“The effect of consumer expertise on consumer behavioral intentions

The role of Involvement and online consumers’ reviews”

Erasmus University of Rotterdam

School of Economics , department of Marketing

Evangelia Makri : 347524

Supervisor: Gui Liberali

Co-reader: Nuno Camacho

December 2012

Does every piece of information “make sense” to everyone???

Do they all think alike?

Every person is unique

We want technical

information-

attributes*

We want to know the

beneficial aspects of

the product*

An example of different informational needs

*Source: Park & Kim, 2008; Everett Rogers, 1962

…so what can a marketer do?

• When and how they should change their strategies

of providing information?

• Should they offer both attribute-centric and benefit-

centric information in their ads?

NO!!

Why? Time-consuming

Costly

Ineffective

Source: D. Maheswaran, B. Sternthal,1990

Introducing the voice of people: Online word of mouth

Influence Purchase intentions

Affect consumers’ information adoption

Increase product sales

Build Brand equity

Source: Chevalier and Mayzlin, 2006; Bambauer-Sachse and Mangold 2011

6 in 10 online shoppers say UGC have a significant impact on

consumers’ buying behavior

Source: http://www.rswus.com/resources/survey-results

“Almost 3 in 10 marketers say they have shifted at least half of their

marketing spending from traditional to digital advertising

over the past 3 years”

Source: http://www.rswus.com/resources/survey-results

But why is e-WOM so important?

• Fro the usto ers’ perspe ti e:

The users who write these reviews are most of times former users at any given stage

of the product life cycle. Their reviews can be attribute or benefit centric and

satisfy the informational needs of potential o su ers

• From the marketers perspective:

Measurable

Traceable

Easier to control than traditional WOM

Research Problem

So…consumers are affected by e-WOM

But.. they are unique and have:

Different informational needs (attribute-centric vs. benefit-centric)

Different cognitive process (cognitive theory)

Different levels of expertise regarding products (experts vs. novices)

Different levels of Involvement (high vs. low, ELM Theory)

Research Question

“How do different levels of consumer’s expertise, product involvement

(high vs. low),and different types of online consumer reviews (benefit

centric online consumer reviews vs. attribute centric online consumer

reviews) will affect consumer’s purchase intention, attitude toward the

product, perception of risk and perceived informativeness of online

consumers’ reviews?

Literature review: Independent variables and moderators

• Research gap, contradictory studies (Wagenheim & Bayon , 2004

• “The ability to perform product related tasks with success” (Alba &

Hutchinson,1987)

• Two ways of measuring expertise : subjective and objective knowledge

(Park & Kim,2008)

Expertise

• Multidimensional variable

• Enduring and situational (Bloch & Richins , 1983)

• Situational involvement has stronger effects than product class

involvement (Mittal , 1995)

Involvement

• attribute-centric vs. benefit centric online consumer reviews

• impact of online consumer reviews greater than marketing-generated

content (Chiou & Cheng, 2003)

Type of the review

Literature review: Dependent variables

• Part of trustworthiness (Dickinger,2010)

• Part of usefulness (Venkatesh & Davis, 2000)

• Three dimensional (Park & Kim)

Informativeness

• subjective judgment of a consumer

• contradictory theories (Hu et al. 2008; Duan et al. 2008) Purchase Intentions

• Predisposition of a consumer about a product that can change

(ELM theory) Attitude towards the product

• Five dimensions (Dholokia, 2001)

• Riskiness in the purchase (Eroglu &Machleit, 1990)

• E-wom is a risk reducer (Engel et. al., 1995)

Perception of risk

Hypothesis Development

Hypothesis 1a: Perceived informativeness is higher for consumers with high level of

expertise when provided with reviews framed as attribute centric than reviews framed

as benefit centric.

Hypothesis 1b: Perceived informativeness is higher for consumers with low level of

expertise when provided with reviews framed as benefit centric than reviews framed

as attribute centric

Hypothesis 2a: For consumers with high expertise reviews framed as attribute-centric

have a stronger effect on the purchase intention than reviews framed as benefit centric.

Hypothesis 2b: For consumers with low expertise reviews framed as benefit-centric have

a stronger effect on the purchase intention than reviews framed as attribute centric.

Cognitive fit theory

Hypothesis 3a: In the high involvement condition, the impact of attribute centric reviews on

purchase intention is greater than the impact of benefit centric reviews

Hypothesis 3b: In the low involvement condition, the impact of benefit centric reviews on

purchase intention is greater than the impact of attribute centric reviews

Hypothesis 3c: In the high involvement condition, the impact of attribute centric reviews on

attitude towards the product is greater than the impact of benefit centric reviews.

Hypothesis 3d: In the low involvement condition, the impact of benefit centric reviews on attitude

towards the product is greater than the impact of attribute centric reviews

Hypothesis Development

ELM theory

Hypothesis Development

Hypothesis 4a: Attribute centric reviews will lower the effect of perceived risk in the high

involvement situation than benefit centric reviews

Hypothesis 4b: Benefit-centric reviews will lower the effect of perceived risk in the low

involvement situation than attribute centric reviews.

ELM theory Laurent and Kapferer,1985: perception of risk is an after effect of situational

involvement

Hypothesis Development

Hypothesis 5a: The effect of cognitive fit on perceived informativeness is greater for the high

involvement condition than the low involvement condition

Hypothesis 5b: The effect of cognitive fit on purchase intention is greater for the high involvement

condition than the low involvement condition

Hypothesis 5c: The effect of cognitive fit on attitude towards the product is greater for the high

involvement condition than the low involvement condition

Hypothesis 5d: The effect of cognitive fit on perceived risk is greater for the high involvement

condition than the low involvement condition

• When a cognitive fit emerges consumers tend to be more stable towards

their preferences in a high-involvement condition (Oliva et al, 1995)

• Involvement affects significantly the cognitive process of a consumer (Park&Do-Hyung,2007)

Conceptual model

Experimental design

Type of reviews High Involvement Low Involvement

Attribute 1 2

Benefit 3 4

2x2 between subject factorial design

Participants randomly assigned to one of the four conditions

Experimental Product: Tablet Pc

Search good

Technological oriented product: many online consumer reviews

Aids the classification of experts and novices

Respondents should be sufficiently familiar with the product

Tablet PCs are currently quite popular

(31% of U.S. internet users report to have a tablet)

Experimental product

Experimental product

Stimuli development: Involvement scenarios

• High Involvement scenario (goal directed):

Imagine yourself working for a multimedia company and you need to make a purchase

of a tablet PC in a short period of time. Your decision will be important as the tablet

will aid you in executing your tasks more efficiently. Please read the following product

characteristics a d o li e co su e s’ e ie s a d a s e the follo i g uestio s:

• Low Involvement scenario (stripped of goal direction):

Imagine yourself wanting to buy a tablet PC for fun. Please read the following product

characteristics and online consumer reviews and answer the following questions:

Stimuli development: Type of review

Attribute-centric

Stimuli development: Type of review

Benefit-centric

Validity of constructs and sample size

Construct measurements with proven validity and reliability (Cronbach’s alpha higher than 0.80)

Factor analysis to improve validity

Type of reviews High Involvement Low Involvement

Attribute n=40 n=33

Benefit n=34 n=41

Total of

148

Participants

76 male , 72 female

82.4% between 18 to 30 years old

64.2% Greek nationality

53.4% income range 501-1000 euros

43.2% University graduates , 47.3% Master students or graduates

Control and manipulation checks

• Control for price

• Control for brand effects

• Control for review positiveness

• Control general susceptibility and credibility of E-wom

Manipulation of Involvement was successful, F (1,146) =8.987, p <0.001

Results: Informativeness

df F Sig.

TypeofReview 1 0.041 0.839

Expertise 1 0.001 0.970

TypeofReview * Expertise 1 574.956 0.000

Error 144

Results: Informativeness

Experts with attribute centric reviews: M high-involvement =6.096 vs. M low-involvement =5.318

Novices with benefit centric reviews : M high-involvement =5.789 vs. M low-involvement =5.625

F (1,140) =199.2, p<0.05

Results: Purchase Intention

df F Sig.

TypeofReview 1 1.056 0.306

Expertise 1 0.488 0.486

TypeofReview * Expertise 1 549.795 0.000

Error 144

• Participants may not have been satisfied by the number of reviews

• Manipulation was successful but the differences between means were not as high as

expected

Experts with attribute centric reviews: M high-involvement=6.519 vs. M low-involvement=5.136.

Novices with benefit centric reviews : Mhigh-involvement=6.289 vs. M low-involvement=6.094

F (1,140) = 12.476, p<0.05

Results: Attitude towards the product

df F Sig.

TypeofReview 1 2.117 0.148

Involvement 1 0.324 0.570

TypeofReview *Involvement 1 0.135 0.714

Error 144

• Participants may have been affected by other extrinsic effects such as the design of the

product

Results: Attitude towards the product

Experts with attribute centric reviews: M high-involvement=6.258 vs. M low-involvement=5.736

Novices with benefit centric reviews : Mhigh-involvement=6.316 vs. M low-involvement=5.925

F (1,140) = 38.292 p<0.05

Results: Perception of risk

df F Sig.

TypeofReview 1 0.406 0.525

Involvement 1 29.375 0.000

TypeofReview *Involvement 1 6.718 0.011

Error 144

Results: Perception of risk

Source df F Sig.

Involvement 1 177.012 0.000

TypeofReview 1 51.141 0.000

Expertise 1 603.231 0.000

Involvement *TypeofReview 1 16.176 0.000

Involvement * Expertise 1 118.721 0.000

TypeofReview * Expertise 1 53.213 0.000

Involvement *

TypeofReview * Expertise 1 3.046 0.083

Error 140

Perception of risk could be an antecedent of Involvement

Investigate more dimensions

The simple interaction Type of review x Expertise not as expected

Conclusions

Expertise plays a critical role of moderating the

relationship between the type of review

informativeness and purchase intention

Cognitive fit is affected by involvement (for

informativeness, purchase intention and

attitude towards the product)

Perception of risk needs to be examined by

future researchers

Cognitive hypothesis>ELM hypothesis

Managerial implications

Data mining

review mining and summarization aims

to e tra t users’ opi io s to ards spe ifi produ ts fro reviews

and provide an easy-to-understand summary of those opinions

to pote tial u ers

CRM technologies

Aid the categorization of Experts vs. Novices

by taking into consideration:

their product preferences

their reviews preferences

their activity through the website

their experiences regarding a specific product

Source: Kantardzic (2011), Meng and Wang (2009)

Limitations and future research

Small sample size

No control group as a baseline

Brand effects may show different results

Mixed reviews may provide interesting results

Involvement and perception of risk needs further examining

Thank you