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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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All You Wanted to Know About How to Write a Case Study

What do you study in your college? If you are a psychology, sociology, or anthropology student, we bet you might be familiar with what a case study is. This research method is used to study a certain person, group, or situation. In this guide from our dissertation writing service , you will learn how to write a case study professionally, from researching to citing sources properly. Also, we will explore different types of case studies and show you examples — so that you won’t have any other questions left.

What Is a Case Study?

A case study is a subcategory of research design which investigates problems and offers solutions. Case studies can range from academic research studies to corporate promotional tools trying to sell an idea—their scope is quite vast.

What Is the Difference Between a Research Paper and a Case Study?

While research papers turn the reader’s attention to a certain problem, case studies go even further. Case study guidelines require students to pay attention to details, examining issues closely and in-depth using different research methods. For example, case studies may be used to examine court cases if you study Law, or a patient's health history if you study Medicine. Case studies are also used in Marketing, which are thorough, empirically supported analysis of a good or service's performance. Well-designed case studies can be valuable for prospective customers as they can identify and solve the potential customers pain point.

Case studies involve a lot of storytelling – they usually examine particular cases for a person or a group of people. This method of research is very helpful, as it is very practical and can give a lot of hands-on information. Most commonly, the length of the case study is about 500-900 words, which is much less than the length of an average research paper.

The structure of a case study is very similar to storytelling. It has a protagonist or main character, which in your case is actually a problem you are trying to solve. You can use the system of 3 Acts to make it a compelling story. It should have an introduction, rising action, a climax where transformation occurs, falling action, and a solution.

Here is a rough formula for you to use in your case study:

Problem (Act I): > Solution (Act II) > Result (Act III) > Conclusion.

Types of Case Studies

The purpose of a case study is to provide detailed reports on an event, an institution, a place, future customers, or pretty much anything. There are a few common types of case study, but the type depends on the topic. The following are the most common domains where case studies are needed:

• Historical case studies are great to learn from. Historical events have a multitude of source info offering different perspectives. There are always modern parallels where these perspectives can be applied, compared, and thoroughly analyzed.
• Problem-oriented case studies are usually used for solving problems. These are often assigned as theoretical situations where you need to immerse yourself in the situation to examine it. Imagine you’re working for a startup and you’ve just noticed a significant flaw in your product’s design. Before taking it to the senior manager, you want to do a comprehensive study on the issue and provide solutions. On a greater scale, problem-oriented case studies are a vital part of relevant socio-economic discussions.
• Cumulative case studies collect information and offer comparisons. In business, case studies are often used to tell people about the value of a product.
• Critical case studies explore the causes and effects of a certain case.
• Illustrative case studies describe certain events, investigating outcomes and lessons learned.

Need a compelling case study? EssayPro has got you covered. Our experts are ready to provide you with detailed, insightful case studies that capture the essence of real-world scenarios. Elevate your academic work with our professional assistance.

Case Study Format

The case study format is typically made up of eight parts:

• Executive Summary. Explain what you will examine in the case study. Write an overview of the field you’re researching. Make a thesis statement and sum up the results of your observation in a maximum of 2 sentences.
• Background. Provide background information and the most relevant facts. Isolate the issues.
• Case Evaluation. Isolate the sections of the study you want to focus on. In it, explain why something is working or is not working.
• Proposed Solutions. Offer realistic ways to solve what isn’t working or how to improve its current condition. Explain why these solutions work by offering testable evidence.
• Conclusion. Summarize the main points from the case evaluations and proposed solutions. 6. Recommendations. Talk about the strategy that you should choose. Explain why this choice is the most appropriate.
• Implementation. Explain how to put the specific strategies into action.
• References. Provide all the citations.

How to Write a Case Study

Let's discover how to write a case study.

Setting Up the Research

When writing a case study, remember that research should always come first. Reading many different sources and analyzing other points of view will help you come up with more creative solutions. You can also conduct an actual interview to thoroughly investigate the customer story that you'll need for your case study. Including all of the necessary research, writing a case study may take some time. The research process involves doing the following:

• Define your objective. Explain the reason why you’re presenting your subject. Figure out where you will feature your case study; whether it is written, on video, shown as an infographic, streamed as a podcast, etc.
• Determine who will be the right candidate for your case study. Get permission, quotes, and other features that will make your case study effective. Get in touch with your candidate to see if they approve of being part of your work. Study that candidate’s situation and note down what caused it.
• Identify which various consequences could result from the situation. Follow these guidelines on how to start a case study: surf the net to find some general information you might find useful.
• Make a list of credible sources and examine them. Seek out important facts and highlight problems. Always write down your ideas and make sure to brainstorm.
• Focus on several key issues – why they exist, and how they impact your research subject. Think of several unique solutions. Draw from class discussions, readings, and personal experience. When writing a case study, focus on the best solution and explore it in depth. After having all your research in place, writing a case study will be easy. You may first want to check the rubric and criteria of your assignment for the correct case study structure.

Read Also: ' WHAT IS A CREDIBLE SOURCES ?'

Although your instructor might be looking at slightly different criteria, every case study rubric essentially has the same standards. Your professor will want you to exhibit 8 different outcomes:

• Correctly identify the concepts, theories, and practices in the discipline.
• Identify the relevant theories and principles associated with the particular study.
• Evaluate legal and ethical principles and apply them to your decision-making.
• Recognize the global importance and contribution of your case.
• Construct a coherent summary and explanation of the study.
• Demonstrate analytical and critical-thinking skills.
• Explain the interrelationships between the environment and nature.
• Integrate theory and practice of the discipline within the analysis.

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Case Study Outline

Let's look at the structure of an outline based on the issue of the alcoholic addiction of 30 people.

Introduction

• Statement of the issue: Alcoholism is a disease rather than a weakness of character.
• Presentation of the problem: Alcoholism is affecting more than 14 million people in the USA, which makes it the third most common mental illness there.
• Explanation of the terms: In the past, alcoholism was commonly referred to as alcohol dependence or alcohol addiction. Alcoholism is now the more severe stage of this addiction in the disorder spectrum.
• Hypotheses: Drinking in excess can lead to the use of other drugs.
• Importance of your story: How the information you present can help people with their addictions.
• Background of the story: Include an explanation of why you chose this topic.
• Presentation of analysis and data: Describe the criteria for choosing 30 candidates, the structure of the interview, and the outcomes.
• Strong argument 1: ex. X% of candidates dealing with anxiety and depression...
• Strong argument 2: ex. X amount of people started drinking by their mid-teens.
• Strong argument 3: ex. X% of respondents’ parents had issues with alcohol.
• Concluding statement: I have researched if alcoholism is a disease and found out that…
• Recommendations: Ways and actions for preventing alcohol use.

Writing a Case Study Draft

After you’ve done your case study research and written the outline, it’s time to focus on the draft. In a draft, you have to develop and write your case study by using: the data which you collected throughout the research, interviews, and the analysis processes that were undertaken. Follow these rules for the draft:

• Your draft should contain at least 4 sections: an introduction; a body where you should include background information, an explanation of why you decided to do this case study, and a presentation of your main findings; a conclusion where you present data; and references.
• In the introduction, you should set the pace very clearly. You can even raise a question or quote someone you interviewed in the research phase. It must provide adequate background information on the topic. The background may include analyses of previous studies on your topic. Include the aim of your case here as well. Think of it as a thesis statement. The aim must describe the purpose of your work—presenting the issues that you want to tackle. Include background information, such as photos or videos you used when doing the research.
• Describe your unique research process, whether it was through interviews, observations, academic journals, etc. The next point includes providing the results of your research. Tell the audience what you found out. Why is this important, and what could be learned from it? Discuss the real implications of the problem and its significance in the world.
• Include quotes and data (such as findings, percentages, and awards). This will add a personal touch and better credibility to the case you present. Explain what results you find during your interviews in regards to the problem and how it developed. Also, write about solutions which have already been proposed by other people who have already written about this case.
• At the end of your case study, you should offer possible solutions, but don’t worry about solving them yourself.

Use Data to Illustrate Key Points in Your Case Study

Even though your case study is a story, it should be based on evidence. Use as much data as possible to illustrate your point. Without the right data, your case study may appear weak and the readers may not be able to relate to your issue as much as they should. Let's see the examples from essay writing service :

‍ With data: Alcoholism is affecting more than 14 million people in the USA, which makes it the third most common mental illness there. Without data: A lot of people suffer from alcoholism in the United States.

Try to include as many credible sources as possible. You may have terms or sources that could be hard for other cultures to understand. If this is the case, you should include them in the appendix or Notes for the Instructor or Professor.

Finalizing the Draft: Checklist

After you finish drafting your case study, polish it up by answering these ‘ask yourself’ questions and think about how to end your case study:

• Check that you follow the correct case study format, also in regards to text formatting.
• Check that your work is consistent with its referencing and citation style.
• Micro-editing — check for grammar and spelling issues.
• Macro-editing — does ‘the big picture’ come across to the reader? Is there enough raw data, such as real-life examples or personal experiences? Have you made your data collection process completely transparent? Does your analysis provide a clear conclusion, allowing for further research and practice?

Problems to avoid:

• Overgeneralization – Do not go into further research that deviates from the main problem.
• Failure to Document Limitations – Just as you have to clearly state the limitations of a general research study, you must describe the specific limitations inherent in the subject of analysis.
• Failure to Extrapolate All Possible Implications – Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings.

How to Create a Title Page and Cite a Case Study

Let's see how to create an awesome title page.

Your title page depends on the prescribed citation format. The title page should include:

• A title that attracts some attention and describes your study
• The title should have the words “case study” in it
• The title should range between 5-9 words in length
• Your name and contact information
• Your finished paper should be only 500 to 1,500 words in length.With this type of assignment, write effectively and avoid fluff

Here is a template for the APA and MLA format title page:

There are some cases when you need to cite someone else's study in your own one – therefore, you need to master how to cite a case study. A case study is like a research paper when it comes to citations. You can cite it like you cite a book, depending on what style you need.

Citation Example in MLA ‍ Hill, Linda, Tarun Khanna, and Emily A. Stecker. HCL Technologies. Boston: Harvard Business Publishing, 2008. Print.

Citation Example in APA ‍ Hill, L., Khanna, T., & Stecker, E. A. (2008). HCL Technologies. Boston: Harvard Business Publishing.

Citation Example in Chicago Hill, Linda, Tarun Khanna, and Emily A. Stecker. HCL Technologies.

Case Study Examples

To give you an idea of a professional case study example, we gathered and linked some below.

Eastman Kodak Case Study

Case Study Example: Audi Trains Mexican Autoworkers in Germany

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Decision-making approaches in process innovations: an explorative case study

Journal of Manufacturing Technology Management

ISSN : 1741-038X

Article publication date: 10 December 2020

Issue publication date: 17 December 2021

The purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations.

Design/methodology/approach

This study reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer.

The findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed.

Research limitations/implications

This study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design.

Practical implications

Empirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems.

Originality/value

Unlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.

• Uncertainty
• Decision making
• Process innovation
• Case studies
• Production systems
• Manufacturing industry

Flores-Garcia, E. , Bruch, J. , Wiktorsson, M. and Jackson, M. (2021), "Decision-making approaches in process innovations: an explorative case study", Journal of Manufacturing Technology Management , Vol. 32 No. 9, pp. 1-25. https://doi.org/10.1108/JMTM-03-2019-0087

Emerald Publishing Limited

Copyright © 2019, Erik Flores-Garcia, Jessica Bruch, Magnus Wiktorsson and Mats Jackson

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Process innovations, which involve new or significantly improved production processes or technologies, are essential for increasing manufacturing competitiveness ( Rönnberg, 2019 ; Yu et al. , 2017 ). The benefits of successfully implementing process innovations include reducing time to market, developing strong competitive barriers and increasing market share ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ). However, implementing process innovations does not always lead to desirable results ( Rönnberg et al. , 2016 ; Frishammar et al. , 2011 ). Instead, literature shows that staff frequently encounter difficulties when identifying decision-making approaches during the implementation of process innovations ( Eriksson et al. , 2016 ; Terjesen and Patel, 2017 ). These difficulties originate when staff responsible for implementing process innovations face unfamiliar circumstances ( Gaubinger et al. , 2014 ; Stevens, 2014 ; Jalonen, 2011 ). In particular, staff must deal with a lack of consensus and understanding (equivocality), and absence of rules or processes facilitating the analysis of information (analyzability) ( Piening and Salge, 2015 ; Milewski et al. , 2015 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ).

Operations management research offers diverse decision-making approaches useful for implementing process innovations ( Gino and Pisano, 2008 ; Hämäläinen et al. , 2013 ; Mardani et al. , 2015 ). This paper focuses on normative, intuitive and mixed-method decision-making approaches. Normative decision making involves quantitative analyses based on a systematic assessment of data ( Cochran et al. , 2017 ; Battaïa et al. , 2018 ; Dudas et al. , 2014 ). Intuitive decision making uses affectively charged judgments that arise through rapid, non-conscious, holistic associations ( Elbanna et al. , 2013 ; Dane and Pratt, 2007 ). The mixed-method approach considers both quantitative data and intuition ( Saaty, 2008 ; Thakur and Mangla, 2019 ; Kubler et al. , 2016 ; Hämäläinen et al. , 2013 ). It is vital to know when each decision-making approach is most suitable ( Zack, 2001 ; Eling et al. , 2014 ). Unless decision-making approaches are aligned with their conditions of use, the results could be disappointing ( Luoma, 2016 ).

Different decision-making approaches are used to solve problems when implementing process innovations ( Bellgran and Säfsten, 2010 ; Gershwin, 2018 ). However, it remains unclear when to select a particular decision-making approach ( Calabretta et al. , 2017 ; Dane et al. , 2012 ; Luoma, 2016 ; Matzler et al. , 2014 ). Recently, it is suggested that the degree of equivocality and analyzability of a decision, the structuredness of a decision, may constitute the main criteria for determining a decision-making approach ( Julmi, 2019 ). While this work provides novel insight, two salient issues require further research. First, there is a need for empirical understanding, as current findings remain purely conceptual. For example, manufacturing companies seldom experience a black-and-white divide between equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Eriksson et al. , 2016 ; Zack, 2007 ). Accordingly, it is necessary to remain open to unanticipated findings and the possibility that current explanations about selecting a decision-making approach require adjustments. Second, current findings give precedence to intuitive decision making over normative or mixed approaches. Identifying when and how to use normative and mixed decision making in addition to intuition is essential for implementing process innovations in the context of increasing computational capabilities and the interconnectedness of systems ( Mikalef and Krogstie, 2018 ; Liao et al. , 2017 ; Schneider, 2018 ; Rönnberg et al. , 2018 ). Thus, the purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. This study focuses on production system design, including conception and planning, because this stage contributes significantly to the performance of process innovations ( Andersen et al. , 2017 ; Rösiö and Bruch, 2018 ).

2. Frame of reference

2.1 understanding equivocality and analyzability in process innovations.

Equivocality is a central organizational challenge that negatively impacts the implementation of process innovations in manufacturing companies ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Parida et al. , 2017 ). The current understanding of equivocality is grounded on organization theory ( Galbraith, 1973 ). Equivocality refers to the existence of multiple and conflicting interpretations, and is associated with problems such as a lack of consensus, understanding and confusion ( Daft and Macintosh, 1981 ; Zack, 2007 ; Zack, 2001 ; Koufteros et al. , 2005 ). Equivocality originates when individuals face new or unfamiliar situations in which additional information will not help resolve misunderstandings ( Frishammar et al. , 2011 ). Individuals may experience equivocality of varying degrees ranging from high equivocality, ambiguous unclear events with no immediate suggestions about how to move forward, to low equivocality, clearly defined situations requiring additional information ( Daft and Lengel, 1986 ). The literature suggests that to reduce equivocality, staff must engage in information processing activities that exchange subjective interpretations, form consensus and enact shared understanding ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Daft and Lengel, 1986 ).

Staff responsible for implementing process innovations frequently encounter problems relating to lack of agreement or consensus, namely, equivocality ( Reichstein and Salter, 2006 ; Jalonen, 2011 ; Stevens, 2014 ). The way individuals respond to such problems is referred to as analyzability ( Daft and Lengel, 1986 ). Analyzability describes the extent to which problems or activities require objective procedures as opposed to personal judgment or experience to resolve a task ( Haußmann et al. , 2012 ; Zelt et al. , 2018 ). Similar to equivocality, analyzability is subject to varying degrees. For example, tasks lacking objectives rules and procedures are regarded as having low analyzability. Conversely, tasks including clear and objective procedures leading to a solution are considered as having high analyzability. The degree of analyzability of a task is associated with its degree of equivocality ( Daft and Lengel, 1986 ; Julmi, 2019 ; Byström, 2002 ). When a task is clear and analyzable, equivocality is low, and staff can rely on the acquisition of explicit information to answer questions. When a task is unclear and of low analyzability, equivocality is high, and staff must process information to generate consensus.

2.2 Decision-making approaches

Operations management literature offers distinct approaches to decision making relevant to implementing process innovations. A first approach involves normative decision making. Normative decision making involves a logical step-by-step analysis involving a quantitative assessment ( Mintzberg et al. , 1976 ) and requires information that is clear, objective and well defined ( Dean and Sharfman, 1996 ). Normative decision making is described as a slow and conscious process where information is logically decomposed and sequentially recombined to generate an output ( Jonassen, 2012 ; Swamidass, 1991 ; Papadakis et al. , 1998 ). The benefits of normative decision-making approaches include economizing cognitive effort, solving cognitively intractable problems, producing insight and integrating knowledge ( Liberatore and Luo, 2010 ). Criticism of the use of normative decision making extend from studies suggesting that individuals are intendedly rational, but only limitedly so ( Luoma, 2016 ; Simon, 1997 ). For example, decision makers may systematically deviate from recommendations produced by decision models ( Käki et al. , 2019 ). Normative decision making, despite its alleged drawbacks, continues to be used by organizations and has frequently led to good outcomes ( Metters et al. , 2008 ; Klein et al. , 2019 ).

A second approach includes intuitive decision making ( Bendoly et al. , 2006 ; Loch and Wu, 2007 ; Gino and Pisano, 2008 ; Elbanna et al. , 2013 ; White, 2016 ). Intuitive decision making involves affectively charged judgments that arise through rapid, non-conscious, holistic association of information ( Dane and Pratt, 2007 ). Intuitive decision making is associated with having a strong hunch or feeling of knowing what is going to occur, and can be advantageous when professionals are confronted with time pressure and possess experience in a field ( Gore and Sadler-Smith, 2011 ; Dane and Pratt, 2007 ; Bennett, 1998 ; Elbanna et al. , 2013 ; Hodgkinson et al. , 2009 ; Khatri and Ng, 2000 ). Intuitive decision making is not without drawbacks. Literature suggests that managers using intuition may ignore relevant facts, have a hard time explaining the reasons for making a choice, or produce gross misjudgments ( Dane et al. , 2012 ; Elbanna et al. , 2013 ; Dane and Pratt, 2007 ).

A third alternative includes the use of mixed decision-making approaches ( Tamura, 2005 ; Hämäläinen et al. , 2013 ). The main strength of this approach lies in reducing personal bias and allowing the comparison of dissimilar alternatives while integrating quantitative analysis ( Saaty, 2008 ). Mixed decision-making approaches provide solutions to problems involving conflicting objectives or criteria affected by uncertainty ( Kahraman et al. , 2015 ). Literature presents a variety of alternatives in relation to mixed decision-making approaches ( Mardani et al. , 2015 ), yet these have the common objective of helping deal with the evaluation, selection and prioritization of problems by imposing a disciplined methodology ( Kubler et al. , 2016 ).

2.3 Structuredness of decisions and decision making

In the past, decisions have been classified along a continuum according to their structure ( Shapiro and Spence, 1997 ). This argument maintains that a decision may range from well- to ill-structured depending on whether rules and processes can be unequivocally applied. Grounded on organization theory, recent studies propose that the structuredness of decisions may provide an indication for understanding the correspondence between the choice of a decision-making approach and its conditions of use ( Julmi, 2019 ).

Well-structured decisions include intellective tasks with a definite objective criterion of success within the definitions, rules, operations and relationships of a particular conceptual system ( Dane and Pratt, 2007 ). A well-structured decision involves rules or procedures and unequivocal interpretations that have developed over time ( March and Simon, 1993 ; Luoma, 2016 ). Therefore, it is argued that well-structured decisions relate to low equivocality and high analyzability, and that normative decision making is appropriate because of the structured rules and computable information involved.

Ill-structured decisions involve judgmental tasks where there are no objective criteria, or demonstrable solutions ( Dane and Pratt, 2007 ). Ill-structured decisions originate from novel situations that do not include widely accepted rules that may help determine the degree to which a decision is correct or biased ( Cyert and March, 1992 ; Luoma, 2016 ; Jacobides, 2007 ). Consequently, it is identified that ill-structured decisions correspond to high equivocality and low analyzability. It is suggested that staff facing ill-structured decisions adopt intuitive decision-making because intuition does not rely on rules to cope with a problem; rather, it relies on integrating information holistically into coherent patterns ( Dane and Pratt, 2007 ). Figure 1 illustrates the correspondence of decision-making approaches to the conditions of use based on the structuredness of decisions.

Conceptually, the structuredness of decisions provides a starting point to understand the correspondence of a decision-making approach to its conditions of use. However, there is a need to submit these conceptual arguments to empirical scrutiny and explore whether the degree of equivocality and analyzability provides guidance in selecting a decision-making approach when implementing process innovations. The empirical study to explore these issues is described in the following section.

3. Methodology

Prior studies have focused on explaining how to choose a decision-making approach; however, there is a need for further empirical insight. This casts doubt on the appropriateness of analysis-based research, which is better suited to evaluating well-developed hypotheses ( Johnson et al. , 2007 ; Mccutcheon and Meredith, 1993 ; Handfield and Melnyk, 1998 ). Accordingly, this study adopts a qualitative-based case study to elaborate on the current theory ( Ketokivi and Choi, 2014 ). Theory elaboration is well suited to explore an empirical context with more latitude, and conduct an in-depth investigation based on identified theoretical concepts ( Whetten, 1989 ). The choice of case study research is justified by prior studies which describe its advantages for observing and describing a complicated research phenomenon such that it conveys information in a way that quantitative data cannot ( Eisenhardt and Graebner, 2007 ; Handfield and Melnyk, 1998 ; Meredith, 1998 ; Mccutcheon and Meredith, 1993 ). In designing and conducting the case study, extant guidelines for qualitative case studies in Operations Management were followed ( Barratt et al. , 2011 ).

The focus of this study is the design of production systems. Decision making at this stage is important for achieving the desired level of competitiveness and the overall goals of implementing process innovations ( Bruch and Bellgran, 2012 ). Process innovations are frequently implemented in the form of projects ( Bellgran and Säfsten, 2010 ). Accordingly, the unit of analysis is the production system design project, and its embedded unit of analysis decisions within these projects. Given the research agenda, the decisions occurring in a production system design project are an appropriate unit of analysis. These decisions should adapt to the structure of the environment ( Gigerenzer and Gaissmaier, 2011 ), and are affected by the information processing capacities of an organization ( Matzler et al. , 2014 ).

This study uses empirical data from two production system design projects at one global manufacturing company, which we refer to as Projects A and B. While case study research at a single organization offers limited generalizability ( Ahlskog et al. , 2017 ), it allows an in-depth exploration of how decision making occurs at manufacturing companies beyond well-structured decisions ( Kihlander and Ritzén, 2012 ). The manufacturing company was selected based on theoretical sampling, with the aim of exploiting opportunities to explore a significant phenomenon under rare or extreme circumstances relevant to the study of single cases ( Yin, 2013 ; Eisenhardt and Graebner, 2007 ). In selecting a manufacturing company, the study focused on four factors associated with the competent implementation of process innovations including: large-sized firms of high capital intensity, established processes for developing production systems, continual design of new products and an emphasis on increasing flexibility of production systems ( Cabagnols and Le Bas, 2002 ; Pisano, 1997 ; Martinez-Ros, 1999 ).

Two aspects influenced the choice of projects. First, the focus was on projects implementing radical process innovations, namely, those projects involving new equipment and management practices and changes in the production processes ( Reichstein and Salter, 2006 ). These types of projects reportedly experience varying degrees of equivocality and analyzability ( Parida et al. , 2017 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ). In addition, radical process innovations depend on normative and intuitive decision-making approaches for their implementation ( Calabretta et al. , 2017 ), which are conditions essential to the focus of this study. Second, this study gave precedence to projects that included experienced staff responsible for implementing process innovations. Prior studies highlight that experience influences the capacity of staff to act under conditions of limited information and equivocality, and facilitates making rapid decisions in the absence of data ( Daft and Macintosh, 1981 ; Liu and Hart, 2011 ; Gershwin, 2018 ; Dane and Pratt, 2007 ). Accordingly, two projects in the heavy-vehicle industry focused on the transition from traditional production systems to multi-product production systems were considered.

One of the authors of this study is a researcher at the manufacturing company. Accordingly, this study adopts an interactive research approach ( Ellström, 2008 ), which is considered a variant of collaborative research. Interactive research is distinguished by the continuous joint learning and close collaboration between industry participants and researchers ( Svensson et al. , 2007 ; Ellström, 2008 ). Despite this close interaction, the primary focus of this study is to provide a theoretical contribution and relevant industrial results.

3.1 Description of Projects A and B

The manufacturing company is a leading producer of heavy-vehicle products with more than 14,000 employees and 13 manufacturing sites in Europe, Asia and North and Latin America. The heavy-vehicle industry is characterized by a high degree of product customization and specialized product families targeting specific markets. Manufacturers of this segment consider a wide offering of products to be a key competitive advantage. Production systems are distinguished by assembly lines that specialize in a single product family, and share little else other than the same manufacturing facility.

The manufacturing company initiated two projects, A and B, which originated from a common corporate goal of reducing time to market, manufacturing footprint, and lead time to customers, and increasing production flexibility. These projects focused on the transformation of traditional production systems to multi-product production systems. Projects A and B were considered process innovations because of their novel approach compared to traditional production in the heavy-vehicle industry, which included: standardizing product interfaces, utilizing new production processes and technologies for product assembly, redesigning facility layouts and developing internal logistic solutions. Projects A and B were considered successful because these upgraded outdated production processes and technologies increased production flexibility, reduced production unit labor cost per output, increased productivity and reduced the assembly area of the production systems. Table I describes Projects A and B, and Table II outlines the profiles of staff participating in these projects.

3.2 Data collection

Data collection took place between January 2014 and January 2016. This period comprised all activities and planning for Projects A and B. Different techniques for data collection were used including field notes, interviews and company documents to help obtain objective and reliable results ( Karlsson, 2010 ). The first author drafted field notes during 12 full-day workshops for Project A and 10 full-day workshops for B. Staff responsible for Projects A and B attended these workshops including project managers, production managers, production engineers, logistics developers, consultants and research and development personnel. These separately held workshops involved three themes. The first theme consisted of generating a common vision of the process innovations, identifying critical issues and proposing solutions to these issues. The second theme included designing, developing and deploying discrete event simulation models. The third theme focused on discussing the results of on-site tests for Projects A and B. In addition, the first author participated regularly as a passive observer in project meetings and drafted field notes, including 60 and 40 1-hour weekly meetings for Projects A and B, respectively.

The authors collected additional data based on five semi-structured interviews for Projects A and B. The interviews began with an explanation of the project, its background and goals. Staff described their professional experience and responsibilities in the project and identified the essential activities and decisions of each project. Next, they narrated the process of achieving agreement for each decision. Finally, they detailed how decisions were made including decision-making approaches, rules, processes, information and outcome. To gain a comprehensive understanding of decision making, the interviews involved staff members from different seniority levels, including project managers, production engineering managers, production engineers, logistics developers and consultants. The authors recorded and transcribed all interviews and sent all transcribed interviews to the interviewees for verification. Finally, data collection included company documents in the form of presentations, minutes and reports drafted during the projects. Table III lists the details of data collection.

3.3 Data analysis

Data analysis included an iterative comparison of the collected data and existing literature, as suggested by Yin (2013) . Following the recommendations of Miles et al. (2013) , data analysis occurred in four steps. First, collected data were concurrently selected, abbreviated and stored in a database during data collection. At this stage, salient decisions were identified for Projects A and B, and the focus was on decisions involving the commitment of resources (e.g. additional meetings, production experts or managerial discussions) leading to actions (selecting a layout, proposing a definition or selecting a group of products) as suggested in the literature ( Frishammar, 2003 ). Afterwards, staff participating in Projects A and B verified these decisions.

The second step involved systematically coding the collected data for Projects A and B. The authors jointly decided on three codes for analyzing data: equivocality, analyzability and decision-making approaches. The literature was heavily relied on to identify the equivocality and analyzability associated with a decision ( Daft and Lengel, 1986 ). High equivocality referred to multiple and conflicting interpretations and ambiguous information. Equivocal situations included partial agreement among the staff and ambiguous information. Low equivocality involved unequivocal interpretations and a lack of information. High analyzability concerned clear rules and processes, and low analyzability a lack of objective rules or rule based procedures. Staff of Projects A and B were left to operate freely when selecting decision-making approaches based on preferences or established processes operating at the manufacturing company. Importantly, no definitions of decision-making approaches were provided to the staff. Instead, the decision-making approach of each decision was identified a posteriori based on the characteristics of intuitive or normative decision making found in literature and shown in Table IV .

Third, the authors reassembled data according to the codes described above and analyzed data in two steps, as suggested by Eisenhardt (1989) . First, the author analyzed the projects separately to become acquainted with and identify patterns. Thereafter, the authors analyzed patterns across Projects A and B.

Fourth, the authors compared all the findings in a joint session with the aim of achieving a comprehensive interpretation of the study. The authors deliberated over differences of interpretation until an agreement was reached. Where there was no agreement, the authors contacted interviewees for further clarification. Finally, the authors drew conclusions and conceptualized the findings of the study. The findings were compared and related to existing theory concerning similarities, contradictions and explanations of differences ( Eisenhardt, 1989 ).

4. Empirical findings

4.1 equivocality.

What is a good assembly sequence for all these different products? You had to propose what to do, and then do it, and then show the results. It is not that you would have asked someone: Are we doing the right thing? Should we do it this way? No one really had an answer for that. (Project manager of Project A)

Each of us (project B) has worked with powertrains for a long time, but this was different. Originally we believed that it was necessary to include the vehicle transmission and an additional component in our scope. This choice was not simple because of the intrinsic differences and functionalities of each product family. In addition, we lacked experience on anything remotely similar, did not have enough information, and held different opinions on the matter. (Production engineer of Project B)

We based all the work on the assumption that there is one common assembly sequence. We regarded that as a backbone in the project. I strongly believe that if you have a common assembly sequence, it has an enormous impact on production. (Project manager of Project A)

First, we decided to do an extensive data collection. That drove the project into the wall. On a second attempt, we decided not to dig so deep into the details and focused on a holistic perspective. We went through our products looking for similarities. Based on discussions with our product and production experts, we identified 17 key components; based on these, we developed a common assembly sequence. (Production engineer of Project B)

After developing a shared understanding of a multi-product assembly, our activities focused on issues that could improve our concept. We collected and analyzed information, and compared alternatives. It was essential to know what choices brought our process innovation closer to objectives set up by management. (Logistics developer of Case A)

4.2 Analyzability

Staff experienced analyzability as a tension between two opposites. On the one hand, staff was subject to familiar circumstances, known problems or decisions encountered in the past. In these situations, they adopted standardized rules and procedures common in production system design projects at the manufacturing company: for example, processes for designing a production system, line balancing strategies or the classification of logistics parts.

On the other hand, staff faced new and unfamiliar decisions originating from the specification of the characteristics of a multi-product production system. For example, the manufacturing company possessed no procedures specifying the grouping of different product families for production in a multi-product production system. Similarly, the manufacturing company did not possess rules for identifying a best choice among alternative product groups. Staff considered both decision-making processes essential for multi-product production systems.

Once we developed a common perspective about a single assembly line, we mapped assembly times, figured out the number of stations, moved as much work as possible to sub-assembly lines, worked with logistics, material handling, kitting in line. With a common objective, it was easier for us to pinpoint what the production system would look like. (Production engineer of Project A)

4.3 Decision-making approaches

Staff of Projects A and B utilized three distinct decision-making approaches including intuitive, normative and a combination of intuitive and normative. Intuitive decision making was frequent at the start of Projects A and B, and relied on gut feeling, best knowledge and a holistic consideration of information. Intuitive decision making did not focus on detailed information. Instead, the staff integrated the results from different reports and argued for a solution based on experience or hunches. The staff utilized intuitive decision making in two distinct instances. First, staff relied on intuitive decision making during open and informal debates to achieve consensus. In these circumstances, they either generated a solution to a decision (e.g. agreeing on the importance of a common assembly sequence) or determined new rules or procedures (e.g. steps for grouping and ranking product groups). Second, they utilized intuitive decision making jointly with normative decision making, e.g. in identifying problems and proposing solutions to the production process. An additional example of the latter includes simulation models. Simulation models originally included rough assumptions and simplifications based on the intuition of experts and their general understanding of the production systems, which were increasingly completed with new information.

The results of the simulation analysis were very important to the outcome of the process innovation. This helped us understand how to eliminate variation in our production process. The simulation also helped us understand how the solutions we tested in the factory floor turned out over weeks or months across different areas. We could not have achieved this detail of understanding any other way. (Consultant of Project A)

Finally, staff jointly applied a combination of intuitive and normative decision-making approaches during Projects A and B. Joint intuitive and normative decision-making approaches were subject to the agreement of the staff, collection of data and clear rules or procedures which could be either new or established ones. Intuitive decision making could precede, follow or be used concurrently with normative decision making (e.g. when determining the advantages or trade-offs of a multi-product production system). In this example, staff utilized normative decision making (e.g. simulations) to compare the production systems of sites in North and Latin America to the multi-product production systems developed in Projects A and B. The results of this comparison were presented in workshops and face-to-face meetings. In these meetings, staff participating in Projects A and B and experts from sites in North and Latin America scrutinized the simulation results and compared them to demand forecasts, production reports and experience. This required several iterations, and the primary concern was that of earning trustworthiness from experts. Afterwards, the results of a decision were escalated to a managerial level. When determining the benefits and trade-offs of a multi-product production system, managers considered information from diverse sources – and not exclusively the results of a simulation analysis. Frequently, managers requested “what if” or sensitivity types of analysis from normative decision-making approaches. Accomplishing this required a new iteration of the steps described above. Finally, managers made decisions based on intuition, considering various sources of information holistically. Tables V and VI describe the salient decisions and equivocality, analyzability and decision making of each decision for Projects A and B.

An important observation is that decisions were subject to different degrees of equivocality and analyzability when implementing process innovations. The findings show that distinct decision-making approaches occur at different degrees of equivocality and analyzability. Understanding the correspondence of equivocality and analyzability to a decision-making choice is difficult to comprehend. Therefore, Figure 2 presents the correspondence and frequency of decision-making approaches to the degree of equivocality and analyzability in Projects A and B.

The correspondence between the degree of equivocality and analyzability of a decision and decision-making approaches is identified based on a synthesis of the choices of decision-making approaches in Projects A and B and extant literature. First, findings show that decision-making approaches were most frequently utilized in conditions of low equivocality and high analyzability. In this approach, the staff interpreted a problem unequivocally, possessed clear rules and procedures; however, they lacked information. The staff utilized three different decision-making approaches in conditions of low equivocality and high analyzability, including intuitive, normative and a combination of intuitive and normative decision making.

Our data show that staff found low equivocality and high analyzability as the only conditions suitable for normative decision making in this study. Normative decision making relied on explicit information, a sequential analysis and well-defined decisions. Staff from Projects A and B utilized normative decision making for detailed technical aspects such as evaluating layouts.

In addition, the staff made use of combined intuitive and normative decision making in conditions of low equivocality and high analyzability. Here, they utilized combined intuitive and normative decision making when facing new situations, having previously agreed on procedures for analysis (e.g. identifying vehicle modules). They utilized combined intuitive and normative decision making for high stake decisions involving an aggregation of prior activities and requiring managerial involvement (e.g. comparing a multi-product production system to existing multi-product production systems).

Finally, the staff utilized intuitive decision making in low equivocality and high analyzability when encountering situations perceived as similar to prior situations. In these instances, they relied on experience, quick decisions and a holistic association of information to produce a result (e.g. agreeing on the need for improving staff competence).

Second, Projects A and B faced conditions of low equivocality and low analyzability. Staff agreed on the nature of a problem; however, they lacked clear rules, procedures and relevant information. They judged that these conditions did not meet the criteria for the exclusive use of normative decision making. Instead, they utilized intuitive or a combination of intuitive and normative decision-making approaches. The staff applied intuitive decision making to decisions where the end goal was that of establishing rules or procedures. In these instances, they were not undecided about the goal of a decision, rather how to arrive at a solution (e.g. establishing the rules and procedures for modular assembly and performance indicators). When combining intuitive and normative decision making, they utilized intuition for agreeing on rules and procedures, associated decisions to those faced in the past, and devised steps that were understandable to others based on experience. Next, quantitative analyses were utilized to provide detailed insight, acquire information and logically decompose a problem (e.g. specifying an assembly sequence).

Third, staff of Projects A and B made decisions in a context of equivocality and high analyzability. This coincided with having clear rules and processes; however, with only a partial agreement about the information necessary to complete a task or the outcome of a decision. In these instances, the staff resorted to intuitive decision making for agreeing on the type of information necessary to complete a task. Next, they utilized normative decision making in the form of quantitative based analysis such as spread sheet calculations or simulations. Finally, they returned to intuitive decision making to arrive at a solution while considering holistic information from a variety of sources. Examples of this include proposing logistics solutions for multi-product production systems, and determining advantages and trade-offs of multi-product production systems. The findings of this study would suggest that the conditions of equivocality and high analyzability do not provide sufficient support for the use of an entirely normative decision-making approach. Empirical results suggest that applying purely intuitive decision-making approaches is undesirable. Actually, the staff recognized that decisions could not rest exclusively on hunches, experience or rapid decisions by acknowledging the need for additional information, and disputing the appropriateness of information to complete a task.

Fourth, staff of Projects A and B made decisions against a backdrop of equivocality and low analyzability. These decisions involved the lack of rules or processes and partial agreement about information necessary to complete a task. Decisions of equivocality and low analyzability were not like small differences of opinion resolved over the course of a meeting or workshop. Instead, these decisions required detailed investigation, resource commitment and weeks of deliberation. Staff in Projects A and B proceeded differently when encountering equivocality and low analyzability.

In Project A, the staff identified the logistics needs for a multi-product production system. They agreed on the need for adapting logistics capabilities; however, the information available did not correspond to the needs of a multi-product production system. They estimated logistics needs based on hunches, discussions and experience. They considered the outcome of this decision provisional and subject to increased knowledge about logistics in a multi-product production system. In Project B, the staff proposed a layout for a multi-product production system. To do so, they utilized intuitive decision making to set an initial direction. This was considered insufficient to finalize a decision, and additional information was acquired, and alternatives were judged based on normative decision making.

Findings suggest that these types of decisions are not readily solvable, and evidence a need for generating agreement about the purpose of the decision, information, rules and processes enabling a solution. Data suggest that intuitive decision making is important in enacting a shared understanding; nevertheless, committing to a decision may require the quantitative insight provided by normative decision making. Consequently, decisions experiencing equivocality and low analyzability were subject to a combined intuitive and normative decision-making approach. Examples include identifying logistics needs for multi-product production systems or proposing layouts for multi-product production systems.

Fifth, findings show that no decisions coincided with high equivocality and high analyzability, namely, multiple and conflicting interpretation, ambiguous information, and clear rules and processes. We argue that high equivocality and high analyzability present a contradiction and suggest that the incidence of decision making in these conditions may signal an error. This error may well indicate the inadequate interpretation of existing rules or processes by staff responsible for implementing process innovations.

Sixth, staff made exclusive use of intuitive decision making in decisions involving high equivocality and low analyzability. These type of decisions were characterized by the absence of objectives rules or processes, multiple and conflicting interpretations, and ambiguous information. These decisions were common in the beginning of Projects A and B, and when the staff faced decisions perceived as different from those encountered in the past. They relied on hunches, approximations or conjectures about the result of a decision to guide consensus. Additional information did not help resolve decisions in high equivocality and low analyzability: for instance, when agreeing on the definition of a powertrain across different product families. Figure 3 outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions.

5. Discussion and implications

The purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. In particular, this study focused on how the conditions of equivocality and analyzability provide guidance to the choice of a decision-making approach. Extant literature is compared to empirical findings from two projects implementing process innovations in the form of a multi-product production system in the heavy-vehicle industry. The findings of this study are particularly relevant in light of the interest from manufacturing managers and academics to better understand when and where a decision-making approach is most suitable during the implementation of process innovations.

5.1 Theoretical implications

Recent studies recommended decision-making approaches in extreme cases of problem structuredness, high equivocality and low analyzability or low equivocality and high analyzability ( Julmi, 2019 ). However, staff face varying degrees of equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Frishammar et al. , 2011 ). This study reveals additional combinations of equivocality and analyzability than those previously described in literature. This finding is important because it extends current understanding of decision structuredness, which thus far had been limited to presenting extreme cases, namely, well- and ill-structured decisions. In addition, this study provides empirical evidence that staff must respond to decisions at varying degrees of equivocality and analyzability when implementing process innovations. In particular, this study identified three degrees of equivocality and two of analyzability when implementing process innovations. This study highlights the need for increased understanding of equivocality and analyzability, which may help manufacturing companies avoid failed choice or erroneous approaches to decision making when implementing process innovations. This finding is important as it may help clarify the selection of decision-making approaches leading to an improved outcome ( Calabretta et al. , 2017 ; Luoma, 2016 ), a situation that is crucial for implementing process innovations ( Frishammar et al. , 2011 ; Milewski et al. , 2015 ).

Current understanding of decision structuredness argues that there are no superior decision-making approaches ( Julmi, 2019 ). Instead, a decision-making approach may be better suited to certain conditions and, under these conditions, lead to an effective outcome ( Gigerenzer and Gaissmaier, 2011 ). Our findings show that, consistent with the literature, well-structured and ill-structured decisions corresponded to normative and intuitive decision making. However, findings show differences with prior studies focused on decision structuredness and decision making. For example, staff applied intuitive decision making at varying degrees of equivocality and analyzability, combined normative and intuitive decision making not described in literature, and utilized more than one decision-making approach in three out of six combinations of equivocality and analyzability. The results of this study suggest that decision structuredness may not prescribe a decision-making approach, but may clarify the conditions in which decisions take place. This finding is important because it suggests that current understanding of decision-making choice based on extreme cases of problem structuredness, namely well- or ill-structured decisions, is insufficient to guide a choice of decision-making approach. Addressing this dearth of understanding, this study outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions. This findings is essential as it suggests that identifying the fit of a decision-making approach to the structuredness of a problem is as important as the technical acumen, resources and experience necessary for using a particular type of decision making ( Jonassen, 2012 ; Dean and Sharfman, 1996 ).

By classifying decisions in relation to their degree of equivocality, this study shows that decisions occur more frequently in situations involving low equivocality, followed by those of high equivocality, and finally by those involving partial agreement and ambiguous information or equivocal. A higher frequency of decisions in situations of low equivocality is expected when implementing process innovations. However, an intriguing finding of this study involves the frequency in which staff made decisions in situations including multiple and conflicting interpretations and ambiguous information (e.g. high equivocality). These decisions appeared when staff identified a problem (e.g. product, production process, tools and technology, layouts, logistics), were based on intuitive decision making and defined subsequent decisions of Projects A and B. This finding is disquieting as prior studies show that manufacturing companies frequently rely on ad hoc practices when making early decisions in production system design projects ( Rösiö and Bruch, 2018 ). Similarly, the literature highlights a limited understanding of equivocality at manufacturing companies when implementing process innovations ( Parida et al. , 2017 ). Therefore, our findings give credibility to the claim that comprehension of equivocality, its reduction and the effective use of intuition may harness a competitive edge for manufacturing companies implementing process innovations ( Rönnberg et al. , 2016 ; Frishammar et al. , 2012 ).

The literature advocates the use of structured processes for implementing process innovations ( Kurkkio et al. , 2011 ). Accordingly, the need for clear rules and procedures facilitating high analyzability is essential. The results of this study show no telling difference in the frequency of decisions involving high analyzability or low analyzability in Projects A and B. Importantly, data do not indicate that staff forwent rules and processes when these were lacking. Instead, staff developed rules and processes when facing decisions not previously experienced or described in established procedures. This result is significant and suggests that the ability of staff to develop rules and processes, or procedures when facing non-recurring situations ( Luoma, 2016 ), is as likely to be necessary as that of structured processes for implementing process innovations. The development of rules and processes during the implementation of process innovations is rarely discussed in literature, and therefore constitutes a venue for future research.

Mixed decision-making approaches constitute a well-established field that may help staff arrive at decisions under uncertainty ( Kubler et al. , 2016 ). This study showed that decisions were frequently reached as a result of combined intuitive and normative decision making. However, the process for arriving at these decisions was unlike the methods used in the literature. The findings of this study suggest both the need of mixed decision-making approaches when implementing process innovations, and increased efforts to bridge the gap between academic findings and manufacturing practice.

5.2 Practical implications

The findings of this study have direct practical implications that may benefit staff and managers responsible for implementing process innovations. First, this study underscores the importance of a structured process, experienced design teams and familiarity with normative, intuitive or mixed decision making that enable the implementation of process innovations ( Rösiö and Bruch, 2018 ). However, the analysis also shows that although these concepts are necessary, they are not sufficient to successfully implement process innovations. Instead, managers must be aware of the importance of determining a decision-making approach that corresponds to the conditions of a decision. Addressing this point, this study emphasized the importance of equivocality and analyzability when determining a decision-making approach during the implementation of process innovations. Accordingly, this study underscores the importance of information processing activities, which are under prioritized or neglected because of a lack of resources or competence ( Rönnberg et al. , 2016 ; Koufteros et al. , 2005 ).

5.3 Limitations and future research

Some key limitations circumscribe this study. Like all case studies, our contributions are limited by the idiosyncrasies of the context of study ( Eisenhardt, 1989 ). This study draws data from a global manufacturing company. Undoubtedly, smaller sized manufacturing companies may have different access to staff, resources and experienced personnel when implementing process innovations. Prior studies suggest that these elements affect decision-making approaches. Therefore, validating our results against cases from varying company sizes is important. Another limitation constitutes our focus on the production of heavy vehicles and their components. A suggestion for future research includes the investigation of cases in additional context: for example, the process industry or batch production.

Process innovations concern new production processes or technologies. This study, like many other process innovation studies ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ), focused on new material, equipment or reengineering of operational processes. In doing so, concern stemmed from the conditions that may determine the choice of a decision-making approach. Process innovation literature reflects increasing interest in the way artificial intelligence, automation and digital technologies connected to the Internet of Things affect decision making ( Rönnberg et al. , 2018 ). While the interplay of intuitive, normative and mixed decision-making approaches is a concern of this study, technological changes enabling decision making is not. Future research could focus on conceptualizing the domain of novel digital technologies and decision making when implementing process innovations.

Choice of intuitive or normative decision making based on decision structuredness

Correspondence of decision-making approaches to degree of equivocality and analyzability in Projects A and B

Choice of decision-making approaches when implementing process innovations according to the degree of equivocality and analyzability of decisions

Description of production system design Projects A and B focused on implementing a multi-product production system as a process innovation

Profiles of staff participating in Projects A and B

Details of data collection for Projects A and B

Characteristics of intuitive and normative decision-making approaches

Description of salient decisions, equivocality, analyzability and decision making in Project A

Notes: Equivocality (HE, high equivocality; E, equivocality; LE, low equivocality), Analyzability (HA, high analyzability; LA, low analyzability)

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Acknowledgements

The authors gratefully acknowledge the contributions of all the participants from the anonymous company used as a case study in this research. Financial support from the Knowledge Foundation (KKS), and the industrial graduate school “Innofacture” is also gratefully acknowledged.

Corresponding author

About the authors.

Erik Flores-Garcia is Doctoral Candidate at the Innofacture Industrial Graduate School, Mälardalen University, Sweden. His research interests include simulation, production decisions and process innovation.

Jessica Bruch is Professor in production systems at Mälardalen University, Sweden. Her research interest concerns various aspects of production development and addresses both technological and organizational aspects on the project, company and inter-organizational level.

Magnus Wiktorsson is Professor in production logistics at the Royal Institute of Technology (KTH), Sweden. His research interests include two ongoing major changes in production logistics: the digitization of all processes and the need for transformation into environmentally sustainable production.

Mats Jackson is Professor in innovative production at Jönköping University, Sweden. His research interests include flexibility of production systems, industrialization and innovation in production systems.

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Do Your Students Know How to Analyze a Case—Really?

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• Case Teaching
• Student Engagement

J ust as actors, athletes, and musicians spend thousands of hours practicing their craft, business students benefit from practicing their critical-thinking and decision-making skills. Students, however, often have limited exposure to real-world problem-solving scenarios; they need more opportunities to practice tackling tough business problems and deciding on—and executing—the best solutions.

To ensure students have ample opportunity to develop these critical-thinking and decision-making skills, we believe business faculty should shift from teaching mostly principles and ideas to mostly applications and practices. And in doing so, they should emphasize the case method, which simulates real-world management challenges and opportunities for students.

To help educators facilitate this shift and help students get the most out of case-based learning, we have developed a framework for analyzing cases. We call it PACADI (Problem, Alternatives, Criteria, Analysis, Decision, Implementation); it can improve learning outcomes by helping students better solve and analyze business problems, make decisions, and develop and implement strategy. Here, we’ll explain why we developed this framework, how it works, and what makes it an effective learning tool.

The Case for Cases: Helping Students Think Critically

Business students must develop critical-thinking and analytical skills, which are essential to their ability to make good decisions in functional areas such as marketing, finance, operations, and information technology, as well as to understand the relationships among these functions. For example, the decisions a marketing manager must make include strategic planning (segments, products, and channels); execution (digital messaging, media, branding, budgets, and pricing); and operations (integrated communications and technologies), as well as how to implement decisions across functional areas.

Faculty can use many types of cases to help students develop these skills. These include the prototypical “paper cases”; live cases , which feature guest lecturers such as entrepreneurs or corporate leaders and on-site visits; and multimedia cases , which immerse students into real situations. Most cases feature an explicit or implicit decision that a protagonist—whether it is an individual, a group, or an organization—must make.

For students new to learning by the case method—and even for those with case experience—some common issues can emerge; these issues can sometimes be a barrier for educators looking to ensure the best possible outcomes in their case classrooms. Unsure of how to dig into case analysis on their own, students may turn to the internet or rely on former students for “answers” to assigned cases. Or, when assigned to provide answers to assignment questions in teams, students might take a divide-and-conquer approach but not take the time to regroup and provide answers that are consistent with one other.

To help address these issues, which we commonly experienced in our classes, we wanted to provide our students with a more structured approach for how they analyze cases—and to really think about making decisions from the protagonists’ point of view. We developed the PACADI framework to address this need.

PACADI: A Six-Step Decision-Making Approach

The PACADI framework is a six-step decision-making approach that can be used in lieu of traditional end-of-case questions. It offers a structured, integrated, and iterative process that requires students to analyze case information, apply business concepts to derive valuable insights, and develop recommendations based on these insights.

Prior to beginning a PACADI assessment, which we’ll outline here, students should first prepare a two-paragraph summary—a situation analysis—that highlights the key case facts. Then, we task students with providing a five-page PACADI case analysis (excluding appendices) based on the following six steps.

Step 1: Problem definition. What is the major challenge, problem, opportunity, or decision that has to be made? If there is more than one problem, choose the most important one. Often when solving the key problem, other issues will surface and be addressed. The problem statement may be framed as a question; for example, How can brand X improve market share among millennials in Canada? Usually the problem statement has to be re-written several times during the analysis of a case as students peel back the layers of symptoms or causation.

Step 2: Alternatives. Identify in detail the strategic alternatives to address the problem; three to five options generally work best. Alternatives should be mutually exclusive, realistic, creative, and feasible given the constraints of the situation. Doing nothing or delaying the decision to a later date are not considered acceptable alternatives.

Step 3: Criteria. What are the key decision criteria that will guide decision-making? In a marketing course, for example, these may include relevant marketing criteria such as segmentation, positioning, advertising and sales, distribution, and pricing. Financial criteria useful in evaluating the alternatives should be included—for example, income statement variables, customer lifetime value, payback, etc. Students must discuss their rationale for selecting the decision criteria and the weights and importance for each factor.

Step 4: Analysis. Provide an in-depth analysis of each alternative based on the criteria chosen in step three. Decision tables using criteria as columns and alternatives as rows can be helpful. The pros and cons of the various choices as well as the short- and long-term implications of each may be evaluated. Best, worst, and most likely scenarios can also be insightful.

Step 5: Decision. Students propose their solution to the problem. This decision is justified based on an in-depth analysis. Explain why the recommendation made is the best fit for the criteria.

Step 6: Implementation plan. Sound business decisions may fail due to poor execution. To enhance the likeliness of a successful project outcome, students describe the key steps (activities) to implement the recommendation, timetable, projected costs, expected competitive reaction, success metrics, and risks in the plan.

“Students note that using the PACADI framework yields ‘aha moments’—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.”

PACADI’s Benefits: Meaningfully and Thoughtfully Applying Business Concepts

The PACADI framework covers all of the major elements of business decision-making, including implementation, which is often overlooked. By stepping through the whole framework, students apply relevant business concepts and solve management problems via a systematic, comprehensive approach; they’re far less likely to surface piecemeal responses.

As students explore each part of the framework, they may realize that they need to make changes to a previous step. For instance, when working on implementation, students may realize that the alternative they selected cannot be executed or will not be profitable, and thus need to rethink their decision. Or, they may discover that the criteria need to be revised since the list of decision factors they identified is incomplete (for example, the factors may explain key marketing concerns but fail to address relevant financial considerations) or is unrealistic (for example, they suggest a 25 percent increase in revenues without proposing an increased promotional budget).

In addition, the PACADI framework can be used alongside quantitative assignments, in-class exercises, and business and management simulations. The structured, multi-step decision framework encourages careful and sequential analysis to solve business problems. Incorporating PACADI as an overarching decision-making method across different projects will ultimately help students achieve desired learning outcomes. As a practical “beyond-the-classroom” tool, the PACADI framework is not a contrived course assignment; it reflects the decision-making approach that managers, executives, and entrepreneurs exercise daily. Case analysis introduces students to the real-world process of making business decisions quickly and correctly, often with limited information. This framework supplies an organized and disciplined process that students can readily defend in writing and in class discussions.

PACADI in Action: An Example

Here’s an example of how students used the PACADI framework for a recent case analysis on CVS, a large North American drugstore chain.

The CVS Prescription for Customer Value*

PACADI Stage

Summary Response

How should CVS Health evolve from the “drugstore of your neighborhood” to the “drugstore of your future”?

Alternatives

A1. Kaizen (continuous improvement)

A2. Product development

A3. Market development

A4. Personalization (micro-targeting)

Criteria (include weights)

C1. Customer value: service, quality, image, and price (40%)

C2. Customer obsession (20%)

C3. Growth through related businesses (20%)

C4. Customer retention and customer lifetime value (20%)

Each alternative was analyzed by each criterion using a Customer Value Assessment Tool

Alternative 4 (A4): Personalization was selected. This is operationalized via: segmentation—move toward segment-of-1 marketing; geodemographics and lifestyle emphasis; predictive data analysis; relationship marketing; people, principles, and supply chain management; and exceptional customer service.

Implementation

Partner with leading medical school

Curbside pick-up

Pet pharmacy

E-newsletter for customers and employees

Employee incentive program

CVS beauty days

Expand to Latin America and Caribbean

Healthier/happier corner

Holiday toy drives/community outreach

*Source: A. Weinstein, Y. Rodriguez, K. Sims, R. Vergara, “The CVS Prescription for Superior Customer Value—A Case Study,” Back to the Future: Revisiting the Foundations of Marketing from Society for Marketing Advances, West Palm Beach, FL (November 2, 2018).

Results of Using the PACADI Framework

When faculty members at our respective institutions at Nova Southeastern University (NSU) and the University of North Carolina Wilmington have used the PACADI framework, our classes have been more structured and engaging. Students vigorously debate each element of their decision and note that this framework yields an “aha moment”—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.

These lively discussions enhance individual and collective learning. As one external metric of this improvement, we have observed a 2.5 percent increase in student case grade performance at NSU since this framework was introduced.

Tips to Get Started

The PACADI approach works well in in-person, online, and hybrid courses. This is particularly important as more universities have moved to remote learning options. Because students have varied educational and cultural backgrounds, work experience, and familiarity with case analysis, we recommend that faculty members have students work on their first case using this new framework in small teams (two or three students). Additional analyses should then be solo efforts.

To use PACADI effectively in your classroom, we suggest the following:

Advise your students that your course will stress critical thinking and decision-making skills, not just course concepts and theory.

Use a varied mix of case studies. As marketing professors, we often address consumer and business markets; goods, services, and digital commerce; domestic and global business; and small and large companies in a single MBA course.

As a starting point, provide a short explanation (about 20 to 30 minutes) of the PACADI framework with a focus on the conceptual elements. You can deliver this face to face or through videoconferencing.

Give students an opportunity to practice the case analysis methodology via an ungraded sample case study. Designate groups of five to seven students to discuss the case and the six steps in breakout sessions (in class or via Zoom).

Ensure case analyses are weighted heavily as a grading component. We suggest 30–50 percent of the overall course grade.

Once cases are graded, debrief with the class on what they did right and areas needing improvement (30- to 40-minute in-person or Zoom session).

Encourage faculty teams that teach common courses to build appropriate instructional materials, grading rubrics, videos, sample cases, and teaching notes.

When selecting case studies, we have found that the best ones for PACADI analyses are about 15 pages long and revolve around a focal management decision. This length provides adequate depth yet is not protracted. Some of our tested and favorite marketing cases include Brand W , Hubspot , Kraft Foods Canada , TRSB(A) , and Whiskey & Cheddar .

Art Weinstein , Ph.D., is a professor of marketing at Nova Southeastern University, Fort Lauderdale, Florida. He has published more than 80 scholarly articles and papers and eight books on customer-focused marketing strategy. His latest book is Superior Customer Value—Finding and Keeping Customers in the Now Economy . Dr. Weinstein has consulted for many leading technology and service companies.

Herbert V. Brotspies , D.B.A., is an adjunct professor of marketing at Nova Southeastern University. He has over 30 years’ experience as a vice president in marketing, strategic planning, and acquisitions for Fortune 50 consumer products companies working in the United States and internationally. His research interests include return on marketing investment, consumer behavior, business-to-business strategy, and strategic planning.

John T. Gironda , Ph.D., is an assistant professor of marketing at the University of North Carolina Wilmington. His research has been published in Industrial Marketing Management, Psychology & Marketing , and Journal of Marketing Management . He has also presented at major marketing conferences including the American Marketing Association, Academy of Marketing Science, and Society for Marketing Advances.

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A Real Case Application of Game Theoretical Concepts in a Complex Decision-Making Process: Case Study ERTMS

• Open access
• Published: 18 October 2021
• Volume 31 , pages 153–185, ( 2022 )

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• Femke Bekius   ORCID: orcid.org/0000-0003-4357-7371 1 ,
• Sebastiaan Meijer 2 &
• Hugo Thomassen 3  

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Engineering systems are complex, amongst others due to the interdependencies between actor and technical aspects. This complexity has consequences for the way of designing such systems and, in particular, for the decision-making process. Recognizing the impossibility of having an optimal system design in such complex systems, this article explores how a game theoretical characterization of a decision-making process assists in the organization and design of the process itself. In contrast to a game theoretical analysis, which results in optimal outcomes, the characterization is fed back to the designers of the decision-making process during the course of the process. The study analyses how the game concept characterization was used, i.e., which strategies were defined during the game theory interventions, and what the consequences of these strategies were for the design of the decision-making process. The design of a new safety system ERTMS for the Dutch railway sector is the context in which the study was performed. The contribution is a successful approach to complex decision-making in multi-actor systems by identification of multiple game concepts over time, with periodic feedback into the designing system, and not the actual decision-making itself. In short, it supported adapting to an actor focus on the process, it affected the role and responsibilities of the program management, it contributed to (de)coupling of issues, and it influenced the capability of creating awareness amongst actors of the urgency of the decision window. The paper ends with reflections on the experience of intervening in a decision-making process with game theoretical concepts.

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1 Introduction

Large scale infrastructures in our society are associated with complex stakeholder constellations and equally complex technological challenges and interdependencies. It is well-known that an integrated management of both aspects is essential for the success of infrastructures and society at large.

Different perspectives on engineering systems exist, as they can be interpreted as a large technological system (Hughes 1987 ), as a Socio-Technical System (STS) (Trist and Bamforth 1951 ), as a System-of-Systems (SoS) (DeLaurentis 2005 ) or as a Complex Adaptive System (CAS) (Holland 1995 ; Miller and Page 2007 ).

Considering this evolution of system theories, we observe an increase in focus on the interactions between actor and technical aspects. According to a CAS perspective, systems consist of interdependent subsystems that need to be aligned to let the entire system function. The relations between subsystems are dynamic and evolving over time and, as a result, systems show emergent and chaotic behavior (Holland 1992 ). The increased understanding of the complexity of systems has consequences for the designing function. In traditional engineering design, one actor could decide on an optimal solution. Nowadays, multiple actors are involved, each responsible for their own subsystem with disjoint perspectives on optimality and incentives. This is embodied in the concept of Engineering Systems (De Weck et al. 2011 ).

To capture and address the uncertainties in decision-making processes, several types or levels of complexity have been proposed. System complexity is rooted in the engineering sciences (Hughes 1987 ) and actor complexity has its origins in the social sciences (Thissen and Walker 2013 ). This distinction provides insight into the different elements of the process, but the real complexity is represented by the interdependencies within and between complexity levels (De Bruijn and Herder 2009 ). According to De Bruijn and Ten Heuvelhof ( 2018 ), decision-making processes are complex due to three main characteristics: (i) unstructured problems: there are many technological uncertainties, problems and solutions; (ii) networks: multiple stakeholders with different incentives are interdependent; (iii) dynamics: context and environment continuously change. Therefore, we distinguish three levels of complexity: technical, actor and context complexity. Moreover, the theory on complex decision-making and multi-actor systems talks about games (Kickert et al. 1997 ; Scharpf 1997 ; Axelrod 1984 ).

Game concepts describe the behavior of and interaction between actors who have to make a decision. The concepts originate from different disciplines - ranging from formal game theory to public administration (Bekius et al. 2016 ; Rasmusen 2007 ). The game concept approach provides structure in the ill-structured decision-making process by making the game elements, such as actors, actions and strategies, precise and specify the type of game. Moreover, the game concepts allow for an analysis of different scenarios and possible outcomes which creates a perspective of action. Different from formal game theory the approach does not make the assumption of rationality explicit or specifies an optimal or right-versus-wrong outcome. Rather, the game concept approach focuses on incentive structures, responsibility and ownership of actors, i.e., actors’ agency, and dilemmas existing in the decision-making process (Bekius 2019 ).

In this paper, we explore how a game theoretical characterization of a decision-making process assists in the organization and design of the process itself. Describing decision-making situations in game theoretical terms is not new. This paper goes one step further: theoretical models are used to assist in the continuous design of a complex negotiation process. The exploratory nature of the paper allows us to assess the value of descriptive insights for a process without being prescriptive.

The objectives of this paper are (i) to apply game theoretical concepts in a real complex decision-making process, (ii) to give back the game theoretical characterization to the designers of the decision-making process and observe the impact on the process, and (iii) to provide lessons learned from the experience (intervening in a decision-making process with game theoretical concepts) for both researchers and practitioners. Our research question is: What are the effects of giving back a game theoretical characterization of a decision-making process to the designers of the process on the process itself?

The paper contributes to the theoretical framework on complex decision-making in multi-actor systems [among others De Bruijn et al. ( 2010 ); De Bruijn and Ten Heuvelhof ( 2018 ); Kickert et al. ( 1997 )]. We identify multiple game concepts, show how they interact and characterize the situation instead of finding an optimal outcome and defining the rules of the game. Moreover, we extend the framework by specifying particular game concepts instead of talking about the decision-making game in general terms.

Moreover, the paper contributes to the use of game theoretical models and formal models in general. It yields an example in which theoretical models are used to assist in a complex decision-making process. Opposed to the classical use of game theoretical concepts in a multi-actor setting which aims at an optimal outcome of a particular decision moment, the game theoretical characterization in this process describes a running process. Additionally, the characterization was given back to the designers of the process while the process was still going on. We explore the effects of periodic feedback to designers of the decision-making process on the process. In particular, we describe how the characterization is translated into strategies and present the consequences of these strategies for the design of the decision-making process.

The focus of this paper is to intervene in the design of the decision-making process without actively intervening in the behavior of actors. This is different from gaming simulation which is an active intervention in actor behavior and in which players are taken out of their usual context to play a game in a so-called safe environment. The use and effect of such interventions have been assessed in different studies (Bekebrede and Meijer 2009 ; Meijer 2012b ; Mayer et al. 2005 ; De Caluwé 1997 ), but an evaluation of the interventions in the design of decision-making processes is missing (Grogan and Meijer 2017 ).

Finally, we contribute to the decision support field by outlining lessons learned from the experience. In several interventions the game theoretical characterization is presented to and discussed with senior program managers. An analysis of the interventions provides insight into the consequences of giving back such information for the process of decision-making. Moreover, apart from a descriptive value at process level, the analysis resulted in learning points for us as researchers. The learning points are framed as points to consider when repeating this study in different organizations or domains to overcome the exploratory character of the current study.

1.1 Case Study: Railways

The European Rail Traffic Management System (ERTMS) is a European project to standardize control and improve safety of the railway system. The aim of the project is to “enhance cross-border interoperability and signaling procurement by creating a single Europe-wide standard for railways with the final aim of improving competitiveness of the rail sector” (Schuitemaker et al. 2018 ). For the Netherlands, it entails a major systems transition from an analogue to a digital system for the entire country. We will introduce the context of the case study using three complexity levels.

The technical complexity consists of the difficult content of the technical aspects and the multiple interdependencies that exist between theses technical aspects. For example, implementation strategies in the Netherlands have immediate consequences for the timing of replacing trains. There is uncertainty whether program goals regarding interoperability, capacity, speed, safety and reliability can be reached. The complexity of and interdependence between these goals makes it nearly impossible to translate them to technical specifications for all subsystems.

The complex multi-actor setting with many formal and informal rules, diffuse responsibilities towards the systems results in a complicated governance and is captured by the actor complexity . The actors (passenger and freight train operators, contractors, leasing companies, infrastructure manager, ministries, the Parliament, and European Union) have different incentives, are not hierarchically organized, but are mutually dependent and responsible for different parts of the system. The main actors, ProRail, NS, the Ministry of Infrastructure and Water management (I&W), and freight operators, have different perspectives on time lines.

The context complexity comprises different dynamics: other decisions made on the railway system such as storage of trains overnight are coupled to ERTMS decision-making, historical decisions which assume the implementation of ERTMS influence the current decision-making; political pressure from European Union requirements exists as well as decisions made by other European Union countries regarding ERTMS. Moreover, the technology is new and developing. Hence, the status of the technology today is not the status at the moment of implementation.

The complexities and interdependencies create an ill-structured, and sometimes messy, decision-making process with many uncertainties. The program management of ERTMS, responsible for the design of the decision-making process and advising on the decisions to be made, can impossibly oversee the entire process. There is thus a need to gain insight into the strategic behavior of actors, actors’ power and responsibilities and how both evolve in a dynamic environment. More understanding of the decision-making process and a perspective of action could help the program management in the design of the decision-making process. To address both points we propose the use of game theoretical concepts as further explored in this paper.

The paper is structured as follows: in Sect.  2 , we introduce the game concept approach and position the approach in the broader field of decision support methods. The methodology for this paper is outlined in Sect.  3 where we distinguish between a characterization of the decision-making process and returning the characterization into the designing system. In Sect.  4 , we present a timeline of events capturing the main elements of the decision-making process together with the game concept elements and interventions. The analysis of the four interventions is shown in Sect.  5 . Finally, we provide lessons learned from the series of interventions in Sect.  6 before we conclude the paper and discuss the results in Sect.  7 .

2 Game Concept Approach

In this paper, we use game concepts to characterize a complex decision-making process. Subsequently, the characterization is given to designers of the decision-making process in interventions to support the process. The game concept approach is introduced by presenting the theoretical framework in which the approach fits and positioning the approach in the broader field of decision support methods. Finally, we introduce the seven game concepts that are central in this paper.

2.1 Theoretical Framework

Complex decision-making in multi-actor systems is a broad field that has been studied by many researchers (among others De Bruijn et al. ( 2010 ); De Bruijn and Ten Heuvelhof ( 2018 ); Kickert et al. ( 1997 )). It goes beyond the scope of this paper to give a detailed overview of this literature. We have restricted ourselves to a couple of features that explain why decision-making processes in multi-actor systems are complex. They include: networks of multiple interdependent actors, wicked or unstructured problems, and a dynamic environment.

Actors perform strategies to deal with the complexity of a decision-making process. This can take place at (at least) two levels. First, at the level of individual actors and their strategies, for example, wait-and-see behavior or keeping-options-open. Second, at the process level actors develop strategies or interventions to deal with the unstructured process itself. For instance, share or hide information, or introduce new issues as an attempt to structure the process.

We consider the following three elements of the theoretical framework on complex decision-making in multi-actor systems to be relevant for the game concept approach: First, the decision-making process can be seen as a game. Actors perform strategic behavior and follow the rules of the game to reach an (optimal) outcome or consensus. Second, descriptive analysis of the decision-making process in terms of game elements takes into account the dynamics and context of the process. Third, the analysis of the decision-making process forms the basis for interventions in the behavior of actors.

In the remainder of this paper, we show how the game concept approach used in multiple interventions extends the theoretical framework by addressing the above mentioned elements.

2.2 Decision Support Methods

As the game concept approach is used to support decision-making processes, more precisely to support the design of the process, we present a selection of approaches and decision support methods applied in cases on large infrastructure systems. We show for each method how the game concept approach is different or complementary.

Many examples of formal applications of game theoretical models to support decision-making exist (Chen et al. 2012 ; Cantarelli et al. 2013 ; Hollander and Prashker 2006 ; Osman and Nikbakht 2014 ). Game theoretic modeling often simplifies the situation to one game and therefore explains only a small part of the process (Cohen 2015 ). In contrast, we identify multiple game concepts in a decision-making process and are interested in the interactions between identified game concepts to represent the dynamics of the process more accurately. Another feature of formal game theoretical modeling is that it usually aims at finding an optimal or stable outcome and thereby assumes the rationality of actors (Howard 1994 ). This assumption is quite strong, especially when we consider the fact that actors have different responsibilities and thus perceive an outcome differently. The game concept approach rather presents different scenarios and outcomes with potential risks. Moreover, game theoretical models are rarely presented to decision makers in their formal way (Camerer 1991 ). The game concept approach does not use the formal presentation of games which makes it suitable for use in interventions with decision makers.

Several design theory frameworks are available which aim to describe the design process of (engineering) systems (Reich 1995 ; Reich et al. 1996 ; Meijer et al. 2014 ). Such frameworks suggest that the components (e.g. product, actor, institution) need to be connected to or reflecting upon one another (Geels 2004 ; Hermans et al. 2013 ; Geyer and Davies 2000 ; Hardy et al. 2005 ). However, the problem with those methods, which is why they are less suitable for application to support decision-making, is that they either do not involve all components (Pluchinotta et al. 2019 ) or the framework is not fully operationalized. The game concept approach is applied to design processes to explain misalignments between different components in the process (Bekius and Meijer 2018 ).

Some well-known decision support models are Multi Criteria Decision Analysis (MCDA) (Ishizaka and Nemery 2013 ; Nikas et al. 2018 ), Cost-Benefit Analysis (CBA) (Flyvbjerg et al. 2008 ) and Analytical Hierarchy Process (AHP) (Ossadnik et al. 2016 ). The models compare different alternatives, or variants, based upon various evaluation criteria that can have different weights (Dodgson et al. 2009 ; Corsair et al. 2009 ). Group Decision Support Systems (GDSS) are specific Information and Communications Technology (ICT) applications for the support of group interaction and decision-making (Mayer and De Jong 2004 ; Eden 1992 ). De Vreede and Dickson ( 2000 ) use a Group Support System (GSS) to develop and evaluate a participatory design process. Their research approach to support the design of organizational processes relates to our study. However, the game concept approach particularly focuses on interactions between actors with selected game theoretical models. Moreover, we evaluate the use of the game concept approach in a running process compared to a complete redesign of a process. The context elements of a decision-making process, such as the impact of the political environment on the decision, are difficult to quantify and these elements are usually not covered by the decision-based models (Mayer et al. 2005 ). The game concept approach is able to reflect the political dimensions of a decision-making process by focusing on incentive structures of actors as shown in two case studies of the Dutch railway sector (Bekius et al. 2018 ).

Gaming simulations have been used in various studies to support decision-making on infrastructure systems (Mayer et al. 2004 ; Bekebrede and Meijer 2009 ), including the Dutch railway sector (Meijer 2012a , 2015 ; Lo et al. 2013 ). Which elements of the process to include in the game design is crucial for the success of a gaming simulation (Salen and Zimmerman 2004 ). The game concept approach helps in identifying the strategic games being played in the decision-making process in terms of actor constellation, its responsibilities and power relations and how the constellation evolves over time (Roungas et al. 2019 ). Gaming simulation is an active intervention in the behavior of actors. Participants are taken out of their usual context to participate in a game. The game concept approach targets a different type of participants and does not intervene directly in the decision-making process, but more indirectly in the design of the process.

Operational research is a discipline which has developed a plethora of methods and tools to support decision-making processes (De Gooyert 2016 ). Problem Structuring Methods (PSM) , and as subcategory Game Structuring Methods (GSM), are a collection of techniques to model a situation one want to change (Cunningham et al. 2014 ; Rouwette et al. 2009 ). Usually the model is applied by a group of people to structure a situation, to facilitate reaching consensus or to negotiate on what needs to change instead of making a decision (Mingers and Rosenhead 2001 ). Hermans and Thissen ( 2009 ) present an overview of actor analysis methods and their limitations and potentials by focusing on the trade-off between analytic quality and practical usability. Although features of the various stakeholder and actor analysis methods overlap with the game concept approach there are three important characteristics that distinguish them: (i) game concepts focus on the behavior of actors and interactions between them, including actor’s agency, i.e., responsibility and ownership of the system resulting in power relations; (ii) game concepts characterize the process of decision-making and thereby include the dynamics; and (iii) game concepts are developed in such a way that they can be used by decision makers themselves and eventually create a ‘perspective of action’ in the form of strategies.

So far, we introduced the theoretical framework in which we position the game concept approach and showed how the game concept approach relates to other methods investigating and supporting decision-making processes. In short, the game concept approach adds a structured way to address the constellation of actors including their responsibilities and power relations and the dynamics. Moreover, the approach is not only theoretical, but can be used with and by decision makers as we will show later in the paper. Next, we introduce the selection of game concepts which are central in this paper.

2.3 Game Concepts

In this paper, we use seven different game concepts to characterize a complex decision-making process. The selection of game concepts is based on characteristics of complex decision-making processes such as multiple actors forming a network of interdependencies and where reaching a collective decision is the aim of the process. A taxonomy of game concepts is provided in an earlier paper, and we refer to Bekius ( 2019 ) and Bekius and Meijer ( 2020 ) for a detailed explanation of the selection criteria. The game concepts are illustrated by an example and defined in terms of the context in which they appear, the process they characterize, their possible results, and potential risks in Table  1 .

The Multi-Issue game (M-I game) characterizes a situation with multiple actors having different incentives aim to reach consensus in a decision-making process that was in a deadlock in the first place (Winter 1997 ; De Bruijn and Ten Heuvelhof 2018 ; De Bruijn and Ten Heuvelhof 2002 ; Sebenius 1983 ).

Imagine you are part of a family (father, mother and three children) and the father sees the opportunity to have a summer holiday together. He decides on a destination and period in summer, but not everyone likes his idea. The family members have different preferences regarding the summer holiday issue and the situation gets stuck in a deadlock. How could the father resolve it? An option is to take control over the agenda and introduce new issues that matter to the family members, such as a skiing holiday during winter or having a pet. The new agenda creates a broadened solution space which enables the forming of coalitions within the family, the exchanging of issues, and the creation of a give-and-take game. As a result, the deadlock could be resolved and the family members could reach consensus on the larger set of issues. A serious risk is that too many issues are added to the agenda such that the game becomes over-complex.

The Principal-Agent game (P-A game) represents a hierarchical relation between a principal and an agent. The principal is dependent on the agent because of its knowledge and expertise regarding a certain decision. The game explains the power position of the subordinate, i.e., the agent (Stauvermann 2004 ; Gintis 2000 ; Braun and Guston 2003 ; Laffont and Martimort 2002 ; Cantarelli et al. 2013 ; Dodgson et al. 2009 ; Cole et al. 2014 ).

Suppose you are an employee in a company and your boss asks you to deliver a report before the deadline on the feasibility of a certain technique for measuring the well-being of elderly people (Roungas et al. 2019 ). Apart from the content of the report, you know it is important for the company to be able to report good results in order to be chosen by the regional government for implementing the technique. As employee, you are the agent in a principal-agent relation with your boss, i.e., the principal. The relation is asymmetrical since your boss puts pressure on the deadline and the content of the report, i.e., the principal has more power. On the other hand, you have more information and know the details of the technique. The information is the agent’s power to argue with or convince the principal.

The Cascade Game (CG) shows the tendency of intelligent actors, in case of uncertainties, to follow the decisions of others independently of the quality of the content of the decisions (Bikhchandani et al. 1992 ; Easley and Kleinberg 2010 ; Anderson and Holt 1996 ; Conradie et al. 2015 ).

Imagine you are in a new place and you want to make a roundtrip. Based on your own research you intent to go for operator A. However, when you arrive at the departure point you notice that it is completely empty, and operator B has a line of people waiting for tickets. At this point, it would make sense to have your private information, decide for operator A, to be outweighed by the public information you infer from others’ decision for operator B. The decision has an either-or character, it is either A or B, players make the decision sequentially, and each player can observe the choices made by those who acted earlier. The Cascade game makes clear that players tend to follow others and this creates a cascade-effect. It has an important implication: the actors who made their choice first have an impact on the following actors.

The Hub-Spoke game (H-S game) describes a situation with multiple actors (spokes) having different incentives who are steered by one actor (hub) via command-and-control. The game creates an incentive for inflated claims, the spokes can reach agreements among each other and create strategic issues for the hub (Elrod and Fortenberry 2017 ; Adler 2005 ; Adler and Smilowitz 2007 ; Adler et al. 2010 ; Takebayashi 2015 ; Markusen 1996 ; De Bruijn et al. 2010 ).

Suppose there is a company, called the hub, which wants to found a business unit in a certain area. To succeed it needs to deal with several parties, called the spokes, for example local companies and municipalities. The hub initiates the plan and starts negotiating with the spokes. The negotiations follow a plan and it can enact several strategies. The hub can make agreements with each spoke separately and propose a unique deal to each. Alternatively, it can propose the same deal to each of the spokes. A local company can try to block the plan of the hub by performing a wait-and-see strategy or exchanging information with other spokes. The communication between spokes can create strategic issues for the hub.

The Volunteers Dilemma (VD) explains why one or more actors take the responsibility for the group to prevent a worst-case scenario from happening. Performing wait-and-see behavior is beneficial, but increases the risk of a bad outcome of the decision-making process (Archetti 2009 ; Goeree and Holt 2000 ; Diekmann 1985 ).

Imagine a situation in which you live in a flat and suddenly the electricity fails. There are many people in your flat, but no one calls an electrician. Performing wait-and-see behavior, not calling the electrician, is beneficial, since it requires no effort and thus no cost. Moreover, there are so many others, why would you be the volunteer? No volunteer acting increases the time of being without electricity. A very bad outcome no one wants. The longer this dilemma, calling or not calling, is present, the higher the risk, and thus the more pressure exists for a volunteer to take action.

The Diners Dilemma (DD) represents a situation in which multiple actors come to an agreement about the process of decision-making, e.g. collaboration and mutual interaction. Due to the agreements made it becomes attractive to be the first one to violate the agreements (Teng et al. 2013 ; Gneezy et al. 2004 ; O’Donovan et al. 2013 ; De Bruijn and Ten Heuvelhof 2018 ).

Suppose you decide to have dinner with a group of people in a restaurant. You agree in advance that the costs of the dinner will be divided equally among the participants. Upon arrival, there appear to be two options: there is a cheap menu and an expensive menu. The participants come to an additional agreement to order the cheap menu. However, there is an incentive to be the first one to violate the collective agreement and order the expensive menu. The profit for this person is high, and there are limited additional costs for the other group members. However, the other group members seeing the bill will understand that the agreement has been violated. It becomes attractive for others to join the first mover and, in the end, the agreement will erode.

The Battle of the Sexes (BS) describes a case in which two actors are completely dependent upon each other. Moreover, they share the same goal, but have different incentives. In order to reach a decision one of the two actors needs to adopt the others’ idea (Shoham and Leyton-Brown 2008 ; Van Benthem 2014 ; Easley and Kleinberg 2010 ; Vollmer 2013 ; Goeree and Holt 1999 ; Camerer 1997 ; Binmore 2007 ; Rasmusen 2007 ).

A man and a woman share the same goal of going out together, but prefer a different destination. The woman prefers a baseball match and the man prefers the opera. The different preferences result in different strategies and create a ‘battle’. To reach a decision and achieve the shared goal they need to anticipate each other’s strategy. One of the two actors needs to adopt the other’s viewpoint and go to the baseball game instead of the opera or the other way around. However, given the complete dependency of actors and the fact that no one can overrule the other by using power or information this is unlikely to happen. The battle will thus continue, probably delay the process, and eventually one of the actors will be the winner and the other the loser, which results in sub-optimal decision-making.

3 Methodology

Action research was the research approach employed in this study. It aligned with the general definition that says “research leading to action” (Lewin 1946 ) and was intended to have a dual outcome of action (in the design of the decision-making process) and research (exploring how the game concept approach was used) (Argyris and Schön 1989 ). The game theoretical characterization of the decision-making process came about as an interplay between researchers and participants in interventions. In the interventions, the participants used the game concept characterization to reflect on the situation and formulated strategies. Moreover, we assessed the uptake from the formulated strategies for the design of the decision-making process.

In this study, we performed an iterative process from data collection to data structuring, data analysis and data validation through various methods such as participatory interventions, interviews and reconciliation meetings.

Important to mention is the dichotomy in this research between (i) characterizing the decision-making process using game concepts, and (ii) giving back the characterization to designers of the decision-making process. The latter always happened during the interventions and, apart from intervention 1, by the researcher. The first point, however, was sometimes performed by researchers beforehand, other times by participants during interventions, and at times by both researchers and participants. In Table  3 , we specified the role of researchers and participants during the interventions.

3.1 Data Collection

In this study, we collected different types of data. The reason why the data was collected varied.

Creation of an overview of decision-making process to understand the context, the actors involved and the issues present.

Identification of game concepts to characterize the decision-making process.

Presentation and discussion of game concepts with and by participants to come to strategies .

Estimate the uptake of defined strategies and its consequences for the design of the decision-making process.

Validation of results, in particular, validation of the overview and identification.

Table  2 lists the types of data collected and the above mentioned purposes. The order of the table does not correspond to the order in which data was collected. Since the interventions were an important part of this research we specify the details of the interventions in Table  3 .

3.2 Data Structuring, Analysis and Validation

To explain how we structured and analyzed the data we use the different purposes of data collection.

An overview of the decision-making process was created by a timeline of events. The timeline contained the important decision moments, the actors and their incentives, and the issues present. Both researchers and program managers contributed to the timeline. Program managers did this during intervention 2 and intervention 3. Researchers provided a timeline, based on documentation, interviews, memos of meetings and recordings of interventions, before interventions 3 and 4 and adapted these afterwards. The interventions together with the validation sessions served to check the details of the process overview.

Identification of game concepts was performed by both program managers and researchers. In intervention 1 program members identified the game concepts using a game concept identification tool (Bekius 2019 ). The tool consists of characteristics of the game concepts. These characteristics were translated to questions regarding the process of decision-making. By answering a sequence of questions the tool presented one or more game concepts. In intervention 3, the game concepts were identified using the descriptions of the game concepts (see Sect.  2 ). In interventions 2, 3 and 4, researchers identified the game concepts before the intervention based on data collected in interviews, meetings, and from documentation. Hence, those interventions were also a validation session for the game concept identification performed by the researchers. Moreover, during these interventions the identified game concepts were connected to the events on the timeline.

Formulation of strategies took place during the interventions. After the game concept characterization was presented to the participants, they discussed its meaning for the process of decision-making. The participants thought of ways to steer the game(s) identified and prevent the potential worst case scenarios from happening. More precisely, they defined strategies to (re)design the decision-making process. Researchers made notes during the interventions or coded the transcripts of the discussion afterwards.

Assessing the uptake from strategies formulated during the interventions happened both directly and indirectly. During validation sessions and meetings with program members we asked them which strategies were implemented and how they worked out. The program members formulated the strategies during the interventions and were able to assess to which extent these actions steered, changed, or contributed to the design of the decision-making process. Additionally, we questioned the consequences of strategies in an indirect way by interviewing different actors involved in the decision-making process. Questions during interviews concerned the quality of the decision-making process, the preparation of the decision weeks, the essential moments in the process, information and time available, and collaboration with other actors. Moreover, we followed the development of the process of decision-making using documentation and reconsolidation meetings with program members. In intervention 4, we described potential future game concept dynamics and interactions based on the previous collected information. Afterwards, we evaluated whether the dynamics and interactions of game concepts happened as expected by interviewing different program members. The interview questions concerned elements of the game concepts, and for each of these elements we questioned which strategies were performed to deal with that situation or what context prevented this situation from happening.

The methodology explained so far described how we accomplished the iterative process of collecting, analyzing, structuring and validating data. It showed how we applied game theoretical concepts in a real complex decision-making process, and returned the game theoretical characterization to the designers of the decision-making process and observed the impact on the process. The third objective, to provide lessons learned from the experience (interventions), is based on a reflection of the researchers and practitioners on the process afterwards.

4 Case Study ERTMS and Game Concepts

The case description considers a specific part of the decision-making process regarding the roll-out of ERTMS starting from the beginning of 2018, when preparations for a first decision week started, until mid-2019, when the Parliament decided on the implementation of ERTMS. This section gives an overview of the important moments in the decision-making process and highlights the elements and strategies of the game concepts present. Figure  1 provides an overview of the game concepts present throughout the decision-making process. Details about the role of the actors and strategies played in the game can be found in the text description.

Game concepts present throughout the decision-making process

Beginning 2018: Letter from freight operators to the Parliament The roll-out of ERTMS needed a timely and technically coordinated refurbishment of trains and infrastructure. Freight operators sent (separate) letters to the Parliament (a principal in the Principal-Agent game) about their worries regarding the process of introducing ERTMS. For the freight operators it was unclear which costs they needed to cover and no agreements were made regarding this issue. The need for investment in new trains by freight operators created a deadlock in the decision-making process (Multi-Issue game). Moreover, contractors wanted to confirm their position. As a result, freight and regional passenger operators started participating in the steering group of ERTMS which brought new perspectives to the decision table. New actors were introduced in the Multi-Issue game.

April 2018: Decision to start preparation phase of program ERTMS The first decision week concerned the approval of the ERTMS dossier for starting a BIT (Bureau ICT-Toetsing) test. Footnote 1 In preparation of this week, the actors of the steering group made process agreements which included finalizing the ERTMS dossier and investigating, and solving, a list of issues. The process agreements were an element of a Diners Dilemma. Final decisions regarding the list of issues needed to be made before summer 2018. The focus of the process so far had been mainly on content issues and new issues arose constantly because of the complexity of the system transition and the many uncertainties present. Content and context issues were on the agenda of the Multi-Issue game. The focus on content issues explained the over-complexity of the decision-making process. Moreover, technical uncertainties influenced the hierarchical relation between the Ministry (principal) and the railway sector: ProRail as infrastructure provider, NS as main operator, regional and freight operators (agents) in a Principal-Agent game.

Mid-May 2018: Intervention 1 “Workshop session Patterns in decision-making processes” Members of the central program management organization ERTMS (in Dutch: programmadirectie ERTMS) identified game concepts for the current stage of the decision-making process and discussed their different views. Moreover, they discussed a possible design of the decision-making process.

Beginning June 2018: Intervention 2 “Follow-up workshop session” Building on Intervention 1, a timeline of events, including actors involved, their incentives, and the main issues was constructed. The participants recognized a Multi-Issue game and discussed its potential effects on the process of decision-making.

End June 2018: Final preparations of the first decision week At this point in time, two main issues were present. The first issue concerned a discussion between the Ministry and freight operators about the Masterplan freight transport. Exchange of issues, such as track access charges and noise pollution, took place in return for freight operators being committed to ERTMS. It influenced their position in the decision-making process. The exchange of issues to broaden the solution space was a Multi-Issue game strategy performed by the Ministry. Moreover, agreements between freight operators and the Ministry in the Masterplan were an element of a Diners Dilemma. The second issue reflected the following situation: there was asymmetry of information between the Ministry (principal) and the ProRail (agent). The Ministry did not have sufficient railway knowledge to fully steer the technical part of the program. Given its knowledge and position, ProRail was the best candidate to take a larger responsibility in the governance of the ERTMS program by being responsible for the program management. Moreover, the principal assigned to the agent the role of referee in conflict situations and distributed its power to close information gaps. This was a Principal-Agent strategy and concerned the positioning of ProRail, and especially the program management, as hub in a Hub-Spoke game. Therefore, one of the main questions to be answered in the decision week was: do the other main actors agree with the steering of the program ERTMS by ProRail?

The decision week was prepared in a very short time, resulting in limited time for reflections on the decisions to be made and late communication of information. This affected the pressure on and quality of the decisions to be made. The program management and the Ministry put pressure on deadlines, heading for a freeze of the dossier for the BIT test. Its aim was to decide on the list of issues prepared and finally decide on starting the BIT test after summer 2018. Pressure to start the BIT test was forced by European Union agreements. The pressure from the European Union (principal) was part of another Principal-Agent game in which the Parliament, and indirectly the Ministry, was one of the agents.

9-13 July 2018: The first decision week The main goal of the first decision week was to decide on the readiness of the ERTMS dossier to start the BIT test. The decision week was set-up as a ‘cascade’ of decisions which needed to be made by the different layers of the organizations. First, the different organizations discussed the decisions internally within their own organization. Then, the actors met at the Director meeting (in Dutch: Directeuren Overleg (DO)) and discussed the list of decisions again. Subsequently, the DO-meetings provided input for the Board of Directors (in Dutch: Raad van Bestuur (RvB)) before the final decisions were made by the ERTMS steering committee at the end of the week. The decision week was organized as a Cascade game and as an effect the entire process before and after the decision week converged to and built on a Cascade game structure. The decisions needed to be adopted by the various predefined decision levels involving representatives of different organizations.

Decisions present in the decision week were connected to one another which is an element of a Multi-Issue game. For example, the issue concerning one or two system suppliers for implementing ERTMS was connected to the number of corridors which could be equipped with ERTMS given the available budget. Moreover, investing in a test corridor affected the number of corridors that could be included in the roll-out strategy. Starting the roll-out with corridor Kijfhoek-Roosendaal would give freight operators the burden of dealing with first-time issues. However, connecting this decision to the decision of assuring a test corridor resolved the issue. For the ATB-NG issue (an old safety system that needs to be replaced before ERTMS is implemented) no solution was found yet. ProRail as infrastructure provider wanted to introduce ERTMS immediately. However, this raised several new issues, contributing to the over-complexity of the process (Multi-Issue game), concerning the roll-out strategy that was decided upon and the need for investments in new trains earlier than expected.

Everyone was committed to provide clarity to the steering committee at the end of the week and postponing decisions until after summer was not challenged. The commitment showed that an agreement on the list of decisions and the shared goal to start the BIT test after summer was made (Diners Dilemma). Moreover, the Cascade game was not blocked by any actor at this point. After the decision week, the starting point for the introduction of ERTMS was clear, but certainly not everything was set. For instance, there were still no (formal) agreements with freight operators about the exact costs and the train specifications. Moreover, many issues remained and new issues were expected, therefore no consensus was reached in the Multi-Issue game.

End July 2018: Intervention 3 “Construct timeline of game concepts” The earlier constructed timeline was adapted and a game concept characterization was added to the timeline based on the dynamics of the first decision week.

Summer 2018: Completion of tasks after the first decision week Although freight operators agreed during the decision week on the list of decisions, afterwards they claimed that they needed more time and they actually disagreed with some decisions made. The freight operators, leasing companies, and contractors had different perspectives and were thus split-up. Additionally, ProRail, NS, and the Ministry sent letters to the program management with additional wishes and issues. The objections of actors after the decision week, due to the fact that time was limited and information was late, could be seen as a sub-optimal results of the Cascade game.

Some actors organized themselves and consulted political parties to influence the political level. This is an example of actors who were ‘freeriding’ (Diners Dilemma). It was in the freight operators’ interest to delay the process since investments in the future were necessary anyway. NS did not have an incentive to delay the process, but they wanted to have sufficient time to prepare the operation before introducing ERTMS and they wished to maintain their autonomy. This situation could eventually result in actors violating the agreements made (Diners Dilemma) and blocking the Cascade game or at least delaying the process. The program management was positioned as independent actor (the hub), i.e., separate from the organization ProRail itself (one of the spokes). Its role was to challenge project plans from different actors and departments (other spokes). The spokes in the Hub-Spoke game were not aligned, moreover, the exact responsibilities of the hub were not fully clear and if they were clear they were not fully accepted by the spokes. The result was that spokes created strategic issues easily which delayed the process and created chaos (Hub-Spoke strategy).

The period after the decision week and before the start of the BIT test appeared to be too short to address all remaining aspects of the decision week. NS was worried about aspects concerning the planning of infrastructure and the readiness of the test corridor. Both situations could lead to a blockade of the Cascade game.

October 2018: The BIT test officially started.

January 2019: Results BIT test The advice from the BIT test arrived and its main point concerned the position of the program management in the process of decision-making. The program should conduct a system engineering approach and provide guidance on technical content towards the main actors. The advice suggested to use additional methods for managing this program. It said to change the role of the hub (program management) to a more strict and controlling role that did not leave room for conflicts and issues between spokes to occur (following a client-contractor model), this was a Hub-Spoke game strategy.

An example of such a conflict was the technological issue of where to build an antenna: on the train (preferred by ProRail) or into the infrastructure (preferred by NS). Both actors shared a common goal of introducing ERMTS in a feasible and good way, however, on this issue their preferences were in conflict. Since no one could overrule the other actor with power or information and no compromise was possible the conflict appeared (Battle of the Sexes). The fact that institutional uncertainty existed, i.e., no one could decide on the issue alone, this resulted in a delay of the process and affected the (trust) relations between actors. A solution (Battle of the Sexes strategy) was to involve a referee, in this case the hub (program management), who decided upon the issue and resolved the conflict.

February 2019: Intervention 4 “Potential future game concepts” A timeline including the period until mid-2019 was presented to participants based on the previous constructed timelines (including events, actors and issues) and a game concept characterization of the process. The timeline also described potential future activation of and interaction between game concepts. Participants validated the timeline and discussed how to deal with the game concept dynamics in the process of decision-making.

March/April 2019: Towards the second decision week In preparation of the second decision week, the program management organized several meetings with directors to make sure that urgent issues were covered. The main issues were the delayed tendering process concerning the infrastructure, the connection of the budget of the high speed line (HSL) and ERTMS, and the fact that freight train operators searched for support from the political level.

Moreover, technical briefings with the Ministry were held to discuss the details of the dossier ERTMS and the role of freight train operators in the process. This was a Principal-Agent strategy to minimize information asymmetry between the Ministry (principal) and the program management (agent).

April 2019: The second decision week The goal of the second decision week was to decide whether the ERTMS program could move from the planning phase to the realization phase. Hence, the decision to be made was: Is the ERTMS dossier ready for the decision by the Council of Ministers in May 2019? Depending on their decision, the decision would be reconsidered by the Parliament. If the Parliament’s decision was positive, the tender process for new rolling stock and infrastructure could start, but only after an independent review approved that the recommendations of the BIT test were adequately implemented, as the Ministry promised to the Parliament. A third Principal-Agent game, the Parliament being the principal and the Ministry being the agent, was present.

During the second decision week the remainder of the decisions from the previous decision week and final points for finalizing the dossier were discussed. The decision week was organized similar to the first decision week starting with decision-making at the DO-level, then decision-making at the RvB level, and finally the steering committee ERTMS decided. Again a Cascade game was present since decisions needed to be adopted by the various predefined decision levels.

May 2019: Council of Ministers decision The Ministry of Infrastructure and Water management decided positively on the start of the implementation of ERTMS on 17th of May 2019.

June 2019: Parliament decision Despite the large budget allocated by the Parliament for implementation of ERTMS, the number of motions was relatively low and the decision of the Council of Ministers was adopted. Hereby, the principal in the second Principal-Agent game, i.e., the Parliament, finalized the cascade of decisions. During the process information asymmetry was minimized by the Ministry (agent) providing access to documentation, technical briefings, and progress reports (a Principal-Agent strategy). As a result, the principal and the agent shared the same incentives which meant that pressure for a certain outcome from the principal was not necessary.

5 Analysis of Interventions With Game Concepts

The previous section presented an overview of the ERTMS decision-making process and highlighted the elements of and strategies performed by actors in terms of game concepts. This section analyses the four intervention moments in which the game concept characterization was given back to designers of the decision-making process, i.e., program management ERTMS. For each intervention, we describe the main observation points by the participants of the intervention and explicate the strategies they formulated during the intervention. Subsequently, we present the consequences of those strategies for the design of the decision-making process. Notice that this section discusses game concept strategies at another level compared to the previous section. Where Sect.  4 characterized game concept strategies performed by actors in the decision-making process, in Sect.  5 , we discuss the game concept strategies (to be) performed by participants of the interventions as a result of the intervention.

5.1 Intervention 1: Workshop “Patterns in Decision-Making Processes”

Short summary: Participants identified the game concepts individually using a game concept identification tool (Bekius 2019 ). They discussed their view on which game concepts were present and reached consensus on two game concepts: the Multi-Issue game and the Cascade game. Additionally, they discussed potential next steps in the process of decision-making given the game concept context.

5.1.1 Observations and Strategies

Actors involved in the decision-making process brought up new content issues continuously, but most new issues were not that much relevant with respect to the overall program goals. There was a risk of creating an over-complex process (Multi-Issue game). The over-complexity could have resulted in no decision being made in the first decision week. Extension of the decision was an undesirable outcome and seen as a worst case scenario due to the planned external BIT-audit. It was unlikely that after all the engineering work new insights would occur. Strategy (1): Change the focus of the process, from content to actors, their incentives, and the process of decision-making.

The incentives of the different actors were not always clear, or at least not made transparent. Some actors could have retained the decision. This was something to pay attention to and affirmed that trust between actors played an important role. Strategy (2): Create an overview of actors’ preferences towards the issues of the Multi-Issue game. Focus on strengthening the trust relation with actors who can block the decision.

The aim of the decision-making process, consensus among actors (Multi-Issue game outcome) or sufficient actors on board regarding the weight of the program goals and design of solutions, changed over time due to politics. Strategy (3): Aim for sufficient support of actors, i.e., their agreement with the main or sufficient decisions, and particularly focus on the interests of potential blocking actors. More specifically, give actors the opportunity to put their final points on paper and discuss them in the steering group.

The process was not about one decision, but a sequence of decisions to be made in the coming years (Cascade game). The program management was part of a much larger game playing at different decision levels. Strategy (4): Create an overview for actors of those decision levels (Cascade game) and how the sequence of decisions evolves over time.

5.1.2 Consequences for the Design of the Decision-Making Process

Strategy (1) led to a focus on actors and the larger context in the process of decision-making. Together with strategies (2), (3) and (4) this resulted in the following: incentives of actors involved in the process were planned to be made explicit in the program plan, the next decision week as extension to make decisions was framed as no option, the number of issues was limited with support of actors, and the program specified a decision-making process as a sequence of decisions rather than as one decision.

In general, awareness of the Multi-Issue game and Cascade game, and how to use them explicitly in the decision-making process, increased. Based on these insights there was a need to further develop and investigate the game concepts identified. This was the motivation for a follow-up session on how to approach and organize the first decision week based on the Cascade game and the Multi-Issue game.

5.2 Intervention 2: Follow up “Patterns in Decision-Making Processes”

Short summary: Participants created a timeline of important events in the decision-making process. Attached to the events we specified the actors involved, their incentives and the main issues present. The discussion then focused on the Multi-Issue game and the Cascade game: How could the program management steer these games and thereby manage the process of decision-making, in particular, towards the first decision week?

5.2.1 Observations and Strategies

The program management had to manage the Multi-Issue game and the Cascade game to reach the desired outcome. Two risks were observed: i) an over-complex Multi-Issue game (more and more issues on the table) and ii) a Cascade game blocked by one or more actors.

There were two main issues: Masterplan freight train operators and the governance of the program ERTMS. Moreover, these two issues influenced decisions on other issues in the Multi-Issues game. In the decision week, decision makers needed to decide over the totality of issues. Strategy (1): Create an overview of the totality of issues and collect actors’ opinions on these issues.

The desired outcome of the process was a decision supported by the actors involved, i.e., consensus reached in the Multi-Issue game. An undesired outcome was no decision since this would delay the process. Strategy (2): Focus on actors who can potentially block the Multi-Issue game. Which issues can be added to the agenda to stimulate exchange of issues?

5.2.2 Consequences for the Design of the Decision-Making Process

Strategy (1) led to a collection of opinions (in favor, neutral, or against) of the actors regarding the issues (decisions) planned for the decision week. It resulted in a categorization of the decisions: decisions that were not ready and would be postponed to a later stage, decisions not objected to by any actor, content decisions with objections from at least one actor, financial decisions, and decisions regarding the roll out of ERTMS in the Netherlands. The categorization provided the basis for the agenda, i.e., the sequence of topics and issues, of the decision week. Apart from collecting opinions on single decisions, the program management questioned the dependencies between the two main issues and preferences for the other decisions. This provided an idea on how potential blocking actors could be kept in the game to support the total set of issues (strategy 2).

The program management used the information to decide on the order of decisions on the agenda and balance decisions that would take short time (since everyone agreed) with decisions that would need more time for discussion (since actors had opposing opinions). This process steered the way in which the cascade of decisions in the decision week was structured (Cascade game) while taking into account the multiple issues that played a role in the context (Multi-Issue game).

Actors involved in the decision-making process were informed about the agenda and the structure of the decision week by the program management before the decision week started. This helped in giving the program management a neutral position (as hub in the Hub-Spoke game) during the decision week in which the actors involved should come to an agreement. The position of the program management as such contributed to the level of trust between actors. It had been reported that the decision week was structured and well prepared, the right issues were discussed and there was room for all actors to present their opinions. Moreover, actors involved in the decision week reported a high level of trust.

5.3 Intervention 3: Analysis of Game Concepts Over Time

Short summary: Participants adapted and extended the timeline of events from Intervention 2 with future events, actors and issues. They were able to identify and describe the game concepts themselves in the process of decision-making. The intervention included the total set of game concepts instead of only focusing on the Multi-Issue and Cascade game. Moreover, the scope was broader since it considered future decisions until the year 2025 and its potential dynamics.

5.3.1 Observations and Strategies

The position and coordination role of the program management was crucial for the continuation of the process. There was an increased focus by the Ministry to strengthen the position of the program direction ERTMS in legal terms. Strategy (1): The Ministry positioned the program management as hub in the Hub-Spoke game and agent in the Principal-Agent game.

One could not change decisions at a later stage without risking a lot of budget to be spend. This is called the convergence of decisions in the Cascade game. Strategy (2): Create awareness of this aspect to prevent sub-optimal outcomes of the Cascade game.

Actors violating agreements (Diners Dilemma) or creating deadlocks (Multi-Issue game) would delay the process. Strategy (3): The Ministry connected ERTMS issues to context issues, i.e., they created a support package for freight train companies (Maatregelenpakket) and thereby broadened the agenda of the Multi-Issue game. Strategy (4): Moreover, the Ministry wanted to stimulate cooperation between operators to prevent violation of agreements in the Diners Dilemma.

5.3.2 Consequences for the Design of the Decision-Making Process

Positioning the program management as a hub (strategy 1) aimed at minimizing information asymmetry and confusion about incentives. The program management functioned as an intermediate actor between the other actors and the Ministry. As a result, the Ministry did not need to be involved in all technical discussions. Moreover, in case of conflicts between actors (the spokes) about system integration aspects, the program management (hub) had the power to decide in favor of one actor and provided the other actor with compensation. The Ministry had limited knowledge of these system aspects and could not make an objective decision. The formulation of the hub-position in legal terms in the governance led to situations in which conflict situations could be dealt with easier and faster.

To prevent sub-optimal outcomes of the decision week (strategy 2), the cascade of decisions was taking place in one week. During the decision week, both internal decision-making within organizations and decision-making between organizations was performed iteratively. The fact that decisions were made consecutively in a short time decreased the risk of forgetting or ignoring what had been decided upon previously and alerted decision makers to inconsistencies among decisions.

The connection between ERTMS issues and context issues (strategy 3) led to a broader agenda. The discussion between freight operators, program management and the Ministry created room for a give-and-take of issues eventually preventing a deadlock in the decision-making process. Together with stimulation of cooperation between operators (strategy 4) this reduced opposition during the decision week.

5.4 Intervention 4: Forecasting Future Game Concepts

Short summary: Participants discussed a characterization of the decision-making process based on Intervention 3. Researchers included a preview of the decision-making process and described potential future game concepts and their dynamics. The total set of game concepts was included. Participants explored options and solutions to prevent situations which could be destructive for or delay the process of decision-making.

5.4.1 Observations and Strategies

Given the game concept characterization over time of Intervention 3 we expected the multiple Principal-Agent games, Multi-Issue game, Cascade game and Hub-Spoke game to continue towards the second decision week. Moreover, a Battle of the Sexes, Volunteers Dilemma and Diners Dilemma could appear and be destructive for the process and its outcome. The program management recognized the game concepts presented and their potential risks. A worst-case scenario for them was the final decision being postponed by the Parliament which could happen for several reasons.

Actors who disagreed with the set of decisions started lobbying by the Parliament. Strategy (1): Show actors that cooperation is key for this process with financial incentives (prevent a Diners Dilemma), and invest in the trust relation between actors by recognizing their worries and providing solutions.

New issues emerged at the table and opened up a new area of issues to be resolved (over-complex Multi-Issue game). Strategy (2): Create a context in which actors are aware that this is the only moment for a decision by the Parliament to go from ERTMS plan to implementation. Focus on actors’ incentives instead of technical issues they bring up.

Actors could leave the process or block the cascade of decisions (Cascade game). Strategy (3): Provide information regarding the second decision week on time and give sufficient time for internal decision-making within organizations. Moreover, focus on the importance of the decision moment at the level of directors of organizations.

Pressure from European Union due to time, budget, and cooperation with other countries increased. Strategy (4): Manage the multiple Principal-Agent games by reducing information asymmetry and differences in incentives. This is crucial to prevent misalignment of actors, in particular between the Ministry and the Parliament.

Conflicts and dilemmas between actors on content issues delayed the process (Battle of the Sexes). Strategy (5): Position the program management as mediator and objective actor in conflict situations between actors (to resolve a Battle of the Sexes). Moreover, introduce an extra condition in the subsidy arrangements such that the program management has the mandate to decide on technical issues.

5.4.2 Consequences for the Design of the Decision-Making Process

In meetings with directors of organizations prior to the second decision week the decision makers accommodated to the message of the program management: “there is a single chance to get the ERTMS dossier from plan to implementation and the chance is now” (strategy 2 and 3). Decision makers came to a common understanding of this situation which helped the discussions and decreased the number of issues coming up. This prevented an over-complex Multi-Issue game. Moreover, it set the basis for actors not performing unexpected behavior in the final stage close to the decision week. For example, blocking a Cascade game or acting as a volunteer in a Volunteers Dilemma.

The Ministry took its role in decreasing the information asymmetry and minimizing power play with the Parliament (strategy 4). This helped in dealing with the limited number of motions and during technical briefing with the Ministry. Moreover, they addressed the urgent issues in the final stage and permitted the actors involved to negotiate about it. These issues therefore did not complicate the decision week or decision-making at the Parliament level.

In case of conflicts or issues such as with the HSL, the program management formulated one consistent answer. Its role as mediator and objective decision maker was to a certain extent accepted by the actors involved (strategy 5). The acceptance was caused by the shared understanding that a decision was needed (strategy 2) and thus actors kept their opinion to themselves and did not bring it up with others.

Freight train operators did not try to block the decision of the Parliament, since they realized that a broad support across the Parliament had emerged and wanted in an upcoming stage to fight again for more budget. The program management acknowledged the position of the freight operators and promised extra support for their issues (strategy 1).

To conclude this section, we summarize the main consequences for the design of the decision-making process as a result of the formulation of strategies based on the game concept characterization.

The program management shifted towards a focus on actors and their incentives instead of on new issues, and they adapted the decision-making process accordingly. A result was the implementation of two separate decision weeks. To prepare for the decision weeks the program management identified the incentive structures of actors. Moreover, the program management adapted its role and responsibility to resemble a client-contractor model in which they became the neutral organizer of the decision-making process. It had been reported that decision weeks were well structured with decisions clustered around themes and the ordering dependent on the incentive structures of actors. This contributed to the level of trust between actors.

We observed a strengthening of the program management to better deal with conflicts in and prevent delays of the decision-making process. As a neutral actor, close to the technical system integration issues, they were better able to make objective decisions than the Ministry. In the final stages, the initiated coupling of issues was prevented by the program management being more secure in its role.

Coupling of issues by the Ministry for the freight train operators resulted in a broader decision-making process. Due to this strategy, smaller freight train operators got involved, which had historically proven difficult. Moreover, the program management invested in the relation with freight train operators, all to prevent them from blocking the decisions.

The game concepts assisted in supporting a consistent communication of the main message: there is a single chance for a decision by Parliament and that is now. This consistency decreased the number of new issues and set the basis for actors not enacting unexpected behavior in the final stage close to the decision week.

Finally, we observed an effect in minimizing the information asymmetry between principal and agent when multiple Principal-Agent games were present. It has been reported that the decision by the Parliament after the decision of the Ministry went quite fast. Due to minimizing the information asymmetry and sharing the incentives a level of trust was created which helped adopting the final decision.

In the next section, we take a step back and provide lessons learned from the experience of intervening in a decision-making process with game concepts.

6 Reflection on Methodology

Pursuing a project like this brings with it a number of challenges, as is common for action research. This section provides some reflections from the experience of intervening in a decision-making process with game concepts. Initially, the lessons are framed as points to consider when repeating this study in different organizations or domains to overcome the exploratory character of the current study. However, these lessons could even be valuable for practitioners who are interested in approaches that assist in the design of a decision-making process.

The research design can be seen as a cycle: process analysis \(\rightarrow \) intervention with designers \(\rightarrow \) evaluation of strategies, and back to process analysis. Zuber-Skerritt ( 1991 ) distinguishes four phases: plan (analysis), act and observe (intervention), and reflect (evaluation). In this study, we performed the cycle four times. However, the phases (analysis, intervention, evaluation) are not always distinct. During interventions, so when one acts and observers, analysis as well as evaluation can take place.

Lesson 1: A rigid design of steps and cycles provides clarity, but requires flexible adjustment during the process.

As with the nature of action research, the role of researchers and participants in the research cycle is dynamic (Robertson 2000 ). The role of researchers and participants can vary between the phases of the cycle and can differ across cycles. In our study, the role of researchers and participants in the interventions was made explicit in Table  3 . We observed that participants had a larger role in the characterization of the process and game concepts in the earlier interventions and researchers were more dominant in giving the information in the later interventions. An explanation for the shift in roles could be that researchers gained more insight in the decision-making during the process and were therefore better able to characterize, and even forecast future dynamics of, the decision-making process. Given the information asymmetry between researchers and process designers, we experienced several moments of tension between roles.

Lesson 2: Be explicit about the role of researchers and participants in the different phases of the research cycles. In particular, regarding the use of game concepts. Questions to clarify are: Who identifies the game concepts? Who validates the identification? (process analysis); Who presents the game concept characterization? Who interprets the game concept characterization? (intervention); Who assesses the effect of the formulated strategies of the intervention? (evaluation).

Although the interventions took place at the level of the design of the decision-making process, i.e., with designers of the process, the perspectives of the other actors at different levels involved in the process needed to be included. This was necessary to obtain an overview of the process itself, but most importantly to evaluate the consequences of strategies formulated for the design of the decision-making process.

Lesson 3: Include information from actors involved at different levels of the decision-making process. This can be done in two ways: Indirectly, by asking the designers of the process to introduce the perspectives of different actors; and directly, by interviews with, observations of, or documentation from actors themselves.

It was crucial to be aware of the changing dynamics of the decision-making process to prevent the game theoretical characterization being inaccurate (or just wrong). Assuming a constant actor constellation is not reasonable for such processes.

Lesson 4: Find a way to keep track of changing dynamics between intervention cycles. There are a couple of solutions here: (i) let participants characterize the process themselves during the intervention; (ii) start the intervention with validating the characterization, or do this beforehand; (iii) have at least one linking actor, part of the design team, who can provide updates about the process and changing dynamics.

Similar strategies were formulated in different interventions. For example, strategy 2 of intervention 1 and strategy 1 of intervention 2 overlap. An explanation could be that different participants were involved in the interventions, but also that the observation of the situation preceding the formulated strategy was still present. During interventions, participants did not always come up with new strategies, but also re-articulated strategies that were formulated before. Sometimes the moment to put the strategy into action was not directly after the current intervention, but only after the next intervention.

Lesson 5: A suggestion for further research is to not only consider the interventions separately, but to view them as a whole and examine how strategies developed over time as well as the consequences of these strategies for the design of the decision-making process.

In all interventions, the participants used the game concepts, but the way in which they used them was different. In this study, the way in which game concepts were used was dependent on (i) the size and constellation of the group, (ii) previous experience with and thus existing knowledge of the game concepts, (iii) the time available for the intervention, and (iv) the information researchers had about the decision-making process beforehand.

Lesson 6: When repeating this study on a larger scale, align a number of those points such that the value of the way in which the game concept approach is used by participants can be better assessed.

7 Conclusion and Discussion

The objective of this paper was to explore the value of descriptive insights from game-theoretic analysis for dynamic interventions in the design of a process. The study applied game theoretical models to characterize a complex decision-making process and gave back the information to the designers of the process. The experience showed that the effects of such an approach could potentially make game theoretical models relevant to practical decision-making in a new way. Moreover, we provided lessons learned based on the experience for both researchers and practitioners. The context in which this research took place is the case of ERTMS in the Dutch railway sector.

First, we described the process of decision-making and highlighted the game concept elements. Second, we analyzed the four interventions with designers of the decision-making process. They used the game concept characterization to formulate strategies. Subsequently, we investigated the consequences of the strategies for the design of the decision-making process. The consequences for the process can be summarized as follows: an actively supported adaptation of an actor focus in the process, it affected the role and responsibilities of the program management, it contributed to the (de)coupling of issues, and it influenced the capability of creating awareness amongst actors of the urgency of the decision window. It also avoided the expected actor behavior of complicating the final stage of the decision-making process. Finally, we reflected upon the study as action research and drew six lessons learned for both researchers and practitioners.

The paper contributed to the theoretical framework on complex decision-making in multi-actor systems. We characterized the process by multiple game concepts instead of talking about a (single) game and an optimal outcome. Parallel interacting game concepts existed in the decision-making process which played at different levels of the organizations involved.

We showed how game theoretical models can assist in a complex decision-making process by giving back the game theoretical characterization to designers of the process. We provided a successful example of a case where a descriptive analysis led to the formulation of strategies instead of using the normative value of game theoretic models. This point is of interest for the broader use of game theory, or formal models in general, in the area of participatory decision-making and design of organizational processes.

Moreover, instead of actively intervening in behavior of actors outside their real context, these interventions targeted the design of the decision-making process. By investigation of the consequences of the formulated strategies in interventions, we contributed to the area of evaluation of participatory interventions. The game concept approach proved to be flexible in aligning with an ongoing process, and did not overtake the process as, for instance, decision-support systems do and to a certain extent also gaming does.

Finally, the paper contributed to the field of decision support and action research by providing methodological reflections on the use of the game concept approach as presented in the paper. The exploratory nature of the study makes it impossible to draw firm conclusions and shows its limitations, however, the reflections provide a starting point for further research in this area.

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Acknowledgements

This research was funded by ProRail (Dutch Railway Infrastructure Manager) and Delft University of Technology. The authors especially thank Peter Scheffel for his input and time during this study. Moreover, we thank the editor and reviewers for the valuable comments which definitely improved the paper.

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Femke Bekius

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Sebastiaan Meijer

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Bekius, F., Meijer, S. & Thomassen, H. A Real Case Application of Game Theoretical Concepts in a Complex Decision-Making Process: Case Study ERTMS. Group Decis Negot 31 , 153–185 (2022). https://doi.org/10.1007/s10726-021-09762-x

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Case Study Application of an Ethical Decision-Making Process for a Fragility Hip Fracture Patient

In Canada, up to 32,000 older adults experience a fragility hip fracture. In Ontario, the Ministry of Health and Long Term Care has implemented strategies to reduce surgical wait times and improve outcomes in target areas. These best practice standards advocate for immediate surgical repair, within 48 hours of admission, in order to achieve optimal recovery outcomes. The majority of patients are good candidates for surgical repair; however, for some patients, given the risks of anesthetic and trauma of the operative procedure, surgery may not be the best choice. Patients and families face a difficult and hurried decision, often with no time to voice their concerns, or with little-to-no information on which to guide their choice. Similarly, health-care providers may experience moral distress or hesitancy to articulate other options, such as palliative care. Is every fragility fracture a candidate for surgery, no matter what the outcome? When is it right to discuss other options with the patient? This article examines a case study via an application of a framework for ethical decision-making.

INTRODUCTION

Every year, over 30,000 Canadian older adults experience a fragility hip fracture. The Ministry of Health and Long Term Care of Ontario has promoted best practice recommendations which advocate for immediate surgical repair, within 48 hours of admission, in order to achieve optimal recovery outcomes. ( 1 , 2 ) The majority of patients are good candidates for surgical repair; however, given the risks of anesthetic and trauma of the operative procedure, surgery may not be the best choice for all. The patients at higher risk of poor outcomes perioperatively deserve the opportunity to explore options and articulate their values. Unfortunately, as a short pre-operative interval predicts the best outcomes, patients and families face a difficult and hurried decision, potentially with limited time to voice their concerns, and little to no information on which to guide their decision.

From a systems perspective, quality of care and health outcomes have not always incorporated the patient-centred perspective. ( 3 ) Patient-centred care is “a moral concept and philosophy, considering it to be the right thing to do when designing and delivering respectful, humane, and ethical care”. ( 4 , 5 ) Patients and families have reported in the past that they feel left out of crucial conversations and decisions surrounding care, ( 6 ) and that relevant information is not always provided. ( 7 )

To better understand the underlying ethical complexities which arise from critical decisions in the acute care setting, this paper will examine a case study to demonstrate application of the Corey et al . ( 8 ) 8-step framework (see Appendix A ) for ethical decision-making.

Ms. Jones is 93 years old and lives in a Long Term Care residence. She was admitted to hospital with a fragility hip fracture after being found on the floor in the middle of the night. Ms. Jones has dementia and is unable to make her own decisions. She has limited mobility, previously used a walker. Her two daughters are at her bedside. They state her health has been declining over the last few weeks, with increasing confusion and she now rarely leaves her room.

On admission, the team discovered a pleural effusion, taking up much of her right lung. Her pre-operative assessment also revealed a heart murmur; the resulting echocardiogram demonstrated a heart in very poor condition, with significant valve issues. Between her cardiac and pulmonary function, the surgery poses an increased risk of perioperative complications—she may never survive the surgery, or come off of the ventilator once she is intubated.

Interprofessional teams (surgery, anesthesia, nursing) are of differing opinions. The issue at hand is very difficult. The family is informed that the risk of not having surgery will likely result in death, yet in this patient’s case, proceeding with surgery carries its own risk. The family is left with an hour to think things over. Should they pursue the palliative care route or proceed with surgery?

Step 1. Identify the Problem or Dilemma

In our case study, 93 year old Ms. Jones is admitted to hospital with a fragility hip fracture. As a first step, we must recognize that there is actually an ethical dilemma; in this case, the dilemma is whether the patient should proceed with surgery or not, given her underlying medical conditions and potential for perioperative complications. We also need to acknowledge that there is an underlying assumption from all involved (staff, Ms. Jones’ family) that surgery will occur, and that health-care providers (HCPs) may not clearly articulate the option of ‘no surgical intervention’. The stakeholders who are required to proceed through the decision-making process include the patient and family, the surgical team, anesthesia, nursing staff, social work, and potentially the palliative care team and bioethics team.

Step 2. Identify the Potential Issues Involved

There are several assumptions made when a patient presents to the hospital with a fragility hip fracture: a) the fracture will be repaired; b) the patient will recover; and c) the patient will eventually go home or to rehabilitation. With a critically ill, frail, and/or previously compromised patient, this standard trajectory should be questioned. Barry and Edgman-Levitan ( 9 ) promote an ideology of patient-centredness, with the argument that an intervention should only be considered standard if there is ‘virtual unanimity amongst patients about the overall desirability… of the outcomes’.

The first potential issue is the ‘standard’ intervention of surgical repair—the assumption to proceed with the surgery, as directed by best practice recommendations. Is this standard intervention appropriate in all patients with a fragility hip fracture? A second potential issue arises with the patient and their family—the presumption that the acute medical issue will be resolved and the patient will eventually return home. Given her underlying health, this concept is in jeopardy. To add to the complexity, Ms. Jones is likely not able to articulate her wishes and values, as she has dementia. Finally, there is the potential issue of moral distress experienced by health-care providers (HCPs) who feel uncomfortable with the expectant surgical trajectory of this patient, and may feel they are not empowered to advocate for the wishes of the patient.

As health-care professionals, we are guided by moral principles in our decision-making process, namely, autonomy, non-malfeasance, beneficence, justice, fidelity, and veracity. ( 10 ) A focused examination and application of the principles to the case study will help to support potential resolutions for the identified issues.

The spirit of ‘patient-centred care’ endorses that patients should be involved at their level of choice to make an autonomous decision. ( 11 ) However, it is important to recognize that no decision is made in isolation. ( 12 ) The decision at hand is not a simple or straightforward one; literature demonstrates that patients and families have a difficult time with making decisions at time of a critical illness, identifying fear, worthlessness, and a lack of autonomy within the hospital system. ( 7 ) Differing levels of patient and family participation requires an individualized approach to convey meaningful, accurate, and timely information. ( 8 ) Older adult patients tend to take a ‘non-participative’ stance in their care. They often have limited participation in the process for decision-making for a variety of reasons, thereby increasing the risk of their inability to understand or find value within the end decision. ( 6 , 7 , 13 )

Non-malfeasance

Hospitalization can cause the patient to experience “needless mental and physical suffering” ( 14 ) in any number of ways (i.e., pain, waiting for surgery, uncertainty of outcomes, patient/family relationship stress). Evidence indicates that the number of different HCPs involved causes immense anxiety to the family, especially when they do not hear the same message from all members of the team. ( 13 , 15 ) HCPs must ensure that they are not withholding information, or are untruthful as to the options in order to expedite a decision. A study by Ekdahl, Andersson, and Friedrichsen ( 13 ) found that physicians perceive they are ‘too short’ of time for patients to participate in the decision making process, that decisions were ‘too complex’ and ‘time consuming’ to fit into the schedule. Ekdahl et al. ( 13 ) also found that physicians feel frustration with the ‘health-care production machine’, especially in those older adult patients with multiple co-morbidities.

Beneficence

Beneficence promotes wellbeing; or is an action that is carried out to benefit another. ( 8 ) The hospitalization ‘process’ promotes assessment of a patient, treatment of the illness, followed by a physical approach to recovery (allowing recovery to be measured against specific milestones), and discharge in a timely manner. ( 15 , 16 ) This ‘process’ may promote beneficence in an overarching global perspective of the system; however, on an individual level, it often falls short. On an individual level, key actions that have been found to be beneficial and meaningful are open communication and sharing of information. ( 6 , 7 , 14 , 17 )

“Practitioners have a responsibility to provide appropriate services to all clients”. ( 8 ) Older adult patients may not receive information about options available, especially if the HCPs feel that it would take too much time to thoroughly explain, or if HCPs assume that patients are too ill to participate in the decision-making process, ( 13 ) or if the assumption is made that all patients want to proceed with surgery. Focusing on each older adult’s individual health goals is time-consuming—in this case, the patient has dementia, and a family meeting would be required. The concept of patient-centred care revolves around patient and HCP partnerships, yet older adult patients face unique problems with hospitalization—a slower communication process, a decreased level of functioning, and a degree of family involvement. ( 14 ) Can we provide this type of relationship and communication effort equally for every patient? Or only for those patients who may be at higher risk of negative outcomes?

Fidelity and Veracity

Fidelity involves fulfilling ones’ professional roles, creating a trusting relationship, and veracity ensures that we are truthful and honest to the patients. How do we ensure that as a HCP we are providing an unbiased opinion? Do we take the same amount of time to present patients with the option of conservative, non-surgical treatment, including palliative care, as we take to advocate for surgery? The HCP team assumes that patients will commit to surgery; however, a patient often displays a suboptimal understanding of the risks and benefits of surgery. ( 18 ) Similarly, there is the very real risk of bias towards an argument of palliative care in those frail patients or those with dementia. HCPs must return to the voice of the patient through their family, to understand that patients’ identity, their meaning of life, and desired goals which emphasize the patients’ dignity. ( 12 )

It is important to acknowledge assumptions that the patient and family may have made upon admission to hospital—that surgery will occur and the patient will recover. Have we presented the patient and their family with as much information as they need to make a decision in a clear format (without medical jargon)? In addition to understanding risks of surgery, it is paramount that the family understands the non-surgical option may result in death or decreased function (if any functional ability returns). It is in an acute situation such as this that families require truthful and open communication with physicians, nurses, and other members of the health-care team. ( 11 )

Self Care (HCPs)

Can we consistently provide care that prioritizes a patient’s values? HCPs are not always able to preserve all of the values and interests at stake. ( 19 ) We know that the most common cause of moral distress in nursing is prolonged, aggressive treatment which we do not believe will be likely to have a positive outcome. ( 20 ) As a HCP, we must look to root causes operating within the larger system, to prevent and/or respond to feelings of moral distress. ( 19 )

From a systems perspective, does the hospital provide an avenue for exploration of patient values within a timely fashion? Is there a framework in place to enhance the HCP’s understanding of moral distress and provide strategies for coping with situations such as these (i.e., an opportunity for a team debriefing with the entire team, or opportunities for learning how to deal with situations that may cause moral distress)?

Step 3. Review the Relevant Ethics Codes

The philosophy of patient-centred care within the hospital encourages active listening, respect, and an attempt to understand individuals. The Canadian Medical Association (CMA) supports “practicing the profession of medicine in a manner that treats the patient with dignity and as a person worthy of respect”. ( 21 ) The College of Nurses of Ontario (CNO) supports the view that nurses “must use the client’s views as a starting point”. ( 22 ) Across all HCPs is the similarity of the need to listen, understand, support, and advocate for a respect of patients’ values with the expected course of treatment.

The importance of collaboration with the patient and respecting a patient’s values are highlighted within similar statements: ”…it is the patient who ultimately must make informed choices about the care he or she will receive”. ( 21 )

Step 4. Know the Applicable Laws and Regulations

In Ontario, legislation and common law require that the wishes of patients or substitute decision-makers be respected. ( 22 ) However, in many systems, health care is not truly patient-centred; rather, patients are required to adapt to the system. ( 11 ) A number of initiatives have been undertaken in the last few years in an attempt to improve the focus of patient-centredness, with the principle assertion that patients should be involved at the level of their choice. ( 11 )

Step 5. Obtain Consultation

It is important to realize that we bring our own biases to the decision-making process, making it difficult to view the current patient/family’s situation objectively. As an individual HCP, our previous experiences will have an impact on the messaging that we provide. From a systems perspective, we are likely to pose a ‘knowledge’ bias towards meeting treatment based outcomes—for example, surgery within 48 hours, immediate post-operative mobility, and the expected length of stay for this type of patient.

Inter-disciplinary consultations with patients and their families ensure review of unbiased information about the risks and benefits of proceeding with surgery, allowing for a fully informed decision. In addition to discussing the operative plan with the surgical team, there is an opportunity to provide Ms. Jones’ family with other options that may be available to her. Consultation with extended family members, clergy, social workers, or an ethics team may help the family to reflect on the patient values; what this illness means to them as a family unit, and how best to proceed. A discussion with palliative care may help the family to better understand what symptom management consists of for their mother. Social work may also be able help explore community services available to the family in this situation—for example, is the patient able to return to home with the future of wheelchair dependence? Are there any other options which may be available to this patient and her family that were not originally considered? How do we, as HCPs, ensure that the family is afforded the opportunity to obtain all the necessary information from differing disciplines to make an informed choice?

Step 6. Consider Possible and Probable Courses of Action

In order to fully understand the options, it is helpful to outline all the possible and probable courses of action that are open to Ms. Jones and her family.

• Surgical team offers a ‘purposeful pause’ to discover Ms. Jones’ core values; to discuss the consequences of a) delaying surgery, b) proceeding with surgery, and c) the non-surgical intervention. From an ethical and legal perspective, this may meet the concept of patient-centred care, but does not likely provide the patient and her family with all the information they need to make an informed choice. They may have more questions that the surgical team may not be able to answer, or they may request more time to consider. Additionally, the patient and her family would still be expected to adapt to the system in place in order to make a decision within the proposed wait time frame (admission to surgery less than 48 hours).
• Advocate for a family meeting with the primary nurse, social work, palliative care team, clergy, internal medicine, in addition to the surgical (surgeon, anesthesia) team, to fully explore both options, and to explore what the ‘non-surgical’ option would mean. From a legal and ethical perspective this embodies the concept of patient-centred care, with as many members of the health-care team at the table to help Ms. Jones’ family fully explore their options.
• Apply the current standard of care recommendations to Ms. Jones’ situation, without consideration of the patient’s needs, values, or preferences. From an ethical and legal perspective, this approach does not represent patient-centred care.

Step 7. Enumerate the Consequences of Various Decisions

With the first option, the surgical team takes a ‘purposeful pause’ to discover the patient’s core values and discusses pros and cons of a surgical intervention. Often, this may be most ‘efficient’ way to deal with the situation at hand. It may also be the preference of the patient; some patients have reported that they value this limited level of involvement—“I get a description of what is going to happen”. ( 13 ) As a consequence, there will be a number of patients who will want to have a greater sense of involvement other than a simple description of planned events. The first option does recognize the principle of autonomy, but does not follow the principle of justice; practitioners have the responsibility to provide information about other options which may be available. The principles of beneficence and non-maleficence are not completely met, as the team approaches the solution primarily to benefit the system (i.e., efficiency). The principles of fidelity and veracity are also partially met, as the surgical team provides an honest perspective, although it may be biased towards proceeding with surgery.

The second option, offering the patient and her family a meeting with all stakeholders, strongly aligns with the fidelity and veracity principles. The information offered is truthful and complete, and is in Ms. Jones’ best interest, as it attempts to discover her values that will affect the family’s final decision. Principles of beneficence and autonomy would be met with patient empowerment through information sharing, and secondly, by allowing the patient and family to arrive at their own decision with that information. As a consequence, taking the time to arrange for a family meeting with all stakeholders may not be possible for all patients, and the principles of justice and non-maleficence are brought to the forefront for future patients. A potential consequence could be harm to the patient, as the time it takes to arrange a meeting could push the time to surgery beyond the recommended 48 hours post-admission, placing the patient at greater risk of negative post-operative outcomes.

The third option is one of passive action, with a lack of communication and recognition of patient-centred care values. Ms. Jones would be placed on the operating room list, and the surgical repair will occur. Consent must legally be obtained for the surgery; however, the family may not think of key questions to ask that may be relevant in this situation. The onus remains on the HCP to provide a full explanation of all options to the family. The only benefit would be to the system, as the procedure will be carried out in a timely manner. Ms. Jones may benefit from the surgery; we cannot assume that surgery is a negative option. As a consequence of this option, HCPs do not explore patient values, and this option is against almost all of the ethical principles. Additionally, this option is likely to cause the highest moral distress amongst staff, as they are unable to meet the unique needs of Ms. Jones and her family.

Step 8. Choose what Appears to be the Best Course of Action

Virtue ethics asks us if we are doing the best action for our patients, and compels us to be conscious of our behaviours. ( 8 ) We need to take the necessary time to discover the patient’s values within the unique situation they are now experiencing. Simply stated, we need to remember that they are a person, with feelings, emotions, past experiences, future hopes/plans, and usually an element of fear and anxiety. The goal is to work with Ms. Jones and her family to decide together on the current care plan and the best plan for action (or inaction), a plan that truly aligns with the patient’s values.

From an ethical perspective, the best course of action is to hold a family meeting with all stakeholders to discover Ms. Jones’ values about a meaningful life and a meaningful death, and come to a consensus as to what the right decision is for this patient. ( 12 ) The team must ensure that the patient and the family have all the necessary tools in which to make this decision. Have we provided them with all the information required? Do they understand the information? Do they understand the consequences of their decision? From a systems perspective, we need to continue to strive towards engaging patients and family members more fully and consistently in care and decision-making processes. ( 6 ) Dissemination of lessons learned from assisting patients and families through difficult decision-making may be helpful to other health-care teams experiencing similar moral conflicts.

As a next step, the HCP team may consider development of an educational reference for future patients to assist with similar decisions, including promotion of an advanced care plan to help communicate goals and concerns to HCPs. ( 12 , 18 ) Additionally, decision aids, such as videos and brochures, can help deliver information to patients and their families. ( 9 ) The use of readily available technology, such as iPads and cellphones, means that families are better able to access these materials at any time of day. A recent Cochrane Review demonstrated that, in comparison to usual care, decision aids can increase knowledge, resulting in a higher proportion of patients choosing the option which most aligns with their values. ( 23 ) Providing patients with information that outlines potential options with risks and benefits clearly explained can also meet many of the ethical principles that are to be considered with ethical decision-making.

The in-depth review of the case study has helped us to examine the underlying issues that come into play when helping this patient and her family to make a critical decision. Although each patient is an individual, literature tells us that many perceive the concept of patient-centredness to represent an ‘involvement in their care’. The level of involvement may vary from person to person, but all patients want the care they receive to reflect their values and preferences, and to make them feel that they have been treated as a whole person. ( 24 )

Clinicians also like to believe that they deliver patient-centred care, yet the characterization of the concept will vary with the health-care provider, their relationship with the patient, and the circumstances surrounding the admission to hospital. Recognizing that there is potential for an ethical dilemma when patients present with a critical illness is important to ensure that we continue to act upon the key concept of understanding a patients’ values and proceeding to align provision of care with those values.

ACKNOWLEDGEMENTS

The author wishes to acknowledge Dr. Tracy Trothen (Queen’s University) for her time and expertise as a ‘practical ethicist’.

Appendix AFramework for Ethical Decision-Making (Corey et al ., 2014)

• Identify the problem or dilemma
• Identify the potential issues involved
• Review the relevant ethics codes
• Know the applicable laws and regulations
• Obtain consultation
• Consider possible and probable courses of action
• Enumerate the consequences of various decisions
• Choose what appears to be the best course of action

CONFLICT OF INTEREST DISCLOSURES

The author declares that no conflicts of interest exist.

Strategic Decision Making – A Case Study

Michael w. jones.

Dr. Michael W. Jones is a Professor of Strategy and Policy with the United States Naval War College in Monterey, California.  His specialty is the French Revolution and the Napoleonic Wars; however, for the past twenty years he has researched and written on a wide array of conflicts; examining them through the political, grand strategic, strategic, and operational levels of warfare.

Throughout history, professional military officers have studied the past to learn strategic planning and decision making. While history remains the best means to study strategy, it is problematic due to imperfect knowledge of actual events and personal biases infecting hindsight. If these are some of the problems, what are solutions to using history in a more effective manner as a tool to sharpen strategic thinking? This paper examines how practitioners can develop strategy by demonstrating a methodology for constructing alternate courses of action in a historical case study. Studying options, using information known at the time and that could have been gleaned with a greater investment in intelligence, is one of the building blocks to developing a strategically analytical mind. Gaming-out options starts with identifying the enemy’s most likely and most dangerous strategic course of action. From this point one can develop a theory of victory (TOV), meaning a concept of what conditions are necessary to defeat the enemy’s strategy, such as gaining command of the sea or winning a decisive land battle. With a theory of victory, one can then develop an overall strategy, effectively a blueprint, to accomplish it. The strategy is then honed by comparison to the enemy’s most likely response. This analysis results in alternate courses of action that are in turn honed until the most efficient and effective strategy to achieve the policy objective has been determined. The goal is to implement a history-driven process that can be carried forward to developing future strategic contingencies.

The 1904-05 Russo-Japanese War serves as our model because its historical record provides clear data of the belligerents’ policy objectives, orders of battle, their internal political structure, the geostrategic landscape, the theater’s infrastructure, and clear geographical features that dictated Japan’s lines of attack. Simplifying the exercise is that this war was a limited conventional struggle between two great powers with little to no interference by allied or third-party nations. Furthermore, the belligerents foresaw a military confrontation well before the first shots and had time to develop and resource a chosen strategy. Due to limitations of space this paper will be confined to an overview of five Russian strategic options.

Nine months prior to the outbreak of the Russo-Japanese War, General Alexiev Kuropatkin, Russia’s Minister of War, toured the Far East and predicted a Japanese attack. The Russian Imperial Navy had also anticipated war with Japan and gone so far as conducting war games to assess the likelihood of victory.[i] Their foresight provides the temporal starting point to examining Russian strategic options to counter a possible Japanese offensive.

The question is: how does one build strategic options? Following Sun Tzu’s prescription to “know thyself and thy enemy” and Carl von Clausewitz’s admonition that policy is the primary determinate of the nature of war, the Russians first needed to discern Japan’s policy objective. By knowing what they sought to gain from the war, Russian leaders could then determine Japan’s optimal TOV and thereafter their strategy. Russian planners could have further dissected this strategy’s operational components, discerning Japan’s course of action by determining the strategic end state and logically discerning how the Japanese military would arrive at it. From this point, Kuropatkin could then develop Russia’s optimal strategic counter. The methodology worked in the following manner. Prior to Japan’s surprise attack on the Russian fleet based at Port Arthur (now Lushan, China), the Japanese government had publicly opposed Russian encroachment into the Korean peninsula and Manchuria. In the case of war the obvious Japanese policy would be to drive the Russian government and military permanently out of these regions and supplant their authority. Defeating Russian forces in Manchuria was their only means to accomplish the policy. This strategic end state required control of the sea to project the army ashore and then secure a land victory to break Russia’s will. Owing to Russia’s drastically larger manpower and financial resources, the Japanese recognized the need for a relatively short war that only decisive battles could deliver. Japan’s most likely strategic course of action informs analysis of Russia’s options to counter it and achieve its policy objective of retaining control of Manchuria and increasing influence in the Far East.

Strategic Naval Option 1: Decisive Naval Battle

The United States’ legendary naval theorist, Alfred Thayer Mahan, argued that Russia’s best option was to prepare for and execute a decisive naval battle using the seven battleships of its Pacific Squadron. Arguably the Russians had critical advantages over the Japanese at sea. Overall, Russia possessed a much larger fleet and if properly concentrated, as Mahan advocated, it could have traded ships with Japan and still won the war. If Russia was victorious at sea, Japan could not have landed on the Asian mainland, hence Russia would have retained Manchuria and achieved a quick, decisive victory! Because Japan could only win the war on land, Russia had the advantage of being able to risk its fleet and if defeated, fall back on the army to deny the Japanese their objective.

The key to adopting Mahan’s strategy was immediate action the moment Kuropatkin realized war was to occur in the near future. First, the Russians should have appointed their best admiral, the dynamic, charismatic and already internationally renowned Vice-Admiral Ossipovitch Makarov, to command the Russian Pacific Squadron at Port Arthur. The history of the war revealed what Russian leaders already knew of Makarov’s capabilities. In one month of command, before his ship, the Petropavlosk , struck a mine and carried him down with it, he drastically improved the sailors’ seamanship, gunnery, and morale to an extent that the Russian Pacific Squadron could challenge the Japanese navy on an equal footing. Second, Kuropatkin should have ordered and resourced a naval “intelligence preparation of the battlefield” (IPB) of the Japanese navy’s order of battle and capabilities to identify his own navy’s requirements. To win control of the sea, Russia needed overwhelming superiority of battleships, a problem Russia could have been rectified with ships idling in European waters. A reinforced fleet, with Makarov at the helm, would have been fully capable of winning decisively at sea. Seeking out the Japanese fleet for a decisive battle would have been relatively easy, because it was bound to protecting the army coming ashore. Makarov could have struck immediately after Japan fired the first shots or waited until a substantial force had come ashore and then destroyed the Japanese warships, leaving a significant portion of the army stranded in Korea. Kuropatkin’s strategic, operational, and tactical naval options would have abounded with proper preparation, which Russia was wholly capable of doing because they foresaw the coming war, possessed the world’s third largest navy, and were blessed with an excellent fighting admiral.

Strategic Naval Option 2: Commerce Raiding

If the Russians had deemed decisive naval battle too risky, a secondary naval option would have been a commerce war. Japan was particularly vulnerable to this strategic option due to its relatively small merchant marine, the refusal of neutral vessels to carry Japanese war materials, and the reality of its navy having to guard against the possibility of a Russian fleet sortie from Port Arthur. Mahan rightly assessed that Russia’s flawed disposition of its commerce raiding cruisers, deployed alongside the battleships based at Port Arthur, rather than dispersed to unguarded Vladivostok, meant it was unprepared to seize opportunity after Japan attacked. Implementing this strategy, though, would have required forethought beyond what Mahan discusses. As with the prior strategy, European based cruisers should have been shifted to Vladivostok in the ten months prior to war to have made this a viable option. Makarov could have conducted exercises, identified his ablest commanders, and used the naval IPB to discern the best operational approaches to this strategic option. Russia did none of these preparations and found itself with ad hoc commerce raiding operations which proved a dedicated strategy of this nature had much potential to change the course of the war, if it had been properly planned for and resourced. For example, three Russian cruisers sank Japanese transports carrying critical war materials such as siege guns for Port Arthur and American made locomotives Japan needed to project its army into Manchuria. Some analysts concluded that loss of the siege guns alone delayed Port Arthur’s fall by months and drastically increased casualties. With proper coordination, the Russian battleships of the Russian Pacific Squadron could have threatened the Japanese army’s sea lines of communication on the western flank of the Korean peninsula to pin Japan’s limited naval forces. If the Japanese navy hunted the commerce raiders they would have exposed the army to a sortie from the main Russian fleet. To leave the raiders unmolested could have crippled the lifeline to Japan, rendering the Japanese forces already ashore vulnerable to a Russian army riposte. Once again, Russia’s failure to explore strategic options before the war left it unprepared in another strategic dimension. Japan was able overcome Russia’s deadly commerce raiders because they were so few and the lethargy of the Russian Pacific Squadron after Makarov’s death allowed them to eventually dispatch naval forces to find and sink the Russian cruisers.

Strategic Land Option 1: Trade Space for Time + Eventual Decisive Battle

Irrespective of the naval options, Russia could have analyzed three land strategies. Kuropatkin’s chosen strategy was a limited withdrawal along the Russian line of communication – the South Manchurian Railway – to await reinforcements before shifting to the strategic offensive. Kuropatkin assessed that in the initial months of the war, Japanese forces outnumbered his men in theater; therefore, he would gain time and preserve his army’s strength by the classic method of trading space. Time would allow Russian engineers and laborers to improve the Trans-Siberian railway, Asiatic Russia’s lifeline to its European counterpart. This strategy necessitated withdrawal of all Russian forces in southern Manchuria to the city of Liaoyang, roughly 120 miles from the Yalu River. The merit of Kuropatkin’s strategy was that it accomplished his goal of buying time to increase Russian numbers over the Japanese. At the Battle of Liaoyang, the Russians possessed 158,000 soldiers and 609 guns against 125,000 Japanese and 170 guns.[ii] Yet the Russian army was defeated at this potentially decisive battle and the subsequent larger engagement at Mukden because it was an untested and poorly trained force, led by a commander who conceded every strategic, operational, and tactical initiative to his opponent!

What Kuropatkin had gained in time and men in his wholesale retreat, he lost in infrastructure (ports and railroads), key terrain (landing sites, mountain passes, and choke points), and opportunities to hone the army’s operational and tactical skill. Retreating into southern Manchuria left all amphibious landing zones throughout Korea and the Liaotung Peninsula undefended. After the Japanese came ashore they found almost every avenue of approach to Dalny, a commercial port and the most significant logistical hub of the entire war, open, with the limited exception of one regiment at Nanshan, where the Liaotung Peninsula narrows to its most defendable point. While the small Russian force fought a heroic defense, it was outnumbered 10:1. Kuropatkin had left Port Arthur’s garrison to defend itself and simply abandoned Dalny, potentially dooming the Russian Pacific Squadron. Perhaps the worst effect of Kuropatkin’s strategy was that the token resistance he did offer was fodder for Japanese victories. Russian battlefield defeats boosted Japan’s international standing, allowing it to float critical loans, unify its people, and devastate Russian morale on the home front, eventually culminating in revolution.

Strategic Land Option 2: Scorched Earth + Trade Space for Time + Eventual Decisive Battle

Assessing his army as initially too weak to fight a decisive battle, Kuropatkin could have moved his forces deeper into Manchuria, beyond Japan’s logistical reach, while destroying all infrastructure in southern Manchuria. Planning for this strategic option would have included sending the Pacific Squadron back to Europe to preserve this valuable asset and avoiding the disastrous effects on Russian morale stemming from its loss. With no fear of abandoning the fleet, Kuropatkin would have possessed a free hand to withdraw the army and destroy war resources without immense political pressure to hold ground. A scorched earth methodology would have destroyed infrastructure Japan required to project its army into southern Manchuria. For example, after capturing Dalny, Japanese General Yasukata Oku reported, “Over 100 warehouses, barracks…were found uninjured. Over 290 railway cars still usable…. Docks and piers uninjured.”[iii] Ashmead-Bartlett Ellis, a British reporter, confirmed Dalny’s value to the Japanese war effort, reporting “Every day numerous trains steam out of the station laden with troops and stores for Oyama (Field-Marshall Iwao Oyama) and his half-a-million of men.” Ellis went on to describe the docks, harbor, and breakwaters as “splendid.”[iv] Furthermore, Dalny’s rail line connected it to Port Arthur and to the South Manchurian Railroad which ran through the towns of Liaoyang and Mukden, sites of the war’s two largest battles. In his memoirs, Kuropatkin would inadvertently incriminate himself regarding leaving the infrastructure intact referencing, “the delivery of heavy howitzers [that destroyed Russian defenses] and the landing of other siege material was greatly facilitated by the existence of Dalny.”[v] Japan’s use of Dalny as a logistical hub illustrates that a scorched-earth methodology would have increased Japan’s war costs and drawn-out the war in Russia’s favor.

If the Russian army had been safely beyond Japan’s reach, Kuropatkin could have improved the Trans-Siberian Railroad while training and equipping his force for a counteroffensive. A primary factor in Japan’s preemptive strike was the recognition that steady improvements in the Trans-Siberian railroad would eventually permit Russia to deploy a force that could overwhelm their manpower and resources. At the outset of the war, the Trans-Siberian Railroad lacked 600 of the necessary 900 locomotives deemed sufficient to sustain a massive force. It had a large gap at Lake Baikal and was single tracked. Through prodigious effort, the Russian supply situation had drastically improved by March of 1905; however, by this stage Kuropatkin’s many defeats had helped spark revolt in European Russia and the army was a demoralized force.[vi] Avoiding costly human losses and husbanding material and manpower until the railroad was prepared to sustain a large army, would have allowed the Russians a transition to the offensive with overwhelming force against a foe attempting to sustain hundreds of thousands of men across too much desolated space, with its manpower and finances exhausted by a long war.

Strategic Land Option 3: Active Forward Defense + Eventual Decisive Battle

Perhaps the most daring, yet rewarding, land option would have been for Russia to conduct an active defense based on defending against Japanese amphibious landings, and waging a fighting withdrawal until reinforcements arrived from Europe to tip the military balance toward a strategic offensive. Preventing the Japanese from coming ashore in sizeable numbers would have preserved Manchuria’s infrastructure, saved the battleship fleet at Port Arthur, and provided Kuropatkin’s forces with all the advantages of the central position. Denying the Japanese easy and early victories would have bolstered the army’s morale and skill, dried up Japanese war loans, and perhaps forestalled the Russian Revolution of 1905, which denied it the option of extending the war.

In the first two months of the war, the Japanese offensive was most vulnerable because it had to conduct a risky series of complimentary amphibious operations. The Japanese First Army initially landed in central Korea, distant from Russian counterattacks, then marched north along dirt tracks, with only Korean coolies providing logistical support. These initial troops seized inlets that allowed the navy to keep advancing the army’s logistical base closer to the Yalu River, at the base of the Liaotung Peninsula. Victory at the Yalu would protect the eastern flank of the Second and Fourth Armies as they came ashore at beaches near Dalny and Port Arthur. If the Russians had contested northern Korea and defended the Yalu River, rather than Kuropatkin’s pitting of a mere 19,000 men against 42,000 Japanese, the Russians could have stalled the entire Japanese offensive, making time a weapon in their favor.

This Russian strategic option would have rested on a combination of prepared forward defenses supported by quick reaction forces (QRF). First, Russian intelligence needed to conduct an IPB of the Korean and Liaotung Peninsula’s topography to determine landing sites, lines of communications, and advantageous defensive terrain. Defending the beaches with a combination of garrison forces and QRFs would have drastically increased Japanese casualties and potentially slowed their advance along the Korean Peninsula to a crawl. On almost every beach the Japanese army was exposed coming ashore. For example, the Japanese Second Army landed in chest high water and had to wade ashore, across a long and vulnerable stretch. Their equipment continued to be offloaded on sandy beaches until the Japanese captured Dalny. If the Russians had opted to defend against amphibious landings, they may have been able to inflict a disaster similar to the British army’s debacle in World War I at Gallipoli. If driven back, the Russians could have fought from a belt of defensive positions to bleed the Japanese army and extend the war, thereby draining Japanese financial resources and exhausting their nation. The Russian army was fully capable of such a defense as it proved at the Battle of Nanshan and the siege of Port Arthur. Drastically outnumbered in both operations, the Russian defenders inflicted massive casualties on the Japanese. What would have been the strategic ramifications of such battles being fought before the Japanese had time to offload their entire army onto the continent and were bottled up on beachheads and narrow lines of communication, all the while Russian reinforcements poured in from Europe to seek the final decisive blow?

For military leaders, prognosticating a future war and predetermining strategy is extremely difficult, but if correctly anticipated, such insights provide opportunity to analyze, plan, resource, and even war game scenarios. In the 1930s, the United States Navy anticipated a naval battle similar to Midway, allowing its students, in particular Chester Nimitz, to study options to defeat the Japanese. This exercise bore fruit in perhaps America’s greatest naval victory. The Navy could not be certain of the future, but the evidence they observed allowed them to visualize realistic scenarios which were the basis of planning. Similar to Nimitz, Kuropatkin also foresaw war but unlike America’s great admiral he failed to subject his strategy to productive counter-thesis. While some may decry analyzing alternative historical strategies as smoke and mirrors, mentally exercising options not taken in the past helps develop critical skills applicable to future wars. A great challenge in the historical method is that historians tend to write on the paths taken, not hypothetical alternatives. And since historical information is the intellectual fuel for analyzing war, one must cobble together evidence and use logic to develop plausible alternatives. This methodology is hard for many analysts to internalize. How can one use strategies that did not take place? Fortunately, the Russo-Japanese War provides ample information to study a range of strategic options. Starting with the belligerents’ policy objectives and working through net assessments, strategic options begin to coalesce. In the Russo-Japanese War Kuropatkin possessed the resources to defeat Japan’s military, but he lacked a means to analyze the best strategic course of action.

[i] John W. Steinberg, Bruce W. Menning, David Schimmelpenninck van der Oye, David Wolff, and Shinji Yokote, eds., The Russo-Japanese War in Global Perspective (Boston: Brill Academic Publishers, 2005), 59. [ii] Warner, Denis and Peggy, The Tide at Sunrise, A History of the Russo-Japanese War 1904-05, (London: Frank Cass, 1974), 354. [iii] Ashmead-Bartlett Ellis, “The Times” 04 Jun 1904, Issue Number 37412, 7. https://bit.ly/2Qgjdhk [iv] Ibid, 9-11. [v] Kuropatkin, Alexiev. 1909. The Russian Army and The Japanese War, Being Historical and Critical comments on the Military Policy and Power of Russia and on the Campaign in the Far East. Translated by Captain A.B. Lindsay. Edited by Major E.D. Swinton, vol 1. New York: E.P. Dutton and Company, 1909, 127. Kuropatkin blamed other ministers for building Dalny’s infrastructure which he neglected to defend and/or destroy!

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Planning the Decision Making Process: A Multiple Case Study

•   Willy Sousa    
•   Maria Porto    
•   Maria Marcatonio    
•   Pedro Takenouchi    
•   Abraham Yu    

The decision-making process involves making decisions about the decision process itself. Understanding better about “how to decide” decision makers can improve the quality of their decisions and using less time and resources. A multiple case study was developed to identify factors that may lead a decision-making process to be planned or unplanned. In the three cases studied we observed the planning of the decision-making process, however, with distinct degrees of effort and the time frame of the problem’s occurrence and the decision-making. We identified five main factors that influence the planning of the decision-making process: i) the nature of the problem—whether the problem is new or recurrent to the firm, ii) awareness regarding the problem, the objectives and alternatives, iii) decision maker’s experience, iv) organizational culture regarding risk taking in decision making, v) decision maker’s autonomy level and holistic view of the firm and the conjuncture embedded. By studying the decision planning process of these three cases we believe we could draw attention to a perspective of the decision process seldom studied and open the possibility of new studies involving the decisions about the decision process—the meta-decisions.

• DOI: 10.5539/emr.v4n1p82

• ISSN(Print): 1927-7318
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Title: A decision support framework to evaluate the main factors affecting the selection of sustainable materials in construction projects

Authors : Ebrahim Aghazadeh; Hasan Yildirim

Addresses : Department of Civil Engineering, Istanbul Technical University, 34469, Sarıyer/İstanbul, Turkey ' Department of Civil Engineering, Istanbul Technical University, 34469, Sarıyer/İstanbul, Turkey

Abstract : In the present paper, a decision support framework is proposed to solve the problem of sustainable material selection in the construction industry. The developed framework is configured based on statistical analysis methods and a hybrid fuzzy MCDM method. By combining fuzzy AHP and fuzzy TOPSIS methods, the proposed framework was created and implemented for a case study in a mass-house building project in Iran. The purpose of the decision-making process was to choose a new optimal construction system considering criteria affecting the sustainable materials. The results of the ranking criteria showed that the most significant sub-criteria for the selection of sustainable materials were minimising the environmental impacts (ozone depletion, etc.), life-cycle cost, capability to optimise energy consumption, compatibility with sustainable development regulations (LCA, LEED, etc.), material investment long-term cost, respectively. The results also showed that the LSF, ICF, and 3DP systems have more priorities than others, respectively.

Keywords : decision support framework; affecting factors; sustainable material selection; factor analysis; hybrid fuzzy MCDM methods.

DOI : 10.1504/IJSOM.2024.137992

International Journal of Services and Operations Management, 2024 Vol.47 No.4, pp.449 - 495

Received: 06 Jun 2021 Accepted: 13 Nov 2021 Published online: 16 Apr 2024 *

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Polycentric governance of commons through multi-stakeholder platforms: Insights from two case studies in India

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ElDidi, Hagar; Rawat, Shivanyaa; Meinzen-Dick, Ruth; Chaturvedi, Rahul; and Sanil, Richu. Polycentric governance of commons through multi-stakeholder platforms: insights from two case studies in India. Environment, Development and Sustainability. Article in press. First published online on April 12, 2024. https://doi.org/10.1007/s10668-024-04896-9

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This paper examines the complexities of commons governance, focusing on the role of multistakeholder platforms (MSPs) in addressing tensions among diverse decision-making centers. Drawing on the experiences of the Indian NGO Foundation for Ecological Security (FES) in Gujarat and Odisha, the study analyzes two MSPs operating at the block level, engaging communities, government, and private sector actors. Through surveys, interviews, and analysis of institutional reports, the research identifies key benefits of MSPs, including enhanced multi-stakeholder engagement, scale, and enabling conditions. It argues that MSPs can effectively support polycentric governance by facilitating inter-community collaboration, strengthening local voices, and building trust over time. The study also underscores the importance of external actors like NGOs in supporting community agency and fostering collaboration across different governance levels.

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