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MGN412 Literature Review

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MGN412 Literature Review

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Course Code: MGN412
University: Queensland University Of Technology is not sponsored or endorsed by this college or university

Country: Australia


Is your thesis statement argumentative, analytical or explanatory.  This determines the position you are taking.  That is,  are you going to argue for or against something (argumentative), break down an issue into its different parts and evaluate it (analytical) or explain a concept or idea (explanatory)?   You may change your thesis statement into an argumentative one if you prefer.   These notes will mainly refer to an argumentative thesis statement.  That is, it will try to convince the reader to adopt a certain point of view about a topic or issue.   An argumentative thesis statement (commonly in the form of a question) may start with words such as:  Do, does, should, would, could, how, when, where, who, why, what.  Decide what your answer is to your thesis statement as this then directs your argument.    If you don’t have an answer then how will you know if you win the argument or not?
Does psychological bullying of staff in small workplaces by their managers create problems for customers and suppliers? – YES, IT DOES (and that is what I want to convince the reader of).  

Directional or action words (often verbs)
Words that can be substituted by others with the same or similar meaning – this may help finding more appropriate articles – for example, ‘technology’ might produce better results than ‘computers’
Important phrases and words
Something causing something (cause and effect/outcome/consequences)
Phrases or words that link to others in the statement


Organizational Behavior
According to Andy (2014), data is one of the factors that make businesses to attain some of the essential things in their operations. In organizations, making decisions is fundamental because it makes managers to address some of the things that affect operations. Although there are many types of decision makings, data-informed decision-making has been termed as one of the important tools in the operation of businesses because it makes managers to come up with outstanding decisions. According to William (2012), data-informed decision making provides references which are essential in guiding decisions making processes.
Abdullah (2015) asserts that in organizational operations, data can be termed as a crucial and influential source of business insight for managing organizations today. However, when it comes to making that insight deliver a positive outcome, managers should not allow the data to completely control their instincts and experience. It is advisable to utilize the data as the basis of strategic decision making while making instincts and experience to be independent.
Andy (2014) asserts that the best way to make decisions is using data-informed decision-making technique. Data-informed decision making derives strategies decisions through a combination of experience and data. According to William (2012), although this technique is essential in making decisions in organizations, it is essential to combine human experience and data in order to maximize the chance for success. The reason why this combination is important is both the decision maker and the data are not always perfect but the combination of the two help a person to overcome the shortcoming of either.
According to Popescu (2014), one of the challenges of making the best decisions is being able to know the extent which one is required to rely on the data, and how much to rely on the decision makers experience and instincts. Essentially, every issue in an organization is addressed through an approach situated on a problem-solving spectrum while depending on past experience (Hagai, 2012). Organizations must understand that while following one’s experiences and instincts during decision making may be crucial in some incidences, it is always important to ensure that decisions are informed by data which is proven be valid and accurate.
Although Data-informed decision making is widely used in education communities, it can also be used in other fields where data is viewed as essential tools in making decisions. According to Abdullah (2015), the use of this technique in decision-making processes yield better decisions because it assists decision makers to make their decisions based on facts (William, 2012). However, it can be misleading especially where there is a lack of expertize especially in various aspects regarding data analysis and evaluation. This means that when using this tactic to make decisions, it is essential to ensure that the decision-making team is made up of people with proper skills regarding various aspects concerning data.
Nguyen (2018) asserts that being data-informed means having a subset of data which is accurate, and relevant to the decision being made. This means that before selecting any time of data to inform decision-makers, it is crucial to ensure that the data is sufficient and can be broken into different subsets (Hagai, 2012). Lack of enough data can lead to wrong conclusions, which in turn can make the decision makers to come up with decisions which can significantly hurt an organization.
Abdullah (2015) asserts that data-informed decision making provides rigorous methods to assist managers and other leaders in making decisions in not only situations where there is sufficient data but also when an organization does not have enough information to rely on. Examples of situations where data come into play during decision making in organizations range from estimating sales and revenue through optimizing various factors such as staff schedules and supply-chain operations, to coming up with strategic decisions in multifaceted environments (Hagai, 2012). Making use of data-informed decisions in management help organizations to think critically through using facts, and therefore enable managers to come up with sound decisions especially when faced with serious challenges.
In the current business operation waves where most of the sectors are affected by various aspects that affect business operations, managers need to turn the data they have into decisions. According to Charles (2014), one of the primary reasons why executives should view data-informed decision-making as an important tool is that it makes them to make decisions that are controlled by facts. In most cases, companies arrive at decisions that negatively impact their performance because they settle on conclusions that have no basis, or that lack any proper reason why they are made (Abdullah, 2015). In most cases, businesses that make such decisions are those that have decision makers who like settling on decisions that serve their interest instead of addressing the issue at hand. The use of data-informed decision-making approach is crucial because it minimizes the chances of bias in decision making.
In letting data to be the center of the decision-making process, organizations are able to come up with sound decisions that are able to address issues even those that seem complicated to solve. When organizations make use of this approach, their decisions affect various stakeholders such as employees, customers, shareholders among others. Although data-driven decision making is termed as a common term, it is a superior term because according to Devettere (2010), decisions should not be solely based on quantitative data.
Andy (2014) asserts that data in most cases raises more questions than it solves. This means that managers should view data-informed decision-making process as a constant process which requires a lot of things to be done in order to come up with sound decisions. For example, it requires experts to analyze the available information and provide their views on whether it is good to be used for the decision-making process.
According to Guion (2011), data-driven decision making leads to various advantages in an organization. Some of these advantages include minimizing the level of bias involved in decision making. In some cases during decision making, people want things to be addressed to their best interest, and this leads to the problem of having biased decisions (Charles, 2014). Data-informed decisions are termed as essential in such organizations because the use of data to come up with decisions makes the team members and stakeholders to be on the same page with less of their own judgment.
The use of data-driven decisions making also makes the decision-making process to be less time consuming because it minimizes discussions on unnecessary matters (Vijayasree, 2012). In most cases, making decisions takes long hours because of various issues such as arguing just for the sake of doing it (Devettere, 2010). Having data-driven decision making is essential because it makes decision makers to have the basis of their arguments, and also to reason faster hence saving time.
According to Marina (2010), although data-driven decision making has various advantages, it also has various drawbacks which organizations should understand before using it as a tool in guiding their decisions. One of the disadvantages is that it requires huge data volume in order to come up with accurate decisions. William (2012) asserts that making decisions based on data requires a lot of information to be present so that the decision makers can make comparisons to come up with a precise conclusion. Sometimes, attaining the huge amount of data for some companies may not be easy because it is a process that requires a lot of efforts.
The other drawback associated with this technique is that it requires someone with good knowledge of data science, and a lot of resources need to be available for this approach to be beneficial. According to Hagai (2012), analyzing data to come up with sound decisions requires someone with proper data science knowledge to be present in order to undertake various things such as analysis in order to find out some of the things that make sense for the decision making process. It also requires various resources such as information management systems to be available (Marina, 2010). Sometimes, having these resources especially for small organizations may be challenging and therefore meaning that this technique may not be beneficial for some businesses.
According to Eleanor (2011), in most cases and especially for organizations that lack experts in matters regarding data collection, people tend to be biased in various matters especially those regarding the manner in which they gather data. When this happens, organizations risk making decisions on the wrong information and this may harm their performances because they may fail in addressing matters that risk business performance.
Although a good decision is the one that makes the best use of the available information, the data-informed decision-making approach does not mean that managers will always come up with right decisions because information is not always perfect (Marina, 2010). However, if properly used, it can lead to a greater success in decision making by improving the quality of decisions that are made.
According to Kline (2010), coming up with a data strategy to inform managers in decision making helps them in identifying, defining, and tracking outcomes. For managers to come up with sound decisions using this approach, they do not need to take years while undertaking performance metrics or establishing a robust data strategy. Instead, they need to come up with a systematic process of identifying the right data sets so that they can be able to inform the organization’s strategy (Yoe, 2012). A compressive data and one which is good for decision making should be relevant, accurate, and reliable.
According to John (2014), irrespective of the size and responsibility of an organization, the use of data to inform decision making can make managers to come up with every kind of decision, whether it be decisions related to marketing strategy, product development, advertising, hiring new personnel and so forth. Guion (2011) asserts that companies that use data as the basis of their decision making tend to be more competitive and productive because having good quality information makes them to make sound decisions concerning how they can stand out in the market.
Although the term data-informed decision may make most people to suggest that the technique entirely relies on data, Donald (2013) affirms that this approach does not completely depend on data in decision making. Instead, it connects with other factors such as the person’s point of view to make judgments concerning how the available information influence the decision being made.
Unlike other approaches such as data-driven, data-informed approach understand that data is not always perfect information as it seems to be because it can be biased, depending on how it was gathered. Organizations that use this approach also understand the limitation of data because according to Yoe (2012), collected data indicates a snapshot of the reality. Because of this factor, people who use this tactic trust that the decision-making should not solely depend on data. Therefore, instead of entirely relying on the data, they test and question it before coming up with their conclusions.
In conclusion, many kinds of literature have tried to explain the meaning of data-informed decisions and its implication to the management of organizations. Although various researcher and scholars have different views concerning this approach, most of them agree that it is an important tool because it makes organizations to not only come up with unbiased decisions but also ones which solve problems even those that seem to be more challenging. One of the major characteristics of this approach is that it does not entirely rely on data in making decisions. It needs to be combined with personal experience and ensure that data does not completely control the instincts and experience of the decision maker.
The data-informed decision-making approach means using data to guide decisions and has various advantages and disadvantages. One of the advantages is that it eliminates bias in decision making. The other major advantage is that it makes the decision-making process to be less time consuming because it enables decision-makers to take the human out of the equation. On the other hand, the major disadvantage associated with this approach is that it requires people who are experts in data related aspects such as analysis and evaluation to be available. The approach also requires an organization to have various resources such as information systems in order to ensure there is sufficient information to guide the decision-making process.
Abdullah, Y. S. (2015). Household Bargaining, Financial Decision-Making and Risk Tolerance.  International Journal of Business and Society, 16(2), 41-58.
Andy, A. L. (2014). Decision-Making in International Relations: A Theoretical Analysis.  Canadian Social Science, 10(5), 54-67.
Charles, C. W. (2014). Navigating Strategic Decisions: The Power of Sound Analysis and Forecasting.  The Journal of Business Forecasting, 33(1), 76-89.
Devettere, R. J. (2010). Practical Decision Making in Health Care Ethics: Cases and Concepts.  Washington, DC: Georgetown University Press.
Donald, G. L. (2013).  Leadership and Decision-Making in Team-Based Organizations: A Model of Bounded Chaotic Cycling in Emerging System States.  Emergence: Complexity and Organization, 15(3), 365-379.
Eleanor, W. M. (2011). You Make the Call: Tips for Making Public Decisions.  The Public Manager, 40(3), 32-56.
Guion, R. M. (2011). Assessment, Measurement, and Prediction for Personnel Decisions.  Routledge
Hagai, G. (2012).  The Greatest Business Decisions of All Time: How Apple, Ford, IBM, Zappos, and Others Made Radical Choices That Changed the Course of Business.  Journal of Multidisciplinary Research, 4(3), 89-102.
John, M. (2014).  Critical Issues Facing School Leaders concerning Data-Informed Decision-Making.  The Professional Educator, 38(1), 43-65.
Kline, J. M. (2010). Ethics for International Business: Decision Making in a Global Political Economy.  New York: Routledge
Marina, G. (2010).  Foresight to Insight to Action: Imaginative Forecasting Helps Shape a Brighter Future. Generations, 34(3), 132-154.
Nguyen, D. M. (2018).  A New Decision Making Model Based on the Made in Vietnam Lean Management Philosophy.  Economics & Sociology, 11(1), 657-671.
Popescu, L. D. (2014).  Decision Making in Public Sector Organizations.  Geopolitics, History and International Relations, 6(1), 78-97.
Vijayasree, K. R. (2012). Effect of Emotions and Sociability on Human Decisions.  Indian Journal of Positive Psychology, 3(4), 76-97.
William, B. (2012).  Sociological Overview of Knowledge Management in Macro Level Health Care Decision-Making.  Journal of Sociological Research, 4(2), 75-98.
Yoe, C. (2012). Principles of Risk Analysis: Decision Making under Uncertainty.   Boca Raton, FL: CRC Press

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