Informational Influence
Floyd van den Berg S2560879
1st supervisor: dr. J. C. Hoekstra
How to become more effective?
› Nowadays, many people are likely to follow an
influencer just because they have become a celebrity, not due to trust (Barker 2017).
› Placing “paid sponsorship” descriptions evokes
feelings of anger in consumers (Ross 2018), due to increased skepticism.
› It is unknown to many practitioners where the
This study aims to...
› Investigate the role of an influencer’s contagiousness on consumer’s purchase intentions in an Online
Social Network environment.
› Gain a deeper understanding of this relationship by investigating consumer’s susceptibility.
Theoretical background
› Providing and validating information with regards to the advantages and disadvantages of products can
affect group behavior (Hinz, Schulze, and Takac 2014) › Contagion has the power to affect purchase
intentions, i.e. by accelerating or slowing down
Consumer’s product knowledge
› Consumers with a high level of product-related experiences tend to negate interest in others’ information and opinion due to a heightened confidence in their own abilities (Bearden,
Netemeyer, and Teel 1990; Clark and Goldsmith 2006; Kahle 1995; Locander and Hermann 1979) › Knowledgeable consumers depend less on others to
Perceived distance consumer - influencer
› The consumer and the influencer have the best fit when the influencer seems likeable, shows passion and conviction or reminds them of the individual (Ross 2018)
› Consumers experience a decrease in level of self-esteem by comparing to individuals with highly desirable characteristics (Morse and Gergen 1970)
Susceptibility to informational influence
› “the tendency to accept information from others as evidence about reality” (Deutsch and Gerard 1955), reflected in the desire to obtain information about products and brand (Netemeyer, Bearden, and Teel 1992).
› Influencers can help reduce perceived risk (Burnkrant and Cousineau 1975) and increase a consumer’s
susceptibility (Woodside and Sims 1976), enabling the evaluation of contagiousness and making
Method
› Sample: 106 respondents mixed in age, gender, and relationship status
› Between-subjects experimental design › 2 conditions:
• High contagiousness
• Low contagiousness
› Respondents were asked to fill in scales with items
Conditions
Analysis
› Independent samples t-test to assess differences in contagiousness
› Analysis of covariance (ANCOVA) to investigate the direct effect and moderation effects
Results independent samples t-test
› In the pre-test:
• Contagiousness is different for the high condition
(M = 4.10, S.D. = 1.71, n = 21) compared to the low condition (M = 2.15, S.D. = 1.09, n = 22).
› In further analysis:
• Contagiousness is marginally different for the high
condition (M = 3.23, S.D. = 1.35, n = 52) compared to the low condition (M = 3.02, S.D. = 1.08, n =
Results ANCOVA
› Using dummy-coded Contagiousness:
• No signs of a direct effect.
• No signs of Perceived distance moderating.
• Unexpected positive direct effect between
Perceived distance and Purchase intentions.
• Unexpected marginally positive moderating effect
of Product knowledge.
› Using scale-measured Contagiousness:
• A positive relationship between Contagiousness
Procedure in linear regression analysis
› Using the assumptions by Baron and Kenny (1986):
• Contagiousness on Purchase intentions has to be
significant (Model 5).
• Contagiousness on Susceptibility to informational
influence has to be significant (Model 6).
• Susceptibility to informational influence on
Purchase intentions has to be significant (Model 7).
• Susceptibility to informational influence has to
make Contagiousness on Purchase intentions non-significant (full) or lower the effect (partial)
Results linear regression analysis
› Using dummy-coded Contagiousness:
• None of the four models are significant, indicating
no effects.
› Using scale-measured Contagiousness:
• Models 5, 6, and 8 are significant, but Model 7 is
not significant.
• Susceptibility to informational influence neither
makes Contagiousness non-significant, nor lowers its effect.
• A positive relationship between Contagiousness
Hypothesis testing (1)
› H1. An influential’s contagiousness is expected to
positively influence consumer’s purchase intentions.
• Accepted
› H2. A consumer’s product knowledge is expected to decrease the effect of an influential’s contagiousness on consumer’s purchase intentions.
Hypothesis testing (2)
› H3. The perceived distance between a consumer and an influencer is expected to decrease the influence of an influential’s contagiousness on consumer’s
purchase intentions.
• Rejected
› H4. An influencer’s contagiousness positively affects consumer’s purchase intentions both directly and indirectly through susceptibility to informational influence
Conclusions
› Offering “contagious” content appeals more to the consumer, resulting in higher purchase intentions.
› Instead of decreasing, pre-existing product knowledge increases a consumer’s confidence and purchase
intentions accordingly.
› Consumers experiencing a distance between him/her and the influencer will not affect their purchase
intentions
