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CONSUMER BEHAVIOR (IN A DIGITAL WORLD)

ATTITUDES

TODAY’S AGENDA

1. Attitudes • Explicit attitudes

2. Multi-attribute attitude model 3. Implicit vs. Explicit Attitudes

• Implicit attitudes • Implicit Association Test (IAT)

4. Conjoint Analysis

EVALUATIONS

We are evaluative creatures • We automatically evaluate everything

we encounter • Most basic level: good or bad

(approach or avoid) Evaluations are stored in our associative networks (i.e., memory)

ATTITUDES

An attitude is a person’s overall evaluation of a target.

(person, product, idea, etc.)

ATTITUDES

The target of an attitude is an attitude object.

(Attitudes have to be about something.)

Category Fast food restaurant

Subcategory Burger joints Pizza places

Brand In-N-Out Rusty’s

Model in Goleta in L.A.

Brand/Model General Situation

Eating lunch with friends at in L.A.

Eating dinner with partner’s parents at . in L.A.

Brand/Model Specific Situation

Lunch with friends at . in Goleta on a Tuesday

Lunch with friends at . in Goleta on my birthday

ATTITUDE OBJECTS

Category Fast food restaurant

Subcategory Burger joints Pizza places

Brand In-N-Out Rusty’s

Model in Goleta in L.A.

Brand/Model General Situation

Eating lunch with friends at in L.A.

Eating dinner with partner’s parents at . in L.A.

Brand/Model Specific Situation

Lunch with friends at . in Goleta on a Tuesday

Lunch with friends at . in Goleta on my birthday

Each attitude has an associated evaluation. Marketers need to measure at the right level.

EXPLICIT ATTITUDES

Explicit attitudes: evaluations that people can consciously think about and report. Related to specific beliefs about the attitude object.

MULTI-ATTRIBUTE ATTITUDE MODEL

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MULTI-ATTRIBUTE ATTITUDE MODEL

• Consumers can have several beliefs about a single object.

• A consumer’s attitude toward an attitude object is a sum of the evaluations of beliefs about an object, weighted by the strength or importance of those beliefs.

Where AO = Attitude toward the object

Ao = biei i=1

∑

n = number of salient beliefs

bi = strength of salient beliefs

ei = evaluation of beliefs

MULTI-ATTRIBUTE ATTITUDE MODEL

Uo = wixi i=1

∑

MULTI-ATTRIBUTE ATTITUDE MODEL

By the way, this is based on the same utility model used in conjoint.

Ao = biei i=1

∑

MULTI-ATTRIBUTE ATTITUDE MODEL

Beliefs • The ICON 4x4 gets 14 MPG • The ICON 4x4 is good for off-roading • The ICON 4x4 is expensive

MEASURING BELIEF STRENGTH

• The belief must be salient; one must be aware of the belief

MEASURING BELIEF STRENGTH

• The belief must be salient; one must be aware of the belief

• How likely is it that the ICON gets 14 MPG? (1=Extremely unlikely to 5=Extremely likely)

MEASURING BELIEF STRENGTH

• The belief must be salient; one must be aware of the belief

• How important is MPG? (1=Not at all important to 5=Extremely important)

MEASURING EVALUATIONS

• Affective positive/negative responses to the individual beliefs

• How good or bad is 14 MPG? (-3=Extremely bad to 3=Extremely good)

MULTI-ATTRIBUTE ATTITUDE MODEL

Where AO = Attitude toward the object

Ao = biei i=1

∑

n = number of salient beliefs

bi = strength (importance) of beliefs

ei = evaluation of beliefs

belief bi ei

Futuristic 3 +18 Cute 3 +30 Useful -2 -20 Good company 2 +4 Could destroy world -3 -15 A +17

MULTI-ATTRIBUTE ATTITUDE MODEL

belief bi ei

Futuristic 6 3 +18 Cute 10 3 +30 Useful 10 -2 -20 Good company 2 2 +4 Could destroy world 5 -3 -15 A +17

Strength/importance

MULTI-ATTRIBUTE ATTITUDE MODEL

belief bi ei

Futuristic 6 3 +18 Cute 10 3 +30 Useful 10 -2 -20 Good company 2 2 +4 Could destroy world 5 -3 -15 A +17

Evaluation

MULTI-ATTRIBUTE ATTITUDE MODEL

belief bi ei

Futuristic 6 3 +18 Cute 10 3 +30 Useful 10 -2 -20 Good company 2 2 +4 Could destroy world 5 -3 -15 A +17

SUMMARY

• The multi-attribute attitude model has an intuitive appeal to researchers and managers because it is relatively easy to use in research.

• The multi-attribute model is useful for identifying which attributes are the most important (or most salient) to consumers.

• By examining the salient beliefs that underlie attitudes toward various brands, marketers can learn how their strategies are performing and make adjustments to improve their effectiveness.

IMPLICIT VS. EXPLICIT ATTITUDES

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BRAND ATTITUDE & BRAND EQUITY

Brand attitude is a key aspect of brand equity. Brand equity concerns the value of the brand to the marketer and to the consumer. Brand equity involves: 1. Strong, positive brand attitude (favorable

evaluation of the brand) 2. based on favorable meanings 3. and beliefs that are accessible in memory (easily

activated).

A CASE STUDY

Increase occupancy rate by 15%

IMPLICIT VS. EXPLICIT ATTITUDES

Explicit attitudes: evaluations that people can consciously think about and report. Implicit attitudes: positive and negative associations that occur even without conscious awareness.

IMPLICIT ATTITUDES & ASSOCIATIVE NETWORKS

Spreading activation means that thinking of one concept automatically activates related concepts.

IMPLICIT ATTITUDES & ASSOCIATIVE NETWORKS

…and positive or negative evaluations of those concepts.

ATTITUDES: ACTIVATION AND AWARENESS

Amodio 2019

ATTITUDES: ACTIVATION AND AWARENESS

Amodio 2019

MEASURING ATTITUDES

Explicit attitudes: can be measured via self-report. Implicit attitudes: must be measured via tests of cognitive associations (e.g., IAT, Stroop).

The IAT quantifies the strengths of associations.

IAT: IMPLICIT ASSOCIATIONS TEST

Do people have an implicit preference for Coke over Pepsi? Which brand has stronger positive associations?

IAT: IMPLICIT ASSOCIATIONS TEST

GROSS

IAT: IMPLICIT ASSOCIATIONS TEST

HAPPY

IAT: IMPLICIT ASSOCIATIONS TEST

Measure of implicit attitudes: difference in response time for one pairing vs. another pairing • Shorter response times indicate closer

associations. • Longer response times indicate more distant

associations.

IAT: IMPLICIT ASSOCIATIONS TEST

IMPLICIT ATTITUDES & IMPLICIT BIAS

The same principle we use to uncover the strengths of positive/negative associations with brands…

IMPLICIT ATTITUDES & IMPLICIT BIAS

…can be used to uncover positive/negative associations with different groups of people

IMPLICIT ATTITUDES & IMPLICIT BIAS

…as well as different, more specific associations…

Implicit.harvard.edu

IAT: IMPLICIT ASSOCIATIONS TEST

IMPLICIT VS. EXPLICIT ATTITUDES

What predicts behavior? • Implicit attitudes are more likely to affect

behavior when consumers lack time and/or cognitive resources

• Explicit attitudes are more likely to affect behavior when consumers have time/resources to deliberate

TAKEAWAY

• Attitudes are affective evaluations created by the cognitive system.

• We can understand our (explicit) attitude toward an object as the evaluation of salient beliefs.

• Explicit attitudes tend to predict behavior we can control, implicit attitudes predict behaviors that are more spontaneous and difficult to control.

OVERVIEW OF CONJOINT ASSIGNMENT

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WHY DO WE LIKE WHAT WE LIKE?

Consumers often know what they like.

However, that doesn’t mean they can (accurately) tell you why.

CONJOINT ANALYSIS

How do the individual features of a product or service contribute to a consumer’s overall preference?

CONJOINT ANALYSIS

In other words… How much value does a consumer get from each individual feature?

CONJOINT ANALYSIS

How much is a color screen worth to you? Accuracy? Battery life?

CONJOINT ANALYSIS

Based on the following utility model:

UO = Overall preference

n = number of features

wi = importance of feature

xi = level of feature

Uo = wixi i=1

∑

CONJOINT ANALYSIS

Consumers can’t report their ‘w’s. Fortunately, they can report their ‘U’s. Conjoint analysis allows us to use information that we determine (‘x’s) and information that consumers can report (‘U’s) to uncover information that consumer can’t accurately report (‘w’s).

CONJOINT ANALYSIS

How much is a color screen worth to you? Accuracy? Battery life?

FEATURES AND LEVELS

Features: • Accuracy: 50 feet vs. 10 feet • Display Color: B&W vs. Color • Battery Life: 12 hrs vs. 32 hrs

Price: $350 vs. $250

(0) (0) (0)

(0)

(1) (1)

(1)

PREFERENCES

REGRESSION ANALYSIS

Regression:

Uo = wixi i=1

∑

Preference = w0+ w1Accuracy + w2Battery + w3Color + w4Price

REGRESSION ANALYSIS

PART-WORTHS

TRADE-OFFS AND WTP

How much is this consumer willing to pay for improved accuracy?

(1) Calculate monetary value of each “util”

(1 util = Change of “1” in preference rating)

(1) Utils gained by price reduction = 43 (partworth of price)

(2) Monetary value of price reduction = $100

(3) Monetary value of each util = $100/43 = $2.33

TRADE-OFFS AND WTP

How much is this consumer willing to pay for improved accuracy?

(2) Calculate monetary value of utils gained by improving accuracy:

(1) Utils gained = partworth of accuracy = 10

(2) Monetary value of utils gained = 10*$2.33

(3) WTP for improved accuracy = $23.30