THE EFFECT OF ONLINE REVIEW CONFIGURATIONS, PRICE, AND PERSONALITY ON ONLINE PURCHASE DECISIONS

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1 THE EFFECT OF ONLINE REVIEW CONFIGURATIONS, PRICE, AND PERSONALITY ON ONLINE PURCHASE DECISIONS Shih Yung Chou H-E-B School of Business and Administration University of the Incarnate Word 4301 Broadway San Antonio, TX Phone: Sergio Picazo-Vela Departamento de Administración de Empresas Escuela de Negocios y Economía Universidad de las Américas Puebla Sta Catarina Mártir Cholula, Puebla México sergiopicazo@yahoo.com.mx Phone: John M. Pearson College of Business Southern Illinois University Carbondale Carbondale, IL jpearson@business.siuc.edu Phone: ABSTRACT Online reviews have been studied intensively as they have a powerful impact on online consumers purchase decisions. Very little attention, however, has been paid to an online consumer s perceptions of online review configurations. Thus, the main purpose of this study is to examine how an online consumer s online purchase decision is affected by different types of online review configurations. In addition, we analyze whether the purchase decision is moderated by price and personality traits. Results of this study may help online retailers and sellers manage online reviews more effectively. INTRODUCTION As the market environment has become much more competitive, firms have recognized the need to integrate information technology (IT) into their sales practices. One of the most prominent examples is the use of the Internet. According to USA Today, Internet sales have increased from 27 billion in 2000 to billion in 2008 ( Although firms are able to maintain or enhance the quality of their products or services by utilizing control or assurance

2 systems, e-commerce is not without risks from consumer s perspective. These risks may include the exposure of credit card information, inability to evaluate a product or service without touching or feeling it, and inability to return a product or service if it fails to meet the seller s approval (Bhatnagar, Misra, and Rao, 2000). To overcome concerns associated with e-commerce, online retailers have devoted much effort to developing a better e-commerce environment. Better security protocols, for example, have been developed and are broadly used by online retailers to enhance consumers willingness to engage in online shopping. Moreover, the improvements of communication facilities and infrastructures have facilitated information interchange between an online retailer and consumers (Valera, Vergara, Moreno, Villagraa, and Berrocal, 2001). Among various developments in e-commerce, online review systems have received much attention because of their potential impact on purchase decisions and online consumer behavior (Resnick, Zeckhauser, Friedman, and Kuwabara, 2000). Although there are various designs of online review systems, an online review system generally refers to a platform that collects, distributes, and aggregates feedback and comments about participants past behavior (Resnick et al., 2000). It is important to study online review systems because they not only provide online sellers information about customers needs and satisfactions or dissatisfactions but also facilitate prospective customers purchase decision process. From this perspective, online sellers are able to improve their sales volumes by leveraging online reviews (Barton, 2006; Clemons, Gao, and Hitt, 2006; Dellarocas, Zhang, and Awad, 2007). As online review systems have been widely adopted by online sellers and online intermediaries such as Amazon.com, ebay, and Buy.com, online consumers might face difficulties in making purchasing decisions when using online reviews. Specifically, in an online marketplace, an online seller could have an all positive reviews profile, an all negative reviews profile, or a mixed reviews profile. As the configurations online reviews can vary across online sellers dramatically, online consumers could introduce themselves into dilemmas when using online reviews to make purchase decisions. To date, however, very little attention has been paid to an online consumer s perceptions of different configurations of online reviews. Specifically, because an online seller s online review profile can be configured by various numbers of positive and negative reviews, it is important to understand how an online consumer s purchase decisions are affected by these configurations. Thus, the main goal of this study is to investigate the relationship between online review configurations and purchase decisions. Additionally, we include Big-Five personality traits into our study as individual behaviors can be largely explained by personality traits (e.g., Barrick and Mount, 1991; Hough, Eaton, Dunnette, Kamp, and McCloy, 1990; Ones, Viswesvaran, and Schmidt, 1993; Tett, Jackson, and Rothstein, 1991). Figure 1 summarizes the proposed research model. The remainder of this study is organized into four additional sections. In section 1, previous studies related to online reviews are reviewed. Section 2 presents the purpose and objectives of

3 this study. Next, the proposed methodology and expected results are discussed. In the final section, a brief discussion on potential theoretical and practical contributions is provided. Figure 1: Proposed Research Model Online Review Configurations Purchase Decision Price Personality Online Review Systems in E-Commerce LITERATURE REVIEW According to Mudambi and Schuff (2010), online reviews can be defined as peer-generated product evaluations posted on an online retailer s or a third party s website. Because the main purpose of online review systems is to help potential consumers make better purchase decisions, online review systems have been designed in many different ways. For instance, Amazon.com creates a five-star system to rate an online seller s activities on Amazon.com. Amazon.com has also constructed a feedback table for each online seller in which buyer reviews are categorized into positive, neutral, and negative reviews. In addition, online reviews are counted by 30 days, 90 days, 365 days, and lifetime. Another well-known online intermediary, ebay, uses a similar online review system to provide sellers and buyers information about ebay users. Specifically, ebay constructs a system where users can leave positive, negative, or neutral ratings along with short comments. A feedback score (+ 1 for a positive feedback, - 1 for a negative feedback, and 0 for a neutral feedback) can, therefore, affect a user s overall feedback profile. Using this system, as ebay claims, users can shop confidently on ebay. Moreover, ebay develops a star system that is different from Amazon s system. Specifically, ebay uses 12 different star colors to represent the degree of a user s reputation. There are also many other online retailers and intermediaries have been using online review systems to help potential buyers make purchase decisions. For instance, Buy.com uses a 5-star

4 system for buyers to rate a product using several key criteria such as performance, appearance, organization, durability, value, ease of use, etc. Based on the criteria, buyers then determine the number of stars for each criterion. Staples.com allows buyers to describe a product in several categories such as pros, cons, best uses, primary use, bottom line, etc. BestBuy.com uses a 5-star rating system to describe overall customer rating of a product along with sub-product ratings such as value for price, quality, features, performance, etc. Moreover, BestBuy.com invites buyers to write a short comment explaining what is great and what is not so great about the product. Most importantly, buyers of BestBuy.com can provide their opinions on whether to recommend a product to others. Outcomes of Online Reviews Because online reviews have been used intensively by most online consumers to make purchase decisions, research on online reviews has increased dramatically in the past few years. Among several research focuses, the relationship between online reviews and sales has been studied intensively. For instance, Basuroy, Chatterjee, and Ravid (2003) showed that both positive and negative online reviews are correlated with magazine firms revenues. Clemons et al. (2006) demonstrated that online reviews play a significant role in determining product growth in the craft beer industry. Duan, Gu, and Whinston (2008) showed that the number of online reviews not the average rating affects product sales. Hu, Liu, and Zhang (2008) investigated quantitative and qualitative aspects of online reviews and found that consumers tend to pay attention to both review scores and contextual information. Forman, Ghose, and Wiesenfeld (2008) found that online reviews containing identity-descriptive information are associated with increases in subsequent online product sales. Black and Kelley (2009) studied the outcomes when online reviews include elements of a good story in service industry. They found that consumers perceive online reviews documenting a service failure to be less helpful than reviews that do not document a failure. Moreover, consumers report high levels of helpfulness of online reviews that contain an effective service failure recovery. Mudambi and Schuff (2010) analyzed and found the effect of review characteristics including review extremity, review depth, and product type on a buyer s perceived helpfulness of the review. Zhu and Zhang (2010) found that the design of an online review system such as how ratings are displayed and how easy for consumers to rate a product/service may affect consumer s reliance on reviews. Consumer Characteristics and Online Reviews Although the ideal purpose of online reviews is to help online consumers make better purchase decisions, an online consumer s perceptions of online reviews also play a critical role on decision-making. For example, Park and Lee (2009) investigated the relationship among consumer characteristics, attitude toward online reviews, and the outcomes of online reviews in the U.S. and Korean cultures. They found that consumer susceptibility to interpersonal influence and Internet shopping experience affect perceived usefulness of online reviews, which in turn influences the online reviews usage frequency and purchase decisions. Sher and Lee (2009) studied the relationship between consumer skepticism and their tendency to believe or disbelieve in online reviews. They found that highly skeptical consumers tend to be biased against certain

5 types of information and indifferent to the online review quality while low skeptical customers are more persuaded by online review quantity. Picazo-Vela, Chou, Melcher, and Pearson (2010) investigated the factors affect consumer s intention to provide an online review by incorporating Big-Five personality traits. These researchers found that neuroticism and conscientiousness are significant predictors of intention to provide an online review. Zhu and Zhang (2010) found that there is a positive relationship between the reliance on online reviews and a consumers Internet experience. THE CURRENT STUDY As mentioned earlier, previous research has investigated the impact of online reviews on e- commerce outcomes such as sales volumes, forecasting, review usefulness and helpfulness, or purchase decisions. Very little attention, however, has been paid to a more practical situation of an online review system. Specifically, almost all previous studies that investigated online reviews used a dichotomous approach where online reviews are categorized into either all positive or all negative reviews. However, it is almost uncommon for an online seller to have an all positive or all negative reviews profile. Because of this unique phenomenon, many online consumers have to make purchase decisions based on different configurations of online reviews. To date, however, very few studies have focused on this particular area. Therefore, our study intends to fill this research gap by investigating how a consumer s purchase decision is affected by the configurations of online reviews. Additionally, we include price and Big-Five personality traits into our study to better understand how a purchase decision is made. Procedure PROPOSED METHODOLOGY We will use a survey approach to collect data. The survey will include four steps. First, we will collect demographic information from participants. Second, we will identify participants personality trait using the Big-Five Personality instrument developed by Goldberg (1992). Next, we will present to participants two products with two price levels: high and low. We will then show participants two online sellers that sell these two products but with different online review configurations. Finally, participants will be asked to make an online purchase decision. Data Collection and Expected Results Participants will be undergraduate students at a large university in the Midwest and a mid-sized university in the Southwest. Students have been found good proxies for online shoppers as they have been identified as a major group of Internet shoppers (Lim, Sia, Lee, and Benbasat, 2006). After the completion of data collection, we will employ Chi Square tests and logistic regression technique to examine whether purchase decisions are affected by online review configurations, price, and personality traits

6 We expect to find that online consumers tend to purchase from an online seller who has a few but consistent positive online reviews when product price is low and that online consumer tend to purchase from an online seller who has a large number of positive and negative online reviews when product price is high. When including personality traits, we expect to find a moderating effect of personality on these purchase decisions. POTENTIAL CONTRIBUTIONS This study may have several theoretical and practical contributions. Specifically, this study is one of the very first few studies in the literature that analyze online consumer shopping behavior using mixed online reviews. Another potential contribution to theory is that this study incorporates personality as a moderator to the analysis of online purchase decisions. Finally, from a practical perspective, this study may help online sellers better understand how online consumers use online reviews to shape their purchase decisions. REFERENCES Barrick, M. R., and Mount, M. K. (1991). The Big Five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), Barton, B. (2006). Ratings, reviews and ROI: How leading retailers use customer word of mouth in marketing and merchandising. Journal of Interactive Advertising, 7(1), 1-7. Basuroy, S., Chatterjee, S., and Ravid, S. A. (2003). How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of Marketing, 67(4), Bhatnagar, A., Misra, S., and Rao, R. H. (2000). On risk, convenience, and Internet shopping behavior. Communications of the ACM, 43(11), Black, H. G., and Kelley, S. W. (2009). A storytelling perspective on online customer reviews reporting service failure and recovery. Journal of Travel and Tourism Marketing, 26, Clemons, E. K., Gao, G., and Hitt, L. M. (2006). When online reviews meet hyperdifferentiation: A study of the craft beer industry. Journal of Management Information Systems, 23(2), Dellarocas, C., Zhang, X., and Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(4), Duan, W., Gu, B., and Whinston, A. B. (2008). Do online reviews matter? An empirical investigation of panel data. Decision Support Systems, 45(4), Forman, C., Ghose, A., and Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19, Goldberg, L. R. (1992). The development of markers for the Big-Five factor structure. Personality Assessment, 4,

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