Full length articleWhat makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score
Introduction
Various online platforms (e.g. social commerce, ecommerce, online communities) are increasingly facilitating consumers in sharing their experiences, opinions, and feedback regarding people, products, services and brands in the form of online reviews, ratings, and ranking scores. Online consumer reviews, a form of electronic word of mouth (e-WOM), can be defined as any positive, neutral, or negative evaluation of a product, a service, a person, or a brand presumably posted by former customers on websites that host consumer reviews. According to a report from Mintel (2015) 81 percent of consumers aged 18–34 in the United States seek out opinions from others before purchasing a product or service. Research has established the power of online consumer reviews in predicting product sales and revenues in different product categories, such as books, beers, restaurants, movies, and hotels (e.g. Chevalier and Mayzlin, 2006, Clemons et al., 2006, Cui et al., 2012, Duan et al., 2005, Liu, 2006, Ye et al., 2011).
Different organizations are increasingly enabling consumers to leave a helpful vote to each review in an attempt to signal to consumers the most helpful reviews for assessing products and services’ quality and performance. Scholars have recently started to examine what makes online review helpful by importing the data from these e-retailers (e.g. Amazon) and using the voting mechanism to measure the characteristics of the reviews that receive more helpful votes (e.g. Ahmad and Laroche, 2015, Baek et al., 2012, Chua and Banerjee, 2016, Huang et al., 2015, Jabr and Zheng, 2014, Mudambi and Schuff, 2010, Pan and Zhang, 2011, Racherla and Friske, 2012, Yin et al., 2014).
However, there is a dearth of studies on the determinants of information diagnosticity from a consumer perspective in e-WOM research (Filieri, 2015). Although existing studies are useful, they have mainly investigated the ‘visible’ aspects of review helpfulness focusing on textual elements such as review extremity, review sentiment, review valence, review length or profile information of the reviewer (e.g. Chua and Banerjee, 2016, Mudambi and Schuff, 2010). Researching consumer's perception of information diagnosticity is important for several reasons: the voting mechanism can be easily manipulated (Filieri, 2016, Lim et al., 2010, Pan and Zhang, 2011); for example it is plausible to expect that given the importance that consumer reviews have on sales, managers may vote as more helpful those reviews that provide a positive rather than a negative evaluation of their business. Moreover, some important ‘qualitative’ information dimensions cannot be measured through quantitative textual analysis. For example, the perceived credibility of a source (i.e. the reviewer), the capacity of a review message to satisfy a consumer's specific information needs (i.e. review relevancy) or to provide plausible, fact-based information (i.e. review factuality) or up to date information (review currency). However, these factors can still be important to consumers to assess information diagnosticity.
This study applies the definition of information diagnosticity to consumer reviews and assesses consumers' perception regarding the ability of the information contained in OCRs to enable consumers to learn and to evaluate the quality and performance of services (information diagnosticity) before purchasing them. Understanding information diagnosticity is paramount for social commerce organizations because the higher the perceived diagnosticity of the information they host the better will be consumer's attitude towards shopping online (Jiang & Benbasat, 2007) and the higher will be the influence on purchase intentions (Filieri, 2015).
Additionally, most studies on e-WOM are cross-sectional and no study has measured the variations of the determinants of information diagnosticity in e-WOM over time. For instance, previous scholars have looked at the temporal evolution of e-WOM, investigating the evolution of different marketing variables and consumer posting behavior (Chen, Fay, & Wang, 2011), while other scholars emphasized the temporal dynamics in the evolution of ratings (Godes & Silva, 2012) or on how e-WOM volume evolves in movies' releases (pre-release and post-release) (Liu, 2006). In this study instead, we focus on the determinants of consumer perception of information helpfulness and how it evolves over time. We conjecture that due to the increasing importance of OCRs, the global echo produced by mass media on the phenomenon of fake reviews (e.g. Gartner, 2012, Smith, 2013, Tuttle, 2012), consumers may have become more cautious and attentive when they scrutinize the recommendations contained in websites publishing OCRs (Filieri, 2016). Thus, what makes a consumer review diagnostic may be subject to changes due to external factors (i.e. negative publicity from mass media) that might have changed consumers’ attitudes towards OCRs. An analysis at different points in time can provide us with some insights into how (and if) the influence of various antecedents of information diagnosticity has changed over time.
Elaboration Likelihood Model (ELM) has been adopted in e-WOM research to explain consumer cognitive processing of product reviews and evaluation of review messages (e.g. Park et al., 2007, Park and Lee, 2008, Zhang and Watts, 2008, Lee et al., 2008, Lee and Lee, 2009, Cheung et al., 2012, Filieri and McLeay, 2014). In this study, ELM (Petty & Cacioppo, 1986) has been used to investigate whether central and peripheral cues of information processing affect perceived information diagnosticity considering the potential moderating effect of consumer's involvement with a purchase. In line with the ELM, we have developed and tested a model that measures the influence of some central cues, namely length, relevancy, currency, and factuality; and of some peripheral cues of information processing, namely source credibility, and overall ranking scores; on information diagnosticity (dependent variable) considering the moderating role of consumer involvement with a purchase. The model was tested using regression analysis respectively in 2011 with 334 respondents and in 2016 with 297 respondents.
Section snippets
The Elaboration Likelihood Model
We have adopted Petty and Cacioppo's (1986) Elaboration Likelihood Model (ELM) to understand the determinants of information diagnosticity in different involvement conditions. ELM postulates that consumers may take a central or a peripheral route when they process information from advertising messages (Petty, Cacioppo, & Schumann, 1983). Consumers take the central route when they are capable, highly motivated or willing to process information, spending more time and providing a rational
Data collection and sample
We collected the data for this study at two different points in time: the first survey was carried out in September 2011, while the second one took place in January 2016 with users of OCRs of accommodation. Accommodation is a classic example of service (e.g. Lovelock and Wirtz, 2011, Lovelock, 1983) and they constitute one of the most important items for which consumers search information online. We have decided to focus on services because the intangibility, variability, perishability,
Findings
Confirmatory factor analysis (CFA) was conducted using AMOS 22 to examine the measurement validity of the constructs used in our study. The data of both the first and the second dataset show a good model fit: χ2 = 385.31; p < 0.001; CFI = 0.94; TLI = 0.92; NFI = 0.91; RMSEA = 0.05; and χ2 = 494.78; p < 0.001; CFI = 0.92; TLI = 0.91; NFI = 0.90; and RMSEA = 0.05 (Hair, Anderson, Babin, & Black, 2010).
Additionally, both the convergent and discriminant validity in the 2011 and 2016 samples were
Discussion
This study has adopted the ELM model and has tested a model to investigate the influence of central and peripheral cues of information processing on perceived information diagnosticity in OCRs of accommodation. This study has tested the proposed framework using data collected at different points in time (2011–2016).
Previous studies have focused on review helpfulness and mainly used a database of consumer reviews from Amazon and ‘helpful votes’ left by readers to shed light on the review that
Managerial implications
Online organizations still struggle to understand what types of information are more important to consumers when they retrieve information from their website. This study provides some insights into the nature of diagnostic information to consumers’ eyes.
The importance of information relevancy emerged in this study, which suggests that social commerce organizations should refine their information search criteria (e.g. product's style, price, availability, friends' recommendations, and the like)
Limitations and future research
The present study has some limitations. First, the sample was composed mainly by European respondents, which may hinder the generalizability of this study's findings to other geographical contexts. Further research in other countries with a more diverse sample is advised.
Another limitation of this study is that it focuses on travel services only (accommodation). However studies focusing on goods or on different service types (e.g. financial service providers) may obtain different results.
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