How online social ties and product-related risks influence purchase

intentions: A Facebook experiment

研究⽣生 鍾聖彥 指導⽼老師 蔡家⽂文 教授

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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 strengthPurchase intention Product-related risks

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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

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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.

WOM marketing > Traditional marketing

Effects

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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).

Strong ties (friends) Weak ties (acquaintances)>Influence

Purchasing decisions�5

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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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).

!Product-related risks are important factors that influence how consumers evaluate products and the information sources (Dowling 1999).

This study as a Mediator

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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?

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Introduce(7/7)

Online tie strength

Perceived diagnosticity

Product-related risks

Decision-making process

Managerial perspective

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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

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Theoretical background(2/6)

Frenzen and Nakamoto (1993) studied the impacts of consumer decisions to transmit or withhold word-of-mouth 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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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. !

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Theoretical background(4/6)

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Theoretical background(5/6)

!

• 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

This Study different:

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Theoretical background(6/6)

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.

Stimuli (S) may appear in different formats (Jacoby 2002).

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。

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Research model and hypotheses

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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.

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Hypotheses(2/3)

Hypothesis 2. Product-related risks moderate the relationship between online tie strength and perceived diagnosticity.

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).

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Hypotheses(3/3)

Hypothesis 3. For positive recommendations, higher perceived diagnosticity will lead to higher purchase intentions.

Perceived diagnosticity can alleviate information asymmetry through the signals and incentives, prevent customers to purchase low-quality 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).

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Research method

Experiment on Facebook to test hypotheses.

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Research methodOnline tie strength

• Tie strength as the frequency of interactions among contacts on SNSs.

Interaction frequency

Strong-tie friends

Weak-tie friends

During the past three months

most leastThree people

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Research methodProducts described in the experiment

• 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.

Product price is a stimulus for customer thinking, high-priced products lead customers to think harder about their purchase intentions (Wathieu and Bertini 2007).

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Research methodExperimental 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.).

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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.

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Research methodQuestionnaire items

Two key constructs

Purchase intentions

Perceived diagnosticity

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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.

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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?’’

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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). This finding confirms hypothesis H1.

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Data analysis and results

(Mstrong = 5.31 vs. Mweak = 4.84, p < 0.01)

(Mstrong = 5.13 vs. Mweak = 5.09, p = 0.746)

This finding supports hypothesis H2.

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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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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.

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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.

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!

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. !

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!

Future research

!• Future studies could choose different product-

related 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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