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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...
論⽂文簡介
•

期刊:Electronic Commerce Research and Applications

•

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

!

K...
Introduce(1/7)
Consumers can obtain product information from online
sources, which are important to their purchasing
decis...
Introduce(2/7)
Trusov et al. (2009) studied the effect of word-of-mouth
(WOM) marketing on the growth of a social networki...
Introduce(3/7)
Marketers and researchers have realized the importance
of social ties with respect to customers’ decision-m...
Introduce(4/7)
In SNSs, users interact with one another, share
information, gather ideas and opinions, and influence
one an...
Introduce(5/7)
Perceived diagnosticity represents consumers’
perceptions of their information sources’ capacity to
assist ...
Introduce(6/7)
Field experiment on Facebook
To investigate the following three questions.
•

Are the recommendations and i...
Introduce(7/7)
Managerial
perspective

Productrelated risks
Perceived
diagnosticity

Online tie
strength

Decision-making
...
Theoretical background(1/6)
Brown and Reingen (1987) used retrospective data
collected exclusively from successful referra...
Theoretical background(2/6)
Frenzen and Nakamoto (1993) studied the impacts of
consumer decisions to transmit or withhold ...
Theoretical background(3/6)
Bruyn and Lilien (2008) observed the reactions of 1100
individuals to receiving an unsolicited...
Theoretical background(4/6)

13
Theoretical background(5/6)

This Study different:
•

Relationship between SNSs and consumers’
decision-making processes i...
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...
Research model and hypotheses

16
Hypotheses(1/3)
Mudambi and Schuff (2010) applied the concept to the context of
online product reviews and found that depe...
Hypotheses(2/3)
Previous studies have noted that the distinction between high-risk
products and low-risk products causes c...
Hypotheses(3/3)
Perceived diagnosticity can alleviate information asymmetry through
the signals and incentives, prevent cu...
Research method
Experiment on Facebook to test hypotheses.

20
Research method
Online tie strength

•

Tie strength as the frequency of interactions among
contacts on SNSs.
During the p...
Research method
Products described in the experiment

Product price is a stimulus for customer thinking, highpriced produc...
Research method
Experimental design

•

Scenario in which a friend invited them to a party and
asked them to bring some sn...
•

•

•

•

24

In the strong-tie and high-risk
treatment, the subjects saw
that products with high prices
were recommende...
Research method
Questionnaire items

Two key constructs

Purchase
intentions

Perceived
diagnosticity

25
Data analysis and results
•

•
•
•
•
•

By inviting randomly selected users on Facebook
to participate in a Facebook appli...
Data analysis and results
To assess our operational definitions of online tie
strength and product-related risks.
!

Follow...
Data analysis and results

T-test to compare the levels of perceived diagnosticity of
information from strong- and weak-ti...
Data analysis and results

(Mstrong = 5.31 vs. Mweak = 4.84, p < 0.01)
(Mstrong = 5.13 vs. Mweak = 5.09, p = 0.746)
29

Th...
Data analysis and results

Perceived diagnosticity was positively correlated with purchase
intentions (b = 0.596, p < 0.01...
Discussion and
conclusions(1/2)
•
•

•

•

Online tie strength is an important factor in
consumer decision making.
Consume...
Discussion and
conclusions(2/2)
•

•

•

Product-related risks moderate the relationship
between online tie strength and p...
!

Future research
!

•
•
•

Consumer decision making may be affected by
various factors.
The effects of individual charac...
!

Future research
!

•
•

Future studies could choose different productrelated risks to confirm that the results hold.
Fut...
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How online social ties and product-related risks influence purchase intentions: A Facebook experiment

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How online social ties and product-related risks influence purchase intentions: A Facebook experiment

  1. 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. 2. 論⽂文簡介 • 期刊:Electronic Commerce Research and Applications • 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 ! Keywords: Social networking sites (SNSs) Information diagnosticity Tie strength
 Purchase intention Product-related risks • 2
  3. 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. 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. 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 5
  6. 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???? 6
  7. 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). 7
  8. 8. Introduce(6/7) Field experiment on Facebook To investigate the following three questions. • Are the recommendations and information provided by different social ties perceived as having different levels of diagnosticity? • Do product-related risks moderate the relationship ! between online tie strength and perceived diagnosticity? • Does perceived diagnosticity have a positive effect on purchase intentions? 8
  9. 9. Introduce(7/7) Managerial perspective Productrelated risks Perceived diagnosticity Online tie strength Decision-making process 9
  10. 10. Theoretical background(1/6) Brown and Reingen (1987) used retrospective data collected exclusively from successful referrals to analyze WOM-referral behavior. ! Weak Social ties Strong Social ties Decision-making process 10
  11. 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. ! 11
  12. 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. ! ! ! 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. ! ! 12
  13. 13. Theoretical background(4/6) 13
  14. 14. Theoretical background(5/6) This Study different: • 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 14
  15. 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. ! The response (R) represents the psychological reactions such as attitudinal and behavioral reactions. 15
  16. 16. Research model and hypotheses 16
  17. 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. ! Hypothesis 1. The recommendations and information provided by strong-tie sources will have a higher level of perceived diagnosticity. 17
  18. 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. 18
  19. 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). ! 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. 19
  20. 20. Research method Experiment on Facebook to test hypotheses. 20
  21. 21. Research method Online tie strength • 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 21
  22. 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). • Snacks as the product analyzed • Low-priced($2~$4) and High-priced($30~$50) products • Low-priced products as less risky products and high-priced products as risky products. 22
  23. 23. Research method Experimental design • 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. ! Second, we added three posts containing product recommendations (e.g., the snack is delicious.). 23
  24. 24. • • • • 24 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. 25. Research method Questionnaire items Two key constructs Purchase intentions Perceived diagnosticity 25
  26. 26. Data analysis and results • • • • • • 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. 26
  27. 27. Data analysis and results To assess our operational definitions of online tie strength and product-related risks. ! 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?’’ 27
  28. 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). 28 This finding confirms hypothesis H1.
  29. 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) 29 This finding supports hypothesis H2.
  30. 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. 30
  31. 31. Discussion and conclusions(1/2) • • • • 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. 31
  32. 32. Discussion and conclusions(2/2) • • • 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. 32
  33. 33. ! Future research ! • • • 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. ! 33
  34. 34. ! Future research ! • • 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). ! ! ! ! 34

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