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Team Performance Management

ISSN : 1352-7592

Article publication date: 1 May 1999

To date, many of the models and theories that seek to explain problem solving and decision making, have tended to adopt an overly reductionist view of the processes involved. As a consequence, most theories and models have proved unsuitable in providing managers with a practical explanation of the dynamics that underpin problem solving. A substantial part of a manager’s time is taken up with problem solving and decision making issues. The question of whether managers possess the necessary problem solving skills, or have access to “tools”, which can be used to manage different types of problems, has become an issue of some importance for managers and organisations alike. This paper seeks to contribute to the current literature on problem solving and decision making, by presenting a conceptual model of problem solving, which is intended to assist managers in developing a more holistic framework for managing problem solving issues.

  • Strategic management
  • Problem solving
  • Decision making

O’Loughlin, A. and McFadzean, E. (1999), "Toward a holistic theory of strategic problem solving", Team Performance Management , Vol. 5 No. 3, pp. 103-120.

Copyright © 1999, MCB UP Limited

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Adopting a holistic approach to problem-solving in business

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“Price discounting is one of my pet peeves,” says Sharmila Chatterjee. No, the academic head for the Enterprise Management (EM) Track of MIT Sloan School of Management’s MBA program doesn’t have it out for customers looking for a deal. Rather, this is a stitch in a running thread of conversation about bigger-picture thinking honed from years of research.

Chatterjee is a business-to-business (B2B) marketing expert and an award-winning case writer who examines issues in the domains of channels of distribution, sales force management, and relationship marketing. “No one should be engaged in price gouging, of course,” she says, “but get an equitable return on the value delivered through your offerings, such that you can sustain investments for the long-term success of your brand.”

Citing Millward Brown’s finding that brands account for more than 30 percent of the stock market value of companies in the S&P 500 index, The Economist acknowledged that for many companies, their brand is their most valuable asset. Consider United Airlines' 2017 branding crisis that resulted in their stock plummeting $1.4 billion in a matter of days after video footage emerged showing the forced removal of a passenger from an overbooked flight.

Brand, according to Chatterjee, is a function of customer experience. In a 2019 opinion piece for USA Today , Chatterjee outlined the importance of customer experience by examining the case of bricks-and-mortar (B&M) retailers. When e-commerce emerged in the early 1990s, traditional retailers tried to compete by slashing prices. As a result, they were forced to cut costs elsewhere: They whittled down sales teams, neglected investment in training, and ignored merchandising and product development — all to the detriment of the in-person customer experience.

Their misapprehension of the situation resulted in a self-fulfilling prophecy. Customers flocked online for discounts, leaving, among others, big brands like RadioShack, Toys R Us, and Sears to file for bankruptcy. In an unexpected twist, the rise of the digital age exposed the importance of the human interface. Today, savvy retailers recognize the benefits of combining e-commerce and B&M, integrating online and offline. “Technology is a great facilitator and enabler. Problems arise when we turn to technology as a panacea,” warns Chatterjee. “Human capital is critical for successful outcomes in most cases — minimizing this comes at a great cost.”

For further evidence of the need for human-tech complementarity, we need look no further than the high rates of technology implementation failures experienced by businesses today. In 2001, Gartner reported a 50 percent failure rate of customer relationship management (CRM) implementation. Fast-forward to 2021, and those numbers have only increased. How to reconcile this with the fact that CRM spending shows no sign of slowing down, and for many businesses, CRM has become the largest revenue area of spending in enterprise software?

Meanwhile, a recent study conducted by MIT Sloan Management Review with Boston Consulting Group found that technology implementation failure rates of 70 percent or higher are the norm, and only 10 percent of companies report significant returns on their AI investments. “There is obviously enormous potential for technology to deliver value, but the potential is not being realized, and therein lies the role of human capital,” says Chatterjee.

In an effort to understand the high rate of technology implementation failures, particularly in the B2B context, Chatterjee conducted an empirical analysis of buying organizations that purchased and implemented business intelligence software. She found that the most significant predictors of both successful technology assimilation and overall customer satisfaction were related to a set of intangibles that the buyer assigned to the software seller; the “soft” service facet, reflecting a holistic understanding of the customer’s business context, value creation potential, and pathway to value.

She calls this the “value mindset.” Once a business has a competitive product, they need to be able to communicate how to best extract the potential value from the technology. This means, among other things, sharing with the buyer the change management needed in the legacy processes as well as best business practices and lessons learned from failure. Sellers that communicated “value mindset,” according to Chatterjee, experienced significantly higher rates of customer satisfaction — to the extent of a three-to-one effect. “Again, this demonstrates in spades the incredible value of the human interface — value not communicated is value not delivered. Again, technology and humans as complements,” she explains.

Chatterjee's research began with a fascination with information silos fostered during her time as a graduate student at the Wharton School, where she first became aware of a statistic that found a mere 30 percent of marketing leads were pursued by sales teams. This revelation regarding the gap in the sales-marketing interface was something Chatterjee channeled into years of research in the domain of sales lead management and customer retention. Focused on this disconnect between human behavior and the rhythms of capitalism, she would go on to conduct some of the first studies in the critical area of the sales-marketing interface, specifically sales lead management, including a study she and her colleagues titled " The Sales Lead Black Hole: On Sales Rep’s Follow-up of Marketing Leads ."

A self-proclaimed empiricist in her research, Chatterjee employs surveys coupled with econometric methods for analysis that tests models in the real world. This focus on real-world business challenges extends to her administrative role leading the EM Track at MIT. All MBA programs deliver content, whether it be in finance, marketing, operations, or leadership. But at the Institute, Chatterjee emphasizes the importance of mindset. From the outset, students in the EM Track are encouraged to think like CEOs while on a pathway to future leadership. In practice, this means training students to move away from siloed thinking while adopting a holistic, cross-functional approach to problem-solving in order to transform organizations.

The best way to instill this leadership mindset is for students to work on real-life business projects and challenges with companies while applying theoretical concepts and frameworks learned in the classroom. In their first year, students are thrown into the deep end. Through the Enterprise Management Lab (EM-Lab), EM Track students are provided the opportunity to work hands-on with large enterprises to address business challenges. "My students' work on real business challenges embodies the MIT motto “mens et manus,” mind and hand,” she says.

Companies like Procter & Gamble/Gillette, Amazon, BMW, and IBM have all participated in EM-Lab projects. “Multinationals come back to us year after year because we deliver useful findings that they can implement in their companies,” says Chatterjee. At the same time, MIT students benefit from the real-world experience, refining their research skills while developing the mindset of future business leaders. “It’s a very symbiotic relationship, a partnership that benefits all involved,” Chatterjee says.

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To date, many of the models and theories that seek to explain problem solving and decision making, have tended to adopt an overly reductionist view of the processes involved. As a consequence, most theories and models have proved unsuitable in providing managers with a practical explanation of the dynamics that underpin problem solving. A substantial part of a manager’s time is taken up with problem solving and decision making issues. The question of whether managers possess the necessary problem solving skills, or have access to “tools”, which can be used to manage different types of problems, has become an issue of some importance for managers and organisations alike. This paper seeks to contribute to the current literature on problem solving and decision making, by presenting a conceptual model of problem solving, which is intended to assist managers in developing a more holistic framework for managing problem solving issues.

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  • Team Performance Management /
  • Volume 5 Issue 3
  • Subject Areas /
  • Business, Management and Accounting

To date, many of the models and theories that seek to explain problem solving and decision making, have tended to adopt an overly reductionist view of the processes involved. As a consequence, most theories and models have proved unsuitable in providing managers with a practical explanation of the dynamics that underpin problem solving. A substantial part of a manager’s time is taken up with problem solving and decision making issues. The question of whether managers possess the necessary problem solving skills, or have access to “tools”, which can be used to manage different types of problems, has become an issue of some importance for managers and organisations alike. This paper seeks to contribute to the current literature on problem solving and decision making, by presenting a conceptual model of problem solving, which is intended to assist managers in developing a more holistic framework for managing problem solving issues.

Team Performance Management – Emerald Publishing

Published: May 1, 1999

Keywords: Strategic management; Problem solving; Decision making; Paradigms; Models

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A More Holistic Approach to Problem Solving

  • B V Krishnamurthy

When you’re stuck on a problem, it often helps to step back and look at the bigger picture. You see things differently and discover new solutions. What I ask participants in my Total Leadership program to do is take the “four-way view” – the interaction among work, home, community, and self – and come up […]

When you’re stuck on a problem, it often helps to step back and look at the bigger picture. You see things differently and discover new solutions. What I ask participants in my Total Leadership program to do is take the “four-way view” – the interaction among work, home, community, and self – and come up with creative ways of bringing them together into a more coherent whole. I’ve found that when you do this, you see opportunities for change to which you were previously blind. The happy result: improved performance and satisfaction all the way around.

holistic theory of strategic problem solving

  • B V Krishnamurthy is the Director and Executive Vice-President of Alliance Business Academy in Bangalore, India, where he is also the ASI Distinguished Professor of Strategy and International Business.

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MIT Executive MBA

Learning a holistic approach for effective strategy

Ana Escandon, EMBA '19

Aug 12, 2020

Ana Escandon '19

Strategy is important for all companies, yet a common pitfall is not looking at the organization holistically. Often, strategy is biased, taking a narrow view of the company. As a result, the strategy might solve a few problems but may not be transformational at all.

A simple analogy for strategy is picking apples. You pick up the fruit and quickly look at it; you might find a few spots and still decide to buy it, thinking it appears to be healthy. I learned through MIT’s Global Organizations Lab (GO-Lab) that by taking a holistic approach, you can observe and extract additional information that can make strategic decisions, or picking the right apples, more effective.  By carefully viewing all sides of the fruit and considering its condition below the surface, you may discover that a simple spot is an indication that the apple is not so healthy after all.

GO-Lab is an international, integrative project of the Executive MBA in which teams are tasked to solve a strategic challenge for a real company. I had the fortune to work alongside an incredible team and a highly invested client. The challenge was to provide strategic guidance to a global organization on whether or not to enter a new business somewhat far from its current capabilities. As I reflected on this experience, it became clear that these three learnings are absolutely critical when it comes to holistic and effective strategy.

Effective strategy is multidisciplinary

When presented with a problem statement, the first step is to “go see and assess” to determine if the stated problem is in fact the real problem. The goal is to identify the actual problem and better understand its cause. Just like the examination of the apple, this means looking at the problem statement from different perspectives.

In the GO-Lab project, this came naturally for our team because we all came from diverse backgrounds, from strategy and R&D to finance and pharma. We asked our client all sorts of questions and requested information from different departments. As a result, we talked with people across the entire organization, gathering data and perceptions from detractors, as well as regions and departments that weren’t obvious internal stakeholders. We also talked to external actors in the client’s ecosystem who could contribute other viewpoints of the organization. We included researchers, universities, and even other companies related to the industry in our interviews.

While it took some time, these discussions gave us a well-rounded idea of the company and its position in the marketplace. Moreover, it revealed that the original problem statement didn’t represent the real problem at stake. Getting to this point was crucial in delivering good results for the project and the client.

Effective strategy is honest

Acknowledging the organization’s capabilities is key. Several times during the project, my teammates would say, “the farther you are from headquarters, the more real it gets.” People at the front line are the ones who experience the true ability of the organization’s capabilities. By detaching from corporate beliefs, you can make more assertive strategic decisions.

Throughout our project, we encountered key people at the company’s headquarters who defined the organization based on past experiences and successes. As a result, the company thought it could enter the new market on its own. From our visits to the company’s sites and talking to frontline employees, we found many gaps in the company’s common knowledge and the new business’ benchmark. When we brought back this information to the client, they were able to be honest with themselves and admit to where they really were in the market. In the end, this honesty enabled them to select a much better strategy to enter the space.

Effective strategy is political

As noted above, problem statements aren’t always accurate. A political lens is helpful to understand the underlying interests of different groups surrounding the problem. Understanding this can help to reduce obstacles in generating commitment towards strategic goals. If you can’t align interests, the organization won’t move towards the desired direction.

As we dug in deeper through the project, we realized that the customer had fundamental interests that weren’t explicit in the problem statement. We had to tap into our political savviness to understand these interests and how they conflicted with the views of other internal stakeholders. Realizing this helped us to frame our proposal in ways that would be more practical for the client to sell their strategy internally.

Applying a holistic approach

The client moved forward with the project, the team graduated, and these learnings lived on for a new purpose. Today, I’m in charge of strategy and business development in my organization and use these GO-Lab learnings to help drive a transformational, strategic agenda for the company.

I follow this holistic approach to include a diversity of people in discussions, learning from their perspectives on the company’s position and understanding conflicting interests in order to move forward. By doing this, I have been able to reveal misconceptions about our capabilities and opportunities, which helped me to take the lead in redesigning our growth strategy for the next five years.

Thanks to this experience, I have changed as a leader and a driver for change. Without coming to MIT, I would never have imagined that something as simple as “picking apples” could deliver transformational results if done well.

Ana Escandon, EMBA '19, is Head of Sales at Citrofrut in Monterrey, Mexico.

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... At that time these ideas will be re-assessed and may be utilized or discarded depending on whether they are in-line with the organizational strategy (O’Loughlin and McFadzean, 1999). ...

... The innovation output is monitored and the information obtained from this monitoring is fed-back (O’Loughlin and McFadzean, 1999), and placed in a repository providing inspiration for subsequent Entrepreneurship and innovation 401 ideas (O’Loughlin, 2001). ...

... The innovation output is monitored and the information obtained from this monitoring is fed-back (O’Loughlin and McFadzean, 1999), and placed in a repository providing inspiration for subsequent Entrepreneurship and innovation ...

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Intuition versus analysis: Strategy and experience in complex everyday problem solving

  • Published: April 2008
  • Volume 36 , pages 554–566, ( 2008 )

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  • Jean E. Pretz 1  

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Research on dual processes in cognition has shown that explicit, analytical thought is more powerful and less vulnerable to heuristics and biases than is implicit, intuitive thought. However, several studies have shown that holistic, intuitive processes can outperform analysis, documenting the disruptive effects of hypothesis testing, think-aloud protocols, and analytical judgments. To examine the effects of intuitive versus analytical strategy and level of experience on problem solving, 1st- through 4th-year undergraduates solved problems dealing with college life. The results of two studies showed that the appropriateness of strategy depends on the problem solver’s level of experience. Analysis was found to be an appropriate strategy for more experienced individuals, whereas novices scored best when they took a holistic, intuitive perspective. Similar effects of strategy were found when strategy instruction was manipulated and when participants were compared on the basis of strategy preference. The implications for research on problem solving, expertise, and dual-process models are discussed.

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Study 1 was part of a doctoral dissertation submitted to Yale University. Support for this project was provided by a Yale University Dissertation Fellowship, an APA COGDOP award, and an APA dissertation award.

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Pretz, J.E. Intuition versus analysis: Strategy and experience in complex everyday problem solving. Memory & Cognition 36 , 554–566 (2008).

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The first chapter, “An overview of strategic thinking in complex problem solving,” gives a general description of the book and introduces the case study that is used in each chapter to exemplify how the tools apply to a practical case. The case study requires no specialized knowledge: a friend’s dog disappears on the very day that the friend dismissed his temperamental housekeeper; did the dog escape or was he kidnapped? The chapter also introduces five key concepts that come at various points along the resolution process: using alternatively divergent and convergent thinking, using issue maps to identify all possible answers to a question exactly once (by using a mutually exclusive and collectively exhaustive (MECE) structure), acquiring the right skills, simplifying to reveal the underlying structure of a problem, and not fooling oneself and others.

On a Wednesday afternoon, your cell phone rings. It’s your friend John, and he is frantic: “My dog, Harry, is gone! I came home a few minutes ago and Harry’s not here. I left my house at noon, and when I came back, around four, he was missing. Our house has a backyard with a doggy door in between. This is really strange, because he hasn’t escaped in months—ever since we fixed the gate, he can’t. I think the housekeeper is holding him hostage. I fired her this morning for poor performance. She blamed Harry, saying he sheds too much. She was really upset and threatened to get back at us. He has no collar; how are we going to find him? Also, the yard crew came today to mow the lawn. Anyway, you’re the master problem solver. Help me find him!”

You and I solve countless problems every day, sometimes even without being aware of it. Harry is a real dog, whose disappearance provided me with an opportunity to describe some tools that are universally applicable through a concrete (and true!) case. This book will help you acquire techniques to become better at solving complex problems that you encounter in your personal and professional life, regardless of your occupation, level of education, age, or expertise.

In some cases, these ideas will not apply as well to your own situation, or you may feel that an alternative is better. For instance, one limitation of this technique is that it is time consuming, so it is ill-suited to Grint’s critical problems that require decision-making under tight deadlines. 1 If that’s the case, you may want to cut some corners (more in Chapter 9 ) or use a different route. This is perfectly fine, because this approach is meant to be a modular system of thinking, one that you can adapt to your needs.

This book shows how to structure your problem-solving process using a four-step approach: framing the problem (the what ), diagnosing it (the why ), finding solutions (the how ), and implementing the solution (the do ) (see Figure 1.1 ).

We use a four-step approach to solving problems.

First, identify the problem you should solve (the what ).

Facing a new, unfamiliar situation, we should first understand what the real problem is. This is a deceptively difficult task: We often think we have a good idea of what we need to do and quickly begin to look for solutions only to realize later on that we are solving the wrong problem, perhaps a peripheral one or just a symptom of the main problem. Chapter 2 shows how to avoid this trap by using a rigorous structuring process to identify various problem statements, compare them, and record our decision.

Second, identify why you are having this problem (the why ).

Knowing what the problem is, move to identify its causes. Chapter 3 explains how to identify the diagnostic key question —the one question, formulated with a why root, that encompasses all the other relevant diagnostic questions. I then show how to frame that question, and how to capture the problem in a diagnostic definition card that will guide subsequent efforts.

Next, we will do a root-cause analysis: In Chapter 4 , we will diagnose the problem by first identifying all the possible reasons why we have the problem before focusing on the important one(s). To do that, we will build a diagnosis issue map : a graphical breakdown of the problem that breaks it down into its various dimensions and lays out all the possible causes exactly once. Finally, we will associate concrete hypotheses with specific parts of the map, test these hypotheses, and capture our conclusions.

Third, identify alternative ways to solve the problem (the how ).

Knowing what the problem is and why we have it, we move on to what people commonly think of when talking about problem solving: that is, actively looking for solutions. In Chapter 5 , we will start by formulating a solution key question , this one formulated with a how root, and framing it. Next, we will construct a solution issue map and, mirroring the processes of Chapters 3 and 4 , we will formulate hypotheses for specific branches of the map and test these hypotheses. This will take us to the decision-making stage: selecting the best solutions out of all the possible ones (Chapter 6 ).

Fourth, implement the solution (the do ).

Finally, we will implement the solution, which starts with convincing key stakeholders that our conclusions are right, so Chapter 7 provides guidelines to craft and deliver a compelling message. Then, we will discuss implementation considerations and, in particular, effectively leading teams (Chapter 8 ).

What, Why, How, Do. That’s our process in four words.

In conclusion, Chapter 9 has some ideas for dealing with complications and offers some reflections on the overall approach.

Note that the book’s primary objective is to provide a way to go through the entire problem-solving process, so it presents one tool to achieve each task and discusses that one tool in depth, rather than presenting several alternatives in less detail. 2 Most of these tools and ideas are not mine; they come from numerous academic disciplines and practitioners that provide the conceptual underpinnings for my approach. I have referenced this material as consistently as I could so that the interested reader can review its theoretical and empirical bases. A few ideas are from my own observations, gathered over 15 years of researching these concepts, applying them in managerial settings, and teaching them to students, professionals, and executives.

1. Finding Harry

Let’s pretend that we just received John’s phone call. Many of us would rush into action relying on instinct. This can prove ineffective, however; for example, if the housekeeper is indeed holding Harry hostage, as John thinks, there is little value in searching the neighborhood. Similarly, if Harry has escaped, calling the police to tell them that the housekeeper is keeping him hostage will not help.

So finding Harry starts with understanding the problem and summarizing it in a project definition card, or what card, as Figure 1.2 shows. This is the what part of the process. You may decide that your project is finding Harry, which you want to do in a reasonable time frame, perhaps 72 hours, and that to do so, you first need to understand why he is missing.

A project definition card—or what card—is useful to capture your plan in writing: what you propose to do by when.

Next, you will want to diagnose the problem. This is the why part of the process. Having identified a diagnostic key question—Why is Harry the dog missing?—you can look for all the possible explanations and organize them in a diagnostic issue map, as in Figure 1.3 .

A diagnostic issue map helps identify and organize all the possible root causes of a problem.

When I present this case to students, someone usually dismisses the possibility of Harry being held hostage as ridiculous. This is not as far fetched, however, as it might look: Statistics show that there is such a thing as dognapping, as it is called, and it is actually on the rise. 3 Others also question that someone would hold a dog hostage, but here, too, there is a precedent: In 1934, Harvard students dognapped Yale’s bulldog mascot—Handsome Dan—and held him hostage on the eve of a Yale–Harvard football game. 4

From here, you can formulate formal hypotheses, identify the evidence that you need to obtain to test them, conduct the analysis, and determine the root cause(s) of Harry’s disappearance.

Knowing why Harry is missing, we can now identify alternative ways to get him back. This is the how part of the process. The procedure mirrors our diagnostic approach: We develop a solution definition card, draw an issue map (this time, a solution issue map), formulate hypotheses, identify and gather the evidence necessary to test the hypotheses, and draw conclusions.

This leads us to identify a number of possible ways to look for Harry. Because our resources are limited, we cannot implement all these solutions simultaneously; therefore, we must discard some or, at least, decide in which order we should implement them. To do so, we use a decision tool that considers the various attributes that we want to take into account in our decision and assign each of them a weight. Then, we evaluate the performance of each possible solution with respect to each attribute to develop a ranking, as Table 1.1 shows.

Now that we have identified how we will search for Harry, the strategizing part is over, and it is time to implement our plan. The do part of the process starts by convincing the key decision makers and other stakeholders that we have come to the right conclusions. We then move on to agreeing on who needs to do what by when and then actually doing it. The implementation also includes monitoring the effectiveness of our approach and correcting it as needed.

The case is a real story—although I changed Harry’s name, to protect his privacy—and we did find him after a few hours. This problem is relatively simple and time-constrained; therefore, it does not need the depth of analysis to which we are taking it. It provides a roadmap, however, for solving complex, ill-defined, and nonimmediate problems (CIDNI, pronounced “seed-nee”). As such, we will come back to Harry in each chapter to illustrate how the concepts apply in a concrete example.

2. Solving Complex, Ill-defined, and Nonimmediate Problems

A problem can be defined as a difference between a current state and a goal state. 5   Problem solving , the resolution of such a difference, is omnipresent in our lives in diverse forms, from executing simple tasks—say, choosing what socks to wear on a given day—to tackling complex, long-term projects, such as curing cancer. This book is about solving the latter: the complex, ill-defined, and nonimmediate problems.

Complex means that the problem’s current and goal states, along with obstacles encountered along the way, are diverse, dynamic during their resolution, interdependent, and/or not transparent. 6   Ill-defined problems have unclear initial and final conditions and paths to the solution. 7 They usually do not have one “right” solution; 8 in fact, they may not have any solution at all. 9 They usually are one of a kind. 10 Finally, nonimmediate means that the solver has some time, at least a few days or weeks, to identify and implement a solution. At the organizational level, a CIDNI problem for a company may be to develop its marketing strategy. On a global scale, CIDNI problems include ensuring environmental sustainability, reducing extreme poverty and hunger, achieving universal primary education, and all the other United Nations’ Millennium Development Goals. 11

A fundamental characteristic of CIDNI problems is that, because they are ill-defined, their solutions are at least partly subjective. Indeed, appropriate solutions depend on your knowledge and values, and what may be the best solution for you may not be for someone else. 12 Another implication is that the problem-solving process is only roughly linear. Despite our best efforts to define the problem at the onset of the project, new information surfacing during the resolution may prompt us to modify that definition later on. In fact, such regression to a previous step may happen at any point along the resolution process. 13

Think about what makes your problem CIDNI.

Problems can be challenging for various reasons, and understanding these may help you choose a direction in which to look for a solution. Some problems are complex because they are computationally intensive. A chess player, for instance, cannot think of all alternatives—and all the opponent’s replies—until late in the game, when the universe of possibilities is much reduced. Chess, however, is a fairly well-defined environment.

Contrast this with opening a hotel in a small village in the Caribbean and discovering that obtaining a license will require bribing local officials. The challenge here is not computational, but the problem is ill-defined in important ways: Do you still want to carry out the project if bribery is a requirement? If you want to avoid bribing officials, how can you do so successfully? And so on.

Indeed, ill definition stems in many ways when human interactions are part of the picture. Consider the case of a graduate student ready to defend her dissertation only to discover that two key members of her jury have just had a bitter argument and cannot sit in the same room for more than five minutes without fighting. How should she proceed?

Or consider the case, during World War II, of the British Navy capturing an Enigma cryptography machine, which gave them deep insight into the operation of German submarines. This gave them a unique opportunity to reduce the risk of attacks to their convoys. However, they could not use this information in any way that would tip off the Germans that their naval codes had been broken; indeed, the Germans would then change the Enigma codes or introduce a new communication system. How then should the British best use this information? 14

So, rather than thinking of CIDNI problems as one type of difficult situation, you may be better served to think about what makes your problem a CIDNI problem, given that doing so may indicate where you can search out solutions. If a problem is computationally complex, for example, exploring the support that computers and artificial intelligence can bring could be of great support. In a situation that has significant moral, emotional, or psychological components, however, such support is not likely to be of much help.

3. Complementing Specialization with Generalist Skills

It’s not so much that STEM [science, technology, engineering, mathematics] graduates do not know how to solve technical problems, because, in fact, they do, but that these graduates lack the non-technical skills needed for the job. That’s one of the points that Meghan Groome, the executive director of education and public programs at the New York Academy of Sciences, emphasized […]. “The problem is universal,” Groome explained. “Students are not learning how to network, manage their time, or to work together.” These skills, Groome insisted, are those that students can learn if they take the right courses. 15

There is widespread agreement that an ideal CIDNI problem solver (or problem-solving team) is “T-shaped,” that is, both a specialist in the relevant disciplines and a generalist. 16

Formal training programs usually focus on the discipline-specific side, the vertical bar of the “T,” but they fall short on the generalist front, 17 which is problematical. For instance, a report by the National Academies notes that, because real-world problems are ill defined and knowledge intensive, they often differ considerably from the ones students solve in class. 18 This leads to some students’ inability to translate what they learn on campus to practical situations, 19 what physics Nobel Prize laureate Richard Feynman called a “fragility of knowledge.” 20

Another drawback of focusing solely on the vertical bar of the T is that it limits innovation as we fall prey to the “not invented here” syndrome. Yet, there is considerable value in “stealing” ideas from other disciplines. For instance, consider the use of checklists that first appeared in airplane cockpits and are now being increasingly used in operating rooms. Despite strong initial resistance by surgeons, their adoption has led to significant reductions in postsurgical complications. 21 Similarly, medical practices also are adopted by other disciplines: The rise in the 1990s of evidence-based medicine—the reliance on evidence from well-designed and conducted research to guide decision making—has helped initiate a practice of evidence-based management in the last decade. 22 In both these cases, an ability to see value in a field different than one’s own was needed and paid off. Developing an ability to see past the surface features of problems to concentrate on the underlying structure, and recognizing that this may be achieved by looking at problems in other disciplines is, therefore, beneficial. As we will see in the ensuing chapters, it is also a requirement for good analogical thinking. 23

In short, Strategic Thinking in Complex Problem Solving offers ways to develop that horizontal, strategic, cross-disciplinary knowledge necessary to be an effective CIDNI problem solver (see Figure 1.4 ).

Effective CIDNI problem solvers are both generalists and specialists; this book helps improve generalist skills.

This approach enables you to tackle any problem, even ones in which you are not a specialist, in a structured and creative way. And in today’s economy, where organizations are constantly reinventing themselves, this skill makes you a very desirable asset. 24

4. Five Key Guidelines that Support Our Approach

Before we look in detail at the four steps of the problem-solving process, let’s conclude this overview by presenting five key principles that each apply to various steps.

1. Use divergent and convergent thinking

Effective problem solving requires both divergent and convergent thinking patterns. 25 As Figure 1.5 shows, this occurs at each step of the process. Diverging, you think creatively: stretching your mind to identify new possibilities. Converging, you think critically: gathering data to analyze each possibility, compare it with others, and select the best. Whenever possible, you should defer judgment, that is, you should keep idea creation (or ideation 26 ) separate from idea evaluation . 27 This is to avoid restricting your creativity. 28 We will address this again in Chapters 3 and 5 .

Effective complex problem solving requires alternating divergent and convergent thinking.

2. Use issue maps

A central tool in our methodology is the issue map, a graphical breakdown of a question that shows its various dimensions vertically and progresses into more detail horizontally. There are many types of cartographic representations of problems, including trees, diagrams, and maps. One attribute they share is that they expose the structure of the problem, thereby promoting better understanding. Graphical breakdowns of arguments, for example, have been shown to significantly improve people’s critical thinking. 29 We will discuss maps extensively in Chapters 3 and 5 .

Figure 1.6 shows a typical issue map. It starts with a key question on the left, in this case a solution key question, with a how root. It then lists and organizes solutions on the right. These solutions do not have to be desirable but, applying the principle of deferred judgment of the previous section, we refrain from evaluating them until later in the process.

Issue maps graphically expose the structure of a question.

Maps enable us to consider all possibilities exactly once: we do not consider a possibility more than once, and we do not leave out any. That is, maps structure the universe of answers in a set of mutually exclusive and collectively exhaustive branches (or MECE , pronounced “me-see”).

Mutually exclusive (ME) means “no overlaps.” Two events are mutually exclusive when the occurrence of one precludes the occurrence of the other. Organizing the answers to a question in mutually exclusive branches means that you consider each one only once, thereby not duplicating efforts. To think ME, you must think in a convergent pattern, determining whether branches are truly distinct.

So if you set yourself to answer the question, “How can I go from New York City to London?” and you reply by first dividing means of transportation between “flying” and “traveling by sea,” you are organizing the possible solutions of your problem in a ME way, because you cannot be flying and traveling by sea at the same time.

Collectively exhaustive (CE) means “no gaps.” Events are collectively exhaustive when they include all possible outcomes. So the branches of an issue map are CE when they include all the possible answers to the key question. To think CE you must think divergently, asking yourself repeatedly, “ What else could be an answer to this question?” So you must be very creative; Chapters 3 and 5 will give you ideas to do that, such as relying on analogies or existing frameworks.

When you are identifying options to go from NYC to London, CE thinking means that you are considering all possibilities. Although we initially thought that traveling by sea or air were the only possibilities, forcing ourselves to be CE results in an expanded list, as shown in Figure 1.7 . The possibility of traveling by sea or air occurs quickly to people thinking about this situation, so let’s stick these options into a branch that we call “conventional.” Then, to be CE, we should have a “nonconventional” branch. What could this include? Well, people also travel by land. What else? Perhaps teletransport. What else? Well, maybe I should not travel to London; instead, London should travel to me. And we could go into further details there: perhaps we could have the people I was going to meet in London come to me or maybe we could create a London where I am. That sounds far-fetched. True. But, first, abiding by the principle of deferred judgment, we should not care whether it is far-fetched—not until later. And second, even if it is far-fetched, there are precedents: Las Vegas has done it with the Eiffel Tower, so why not us? Again, these new options may not be desirable. What is important is that, if we end up discarding them, we will do so because of a conscious decision, not because we forgot to consider them. We will talk more about MECE thinking in Chapters 3 and 5 .

Part of the process is to think divergently to identify as many solutions as possible so as to leave no gaps.

3. Acquire the right skills

In 2001, the United Kingdom’s Research Councils and the Arts and Humanities Research board released a joint statement highlighting the skills that doctoral students are expected to develop during their research training. 30 Table 1.2 summarizes some of these skills. These are relevant to you even if you are not working on a doctorate. Indeed, solving problems requires doing research: identifying which evidence you need to gather and assessing it. We will talk about working with evidence in Chapters 4 and 6 .

After Research Councils, United Kingdom. (2001).

This book provides pathways to develop many of these skills. You may find value using this list as a roadmap for your own development. 31 Alternatively, you may elaborate your own list. But you may also face a problem before you get a chance to develop the skills; when that happens, and you should probably assume that it will, you should consider teaming up with people who have complementary skills.

Enlist others.

Working with others may increase quality and visibility. It used to be that the works of lone geniuses were the most impactful, but this might be changing. Collaborative work has resulted in many contributions, including the discovery of DNA, the creation of the Linux operating system, and the development of the Internet. 32 Also, scientific papers with multiple authors are cited more than twice as frequently as those by single authors. 33

Leverage diversity.

When I teach this method in a course, it is a practical workshop. Each student brings a project that he or she is interested in and we use these as case studies. Students come from all disciplines, but they must help others and seek help from others (a large chunk of their grade depends on it) and they need to sit next to a different colleague in each session. Although this collaboration across disciplines does not come naturally to many, they quickly see its value: People with different training bring different perspectives, which helps each of them be more creative. This is in line with observations from a committee of the National Research Council: “Analysis improves when analysts with diverse perspectives and complementary expertise collaborate to work on intelligence problems.” 34 We will talk extensively about the value of collaboration and diversity throughout the book.

4. Simplify to reveal the underlying structure

Simplicity is central to numerous practices in many fields. In the scientific method, the parsimony principle recommends that, all other things being equal, the simplest theory that fits the facts should be preferred. 35 Copernicus used it to propose his model of motion of the earth (the heliocentric one, i.e., a daily revolution around its axis and an annual revolution around the sun) over the then-favored Ptolemaic one. Copernicus’s model did not generate a better fit, but it was simpler. 36

In design, simplicity is often linked to quality and usability. 37 At Apple, Steve Jobs viewed it as the ultimate sophistication, which resulted in many Apple products not having the features of their competitors’ and yet outselling them. 38

Though the end product may be simple, the process to get there usually is not. Here is Steve Jobs again: “When you start looking at a problem and it seems really simple with all these simple solutions, you do not really understand the complexity of the problem. And your solutions are way too oversimplified. Then you get into the problem, and you see it’s really complicated. And you come up with all these convoluted solutions … that’s where most people stop, and the solutions tend to work for a while. But the really great person will keep on going and find … the key, underlying principle of the problem. And come up with a beautiful elegant solution that works.” 39

I have seen this happen multiple times. In my course, my students must reformulate their problem to make it understandable to the rest of us. This is difficult for some of them, particularly those versed in highly technical subjects, and some invariably claim that expressing their problem in simple, accessible terms is not possible. They all, however, eventually discover that it is. Moving beyond the surface features of their disciplines, they learn to focus on their problem’s underlying structure, and by expressing it in simple terms, they enable others to assist them in solving it.

This challenge of simplification is worthy not just because they now have a larger and more diverse network of people to help them, but because it also forces them to clarify their understanding of their problem: having to do away with the jargon of their field, they can no longer present their problem in the terms that they have heard it expressed by specialists. They now have to answer “dumb” questions that they have been trained not to ask, which forces them to understand why (or why not!) these questions are dumb. Moving beyond surface characteristics to focus on the structure of problems is also an essential component of successful analogies, 40 so by going through this process, students learn to see similarities among disciplines.

Transcend “that’s interesting”: understand the “so what?” Gathering lots of data about a problem is not necessarily helpful; in fact, it can be counterproductive (see Table 1.3 ). So finding that something is interesting should not be an end point but, rather, a starting point to dig deeper. Analyze your thinking: If you find something interesting, why is it so? What is the “so what?” of your finding? Keep on assaulting your problem with critical thinking until you reach simplicity. We will talk more about this in Chapters 3 , 4 , and 5 .

The table is adapted from ( Makridakis & Gaba, 1998 ) and ( Arkes & Kajdasz, 2011 ) [pp. 143–168]. For an example of how more information can result in worse outcomes in a medical setting, see ( Welch, 2015 ) [pp. 84–95].

5. Do not fool yourself (and others)

In his address to the graduating class of 1974 at Caltech, Richard Feynman urged students to “not fool yourself—and you are the easiest person to fool.” 41 This is in line with findings on biases: humans are biased in many ways, often without realizing it. For instance, we have a high propensity to be overconfident; 42 to think that, had we been asked, we would have predicted an event’s occurrence in advance (hindsight bias); 43 or to interpret information partially (confirmation bias). 44

Table 1.3 summarizes some common ways in which we fool ourselves, compares those to empirical findings, and proposes remedies.

Adopt an evidence-based approach.

In medicine, the belief that physicians’ actions should be guided by evidence dates back at least 200 years. 45 And yet, many destructive practices remain in use; in some settings, over 30% of patients are estimated to receive care that is not consistent with scientific evidence. 46

The modern evidence-based medicine movement advocates for integrating the best external evidence available with one’s expertise and the specifics of one’s situation. 47 Started in the early 1990s, it has garnered considerable attention and is credited for dramatically speeding up the process of finding effective treatments instead of relying on intuition and personal experience. 48

Some disciplines, such as management, are now trying to emulate it, 49 while others, including the intelligence community, have been strongly advised to follow the trend. 50 This book argues that you should adopt an evidence-based approach to problem solving and we will talk about how to do this across chapters.

Confidence-wise, brace yourself.

Steve Jobs’s earlier quote illustrates how, when we approach new problems, we sometimes feel that we instantaneously understand them and know how to solve them. This is, in part, because we bring our own preconceptions. The four-step process described in this book aims at replacing these preconceptions and the unwarranted confidence they generate with warranted confidence. Although we hope that, at the end of it, you are rightfully confident in your views, getting there will probably be tumultuous.

Going through a rigorous evidence-based analysis of your preconceived ideas, you may soon feel that you become unsure of what you know and do not know, and your overall confidence will plunge before it rises. It is important to be able to welcome these doubts, because they are an integral part of Socratic wisdom, that is, of “knowing what you know and knowing what you do not know.” 51

Replacing unwarranted confidence requires you to take the risk of reducing your confidence, at least briefly. Although this may sound demoralizing, see it as progress: You may not yet know what the right paradigm is, but at least you now know that the one you trusted was wrong.

Following this approach, this book advocates that you base your practices on sound logic and solid evidence, synthesizing reliable external information with your own expertise, and integrating that approach with the judicious use of intuition. The book presents tools to help you do so.

Respect the scientific ideal.

Cambridge’s fluid dynamist Michael McIntyre defines respecting the scientific ideal as attempting to keep an open mind while deploying logical thinking, putting up with nagging uncertainty, being willing to admit ignorance, avoiding prior judgments about candidate hypotheses, and remaining skeptical about any reason to favor a theory other than the cautious application of Occam’s razor (see Chapter 4 ). It also includes revising one’s position when new evidence appears and taking a look from various viewpoints. An illustration of respecting the scientific ideal is being the skeptical juror in the movie “Twelve Angry Men,” the one who insists on having one last look at the evidence in a murder trial when the other eleven already think that they know the truth. 52 These characteristics and a few more are all central to the approach described in this book.

5. Summary: CIDNI problem solving in a nutshell

Our approach to solving complex, ill-defined, and nonimmediate problems allows us to go from where we are to where we want to be, namely, to solve problems with a four-step process (What, Why, How, Do) that rests upon five key principles (see Figure 1.8 ).

Five key principles support our approach to problem solving.

We can visualize these key principles as a bridge with three pillars: using convergent and divergent thinking, using maps, and acquiring the right skills. In turn, these three pillars rely on two layers of foundation: simplifying and not fooling yourself.

Do not over-design your resolution process.

Before we jump into the heart of the matter, I would like to stress one last point: the methodology described in the book assumes that you have the time and resources to conduct an in-depth analysis of all stages and that it is beneficial to do so. If this is not the case—for whatever reason, maybe because you do not have enough time to conduct a full-blown analysis or maybe because you already have trustworthy answers for, say, the diagnostic—you should cut some corners. We will discuss this further in Chapter 9 , but you should keep this in mind as you walk your way through the resolution process.

So, if investing effort in a specific part of the resolution process seems inappropriate for your specific problem, first question this feeling, because it is easy to bypass, say, thoughtful problem framing even in situations where it is precisely what you should do. But, if after careful consideration, you think that you should fast-forward over some steps, then do so.

Having laid out a general description of our problem-solving process and an overview of each chapter, we can now move to a more detailed analysis. This starts with Chapter 2 giving some guidelines for framing the problem.

Steps in solving problems. Our approach has four steps, but this is not universal. For instance, Basadur presents a three-step process (problem finding, problem solving, solution implementation). 53 The difference here is that we have broken the problem-finding stage into two, to separate the what from the why , in an effort to bring light to the importance of these stages. Other approaches exist: Woods identified 150 published strategies used in numerous disciplines. 54

Treating symptoms. Peter Senge calls treating symptoms, rather than the problem itself, “shifting the burden.” This may result in having the problem recur. 55

The two dimensions of the T. Being a specialist requires domain-specific or local knowledge and skills. Being a generalist relies on knowledge and skills that are transferrable across disciplines, that is, domain independent.

From T to π. The T-shaped metaphor can extend to π-shaped or even comb-shaped skill sets where individuals have a breadth of knowledge and expertise in more than one field. 56

Improve your “foxiness.” Related to the specialist/generalist differentiation is that of hedgehogs versus foxes, a dichotomy invented by philosopher Isaiah Berlin. 57 Hedgehogs are specialized, stubborn, order-seeking, and confident. Foxes are multidisciplinary, self-critical, and cautious; they accept ambiguity and contradiction as an inherent part of life. Having compared the two groups, political scientist Philip Tetlock observes that foxes are better forecasters than hedgehogs. 58

Strategic thinking in complex problem solving. We define strategic thinking in complex problem solving, loosely following Beaufre: Facing a problem—that is, a gap between a current and a desired positions—it is a process that includes design, analysis, and synthesis. Design to identify the key activities needed to bridge the gap, analysis to assemble and process the necessary data, and synthesis to elect a solution from various alternative courses of action. In the process, strategic thinking requires rationality, intuition, and innovation. 59 Beaufre’s view: Strategic thinking “is a mental process, both abstract and rational, that combines psychological and material data. The process relies on a great capacity for analysis and synthesis; analysis is necessary to assemble the data on which to make a diagnosis, synthesis is necessary to extract the diagnosis from the data. The diagnosis amounts to a choice between alternatives.” 60

Taxonomies of problems. There are many types of problems and many taxonomies to describe them. Savransky defines routine problems as those where all critical steps are known (a critical step is one that is required to reach the solution). 61   Inventive problems are a subset of nonroutine ones where both the solution and at least one critical step are unknown. Also, a closed problem is one with a finite number of correct solutions. 62

Biases. They abound! (See Bazerman & Moore (2008 , pp. 13–41) for a review.)

Using case studies. Using my students’ problems as cases for the class is an example of problem-based learning, which has shown superior long-term retention and skill development. (Traditional methods, in turn, are superior for short-term retention as measured by standardized exams.) 63

( Grint, 2005 ) [pp. 1473–1474].

For the latter, see, for instance ( Polya, 1945 ), ( VanGundy, 1988 ).

( Leach, 2013 ).

( Holley, 1997 ). One can only imagine the psychological damage to Yale students when they saw the next day their beloved Dan in the newspaper … happily eating a hamburger in front of John Harvard’s statue.

See, for instance ( David H. Jonassen, 2000 ), ( G. F. Smith, 1988 ).

( Wenke & Frensch, 2003 ) [p. 90], ( Mason & Mitroff, 1981 ) [p. 5].

( Simon, 1974 ), ( David H. Jonassen, 1997 ), ( Pretz et al., 2003 ) [p. 4], ( S. M. Smith & Ward, 2012 ) [p. 462], ( Mason & Mitroff, 1981 ) [p. 30].

( Bardwell, 1991 ).

( David H. Jonassen, 2000 ).

( Brightman, 1978 ).

(United Nations).

( Hayes, 1989 ) [p. 280].

See Rittel’s wicked problems ( Rittel, 1972 ).

See ( Blair, 2000 ) [p. 298].

( Weiner, 2014 ).

( Perkins & Salomon, 1989 ), ( Gauch, 2003 ) [pp. 2–3], ( Grasso & Burkins, 2010 ) [pp. 1–10]; ( Kulkarni & Simon, 1988 ) [p. 140], ( Sanbonmatsu, Posavac, Kardes, & Mantel, 1998 ), ( Sheppard, Macatangay, Colby, & Sullivan, 2009 ) [p. 175], ( Katzenbach, 1993 ), ( Savransky, 2002 ) [p. 18], ( M. U. Smith, 1991 ) [pp. 10–15], ( Brown & Wyatt, 2010 ).

( Theocharis & Psimopoulos, 1987 ), ( Manathunga, Lant, & Mellick, 2006 ).

( National Research Council, 2012 ) [p. 76]. See also ( Manathunga, Lant, & Mellick, 2007 ).

( Chi, Bassok, Lewis, Reimann, & Glaser, 1989 ), ( David H. Jonassen, 2000 ). See also ( National Research Council, 2014 ) [pp. 53–55].

( Feynman, 1997 ) [pp. 36–37].

( Gawande, 2009 ).

( Rousseau, 2006 ), ( Rousseau & McCarthy, 2007 ), ( Rousseau, 2012 ), ( Pfeffer & Sutton, 2006b ), ( Pfeffer & Sutton, 2006a ).

See, for instance ( Keith J. Holyoak & Koh, 1987 ), ( National Research Council, 2011a ) [pp. 136–138].

( National Association of Colleges and Employers, 2014 ) [p. 4].

See, for instance ( Basadur, Runco, & Vega, 2000 ), ( Adams, 2001 ) [pp. 120–121], ( Assink, 2006 ), ( Basadur, Graen, & Scandura, 1986 ). For a review of divergent thinking in generating alternatives, see ( Reiter-Palmon & Illies, 2004 ).

( S. M. Smith & Ward, 2012 ) [p. 465], ( VanGundy, 1988 ) [p. 5], ( Adams, 2001 ) [p. 121].

Although we prefer deferring judgment, an alternative approach allows applying some convergent thinking during idea production. See ( Basadur, 1995 ) for a review.

See ( Hammond, Keeney, & Raiffa, 2002 ) [p. 53].

( Twardy, 2010 ).

( Research Councils UK, 2001 ).

For other lists, see ( Reeves, Denicolo, Metcalfe, & Roberts, 2012 ) and ( Careers Research and Advisory Centre, 2010 ).

( Ness, 2012 ).

( Wuchty, Jones, & Uzzi, 2007 ).

( National Research Council, 2011a ) [p. 61]. See also ( National Research Council, 2014 ) [p. 64].

( Gauch, 2003 ) [pp. 269–270].

( Gauch, 2003 ) [p. 273].

( Karvonen, 2000 ).

( Thomke & Feinberg, 2009 ).

Cited in ( Thomke & Feinberg, 2009 ).

( Keith J. Holyoak, 2012 ), ( Keith J. Holyoak & Koh, 1987 ), ( National Research Council, 2011b ) [pp. 136–138].

( Feynman, 1998 ).

( Fischhoff, 1982 ) [p. 432].

( Arkes, Wortmann, Saville, & Harkness, 1981 ).

( Klayman & Ha, 1987 ), ( Klayman & Ha, 1989 ), ( Nickerson, 1998 ).

( Pfeffer & Sutton, 2006b ) [p. 13].

( Grol, 2001 ), ( Heyland, Dhaliwal, Day, Jain, & Drover, 2004 ), ( Rauen, Chulay, Bridges, Vollman, & Arbour, 2008 ). See also ( Golec, 2009 ), ( Sheldon et al., 2004 ), ( Straus & Jones, 2004 ).

( Sackett, Rosenberg, Gray, Haynes, & Richardson, 1996 ), ( Straus, Glasziou, Richardson, & Haynes, 2011 ) [p. 1].

( National Research Council, 2011a ) [p. 28].

See, for instance ( Allen, Bryant, & Vardaman, 2010 ), ( Pfeffer & Sutton, 2006b , 2007 ), ( Rousseau, 2006 ), ( Rousseau & McCarthy, 2007 ).

( National Research Council, 2011b ) [pp. 95–97], ( National Research Council, 2011a ) [pp. 2–4; 88, 91, 92].

( Pfeffer & Sutton, 2006b ) [pp. 52–53].

( McIntyre, 1998 ).

( Basadur, 1995 ).

( Woods, 2000 ).

See ( Leung & Bartunek, 2012 ) [pp. 170–173].

( National Research Council, 2014 ) [pp. 62–63].

See ( National Research Council, 2011b ) [pp. 155–156], ( Silver, 2012 ) [pp. 53–73].

( Tetlock, 2005 ) [pp. 20–21].

See also ( Graetz, 2002 ), ( Mintzberg, 1994 ), ( Liedtka, 1998 ), ( Heracleous, 1998 ).

( Beaufre, 1963 ) [p. 23].

( Savransky, 2002 ) [p.4].

( Savransky, 2002 ) [p. 5]. For more on taxonomies of problems, see also ( G. F. Smith, 1988 ), ( M. U. Smith, 1991 ), ( Bassok & Novick, 2012 ), ( Kotovsky, 2003 ). See also ( David H. Jonassen, 2000 ) [p. 67] for a description of well-defined and ill-defined problems. For tame, wicked, and critical problems and how they relate to managers and leaders, see ( Grint, 2005 ) [p. 1473], ( Rittel, 1972 ).

( Strobel & van Barneveld, 2009 ), ( David H. Jonassen, 2011 ) [pp. 153–158].

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