To Buy or Not to Buy: Online Reviews and Social Networks Establish the Subjective Norm

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The intention of this research is to understand the influence of the online review in the shopping process and how that influence may be moderated by the shopper’s social network.

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To Buy or Not to Buy: Online Reviews and Social Networks Establish the Subjective Norm

  1. 1. Running head: TO BUY OR NOT TO BUY 1 To Buy or Not to Buy: Online Reviews and Social Networks Establish the Subjective Norm Nicole Cathcart The Johns Hopkins University
  2. 2. TO BUY OR NOT TO BUY 2 To Buy or Not to Buy: Online Reviews and Social Networks Establish the Subjective Norm A projected $680 billion industry in 2011, and growing at 19 percent in just the last year,e-commerce, or online purchasing, revenue represents a substantial and growing method ofpurchase within the global marketplace (Rao, 2011). A fundamental driver of e-commercebehavior is the parallel explosive growth of online usage. The Pew Internet and AmericanProject reports (Jansen, 2009) that over half of American adults research products online beforepurchasing, and that 21 percent of adults search online daily—an increase from nine percent in2004. This rapid move online shows no sign of slowing. Parallel to the growth of general online activity is the adoption of social networks. Fivepercent of Americans reported using social media sites such as LinkedIn or Facebook in 2005,and in 2010, that number reached 46 percent (Jansen, 2009). As both e-commerce and social network adoption grow in size, the overlap betweenshopping behavior and social networks online grows stronger. The recent feature launch ofsocial shopping from the search engine Bing, in partnership with Facebook,where you can seethe services and products your social network purchases and likes at the point of search,represents the growing understanding of the value in-network recommendations might have onpurchasing (The Bing Team, 2010). While marketers may be applying existing social networks more in the purchasingprocess as technology advances to allow for greater integration, the online review or commenthas become a staple of the e-commerce experience. The intention of this research is tounderstand the influence of the online review in the shopping process and how that influencemay be moderated by the shopper’s social network.
  3. 3. TO BUY OR NOT TO BUY 3 Theoretical Framework and Background To understand why the online review influences purchasing behavior, the application ofthe theory of reasoned activity (TRA) provides a useful framework. Additionally, TRA explainshow the influence of online reviews increases as the credibility of the reviewer increases. TRA presumes that antecedents of behavior change, including attitude and subjectivenorms, both affect behavior intention, then behavior (Montano&Kasprzyk, 2008). The onlinereview influences the subjective norm, creating an expectation for the outcome of the intendedbehavior. In utilizing TRA to explain the effectiveness of online reviews, the theory provides formoderating subjective norms through the shopper’s general receptivity of behaving according tosocial norms. This research suggests that receptivityincreases as the influencing reviewer movesfrom anonymous to connected within the shoppers network. Within this model, online reviewsaffect normative beliefs, and they are moderated by the reviewer’s proximity to the shopper inthe social network. These factors converge to increase the shopper’s intention to purchase (seeFigure 1). Even within the TRA, additional influences are identified to contribute to onlinepurchasing. The purpose of this research is not to identify or prioritize other influences, butrather to explain the important role of the online review and its moderators within the e-commerce decision-making process. The evaluation of the literature establishes the impact of online reviews in purchasingbehavior, the influence of quality of the reviewer in driving purchasing behavior, and themoderating influence of proximity within a social network. The combination of these factors
  4. 4. TO BUY OR NOT TO BUY 4creates a normative expectation of an intended behavior and then enhances that expectationbased on trust within an existing social network. Online Reviews Attitude Normative Behavior Intent to Behave Beliefs Subjective Norms Intention to Comply Proximity of Trust in Members within Members of a the Social Social Network NetworkFigure 1.Role of online reviewers and the social network within the theory of reasoned action.Adapted from“Theory of Reasoned Action and Theory of Planned Behavior,” by D.E. Montano, and D. Kasprzyk, 2008).Theory ofreasoned action, theory of planned behavior, and the integrated behavioral model [Chapter 4]. In K. Glanz, B. K.Rimer, & K. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice, pp. 70, SanFrancisco, CA: John Wiley. Although not an exhaustive review of how TRA can be used to understand theonline purchasing process, the literature presented shows that the online review represents anestablished and important element for understanding and influencing online purchasing behavior,and that social networks can magnify that element.
  5. 5. TO BUY OR NOT TO BUY 5 Consistency, Quality and Quantity of Positive and Negative User Reviews The online review is now a common feature in e-commerce, allowing shoppers to rely onthe public recommendations of previous purchasers. These anonymous reviews contribute tocreate a normative belief about the outcome of purchasing—whether the user will be satisfiedwith their product after purchasing. Many factors influence purchasing behavior, but bothpositive and negative online reviewshave consistently demonstrated a significant correlation withpurchasing behavior. In fact, some evidence exists that online reviews may be one of the most significantfactors in purchasing. In an experiment with 90 undergraduate students, researchers comparedfive different stimuli but found customer reviews to have the highest impact on online shoppingbehavior (Fagerstom, 2010). The results of the study included a high impact for a high quantityof positive reviews and a very high impact for a high quantity of negative reviews. Notable inthis study is both the moderating influence of quantity and that negative reviews held moresignificance than positive reviews. Further evidence of the more significant impact of negative reviews was found in a multi-year meta-analysis of video game shopping reviews. Researchers gathered online reviews from2003-2005 from Gamespot.com and found that online reviews were most influential forunpopular games (Zhu and Zhang, 2010). The more significant impact of negative reviewssuggests that while reviews can help shape a positive outcome, they are more effective indisproving a positive outcome to purchasing. However, a high quantity of positive reviews remains a significant influencer. Chen, Ma,Li, Dai, Wang and Shu (2010) demonstrated that when positive reviews were consistent, subjectsfollowed the herd in 90% of cases. In their experiment including sixteen undergraduates, Chen
  6. 6. TO BUY OR NOT TO BUY 6et al. (2010) supplied participants with book titles, authors and positive and negative reviews ofbooks and asked them to decide whether to purchase a book as quickly as possible. When all thereviews were consistently positive, the subject purchased the product 90% of the time. When thereviews were mixed between positive and negative, the subject purchased the product 50% of thetime. The results suggest that the greater the appearance of a positive result based on theconsistency of positive reviews, the more likely the purchasing. In addition to consistency of reviews, the quality and quantity of reviews can influencepurchasing. In an online experiment utilizing the Elaboration Likelihood Model (ELM), 263undergraduate students were presented with reviews of either high or low quality. ELM suggeststhat success of a persuasive message can be different depending on the need for cognition, orhow much a person has to think about a topic or decision. Results showed that for thoseparticipants with high need for cognition, the quality of reviews was important, while with thosewith a low need for cognition, the quantity of reviews was most important in influencingpurchasing behavior (Lin, Lee and Horng, 2011). Anticipating the role cognition plays in thepurchasing process, perhaps by understanding the target audience or product for sale, may thenquantify the importance of soliciting large amounts of reviews instead of focusing on the qualityof the opinion. Cognition is just one of the potential factors that can affectthe role of reviews on thepurchasing process. The literature shows that positive and negative review types influencepurchasing, creating a standard for how online shoppers anticipate the outcome of purchase.This standard can be explained as the normative belief, which gains more clarity through theconsistency, quality and quality of reviews.
  7. 7. TO BUY OR NOT TO BUY 7 Trust, Expertise and Credibility Form a Quality Reviewer While the quantities of reviews establish the normative beliefs behind the likelihood of apositive outcome based on purchasing behavior, the quality of a review, measured through thecharacteristics of the reviewer, can help establish the subjective norm. Levels of trust, expertiseand credibility influence the individual’s intention to comply, or purchase.The research shows alink between the perceived quality of the reviewer and the influence of the review. Researchers surveyed users of the Bahamut website (www.gamer.com.tw) for differentinfluences impacting online shopping, including ability, benevolence/integrity, and critical massof reviewers (Hsiao, Lin, Wang, Lu & Yu, 2010). Of the 1,219 qualified respondents,researchers concluded a significant connection between purchasing and perceivedbenevolence/integrity of reviewers. In this case, the results indicate a connection between thelevel of trust the shopper has for the reviewer as the most important predicator of purchasingbehavior. In another study isolating the impact of quality reviewers, researchers used a subsampleof 26 batches of panel data over the course of several months from Amazon.com’s salesinformation where a review was present (Hu, Liu & Zhang, 2008). To determine whether areviewer could be determined as high quality, the researchers evaluated the number of reviewssubmitted per reviews compared against the number of times another user considered a reviewhelpful in deciding to purchase. Their results showed a positive and significant correlationbetween the quality of a reviewer and purchasing behavior. While both the previous studies establish a level of trust, either explicitly defined or asindicated by how helpful and prolific the reviewer was, Mackiewicz (2010) empiricallyevaluated the different characteristics and actions of reviewers that indicate credibility, a
  8. 8. TO BUY OR NOT TO BUY 8construct composed of expertise and trust. In a study analyzing digital camera reviews within750 online reviews on Epinions.com, the research created three models of reviewer expertise—including assertions of expertise about the product, expertise about related products, and a role orfunction that indicates expertise in the field. In testing the reliability of these categories,Mackiewicz (2010) found all significant, but did not weigh any categories as more effective. The literature suggests that trust, expertise, and the two combinedto create credibility inthe reviewer, all indicate ameasure of quality. While the consistency, quality and quantity of thereviews themselves create the normative beliefs about the outcome of purchasing products, thequality of the reviewer can influence the intention to comply, or purchase. These levels of trust,expertise and credibility only increase when the reviewer is a known part of a social network. The Influence of Tie Strength, Homogeneity and Trust inSocial Networks The influence of an individual’s social network may affect their intention to comply withsubjective norms. The literature pinpoints different characteristics that grow stronger withinsocial networks, suggesting influence may grow as individuals are more closely connected.When evaluating the elements that that strengthen social networks, researchers identify tiestrength, homogeneity and trust. In a study of trust dynamics, researchers surveyed 572 international university studentsand were able to determine greater levels of trust based on relationism, or the proximity ofindividuals within a social network (Igarashi, Kashima, Kashima, Farsides, Kim, Strack, Werth,& Yuki, 2008). The closer the tie to the individual, the results indicated more significance in thetrust. While the study does not relate to behavior, the findings of continually amplified trust as
  9. 9. TO BUY OR NOT TO BUY 9networked individuals are more closely related creates a directional model for how socialnetworks, online or offline, can work. Also researching smaller, more connected networks, Shu and Chuang (2011) evaluatedtrust in social networking sites built around six degrees of separation versus broader connections.The researcher used a pre-test-post-test experiment of 123 Taiwanese college students, with 61 inthe control, to determine the impact of trust. The students in the experimental group usedFacebook during the two months between pre- and post-testing. The results indicated a greaterdegree of trust in the experimental group, suggesting that networks with greater ties, in this case,no more than six degrees of separation, have greater levels of trust. The importance of trust in social networks was also found significant in an analysis ofcompleted online surveys of 363 undergraduate students measuring influences for brandrecommendations in online social networks (Chu and Kim, 2011). Researchers found that tiestrength within a network significantly impacted opinion giving and passing;that homogeneitysignificantly impacted opinion seeking and passing;but that trust significantly impacted opiniongiving, seeking and passing. Ultimately, results indicated that trust in one’s social networkingsite contacts was the most significant factors in brand engagement. The literature suggests that more connected the network, the greater the level of trust—atrust that can be extended to belief in brand recommendations. This model exists offline andonline in these studies—one generalizes the concept of trust within closer networks, and theothers show the both the greater levels of trust in online social networks, and the impact of thattrust in recommending brands.
  10. 10. TO BUY OR NOT TO BUY 10 Conclusions While offline, personal recommendations may influence purchasing intentions, thisprocess is transparent and powerful in its online form. Just as high quantities of positive andnegative reviews have been shown to influence purchasing behavior (Fagerstom, 2010; Zhu andZhang, 2010;Chen, Ma, Li, Dai, Wang &Shu, 2010), the added value of the credibility of apersonal review within a social network can influence intention to comply based on increasedlevels of trust and credibility (Chu and Kim, 2011). As more individuals purchase online, the e-commerce experience grows in importancefor marketing practitioners. Isolating the influencing variables in this shopping experiencebecomes a necessary part of creating a better and more predictable outcome to the shoppingprocess. In utilizing TRA, researchers can use an existing theoretical model to understand howonline reviews act as antecedents to purchasing intention and behavior.
  11. 11. TO BUY OR NOT TO BUY 11 ReferencesChen, M., Ma, Q, Li, M., Dai, S., Wang, X, &Shu, L. (2010). The neural and psychological basis of herding in purchasing books online: An event-related potential study. Cyberpsychology, Behavior, and Social Networking, 13(3), 321-328.Chu, S., & Kim, Y. (2011). Determinants of consumer engagement in electronic word- of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47–75.Fagerstrom, A. (2010). The motivating effect of antecedent stimuli on the web shop: A conjoint analysis of the impact of antecedent stimuli at the point of online purchase.Journal of Organizational Behavior Management, 30, 199-220.Hsiao, K., Lin, J.C., Wang, X., Lu, H., & Yu, H. (2010). Antecedents and consequences of trust in online product recommendations: An empirical study in social shopping. Online Information Review, 34(6), 935-953.Hu, N., Liu, L., & Zhang, J.J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology Management, 9, 201-214.Igarashi, T., Kashima, Y., Kashima, E.S., Farsides, T., Kim, U., Strack, F., Werth, L., & Yuki, M. (2008).Culture, trust and social network.Asian Journal of Social Psychology, 11, 88- 101.Jansen, J. (2010). Online Product Research.Retrieved from Pew Internet and American Life Project website: http://pewinternet.org/Reports/2010/Online-Product- Research/Findings.aspxLin, C., Lee, S., Horng, D. (2011). The effects of online reviews on purchasing
  12. 12. TO BUY OR NOT TO BUY 12 intention: The moderating role of need for cognition. Social Behavior and Personality, 29(1), 71-82.Mackiewicz, J. (2010). Assertions of expertise in online product reviews.Journal of Business and Technical Communication, 24(1), 3-28.Montano, D. E., &Kasprzyk, D. (2008). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model [Chapter 4]. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice (pp. 67-92). San Francisco, CA: John Wiley.Rao, L. (2011, January 3). J.P. Morgan: Global e-commerce revenue to grow by 19 percent in 2011 to $680B.Techcrunch. Retrieved from http://techcrunch.com/2011/01/03/j-p- morgan-global-e-commerce-revenue-to-grow-by-19-percent-in-2011-to-680b/Shu, W., & Chuang, Y. (2011).The perceived benefits of six-degree-separation social networks. Internet Research, 21(1), 25-45.The Bing Team. (2010, November 2). Bing’s new social search features arrive today [Web log post]. Retrieved from http://www.bing.com/community/site_blogs/b/search/archive/ 2010/11/02/search-blog-bing-s-new-social-search-features-arrive-today.aspxZhu, F., & Zhang, X. (2010). Impact of online consumer reviews of product and consumer characteristics. Journal of Marketing, 74, 133-148.

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