For your second assignment, you will collect quantitative data to conduct a conjoint analysis. I provide conceptual background for the conjoint analysis technique, in-depth in

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 1 Professor Hamilton

CONJOINT ANALYSIS ASSIGNMENT OVERVIEW Due: Thursday, February 15 by 11:59pm

This assignment is to be completed individually.

Deliverable:

1. A 3-4 page paper in APA format that includes a/an: a. explanation of your research methodology b. report of the major findings from your analysis (further described below)

2. An APA style appendix (in addition to the report) that includes a(n): a. matrix of features/levels with overall ratings (‘U’s), b. raw regression output (from Excel or other stats tool), c. graphical representation of the partworths you calculated, and d. table of your willingness to pay (WTP) calculations.

Overview: Conjoint analysis is a method for determining the relative importance of distinct features of a product or service for determining overall preferences.

In this assignment, you will create a conjoint choice task related to a product or service of your choosing, collect preference ratings from a friend or classmate, analyze these data using a regression, and interpret your results. Your results will yield insight into how much each feature of a product/service contributes to overall preferences, and you will use this insight to calculate your respondent’s trade-offs between levels of each feature in terms of willingness to pay (WTP). Most importantly, you will use the paper component of this assignment to communicate what your data mean and why they matter (see requirements and guidelines below). Just like the Laddering Assignment you just completed, your goal is to collect and analyze data and then interpret your data in a white paper report.

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 2 Professor Hamilton

Detailed instructions 1. Step One – Conduct a Survey

a. Choose a product (e.g., Garmin GPS). i. Specify four features of the product (price + three features of your choice;

e.g., price, accuracy, battery life, color), each with two levels (e.g., 10 feet vs. 32 feet).

ii. Deliverable: In your report, briefly explain why you picked your product. Then, provide a definition of the features of this product and the levels of each feature.

b. Survey someone else (not yourself). i. Ask your respondent to indicate their overall preference for a hypothetical

product with each combination of features. (Hint: Because there are 4 features with 2 levels each, there are 16 combinations. The 16 combinations are reflected in Step 2a).

ii. Here are some ways you can ask them: 1. “On a scale of 1-100, how likely are you to recommend the

[PRODUCT] featured to a friend or colleague? 2. “On a scale of 1-100, How likely or unlikely are you to purchase the

[PRODUCT]?” 3. (Hint: You can do this via survey or through an interview. But,

remember your goal is to ask them about 16 different products that each have their own unique attributes. The better you can help them visualize each product, the more reliable your results will be.)

iii. Record their preferences for all 16 combinations. iv. Transcribe their preferences to a Google sheet (or Excel) where preference

ratings are in Column A and the code for each level (e.g., 10 feet = 0 and 32 feet = 1 for Accuracy) of the four features are in Columns B-E

v. Deliverable: Include a screenshot of recorded data (similar to the image below) in Appendix A of your white paper.

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 3 Professor Hamilton

2. Step Two – Run a Conjoint Analysis

a. Conduct a regression predicting preferences (‘U’s) from features (‘x’s). Make sure to code the levels of your features as ‘0’s and ‘1’s. Then, select “Regression” where Y is the preference ratings column and X is the four feature columns. (I will include a demo in the lecture of the process of running your regression in Google Sheets in our lecture). i. Deliverable: Include a screenshot of your regression output (similar as below)

in Appendix B.

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 4 Professor Hamilton

b. Compute the partworths of each feature. i. The table below describes how to calculate partworths.

ii. Deliverable: In your report, describe the partworth of each feature and interpret the direction of the relationship between levels on preference ratings. For example, “This individual preferred a GPS with a 32 hour battery life over 12 hour battery life, which accounted for 32% of the variance in their preference ratings.”

iii. Deliverable: Show the relative importance of each feature using a bar graph or pie chart and include the illustration in Appendix C.

3. Step Three – Calculate and Interpret WTP a. Compute Willingness to Pay (WTP) for all the attributes.

i. Deliverable: In your report, explain what the trade-off for price you computed means using plain language and refer to these calculations while providing recommendations about the price trade-off of including different features.

ii. Deliverable: Include a table (like the one below) that explains your WTP calculations in Appendix D.

Feature Partworth (%) WTP Calculation WTP Price ($250 vs. $350) 43% $2.33*43 = $100.19 $100.19 Accuracy 10% $2.33*10 = $23.30 $23.30 Battery 32% $2.33*32 = $74.56 $74.56 Color 16% $2.33*16 = $37.28 $37.28

4. Step Three – Report your findings a. Report your findings in an APA style paper. Your paper must explain your research

methodology and your findings before providing an evidence-based recommendation about the design or promotion of your product. More specifically, your report must: i. Give detail about why you picked your product. Then, provide a definition of

the features of this product, and the levels of each feature (see step 1a.ii). ii. Give detail about your respondent; when presenting your results, make sure to

discuss the extent to which you think your respondent’s ‘w’s are representative of the general population, a particular segment, or perhaps just that individual. Your description of this person should inform your generalizable findings in the recommendation section.

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 5 Professor Hamilton

iii. Describe the partworth of each feature and interpret the direction of the relationship between levels on preference ratings (see step 2b.ii).

iv. Explain what the trade-off for price you computed means using plain language (see step 3a.i). A discussion of trade-offs can inform segmentation, new product creation, profit maximization, and so on.

v. Most importantly, talk about your results in depth. You must show that you’re making connections between the numbers produced by your analysis and what these numbers mean. If you have any odd results, discuss why they might have turned out this way.

vi. Discuss limitations of your data. Limitations could be related to the nature of the person you surveyed, the way you surveyed them, etc.

vii. Describe the ideal product to promote to the perceived “population” you sampled. For example, if you survey (i.e., sample from) a college student about their preferences of laptops, you should be careful not to generalize your insights to all people, and instead be clear to whom you believe you are generalizing your insights based on the details you provide about your respondent. Then, provide a more practical product that considers WTP tradeoffs. This product can have the same features as the ideal product, but you must rationalize why you believe including these features are worth the price they may cost. The insight you generate should be based on evidence you have aggregated about the importance of features, WTP trade-offs, or theoretical findings discussed in lecture.

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 6 Professor Hamilton

CONJOINT ASSIGNMENT EVALUATION CRITERIA

Your assignment will be evaluated out of 22 points, following the rubric below.

Description Level grade

Definition

1 Submission ● Was the assignment fully submitted and

on time?

0 No

1 Yes

1 Formatting and Required Elements ● Does the white paper follow the

prescribed format (i.e., 3-4 page APA format with an appendix)?

● Here is a reference: https://guides.rasmussen.edu/apa6th/abstra ct-appendix

0 No–not all formatting requirements are met.

1 Yes–all formatting requirements are met.

2 Methodology: Product details ● Does the report provide a detailed

discussion about 1) what the product/service is, 2) why the product/service was chosen, 3) definitions of the features of this product, and 4) levels of each feature of the product/service?

0 No– definitions of the features and/or levels of each feature are not described in the report.

1 Somewhat– product details are discussed in the report, but one (or more) crucial aspect is missing. This missing detail makes it harder to follow the conjoint analysis.

2 Yes– All criteria are met.

3 Methodology: Respondent details ● Does the report provide details about the

respondent and whether this respondent may (or may not) be representative of a particular segment (or segments) of the population?

0 No– details about who the respondent reflects in the population are missing.

1 Somewhat– respondent details are discussed in the report, but one (or more) crucial aspect is missing about the product details. This makes it difficult to connect these details to the

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 7 Professor Hamilton

limitations they report.

2 Yes– All criteria are met.

4 Conjoint: Product features + Regression ● Does the product/service reported in

Appendix A vary on four features (price + three others), with two levels for each feature?

● Does the report describe the regression analysis and include the raw regression output in Appendix B?

0 No– one (or more) crucial aspect is missing from the conjoint analysis.

1 Somewhat– it is clear a conjoint analysis was conducted, but one (or more) aspect is not reported or incorrect.

2 Yes– All criteria are met.

5 Partworths: Computation + Interpretation ● Does the report include partworth

calculations for each feature, and explain what these partworths mean with respect to the relationship between levels of that feature?

0 No– the partworth calculations are missing.

1 Somewhat– it is clear partworths were calculated, but one (or more) aspect is not reported or the calculations are incorrect.

2 Yes– All criteria are met.

Partworths: Computation + Interpretation ● Does the report include a graph (e.g., bar

graph, pie chart) of partworths in Appendix C? Give credit based on whether the illustration clarifies the weighted preference (i.e., importance) for a particular feature.

0 No– a graphical representation of partworths is missing or something about the figure is incorrect.

1 Yes– All criteria are met.

6 Trade-offs: WTP + non-monetary features ● Does the report include WTP for each

non-monetary feature and explain what these WTP calculations mean?

0 No– WTP calculations are not explained in the report

1 Somewhat– it is clear WTP was calculated for each non-monetary feature, but one (or more) aspect is not reported or the calculations are incorrect.

2 Yes– All criteria are met.

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 8 Professor Hamilton

Trade-offs: WTP + non-monetary features ● Does the report describe WTP

calculations in Appendix D?

0 No– WTP calculations are not reported in Appendix D or the calculations are incorrect.

1 Yes– All criteria are met.

7 Recommendation ● Does the report provide details about an

ideal product with features that are based on partworth or WTP calculations, or are in consideration of theoretical findings discussed in lecture?

● Ultimately, does the recommendation illuminate how a company might design or promote this product based on their conjoint analysis data?

0 No– the submission does not provide a recommendation for the product analyzed.

1 Somewhat– It is clear the report fully attempts to provide a recommendation, but their recommendation is not clearly connected to their conjoint data or course material.

2 Yes– All criteria are met.

8 Insight ● Did the conjoint data yield insight that

was explained in a way that felt novel and interesting?

0 No– The insights generated from the conjoint data were not interpreted in a way that yielded much insight (Hint: Usually in this case data are described, and then ignored).

1 Yes– conjoint data were interpreted in a way that yielded insight someone could use to design or promote a better product.

2 Yes (+ special recognition)– conjoint data were interpreted in a way that yielded insight someone could use to design or promote a better product AND the insight generated was probably more novel and interesting than 90% of class submissions.

9 Clarity ● Is it clear how data from the analysis

connects to the insight generated in the

0 No– it is not clear whether insight was generated from data.

Consumer Behavior in a Digital World, Winter 2024 Conjoint Analysis 9 Professor Hamilton

report? Is the insight generated from the results confusing to understand?

1 Somewhat– the insight generated was confusing to follow.

2 Yes– the insight is clear and it is easy to see how this insight was informed by data.

10 Organization ● In general, was the paper well-organized

(i.e., did the formatting contribute to a better reading experience)?

0 No– the formatting made the paper harder to read.

1 Somewhat– the paper followed basic formatting standards.

2 Yes–the formatting structure or writing organization made the paper easy to read.

CUSTOMERS

Why Consumers Rebel Against

Slogans by Juliano Laran, Amy N. Dalton, and Eduardo B. Andrade

FROM THE NOVEMBER 2011 ISSUE

B

Logos Can Be Tricky, Too

Bigger isn’t always better. Unsurprisingly, consumers of low-end goods favor unobtrusive logos—but so do many

rand names, logos, and slogans are integral parts of any company’s marketing

message. All have the same aim: to make consumers react positively to a product or

a business.

Our research shows, however, that many slogans backfire—for example, causing consumers

to spend money when they’re told they can save, or vice versa.

In five studies of several hundred undergraduates each, in which computers were used to

simulate shopping behavior, we found that consumers typically follow the prompt of a brand

name or a logo. After participants were exposed to brands associated with luxury (such as

Tiffany and Neiman Marcus), they decided to spend 26% more, on average, than after they

were exposed to neutral brands (such as Publix and Dillard’s). After they were exposed to

brands associated with saving money (such as Dollar Store and Kmart), they decided to spend

37% less than after they were exposed to neutral brands. The brands had the intended

“priming” effect.

But when it came to slogans, the same

participants exhibited the opposite of the

desired behavior. After reading a slogan meant

consumers of ultra-high-end goods, who prefer to send a quiet signal to others in the know.

Source: Jonah Berger and Morgan Ward, “Subtle Signals of Inconspicuous Consumption,” Journal of Consumer Research

to incite spending (“Luxury, you deserve it”),

they decided to spend 26% less than after

reading a neutral slogan (“Time is what you

make of it”). When a slogan invited them to

save (“Dress for less”), they decided to spend—

an additional 29%, on average. The slogans

had a “reverse priming” effect.

In many cases, then, brands and slogans work

at cross-purposes. For example, the name

Walmart tends to induce thriftiness, but the

company’s slogan (“Save money. Live better”) causes indulgence.

What makes slogans so different? Our studies suggest that reverse priming occurs because

consumers recognize that slogans deliberately attempt to persuade them, whereas (in their

perception) brands do not. The recognition may not be conscious: We found that consumers

automatically resisted a slogan’s message.

There’s actually good news here for marketers, who need not simply abandon slogans for fear

of adverse reactions. Slogans can exert a positive influence, we believe, if the consumer is led

to focus on something other than the effort to persuade. To test this theory, we asked one

group of participants to rate a set of slogans on the basis of intent to persuade, while a second

group rated them on creativity. The group that evaluated creativity decided to spend 58%

more than the other group. Of course, getting consumers to focus on creativity instead of

persuasion may be easier in a lab setting than in real-world marketing.

More research is needed to understand why consumers perceive certain tactics as efforts to

persuade. In the meantime, marketers should be aware that messages seen even

subconsciously as manipulative can cause significant backlash.

A version of this article appeared in the November 2011 issue of Harvard Business Review.

B E H A V I O R   &   S O C I E T Y

How to Think about "Implicit Bias"

Amidst a controversy, it’s important to remember that implicit bias is real—and it matters

By Keith Payne, Laura Niemi, John M. Doris on March 27, 2018

Credit: theprint Getty Images

When is the last time a stereotype popped into your mind? If you are like most

people, the authors included, it happens all the time. That doesn’t make you a racist,

sexist, or whatever-ist. It just means your brain is working properly, noticing

patterns, and making generalizations. But the same thought processes that make

people smart can also make them biased. This tendency for stereotype-confirming

thoughts to pass spontaneously through our minds is what psychologists call implicit

bias. It sets people up to overgeneralize, sometimes leading to discrimination even

when people feel they are being fair.

Studies of implicit bias have recently drawn ire from both right and left. For the right,

talk of implicit bias is just another instance of progressives seeing injustice under

every bush. For the left, implicit bias diverts attention from more damaging instances

of explicitbigotry. Debates have become heated, and leapt from scientific journals to

the popular press. Along the way, some important points have been lost. We highlight

two misunderstandings that anyone who wants to understand implicit bias should

know about.

First, much of the controversy centers on the most famous implicit bias test, the

Implicit Association Test (IAT). A majority of people taking this test show evidence of

implicit bias, suggesting that most people are implicitly biased even if they do not

think of themselves as prejudiced. Like any measure, the test does have limitations.

The stability of the test is low, meaning that if you take the same test a few weeks

apart, you might score very differently. And the correlation between a person’s IAT

scores and discriminatory behavior is often small.

The IAT is a measure, and it doesn’t follow from a particular measure being flawed

that the phenomenon we’re attempting to measure is not real. Drawing that

conclusion is to commit the Divining Rod Fallacy: just because a rod doesn’t find

water doesn’t mean there’s no such thing as water. A smarter move is to ask, “What

does the other evidence show?”

In fact, there is lots of other evidence. There are perceptual illusions, for example, in

which white subjects perceive black faces as angrier than white faces with the same

expression. Race can bias people to see harmless objects as weapons when they are in

the hands of black men, and to dislike abstract images that are paired with black

faces. And there are dozens of variants of laboratory tasks finding that most

participants are faster to identify bad words paired with black faces than white faces.

None of these measures is without limitations, but they show the same pattern of

reliable bias as the IAT. There is a mountain of evidence—independent of any single

test—that implicit bias is real.

The second misunderstanding is about what scientists mean when they say a measure

predicts behavior. It is frequently complained that an individual’s IAT score doesn’t

tell you whether they will discriminate on a particular occasion. This is to commit the

Palm Reading Fallacy: unlike palm readers, research psychologists aren’t usually in

the business of telling you, as an individual, what your life holds in store. Most

measures in psychology, from aptitude tests to personality scales, are useful for

predicting how groups will respond on average, not forecasting how particular

individuals will behave.

The difference is crucial. Knowing that an employee scored high on conscientiousness

won’t tell you much about whether her work will be careful or sloppy if you inspect it

right now. But if a large company hires hundreds of employees who are all

conscientious, this will likely pay off with a small but consistent increase in careful

work on average.

Implicit bias researchers have always warned against using the tests for predicting

individual outcomes, like how a particular manager will behave in job interviews—

they’ve never been in the palm-reading business. What the IAT does, and does well, is

predict average outcomes across larger entities like counties, cities, or states. For

example, metro areas with greater average implicit bias have larger racial disparities

in police shootings. And counties with greater average implicit bias have larger racial

disparities in infant health problems. These correlations are important: the lives of

black citizens and newborn black babies depend on them.

Field experiments demonstrate that real-world discrimination continues, and is

widespread. White applicants get about 50 percent more call-backs than black

applicants with the same resumes; college professors are 26 percent more likely to

respond to a student’s email when it is signed by Brad rather than Lamar; and

physicians recommend less pain medication for black patients than white patients

with the same injury.

Today, managers are unlikely to announce that white job applicants should be chosen

over black applicants, and physicians don’t declare that black people feel less pain

than whites. Yet, the widespread pattern of discrimination and disparities seen in

field studies persists. It bears a much closer resemblance to the widespread

stereotypical thoughts seen on implicit tests than to the survey studies in which most

people present themselves as unbiased.

One reason people on both the right and the left are skeptical of implicit bias might be

pretty simple: it isn’t nice to think we aren’t very nice. It would be comforting to

conclude, when we don’t consciously entertain impure intentions, that all of our

intentions are pure. Unfortunately, we can’t conclude that: many of us are more

biased than we realize. And that is an important cause of injustice—whether you know

it or not.

ABOUT THE  AUTHOR(S)

Keith Payne

Keith Payne is a Professor of Psychology and Neuroscience at UNC Chapel Hill. He studies

implicit bias and the psychological effects of inequality.

Laura Niemi

Laura Niemi is a Postdoctoral Fellow in the Department of Philosophy and the Center for

Cognitive Neuroscience at Duke University and an Affiliate of the Department of Psychology at

Harvard University. She studies moral judgment and the implications of differences in moral

values.

John M. Doris

John M. Doris is Professor in the Philosophy–Neuroscience–Psychology Program and

Philosophy Department, Washington University in St. Louis. He works at the intersection of

cognitive science, moral psychology, and philosophical ethics.

126

The Gap began in 1969 with a single

store in San Francisco selling jeans

and records. Fueled by heavy adver-

tising, The Gap grew rapidly in the 1970s to

200 stores. In the process, The Gap became

the epitome of “cool” by offering basic items

such as T-shirts and jeans t hat looked like

designer clothing, but without the arrogance.

Although the company experienced a few

bumps along the way, growth continued

through the 1980s and most of the

1990s.

By 2000 there were 1,800

stores in Europe, North

America, and Japan, includ-

ing new stores such as Gap-

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