1. How online social ties and productrelated risks influence purchase
intentions: A Facebook experiment
研究⽣生
指導⽼老師
1
鍾聖彥
蔡家⽂文 教授

2. 論⽂文簡介
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期刊：Electronic Commerce Research and Applications
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作者：⺩王俊程 教授 國⽴立清華⼤大學 服務科學研究所
張景惠 博⼠士⽣生 國⽴立清華⼤大學 科技管理研究所
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Keywords:
Social networking sites (SNSs)
Information diagnosticity
Tie strength
Purchase intention
Product-related risks
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2

3. Introduce(1/7)
Consumers can obtain product information from online
sources, which are important to their purchasing
decisions. Several studies have discussed the impact of
online consumer reviews on the purchasing process
(Chevalier and Mayzlin 2006, Dellarocas et al. 2007, Duan
et al. 2008a,b, Godes and Mayzlin 2004, Koh et al. 2010,
Lee et al. 2008, Liu 2006, Park and Kim 2008, Park et al.
2007).
Online consumer reviews
Purchasing process
3

4. Introduce(2/7)
Trusov et al. (2009) studied the effect of word-of-mouth
(WOM) marketing on the growth of a social networking
site and compared WOM marketing with traditional
marketing techniques.
Effects
WOM marketing
> Traditional marketing
4

5. Introduce(3/7)
Marketers and researchers have realized the importance
of social ties with respect to customers’ decision-making
processes (Arndt 1967).
The different types of social ties have different impacts
on purchasing decisions. (Brown and Reingen 1987).
Influence
Strong ties (friends)
>
Weak ties (acquaintances)
Purchasing decisions
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6. Introduce(4/7)
In SNSs, users interact with one another, share
information, gather ideas and opinions, and influence
one another’s perceptions (Centola 2010).
SNSs are changing existing marketing practices because
people are relying more on the members of their own
networks, such as friends and peers, to guide their
decision making instead of relying only on authority
figures, experts, the mainstream media, and mass
advertising (Brymer 2009).
!
Specific opinion leaders????
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7. Introduce(5/7)
Perceived diagnosticity represents consumers’
perceptions of their information sources’ capacity to
assist them in evaluating the quality of products (Jiang
and Benbasat 2005, Kempf and Smith 1998, Mudambi
and Schuff 2010).
This study as a Mediator
Product-related risks are important factors that influence
!
how consumers evaluate products and the information
sources (Dowling 1999).
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8. Introduce(6/7)
Field experiment on Facebook
To investigate the following three questions.
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Are the recommendations and information provided by
different social ties perceived as having different levels of
diagnosticity?
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Do product-related risks moderate the relationship
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between online tie strength and perceived diagnosticity?
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Does perceived diagnosticity have a positive effect on
purchase intentions?
8

9. Introduce(7/7)
Managerial
perspective
Productrelated risks
Perceived
diagnosticity
Online tie
strength
Decision-making
process
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10. Theoretical background(1/6)
Brown and Reingen (1987) used retrospective data
collected exclusively from successful referrals to analyze
WOM-referral behavior.
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Weak
Social ties
Strong
Social ties
Decision-making
process
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11. Theoretical background(2/6)
Frenzen and Nakamoto (1993) studied the impacts of
consumer decisions to transmit or withhold word-ofmouth information on the flow of information in a market.
Consumers are likely to share all types of information
with strong-tie contacts.
!
!
Duhan et al. (1997) studied the factors that influence the
likelihood that a consumer will use recommendations
from strong-tie sources and weak-tie sources.
!
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12. Theoretical background(3/6)
Bruyn and Lilien (2008) observed the reactions of 1100
individuals to receiving an unsolicited e-mail invitation
from one of their acquaintances to participate in a survey.
!
!
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Wen et al. (2009) conducted an experiment using SNS
pages to investigate the effects of tie strength, endorser
expertise, and product type on advertising effectiveness.
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!
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13. Theoretical background(4/6)
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14. Theoretical background(5/6)
This Study different:
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Relationship between SNSs and consumers’
decision-making processes in detail.
!
!•
How the consumers’ decision-making processes
involve their cognitive systems in processing
product information and evaluating its sources.
!
•
Consider the effect of product-related risks
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15. Theoretical background(6/6)
S－O－R 模式 ＜Stimulus－Organism－Response＞
該模式表明消費者的購買⾏行為是由刺激所引起的，這種刺激既來⾃自於消費者⾝身體內部的⽣生理、⼼心
理因素和外部的環境。
Stimuli (S) may appear in different formats (Jacoby 2002).
Past works have said that website features are important stimuli for
the online purchase process (Eroglu et al. 2003, Jiang et al. 2010,
Koufaris 2002, Parboteeah et al. 2009).
The organism (O) refers to an individual’s cognitive systems,
including the cognitive network and schema.
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The response (R) represents the psychological reactions such as
attitudinal and behavioral reactions.
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16. Research model and hypotheses
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17. Hypotheses(1/3)
Mudambi and Schuff (2010) applied the concept to the context of
online product reviews and found that depending on the type of
product, the characteristics of product reviews, review rating, and
review length have different effects on their perceived diagnosticity.
Rogers (1995) argues that strong-tie sources are perceived as more
credible and trustworthy than weak-tie sources.
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Hypothesis 1.
The recommendations and information provided by
strong-tie sources will have a higher level of perceived
diagnosticity.
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18. Hypotheses(2/3)
Previous studies have noted that the distinction between high-risk
products and low-risk products causes consumers to utilize
different psychological processes while evaluating a product
(Dowling and Staelin 1994, Payne et al. 1993).
Hypothesis 2. Product-related risks moderate the
relationship between online tie strength and perceived
diagnosticity.
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19. Hypotheses(3/3)
Perceived diagnosticity can alleviate information asymmetry through
the signals and incentives, prevent customers to purchase lowquality products (Pavlou et al. 2007), and strengthen customers’
confidence in their purchase decisions (Kempf and Smith 1998).
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If customers feel that the product information is diagnostic, they are
certain about estimating product quality, and more confident about
their purchase decisions (Kempf and Smith 1998).
Hypothesis 3. For positive recommendations, higher
perceived diagnosticity will lead to higher purchase
intentions.
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20. Research method
Experiment on Facebook to test hypotheses.
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21. Research method
Online tie strength
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Tie strength as the frequency of interactions among
contacts on SNSs.
During the past three months
Interaction
frequency
most
least
Three people
Strong-tie
friends
Weak-tie
friends
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22. Research method
Products described in the experiment
Product price is a stimulus for customer thinking, highpriced products lead customers to think harder about
their purchase intentions (Wathieu and Bertini 2007).
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Snacks as the product analyzed
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Low-priced($2~$4) and High-priced($30~$50) products
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Low-priced products as less risky products and high-priced
products as risky products.
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23. Research method
Experimental design
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Scenario in which a friend invited them to a party and
asked them to bring some snacks.
First, we replicated the 10 most recent posts on the
subject’s actual Facebook wall on the day before he or
she participated in the experiment.
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Second, we added three posts containing product
recommendations (e.g., the snack is delicious.).
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24. •
•
•
•
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In the strong-tie and high-risk
treatment, the subjects saw
that products with high prices
were recommended by their
strong-tie friends.
In the strong-tie and low-risk
treatment, the subjects saw
that products with low prices
were recommended by their
strong-tie friends.
In the weak-tie and high-risk
treatment, the subjects saw
that products with high prices
were recommended by their
weak-tie friends.
In the weak-tie and low-risk
treatment, the subjects saw
that products with low prices
were recommended by their
weak-tie friends.

25. Research method
Questionnaire items
Two key constructs
Purchase
intentions
Perceived
diagnosticity
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26. Data analysis and results
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•
•
•
•
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By inviting randomly selected users on Facebook
to participate in a Facebook application created for
this experiment.
The 420 subjects by using Facebook.
216 (51.4%) were female, 204 (48.6%) were male.
Most of the participants (78.5%) were between 21
and 30 years old.
Most had been using Facebook for more than a
year (74.5%)
Majority (68.1%) checked their Facebook wall
messages at least once a day.
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27. Data analysis and results
To assess our operational definitions of online tie
strength and product-related risks.
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Following two questions in the questionnaire:
‘‘Do you think you interact often on Facebook with the
friends who provided recommendations?’’
‘‘Do you think the price you saw for the snack product is
high?’’
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28. Data analysis and results
T-test to compare the levels of perceived diagnosticity of
information from strong- and weak-tie sources
(Mstrong = 5.22 vs. Mweak = 4.96, p < 0.05).
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This finding confirms
hypothesis H1.

29. Data analysis and results
(Mstrong = 5.31 vs. Mweak = 4.84, p < 0.01)
(Mstrong = 5.13 vs. Mweak = 5.09, p = 0.746)
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This finding supports
hypothesis H2.

30. Data analysis and results
Perceived diagnosticity was positively correlated with purchase
intentions (b = 0.596, p < 0.01), fully mediated the relationship
between online tie strength and purchase intentions, and explained
36.3% of the variance in purchase intentions.
This finding supports hypothesis H3.
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31. Discussion and
conclusions(1/2)
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•
•
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Online tie strength is an important factor in
consumer decision making.
Consumers believe that a strong-tie source can
help them understand and evaluate the quality and
performances of products.
Online social relationship data are important and
provide an effective means of analyzing consumer
behavior.
The evidence indicates that theories about the
impact of social relationships on traditional WOM
communication may be applicable to SNSs.
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32. Discussion and
conclusions(2/2)
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•
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Product-related risks moderate the relationship
between online tie strength and perceived
diagnosticity.
Recommendation sources can influence
perceived diagnosticity by determining whether
a consumer will purchase a product.
This result supports Fang’s (2012) findings,
which found that perceived diagnosticity directly
influences customers’ transaction intention with
a specific seller from a website.
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33. !
Future research
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•
•
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Consumer decision making may be affected by
various factors.
The effects of individual characteristics.
Future research is encouraged to investigate
other possible mediators such as attitudes
toward products that may enhance the
association between perceived diagnosticity and
purchase intention.
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34. !
Future research
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•
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Future studies could choose different productrelated risks to confirm that the results hold.
Future studies could emphasize in distinguishing
the effect of influence (friends induce to
purchase) and homophily(friends have similar
backgrounds and tastes).
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!
!
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