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Behavioral operations management (BOM) has gained popularity in the last two decades. The main theme in this new stream of research is to include the human behavior in Operations Management (OM) models to increase the effectiveness of such models. BOM is classified into 4 areas: cognitive psychology, social psychology, group dynamics and system dynamics (Bendoly et al. 2010). This dissertation will focus on the first class, namely cognitive psychology. Cognitive psychology is further classified into heuristics and biases. Tversky and Kahneman (1974) discussed 3 heuristics and 13 cognitive biases that usually face decision makers. This dissertation is going to study 6 cognitive biases under the representativeness heuristic. The model in this dissertation states that cognitive reflection of the individual (Frederick 2005) and training about cognitive biases in the form of warning (Kaufmann and Michel 2009) will help decisions' makers make less biased decisions. The 6 cognitive biases investigated in this dissertation are insensitivity to prior probability, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity and misconception of regression. 6 scenarios in OM contexts have been used in this study. Each scenario corresponds to one cognitive bias. Experimental design has been used as the research tool. To see the impact of training, one group of the participants received the scenarios without training and the other group received them with training. The training consists of a brief description of the cognitive bias as well as an example of the cognitive bias. Cognitive reflection is operationalized using cognitive reflection test (CRT). The survey was distributed to students at University of North Texas (UNT). Logistic regression has been employed to analyze data. The research shows that participants show the cognitive biases proposed by Tversky and Kahneman. Moreover, CRT is significant factor to predict the cognitive bias in two scenarios. Finally, providing training in terms of warning helps participants to make more rational decisions in 4 scenarios. This means that although cognitive biases are inherent in the mind of people, management of corporations has the tool to educate its managers and professionals about such biases which helps companies make more rational decisions.
Decision making or making judgments is an essential function in the ordinary life of any individual. Decisions can often be made easily, but sometimes, it can be difficult due to conflict, uncertainty, or ambiguity of the variables required to make the decision. As human beings, we constantly have to decide between different activities such as occupational, recreational, political, economic, etc. These decisions can be transcendental or inconsequential. Analyzing the Role of Cognitive Biases in the Decision-Making Process presents comprehensive research focusing on cognitive shortcuts in the decision-making process. While highlighting topics including jumping to conclusion bias, personality traits, and theoretical models, this book is ideally designed for mental health professionals, psychologists, sociologists, managers, academicians, researchers, and upper-level students seeking current research on cognitive biases that affect individual decision making in daily life.
Behavioral decision research provides many important insights into managerial behavior. From negotiation to investment decisions, the authors weave behavioral decision research into the organizational realm by examining judgment in a variety of managerial contexts. Embedded with the latest research and theories, Managerial Decision Making 8th Edition gives students the opportunity to understand their own decision-making tendencies, learn strategies for overcoming cognitive biases, and become better decision makers.
Research Paper (undergraduate) from the year 2020 in the subject Business economics - Miscellaneous, grade: 1,7, University of Applied Sciences Constanze, language: English, abstract: Human's mind cannot grasp the causes of events in their completeness, but the desire to find those causes is implanted in man's soul. And without considering the multiplicity and complex-ity of the conditions any one of which taken separately may seem to be the cause, he snatches at the first approximation to a cause that seems to him intelligible and says: "This is the cause!". There are many models and frameworks in use in the business world today, and it is hard to keep track of them all. The MBA Model is designed to provide people with a broad groundling in all the key aspects of business. It is a simplified version of something more complex – it helps to understand a specific phenomenon by identifying its key elements. Management is the art of getting work done through others. It involves marshalling a set of resources to achieve desired objectives. Managers make decisions about allocating people and money in an effective way. There are many analytical tools to help decision making, including decision trees and net present value analysis. Most decision making is not as rational as we might expect it to be. Cognitive biases in decision making discusses why people often make snap judgements that are flawed, and how effective managers can overcome these biases to make better decisions. The following work is based on the theoretical foundations of the MBA model (25 need-to-know MBA models, Birkinshaw, 2017). After clarifying the basics in Part 1, examples of Cognitive Biases will follow. In the end of the Scientific Report the Management failure traced back to cognitive bias get explained.
Behavioral Operations Management has been identified in the last years as one of the most promising emerging fields in Operations Management. Behavioral Issues in Operations Management explains and examines up-to-date research in this field, which works to analyze the impact of human behavior on the management of complex operating systems. A collection of studies from leading scholars presents different methodologies and approaches, supported by real data and case studies. Issues such as building trust and strong cooperative relationships with suppliers, enhancing motivation and designing proper incentives for stimulating more effective decision maker behaviours are considered. The main decision-making processes affected by behavioral issues are also analyzed with a focus on new product development, logistics, and supply chain integration. The broad coverage of methodologies and practical implications makes Behavioral Issues in Operations Management an ideal reference for both researchers developing new topics such as NK fitness landscapes and managers with an interest in behavioral management operations.
Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases examines how a wide range of factual evidence, primarily derived from a variety of data available to organizations, can be used to improve the quality of business decision-making, by helping decision makers circumvent the various cognitive biases that adversely impact how we all think. The book is built on the following premise: During the past decade, the new ‘data world’ emerged, in which the rush to develop competencies around business analytics and data science can be characterized as nothing less than the new commercial arms race. The ever-expanding volume and variety of data are well known, as are the great advances in data processing/analytics, data visualization, and related information production-focused capabilities. Yet, comparatively little effort has been devoted to how the informational products of business analytics and data science are ‘consumed’ or used in the organizational decision-making processes, as the available evidence shows that only some of that information is used to drive some business decisions some of the time. Evidence-Based Decision-Making details an explicit process describing how the universe of available and applicable evidence, which includes organizational and other data, industry benchmarks, scientific studies, and professional experience, can be assessed, amalgamated, and funneled into an objective driver of key business decisions. Introducing key concepts in relation to data and evidence, and the history of evidence-based management, this new and extremely topical book will be essential reading for researchers and students of data analytics as well as those working in the private and public sectors, and in the voluntary sector.
The persistent presence of cognitive biases has influenced rational decisions and strategic management since the 1970s. These prejudiced errors in judgment, often systematic and predictable, breach the foundational assumptions of economic theory, leading to dire consequences such as social inequality, financial collapse, and governmental inefficiency. Even the brightest minds are not immune, making it crucial to address these biases head-on. Overcoming Cognitive Biases in Strategic Management and Decision Making unravels the complex tapestry of biases that infiltrate decision-making processes at all levels. From social injustice biases and reasoning errors to action-inaction and social biases, the book confronts the myriad of ways that biases manifest in critical moments. These pose a significant threat to sound decision-making in various fields, impacting professionals ranging from judges and doctors to public officials. The repercussions of unchecked biases are far-reaching, leading to flawed outcomes that echo through society. The urgent need for a strategic response to mitigate these biases and enhance decision-making processes forms the crux of the problem this book seeks to address.
Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases examines how a wide range of factual evidence, primarily derived from a variety of data available to organizations, can be used to improve the quality of business decision-making, by helping decision makers circumvent the various cognitive biases that adversely impact how we all think. The book is built on the following premise: During the past decade, the new ‘data world’ emerged, in which the rush to develop competencies around business analytics and data science can be characterized as nothing less than the new commercial arms race. The ever-expanding volume and variety of data are well known, as are the great advances in data processing/analytics, data visualization, and related information production-focused capabilities. Yet, comparatively little effort has been devoted to how the informational products of business analytics and data science are ‘consumed’ or used in the organizational decision-making processes, as the available evidence shows that only some of that information is used to drive some business decisions some of the time. Evidence-Based Decision-Making details an explicit process describing how the universe of available and applicable evidence, which includes organizational and other data, industry benchmarks, scientific studies, and professional experience, can be assessed, amalgamated, and funneled into an objective driver of key business decisions. Introducing key concepts in relation to data and evidence, and the history of evidence-based management, this new and extremely topical book will be essential reading for researchers and students of data analytics as well as those working in the private and public sectors, and in the voluntary sector.
From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—"a tour de force” (New York Times). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical. In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions. Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.
This book brings together the latest research in this new and exciting area of visualization, looking at classifying and modelling cognitive biases, together with user studies which reveal their undesirable impact on human judgement, and demonstrating how visual analytic techniques can provide effective support for mitigating key biases. A comprehensive coverage of this very relevant topic is provided though this collection of extended papers from the successful DECISIVe workshop at IEEE VIS, together with an introduction to cognitive biases and an invited chapter from a leading expert in intelligence analysis. Cognitive Biases in Visualizations will be of interest to a wide audience from those studying cognitive biases to visualization designers and practitioners. It offers a choice of research frameworks, help with the design of user studies, and proposals for the effective measurement of biases. The impact of human visualization literacy, competence and human cognition on cognitive biases are also examined, as well as the notion of system-induced biases. The well referenced chapters provide an excellent starting point for gaining an awareness of the detrimental effect that some cognitive biases can have on users’ decision-making. Human behavior is complex and we are only just starting to unravel the processes involved and investigate ways in which the computer can assist, however the final section supports the prospect that visual analytics, in particular, can counter some of the more common cognitive errors, which have been proven to be so costly.