and the fault tree analysis (FTA). The FMEA methodology focuses on analyzing single-point failures. It uses a bottom-up approach to analyze the causes of the failure and the results are usually presented in tables. On the other hand, the FTA method uses a top-down approach to analyze several failures simultaneously. The results of the analyses are usually presented in the form of logic diagrams.
The total system intervention (TSI) has been introduced as an advanced method for preventing the occurrence of system failures. TSI enables scientists to manage complex and varied viewpoints. In this regard, the system of systems failure (SOSF) technique can be used by different stakeholders as a shared system of communication to facilitate effective understanding of failures. TSI helps organizations to identify the stakeholders who are involved in a particular system failure. This involves using a matrix to outline the factors that are considered by various stakeholders to have caused the failure. Thus, it is possible to understand the viewpoints of various stakeholders concerning the failure.
There are two approaches that can be used to analyze specific types of system failures. These include the failure factor structuring methodology (FFSM) and the system failure dynamic model (SFDM). FFSM addresses the system failures that are attributed to complex failure factors. FFSM addresses system failures by facilitating double-loop learning. SFDM, on the other hand, overcomes the system failures that occur due to environmental changes. It ensures that systems are operating optimally, and the side-effects of quick fixes are minimized.
System failures can be addressed by taking the following actions. First, the perception gaps among stakeholders should be closed so that the adopted solution reflects the stakeholders’ needs. Second, the key performance indicators should be linked to absolute goals. This ensures that the solutions adopted to address system failures achieve the predetermined objectives. Finally, the boundary of the existing systems should be defined clearly in order to identify the improvements that should be made without introducing undesirable side-effects.
Success of IT Projects
One of the major challenges that organizations face when implementing IT projects is how to define and measure success. Defining and measuring success and failure is problematic because they are understood differently by different people. This problem is exacerbated by the fact that there is no universally accepted definition of failure and success. Generally, some scholars argue that success is realized if the stakeholders of an IT project perceive it to be successful. In addition, some scholars associate success with the survival of the IT project. Lack of a clear definition of success and failure presents significant challenges to IT project management because the projects are often initiated without a clear declaration of what will be considered as success.
Several scholars have provided different definitions of success of IT projects and the criteria to measure it. Cooke-Davies asserted that project success is different from project management success. The later is measured by three variables namely, time, cost, and quality. The former is measured in terms of the extent to which the overall objectives of the IT project were achieved. The criteria provided by DeLone and Mclean to measure success are based on six variables. These include the quality of the system, quality of the service provided, information quality, net benefits, use of the IT system, and the satisfaction of the end-user. These variables often provide inadequate measurement of success.
For instance, satisfaction of the user is considered to be an inappropriate measure since it lacks theoretical underpinning. In addition, frequent use of a system is often not considered as an indicator of success in the context of IT projects such as data warehousing.
Three practices enhance the success of IT projects. These include having a formal definition of success, consistent measurement of success, and using the results of the measurement to improve performance. Empirical studies show that there is no single method of measuring success that is better than others. Companies that effectively define and measure success utilize a variety of success indicators, which include timeliness, cost-effectiveness, business continuity, ease of use, and satisfaction.
Moreover, distinguishing between project management success and business success facilitates effective definition and measurement of success in the context of IT projects. Companies that succeed in achieving the expected project outcomes usually define success prior to the implementation of their projects. The rationale of this strategy is that defining success at the beginning provides a clear vision of what has to be achieved at the end of the project.
In sum, three factors namely, an accepted definition of success, continuous measurement of progress, and utilizing the measurement results to ensure improvements determine the success of IT projects. Thus, companies can improve the outcomes of their IT projects if they know what they need, monitor their progress, and make changes when necessary.
Conclusion
The science of complex systems is associated with numerous challenges, which include difficulty in data collection and modeling. Qualitative and quantitative models have both strengths and weaknesses. Thus, they can be used together to improve the findings of studies that focus on complex systems. ICT plays an integral role in the study of complex systems by facilitating modeling and analysis. Thus, appropriate measures should be taken to prevent ICT system failures and to improve the success of IT projects.
Years of research have revealed that learning is a continuous process that is essential for the existence and effective performance of organizations. Unexpected changes may take place in companies, for example, retirements. Some of them may require the organization to alter their goals, policies and products. The remaining members of staff should have the ability to adapt seamlessly.
Proponents of individual learning assert that it is influenced by the assumption that education has social benefits, and can be achieved in three forms thus formal, informal, and incidental. The participants are required to form a relationship between themselves and their environment.
A good method of determining whether there is progress is proposed by the activity theory. Organizations should engage their employees in group activities, instead of assigning responsibilities to individuals. Any notable changes in employee behavior provide a new concept of education referred to as organizational learning.
Parsons Theory is known to stress the importance of the relationship between individual behavior in a social setting, and their ability to cope to their environment. This provides an option for learners to undertake actions individually or collectively since they have a plethora of options at their disposal. This concept, if properly taken advantage of will establish the most excellent scaffold on which learning interventions can be developed.
The dance supposition has been proposed in a bid to explain another mode of learning. It is widely used, for the fact that it illustrates the process one has to go through during the learning process. It gives prominence to the need of widespread participation by every stakeholder. A person engages his whole being, by involving both his emotional and spiritual self. Dialog in emphasized since an organization is believed to be human a construction.
The theory explains that shared understanding can be achieved among employees, if a course of action that is specifically formulated for the purpose is employed. Although there is no single procedure for attaining this, it lays emphasis on the fact that collective learning calls for engaging of multiple aspects, as opposed to individual ideas.
The learners should have excellent inter personal skills that are mandatory for a harmonious coexistence with their peers. Since it is mandatory for them to possess knowledge of dialog techniques, the above mentioned skills will be complementary. Overall, it is essential to maintain the identity of all participants while getting the best out of their collective effort.
Lastly, the theory can be employed to determine a proper chronology of strategies that can be used in different scenarios. It proposes the use of interventions and other requisite measures to ensure the team works in harmony towards achieving established goals.
Experts have realized the importance of reviewing the role of metaphors, especially in the process of collective learning. Improper use has greatly affected our capacity to rightfully understand the dynamic procedures that take place during such activities. Critics argue that most people define this concept basing on singular results, regardless of whether they are positive or negative.
The proponents in turn deal with people individually as opposed to giving group tasks to be performed. Subsequent evaluation is also done individually, despite having the respondents clustered in groups. They propose that more emphasis should be placed on group tasks and learning systems should be altered accordingly to accommodate these changes.
Create one spray diagram to summarize the different ideas of the article respecting the conventions, and techniques
Draw a multiple cause diagram to show all the sources of the Limitations of current Methods for Studying Mental Models of Systems. Reflect on your diagram in no more than 200 words
Some of the limitations with the current study method of the Mental model are caused by the lack of facilitators to measure the present model before implementing the new one. Consequently, there is no way for the facilitators to evaluate the impact of the new model. This causes wrong interventions that may be influenced by a few subjects who impose their ideas and assumptions on other members.
This alters the overall intervention, and its efficiency in that setting cannot be determined. On the other hand, the previous mental process may be evaluated, forming a way for the intervention to be assessed. However, eliciting the mental model based on the views of the group, as opposed to individual perceptions leads to wrongful representation of the impact of the intervention.
On the other hand, the facilitator may influence the opinions of the subjects, leading to subject bias, which poses problems in determining the efficiency of the intervention.
If the individuals administering the intervention use a facilitation process that is unfamiliar with how the clients make their decisions, or use poor representation techniques when introducing the clients to the new mental model, then this increases the chances of the clients reverting to the old model.
Conversely, the lack of periodic assessments of the new model may pose adaptation problems to the clients, causing them to revert to the old model.
Concept of mental models
Mental models reveal and assist individuals in an educational or corporate setting to understand the thought and decision-making process. According to Vosniadou (2002, p. 4) “mental models are analog representations that preserve the structure of the item they represent”.
Mental models reside in the mind of individuals for long periods of time, which poses challenges when introducing new systems. However, Vosniadou claims that most mental models are created on the spot to help individuals cope with the dynamics of the situations that they are facing.
The design process of mental models aims at enhancing the efficiency of individuals when tackling a problem, which enables them to arrive at solution with ease. Vosniadou states “mental models are also constrained by the framework and specific theories within which they are embedded and thus can be valuable sources of information about them” (2002, p.4).
One of the key features of robust mental models is flexibility. Flexibility refers to the ability of a mental model to vary in order to provide individuals with both a positive and negative thought process (Doyle & Ford 1998). Robust models should also lead to selective perception in the decision making process.
This is a necessary aspect of a mental model since it allows the system to reveal core information (Doyle & Ford 1998). Selective perception facilitates filtering of redundant information (WebFinance 2012, p. 1). The third characteristic of a robust mental model is that it should be limited in its scope in order to avoid straining the individual’s memory.
How mental models are central to The Systemic/Holistic Thinking
Positive and high quality system thinking are key attributes for organizations in order to achieve their objectives. This can be achieved through the implementation of mental models. Organizational-positive and high quality system thinking are characterized by consistent, dynamic and complete mental models.
The systematic point of view provided by Doyle and Ford (1998, p. 3) suggests “mental models are the “products” that modelers take from students and clients, disassemble, reconfigure, add to, subtract from and return with value added”.
Mental models provide modelers with a way of measuring the effectiveness of a particular system-thinking-intervention exercise. There are three key features of an effective system intervention exercise. First, it is a technique for eliciting the right mental models. Second, it is a technique for changing mental models.
Third, it is a technique for measuring the change in mental models. Current methods for studying mental models suffer from serious limitations (Doyle 2011, p. 131). According to Doyle, the elicit mental models in use are “substantially different from the mental models that clients actually use to make business decisions” (p. 131).
Limitations of current methods for studying mental models as cited in the article
The inefficiency of the current methods in measuring mental models is mainly due to their primary design function, which is to improve and not to measure mental models (Doyle 2011, p. 129).
The assessment exercise either alters the mental models or is undertaken when the alteration has already occurred. This makes it difficult for modelers to determine the effectiveness of a system-thinking-intervention-exercise since they lack pre-intervention data with which to compare the new data set.
The current methods also lead to reduced effort at the end of the intervention exercise. This is a necessary step that is aimed at gathering evidence that supports the realization of desirable change in mental models (Doyle et al 2011, p.129). Typically, much of the effort in these current methods is skewed towards the beginning of the intervention exercises, whereby it is directed at eliciting the mental models.
The third limitation is due to the disparity in the measurement of the mental models at the beginning and the end of the intervention exercise. This disparity makes it impossible for modelers to determine whether there is actual change in mental models. In addition, modelers cannot detect the implied change due to use of different measurement tools and procedures.
In view of this limitation, it is recommended that, first, the same measurement procedure and instruments be used at the beginning and the end of the intervention exercise, and second, that mental models be elicited again at the end of the intervention exercise instead of asking participants whether or not they perceive any change in their mental models.
Doyle et al (2011, p. 130) claims that inquiring from the subjects about the impact of an intervention enhances the probability of subject-bias since it encourages participants to communicate the true state of affairs and discourages them from giving the answers that the interviewer wants to hear.
Current methods are also ineffective in monitoring the progress of participants when adopting a new mental model (Doyle et al 2011, p. 130). According to cognitive psychology, a new mental model is often in competition with the incumbent and inferior mental model it has set out to replace.
The old mental model is better placed since it has established multiple connections with a participant’s long-term memory (Doyle et al 2011, p.129). The new model can only replace the older mental model entirely if it is used frequently, so that its details are permanently written in the clients’ memory. It is imperative to assess the progress of participants in adopting a new mental model.
This ensures that the adoption process is successful and is not corrupted. Current methods for studying mental models overlook this fact. According to Doyle et al (2011, p.129) the current methods make the assumption “improved, more dynamic mental models facilitated by the system thinking interventions are easily accepted and stable”.
The fifth limitation exhibited by current methods is that they allow eliciting of mental models by participants who are in a group setting (Doyle et al 2011, p. 130). It is mistakenly assumed that there is a shared consensus in a group and that each group member adopts the mental model put forward by the group (Doyle et al 2011, p.129).
Furthermore, psychological studies show that groups facilitate the generation of low quality ideas since they promote social loafing, anxiety and thought process interruptions (Doyle et al 2011, p. 129).
It is advisable that mental models be elicited from individuals when in isolation and not when in a group. This is mainly because in an isolated individual setting, there is no pressure to conform to a collective mental model, and there is a reduced risk of losing valuable information.
Current methods inherently assign facilitators with a problematic role, especially when mental models are being measured (Doyle et al 2011, p.129). Facilitators are required to generate and direct the discussions that elicit mental models. In addition, the current system requires facilitators to summarize the ideas of the subjects that surface.
This arrangement is associated with a high probability of experimenter bias since “the facilitator may inadvertently give the participants clues about what ideas are better than others or lead the discussion in a direction that the participants would not choose on their own” (Doyle et al 2011, p.129).
Furthermore, facilitators may inadvertently loose valuable information or dilute the information they have collected when they determine the lifespan of discussions. Facilitators lose vital information when they end discussions too soon, whereas the latter occurs when they end them too late.
The seventh limitation involves the task assigned to participants. According to Doyle et al (2011, p. 130) these tasks are “often ill-defined and quite different from the way people naturally go about making decisions”.
This technique encourages participants to create on-the-spot mental models instead of encouraging them to elicit the mental models they normally use to make decisions. Such situations arise when modelers change the questions that the participants are asked to monitor the intervention.
The challenge can also be caused by altering the order of questions. Doyle et al (2011, p. 130) points out that “what people remember and think about can be highly dependent on subtle characteristics of the situation they are in at the time and even seemingly inconsequential differences in how questions are worded”.
Hence, it is recommended that participants be subjected to a well formulated decision task that resembles, as much as possible, the one that they encounter on a daily basis.
In addition to the limitations stated above, some current methods also employ intricate communicating techniques when introducing new mental models. Participants are taught using complex techniques that cause them to make numerous errors.
In some instances, these techniques are so complex that they encourage participants to revert to the old techniques for communicating mental models that they were used to. It is, therefore, recommended that the technique for communicating (or expressing) mental models employed in an intervention exercise factor in the manner in which the individuals of interest normally communicate their ideas.
This increases the accuracy of mental model representation and enables modelers, at the end of the intervention exercise, to determine whether there has been any significant progress in changing the mental models of participants.
How mental models can be changed according to the systems thinking approach
Taking these limitations into account Doyle et al (2011 p. 131) proposes a methodology for changing mental models that is based on systems thinking approach. This method is not only effective in changing mental models, but also in measuring the ability of an intervention exercise to change the same.
The new methodology proposes that the intervention exercise should begin with a pretest survey in which participants are asked to explain the cause of a particular pattern of data. At this point, the primary goal of the methodology is to establish the events, factors and variables that caused the pattern. Afterwards, the relationship between them can be determined (Doyle et al 2011, p. 131).
An advantage of this approach is that the participants decide on their own volition, the extent of information to give in their responses. The new methodology also suggests that the intervention exercise ends with a posttest survey, which is conducted in exactly the same manner as the pretest survey.
The advantages of this method include “separation of the processes of changing and measuring mental models, minimizing the potential for the experimenter and subject bias; and increasing the likelihood that the measured models are those that are used in real-life decision making” (Doyle et al 2011, p. 131).
This new model also employs causal loop diagrams technique to represent the participant’s mental models. Participants are supposed to use the techniques they are familiar with to communicate (elicit) their mental models, such as narratives.
Experimenters are then required to code these narratives into the causal loop diagrams. They achieve these using known psychological techniques that are effective in uncovering underlying explicit and implicit structures that are present in narrative text.
Doyle et al (2011, p. 131) states “by identifying the number of subjects who include a variable and counting the number of variables, connections between variables, and feedback relationships, pre-post differences in the content, structure, complexity and dynamics of mental models can be quantified”. This quantification is essential in measuring how effective an intervention is in improving the system thinking of participants.
Conclusion
Mental models are useful tools in aiding us to understand the decision-making process of an individual. They have been and will continue to be central in improving systems thinking in the organization since they provide modelers with a way of getting inside an individual’s mind and tuning it to reason appropriately.
Furthermore with Doyle’s et al’s proposed model, it is possible to measure the effect of a given system intervention exercise on an individual’s mental models (Doyle et al 2011, p. 131). As such, organizations can determine if indeed the system thinking intervention exercises they invest in are worthwhile
It is important to note that the reading titled “Complex systems thinking is being used for policymaking. Is it the future?” written by Sarah Quarmby highlights the current use of the complex systems thinking framework in policy research. Among many definitions of the latter, one defines it as the following: “Complex systems behave in a way that is greater than the sum of their parts” (Quarmby, 2018, para. 4). In other words, no complex system is comprehensible or understandable by observing its core components only. Thus, policy research and practice need to approach the given endeavor by studying and analyzing the system as a whole.
One of the underlying reasons is the fact that a complex system includes feedback mechanisms, which complicate the interactions between its components. Such interconnectivity inherent to the complex system requires a comprehensive assessment of a problem. In the past, a similar model was the garbage can model, which describes organizations as types of anarchies (Quarmby, 2018). Therefore, a decision is not made uniformly by a single competent entity, but rather decisions are the results of the chaotic mixing of many intertwined issues and their corresponding solutions, creating an enterprise resembling garbage in a bin.
In my current organization, both positive and negative feedback loops exist. For example, some departments will be encouraged to perform their duties through positive feedback loops, whereas others might be instructed to slow their operations. Based on the readings, the organization could rely more on evidence and analytics for its decisions. In addition, it could view concepts more critically instead of blindly introducing them into the policy (Quarmby, 2018). This will ensure that new measures are properly assessed for their validity for each particular case before integrating them systematically.
At my organization, I want to examine the department, company, and industry. Figure 1.1 in the Learning Activity illustrates how the environment encompasses society, and the latter contains business (Gittell et al., 2012). I would place the department in the inner circle and the company in the middle, whereas the industry itself would be placed in the outer circle. The proposed model is made in this manner primarily because each smaller element is part of a larger whole. These particular choices were made because departments tend to operate with a degree of autonomy depending on the organizational design. Each department follows a strict set of its procedures to meet its quarterly or yearly goals in performance. For example, a sales department wants to increase sales, whereas an HR department wants to retain the existing talent and attract new ones as well.
However, departments can become excessively focused on their performances, issues, and achievements that a larger and more important vision can be lost in regards to the company itself. Most companies and businesses operate as a cohesive whole competing in a particular industry. A company seeks to gain a competitive advantage and a larger market share to dominate over its rivals. Businesses devise business models, marketing plans, long-term goals, and strategic positioning to gain more competitiveness in the market. Thus, even at a company level, businesses and their leaders should not forget that they are mostly operating in a larger industry. One should note that industries and their corresponding markets are vulnerable, such as coal mines declining in the face of green energy and climate concerns, as well as the oil and gas industry.
It is important to note that integrated social systems understand and view major problems as a complex whole rather than a separate case. It is applied by accounting for the consequences and implications of one’s actions in the larger environment in which one operates. The core steps include topic identification, boundaries and environment, society or people, economics, conceptual framework, challenges, and solutions (Gittell et al., 2012). The two most critical steps include boundaries and environment, as well as a conceptual framework. The main reason for the former is rooted in the fact that a problem’s influence needs to be properly outlined. It is vital to be an effective problem-solver or researcher to focus on the key aspects.
However, one should additionally be aware that he or she could misjudge the scale of the issue, which could be far larger or smaller than anticipated. It is necessary to approach the conceptual framework as accurately as possible because it determines the effectiveness of the solution. Thus, all seven steps are appropriate, but I would ask about merging challenges into a conceptual framework since the former is identified in the latter. An example of a challenge being an opportunity for action is Europe’s dependence on Russian oil, which became exacerbated by the invasion of Ukraine. The first question is about where specifically a change needs to take place, whereas the second one is about policies to improve the situation. These questions enable more holistic solutions because they seek to identify and address the root cause of the issue. As a result, many European nations, such as Germany, might decide to shift towards energy independence through nuclear power plants.
References
Gittell, R., Magnusson, M., & Merenda, M. (2012). The sustainable business case book. Saylor Foundation.
Quality and safety are critical determinants of the nature of healthcare available in different medical settings. Emerging concepts and frameworks can guide different stakeholders to promote desirable practices that have the potential to maximize patients’ overall experiences. Systems thinking is a powerful strategy that analysts associate with improved healthcare services. Regulators and accreditors can rely on systems thinking as key players to introduce systems thinking, introduce additional skills to practitioners, and identify new guidelines to drive safety and quality in medical practice.
Linking the Work of Accreditors and Regulators to Systems Thinking
Nursing practice is a complex process characterized by numerous procedures, clinical guidelines, participants, and policy initiatives. Each element plays a critical role towards influencing the nature and quality of medical support available to the targeted patient. Systems thinking presents a unique approach for examining most of these factors, their possible interactions, and the manner in which they contribute to the quality of care (Dolansky et al., 2013). Involved stakeholders relying on this framework will monitor the existing complexities and analyze each form of design, activity, and process that impacts the available health services.
Accreditors and regulators play a unique role in the field of healthcare to guarantee quality and safety. Through systems thinking, these stakeholders can identify most of the promoted initiatives to ensure that they resonate with the needs of the patients. For example, decision-makers in such agencies can begin by identifying the educational procedures for nurses and physicians to ensure that are timely and do not contribute to care delivery gaps (McNamara & Teeling, 2021). The involved leaders will revise the existing curriculum and clinical guidelines in accordance with the identified needs.
Accreditation organizations need to implement additional mechanisms to maximize adherence while analyzing the subsequent outcomes continuously. Leaders can examine the level of certifications and requirements and compel other stakeholders to meet them (McNamara & Teeling, 2021). Through systems thinking, these regulators and accreditors will monitor various areas to examine whether they contribute to improved healthcare or nursing procedures (Linnéusson et al., 2022). They will go further to analyze the manner in which such attributes relate to the other strategies undertaken in practice.
Driving Safety and Quality
The Joint Commission is a good example of an accreditor that has the potential to drive quality and safety by employing the concept of systems thinking. The above section has presented a unique approach for maximizing certification while at the same time ensuring that all stakeholders monitor their activities. The model provides a holisitic view of all procedures to identify possible gaps and unearth additional opportunities for continuous improvement (Linnéusson et al., 2022). The relevant agencies can go further to educate practitioners about the use of systems thinking in their respective units. The selected institutions will introduce additional programs to train and encourage more professionals to start embracing the idea.
Through the identified multifaceted approach, key participants will identify possible causes of medication errors and sentinel events in their respective settings. Practitioners who apply systems thinking as a regulatory requirement will monitor the healthcare system and propose additional interventions to support the delivery of personalized and culturally-competent services (McNab et al., 2020). These insights show conclusively that systems thinking can result in the reduction of sentinel events and gaps, thereby taking safety and quality to the next level.
Conclusion
The mission to deliver quality and safe medical care is the responsibility of all key stakeholders, including practitioners and regulators. These accreditors can rely on the concept of systems thinking to support the introduction and implementation of various policies and clinical guidelines. The move to educate and encourage more practitioners to apply systems thinking as a major practice requirement can transform nursing practices. This initiative is evidence-based and capable of improving the quality of care and services available to more patients.
McNamara, M., & Teeling, S. P. (2021). Introducing health care professionals to systems thinking through an integrated curriculum for leading in health systems. Journal of Nursing Management, 29(8), 2325-2328. Web.
Systems thinking is an approach that implies focusing the analysis on observation of interrelation in the system through the course of time and within the context of other more extensive systems. For example, in an article on human-centered crash analysis, Adanu et al. (2019) incorporated the systems thinking approach to provide a multilevel framework to better understand factors that cause human-centered crashes. Another study conducted by Desbois et al. (2021) used systems thinking approach to identify and assess the feasibility of potential interventions that could be used to reduce antibiotic involvement in tilapia farming in Egypt. Thus, system thinking is appropriate in cases where there is a need to detect valuable connections and identify issues within the work of the system’s segments.
On the other hand, the strategic planning approach is a systematic process that implies setting strategic objectives and involves valuable aspects, such as organizational priorities and energy and resources management. For example, in researching protecting coastlines from oil spills, Grubesic et al. (2019), implemented a strategic planning approach to fit the response resources in limited budgeting. Alternatively, in a study on virtual museums exhibitions, Kamariotou et al. (2021), utilized the strategic planning approach to define key elements in the development of digital museums. Because strategic planning features different sides of the subject, the approach is appropriate in determining different aspects of future development.
The two approaches are not interchangeable because they serve to achieve different goals. Both approaches are similar in viewing the system in the context of different conditions, such as connections in the systems thinking approach and factors in the strategic planning approach. However, systems thinking helps evaluate the system’s work in the context of existing connections, while strategic planning allows altering or making new connections to improve the overall work. Strategic planning also differs from systems thinking as it implements the feature of fitting specific criteria, like resource limitation or goal achievement. I can see myself as an agent of change as I believe that changes imply overcoming obstacles and improving the overall work of the system.
System thinking is a complex element, which implies how one of the systems’ parts influences the other essential constituents of the system (Meadows, 2008). Moreover, it is evident that system thinking is a core contributor to the cultivation of the learning organization (Senge, 1990).
In this instance, I understand that the small issues on the lower levels affect the performance of the organization, and considering them remains a necessity for the organizational performance of the whole organization. The system thinking allows avoiding adverse consequences and improving company’s sustainability.
As for the interludes, Meadows uses them to emphasize the significance of the existing problems since the system thinking might affect the original purpose of the objects and activities (2008). I claim that this technique remains efficient, as it helps understand that one small event can contribute to the changes of the purposes and structure of the whole organization with the assistance of the case scenarios.
I consider the interlude the Blind Men and the Matter of the Elephant as the most applicable in the context of my organization, as sometimes people have a tendency to see particular parts of the enterprise only and are not able to interrelate them to each other systematically. The system has to be considered as a composite body, which functions as one organism.
Sometimes change is essentiality due to the alteration of the flow of the events in the marketplace (Wilson & Ralston, 2006). Moreover, understanding that the modifications in the flow of the routine processes might be the case of the changes to the performance of the organization. In this instance, the maintenance and sufficient monitoring of the stages remains an essentiality (Harvard Business School Press and the Society for Human Resource Management, 2006).
Lastly, taking into account the organization as a sophisticated mechanism leads to the optimization of all processes, as they lead to the common goal. Apple is one of the examples of the companies, which utilize system thinking as a primary strategy for the innovation and product development. Application of this technique was essential for the business’ sustainability.
It is evident that system thinking causes alteration to the initial routine stages (Case, 2010). As taking into account the specific steps, it is apparent that I consider these processes necessary, as the flow of the events, especially, if they contribute to the achievement of the common goal.
However, it is evident that they are done automatically since they are considered as routine activities. In this instance, any changes might be a potential reason for the development of uncertainty, as the flow of the events will be modified. In this case, the alternative actions have to be considered as a possibility since they will contribute to the development of the understanding that alterations are a necessity to improve the quality of the processes.
As for the ‘aha’ moment, the vehement implementation of the system thinking as a global phenomenon, as all of the business entities, institutions, and so on are interdependent (Mark Alpert, 2010). The global orientation of the system thinking is surprising. It emphasizes the presence of the world as a complex functioning system, which consists of co-dependent elements. Nonetheless, in conclusion, system thinking is an essentiality, as it contributes to the positive changes in the organization.
Moreover, it has to be considered on the global level, as the complexity of the world cannot be underestimated. It is evident that one part of the mechanism can highly change the flow of the events. It is apparent that the effect will be dramatic since one small alteration leads to the development of the different movement of events and adjustments in the everyday actions.
Harvard Business School Press and the Society for Human Resource Management (2006). The essentials of strategy. Boston, MA: Harvard Business School Publishing.
The systems thinking as a concept aims to alleviate obstacles hindering growth in an organization. Therefore, it is an appropriate problem-solving model when it comes to developing a firm into a learning organization. Peter Senge developed this model in his book, The Fifth Discipline: The Art and Practice of the Learning Organization (1990). Systems thinking enables leaders or individuals to see and understand the connections between parts and events.
Such interrelations include structural organization, customer relations, sales, decision-making process, among other procedures. However, this article will borrow from the fundamental discipline that Senge refers to as a learning organization. Consequently, this will help to unearth the shortcomings within the business world, particularly the motor manufacturing industry. This article seeks to analyze the perceptions of the workers towards team learning since it forms the integral part of a successful organization.
The main problem and the factors contributing to the problem
In the business world today, everyone aspires to remain competitive to survive in the market. However, to achieve visible results has proved to be costly in terms of planning and management. In a bid to achieve efficacy in an organization, it is necessary to evaluate the drawbacks curtailing growth within the organization. In this case, the problem is lack of coordination due to the hierarchy within the organization. Thus, it presents the management of such industry the challenge to come up with the best policies that promote teamwork as a way to improve coordination. Poor decision-making results from the lack of team coordination, which in turn requires team learning (Meadows & Wright, 2008).
The main themes leading to poor coordination include the bureaucratic structure and poor leadership. The bureaucratic system embraces a situation whereby workers receive orders from their leaders thus restricting them from performing fully within the organization. According to Senge (2014), systems thinking in business works in a similar way as the human body system whereby the coordination of various body organs results in functioning of the whole. Handling a complex situation requires from one to identify where the leverage lies. Leverage is an action that can lead to a sustainable and profound change. The main problem in the motor industry is that the leverage changes often appear insignificant to most of the team members.
“What is really going on?”
Identifying the central connection underlying an issue enables one to identify what is going on and sheds light on what might be the solution. In the case of the motor industry, balancing industry growth and capacity building are an evolving problem. Thus, team learning should be a regular process. From a systems perspective, one can see the trends that are repeated lead to growth or decline (Meadows & Wright, 2008). Some of the patterns that repeat include production of low-quality products resulted from poor management. The leverage, in this case, might involve unearthing the sources of stability without necessarily having to cut on costs.
In the motor industry, managers tend to put the blame about poor production on budget constraints. In the process of finding leverage, managers often lay off staff, only to realize that the remaining staff lacks the capacity to handle the workload. Leaders who want to keep their firms competitive mostly find themselves rambling in the balancing process. For an organization to evade this unexpected behavior, it should come up with better ways of thinking and embrace teamwork to establish leverage (Williams & Hummelbrunner, 2011). For example, the American manufacturers happen to be more individualistic and cherish personal mastery, while their Chinese counterparts are more team aligned. The latter eliminates delays and increases diversity hence putting forth a much more successful process.
The archetype in the problem
In the business realm, for instance, in the motor manufacturing industry, there are various archetypes that managers seem to have little understanding about them. Producing a quality product can be costly and time-consuming because it involves more deep controls as opposed to those involved in a low-quality production. When assessed from a systems perspective, such manufacturers fail to acknowledge that given time, high-quality processes will eliminate rework, improve customer satisfaction and reduce rejects. Thus, this approach minimizes the overall costs in the long term (Erjavec, 2010). Therefore, working as a team to generate inclusive ideas helps achieve both goals. When workers focus on quality production with a shared vision, the cost goes down with time. Just as human systems rely on each other, the same case applies to manufacturing.
In a bid to establish the areas where learning is highly needed, one has to consider the entire system that generates the issues (Senge 2014). It is easy to identify the barriers at departmental levels, but most of the heads lack the knowledge on how their departments should liaise with the rest. An immediate leverage to this problem is incorporating cross-functional teams to ensure openness and a steady flow of information without distortion. For instance, consider cases where duplication or omission might occur. Duplication of efforts occurs because of the failure to account for various parts of the system. Learning should come in to create awareness about the missed opportunity for saving human efforts. Viewing parts broadly, helps see the bonds between factors previously considered as divergent variables (Erjavec, 2010).
The leverage
The cross-functional structure in an organization offers a stabilizing aspect that encourages growth. Often, in the motor industry there is bureaucracy when handling classified information. Following this, the rate of growth might diminish or even stop. However, understanding how to manage the structure becomes inevitable. Reinforcing the indicators of growth requires continued learning to ensure that as products saturate in the market, a firm can stay relevant (Williams & Hummelbrunner, 2011).
Some managers respond or find leverage through investing more in a bid to sustain growth. Contrarily, this approach may not necessarily work, instead if the market is saturated, as is the case in the motor industry, it is advisable to innovate new products. Managers need smart ideas to know when the time is due to act and reinvent procedures rather than settling for lesser goals (Senge 2014). In this case, lack of coordination becomes a limiting factor. To increase coordination, the behavior of the team has to change. For example, appreciating workers better through salary increase is an effective way to promote desirable team behavior. Besides, maintaining morale through equal treatment of employees and awarding pay based on production increases growth. When this ceases, the growth slows down tremendously implying that the factors leading to improvement are weak.
Conclusion
As aforementioned, systems thinking suggests that there are no external generators of organizational issues. The root of the problem is within, and the whole network is to be blamed for any failures. Therefore, the solution should be creating the team’s ability to learn how to alleviate barriers to growth. The systems thinking framework provides diverse approaches concerning how to learn in a more inclusive way and why it is essential. Such analysis and consideration of the whole enhances careful uncovering of issues that might end up being misunderstood. Ultimately, working as a team helps to map out the comprehensive process of leadership and training.
References
Erjavec, J. (2010). Automotive technology: A systems approach. Australia: Delmar Cengage Learning. Web.
Meadows, D. H., & Wright, D. (2008). Thinking in systems: A primer. White River Junction, VT: Chelsea Green Pub. Web.
Senge, P. M. (2014). The Fifth Discipline Fieldbook. In Exploring your own story (pp. 46-71). S.I: Crown Publishing Group. Web.
Williams, B., & Hummelbrunner, R. (2011). Systems concepts in action: A practitioner’s toolkit. Stanford, CA: Stanford Business Books. Web.