alternative course of action in case study example

Alternative Courses of Action in Case Study: Examples and How To Write

The ultimate goal of creating a case study is to develop a feasible action that can solve the problem it raised.

One way to achieve this is by enumerating all the possible solutions for your case study’s subject. The portion of the case study where you perform this is called ACA or Alternative Courses of Action.

Are you struggling with writing your case study’s ACA?  Do not worry; we have provided you with the most detailed guide on writing the Alternative Courses of Action (ACA) of a case study.

Table of Contents

What are alternative courses of action (aca) in a case study.

Alternative Courses of Action (ACA) are the possible actions a firm or organization can implement to address the problem indicated in the case study. These are suggested actions that a firm can consider to arrive at the most feasible and effective solution to the problem. 

This portion doesn’t provide the actual and optimal solution yet. Instead, it contains proposed alternatives that will still undergo an evaluation of their respective advantages and disadvantages to help you come up with the best solution. 

The ACA you will offer and indicate will be based on your case study’s SWOT analysis in the “ Areas of Consideration ” portion. Thus, a SWOT analysis is performed first before writing the ACA.

What Is the Importance of Alternative Courses of Action (ACA) in a Case Study?

Given the financial, logistical, and operational limitations, developing solutions that the firm can perform can be challenging. By enumerating and evaluating the ACA of your case study, you can filter out the alternatives that can be a potential solution to the problem, given the business’s constraints 1 . This makes your proposed solutions feasible and more meaningful.

How To Write Alternative Courses of Action in Case Study

Here are the steps on how to write the Alternative Courses of Action for your case study:

1. Analyze the Results of Your SWOT Analysis

Using the SWOT analysis, consider how the firm can use its strengths and opportunities to address its weaknesses, mitigate threats, and eventually solve the case study’s problem. 

Suppose that the case study’s problem is declining monthly sales, and the SWOT analysis showed the following:

• Strength : Creative marketing team 
• Opportunity : Increasing trend of using social media to promote products

Then, you may include an ACA about developing the digital marketing arm of the firm to attract more customers and boost monthly sales. This can also address one of the possible threats the firm faces, which is increasing direct marketing costs.

2. Write Your Proposed Solutions/Alternative Courses of Action (ACA) for Your Case Study’s Problem

Once you have reviewed your SWOT analysis and come up with possible solutions, it’s time to write them formally in your manuscript. Each solution does not have to be too detailed and wordy. State the specific action that the firm must perform concisely.

Going back to our previous example in Step 1, here is one of the possible ACA that can be included:

ACA #1: Utilize digital platforms such as web pages and social media sites as an alternative marketing platform to reach a wider potential customer base. Digital marketing, together with the traditional direct marketing strategy currently employed, maximizes the business’ market presence, attracting more customers, and potentially driving revenues upward.

In our example above, there is a clear statement of the firm’s action: to use web pages and social media sites to reach more potential customers and increase market presence. Notice how the ACA above provides only an overview of “what to do” and not a complete elaboration on “how to do it.” 

3. Identify the Advantages and Disadvantages of Each ACA You Have Proposed

After specifying the ACA, you must evaluate them by stating their respective advantages (pros) and disadvantages (cons). In other words, you must state how your ACA favors the firm (advantages) and its downsides and limitations (disadvantages).

Again, your evaluation does not have to be too detailed but make sure that it is relevant to the ACA that it pertains to. 

Let’s return to the ACA we developed from step 2, utilizing digital platforms (e.g., social media sites) to reach more potential customers. What do you think will be the pros and cons of this ACA?

Let’s start with its potential benefits (advantages). Using digital platforms is cheaper than using print ads or direct marketing. So, this will save some funds for the firm. In short, it is cost-effective. 

Second, digital platforms offer analytical tools to measure your ads’ reach, making it easier to evaluate people’s perceptions of your offering. 

Third, using social media sites makes communicating with any potential customer easier. You can quickly respond to their queries, especially if they are interested in your product. 

Lastly, you can reach as many types of people as possible by taking advantage of the internet algorithm.

Now, let us consider its disadvantages 2 . First, using digital marketing takes time and effort to learn, and you must be able to adapt quickly to the changes in trends and new strategies to keep up with the competition. 

Second, you must deal with the increasing market competition, as many businesses already use digital platforms. 

Third, you have to deal with negative feedback from your customers that are visible to the public and may affect their perception of your brand.

After pondering over the pros and cons of your ACA, it’s time to write them concisely in your manuscript. You can present it in two ways: by tabulating it or by simply listing them.

Example in Table Form:

Examples of Alternative Courses of Action (ACA) in a Case Study

Case Study Problem: Xenon Pastries faces a problem handling larger orders as Christmas Day approaches. With an estimated 15% increase in customer demand, this is the most significant increase in their daily orders since 2012. The management aims to maximize profit opportunities given the rise in customer demand. 

ACA #1: Hire part-time workers to increase staff numbers and meet the overwhelming seasonal increase in customer orders. Currently, Xenon Pastries has a total of 9 workers who are responsible for the accommodation of orders, preparation, and delivery of products, and addressing customers’ inquiries and complaints. Hiring 2 – 3 part-time workers can increase productivity and meet the daily order volume.

• Do not require too much effort to implement since hiring announcements only require signages or social media postings
• High certainty of finding potential workers due to the high unemployment rate
• Improve overall productivity of the business and the well-being of other workers since their workload will be lessened

Disadvantages

• Increase in operating expense in the form of wages to the new workers
• Managing more employees and monitoring their performance can be challenging
• New workers might find it challenging to adapt essential skills required in the operation of the business

ACA #2: Increase the prices of Xenon pastries’ products to increase revenues . This option can maximize Xenon Pastries’ profit even if not all customers’ orders are accommodated. 

• Cost-effective
• Easy to implement since it only requires changing the price tags of the products
• If customers’ desire to buy the products does not change, the price increase will certainly increase the business’ revenue
• Some customers might be discouraged from buying because of an increase in prices
• There’s a possibility that the increase in the price of the products will make it more expensive relative to competitors’ products

Case Study Problem: Delta Motors has been manufacturing motorcycles for ten years. Recently, the business suffered a gradual shrink in its quarterly revenues due to the increasing popularity of traditional and newly-developed electric bikes. Delta Motors seeks a long-term strategy to attract potential customers to bounce back sales.  

ACA #1: Develop a “regular installment payment” scheme to attract customers who wish to purchase motorcycles but have insufficient lump-sum money to acquire one.  This payment scheme allows customers to pay an initial deposit and the remaining amount through smaller monthly payments.

• Enticing for middle to low-income individuals who comprise a large chunk of the population
• Requires low initial capital to implement 
• Provides a new source of monthly income streams that can benefit the financial standing of the company
• Risk of default or delays in installment payments
• Requires additional human resources to manage and collect installment payments
• The payment scheme requires time to gain returns due to the periodic flow of funds
• Requires a careful creation of guidelines and terms and conditions to ensure smooth facilitation of the installment payment scheme

ACA #2: Introduce new motorcycle models that can entice different types of customers. These models will feature popular designs and more efficient engines.

• This may capture the public’s interest in Delta Motors, which can lead to an increase in the number of potential customers and earning opportunities
• Enables the business to keep up with the intense market competition by providing something “fresh” to the public
• Provides more alternatives for those who already support Delta Motors, strengthening their loyalty to the brand
• Conceptualization of a new model takes a lot of brainstorming to test its feasibility and effectiveness
• Requires sufficient funds to sustain the investment for the development of a new model
• It requires effective marketing strategies to promote the new model to the public

Tips and Warnings

• Do not include in this portion your case study’s conclusion . Think of ACA as a list of possible ways to address the problem. In other words, you suggest the possible alternatives to be selected here. The “ Recommendation ” portion of your case study is where you pick the most appropriate way to solve the problem.
• Use statistical data to support the advantages and disadvantages of each ACA. Although this is optional, presenting numerical data makes your analysis more concrete and factual than just stating them descriptively. 
• Do not fall into the “meat sandwich” trap. This happens when you intently make some of the alternatives less desirable so that your preferred choice stands out. This can be done by refusing to elaborate on their benefits or excessively concentrating on their disadvantages. Make sure that each ACA has potential and can be implemented realistically.

Frequently Asked Questions

1. how many alternative courses of action (aca) can a case study have.

Sometimes your instructor or teacher will tell you the required number of ACA that must be included in your case study . However, there’s no “standard” limit to how many ACA you can indicate.

2. What is the difference between Alternative Courses of Action (ACA) and Recommendations?

As mentioned earlier, the case study’s ACA aims to enumerate all possible solutions to the problem. It is not the stage where you state the “final” action you deem most appropriate to address the issue. The case study portion where you explicitly mention your “best” alternative is called the “Recommendation.” 

To help you understand the point above, let’s return to our Delta Motors example. In our previous section, we have provided two ACA that can solve the problem, namely (1) developing a regular installment payment plan and (2) introducing a new motorcycle model. 

Suppose that upon careful analysis and evaluation of these ACA, you came up with ACA #2 as the more fitting solution to the problem. When you write your case study’s recommendation, you must indicate the ACA you chose and your reasons for selecting it. 

Here’s an example of the Recommendation of the case study:

Recommendation

Introducing new motorcycle models that feature popular designs and more efficient engines to entice different types of customers is the most promising alternative course of action that Delta Motors can implement to bounce back its quarterly revenues and keep up with the competitive market. This creates a strong impression on the public of the company’s dedication to promoting high-quality motorcycles that can withstand changes in consumer preferences and market trends. Furthermore, this action proves that the company is continuously evolving to offer a variety of alternative models to suit everyone’s tastes. With proper promotion, these models can rekindle the company’s popularity in the automotive and motorcycle industry.

• How to Analyze a Case Study. Retrieved 23 May 2022, from https://wps.prenhall.com/bp_laudon_essbus_7/48/12303/3149605.cw/content/index.html
• Develop a Digital Marketing Plan. Retrieved 23 May 2022, from https://www.nibusinessinfo.co.uk/content/advantages-and-disadvantages-digital-marketing

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Last Updated July 8, 2023 08:29 PM

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

Browse all articles written by Jewel Kyle Fabula

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Business Case Analysis Format and Guidelines for Students

This Case Analysis Guideline will help you to have an idea of how to analyze a Business case properly. It will also give you pointers on how to construct and what to include in the different parts of your Case Analysis from the Point of View, Problem Statement, down to the decision-making, and Plan of Action.

May this post be of help to all of you, so you can come up with a better analysis of your group’s homework such as thesis or projects?

I. Point of View

The Point of View refers to the perspective of the decision-maker or person who is in the position to make the final recommendations as mentioned in the case.

For example, the problem is related to the manufacturing division. It can be about Engineering, manufacturing processes, quality assurance, and warehousing. The possible decision-maker or point of view is the Vice President of the Manufacturing division.

If the concern or problem is related to product quality which is under a Quality Department within the manufacturing division,  then it is possible to put the ‘Manager of Quality Department’ at the Point of View.

II. Time Context

The Time Context is the time in the case when you will start your analysis. It can be an imaginary time or the last-mentioned date in the case. Make sure that you can justify the reason behind your given time context. Because if your stated time is not relevant, it is possible that your analysis is also not relevant.

Assuming that the problem arises during the summer / dry season in the Philippines. You cannot put June to November in the time context as it is usually the rainy/wet season in PH.

If the problem arises in 2021, you can use that year in time context. For example, ‘First Quarter of 2021’ or ‘February 2021.’

III. Statement of the Problem

The Statement of the Problem defines the perceived problem in the case which becomes the subject of the analysis. You can present this in declarative or in question format.

For example: How to expand the business of Company A while in the middle of the current situation of the food industry.

IV. Statement of the Objectives

The Statement of the Objectives are goals that the case analysis hopes to achieve. It should basically satisfy the test of SMART (Specific, Measurable, Attainable, Realistic, and Time-bound)

For example: To improve the company’s performance in terms of product quality in 12 months. Or to increase the company’s sales for its dog food product lines in 6 months.

IV. Areas of Consideration

For the areas of consideration in your case study, you have to state the internal and external environment of the company/firm through SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis.

You can indicate in the internal environment the facts relating to the company’s financial situation, manufacturing, marketing, and human resources.

For example, does the business have a high employee turnover rate? Does the business’ revenue continuously increase year after year? How about product quality, can it keep up with the industry competition? You should focus on the factors that can help solve the issues and problems that the business is facing .

For the external environment, indicate the economic situation of the city or country. If the government policy affects your business then you can also state it. Indicate here also your competition which company it is or which product. If your chosen company sells dog food or mobile phone, state your competitor.

Now that you have the list of the internal and external environments. You should now list your company’s Strengths, Weaknesses, Opportunities, and Threats.

Under ‘Strengths,’ of course, depending on what is stated in the case. You can indicate if the company is prominent in the industry, awards such as ‘Best Manpower Agency for 10 years,’ ‘Best Hotel in terms of service.’

For ‘Weakness,’ include if the company has a high manpower turnover ratio, lowest quality in the market, and low budget for marketing/advertisement.

Under Opportunities, indicate if the country the company is located in a ‘Free Trade Zone,’ rising population which can equate to increasing product consumption. For example, increasing toothpaste consumption. The Philippine Government has a build build build program which means they will need an increase in cement usage.

VI. Assumptions

The Assumptions are the factors that are not clear or not specifically stated in the case. You need to clarify these factors and state them as assumptions to limit the analysis.

In layman’s terms, you will list in the Assumptions the boundaries of your analysis. It will also help the panelist to understand the reason behind the items you list in your case analysis.

VII. Alternative Courses of Action (ACA)

The Alternative Courses of Actions (ACAs) are the possible solutions to the identified problem. Each of the ACA must stand alone and must be able to solve the stated problem and achieve the objectives. The ACA must be mutually exclusive. In this regard, the student must choose an ACA to the exclusion of the others.

Also, you have to analyze each ACA in the light of the SWOT analysis and assumptions that is if there are any. You have to state clearly the advantages and disadvantages of each ACA. If the case contains enough information or data. Your stated advantages and disadvantages should be supported quantitatively to minimize bias.

VIII. Analysis of ACAs

The analysis of ACAs will state the list of advantages and disadvantages of each alternative course of action.

I have here examples of the courses of action. Again, these ACAs should be mutually exclusive and should solve the issues of the company. If the ACAs are somewhat related to each other, it is best to combine them and then think of a new one that is totally independent.

ACA 1. Increasing the Salary of the Employees

• Advantages of this course of action
• Disadvantages of this course of action

ACA 2. Reduce the Price of the Products Sold

ACA 3. Buyout the Competition

• Disadvantages

IX. Conclusion/Recommendation

After the analysis of the different Alternative Courses of Action (ACAs), you can now come up with the conclusion, recommendations, and decisions. You do not need to repeat the analysis which you have done in the ACA section of the analysis.

To make this part clearer, it is best to come up with a decision matrix similar to the photo.

Decision Matrix Sample

• 1 – Least Favorable
• 2 – Favorable
• 3 – Most Favorable

Here are the examples of criteria that you can use in the decision matrix.

• Ease of Implementation – refers to the effort required to implement the ACA, the least number of people involved or lesser process in implementing the ACA is the highest score
• Time Frame – This is the time required to implement the ACA, the highest score means the least amount of time needed
• Cost-Efficient – This is the amount of capital requirement, the highest score means the least amount of capital needed to implement the task

Recommendation:

Based on the decision matrix, ‘ACA 3 which is Buying out the competition is the best course of action to solve the problem.

X. Plan of Action

The Plan of Action outlines the series of actions to be undertaken to implement the adopted ACA. The plan of action should reflect, the list of activities, the person in charge, the time frame, and the budget to implement the ACA.

To ensure that you have done the analysis comprehensively, it would be best to program the plan according to the basic functional areas. You should present the plan by having column headings for activity, person/unit responsible/ time frame, and budget.

*There you have it guys! May this business case analysis format and guidelines will be able to help students like you in coming up with a logical solution to business-related cases that your teacher gave your group. Good luck!!!

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

Explore more.

• 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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Advancing Alternative Analysis: Integration of Decision Science

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2 UCLA Fielding School of Public Health, UCLA, Los Angeles, California, USA

3 University of California Center for the Environmental Implications of Nanotechnology, UCLA, Los Angeles, California, USA

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Christina m. batteate.

4 Environmental and Public Health Consulting, Alameda, California, USA

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5 Department of Chemistry, Indiana University Bloomington, Bloomington, Indiana, USA

Charles J. Corbett

6 UCLA Anderson School of Management, UCLA, Los Angeles, California, USA

7 UCLA Institute of the Environment and Sustainability, UCLA, Los Angeles, California, USA

Steffen Foss Hansen

8 Department of Environmental Engineering, Technical University of Denmark, Copenhagen, Denmark

Robert J. Lempert

9 RAND Corporation, Santa Monica, California, USA

Igor Linkov

10 U.S. Army Engineer Research and Development Center, Concord, Massachusetts, USA

Roger McFadden

11 McFadden and Associates, LLC, Oregon, USA

Kelly D. Moran

12 TDC Environmental, LLC, San Mateo, California, USA

Elsa Olivetti

13 Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

Nancy K. Ostrom

14 Safer Products and Workplaces Program, Department of Toxic Substances Control, Sacramento, California, USA

Michelle Romero

Julie m. schoenung.

15 Henry Samueli School of Engineering, University of California, Irvine, Irvine, California, USA

Thomas P. Seager

16 School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA

Peter Sinsheimer

Kristina a. thayer.

17 Office of Health Assessment and Translation, National Toxicology Program, National Institute of Environmental Health Sciences, Morrisville, North Carolina, USA

Supplemental Material is available online ( https://doi.org/10.1289/EHP483 ).

The authors declare they have no actual or potential competing financial interests.

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Associated Data

Background:.

Decision analysis—a systematic approach to solving complex problems—offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.

Objectives:

We assessed whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.

A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and were prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups’ findings.

We concluded that the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients and would also advance the science of decision analysis.

Conclusions:

We advance four recommendations: a ) engaging the systematic development and evaluation of decision approaches and tools; b ) using case studies to advance the integration of decision analysis into alternatives analysis; c ) supporting transdisciplinary research; and d ) supporting education and outreach efforts. https://doi.org/10.1289/EHP483

Introduction

Policy makers are faced with choices among alternative courses of action on a regular basis. This is particularly true in the environmental arena. For example, air quality regulators must identify the best available control technologies from a suite of options. In the federal program for remediation of contaminated sites, government project managers must propose a clean-up method from a set of feasible alternatives based on nine selection criteria ( U.S. EPA 1990 ). Rule makers in the Occupational Safety and Health Administration (OSHA) compare a variety of engineering controls and work practices in light of technical feasibility, economic impact, and risk reduction to establish permissible exposure limits ( Malloy 2014 ). At present, as we describe below, some agencies must identify safer, viable alternatives to chemicals for consumer and industrial applications. Such evaluation, known as alternatives analysis, requires balancing numerous, often incommensurable, decision criteria and evaluating the trade-offs among those criteria presented by multiple alternatives.

The University of California Sustainable Technology and Policy Program, in partnership with the University of California Center for Environmental Implications of Nanotechnology (CEIN), hosted a workshop on integrating decision analysis and predictive toxicology into alternatives analysis ( CEIN 2015 ). The workshop brought together approximately 40 leading decision analysts, toxicologists, law and policy experts, and engineers who work in national and state government, academia, the private sector, and civil society for two days of intensive discussions. To provide context for the discussions, the workshop organizers developed a case study regarding the search for alternatives to copper-based marine antifouling paint, which is used to protect the hulls of recreational boats from barnacles, algae, and other marine organisms. Participants received data regarding the health, environmental, technical, and economic performance of a set of alternative paints (see Supplemental Material, “Anti-Fouling Paint Case Study Performance Matrix”). Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups’ findings. This article focuses on the workshop discussion and on conclusions regarding decision making.

We first review regulatory decision making in general, and we provide background on selection of safer alternatives to hazardous chemicals using alternatives analysis (AA), also called alternatives assessment. We then summarize relevant decision-making approaches and associated methods and tools that could be applied to AA. The next section outlines some of the challenges associated with decision making in AA and the role that various decision approaches could play in resolving them. After setting out four principles for integrating decision analysis into AA, we advance four recommendations for driving integration forward.

Regulatory Decision Making and Selection of Safer Alternatives

The consequences of regulatory decisions can have broad implications in areas such as human health and the environment. Yet within the regulatory context, these complex decision tasks are traditionally performed using an ad hoc approach, that is to say, without the aid of formal decision analysis methods or tools ( Eason et al. 2011 ). As we discuss later, such ad hoc approaches raise serious concerns regarding the consistency of outcomes across different cases; the transparency, predictability, and objectivity of the decision-making process; and human cognitive capacity in managing and synthesizing diverse, rich streams of information. Identifying a systematic framework for making effective, transparent, and objective decisions within the dynamic and complex regulatory milieu can significantly mitigate those concerns ( NAS 2005 ). In its 2005 report, the NAS called for a program of research in environmental decision making focused on:

[I]mproving the analytical tools and analytic-deliberative processes necessary for good environmental decision making. It would include three components: developing criteria of decision quality; developing and testing formal tools for structuring decision processes; and creating effective processes, often termed analytic-deliberative, in which a broad range of participants take important roles in environmental decisions, including framing and interpreting scientific analyses. ( NAS 2005 )

Since that call, significant research has been performed regarding decision making related to environmental issues, particularly in the context of natural resource management, optimization of water and coastal resources, and remediation of contaminated sites ( Gregory et al. 2012 ; Huang et al. 2011 ; Yatsalo et al. 2007 ). This work has begun the process of evaluating the application of formal decision approaches to environmental decision making, but numerous challenges remain, particularly with respect to the regulatory context. In fact, very few studies have focused on the application of decision-making tools and processes in the context of formal regulatory programs, taking into account the legal, practical, and resource constraints present in such settings ( Malloy et al. 2013 ; Parnell et al. 2001 ). We focus upon the use of decision analysis in the context of environmental chemicals.

The challenge of making choices among alternatives is central in an emerging approach to chemical policy, which turns from conventional risk management to embrace prevention-based approaches to regulating chemicals. Conventional risk management essentially focuses upon limiting exposure to a hazardous chemical to an acceptable level through engineering and administrative controls. In contrast, a prevention-based approach seeks to minimize the use of toxic chemicals by mandating, directly incentivizing, or encouraging the adoption of viable safer alternative chemicals or processes ( Malloy 2014 ). Thus, under a prevention-based approach, the regulatory agency would encourage or even mandate use of what it views as an inherently safer process using a viable alternative plating technique. Adopting a prevention-based approach, however, presents its own challenging choice—identifying a safer, viable alternative. Effective prevention-based regulation requires a regulatory AA methodology for comparing and evaluating the regulated chemical or process and its alternatives across a range of relevant criteria.

AA is a scientific method for identifying, comparing, and evaluating competing courses of action. In the case of chemical regulation, it is used to determine the relative safety and viability of potential substitutes for existing products or processes that use hazardous chemicals ( NAS 2014 ; Malloy et al. 2013 ). For example, a business manufacturing nail polish containing a resin made using formaldehyde would compare its product with alternative formulations using other resins. Alternatives may include drop-in chemical substitutes, material substitutes, changes to manufacturing operations, and changes to component/product design ( Sinsheimer et al. 2007 ). The methodology compares the alternatives with the regulated product and with one another across a variety of attributes, typically including public health impacts, environmental effects, and technical performance, as well as economic impacts on the manufacturer and on the consumer. The methodology identifies trade-offs between the alternatives and evaluates the relative overall performance of the original product and its alternatives.

In the regulatory setting, multiple parties may be involved to varying degrees in the generation of an AA. Typically, the regulated firm is required to perform the AA in the first instance, as in the California Safer Consumer Products program and the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) authorization process ( DTSC 2013 ; European Parliament and Council 2006 ). The AA, which may be done within the firm or by an outside consultant retained by the firm, is generally performed by an interdisciplinary team of experts (hereafter collectively referred to as the “analyst”) ( DTSC 2013 ). The firm submits the AA to the regulatory agency for review. The regulatory agency will often propose a final decision regarding whether a viable, safer alternative exists and the appropriate regulatory action to take. ( DTSC 2013 ; European Parliament and Council 2006 ). Possible regulatory actions include a ban on the existing product, adoption of an alternative, product labeling, use restrictions, or end-of-life management. Stakeholders such as other government agencies, environmental groups, trade associations, and the general public may provide comments on the AA and regulatory response. Ultimately, the agency retains the authority to require revisions to the analysis and has the final say over the regulatory response ( Malloy 2014 ).

Development of effective regulatory AA methods is a pressing and timely public policy issue. Regulators in California, Maine, and Washington are implementing new programs that call for manufacturers to identify and evaluate potential safer alternatives to toxic chemicals in products ( DTSC 2013 ; MDEP 2012 ; Department of Ecology, Washington State 2015 ). At the federal level, in the last few years, the U.S. Environmental Health Protection Agency ( EPA ) began to use AA as part of “chemical action plans” in its chemical management program ( Lavoie et al. 2010 ). In the European Union, the REACH program imposes AA obligations upon manufacturers seeking authorization for the continued use of certain substances of very high concern ( European Parliament and Council 2006 ). The stakes in developing effective approaches to regulatory AA are high. A flawed AA methodology can inhibit the identification and adoption of safer alternatives or support the selection of an undesirable alternative (often termed “regrettable substitution”). An example of the former is the U.S. EPA’s attempt in the late 1980s to ban asbestos, which was rejected by a federal court that concluded, among other things, that the AA method used by the agency did not adequately evaluate the feasibility and safety of the alternatives ( Corrosion ProoFittings v. EPA 1991 ). Regrettable substitution is illustrated by the case of antifouling paints used to combat the buildup of bacteria, algae, and invertebrates such as barnacles on the hulls of recreational boats. As countries throughout the world banned highly toxic tributyltin in antifouling paints in the late 1980s, manufacturers turned to copper as an active ingredient ( Dafforn et al. 2011 ). The cycle is now being repeated as regulatory agencies began efforts to phase out copper-based antifouling paint because of its adverse impacts on the marine environment ( Carson et al. 2009 ).

AA frameworks and methods abound, yet few directly address how decision makers should select or rank the alternatives. As the 2014 NAS report on AA observed, “[m]any frameworks … do not consider the decision-making process or decision rules used for resolving trade-offs among different categories of toxicity and other factors (e.g., social impact), or the values that underlie such trade-offs” ( NAS 2014 ). Similarly, a recent review of 20 AA frameworks and guides identified methodological gaps regarding the use of explicit decision frameworks and the incorporation of decision-maker values ( Jacobs et al. 2016 ). The lack of attention to the decision-making process is particularly problematic in regulatory AA, in which the regulated entity, the government agency, and the stakeholders face significant challenges related to the complexity of the decisions, uncertainty of data, difficulty in identifying alternatives, and incorporation of decision-maker values. We discuss these challenges in detail below.

A variety of decision analysis tools and approaches can assist policy makers, product and process designers, and other stakeholders who face the challenging decision environment presented by AA. For these purposes, decision analysis is “a systematic approach to evaluating complex problems and enhancing the quality of decisions.” ( Eason et al. 2011 ). Although formal decision analysis methods and tools suitable for such situations are well developed ( Linkov and Moberg 2012 ), for the reasons discussed below, they are rarely applied in existing AA practice. The range of decision analysis methods and tools is quite broad, requiring development of principles for selecting and implementing the most appropriate ones for varied regulatory and private settings. Following an overview of the architecture of decision making in AA, we examine how various formal and informal decision approaches can assist decision makers in meeting the four challenges identified above. We conclude by offering a set of principles for developing effective AA decision-making approaches and steps for advancing the integration of decision analysis into AA practice.

Overview of Decision Making in Alternatives Analysis

In the case of regulatory AA, the particular decision or decisions to be made will depend significantly upon the requirements and resources of the regulatory program in question. For example, the goal may be to identify a single optimal alternative, to rank the entire set of alternatives, or to simply differentiate between acceptable and unacceptable alternatives ( Linkov et al. 2006 ). As a general matter, however, the architecture of decision making is shaped by two factors: the decision framework adopted and the decision tools or methods used. For our purposes, the term “decision framework” means the overall structure or order of the decision making, consisting of particular steps in a certain order. Decision tools and methods are defined below.

Decision Frameworks

Existing AA approaches that explicitly address decision making use any of three general decision frameworks: sequential, simultaneous, and mixed ( Figure 1 ). The sequential framework includes a set of attributes, such as human health, environmental impacts, economic feasibility, and technical feasibility, which are addressed in succession. The first attribute addressed is often human health or technical feasibility because it is assumed that any alternative that does not meet minimum performance requirements should not proceed with further evaluation. Only the most favorable alternatives proceed to the next step for evaluation, which continues until one or more acceptable alternatives are identified ( IC2 2013 ; Malloy et al. 2013 ).

Decision frameworks. Compares the process for decision making under sequential, simultaneous, and mixed frameworks.

The simultaneous framework considers all or a set of the attributes at once, allowing good performance on one attribute to offset less favorable performance on another for a given alternative. Thus, one alternative’s lackluster performance in terms of cost might be offset by its superior technical performance, a concept known as compensation ( Giove et al. 2009 ). This type of trade-off is not generally available in the sequential framework across major decision criteria. Nevertheless, it is important to note that even within a sequential framework, the simultaneous framework may be lurking where a major decision criterion consists of sub-criteria. For example, in most AA approaches, the human health criterion has numerous sub-criteria reflecting various forms of toxicity such as carcinogenicity, acute toxicity, and neurotoxicity. Even within a sequential framework, the decision maker may consider all of those subcriteria simultaneously when comparing the alternatives with respect to human health ( NAS 2014 ; IC2 2013 ).

The mixed or hybrid framework, as one might expect, is a combination of the sequential and simultaneous approaches ( NAS 2014 ; IC2 2013 ; Malloy et al. 2013 ). For example, if technical feasibility is of particular importance to an analyst, she may screen out certain alternatives on that basis, and subsequently apply a simultaneous framework to the remaining alternatives regarding the other decision criteria. A recent study of 20 existing AA approaches observed substantial variance in the framework adopted: no framework (7 approaches), mixed (6 approaches), simultaneous (4 approaches), menu of all three frameworks (2 approaches), and sequential (1 approach) ( Jacobs et al. 2016 ).

Decision Methods and Tools

There are a wide range of decision tools and methods, that is to say, formal and informal aids, rules, and techniques that guide particular steps within a decision framework ( NAS 2014 ; Malloy et al. 2013 ). These methods and tools range from informal rules of thumb to highly complex, statistically based methodologies. The various methods and tools have diverse approaches and distinctive theoretical bases, and they address data uncertainty, the relative importance of decision criteria, and other issues differently. For example, some methods quantitatively incorporate the decision maker’s relative preferences regarding the importance of decision criteria (a process sometimes called “weighting”), whereas others make no provision for explicit weighting. For our purposes, they can be broken into four general types: a ) narrative, b ) elementary, c ) multicriteria decision analysis (MCDA), and d ) robust scenario analysis. Each type can be used for various decisions in an AA, such as winnowing down the initial set of potential alternatives or for ranking the alternatives. As Figure 2 illustrates, in the context of a mixed decision framework, two different decision tools and methods could even be used at different decision points within a single AA.

Multiple decision tool use in mixed decision framework. Demonstrates one potential scenario for using multiple decision tools in one chemical selection process. (Derived from Jacobs et al. 2016 ).

Narrative Approaches

In the narrative approach, also known as the ad hoc approach, the decision maker engages in a holistic, qualitative balancing of the data and associated trade-offs to arrive at a selection ( Eason et al. 2011 ; Linkov et al. 2006 ). In some cases, the analyst may rely on explicitly stated informal decision principles or on expert judgment to guide the process. No quantitative scores are assigned to alternatives for the purposes of the comparison. Similarly, no explicit quantitative weighting is used to reflect the relative importance of the decision criteria, although in some instances, qualitative weighting may be provided for the analyst by the firm charged with performing the AA. The AA methodology developed by the European Chemicals Agency (ECHA) for substances that are subject to authorization under REACH is illustrative ( ECHA 2011 ). Similarly, the AA requirements set out in the regulations for the California Safer Consumer Products program, which mandates that manufacturers complete AAs for certain priority products, adopt the ad hoc approach, setting out broad, narrative decision rules without explicit weighting ( DTSC 2013 ). This approach could be particularly subject to various biases in decision making, which we will address later.

Elementary Approaches

Elementary approaches apply a more systematic overlay to the narrative approach, providing the analyst with specific guidance about how to make a decision. Such approaches provide an observable path for the decision process but typically do not require sophisticated software or specialized expertise. For example, Hansen and colleagues developed the NanoRiskCat tool for prioritization of nanomaterials in consumer products ( Hansen et al. 2014 ). The structure may take the form of a decision tree that takes the analyst through an ordered series of questions. Alternatively, it may offer a set of checklists, specific decision rules, or simple algorithms to assist the analyst in framing the issues and guiding the evaluation. Elementary approaches can make use of both quantitative and qualitative data and may incorporate implicit or explicit weighting of the decision criteria ( Linkov et al. 2004 ).

MCDA Approaches

The MCDA approach couples a narrative evaluation with mathematically based formal decision analysis tools, such as multi-attribute utility theory (MAUT) and outranking. The output of the selected MCDA analysis is intended as a guide for the decision maker and as a reference for stakeholders affected by or otherwise interested in the decision. MCDA itself consists of a range of different methods and tools, reflecting various theoretical bases and methodological perspectives. Accordingly, those methods and tools tend to assess the data and generate rankings in different ways ( Huang et al. 2011 ). However, they generally share certain common features, setting them apart from the type of informal decision making present in the narrative approach. Each MCDA approach provides a systematic, observable process for evaluating alternatives in which an alternative’s performance across the decision criteria is aggregated to generate a score. Each alternative is then ranked relative to the other alternatives based on its aggregate score. Figure 3 provides an example of the type of ranking generated from an MAUT tool. In most of these types of ranking approaches, the individual criteria scores are weighted to reflect the relative importance of the decision criteria and sub-criteria ( Kiker et al. 2005 ; Belton and Stewart 2002 ).

Sample output from MAUT decision tool comparing alternatives to lead solder. SnPb is a solder alloy composed of 63% Sn/37% Pb; SAC (Water) is a solder alloy composed of 95.5% Sn/3.9% Ag/0.6% Cu; water quenching is used to cool and harden solder; SAC (air) is a solder alloy composed of 95.5% Sn/3.9% Ag/0.6% Cu; air is used to cool and harden solder; SnCu (water) is a solder alloy composed of 99.2% Sn/0.8% Cu; water quenching is used to cool and harden solder; SnCu (air) solder alloy composed of 99.2% Sn/0.8% Cu; air is used to cool and harden solder [ Malloy et al. 2013 with permission from Wiley Online Library http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1551-3793/homepage/Permissions.html )]. Note: Ag, silver; Cu, copper; Pb, lead; Sn, tin.

Some MCDA tools, such as MAUT, are optimization tools that seek to maximize the achievement of the decision maker’s preferences. These optimization approaches use utility functions, dimensionless scales that range from 0 to 1, to convert the measured performance of an alternative for a given decision criterion to a score between 0 and 1 ( Malloy et al. 2013 ). In contrast, outranking methods do not create utility functions or seek optimal alternatives. Instead, outranking methods seek the alternative that outranks other alternatives in terms of overall performance, also known as the dominant alternative ( Belton and Stewart 2002 ). The diverse MCDA tools use various approaches to address uncertainty regarding the performance of alternatives and the relative importance to be placed on respective attributes. Some, such as MAUT, use point values for performance and weighting and rely upon sensitivity analysis to evaluate the impact of uncertainty ( Malloy et al. 2013 ). Sensitivity analysis evaluates how different values of uncertain attributes or weights would affect the ranking of the alternatives. Others, such as stochastic multi-criteria acceptability analysis (SMAA), represent performance information and relative weights as probability distributions ( Lahdelma and Salminen 2010 ). Still others, such as multi-criteria mapping, rely on a part-quantitative, part-qualitative approach in which the analyst facilitates structured evaluation of alternatives by the ultimate decision maker, eliciting judgments from the decision maker regarding the performance of the respective alternatives on relevant attributes and on the relative importance of those attributes. The analyst then generates a ranking based upon that input ( SPRU 2014 ; Hansen 2010 ). MCDA has been used, though not extensively, in the related field of life-cycle assessment (LCA) ( Prado et al. 2012 ). For example, Wender et al. ( 2014 ) integrated LCA with MCDA methods to compare existing and emerging photovoltaic technologies.

Robust Scenario Approaches

Robust scenario analysis is particularly useful when decision makers face deep uncertainty, meaning situations in which the decision makers do not know or cannot agree upon the likely performance of one or more alternatives on important criteria ( Lempert and Collins 2007 ). Robust scenario analysis uses large ensembles of scenarios to visualize all plausible, relevant futures for each alternative. With this range of potential futures in mind, it helps decision makers to compare the alternatives in search of the most robust alternative. A robust alternative is one that performs well across a wide range of plausible scenarios even though it may not be optimal or dominant in any particular one ( Kalra et al. 2014 ).

Robust scenario decision making consists of four iterative steps. First, the decision makers define the decision context, identifying goals, uncertainties, and potential alternatives under consideration. Second, modelers generate ensembles of hundreds, thousands, or even more scenarios, each reflecting an outcome flowing from different plausible assumptions about how each alternative may perform. Third, quantitative analysis and visualization software is used to explore the benefits and drawbacks of the alternatives across the range of scenarios. Finally, trade-off analysis (i.e., comparative assessment of the relative pros and cons of the alternatives) is used to evaluate the alternatives and to identify a robust strategy ( Lempert et al. 2013 ).

Decision-Making Challenges Presented by Alternatives Analysis

Like many decisions involving multiple criteria, identifying a safer viable alternative or set of alternatives is often difficult. Finding potential alternatives, collecting information about their performance, and evaluating the trade-offs posed by each alternative are all laden with problems. Those difficulties are exacerbated in the regulatory setting because of additional constraints associated with that regulatory setting, such as the need for accountability, transparency, and consistency across similar cases ( Malloy et al. 2015 ). In this review, we focus on four challenges that are recognized in the decision analysis field to be of particular importance to regulatory AA:

• • Dealing with large numbers of attributes
• • Uncertainty in performance data
• • Poorly understood option space
• • Incorporating decision-maker values (sometimes called weighting of attributes)

Large Numbers of Attributes

In its essential form, AA focuses upon human health, environmental impacts, technical performance, and economic impact. But in fact, AA involves many more than four attributes. Each of the four major attributes, particularly human health, includes numerous sub-attributes, many more than any human can process without some form of heuristic or computational aid. An example is the case of California Safer Consumer Products regulations, which require that an AA consider all relevant “hazard traits” ( DTSC 2013 ). Hazard traits are “properties of chemicals that fall into broad categories of toxicological, environmental, exposure potential, and physical hazards that may contribute to adverse effects …” ( DTSC 2013 ). For human health alone, the California regulations identify twenty potentially relevant hazard traits ( DTSC 2013 ). Similarly, the U.S. EPA considers a total of twelve hazard end points in assessing impacts to human health in its alternatives assessment guidance ( U.S. EPA 2011 ).

Large numbers of attributes raise two types of difficulties. First, as the number of attributes increases, data collection regarding the performance of the baseline product and its alternatives becomes increasingly difficult, time-consuming, and expensive. Because not all attributes listed in regulations or guidance documents will be salient or have an impact in every case, decision-making approaches that judiciously sift out irrelevant or less-important attributes are desirable. Second, given humans’ cognitive limitations, larger numbers of relevant attributes complicate the often inevitable trade-off analysis that is needed in AA. Consider an example of two alternative solders, one of which performs best in terms of low carcinogenicity, neurotoxicity, acute aquatic toxicity, and wettability (a very desirable feature for solders), but not so well with respect to endocrine disruption, respiratory toxicity, chronic aquatic toxicity, and tensile strength (another advantageous feature for solders). Suppose the second alternative presents the opposite profile. Now, add dozens of other attributes relating to human health and safety, environmental impacts, and technical and economic performance to the mix. Even in the relatively simple case of one baseline product and two potential alternatives, evaluating and resolving the trade-offs can be treacherous. In assessing the alternatives, decision makers must determine whether and how to compensate for poor performance on some attributes with superior performance on other attributes. Similarly, the nature and scale of the performance data for the attributes varies wildly; using fundamentally different metrics for diverse attributes generates a mixture of quantitative and qualitative information.

Decision frameworks and methods should provide principled approaches to integrating or normalizing such information to support trade-off analysis. Elementary approaches often use ordinal measures of performance to normalize diverse types of data. For example, the U.S. EPA AA methodology under the Design for the Environment program characterizes performance on a variety of human health and environmental attributes as “low,” “medium,” or “high” ( U.S. EPA 2011 ). The increased tractability comes with some decrease in precision, potentially obscuring meaningful differences in performance or exaggerating differences at the margins. As the number of relevant attributes increases, it becomes more difficult to rely upon narrative and elementary approaches to manage the diverse types of data and to evaluate the trade-offs presented by the alternatives. MCDA approaches are well suited for handling large numbers of attributes and diverse forms of data. ( Kiker et al. 2005 ). In an AA case study using an MCDA method to evaluate alternatives to lead-based solder, researchers used an internal normalization approach to convert an alternative’s scores on each criterion to dimensionless units ranging from 0 to 1 and then applied an optimization algorithm to trade-offs across more than fifty attributes ( Malloy et al. 2013 ).

Uncertain Data Regarding Attributes

Uncertainty is not unique to AA; it presents challenges in conventional risk assessment and in many environmental decision-making situations. However, the diversity and number of the relevant data streams and potential trade-offs faced in AA exacerbate the problem of uncertainty. In thinking through uncertainty in this context, three considerations stand out: defining it, responding to it methodologically, and communicating about it to stakeholders.

The meaning of the term “uncertainty” is itself uncertain; definitions abound ( NAS 2009 ; Ascough et al. 2008 ). For our purposes, uncertainty includes a complete or partial lack of information, or the existence of conflicting information or variability, regarding an alternative’s performance on one or more attributes, such as health effects, potential exposure, or economic impact ( NAS 2009 ). Uncertainty includes “data gaps” resulting from a lack of experimental studies, measurements, or other empirical observations, along with situations in which available studies or modeling provide a range of differing data for the same attribute ( NAS 2014 ; Ascough et al. 2008 ). It also includes limitations inherent in data generation and modeling such as measurement error and use of modeling assumptions, as well as naturally occurring variability caused by heterogeneity or diversity in the relevant populations, materials, or systems. Uncertainty regarding the strength of the decision maker’s preferences, also known as value uncertainty, is discussed below.

There are a variety of methodological approaches for dealing with uncertainty. Some approaches (typically within narrative or elementary approaches) simply call for identification and discussion of missing data or use simple heuristics to deal with uncertainties, for example by assuming a worst-case performance for that attribute ( DTSC 2013 ; Rossi et al. 2006 ). Others rely upon expert judgment (often in the form of expert elicitation) to fill data gaps ( Rossi et al. 2012 ). Although MCDA approaches can make similar use of simple heuristics and expert estimations, they also provide a variety of more sophisticated mechanisms for dealing with uncertainty ( Malloy et al. 2013 ; Hyde et al. 2003 ). Simple forms of sensitivity analysis in which single input values are modified to observe the effect on the MCDA results are also often used at the conclusion of the decision analysis process—the lead-based solder study used this approach to assess the robustness of its outcomes—although this type of ad hoc analysis has significant limitations ( Malloy et al. 2013 ; Hyde et al. 2003 ).

Diverse MCDA methods also offer a variety of quantitative probabilistic approaches relying upon such tools as Monte Carlo analysis, fuzzy sets, and Bayesian networks to investigate the range of outcomes associated with different values for the uncertain attribute ( Lahdelma and Salminen 2010 ). Canis and colleagues used a stochastic decision-analytic technique to address uncertainty in an evaluation of four different processes for synthesizing carbon nanotubes (arc, high-pressure carbon monoxide, chemical vapor deposition, and laser) across five performance criteria. Rather than generating an ordered ranking of the alternatives from first to last, the method provided an estimate of the probability that each alternative would occupy each rank ( Canis et al. 2010 ). Robust scenario analysis takes a different approach, using large ensembles of scenarios in an attempt to visualize all plausible, relevant futures for each alternative. With this range of potential futures in mind, decision makers are enabled to compare the alternatives in search of the most robust alternative given the uncertainties ( Lempert and Collins 2007 ).

Choosing among these approaches to uncertainty is not trivial. Studies in the decision analysis literature (and in the context of multi-criteria choices in particular) demonstrate that the approach taken with respect to uncertainty can substantially affect decision outcomes ( Hyde et al. 2003 ; Durbach and Stewart 2011 ). For example, one heuristic approach—called the “uncertainty downgrade”—essentially penalizes an alternative with missing data by assuming the worst with respect to the affected attribute. In some cases, such a penalty default may encourage proponents of the alternative to generate more complete data, but it may also lead to the selection of less-safe but more-studied alternatives ( NAS 2014 ).

How the evaluation of uncertainties is presented to the decision maker can be as important as the substance of the evaluation itself. Decision-making methods and tools are of course meant to assist the decision maker; thus, the results of the uncertainty analysis must be salient and comprehensible. In simple cases, a comprehensive assessment of uncertainty may not be necessary. In complicated situations, however, simply identifying data gaps without providing qualitative or quantitative analysis of the scope or impact of the uncertainty can leave decision makers adrift. Alternatively, the door could be left open to strategic assessment of the uncertainties aimed at advancing the interests of the regulated entity rather than achieving the goals of the regulatory program. Providing point estimates for uncertain data can bias decision making, and presenting ranges of data in probability distributions without supporting analysis designed to facilitate understanding can lead to information overload ( Durbach and Stewart 2011 ). Decision analytical approaches such as MCDA can provide insightful, rigorous treatment of uncertainty, but that rigor comes at some potential cost in terms of resource intensity, complexity and reduced transparency ( NAS 2009 ).

Poorly Understood Option Space

The range of alternatives considered in AA (often referred to as the “option space” in decision analysis and engineering) can be quite wide ( Frye-Levine 2012 ; de Wilde et al. 2002 ). Alternatives may involve a ) the use of “drop‐in” chemical or material substitutes, b ) a redesign of the product or process to obviate the need for the chemical of concern, or c ) changes regarding the magnitude or nature of the use of the chemical ( Sinsheimer et al. 2007 ). Option generation is a core aspect of decision making; identifying an overly narrow set of alternatives undermines the value of the ultimate decision ( Del Missier et al. 2015 ; Adelman et al. 1995 ). Accordingly, existing regulatory programs emphasize the importance of considering a broad range of relevant potential alternatives ( DTSC 2013 ; ECHA 2011 ).

We highlight three issues that complicate the identification of viable alternatives. For these purposes, viability refers to technical and economic feasibility. First, information regarding the existence and performance of alternatives is often difficult to uncover, particularly when searching for alternatives other than straightforward drop-in chemical replacements. Existing government, academic, and private publications do offer general guidance on searching for alternatives ( NAS 2014 ; U.S. EPA 2011 ; IC2 2013 ; Rossi et al. 2012 ), and databases and reports provide specific listings of chemical alternatives for limited types of products [U.S. EPA Safer Chemical Ingredients List (SCIL)]. However, for many other products, information regarding chemical and nonchemical alternatives may not be available to the regulated firm. Rather, the information may reside with vendors, manufacturers, consultants, or academics outside the regulated entity’s normal commercial network.

Second, for any given product or process, alternatives will be at different stages of development: Some may be readily available, mature technologies, whereas others are emerging or in early stages of commercialization. Indeed, selection of a technology through a regulatory alternative analysis can itself accelerate commercialization or market growth of that technology. Because the option space can be so dynamic, AA frameworks that assume a static set of options may exclude innovative alternatives that could be available in the near term ( ECHA 2011 ). Thus, identifying the set of potential alternatives for consideration can itself be a difficult decision made under conditions of uncertainty.

Third, the regulated entity (or rather, its managers and staff) may be unable or reluctant to cast a broad net in identifying potential alternatives. Individuals face cognitive and disciplinary limitations that can substantially shape their evaluation of information and decision making. For example, cognitive biases and mental models that lead us to favor the status quo and to discount the importance of new information are well documented ( Samuelson and Zeckhauser 1988 ), even in business settings with high stakes ( Kunreuther et al. 2002 ); this status quo bias is amplified when executives have longer tenure within their industry ( Hambrick et al. 1993 ). These unconscious biases can be mitigated to some degree through training and the use of well-designed decision-making processes and aids. Thaler and Benartzi ( 2004 ) demonstrated how changing the default can influence behavior in the context of saving for retirement, and Croskerry ( 2002 ) provided an overview of biases that occur in clinical decision making with strategies of how to avoid them. However, such training, processes, and aids are largely ineffective when the decision maker is acting strategically to limit the set of alternatives to circumvent the goals of the regulatory process. Many regulated firms have strong business reasons to resist externally driven alterations to successful products, including costs, disruption, and the uncertainty of customer response to the revised product.

Incorporating Decision-Maker Preferences/Weighting of Attributes

By its very nature, AA involves the balancing of attributes against one another in evaluating potential alternatives. Consider the example of antifouling paint for marine applications: One paint may be safer for boatyard workers, whereas another may be more protective of aquatic vegetation. In most multi-criteria decision situations, however, the decision maker is not equally concerned about all decision attributes. An individual decision maker may place more importance on whether a given paint kills aquatic vegetation than on whether it contributes to smog formation. Weighting is a significant challenge. In many cases, the individual decision maker’s preferences are not clear, even to that individual. This so-called “value uncertainty” is compounded in situations such as regulatory settings, in which many stakeholders (and thus many sets of preferences) are involved ( Ascough et al. 2008 ).

Existing approaches to AA vary significantly in how they address incorporation of preferences/weighting. Narrative approaches typically provide no explicit weighting of the decision attributes, although in some instances, qualitative weighting may be provided for the analyst. More often, whether and how to weight the relevant attributes are left to the discretion of the analyst ( Jacobs et al. 2016 ; Linkov et al. 2005 ). Elementary approaches usually incorporate either implicit or explicit weighting of the decision attributes. For example, decision rules in elementary approaches that eliminate alternatives based on particular attributes by definition place greater weight upon those attributes. Most MCDA approaches confront weighting explicitly, using various methods to derive weights. Generally speaking, there are three methods for eliciting or establishing explicit attribute weights: the use of existing generic weights such as the set in the National Institute of Standards and Technology’s life cycle assessment software for building products; calculation of weights using objective criteria such as the distance-to-target method; or elicitation of weights from experts or stakeholders ( Hansen 2010 ; Zhou and Schoenung 2007 ; Gloria et al. 2007 ; SPRU 2004 ; Lippiatt 2002 ). The robust scenario approach does not attempt to weight attributes; instead, it generates outcomes reasonably expected from a set of plausible scenarios for each alternative, allowing the decision maker to select the most robust alternative; that is, the alternative that offers the best range of outcomes across the scenarios.

Each strategy for addressing value uncertainty raises its own issues. For example, in regulatory programs such as Superfund and the Clean Air Act, which use narrative decision making, weighting is typically performed on a largely ad hoc basis, generally without any direct, systematic discussion of the relative weights to be accorded to the relevant decision criteria ( U.S. EPA 1994 ; U.S. EPA 1990 ). Such ad hoc treatment of weighting raises concerns regarding the consistency of outcomes across similar cases. Over time, regulators may develop standard outcomes or rules of thumb, which provide some consistency in outcome, but such conventions and the tacit weighting embedded in them can undermine transparency in decision making. Moreover, a lack of clear guidance regarding the relative weight to be accorded to criteria could allow political or administrative factors to influence the decision. However, incorporation of explicit weighting in regulatory decisions creates complex political and methodological questions beyond dealing with value uncertainty. For example, agencies generating explicit weightings would have to deal with potentially inconsistent preferences among the regulated entity, the various stakeholder groups, and the public at large. Similarly, they must consider whether pragmatic and strategic considerations related to implementation and enforcement of the program are relevant in establishing weighting ( Department for Communities and Local Government 2009 ).

Principles for Developing Effective Alternatives Analysis Decision-Making Approaches

The previous section focused on the ways in which the various decision-making approaches can be used to address the four challenges presented by AA. However, integrating such decision making into AA itself raises thorny questions: for example, which of the decision approaches and tools should be used and in what circumstances. In this section, we propose four interrelated principles regarding the application of those approaches and tools in regulatory AA.

Different Decision Points within Alternatives Analysis May Require Different Decision Approaches and Tools

In the course of an AA, one must make a series of decisions. These decisions include selecting relevant attributes, identifying potential alternatives, assessing performance regarding attributes concerning human health impacts, ecological and environmental impacts, technical performance, and economic impacts; the preferred alternatives must also be ranked or selected. Different approaches and tools may be best suited for each of these decisions rather than a one-size-fits-all methodology. Consider decisions regarding the relative performance of alternatives on particular attributes. For some attributes such as production costs or technical performance, there may be well-established methods in industry for evaluating relative performance that can be integrated into a broader AA framework. Similarly, GreenScreen ® is a hazard assessment tool that is used by a variety of AA frameworks ( IC2 2013 ; Rossi et al. 2012 ). However, these individual tools are not designed to assist in the trade-off analysis across all of the disparate attributes; for this task, other approaches and tools will be needed. Some researchers recommend using multiple approaches for the same analysis with the aim of generating more robust analysis to inform the decision maker ( Kiker et al. 2005 ; Yatsalo et al. 2007 ).

Decision-Making Approaches and Tools Should Be as Simple as Possible

Not every AA will require sophisticated analysis. In some cases, the analyst may conclude after careful assessment that the data are relatively complete and the trade-offs fairly clear. In such cases, basic decision approaches and uncomplicated heuristics may be all that are necessary to support a sound decision. Thus, a simple case involving a drop-in chemical substitute with substantially better performance across most attributes may not call for sophisticated MCDA approaches. Other situations will present high uncertainty and complex trade-offs; thus, these situations will require more advanced approaches and tools. The evaluation of alternative processes for synthesizing carbon nanotubes, which involved substantial uncertainty regarding technical performance and health impacts was more suited for probabilistic MCDA ( Canis et al. 2010 ). Similarly, not every regulated business or regulatory agency will have the resources or the capacity to use high-level analytical tools. Accordingly, the decision-making approach/tool should be scaled to reflect the capacity of the decision maker and the task at hand while seeking to maximize the quality of the ultimate decision. Clearly, if the decision will have a major impact but the regulated entity is currently not equipped to apply the appropriate sophisticated tools, other entities such as nongovernmental organizations, trade associations, or regulatory agencies should support that firm with technical advice or resources rather than running the risk of regrettable outcomes.

The Decision-Making Approach and Tools Should Be Crafted to Reflect the Decision Context

Context matters in structuring decision processes. In particular, it is important to consider who will be performing the analysis and who will be making the decision. As discussed above, when AA is used in a regulatory setting, the regulated business will typically perform the initial alternative analysis and present a decision to the agency for review. These businesses will have a range of capabilities and objectives. Some will engage in a good faith or even a fervent effort to seek out safer alternatives. Others will reluctantly do the minimum required, and still others may engage in strategic behavior, appearing to perform a good faith AA but assiduously avoiding changes to their product. The decision-making process should be designed with all of these behaviors in mind. For example, it might include meaningful minimum standards to ensure rigor and consistency in the face of strategic behavior while incorporating flexibility to foster innovation among those firms more committed to adopting safer alternatives.

Multicriteria Decision Analysis Should Support but Not Supplant Deliberation

The output of MCDA is meant to inform rather than to replace deliberation, defined for these purposes as the process for communication and consideration of issues in which participants “discuss, ponder, exchange observations and views, reflect upon information and judgments concerning matters of mutual interest, and attempt to persuade each other” ( NAS 1996 ). MCDA provides analytical results that systematically evaluate the trade-offs between alternatives, allowing those engaged in deliberation to consider how their preferences and the alternatives’ respective performance on different attributes affect the decision ( Perez 2010 ). MCDA augments professional, political, and personal judgment as a guide and as a reference point for stakeholders affected by or otherwise interested in the decision. However, the output of many MCDA tools can appear conclusive, setting out quantified rankings and groupings of alternatives and striking visualizations. Therefore, care must be taken to ensure that MCDA does not supplant or distort the deliberative process and to ensure that decision makers and stakeholders understand the embedded assumptions in the MCDA tool used as well as the tool’s limitations. For example, multicriteria mapping methods specifically attempt to facilitate such deliberation through an iterative, facilitated process involving a series of interviews with identified stakeholders. ( SPRU 2004 ; Hansen 2010 ). Moreover, although MCDA tools summarize the performance of alternatives under clearly defined metrics and preferences, they do not define standards for determining when a difference between the performance of alternatives is sufficient to justify making a change. Consider a case in which a manufacturer finds an alternative that exhibits lower aquatic toxicity by an order of magnitude but does somewhat worse in terms of technical performance. Without explicit input regarding the preferences of the decision maker, the MCDA tool cannot answer the question of whether the distinction is sufficiently large to justify product redesign. Ultimately, the decision maker must determine whether the differences between the incumbent and an alternative are significant enough to justify a move to the alternative.

With these challenges and principles in mind, we now turn to the question of how decision analysis and related disciplines can best be incorporated into the developing field of AA.

Next Steps: Advancing Integration of Alternatives Analysis and Decision Analysis

Decision science is a well-developed discipline, offering a variety of tools to assist decision makers. However, many of those tools are not widely used in the environmental regulatory setting, much less in the emerging area of AA. The process of integration is complicated by several factors. First, AA is by nature deeply transdisciplinary, requiring extensive cross-discipline interaction. Second, choosing among the wide range of available approaches and tools, each with its own benefits and limitations, can be daunting to regulators, businesses, and other stakeholders. Moreover, many of the tools require significant expertise in decision analysis and are not within the existing capacities of entities engaged in AA. Third, given the limited experience with formal decision tools in AA (and in environmental regulation more generally), there is skepticism among some regarding the value added by the use of such tools. Nonetheless, we see value in exploring the integration of decision analysis and its tools into AA, and we provide four recommendations to advance this integration.

Recommendation 1: Engage in Systematic Development, Assessment, and Evaluation of Decision Approaches and Tools

Although there is a rich body of literature in decision science concerning the development and evaluation of various decision tools, there has been relatively little research focused on applications in the context of AA in particular or in regulatory settings more broadly. Although recent studies of decision making in AA provide some insights, they ultimately call for further attention to be paid to the question of how decision tools can be integrated ( NAS 2014 ; Jacobs et al. 2015). Such efforts may include, among other things:

• • Developing or adapting user-friendly decision tools specifically for use in AA, taking into account the capacities and resources of the likely users and the particular decision task at hand.
• • Analyzing how existing and emerging decision approaches and tools address the four decision challenges of dealing with large numbers of attributes, uncertainty in performance data, poorly understood option space, and weighting of attributes.
• • Evaluating the extent to which such approaches and tools are worthwhile and amenable to use in a regulatory setting by agencies, businesses, and other stakeholders.
• • Considering how to better bridge the gap between analysis (whether human health, environmental, engineering, economic, or other forms) and deliberation, with particular focus on the potential role of decision analysis and tools.
• • Articulating objective technical and normative standards for selecting decision approaches and tools for particular uses in AA.

The results of this effort could be guidance for selecting and using a decision approach or even a multi-tiered tool that offers increasing levels of sophistication depending on the needs of the user. The experience gained over the years with the implementation of LCA could be useful here. For instance, the development of methods such as top-down and streamlined LCA has emerged in response to the recognition that many entities do not have the capacity (or the need) to conduct a full-blown process-based LCA, and standards such as the International Organization for Standardization (ISO) 14,040 series have emerged for third-party verification of LCA studies.

Recommendation 2: Use Case Studies to Advance the Integration of Decision Analysis into AA

Systematic case studies offer the opportunity to answer specific questions about how to integrate decision analysis into AA, and they demonstrate the potential value and limitations of different decision tools in AA to stakeholders. Case studies could also build upon and test outcomes from the activities discussed in “Recommendation 1.” For example, a case study may apply different decision tools to the same data set to evaluate differences in the performance of the tools with respect to previously developed technical and normative standards. To ensure real-world relevance, the case studies should be based upon actual commercial products and processes of interest to regulators, businesses, and other stakeholders. Currently relevant case-study topics that could be used to examine one or more of the decision challenges discussed above include marine antifouling paint, chemicals used in hydraulic fracturing (fracking), flame retardant alternatives, carbon nanotubes, and bisphenol A alternatives.

Recommendation 3: Support Trans-Sector and Trans-Disciplinary Efforts to Integrate Decision Analysis and Other Relevant Disciplines into Alternatives Analysis

AA brings a range of disciplines to bear in evaluating the relative benefits and drawbacks of a set of potentially safer alternatives, including toxicology, public health, engineering, economics, chemistry, environmental science, decision analysis, computer science, business management and operations, risk communication, and law. Existing tools and methods for AA do not integrate these disciplines in a systematic or rigorous way. Advancing AA will require constructing connections across those disciplines. Although this paper focuses on decision analysis, engagement with other disciplines will also be needed. Existing initiatives such as the AA Commons, the Organisation for Economic Co-operation and Development (OECD) Working Group, the Health and Environmental Sciences Institute (HESI) Committee, and others provide a useful starting point, but more systematic, research-focused, broadly trans-disciplinary efforts are also needed ( BizNGO 2016 ; OECD 2016 ). The AA case studies from Recommendation 2 could promote transdisciplinary efforts by creating a vehicle for practitioners to combine data from different sectors into a decision model. A research coordination network would provide the necessary vehicle for systematic collaboration across disciplines and public and private entities and institutions.

Recommendation 4: Support Undergraduate, Graduate, and Postgraduate Education and Outreach Efforts Regarding Alternatives Analysis, Including Attention to Decision Making

Advancing AA research and application in the mid-to-long term will require training the next generation of scientists, policy makers, and practitioners regarding the scientific and policy aspects of this new field. With very limited exceptions ( Schoenung et al. 2009 ), existing curricula in relevant undergraduate, graduate, and professional programs do not cover AA or prevention-based regulation. Curricular development will be particularly challenging for two reasons: the relative emerging nature of AA and the transdisciplinary nature of the undertaking. Its emerging nature means that there is little in terms of curricular materials to begin with, requiring significant start-up efforts. In addition, the subject matter is something of a moving target as new research and methods become available and as regulatory programs develop. In terms of the many disciplines that affect AA and prevention-based policy, effective education will itself have to be transdisciplinary and will have to reach across disciplines in terms of readings and exercises and engage students and faculty from those various disciplines.

The societal value of research regarding AA methods depends largely on the extent to which research is accessible to and understood by its end users—policy makers at every level, nongovernmental organizations (NGOs), and businesses. Ultimately, adoption of the frameworks, methods, and tools developed by researchers also requires broader acceptance by the public. This acceptance requires systematic education and outreach: namely, nonformal education in structured learning environments such as in-service training and continuing education outside of formal degree programs and informal or community education facilitating personal and community growth and sociopolitical engagement ( Bell 2009 ). For some, the education and outreach will be at the conceptual level alone, informing stakeholders about the general scope and nature of AA. For others who are more deeply engaged in chemicals policy, the education and outreach will focus upon more technical and methodological aspects.

Conclusions

There is immediate demand for robust, effective approaches to regulatory AA to select alternatives to chemicals of concern. Translation of decision analysis tools used in other areas of environmental decision making to the chemical regulation sphere could strengthen existing AA approaches but also presents unique questions and challenges. For instance, AAs must meet evolving regulatory standards but also be nimble enough for the private sector to employ as a tool during product development. To be useful, different tools crafted for the particular context may be required. The decision approaches employed should be as simple as possible and are intended to support rather than supplant decision making. Transdisciplinary work, mainly organized around case studies designed to address specific questions, and increased access to education and training would advance the use of decision analysis to improve AA.

Supplemental Material

(145 kb) pdf, acknowledgments.

This paper came from discussions at a workshop that was supported by the University of California (UC) Sustainable Technology and Policy Program, a joint collaboration of the University of California, Los Angeles (UCLA) School of Law and the Center for Occupational and Environmental Health at the UCLA Fielding School of Public Health in partnership with the UC Center for Environmental Implications of Nanotechnology (UC CEIN). UC CEIN is funded by a cooperative agreement from the National Science Foundation and the U.S. Environmental Protection Agency (NSF DBI-0830117; NSF DBI-1266377). Support for this workshop was also provided by the Institute of the Environment and Sustainability and the Emmett Institute on Climate Change and the Environment, both at UCLA.

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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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Alternative Courses of Action in Case Study: Examples and How To Write

The ultimate goal of creating a case course is to develop a feasible action that can solution the problem it raised.

One way to achieve diese is via enumerating all the possible solutions used your case study’s subject. The portion of aforementioned koffer study where you perform this is called ACA or Alternative Courses of Active.

Are you struggling with writing your case study’s ACA?  To not worry; we have provided you with the most extensive guide turn writing one Alternative Study of Action (ACA) of adenine crate study. Alternative Courses of Action in Case Course: Examples real How To Write - FilipiKnow

Table of Contents

What are alternative teaching of measures (aca) in an case study.

Variant Courses about Action (ACA) were the possible actions a firm or organization can implement to address the matter indicated in who case choose. These is recommended actions that a firm can consider to treffen at the most feasible and effective solution to the problem. 

Dieser portion doesn’t give the actual the optimal solution yet. Instead, it contains proposals alternatives that will still undergo an evaluation regarding they respective features and disadvantages to help you come up in the best solution. 

The ACA you will request the indicate will be based on my case study’s SWOT analysis in the “ Areas of Consideration ” portion. Thus, a SWOTT analyses be performed initial previously writing one ACA.

What Is the Impact of Alternative Courses of Action (ACA) with a Kasus Study?

Given to financial, strategic, also operational limitations, development solutions that the firm bucket perform can subsist challenging. By batch and evaluating the ACA of your kasus study, you bottle filter out this alternatives that can be a potential solution up the problem, default the business’s constraints 1 . This causes your proposed solutions feasible furthermore more meaningful.

How To Write Alternative Courses of Action in Hard Study

Here are the steps on how to write the Alternative Courses of Action in your lawsuit study:

1. Analyze which Erkenntnisse of Your SWOT Analysis

Using the SWOT analysis, considers how the firm able use its strengths and opportunities toward address its weaknesses, mitigate threats, and eventual solve the case study’s problem. 

Suppose that the case study’s problem is declining monthly sales, and that SWOT analysis showed the following:

• Strength : Creative marketing team 
• Opportunity : Increasing trend of using social media to promote merchandise

Than, you may inclusion an ACA about developing the digital branding arm of the firm to attract more our and boost monthly sales. This can also mailing can off the possible threats the firm faces, which exists increasing direct merchant shipping. Solved Incremental analyses is used to help companies make ...

2. Writing Your Propose Solutions/Alternative Courses of Action (ACA) by Get Case Study’s Problem

Once you have reviewed your SWOT examination and come up equal possible solutions, it’s hours until write you formally in you print. Each resolving does not have until be too detailed and wordy. State the specific action that the establish must perform concisely. Method to favorite consider your options whenever making an significant making

Going back to our previous example in Next 1, here is one of the possible ACA that can be included:

ACA #1: Utilize digital platforms such as web pages and social media sites as an alternative commercialize platform to reach a wider potentially customer base. Digital marketing, together with the customary direct commercialize strategy currently employed, maximizes the business’ market presence, drawing better consumers, and potentially driven total upward.

In our example above, there is a clear statement of the firm’s action: to use web pages both social media website to reach more possibility customers and increase local presence. Notice wie aforementioned ACA upper provides available an overview of “what to do” and not a complete elaboration on “how in do it.”  During a period of crisis, like a recession, many organizations are unsure learn the courses of action into take. Due every crisis capacity be and a threat and einer opportunity, organizations feel indecisive about the strategy which will best guide her to the crisis period and – hopefully – will help them to come going on peak.

3. Identify the Advantages and Drawbacks of Each ACA You Have Proposed

After specifying the ACA, you must evaluate them by showing their respective gains (pros) furthermore disadvantages (cons). In other words, you have state how your ACA likes the business (advantages) and its downsides and limitations (disadvantages).

Again, your evaluation does not have to been far extended but making securely so it is relevant to of ACA that it pertains to. 

Let’s return to the ACA ours developed from step 2, employ digital platforms (e.g., social media sites) for reach read possibility customer. What do you think will may the pros and disadvantages of this ACA? ... alternative courses of action. Person use included analysis in our own decision making as fine. Provide a hypothetical model from your ...

Let’s start with yours potential benefits (advantages). Using digital platforms is cheaply faster using print ads or direct marketing. So, this willing save some funds for an firm. In quick, e is cost-effective. 

Second, digital platforms offer analytical tools to measure your ads’ how, making thereto lighter to evaluate people’s human are your offering. 

Third, using social media sites makes communicating with any potential customer easier. You can quickly respond to their queries, particular if people are concerned in owner product. 

Lastly, you can reach as many types of people while possible on taking advantage of the net algorithm.

Now, let us consider its drawback 2 . First, uses digital marketplace takes time and effort to learn, plus you must be ably to adapt quickly to aforementioned changes in trends or new corporate to keep up with the competition. 

Second, you have deal with the increasing market competition, as many businesses already use digital platforms. 

Third, you have to deal with negative feedback from your client that are display to this public and may affect their perception on your brand.

After pondering over the pros the pros of thine ACA, it’s time at write them concisely the your manuscript. She may present it in two routes: by tabulating it or by simply listings them. How Green Is the Grass on the Other Side? Frontopolar Crust and the Show in Favor of Replacement Courses of Action

Example in Defer Form:

Instances of Alternatively Courses away Measures (ACA) in a Case Study

Case Survey Problem: Xenon Pastries faces a problem handling larger orders as Christmas Daylight approaches. With somebody estimated 15% increase in customer demand, this is the most significant increase in their daily orders since 2012. And management aims to maximize profit opportunities given the rise with customer demand. 

ACA #1: Hire part-time workers to boost workers numbers and meet which overwhelming seasonal increase in customer ordered. Currently, Xenon Pastries has ampere total of 9 workers who are responsible for the shelter of commands, preparation, the delivery of browse, the address customers’ inquiries and complaints. Hiring 2 – 3 part-time workers capacity increase productivity and match the daily order volume.

• Go not require to much effort to implement since hiring announcements only require signages or social media postings
• High certainty of finding potential workers due to the high unemployment rate
• Improve overall productivity of the business and the well-being of other work since their workload will be diminishes
• Raising at operating expense in the form of wages to to new workers
• Managing extra employees and monitoring their performance can be challenging
• New workers might find e ambitious to fitting essential skills requires in of company of the business-related

ACA #2: Increase the charges of Xenon pastries’ products to increase revenues . This option can maximize Xenon Pastries’ profit even if nay all customers’ orders are accommodated. 

• Cost-effective
• Easily to implement since it only supports changing the price badges von who products
• If customers’ desire to buy the products does not change, to price increase will secure increase this business’ revenue

Disadvantages

• Some customers magie to discouraged from buying because of an increase in prices
• There’s a possibility that that increase in this price of the items will make it more expensive relative to competitors’ products

Case Study Trouble: Delta Engines has been manufacturing motorcycles for ten year. Recently, the business lived a gradual shrink in its quarterly billing due to the increasing popularity of traditional and newly-developed electric bikes. Delta Motors seeks one long-term strategy to attract potential customers to rebound back sales.  

ACA #1: Develop a “regular installment payment” scheme to attract clients who wish to acquisition motorcycles but have lacking lump-sum money to acquire one.  This payment scheme allows customers to paypal an initial make plus an remaining amount using smaller monthly payments.

• Enticing for middle to low-income individuals who comprise a large chunked off one your
• Requires low initial capital to implement 
• Provides a new source of monthly income streams that can benefit the financial standing of the company
• Risk of default or delays in installment payments
• Requires added human resources to managed and collect installment payments
• This payment scheme requires time to net returns due to the periodic flow of funds
• Requires adenine careful creation of guidelines and terms or conditions in ensure smooth facilitation of the installment payment scheme

ACA #2: Introduced new motorcycle copies such can entice other types of customers. These models will feature popular designs and get efficient engines.

• This might capture the public’s interest within Delta Motors, which can lead to and increase in that number of potential consumers and merit opportunities
• Enables of business to retain up with the intense markt competitors by providing something “fresh” go one public
• Provides more alternatives for diese who have support Delta Motors, enhancement their loyalty go the brand
• Conceptualization from a new model takes a lot of brainstorming to test its feasibility and effectiveness
• Requires sufficient funds to keep who investment for the development of ampere new model
• Items requires effective marketing strategies to promote the new model to to public

Tips and Security

• Do not inclusive for this portion your kiste study’s conclusion . Think of ACA as a list of possibly ways at address and fix. In other words, you suggest the possible alternatives to be selected here. To “ Recommendation ” portion of thine case study belongs where you dial the most applicable route to solve the problem.
• Use statistical data to support the advantages and disabilities of each ACA. Although on is optional, presenting numerical data manufacture is analysis more concrete and fact-based than just stating them descriptively. 
• Do no fall the the “meat sandwich” trap. Dieser happens wenn you intently make some of the alternatives less desirable so that your preferred choice stands out. Dieser can be done according refuses to elaborate upon the advantages with excessively focus on them disadvantages. Make sure that each ACA has ability and can be implemented realistically.

Frequently Asked Questions

1. how many alternative course on measure (aca) can a case study have.

Sometimes your professor with teacher will tell you the required number of ACA that must be includes in your case choose . Any, there’s nay “standard” limit go how large ACA you bucket ausweisen.

2. What is the difference between Alternative Courses of Action (ACA) furthermore Recommendations?

How mentioned earlier, the case study’s ACA aims to enumerate all optional solutions in the report. This is not the stage where thou state the “final” action you deep most appropriate to meet the issue. The case studies portion where to definitely mention your “best” alternative is called the “Recommendation.”  Scribd is the world's largest social reading additionally publishing place.

Into help you understand the point above, let’s return go our Delta Driving model. Inside our previous section, we have provided two ACA that can solve the finding, is (1) developing a regular installment payment plan and (2) introducing a new motorcycle model. 

Suppose that upon careful analysis also evaluation of these ACA, you came up using ACA #2 as the more fitting solution to the problem. When you start your case study’s testimonial, it must indicate the ACA your chose and your reasons with selecting it. 

Here’s an example of the Recommendation of the case study:

Recommendation

Introduce new motorcycle models that feature popular designs and moreover efficient engines to entice different types of customers is the best promising alternative course off action so Delta Motors can implement to bounce back its quarterly revenues and keep up with the competitive market. This creates a strong impression on the public of the company’s dedication to promoting high-quality motorcycles that ca withstand changes in consumer preferences and market trends. Furthermore, this action proves that the company belongs continuously advanced to range a variety of alternatively models to suit everyone’s tastes. Equipped proper promotion, these models can rekindle the company’s popularity for the automotive and motorcycle industry.

• How to Analyze ampere Case Study. Retrieves 23 Mayor 2022, from https://wps.prenhall.com/bp_laudon_essbus_7/48/12303/3149605.cw/content/index.html
• Developers a Digital Selling Scheme. Retrieved 23 May 2022, with https://www.nibusinessinfo.co.uk/content/advantages-and-disadvantages-digital-marketing

Written on Jewel Kyle Fabula

by History and Educational , Juander How

Last Updated July 8, 2023 08:29 PM

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelors of Science in Economics student at the University of the Home Diliman. His passion for learning computation developed as he competed in some mathematics competition during his Junior High School years. He loves cats, playing video games, and listening to music. She Required Decide. What’s the Highest Course of Action?

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Papa John’s Pizza Restaurant’s Analysis Case Study

Introduction, performance, strategic intent, difference from the rivals, swot analysis, alternative courses of action.

Pizza is among the most favored meals in the United States. It is worth noting though that pizzas are sold all over the world very successfully. People like pizza for its taste and the opportunity to have it delivered already cooked considerably quickly. The combination of all these characteristics makes pizza businesses very successful. Papa John’s Pizza, as one of the top four brands in the world’s market of pizza producers, has a strong position on it, but the issues are still present. Considering the situation at Papa John’s Pizza, the paper aims at exploring the current issues, the strategic intent of the company, its difference from the rivals, conducting SWOT analysis, and proposing the alternative course of action.

Currently, Papa John’s Pizza shows good performance, assessing the increasing revenues year per year ($1,439 million in 2013). The company opens new company-owned and franchised units domestically and abroad every year (82 and 183 respectively). The market share grows as well. The competitive standing of the company has remained stable since competitors such as Pizza Hut, Domino’s, and Little Caesar did not implement radical changes to the business processes, so they did not get a competitive advantage. The company has the reputation of the socially responsible company that supports different social programs for children, for example.

The challenges faced by Papa John’s Pizza are not critical but still considerably noticeable. They are increasing rivalry, the changing trends in the awareness of customers regarding the healthiness of the ingredients, changes in the technologies used in the industry, and the unclear economic situation on several markets where the company is present. The combination of these issues had led to the negative dynamics of revenue growth.

The strategic intent of the company is to build a very strong brand loyalty in the industry of pizza production. The reasoning of such a strategic intent is simple if to think about the peculiarity of pizza market. The product of all brands is rather similar in its concept, and it differs in details only, so to gain and keep customers, the brand has to propose an appropriate atmosphere and environment to them.

Papa John’s Pizza is different from the rivals because of its approach to the customers’ satisfaction assurance. The company uses the Quality Control Centers to assure the standard (the best) quality of the products sold under its brand regardless of location. Domino’s, as the major competitor, has a greater number of outlets and franchises more units than Papa John’s Pizza does, having larger total sales and total revenues. However, the focus of Domino’s on the sales generation was on the delivery of pizzas.

Strengths of the company are in its focus on the customer satisfaction through providing the highest quality of the products. The loyal franchising policy has also provided Papa John’s Pizza with a competitive advantage.

Papa John’s Pizza has no apparent weaknesses except for, perhaps, a certain influence of the unstable markets’ situation on the company’s sales and revenue. However, the company is still not at the first position in the top four of the major pizza brands, so certain issues are present.

As for the opportunities, the company should focus on the use of new technologies that would provide it with a competitive advantage. One of such opportunities could be the use of the advancements in drones’ design and manufacturing costs. Pizzas delivered by drones can become a buzz factor for the hesitating customers.

The threats are the new potential competitors entering the market and the growing interest of pizza consumers to the healthier food. It would require from Papa John’s Pizza new products to develop and grant their cost effectiveness.

Papa John’s Pizza should develop an alternative approach to the process of pizza delivery. Currently, the delivery drivers have a dangerous job to perform. It adds expenses for delivery services. The potential of pizza delivery using drones is tremendous. Such an approach has the following advantages: the image of the technologically advanced company; the environment friendliness of the company; and cost effectiveness considering the cut of expenses for the delivery drivers.

There are some disadvantages as well. They are as follows: the complexity of the new technology; potential theft of equipment; issues at the first stage of implementation due to the unusualness of the delivery method; and lack of the offers on the current market that would provide professional solutions for solving such a complex task

Summing, the paper explored the current issues of Papa John’s Pizza, its strategic intent, the difference between rivals, and conducted SWOT analysis as well as proposed the alternative course of action. Despite the challenges Papa John’s Pizza currently has, the potential of the company to expand in the foreign markets and develop as the pizza producer is still substantial. The company should implement more innovative methods of pizza (and other products offered by the company) delivery to gain a competitive advantage.

In addition, these methods should be cost effective and technologically advanced. Considering the trends and successes in the area of unmanned aircrafts usage for various purposes, it can be a potentially advantageous idea to use drones for pizza delivery.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2020, September 11). Papa John’s Pizza Restaurant’s Analysis. https://ivypanda.com/essays/papa-johns-pizza-restaurants-analysis/

"Papa John’s Pizza Restaurant’s Analysis." IvyPanda , 11 Sept. 2020, ivypanda.com/essays/papa-johns-pizza-restaurants-analysis/.

IvyPanda . (2020) 'Papa John’s Pizza Restaurant’s Analysis'. 11 September.

IvyPanda . 2020. "Papa John’s Pizza Restaurant’s Analysis." September 11, 2020. https://ivypanda.com/essays/papa-johns-pizza-restaurants-analysis/.

1. IvyPanda . "Papa John’s Pizza Restaurant’s Analysis." September 11, 2020. https://ivypanda.com/essays/papa-johns-pizza-restaurants-analysis/.

Bibliography

IvyPanda . "Papa John’s Pizza Restaurant’s Analysis." September 11, 2020. https://ivypanda.com/essays/papa-johns-pizza-restaurants-analysis/.

• Papa John’s International, Inc.
• Papa John’s Company: Profile and Competitive Standing
• Financing a Franchise Acquisition
• My papa’s waltz
• Effects of Lack of Social Media Marketing on Papa Pita Bakery
• Anthropology Oral: Interview with Papa
• Domino's Pizza Case Study: Strategic Management
• "My Papa's Waltz" Poem by Theodore Roethke
• Product Feasibility Analysis
• Consumer Behavior in the Context of Restaurant Domino Pizza
• GLO-BUS Company's Low Revenue and Marketing
• Loganville Window Treatments: Manufacturing and Service
• Tesla Inc.'s PESTEL, Five Forces, SWOT Analyses
• Tesla Motors' Competitive Profile in Abu Dhabi
• Google, Apple and Facebook Companies Competition

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11.3 Understanding Decision Making

Learning objectives.

• Define decision making.
• Understand different types of decisions.

What Is Decision Making?

Decision making refers to making choices among alternative courses of action—which may also include inaction. While it can be argued that management is decision making, half of the decisions made by managers within organizations fail (Ireland & Miller, 2004; Nutt, 2002; Nutt, 1999). Therefore, increasing effectiveness in decision making is an important part of maximizing your effectiveness at work. This chapter will help you understand how to make decisions alone or in a group while avoiding common decision-making traps.

Individuals throughout organizations use the information they gather to make a wide range of decisions. These decisions may affect the lives of others and change the course of an organization. For example, the decisions made by executives and consulting firms for Enron ultimately resulted in a $60 billion loss for investors, thousands of employees without jobs, and the loss of all employee retirement funds. But Sherron Watkins, a former Enron employee and now-famous whistleblower, uncovered the accounting problems and tried to enact change. Similarly, the decisions made by firms to trade in mortgage-backed securities is having negative consequences for the entire U.S. economy. Each of these people made a decision, and each person, as well as others, is now living with the consequences of his or her decisions.

Because many decisions involve an ethical component, one of the most important considerations in management is whether the decisions you are making as an employee or manager are ethical. Here are some basic questions you can ask yourself to assess the ethics of a decision (Blanchard & Peale, 1988).

• Is this decision fair?
• Will I feel better or worse about myself after I make this decision?
• Does this decision break any organizational rules?
• Does this decision break any laws?
• How would I feel if this decision was broadcast on the news?

Types of Decisions

Despite the far-reaching nature of the decisions in the previous example, not all decisions have major consequences or even require a lot of thought. For example, before you come to class, you make simple and habitual decisions such as what to wear, what to eat, and which route to take as you go to and from home and school. You probably do not spend much time on these mundane decisions. These types of straightforward decisions are termed programmed decisions; these are decisions that occur frequently enough that we develop an automated response to them. The automated response we use to make these decisions is called the decision rule . For example, many restaurants face customer complaints as a routine part of doing business. Because this is a recurring problem for restaurants, it may be regarded as a programmed decision. To deal with this problem, the restaurant might have a policy stating that every time they receive a valid customer complaint, the customer should receive a free dessert, which represents a decision rule. Making strategic, tactical, and operational decisions is an integral part of the planning function in the P-O-L-C (planning-organizing-leading-controlling) model.

However, decisions that are unique and important require conscious thinking, information gathering, and careful consideration of alternatives. These are called nonprogrammed decisions . For example, in 2005, McDonald’s became aware of a need to respond to growing customer concerns regarding foods high in fat and calories. This is a nonprogrammed decision because for several decades, customers of fast-food restaurants were more concerned with the taste and price of the food, rather than the healthiness. In response, McDonald’s decided to offer healthier alternatives, such as substituting apple slices in Happy Meals for French fries and discontinuing the use of trans fats. A crisis situation also constitutes a nonprogrammed decision for companies. For example, the leadership of Nutrorim was facing a tough decision. They had recently introduced a new product, ChargeUp with Lipitrene, an improved version of their popular sports drink powder, ChargeUp. But a phone call came from a state health department to inform them that several cases of gastrointestinal distress had been reported after people consumed the new product. Nutrorim decided to recall ChargeUp with Lipitrene immediately. Two weeks later, it became clear that the gastrointestinal problems were unrelated to ChargeUp with Lipitrene. However, the damage to the brand and to the balance sheets was already done. This unfortunate decision caused Nutrorim to rethink the way decisions were made under pressure so that they now gather information to make informed choices even when time is of the essence (Garvin, 2006).

Figure 11.5

To ensure consistency around the globe such as at this St. Petersburg, Russia, location, McDonald’s trains all restaurant managers (over 65,000 so far) at Hamburger University where they take the equivalent of two years of college courses and learn how to make decisions. The curriculum is taught in 28 languages.

Wikimedia Commons – McDonalds in St Petersburg 2004 – CC BY-SA 1.0.

Decision making can also be classified into three categories based on the level at which they occur. Strategic decisions set the course of organization. Tactical decisions are decisions about how things will get done. Finally, operational decisions are decisions that employees make each day to run the organization. For example, remember the restaurant that routinely offers a free dessert when a customer complaint is received. The owner of the restaurant made a strategic decision to have great customer service. The manager of the restaurant implemented the free dessert policy as a way to handle customer complaints, which is a tactical decision. And, the servers at the restaurant are making individual decisions each day evaluating whether each customer complaint received is legitimate to warrant a free dessert.

Figure 11.6 Decisions Commonly Made within Organizations

In this chapter, we are going to discuss different decision-making models designed to understand and evaluate the effectiveness of nonprogrammed decisions. We will cover four decision-making approaches starting with the rational decision-making model, moving to the bounded rationality decision-making model, the intuitive decision-making model, and ending with the creative decision-making model.

Making Rational Decisions

The rational decision-making model describes a series of steps that decision makers should consider if their goal is to maximize the quality of their outcomes. In other words, if you want to make sure you make the best choice, going through the formal steps of the rational decision-making model may make sense.

Let’s imagine that your old, clunky car has broken down and you have enough money saved for a substantial down payment on a new car. It is the first major purchase of your life, and you want to make the right choice. The first step, therefore, has already been completed—we know that you want to buy a new car. Next, in step 2, you’ll need to decide which factors are important to you. How many passengers do you want to accommodate? How important is fuel economy to you? Is safety a major concern? You only have a certain amount of money saved, and you don’t want to take on too much debt, so price range is an important factor as well. If you know you want to have room for at least five adults, get at least 20 miles per gallon, drive a car with a strong safety rating, not spend more than $22,000 on the purchase, and like how it looks, you’ve identified the decision criteria. All of the potential options for purchasing your car will be evaluated against these criteria.

Figure 11.7

Using the rational decision-making model to make major purchases can help avoid making poor choices.

Lars Plougmann – Headshift business card discussion – CC BY-SA 2.0.

Before we can move too much further, you need to decide how important each factor is to your decision in step 3. If each is equally important, then there is no need to weight them, but if you know that price and gas mileage are key factors, you might weight them heavily and keep the other criteria with medium importance. Step 4 requires you to generate all alternatives about your options. Then, in step 5, you need to use this information to evaluate each alternative against the criteria you have established. You choose the best alternative (step 6) and you go out and buy your new car (step 7).

Of course, the outcome of this decision will be related to the next decision made; that is where the evaluation in step 8 comes in. For example, if you purchase a car but have nothing but problems with it, you are unlikely to consider the same make and model in purchasing another car the next time!

Figure 11.8 Steps in the Rational Decision-Making Model

While decision makers can get off track during any of these steps, research shows that limiting the search for alternatives in the fourth step can be the most challenging and lead to failure. In fact, one researcher found that no alternative generation occurred in 85% of the decisions studied (Nutt, 1994). Conversely, successful managers are clear about what they want at the outset of the decision-making process, set objectives for others to respond to, carry out an unrestricted search for solutions, get key people to participate, and avoid using their power to push their perspective (Nutt, 1998).

The rational decision-making model has important lessons for decision makers. First, when making a decision you may want to make sure that you establish your decision criteria before you search for all alternatives. This would prevent you from liking one option too much and setting your criteria accordingly. For example, let’s say you started browsing for cars before you decided your decision criteria. You may come across a car that you think really reflects your sense of style and make an emotional bond with the car. Then, because of your love for this car, you may say to yourself that the fuel economy of the car and the innovative braking system are the most important criteria. After purchasing it, you may realize that the car is too small for all of your friends to ride in the back seat when you and your brother are sitting in front, which was something you should have thought about! Setting criteria before you search for alternatives may prevent you from making such mistakes. Another advantage of the rational model is that it urges decision makers to generate all alternatives instead of only a few. By generating a large number of alternatives that cover a wide range of possibilities, you are likely to make a more effective decision in which you do not need to sacrifice one criterion for the sake of another.

Despite all its benefits, you may have noticed that this decision-making model involves a number of unrealistic assumptions. It assumes that people understand what decision is to be made, that they know all their available choices, that they have no perceptual biases, and that they want to make optimal decisions. Nobel Prize–winning economist Herbert Simon observed that while the rational decision-making model may be a helpful tool for working through problems, it doesn’t represent how decisions are frequently made within organizations. In fact, Simon argued that it didn’t even come close!

Think about how you make important decisions in your life. Our guess is that you rarely sit down and complete all eight steps in the rational decision-making model. For example, this model proposed that we should search for all possible alternatives before making a decision, but this can be time consuming and individuals are often under time pressure to make decisions. Moreover, even if we had access to all the information, it could be challenging to compare the pros and cons of each alternative and rank them according to our preferences. Anyone who has recently purchased a new laptop computer or cell phone can attest to the challenge of sorting through the different strengths and limitations of each brand, model, and plans offered for support and arriving at the solution that best meets their needs.

In fact, the availability of too much information can lead to analysis paralysis , where more and more time is spent on gathering information and thinking about it, but no decisions actually get made. A senior executive at Hewlett-Packard admits that his company suffered from this spiral of analyzing things for too long to the point where data gathering led to “not making decisions, instead of us making decisions (Zell, et. al., 2007).” Moreover, you may not always be interested in reaching an optimal decision. For example, if you are looking to purchase a house, you may be willing and able to invest a great deal of time and energy to find your dream house, but if you are looking for an apartment to rent for the academic year, you may be willing to take the first one that meets your criteria of being clean, close to campus, and within your price range.

Making “Good Enough” Decisions

The bounded rationality model of decision making recognizes the limitations of our decision-making processes. According to this model, individuals knowingly limit their options to a manageable set and choose the best alternative without conducting an exhaustive search for alternatives. An important part of the bounded rationality approach is the tendency to satisfice , which refers to accepting the first alternative that meets your minimum criteria. For example, many college graduates do not conduct a national or international search for potential job openings; instead, they focus their search on a limited geographic area and tend to accept the first offer in their chosen area, even if it may not be the ideal job situation. Satisficing is similar to rational decision making, but it differs in that rather than choosing the best choice and maximizing the potential outcome, the decision maker saves time and effort by accepting the first alternative that meets the minimum threshold.

Making Intuitive Decisions

The intuitive decision-making model has emerged as an important decision-making model. It refers to arriving at decisions without conscious reasoning. Eighty-nine percent of managers surveyed admitted to using intuition to make decisions at least sometimes, and 59% said they used intuition often (Burke & Miller, 1999). When we recognize that managers often need to make decisions under challenging circumstances with time pressures, constraints, a great deal of uncertainty, highly visible and high-stakes outcomes, and within changing conditions, it makes sense that they would not have the time to formally work through all the steps of the rational decision-making model. Yet when CEOs, financial analysts, and healthcare workers are asked about the critical decisions they make, seldom do they attribute success to luck. To an outside observer, it may seem like they are making guesses as to the course of action to take, but it turns out that they are systematically making decisions using a different model than was earlier suspected. Research on life-or-death decisions made by fire chiefs, pilots, and nurses finds that these experts do not choose among a list of well-thought-out alternatives. They don’t decide between two or three options and choose the best one. Instead, they consider only one option at a time. The intuitive decision-making model argues that, in a given situation, experts making decisions scan the environment for cues to recognize patterns (Breen, 2000; Klein, 2003; Salas & Klein, 2001). Once a pattern is recognized, they can play a potential course of action through to its outcome based on their prior experience. Due to training, experience, and knowledge, these decision makers have an idea of how well a given solution may work. If they run through the mental model and find that the solution will not work, they alter the solution and retest it before setting it into action. If it still is not deemed a workable solution, it is discarded as an option and a new idea is tested until a workable solution is found. Once a viable course of action is identified, the decision maker puts the solution into motion. The key point is that only one choice is considered at a time. Novices are not able to make effective decisions this way because they do not have enough prior experience to draw upon.

Making Creative Decisions

In addition to the rational decision making, bounded rationality models, and intuitive decision making, creative decision making is a vital part of being an effective decision maker. Creativity is the generation of new, imaginative ideas. With the flattening of organizations and intense competition among organizations, individuals and organizations are driven to be creative in decisions ranging from cutting costs to creating new ways of doing business. Please note that, while creativity is the first step in the innovation process, creativity and innovation are not the same thing. Innovation begins with creative ideas, but it also involves realistic planning and follow-through.

The five steps to creative decision making are similar to the previous decision-making models in some keys ways. All of the models include problem identification , which is the step in which the need for problem solving becomes apparent. If you do not recognize that you have a problem, it is impossible to solve it. Immersion is the step in which the decision maker thinks about the problem consciously and gathers information. A key to success in creative decision making is having or acquiring expertise in the area being studied. Then, incubation occurs. During incubation, the individual sets the problem aside and does not think about it for a while. At this time, the brain is actually working on the problem unconsciously. Then comes illumination or the insight moment, when the solution to the problem becomes apparent to the person, usually when it is least expected. This is the “eureka” moment similar to what happened to the ancient Greek inventor Archimedes, who found a solution to the problem he was working on while he was taking a bath. Finally, the verification and application stage happens when the decision maker consciously verifies the feasibility of the solution and implements the decision.

A NASA scientist describes his decision-making process leading to a creative outcome as follows: He had been trying to figure out a better way to de-ice planes to make the process faster and safer. After recognizing the problem, he had immersed himself in the literature to understand all the options, and he worked on the problem for months trying to figure out a solution. It was not until he was sitting outside of a McDonald’s restaurant with his grandchildren that it dawned on him. The golden arches of the “M” of the McDonald’s logo inspired his solution: he would design the de-icer as a series of M’s! 1 This represented the illumination stage. After he tested and verified his creative solution, he was done with that problem except to reflect on the outcome and process.

Figure 11.9 The Creative Decision-Making Process

How Do You Know If Your Decision-Making Process Is Creative?

Researchers focus on three factors to evaluate the level of creativity in the decision-making process. Fluency refers to the number of ideas a person is able to generate. Flexibility refers to how different the ideas are from one another. If you are able to generate several distinct solutions to a problem, your decision-making process is high on flexibility. Originality refers to an idea’s uniqueness. You might say that Reed Hastings, founder and CEO of Netflix, is a pretty creative person. His decision-making process shows at least two elements of creativity. We do not exactly know how many ideas he had over the course of his career, but his ideas are fairly different from one another. After teaching math in Africa with the Peace Corps, Hastings was accepted at Stanford University, where he earned a master’s degree in computer science. Soon after starting work at a software company, he invented a successful debugging tool, which led to his founding the computer troubleshooting company Pure Software in 1991. After a merger and the subsequent sale of the resulting company in 1997, Hastings founded Netflix, which revolutionized the DVD rental business through online rentals with no late fees. In 2007, Hastings was elected to Microsoft’s board of directors. As you can see, his ideas are high in originality and flexibility (Conlin, 2007).

Figure 11.10 Dimensions of Creativity

Some experts have proposed that creativity occurs as an interaction among three factors: (1) people’s personality traits (openness to experience, risk taking), (2) their attributes (expertise, imagination, motivation), and (3) the context (encouragement from others, time pressure, and physical structures) (Amabile, 1988; Amabile, et. al., 1996; Ford & Gioia, 2000; Tierney, et. al., 1999; Woodman, et. al., 1993). For example, research shows that individuals who are open to experience, are less conscientious, more self-accepting, and more impulsive, tend to be more creative (Feist, 1998).

There are many techniques available that enhance and improve creativity. Linus Pauling, the Nobel prize winner who popularized the idea that vitamin C could help build the immunity system, said, “The best way to have a good idea is to have a lot of ideas.” One popular way to generate ideas is to use brainstorming. Brainstorming is a group process of generated ideas that follows a set of guidelines that include no criticism of ideas during the brainstorming process, the idea that no suggestion is too crazy, and building on other ideas (piggybacking). Research shows that the quantity of ideas actually leads to better idea quality in the end, so setting high idea quotas where the group must reach a set number of ideas before they are done, is recommended to avoid process loss and to maximize the effectiveness of brainstorming. Another unique aspect of brainstorming is that the more people are included in brainstorming, the better the decision outcome will be because the variety of backgrounds and approaches give the group more to draw from. A variation of brainstorming is wildstorming where the group focuses on ideas that are impossible and then imagines what would need to happen to make them possible (Scott, et. al., 2004).

Ideas for Enhancing Organizational Creativity

We have seen that organizational creativity is vital to organizations. Here are some guidelines for enhancing organizational creativity within teams (Amabile, 1998; Gundry, et. al., 1994; Keith, 2008; Pearsall, et. al., 2008; Thompson, 2003).

Team Composition (Organizing/Leading)

• Diversify your team to give them more inputs to build on and more opportunities to create functional conflict while avoiding personal conflict.
• Change group membership to stimulate new ideas and new interaction patterns.
• Leaderless teams can allow teams freedom to create without trying to please anyone up front.

Team Process (Leading)

• Engage in brainstorming to generate ideas—remember to set a high goal for the number of ideas the group should come up with, encourage wild ideas, and take brainwriting breaks.
• Use the nominal group technique in person or electronically to avoid some common group process pitfalls. Consider anonymous feedback as well.
• Use analogies to envision problems and solutions.

Leadership (Leading)

• Challenge teams so that they are engaged but not overwhelmed.
• Let people decide how to achieve goals , rather than telling them what goals to achieve.
• Support and celebrate creativity even when it leads to a mistake. But set up processes to learn from mistakes as well.
• Model creative behavior.

Culture (Organizing)

• Institute organizational memory so that individuals do not spend time on routine tasks.
• Build a physical space conducive to creativity that is playful and humorous—this is a place where ideas can thrive.
• Incorporate creative behavior into the performance appraisal process.

And finally, avoiding groupthink can be an important skill to learn (Janis, 1972).

The four different decision-making models—rational, bounded rationality, intuitive, and creative—vary in terms of how experienced or motivated a decision maker is to make a choice. Choosing the right approach will make you more effective at work and improve your ability to carry out all the P-O-L-C functions.

Figure 11.11

Which decision-making model should I use?

Key Takeaway

Decision making is choosing among alternative courses of action, including inaction. There are different types of decisions, ranging from automatic, programmed decisions to more intensive nonprogrammed decisions. Structured decision-making processes include rational decision making, bounded rationality, intuitive, and creative decision making. Each of these can be useful, depending on the circumstances and the problem that needs to be solved.

• What do you see as the main difference between a successful and an unsuccessful decision? How much does luck versus skill have to do with it? How much time needs to pass to answer the first question?
• Research has shown that over half of the decisions made within organizations fail. Does this surprise you? Why or why not?
• Have you used the rational decision-making model to make a decision? What was the context? How well did the model work?
• Share an example of a decision where you used satisficing. Were you happy with the outcome? Why or why not? When would you be most likely to engage in satisficing?
• Do you think intuition is respected as a decision-making style? Do you think it should be? Why or why not?

1 Interview by author Talya Bauer at Ames Research Center, Mountain View, CA, 1990.

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