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The effect of homophily on perceived risk and

the relative effect of demographic indicators on

perceived demographic homophily in OCRs

Ton van den Berg (S2892294)

Master thesis Msc. Marketing Management

July 13

th

, 2017

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Agenda

1

Introduction & Research Question

Conceptual Models

Research Designs

Homophily

2

(3)

1. Introduction (1)

• Customers often experience some kind of perceived risk during

their purchase decisions. Especially in an online setting

1

.

– Customers WTP when perceived risk

2

.

– Several studies found that OCRs reduces the perceived risk of customers

during their online purchase decisions

1, 2

.

– Some studies even argue that OCRs are one of the most effective

information sources for reducing perceived risk online

1

.

• How to further abate the degree of perceived risk in OCRs?

– Homophily: the degree to which people are similar regarding certain

attributes

3

.

• Several theories and mechanisms suggests that homophily decreases risk in OCRs, such as: the cognitive dissonance theory , the cognitive balance theory, hedonic fluency model, the liking principle theory, the social comparison theory, social identity theory, social network theory, the attractiveness model and the uncertainty reduction

(4)

1. Introduction (2)

2 dimensions of homophily

– Demographic homophily: the degree to which people are similar regarding demographic

indicators such as gender, age, occupation, location and education

3

.

– Perceptual homophily: refers to the degree to which people are similar regarding values,

attitudes, beliefs and lifestyles

3

.

Validity of generalizations of homophily in most studies questionable

– Current OCR studies mainly focus on demographic homophily, lacking the perceptual

dimension.

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1. Introduction (3)

Therefore, this paper has conducted two studies:

• Study 1: Examine the joint effect of both perceptual and demographic

homophily on perceived risk.

– Relevant for academics: first study measuring joint effect in OCRs. – Relevant for practitioners to decrease risk and increase sales online.

• Study 2: Examine the relative strength of demographic indicators on

perceived demographic homophily.

– Relevant for academics to have a solid construct.

– Relevant for practitioners due to increased machine learning.

(6)

1. Research Question

“What is the effect of homophily on perceived risk in OCRs and what is the relative

effect of each demographic indicator towards perceived demographic

homophily?”

• 1. What is the effect of demographic homophily between sender and perceiver on perceived risk in OCR?

• 2. What is the effect of perceptual homophily between sender and perceiver on perceived risk in OCR?

• 3. What is the effect of demographic and perceptual homophily on perceived risk?

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1. Hypotheses

• H1: Perceived demographic homophily has a negative effect on the degree of perceived risk. • H2: Perceived perceptual homophily has a negative effect on the degree of perceived risk.

• H3: The negative effect of perceived perceptual homophily is stronger on the degree of perceived risk than the negative effect of demographic homophily.

• H4: The negative effect of demographic homophily on perceived risk becomes more negative in the presence of perceived perceptual homophily.

• H5: The negative effect of homophily on the degree of perceived risk is weaker when individuals have an individualistic rather than an collectivist orientation.

• H6-8: The negative effect of homophily on the degree of perceived risk is stronger when:

– controlled for individuals with a prevention and promotion focus. – controlled for individuals experience with OCRs.

– controlled for individuals attitudes towards OCRs.

• H9: Location homophily has the strongest relative effect on perceived demographic homophily, followed by age, occupation, gender, and name homophily respectively.

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2. Conceptual models

Study 1

Homophily on perceived risk

Study 2

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3. Research Designs

Study 1

Homophily on perceived risk

Multiple regression analysis

2x2 between subject design

30 x 4 = 120 respondents needed

15

Study 2

Demographic indicators on demographic homophily

Demographic /

Perceptual

Similar

Dissimilar

Similar

C1

C2

Dissimilar

C3

C4

• Decompositional choice-based

conjoint-analysis

• 4 choice sets (8 choice alternatives)

• 120 respondents is sufficient

15

Sample:

• 146 respondents.

• Gender: 44,5% male, 55,5% female.

• Age: average 33 years (S.D. 13,13).

(10)

3. Manipulation of

Homophily

How to match respondents with dissimilar or similar demographic

and/or perceptual traits?

(11)

3. Manipulation of

Homophily

Stimuli back-end Qualtrics

(12)

3. Stimuli (1)

Study 1

(13)

3. Stimuli (2)

Study 2

Demographic indicators on demographic homophily

(14)

4. Results study 1

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Constant 2,735*** 2,649*** 2,830*** 2,623*** 2,074*** 2,590*** 2,118*** Demo ,158 ,340 -,115 ,337 ,311 ,338 -,092 Per -,431** -,264 -,896 -,270 -,246 -.261 -,700 Demo*Perc -,353 -,343 -,349 -,392 -.357 -,345 Indi -,036 ,020 Indi*Demo ,085 ,069 Indi*Per ,123 ,083 Prevention ,007 ,005

Hypotheses

(15)

4. Results study 2

Attributes and levels β Standard Error Z-score P-value

Name Similar 0,16 0,05 2,96 0,00 Dissimilar -0,16 0,05 -2,96 Age Similar 0,26 0,09 1,27 0,00 Dissimilar -0,26 0,09 -1,27 Gender Similar 0,33 0,08 2,16 0,03 Dissimilar -0,33 0,08 -2,16 Occupation Similar 0,92 0,11 8,30 0,00 Dissimilar -0,92 0,11 -8,30 Location Similar 0,15 1,00 0,00 1,00 Dissimilar -0,15 1,00 -0,00

A similar name, age, gender and occupation is of significant utility to perceive demographic homophily

Individuals do not need to share a similar residence in order to perceive demographic homophily

(16)

4. Results study 2

Attributes and levels β Range Importance

(17)

5. Discussion study 1

Homophily on perceived risk

Study 1 found no significant effects of demographic homophily, perceptual

homophily and their joint effect on the degree of perceived risk in an OCR setting.

In addition, the degree of perceived risk significantly increases by individuals with

a positive attitude towards OCRs.

– Postive attitudes towards OCR  use heuristics > having less involvement and

elaboration when reading  not reading the OCR thoroughly may lead to an increase in perceived risk.

Limitations study 1

Hard to perceive risk in an artificial experiment. imagine to book a hotel… Thus,

the amount at stake and the subjective uncertainty may be too defective. Hence,

insufficient measurement of perceived risk.

The manipulation, using the piped text insertion may have been too obvious.

Positive OCRs, instead of negative OCRs, may exert less risk (i.e. another individual

has positive experience with the hotel), which may subsequently result in less

(18)

5. Discussion study 2

Demographic indicators on demographic homophily

The demographic indicator ´occupation´ has the strongest relative effect towards

perceived demographic homophily, followed by gender, age, name and location.

– Indicate the lifestyle of another individual with occupation 16. For example, when the

occupation of the reviewer is ‘student’, one can associate it with some sort of lifestyle of the individual. A lifestyle involves motivation and choice of individuals which could have a strong effect on perceived homophily9, hence making occupation of such importance.

– Choice domination

Implications study 2

Academics: insert occupation as a demographic indicator to measure the

demographic homophily concept.

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5. Discussion

Future Research

Study 1

• Conduct study with negative

OCRs rather than positive OCRs.

• Study the effects on other goods

such as search goods.

• For external validation purposes,

future research may study the

effects with respondents of other

nationalities

Study 2

• Study the relative importance of

additional demographic

indicators such as family cycle or

race.

• Examine the interaction effects

between demographic indicators.

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Questions?

(21)

Reference (1)

• (1) Park, D. H., & Park, S. B. (2008). The Multiple Source Effect of Online Consumer Reviews on

Brand Evaluations: Test of the Risk Diversification Hypothesis. Advances in Consumer Research, 35, 744-745.

• (2) Moe, W., & Trusov, M. (2011). The Value of Social Dynamics in Online Product Ratings Forums,

Journal of Marketing Research. 48, 444–56.

• (3) Lazarsfeld, P. F., & Merton. R. K. (1954). Friendship as a Social Process: A Substantive and

Methodological Analysis. In Freedom and Control in Modern Society, edited by Berger, M., Abel, T., & Page, C. H, 18–66.

• (4) Rogers, E. M. (1983). Diffusion of innovations (3e ed.). New York, United states of America: The

free press.

• (5) Heider, F. (1958) The psychology of interpersonal relations. Wiley, New York.

• (6) Laumann, E. (1966). Prestige and association in an urban community. Indianapolis, IN:

Bobbs-Merrill.

• (7) Price, L., & Feick L. (1984). The Role of Interpersonal Sources in External Search: An

Informational Perspective. Advances in Consumer Research, 10: 250-255.

• (8) Fennis, B. M., & Stroebe, W. (2016). The psychology of advertising (2e ed.). London, United

Kingdom: Routledge.

• (9) Festinger, L. (1954) A theory of social comparison processes. Human relations, 7, 117-140.10.

Granovetter, 1973

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Reference (2)

• (10) Tajfel, H., & Turner, J. C. (1986). The Social Identity Theory of Intergroup Behavior. In Psychology

of Intergroup Relations, edited by S. Worchel and W. G. Austin. 7–24. Vol. 2, Chicago, IL: Nelson-Hall.

• (11) Berger, C. R., & Calabrese, R. J. (1975). Some explorations in initial interaction and beyond:

Toward a developmental theory of interpersonal communication. Human communication research, 1(2), 99-112.

• (12) Rosario, A. B., Sotgiu, F., Valck, K. D., & Bijmolt, T. H. A. (2016). The Effect of Electronic Word of

Mouth on Sales: A Meta-Analytic Review of Platform, Product, and Metric Factors. Journal of Marketing Research, 53, 297-318.

• (13) Naylor, R. W., Lamberton, C. P., & Norton, D. A. (2011). Seeing ourselves in others: Reviewer

ambiguity, egocentric anchoring, and Persuasion. Journal of Marketing Research, 48, 617-631.

• (14) Racherla, P., Mandviwalla, M., & Connolly, D. J. (2012). Factors affecting consumers’ trust in

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