alternatives and decision criteria case study example

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

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• Case Teaching
• Student Engagement

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

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

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

The Case for Cases: Helping Students Think Critically

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

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

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

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

PACADI: A Six-Step Decision-Making Approach

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

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

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

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

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

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

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

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

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

PACADI’s Benefits: Meaningfully and Thoughtfully Applying Business Concepts

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

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

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

PACADI in Action: An Example

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

The CVS Prescription for Customer Value*

PACADI Stage

Summary Response

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

Alternatives

A1. Kaizen (continuous improvement)

A2. Product development

A3. Market development

A4. Personalization (micro-targeting)

Criteria (include weights)

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

C2. Customer obsession (20%)

C3. Growth through related businesses (20%)

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

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

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

Implementation

Partner with leading medical school

Curbside pick-up

Pet pharmacy

E-newsletter for customers and employees

Employee incentive program

CVS beauty days

Expand to Latin America and Caribbean

Healthier/happier corner

Holiday toy drives/community outreach

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

Results of Using the PACADI Framework

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

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

Tips to Get Started

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

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

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

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

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

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

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

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

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

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

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

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

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

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What are Decision Criteria? (Explained With Examples)

Oct 11, 2023

Decision criteria are essential factors that individuals or organizations consider when making a decision. They serve as the basis for evaluating different options and ultimately choosing the best course of action. In this article, we will delve into the definition of decision criteria, explore their advantages and disadvantages, and provide practical examples in various contexts, such as startups, consulting, and digital marketing agencies. We will also utilize analogies to help illustrate the concept further.

1. What are Decision Criteria?

Decision criteria encompass the specific factors that individuals or organizations prioritize when making a decision. These factors can include financial considerations, strategic objectives, customer preferences, technological feasibility, and many other relevant aspects. By establishing decision criteria, individuals or organizations can systematically evaluate different options and determine the best course of action.

When it comes to decision-making, having clear and well-defined criteria is crucial. Decision criteria serve as a set of guidelines or standards that help individuals or organizations assess and compare different alternatives. These criteria can be quantitative or qualitative, depending on the nature of the decision and the available information.

1.1 Definition of Decision Criteria

Decision criteria refer to the specific standards or benchmarks used to assess different alternatives during a decision-making process. These criteria can be quantitative or qualitative and are often tailored to the specific context or goals of the decision-making process.

Quantitative decision criteria involve measurable factors that can be assigned numerical values. For example, in a financial decision, criteria such as return on investment (ROI), net present value (NPV), or payback period may be used. These criteria provide a clear and objective basis for evaluating options.

On the other hand, qualitative decision criteria involve subjective factors that are difficult to quantify but still play a significant role in the decision-making process. These criteria can include factors such as brand reputation, customer satisfaction, or ethical considerations. While they may not have precise numerical values, they are essential for capturing the intangible aspects that influence decision outcomes.

1.2 Advantages of Decision Criteria

Using decision criteria provides several benefits. Firstly, decision criteria help ensure that all relevant aspects are taken into account, leading to more comprehensive and informed decisions. By considering a range of factors, decision-makers can avoid overlooking critical aspects that could impact the success of the chosen option.

Secondly, decision criteria enable individuals or organizations to compare and prioritize different options based on their importance. By assigning weights or rankings to each criterion, decision-makers can objectively assess the pros and cons of each alternative. This allows for a more structured decision-making process and reduces the likelihood of relying on subjective judgments solely.

Lastly, decision criteria facilitate stakeholder collaboration and transparency as they provide a clear framework for evaluating options and aligning diverse perspectives. When decision criteria are well-defined and communicated, stakeholders can understand the rationale behind the chosen option and feel more engaged in the decision-making process.

1.3 Disadvantages of Decision Criteria

While decision criteria offer significant advantages, they are not without their limitations. One potential disadvantage is that decision criteria can oversimplify complex problems or situations. By focusing on specific factors, decision criteria may overlook critical nuances or underlying complexities that could impact the final decision. It is important for decision-makers to be aware of this limitation and consider additional information or expert opinions to supplement the criteria.

Additionally, decision criteria can be influenced by biases or limited perspectives, potentially leading to suboptimal outcomes. For example, if decision criteria heavily prioritize short-term financial gains, long-term sustainability or ethical considerations may be overlooked. To mitigate this risk, decision-makers should strive for a diverse and inclusive decision-making process that incorporates a wide range of perspectives and expertise.

In conclusion, decision criteria play a vital role in the decision-making process. They provide a structured framework for evaluating alternatives and help ensure that all relevant aspects are considered. However, decision criteria should be used judiciously, taking into account the limitations and potential biases they may introduce. By combining decision criteria with broader considerations and expert insights, individuals and organizations can make more informed and effective decisions.

2. Examples of Decision Criteria

Examining practical examples will further illuminate the concept of decision criteria. Let's explore how decision criteria can be applied in different contexts:

2.1 Example in a Startup Context

In a startup context, decision criteria could include factors such as market potential, cost-effectiveness, scalability, and alignment with the company's vision and mission. By prioritizing these criteria, startup founders can evaluate potential business opportunities and make data-driven decisions that maximize chances of success.

For example, let's consider a tech startup that is developing a new mobile app. The decision criteria for this startup could involve analyzing the size of the target market, assessing the potential demand for the app, and evaluating the competition in the app market. Additionally, the startup may consider the cost-effectiveness of developing and maintaining the app, as well as its scalability potential in terms of user growth and revenue generation.

By carefully considering these decision criteria, the startup can make informed choices about whether to proceed with the app development, how to allocate resources, and how to position the app in the market.

2.2 Example in a Consulting Context

When working on a consulting project, decision criteria might involve client requirements, industry best practices, profitability, and long-term sustainability. By utilizing these criteria, consultants can assess alternative solutions and recommend the most suitable strategy to address the client's challenges and achieve their goals.

For instance, let's imagine a consulting firm that is tasked with helping a manufacturing company optimize its production processes. The decision criteria in this context could include analyzing the client's specific requirements, benchmarking against industry best practices, evaluating the potential profitability of proposed solutions, and considering the long-term sustainability of the implemented changes.

By carefully evaluating these decision criteria, the consulting firm can provide the client with a well-informed recommendation on how to streamline their production processes, improve efficiency, and ultimately achieve their desired business outcomes.

2.3 Example in a Digital Marketing Agency Context

In the context of a digital marketing agency, decision criteria could include factors such as target audience reach, cost per acquisition, return on investment, and campaign performance metrics. By incorporating these criteria, digital marketers can make data-informed decisions on the optimal marketing channels, strategies, and campaigns to maximize client's online presence and achieve desired business outcomes.

For example, let's consider a digital marketing agency that is working with an e-commerce client to increase their online sales. The decision criteria for this agency could involve analyzing the potential reach of different marketing channels, calculating the cost per acquisition for each channel, evaluating the expected return on investment for various marketing strategies, and monitoring campaign performance metrics such as click-through rates and conversion rates.

By carefully considering these decision criteria, the digital marketing agency can develop a tailored marketing plan that focuses on the most effective channels, strategies, and campaigns to drive targeted traffic to the client's website, increase conversions, and ultimately boost online sales.

2.4 Example with Analogies

To grasp the concept of decision criteria further, let's consider an analogy. Imagine you are choosing a holiday destination. The decision criteria might encompass factors such as travel costs, weather preferences, available activities, and cultural experiences. By evaluating these criteria, you can prioritize destinations that align with your preferences and make an informed decision on the best holiday spot.

For instance, let's say you are planning a vacation and have several potential destinations in mind. The decision criteria you might consider could include analyzing the cost of travel and accommodation, assessing the weather conditions and climate preferences, evaluating the availability of activities and attractions, and considering the cultural experiences each destination offers.

By carefully evaluating these decision criteria, you can narrow down your options and choose a holiday destination that suits your budget, weather preferences, desired activities, and cultural interests.

In conclusion, decision criteria play a crucial role in the decision-making process. They allow individuals or organizations to assess alternatives systematically, considering crucial factors and aligning decisions with their goals. While decision criteria have advantages such as comprehensiveness and clarity, it is important to be aware of their limitations and use them judiciously alongside broader considerations. By examining practical examples and analogies, we can better understand how decision criteria apply in various contexts and enhance our decision-making capabilities.

About the author

Arnaud Belinga

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Analyzing case studies, analyzing case studies.

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This page is a very simplified version of the one found here (F.C. Manning School of Business Administration, Acadia University, Canada) and has been adapted to suit english levels for non-native speakers.

What is a Case Study

A case study is a description of an actual situation where a decision has to be made or a problem to be solved. It can be a real situation that has actually happened. Most case studies are written in such a way that the reader takes the place of the manager whose responsibility is to make decisions to help solve the problem. In almost all case studies, a decision of some sort must be made.  

How to do a Case Study

Step 1: the short cycle process.

You should now be familiar with what the case study is about, and are ready to begin the process of analyzing it.  You are not done yet!  You need to go further to prepare the case, using the next step. One of the main reasons for doing the short cycle process is to give you an indication of  how  much work will need to be done to prepare the case study properly. 

Step 2: The Long Cycle Process

At this point, the task consists of two parts:

When you are doing the detailed reading of the case study, look for the following sections:

Most, but not all case studies will follow this format. The purpose here is to thoroughly understand the situation and the decisions that will need to be made. Take your time, make notes, and keep focussed on your objectives.  Analyzing the case should take the following steps:

Defining the issue(s)/Problem Statement

The problem statement should be a clear statement of exactly what needs to be addressed. This is not easy to write! The work that you did in the short cycle process answered the basic questions. Now it is time to decide what the main issues to be addressed are going to be in much more detail. Asking yourself the following questions may help:

The problem statement may be framed as a question, eg: What should Fatima do? or How can Mr Omran improve market share?

Usually the problem statement has to be re-written several times during the analysis of a case, as you go deeper into ALL of the symptoms.

Analyzing Case Data

In analyzing the case data, you are trying to answer the following:

Generating Alternatives

This section deals with different ways in which the problem can be resolved. Typically, there are many and you need to be a little creative.

Things to remember at this stage are:

Once the alternatives have been identified, a method of evaluating them and selecting the most appropriate one needs to be used to arrive at a decision.

Key Decision Criteria

For a business situation, the key decision criteria are those things that are important to the organization making the decision, and they will be used to evaluate the suitability of each alternative recommended.  Key decision criteria should be:

Students tend to find the concept of key decision criteria very confusing, so you will probably find that you re-write them several times as you analyze the case. They are similar to constraints or limitations, but are used to evaluate alternatives. 

Evaluation of Alternatives

If you have done all the above properly, this should be easy. You measure the alternatives against each key decision criteria . Often you can set up a simple table with key decision criteria as columns and alternatives as rows, and write this section based on the table. Each alternative must be compared to each criteria and its suitability ranked in some way, such as met/not met, or in relation to the other alternatives, such as better than, or highest. This will be important to selecting an alternative.

Another method that can be used is to list the advantages and disadvantages (pros/cons) of each alternative, and then discussing the short and long term implications of choosing each. Note that this implies that you have already predicted the most likely outcome of each of the alternatives. Some students find it helpful to consider three different levels of outcome, such as best, worst, and most likely, as another way of evaluating alternatives. 

Recommendation

You must have one! Business people are decision-makers; this is your opportunity to practice making decisions. Give a justification for your decision (use the Key Decision Criterias ). Check to make sure that it is one (and only one) of your Alternatives and that it does resolve what you defined as the Problem. 

Other Case Study Resources

• Introduction
• About Case Study Reports
• Section A: Overview
• Section B: Planning and Researching
• Section C: Parts of a Case Study
• Section D: Reviewing and Presenting
• Section E: Revising Your Work
• Section F: Resources
• Your Workspace
• Guided Writing Tools

• About Lab Reports
• Section C: Critical Features
• Section D: Parts of a Lab Report

• About Literature Review
• Section C: Parts of a Literature Review
• Section D: Critical Writing Skills

• About Reflective Writing
• Section B: How Can I Reflect?
• Section C: How Do I Get Started?
• Section D: Writing a Reflection

Case Study Report Annotated Example: Alternatives and Decision Criteria

Use the arrows that appear in this window to navigate through the annotations. Read the corresponding highlighted text in the content box below. Click Begin Activity to start.

Present all viable, mutually exclusive alternatives.

Outline the criteria that will be systematically applied in order to determine the best solution to the problem being faced.

Be sure to include ranking and weighting information associated with the decision criteria.

There are two separate organizational structures that are consistent with the recommended strategy and that fit Company XYZ’s needs. Each structure takes a different approach to balancing innovation with a focus on profitability and efficiency.

Alternative 1

The first alternative organizational structure, referred to as a matrix structure, is a unique structure associated with an equal balance of profitability and innovation. This includes having two separate groups of managers; one group will be responsible for the functional aspects of the organization, including marketing, accounting, and any other activities necessary for Company XYZ’s operations to continue. The second group of managers will be responsible for specific product lines including innovation and development. This structure will likely see one employee with two different managers; one for their product line and one for their associated functional department.

Decision Criteria

Each of the aforementioned alternatives can be evaluated using the following decision criteria (listed in order of importance and including the weighting of each criterion):

• The recommendation must increase both sales and employee morale ( 60% );
• The recommendation must align with a strategy that promotes innovation and product development (25%); and
• The recommendation must be able to absorb Company XYZ’s growth in the future, including the ability to add or remove product lines as needed (15%).
• Executive Summary
• Alternatives
• Recommendations

• Privacy Policy

Case Study Format

Following are the main Components/Parts of a Case Study;

Executive Summary

Introduction, alternatives and decision criteria, recommendations and implementation plan, conclusion and references, citing sources.

Writing a case study is not a simple process as it can take several months to write it successfully. There are many stages you need to complete first and after that, you finally come at the stage of writing your case study like selecting the topic, a lot of research ( qualitative research, quantitative research or interview with subjects etc), etc. While writing a case study, bear in mind that all the case studies aren’t the same for sure and vary largely in size, type, and design. It is necessary for a writer to follow a proper case study format while writing it, no matter what.

If you are going to write your very first case study, you need to know that every case study has a specific format, as mentioned above. Students or businesses all over the globe must follow that format in order to make their case study successful. However, one may choose to be a little bit different but the basics remain the same for everyone. Here we are going to discuss different case study sections. The purpose of every section in case study format is different from other and comprise of several unique key elements.

You may also study: How To Write a Case Study

It will definitely assist you to write your own case study with the utmost ease. The sections that are included in the case study are executive summary, introduction, analysis, alternatives and decision criteria, recommendations and implementation plan, conclusions and references, citing sources.

Before moving towards a detailed view of the case study format , let us have a look at the case study benefits.

Value of Case Study

Have you ever think why case studies are developed? This is the question you must ask yourself before writing your very first case study. Knowing the answers and keeping all that stuff in mind will let you write a successful and worth-reading case study.

It is the best way to put the students or learners in an active learning mode. Instead of just testing their memory, case study challenges the students to test their learning via practice, which is usually the easiest approach. In short, case studies proffer students a great opportunity to analyze and resolve the real-world problem with a practical approach. It makes the interpretation as well as problem-solving tasks easy for the learners.

A detailed look at the Case Study Format

Executive summary for a case study is usually similar to the general summary. It is basically a short snapshot that shed the image of your entire case precisely, which consists of a page, most of the time. It doesn’t include too much detail about your case but focuses on key elements or main highlights of your case study. Reading the executive summary of your case study must give the reader an idea about the entire case study and its key elements. There are two approaches adopted to write case studies.

• The first approach is to write the case study’s executive summary in short paragraphs.
• On the other hand, the second approach is to write it in form of points.

Key Points to Cover in an Executive Summary

There are a few things which are necessary to include in your case study’s executive summary i.e. problem statement, recommendation, evidence and supporting arguments, and last but not the least conclusion.

All these things comprise to form a perfect executive summary, which let the reader walk through the entire case study, just by reading it.

• The first thing to include in a case study format is an executive summary, as mentioned above. The very first thing to add in executive summary is problem statement. It let the reader know about the key issue discussed in the entire case in just a few lines. Problem statement usually comprises of one or two statement but may vary according to the case.
• The second thing to add is recommendation after stating the problem statement. What is this recommendation about? It presents one or a few ideas to resolve the problem stated in problem statement.
• The next thing to add in the executive summary is supporting arguments and evidence. It is all about highlighting key areas of your entire case and the arguments of the case. Moreover, it also states one or a few pieces of evidence that support your recommendation section.
• This is the last thing to add in the executive summary is conclusion that definitely concludes everything stated here in this portion. You must let the reader know the key message you want to deliver. Also, state why it is essential to resolve this problem and what are the expected outcomes if the reader follows your recommendations you stated in your case study.

The introduction section of the case study is somehow different from the introduction section of research paper . What is this section intended to have? It is usually here to formulate the stage for your entire case study. It must not only introduce the report of your case but also should state the key problem being faced and discussed thoroughly in a clear and accurate tone.

One thing which is worth-mentioning here is that case study is not like a scientific research report, which is only read by the experts or scientists. It must be written in such a way that a layperson could read and understand it well. Reading the introduction section of case study must let the user know about full case study i.e. what it is about, what are the key areas discussed in this and how the reader will get benefit from it etc. It must not be short enough to miss the necessary details. On the other hand, it must not be long enough that it becomes boring.

Don’t include irrelevant or unnecessary details in it. Just be precise and accurate, and try to include the following:

• A perfect and well-written introductory sentence.
• A short but precise problem statement.
• All necessary problem details.
• The best recommendations for the stated problem.
• And last but not the least: roadmap of the entire case study.

The next section of the case study, when it comes to case study format is analysis. It is usually a detailed section of your case study and it is supposed to examine the problem (which is identified in the previous section) in detail.

When it comes to the right way to structure the analysis section, make sure to ask from your instructor about this, whether there is any format to follow specifically when writing it i.e. SWOT or PEST etc? If your instructor tells you to write it generally, here are a few important things you need to know.

• Start with examining the problem and try to focus on its most crucial or sensitive parts. Here, you are not meant to include any irrelevant or unnecessary details. Your main focus should be the main problem and its critical areas.
• Make sure to mention the causes as well as effects, or any other detail you think is necessary to include. Also, make use of headings to highlight every single portion.
• Here, you are also meant to provide a meaningful conclusion to your analysis. It must conclude all the points, ideas and thoughts you discussed previously into some meaningful ending.

This section of the case study format addresses two key areas. The first one is alternatives and the second one is the decision criteria.

As the name suggests, alternatives must mention all the potential ways the identified problem can be addressed. It let the reader think about the different directions (which are successful as well) to solve the problem. Knowing all the alternatives or the available options to solve the problem, the reader can definitely identify the best possible solution to the problem, as per knowledge and thinking criteria.

One thing which is worth mentioning here is that all the presented solutions to the problems in the alternatives portion must be mutually exclusive. Why is it important to present the mutually exclusive alternatives? What are basically mutually exclusive alternatives?

Mutually exclusive alternatives refer to the situation in which selecting one alternative eliminate all others. There is a specific and a single solution to the identified problem. Mutually exclusive alternatives prevent a scenario in which it becomes essential to implement several available alternatives. When the alternatives will be mutually exclusive, it means that choosing one will eliminate the chances of selection of all others and thus, one alternative will be implemented.

The second thing which needs to be stated here in this portion is decision criteria. It means that you must state precisely your decisive factor i.e. key requirements one need to meet successfully for solving the problem. It is the most important thing here in this portion and you must state it in easy to read and simple words so that the reader could understand it well.

In this section of the case study format , the reader is well aware of all the recommendations for sure. So, there is no need to introduce the reader to the basics of the recommendations again. Rather, you are supposed to let the reader know the specifics of recommendation for solving the identified problem. In this regard, the reader will automatically get all the aspects of the recommended solution to the problem and will see how it will take you to the path of success i.e. towards the path of resolving the problem. For executing the recommendation in a successful manner, here you need to proffer the reader a well thought-out and a comprehensive implementation plan so that the reader could execute the recommended solution, making sure the success.

The recommendations and implementation plan is supposed to include a few things must, which are the following:

A detailed overview of what your recommendation entails, which are necessary steps to follow to implement this successfully and also, the required expertise or a list of equipment needed.

When it comes to the implementation plan, here are a few things which are essential to state here:

• The most important parts of the entire plan of implementation, and who will be accountable for those parts separately.
• Whether it is short term, long term or medium term implementation plan.
• The overall cost required to implement the recommendation.
• The effects of the implementation of recommendation on the entire organization.
• The last but not the least thing to mention here is the potential things which could fail while implementation and plan to recover that failure, if any.

This is the portion of your case study where you are going to make a final ending note for your reader in a few easy to understand yet powerful statements. These statements must emphasize the proposed recommendations. As per common observation, a few instructors don’t suggest you to include this portion of conclusion in your case study but it is helpful in providing a strong endnote to your case study.

There are a few things which are essential to add in this conclusion section, which are the following:

• If the purpose of your case is complex, make sure to summarize it here, in point form, so that the reader could have a review at the entire case again, before approaching the conclusion.
• If you haven’t yet stated the importance of your findings, make sure to do it here in this conclusion section.
• A few concluding sentences that shed the case’s summary and let the reader know what he has learned from this. Moreover, choose to finalize with a few memorable and impactful sentences.

However, the conclusion is the most important section of your case study as you are going to give your entire case an end note, so here are a few things you must keep in your mind while writing this portion.

• A few people give an abrupt ending to the case study, which is one of the biggest mistakes ever. The reason is that the ending must be impactful and must not leave the reader disappointed. So, formulate a few sentences to create a path towards a natural close.
• While stating your recommendation, try to summarize the ways problem will be resolved at hand.
• Make sure everything you write in conclusion portion is convincing enough to persuade the reader to believe that the recommended solution will work the best for solving the particular problem.

As per universal rule, you must cite any idea, though, or expression that is not yours and is presented by someone else. These citations are must to include at the end of your case study. The plagiarism policies or academic misconduct policies vary from one institute to another so you must familiarize yourself with the ones of your institute. Other than this, try your level best to make your case study written in a perfect manner and make sure to cite all of the following:

• Ideas presented by others, which are originally not by you.
• Use of quotations is not recommended while writing the case study. But, if you do, make sure to cite it properly.
• Any summarized work by any other writer.
• Definitions, models or theories etc presented by others must also be properly cited.
• Any information from company websites, annual reports, or press releases must also be cited in a proper manner.

What is the Proper Way to Write Citations?

If you are going to write the citation for the very first time, you need to know that these aren’t written generally. Rather, there is a specific format to follow while writing them. There are a few citations styles which are used by the students universally but before finalizing, you must ask your instructor to suggest you the best one.

A few most commonly used citation styles are the following:

An Ideal Case Study should be LOGICAL, ALL INCLUSIVE & THOROUGH

There are a few characteristics your case study must possess, which are the following:

The first and foremost thing is that each and every section or part of your case study must be logical. Any guesses or estimations must not be included in your case study as everything is supposed to be logical and authentic. However, you can choose to write your observation generally in your case study but it must not state any sort of assumptions made from that.

All INCLUSIVE

The next thing is that your case study must not miss any data or findings. It should be all inclusive i.e. you aren’t given the authority to choose data or findings to include or skip. You are liable to put everything in it. Otherwise, it could fail to be a successful case study.

The last thing to mention here is that your case study must be thorough. You aren’t just meant to write down all the observations during your research but rather, you must proffer the in-depth detail to every observation as well.

So, this is all about writing your case study as well as the right case study format to follow while writing it. Keep all the above-listed things in mind and start your writing process now.

Case Study Format Example 01:

Case study format example 02:, case study format example 03:.

Lots of Luck!

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Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Criteria for Case Scenario Analysis

Define the problem : Students should focus on defining the problem by determining the root cause, not the underlying symptom(s).

Develop reasonable alternatives : Students should develop three to four reasonable alternatives to deal with the problem. Most laws are written around the concept of what a reasonable person would do.

Evaluate each alternative: Generally, any alternative has both advantages and disadvantages. Students should provide at least two advantages and two disadvantages for each alternative.

Select the preferred alternative: Students should select one alternative or a combination of alternatives to resolve the underlying problem. Additionally, students should provide a reasonable and logical explanation as to why one alternative or combination of alternatives is better than another alternative.

Support the decision with empirical evidence : Students should support their decisions with empirical evidence as applicable. Not all empirical evidence is generalizable to every problem.

Critical Employment, Ethical, and Legal Scenarios in Human Resource Development Copyright © 2020 by Claretha Hughes is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Environ Health Perspect
• v.125(6); 2017 Jun

Advancing Alternative Analysis: Integration of Decision Science

Timothy f. malloy.

1 UCLA School of Law, University of California, Los Angeles (UCLA), Los Angeles, California, USA

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

Virginia M. Zaunbrecher

Christina m. batteate.

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

William F. Carroll, Jr.

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.

Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact vog.hin.shein@enilnophe . Our staff will work with you to assess and meet your accessibility needs within 3 working days.

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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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

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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.

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Recommendation Framework for Products Using Optimization Algorithms

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• Published: 17 March 2024

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• Neha Punetha   ORCID: orcid.org/0000-0001-8173-4003 1 &
• Goonjan Jain 1  

The implementation of multi-criteria decision-making (MCDM) methodologies has been observed to be on the rise in the context of product recommendation decisions, which generally involve the consideration of multiple factors. The objective of this research is to showcase the execution of a new approach centered on PROMETHEE-I MCDM methodologies as the fundamental component of a Decision Support System for consumers. The proposed system recommends the most optimal choices from a provided range of alternatives. Despite its significance, relatively little research has been done on the topic of ranking products based on online product ratings and consumer preferences. The present study puts forth optimization techniques to rank products through the utilization of multi-attribute online ratings. Our study presents a new approach for recommending optimal alternatives through a mobile recommendation-ranking system-based (MCDM) method. The study utilized the PROMETHEE-I methodology to effectively rank the alternatives and address the optimal mobile recommendation issue. A case study illustrating the proposed methodology for selecting a mobile phone. This decision-making system may show to be the best long-term solution for e-commerce sites and websites due to its superior product comparison abilities and capacity to provide a recommendation to the user as a final ranking of alternatives.

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Punetha N, Jain G (2023) Integrated Shannon entropy and COPRAS optimal model-based recommendation framework. Evolut Intell. https://doi.org/10.1007/s12065-023-00886-4

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Punetha, N., Jain, G. Recommendation Framework for Products Using Optimization Algorithms. Natl. Acad. Sci. Lett. (2024). https://doi.org/10.1007/s40009-024-01401-8

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Received : 02 February 2023

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Published : 17 March 2024

DOI : https://doi.org/10.1007/s40009-024-01401-8

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