Online consumer reviews Why do we adopt them?

Transcription

1 Online consumer reviews Why do we adopt them? Abstract Consumers are increasingly using online consumer reviews (OCRs) to help inform their purchasing decisions. As such, it is important for marketers and academics to improve their understanding of the behavioral consequences of e-word-of-mouth. In this study, we adopt the information adoption moderated model (IAMM) to investigate the predictors of consumer s adoption of OCRs. Drawing on the IAMM, we analyse the influence of OCRs information quality, source credibility, and overall product ranking on perceived information usefulness. In addition, we explore the relationship between information usefulness and information adoption using a structural equation model and data from 366 users of OCRs. The findings of this study suggest that consumers are primarily influenced by the quality of information in OCRs and subsequently by overall product ranking information. This relationship is not moderated by a consumer s degree of involvement. These results imply that in high involvement situations, consumers will adopt both central and peripheral cues of information processing. Central cues include information quality while peripheral cues include overall product ranking and source credibility. Keywords: electronic word of mouth, online reviews, information quality, source credibility product ranking, information usefulness, information adoption. 1

2 Introduction The advances of Web 2.0 are enabling consumers to share their experiences, opinions, and feedback regarding products, services and brands in the form of online reviews for other consumers on consumer review websites (CRWs). Online consumer reviews (OCRs) are a type of electronic word of mouth (WOM) that are becoming popular among consumers worldwide who read them before making purchase decisions (Senecal & Nantel, 2004). Current research has demonstrated that e-wom can be even more influential than WOM on consumer decision making (Steffes & Burgee, 2009). Electronic WOM (e-wom) refers to any positive or negative statement made by potential, actual or former consumers about a product or company, which is made available to a multitude of people and institutions via the Internet (Hennig-Thurau et al., 2004, p. 39). OCRs have been identified as a new element in the marketing communication mix because they work as free sales assistants that facilitate consumer choices of the products or services that best match their usage needs (Chen & Xie, 2008). In this paper, the term OCRs is used to refer to any positive, neutral, and negative comments or reviews on products and services created and published on CRWs by consumers. Previous studies of OCRs have primarily focused on the power of e-wom in predicting product sales in different product categories, such as books, movies, and hotels (Godes & Mayzlin, 2004; Liu, 2006; Duan, Bin, & Whinston, 2005; Chevalier & Mayzlin, 2006; Ye, Law, & Gu, 2009; Zhu & Zhang, 2010). Other studies have adopted the Elaboration Liklehood Model (ELM) to explain whether high and low involvement purchasing decisions result in consumers adopting central or peripheral cues to information processing (Park, Lee, & Han, 2007; Park & Lee, 2008; Lee, Park, & Han, 2008; Sher & Lee, 2009; Lee & Lee, 2009; Gupta & Harris, 2010). Instead, in this research we have adopted the Sussman & Siegal s (2003) information adoption moderated model (IAMM) to investigate the main antecedents of consumer s adoption of OCRs. Moreover, a large body of literature has focused on the influence exerted by either negative or positive reviews on consumer decisions (Park, Lee, & Han; 2007; Sen & Lerman, 2007; Park & Kim, 2008; Lee, Park, & Han, 2008; Vermeulen & Seegers, 2009; Gupta & Harris, 2010). Instead, we have introduced the concept of overall product ranking, which is a type of information about average consumer evaluations that summarise the number of positive, negative, and neutral reviews on a product or service. Theoretical Background The Information Adoption Moderated Model The IAMM was developed by Sussman & Siegal (2003), who integrated the theory of reasoned action (Fishbein & Ajzen, 1975; Ajzen & Fishbein, 1980) and the Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1986) to explain the ability of computermediated communications such as messages to provoke behavioural modification in receivers (Sussman & Siegal, 2003). Information adoption is the process by which people purposefully engage in using information (Sussman & Siegal, 2003; Cheung, Lee, & Rabjhon, 2008). In this study, information adoption reflects the informational influence of e-wom, which implies that consumers who adopt information from OCRs accept the recommendations contained in OCRs and subsequently take action by following these recommendations. The IAMM suggests that source credibility and argument quality predict the perceived usefulness of information, which in turn influences information adoption (Sussman & Siegal, 2003). Consumer involvement is considered a moderating variable in this relationship. There is a paucity of studies that have examined the determinants of information adoption in e-wom research. Exceptions include Cheung et al. (2008) and Cheung et al. (2009) who conclude that information usefulness and e-wom credibility predicted information adoption in Chinese online discussion forums. The current research has adopted the IAMM and the ELM (Sussman & Siegal, 2003) in an attempt to understand whether users of online consumer reviews are primarily influenced by the quality of information in OCRs, 2

3 the credibility of the source (reviewer), or by summary information such as overall product ranking and if such relationships are moderated by consumer s level of involvement with a product or service. In Figure 1, we illustrate the constructs of this study and the hypothesised relationships ADD FIG. 1 HERE Information quality and perceived information usefulness Information quality is defined as the quality of the content of a consumer review from the perspective of information characteristics (Park, Lee, & Han, 2007, p. 128). The importance of information quality of OCRs and its influence on consumer decision making has been investigated in information systems literature (Park, Lee & Han, 2007; Lee, Park, & Han, 2008; Cheung, Lee, & Rabjhon, 2008) but marketing research on this topic is scant. The dimensions adopted in this study to measure information quality have been developed following Churchill s (1979) approach and include: information timeliness, information depth, information breadth, information format, information comparability, and information credibility. OCRs provide information that may be considered useful by consumers if of high quality. Based on the IAMM, consumers will scrutinise the quality of the arguments in OCRs to make a decision about purchasing a product or service. Researchers in e-wom have demonstrated that the quality of arguments (information relevance, understandability, sufficiency, and objectivity) contained in OCRs influence high-involvement consumer behaviour (Park, Lee, & Han, 2007; Lee, Park, & Han, 2008), while Cheung et al. (2009) suggested that e-wom information credibility influenced information adoption among Chinese consumers. We argue that consumers will find information to be more useful in their decision-making process if the information in OCRs is determined to be of high quality. Thus: H1: There is a positive relationship between information quality and usefulness of information. Source credibility and perceived information usefulness Source credibility and trustworthiness are considered as fundamental predictors of a consumer s acceptance of a message in WOM (McGinnies and Ward; 1980; Lazarsfeld, Berelson and Gaudet, 1948). In this study we focus on online consumer reviews which are generally generated by anonymous sources or consumers that have no prior relationship with the receiver (Dellarocas, 2003; Goldsmith & Horowitz, 2006; Sen & Lerman, 2007). In an online environment it is difficult to infer the credibility and the reliability of a reviewer (Chatterjee, 2001; Schindler & Bickart, 2005). However, many CRWs have started to provide some meta-information about the expertise and the credibility of reviewers to enable consumers to infer the reliability of a source (e.g., the feedback system to evaluate the reliability of a seller in Amazon and ebay). The influence of source credibility on consumer behaviour has been studied by Senecal and Nantel (2004), who found that the expert systems source (e.g., recommender system) is the most influential source even if it was perceived as possessing less expertise than human experts (e.g., salespersons, independent experts), and as less trustworthy than other consumers (e.g., friends, relatives, acquaintances). However, we believe that a source that is considered as credible will be perceived as one that is likely to provide useful and correct information for the receiver. Therefore we hypothesis: H2: There is a positive relationship between source credibility and information usefulness Overall product ranking and perceived information usefulness The overall product ranking is a common feature in CRWs and refers to the average evaluation of all individual consumer ratings of a product in a specific category. The overall ranking of a product does not refer to the quality of arguments. Rather, it refers to an 3

4 information cue (or shortcut) with respect to how all reviewers have evaluated one product or different products in a specific category (e.g., hotels in a destination). Consumers benefit from the aggregation of single review ratings into summary statistics that are presented as product rankings, which are generally displayed as average or mean star ratings (or the proportion of positive, neutral or negative reviews) and are located beside a products picture (Godes & Silva, 2012). For example, on Tripadvisor.com, every reviewer can rate the overall quality of a hotel using a scale from one (terrible) to five (excellent) stars. Existing studies have focused on: the ranking behaviour of reviewer s (Schlosser, 2005; Godes & Silva, 2012; Moe & Schweidel, 2012); how ratings change over time and sequence (Godes & Silva, 2012), or the importance of ratings on the perceived trustworthiness of retailers (Bolton, Katok, & Ockenfels, 2004; Aiken & Boush, 2006; Benedicktus, 2011). The current study investigates the influence of overall product raking on perceived information usefulness in OCRs. We argue that overall product ranking is valued as useful information by consumers because by classifying products in a category from the best to the worst they reduce the number of alternatives available and make the consumer decision making process easier. Moreover, summary information such as star rankings that represent average evaluations from a number of consumers can be considered as more useful by consumers than single reviews. Accordingly: H3: There is a positive relationship between overall product ranking and information usefulness. Perceived information usefulness and information adoption Information usefulness is a key construct in adoption behaviour (Sussman & Siegal, 2003). Drawing on TAM theories, perceived information usefulness refers to an individual s perception that using a new technology will enhance or improve his or her performance (Cheung et al., 2008). For instance, users of a CRW would consider whether the opinions and information contained in OCRs are useful to help them to make a better buying decision. Therefore, if consumers believe that OCRs are useful, there will be a greater likelihood that they will adopt the recommendations provided. Drawing on the IAMM, we argue that perceived information usefulness will predict consumer information adoption. Thus, we expect that consumer s perceptions of information usefulness will mediate the relationship between information quality, source credibility, overall product ranking and information adoption. Accordingly: H4: There is a positive relationship between information usefulness and information adoption Moderating variable Based on the IAMM (Sussman & Siegal s, 2003), involvement in the processing of OCRs is considered a moderating variable. According to the ELM, once a consumer receives a message, he or she begins to process it. Depending on the consumer s degree of involvement, two routes may be adopted to influence the consumer (a central route or a peripheral route (Petty & Cacioppo, 1986). High-involvement consumers spend more time and exert greater effort to understand marketing messages and primarily focus on the quality of the arguments. Conversely, low-involvement consumers are less motivated or less capable of thinking about a message and thus exert less cognitive effort. As such, they use peripheral cues or information shortcuts to evaluate a message rather than analysing its content (Petty & Cacioppo, 1986). This leads to the following hypotheses: H 5: The higher the receiver s involvement, the higher the influence that information quality plays on information usefulness H 6: The higher the receiver s involvement, the lower the influence of source credibility and overall product ranking on information usefulness 4

5 Figure 2 in the appendix illustrates the hypothesised relationships. Methodology - Data Collection, Measures, and Sample An online questionnaire was created using professional survey-design software which was primarily composed of closed-ended questions that were measured using a 7-point Likert scale. Almost all items required an answer from strongly disagree (1) to strongly agree (7). The questionnaire was pilot-tested two times. The first time was with 18 and the second time with 31 users of ORs who were familiar with questionnaire design. The questionnaire was improved considerably by paraphrasing or deleting ambiguous or similar items. A link to the questionnaire was distributed by to a convenience sample of academic and administrative staffs and students from an Irish and an English university. To ensure the suitability of the participants, we selected the sample by attempting to concentrate only on individuals who had recently adopted ORs (purposive sampling). Accompanying instructions asked respondents to name the CRW from where they had read ORs, and to provide the product category for which they had searched for information. We received a total of 416 responses but 51 were deleted because they were not complete or not filled properly to give a total of 366 usable questionnaires. Source credibility and trustworthiness was measured by a scale developed by Ohanian (1990), and recently used by Senecal and Nantel (2004). Information usefulness was measured using three items derived from Cheung et al. (2008). Consumer information adoption was measured by three items used in previous studies (Sussman & Siegal, 2003; Cheung et al., 2009). Involvement with message processing was measured using a scale that was developed and tested by Wheeler, Petty, & Bizer (2005). For the sake of simplicity, only three items were used. The scale for measuring information quality and overall product ranking were developed for this study following Churchill s (1979) approach. The socio-demographic characteristics of the sample are presented in Table 1. Analysis and Findings Both the convergent and discriminant validity of the model were tested. The convergent validity was assessed using the average variance extracted (AVE). Factor loadings for all constructs ranged from to 0.887, which is higher than the recommended cut-off of 0.5, thus demonstrating that the scales measure the concepts that they were designed to measure (Fornell & Larcker, 1981). All factor correlations were below the 0.85 threshold (Fornell & Larcker, 1981). Moreover, for adequate discriminant validity, the AVE for each latent variable included in the model should be greater than the squared correlation estimate. Table 3 demonstrates that these requirements have been satisfied (Fornell & Larcker, 1981). Reliability was assessed for each construct with Cronbach s α (Nunnally, 1978). Cronbach s α ranged from for information usefulness to for overall product ranking. All items had an overall Cronbach s α value of 0.850, which signifies a very good level of reliability for the items and the scale that were used in this study (see Table 2). Based on the x 2 /df, GFI, CFI, RMSEA, SRMR, the structural equation model shows a good goodness of fit (Table 4). The structural equation model was tested using the statistical software Amos The results are presented in Table 4. The resulting relationship among information quality, overall product ranking, source credibility and information usefulness is strong and highly significant (R 2 = 0.585; F = ; p<0.001). The strongest antecedents of consumer s adoption of OCRs were information quality (stand. β = 0.486; p<0.001) and overall product ranking (stand. β = 0.309; p<0.001) which supports H1 and H3, respectively. Contrary to our predictions, source credibility did not exhibit a strong predictive power in its relationship with the dependent variable (stand. β = 0.121, p< 0.050); thus, H2 was rejected. The influence of perceived information usefulness on information adoption was high and significant (stand. β = 0.546; p<0.001), thus confirming H4. We have then divided respondents according to their 5

6 level of involvement. The relationship between information quality and information usefulness improves in high involvement situations, supporting hypothesis 5. However, the relationship between overall product ranking and source credibility decreases in low involvement conditions, which means hypothesis 6 is rejected ADD TABLE 2, 3, 4, 5 HERE Discussion In this study we have adopted the IAMM and have investigated the determinants of information adoption from OCRs. In contrast with much literature that has focused on the influence of either on positive or negative reviews (Park, Lee, & Han; 2007; Sen & Lerman, 2007; Park & Kim, 2008; Lee, Park, & Han, 2008; Vermeulen & Seegers, 2009; Gupta & Harris, 2010), this study has introduced the construct of overall product ranking that consists of a graphical representation of statistics that summarise the average or mean ratings or the proportion of positive, neutral, and negative reviews for a product or service. The results of this study demonstrate that information quality and overall product ranking are strong predictors of perceived usefulness of information, while source credibility does not have a strong predicting power. This research confirms the importance of information quality as an influencer of information usefulness. Moreover, a new scale was developed for measuring the information quality dimension. The predictive power of overall product ranking as an antecedent of information usefulness is also new in e-wom research. In contrast, previous studies investigated the ranking behaviour of consumers (Schlosser, 2005; Godes & Silva, 2012; Moe & Schweidel, 2012) or the manner in which ratings influence the trust or reliability of an online retailer (Bolton, Katok, & Ockenfels, 2004; Aiken & Boush, 2006; Benedicktus, 2011). This result contrasts with social cognition theorists who assume that while making judgements consumers tend to underuse base-rate information such as summary statistics (Borgida & Nisbett, 1977; Nisbett & Ross, 1980; Bar-Hillel, 1980) that are used to develop product rankings. In accordance with previous studies (Cheung, Lee, & Rabjhon, 2008), we have demonstrated that perceived information usefulness acts as a mediating variable between information quality and information adoption. Previous findings suggest that the quality of information in online review influences consumer decision in high-involvement conditions (Park, Lee, & Han, 2007; Lee, Park, & Han, 2008). In this study, information quality had an increasing influence on information usefulness in high-involvement conditions. However, overall product ranking and source credibility also increased the predicting power of information usefulness when consumer involvement was high. This implies that a higher level of consumer involvement with a product or service, results in a consumer considering a higher number of variables when making a purchase decision. These variables include both central (information quality) and peripheral information cues (overall product ranking, source credibility). Our results suggest that in an online environment, both central and peripheral routes have an important influence on consumer behaviour, and that both routes can be used by marketers to influence information adoption among high-involvement consumers. Thus, the central and the peripheral routes should not be considered as alternative ways to influence differentially involved consumers, rather as interactive processing models (Bitner & Obermiller, 1985). This finding implies that marketers of CRWs could consider adopting some peripheral cues on crowd opinions in order to influence consumer behaviour. The present study has some limitations. First, we do not investigate the potential differences existing between different product categories such as experience and search products (Senecal and Nantel, 2004). Finally, the sample is composed mainly by UK and Irish respondents which may hinder the generalisability of this study s findings to other geographical contexts. Further research in other countries with a more diverse sample is advisable. 6

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9 Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28(1), Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), Appendix 1 Figure 1. The information adoption moderated model (Sussman & Siegal, 2003) Figure 2. Conceptual model Receiver involvement Information Quality Source credibility H1 H2 H5, 6 Information Usefulness H4 Information Adoption Product Ranking H3 9

10 Table 1. Socio-Demographic Characteristics of the Respondents Dimension Items Percentage Gender F 46.7 Age Economic Status Nationality M > and above Under No answer 21.8 Republic of Ireland or UK Europe United States South-America Asia Australia 0 Missing

11 Table 2. Items used in the study, Cronbach s alpha, and CR Construct Items Loadings α Overall Product Ranking Information quality Reduced the number of alternative products/services that I was considering buying Has helped me to rapidly identify the best (and the worst) products/services Has guided my purchase decision to a specific product/service Has facilitated my purchase decision Has enabled me to identify the product/service that could satisfy my needs The information from online reviews was current for my needs The information from online reviews was easy to combine The information from online reviews was credible The information from online reviews were in the same format The information from online reviews were easy to compare The information from online reviews was of sufficient depth (degree of detail) The information from online reviews was of sufficient breadth (spanning different subject areas) Source Credibility Information usefulness The reviewers were credible The reviewers were experienced The reviewers were trustworthy The reviewers were reliable The information in online reviews was valuable The information in online reviews was helpful Involvement Information Adoption How much effort did you put into evaluating the given information? Did you think deeply about the information contained in online reviews? How personally involved did you feel with the issue you read about? Review made it easier for me to make purchase decision. (e.g., purchase or not purchase). Online reviews have enhanced my effectiveness in making purchase decision Online reviews have motivated me to make a purchase decision The last time I read online reviews I adopted consumers recommendations and purchased (or not purchased) the recommended product/service

12 Table 3. Mean, SD, correlations, and average variance extracted Variable Mean SD Overall Product Ranking 2.Information Quality Source Credibility 4.Infomation Usefulness 5. Involvement Information Adoption Note. All correlations were significant at p < Table 4. Goodness of fit of the model x 2 /df Chi-squared , Goodness of Fit Index, GFI Comparative Fit Index, CFI Root Mean Squared Error of Approximation, RMSEA 0.06 Standardised root mean square residual, SRMR Table 5. Structural equation modelling results without involvement Hypotheses Relationship Standardised regression weight Supported vs. non supported H1 IQ > USE 0.486*** Supported 12

13 H2 SC > USE 0.121** Non-Supported H3 PR > USE 0.309*** Supported H4 USE > ADO 0.546*** Supported Note: *** indicates p < 0.001, ** indicates p < Table 6. Structural equation modelling results with involvement Hypotheses Relationship Standardised regression weight Supported vs. non supported H5 IQ > USE 0.653*** Supported H6 PR > USE 0.519*** Non-Supported SC > USE 0.257*** Note: *** indicates p < 0.001, ** indicates p < SC = Source Credibility IQ = Information Quality USE = Information Usefulness PR = Overall Product Ranking ADO= Information Adoption 13