perceived Credibility of

A Study of Heuristic Cues affecting the

perceived Credibility of

Online Consumer Review Sources

from an ELM Perspective

Sven Giesen (s3002454)

Master Thesis Msc. Marketing Management

1

_{supervisor: Dr. J. A. Voerman}

Agenda

• Introduction

• Research Questions

• Academic & Managerial Relevance

• Hypothesis & Conceptual Model

• Research Design

• Results

• Discussion & Implications

Introduction (1)

• Electronic Word of Mouth blows up the boarders of traditional

WOM

– Unlimited reach

– Communication with total strangers

– No Face-to-Face Situation

– Written words

• Problem: „How could I trust him?“

– Enormous amount of OCRs available

– Detecting credibility of a source online very difficult

 Cues of a traditional WOM setting (facial expressions, gestures) not available

Introduction (2)

• The uncertainty reduction theory

 High uncertainty in computer-mediated environment

 Motivation to reduce uncertainty about interaction

partner

 Solution: Interpreting heuristic cues (hidden

credibility markers) to judge credibility

Profile Picture

Real Person Avatar No Picture

Stated Reputation

Low (negative user ratings) High (positive user ratings)

The heuristic role of the IVs

Real person picture

• Social Presence Heuristic4

 Triggers impression of communicating with a real person • Identity Heuristic4

 Users see themselves identified by a reviewer

Avatar picture

• Social Presence Heuristic4

 Also present for computer-controlled entity

• Machine Heuristic4,5

 Automatically believe information originating from a computer

Benchmark No Picture  Heuristics are not applicable Reputation • Reputation/Endorsement Heuristic6

 A source is credible if others rate it as such • Bandwagon Heuristic/Principle of social proof4,7

 Looking at others to infer one‘s own opinion

Introduction of the DV & moderator

• DV

– Conceptualization of credibility

• Source-credibility Model by Hovland et al. (1953)

• Source-attractiveness model by McGuire (1985)

• The Three-Component-Model of Credibility

Three-component

model of credibility

• Moderator

– Involvement

• Two ways of processing information (ELM)

 central, effortful route vs. Peripheral, low effort route

Research Questions

• How do the perception of trustworthiness, expertise and

homophily impact the perceived source credibility?

• What is the effect of a reviewer‘s profile picture and stated

reputation on the perception of:

– Trustworthiness?

– Expertise?

– Homophily?

– Credibility?

Relevance

• Academic relevance

– The three-component model of source credibility – a new

conceptualization

– Insights about credibility judgements in OCRs based on heuristic

cues

– Testing the link between the involvement and the peripheral,

heuristic style of processing (ELM, HSM)

• Managerial relevance

– OCRs influential for the decision making process

Hypotheses & Conceptual Model

H1: The perception of trustworthiness positively impacts the perceived

credibility of the OCR source.

H2: The perception of expertise positively impacts the perceived

credibility of the OCR source.

H3: The perception of homophily between the source and the reader positively impacts the perceived

credibility of the OCR source. Stated Reviewer

Reputation

Credibility

Involvement Profile Picture

(real, avatar, no)

Hypotheses & Conceptual Model

H4: Compared to the no picture

condition, a real person profile picture positively impacts the perceived

trustworthiness (a), expertise (b), homophily (c) and indirectly credibility (d) of the OCR source.

H5: Compared to the no picture condition, an avatar profile picture positively impacts the perceived trustworthiness (a), expertise (b) and homophily (c) and indirectly credibility (d) of the OCR source.

H6: Compared to the real picture condition, an avatar similarly affects the perceived credibility,

trustworthiness, expertise and

homophily and indirectly credibility (d) of the OCR source.

Stated Reviewer Reputation

Credibility

Involvement Profile Picture

(real, avatar, no)

Hypotheses & Conceptual Model

H7: Compared to the low reputation condition, a high reputation positively impacts the perceived trustworthiness (a), expertise (b), homophily (c) and indirectly credibility (d) of the OCR source.

H8: The presence of a reputation cue

lowers the impact of a profile picture

on the perceived source credibility and its determinants.

H9: The positive effect of the picture and stated reputation cues on the outcome variables perceived

credibility, trustworthiness, expertise and homophily will increase when

involvement is low. Stated Reviewer Reputation Credibility Involvement Profile Picture

(real, avatar, no)

Research Design & Sample

Experimental Design

Results: Mediation Analysis

Positive effect of

three-component model on credibility No significant effect of a real person picture compared to no picture

Positive effect of an avatar compared to no picture Positive effect of an avatar compared to a real picture Positive effect of the reviewer‘s reputation (stronger than the ones of the profile picture) Interaction terms not significant

Throughout negative effect of involvement not present

 Mutiple role assumption of the ELM

 Unimodel

Effects of the profile picture partially not as expected, BUT…  Likability of the reviewer is an essential covariate needed

Results: Serial Mediation Analysis

Conceptual Model updated

Profile Picture / Reputation Likability Expertise Trust Homophily Credibility • Ranking of Liking Real picture Avatar No picture

• Reputation affects liking

• Positive effect of real picture on perceived trustworthiness,

expertise, homophily and credibility through likability • Positive effect of avatar not

mediated by likability • No significant difference

between avatar and real picture • Reputation effect as well

partially mediated by likability Both types of profile pictures (real or avatar) positively impact

perceptions, BUT functional chain is different!  Real picture: emotional response – liking

Additional Findings

• 76.3% would not upload a picture at all

 privacy concerns

• Manipulation Check supports ELM

 Hair color, reputation better noticed in case of low involvement

 Arguments better remembered in case of high involvement

• Direct effect of involvement on DVs significantly negative

 Quick & dirty evaluation

 Believing = default option

• Argument quality has a significantly positive effect on the

DVs

• Women generally judge the reviewer as more trustworthy,

Discussion & Implications

Dicussion

• A picture (being either an avatar or a real person) is perceived as more trustworthy, expert,

homophile and credible than no picture

– A real person drives a more emotional response (likability) – An avatar triggers an automatic response (machine heuristic)

• The reputation as stated by other users is even more influential on those perception (social proof)

• No definite effect of the level of involvement

– Multiple Role assumption of the ELM

Implications

• OCR interfaces encouraging consumers to upload a profile picture • Personalize service employees online

• Establishing a user-to-user rating system

Limitations & Future Research

Limitations

• Cues limited to source and receiver

characteristics

 Cues can also be present in the content of the review (e.g. spelling)

• Limited to positive review valence • Limited to speific male pictures • Limited to experience goods

• Only one review – cognitive effort of real life setting missing

Future Research

• Extending the model with content cues such as spelling

• Type of picture can be varied in several ways

 Content (i)relevant avatars  non-anthropomorphic avatars

 features of the person on the picture (e.g.

glasses, facial expressions, ethnicities)

 female profile pictures

• Increasing cognitive effort to test the ELM assumptions

 Higher amount of reviews  Adding a distraction task

A Study of Heuristic Cues affecting the

perceived Credibility of

Online Consumer Review Sources

from an ELM Perspective

References (1)

1 _{Sun, T., Youn, S., Wu, G., & Kuntaraporn, M. (2006). Online word‐of‐mouth (or mouse): An}

exploration of its antecedents and consequences. Journal of Computer‐Mediated Communication, 11(4), 1104-1127.

2 _{Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technology design, identity} verification, and knowledge contribution in online communities. Information systems research, 18(1), 42-67.

3 _{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.

4 _{Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects} on credibility. Digital media, youth, and credibility, 73-100.

5 _{Fennis, B. M., & Stroebe, W. (2015). The psychology of advertising. Psychology Press.} 6 _{Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and heuristic approaches to} credibility evaluation online. Journal of communication, 60(3), 413-439.

7 _{Cialdini, R.B. (1993). Influence: Science and Practice (3rd edn). New York: HarperCollins}

References (2)

8 _{Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion; psychological} studies of opinion change.

9 _{McGuire, W. (1985) Attitudes and attitude change. In: Gardner L, Elliott A (eds) Handbook of}

social psychology, 2, 233–346. New York

10 _{Petty, R., & Cacioppo, J. T. (2012). Communication and persuasion: Central and peripheral}

routes to attitude change. Springer Science & Business Media.

11 _{Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM}

Transactions on the Web (TWEB), 1(1), 5.

12 _{Pesce, J. P., Casas, D. L., Rauber, G., & Almeida, V. (2012). Privacy attacks in social media using} photo tagging networks: a case study with Facebook. In Proceedings of the 1st Workshop on

Privacy and Security in Online Social Media (p. 4). ACM.