Antecedents of information helpfulness and purchase intentions in e-retailers providing
consumer reviews
Consumers are increasingly turning to online reviews to diagnose the real quality of the
products and services that they plan to buy. Thus, it is very important for online retailers to
understand the determinants of online reviews helpfulness and their influence on consumer
behavior. However, there is a dearth of studies on the determinants of review helpfulness,
especially from the consumer perspective. To fill this gap, we adopt dual-process theory and
explore the influence of informational and normative cues on information diagnosticity, as
well as its link with consumers’ purchase intentions. Predictions are tested using structural
equation modelling with 401 users of travel reviews. Results show that information quality,
overall product ranking, product popularity are strong predictor of review helpfulness and that
high ranking scores together with helpful reviews provided by highly credible sources will
affect consumers’ purchase intentions. This study extends the application of dual-process
theory to e-word of mouth.
1. Introduction
More and more consumers are trusting online consumer reviews (OCRs) and using them to
assess the quality and performance of the products and services that they plan to purchase.
The importance of consumer reviews has fostered e-retailers to provide their products and
services with customer reviews (Mayzlin, 2006). Scholars have provided evidence of the
influence that online reviews have on product sales (e.g., Liu, 2006; Dellarocas et al., 2007;
Duan, Bin, Whinston, 2008; Zhu & Zhang, 2010), information processing and adoption, and
purchasing decisions (Park, Lee & Han, 2007; Zhang & Watts, 2008; Filieri & McLeay,
2014). However, little is known about what makes online reviews diagnostic by consumers
using Electronic word-of mouth (e-WOM) (Pan & Zhang, 2011; King et al., 2014).
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Information is diagnostic if consumers perceive it helps them to understand and evaluate the
quality and performance of products sold online (Jiang & Benbasat, 2004). Diagnosticity is
often conceptualised as the degree of helpfulness of information (Skowronski & Carlston,
1987; Qiu et al., 2012). Not all consumer reviews are helpful and understanding the
antecedents of information helpfulness is paramount for e-retailers because the more helpful
the reviews the higher will be e-retailers’ sales (Chen et al., 2008). In addition to customer
reviews, e-retailers provide several cues and signals to help customers diagnose the quality
and performance of products including overall ranking scores, product popularity signals, and
product quality marks. In order to address gaps in the e-WOM literature, we investigate the
factors which contribute the most to consumers’ perceptions of online review helpfulness.
Existing studies on review helpfulness mostly use databases of reviews from e-retailers such
as Amazon using voting mechanisms which ask readers ‘was this review helpful ?’ to assess
review helpfulness (e.g., Mudanbi & Schuff, 2010; Pan & Zhang, 2011; Baek et al., 2012).
However, in this study we analyse review helpfulness from the consumer’s perspective for
two main reasons: firstly, scholars have found that voting mechanisms can be easily
manipulated (Lim et al., 2010); second, some aspects that might affect review helpfulness
such as perceived source homophily cannot be assessed with textual analysis. We attempt to
address gaps in the extant literature by identifying the determinants of online reviews
helpfulness and its link with purchase intentions. We have used dual-process theory because it
can explain the influence of social and informational factors on an individual’s psychological
processes (Deutsch & Gerard, 1955).
1. E-WOM Literature
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 via
the Internet’ (Hennig-Thurau et al., 2004, p. 39). Third-party e-retailers, namely online
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agencies who sell on behalf of a service provider (e.g. Booking.com), are increasingly
providing customer reviews on their websites (Mayzlin, 2006) in an attempt to facilitate the
consumer decision journey and increase their sales.
Online consumer reviews have attracted considerable interest from researchers who have
found that OCRs directly affect sales of products (e.g., Liu, 2006; Dellarocas et al., 2007; Zhu
& Zhang, 2010) and influence elements of consumer behavior including: information
adoption (Cheung et al., 2008; Zhang & Watts, 2008; Filieri & McLeay, 2014); product
considerations and choice (Huang & Chen, 2006); attitudes towards products (Lee et al.,
2008) and purchase intentions (Park et al., 2007; Park & Lee, 2008; Lee & Lee, 2009).
Despite the importance of information diagnosticity in explaining persuasion in WOM, e-
WOM research on this construct is still scant (Pan & Zhang, 2011).
2. Theoretical background: Dual-process theory
Dual-process theory (DPT) was developed by social psychologists to differentiate between
two types of social influences: informational and normative (Deutsch & Gerard, 1955). DPT
postulates that individuals are influenced by others because they are dependent on others
either for information that removes ambiguity and thus establishes subjective validity, or for
reasons of social approval and social acceptance. Informational influence includes the
relevant components of the information, such as the content, source, and receiver, which are
considered as important sources of influence (Hovland, Janis & Kelley, 1953; Cheung et al.,
2009). Normative influences is defined as ‘an influence to conform to the positive
expectations of another, while informational influences is defined as an influence to accept
information obtained from another as evidence of reality’ (Deutsch & Gerard, 1955; p.629).
Drawing on DPT, we argue that social influence in e-WOM communications may occur via
informational influences, which include: the quality of the argument provided by others in
consumer reviews, the credibility of a source, its similarity (homophily) with the reader,
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product quality signals provided by e-retailers; as well as via normative influence; which
include consumer’s overall evaluations (ranking) of products and their popularity. In the
discussion that follows, we conceptualize and discuss each of these constructs in more detail.
4. Hypotheses Development
4.1 Informational influences
4.1.1 Information quality
Information quality is defined as ‘the quality of the content of a consumer review from the
perspective of information characteristics’ (Park et al., 2007, p. 128). Information quality has
been shown to affect information usefulness (Cheung et al., 2008), information adoption
(Filieri & McLeay, 2014), and review credibility (Cheung et al., 2009, 2012). In studies using
datasets of customer reviews from e-retailers, scholars have identified that review depth and
review length are information quality dimensions that affect review helpfulness (Mudanbi &
Schuff, 2010; Pan & Zhang, 2011; Baek et al., 2012). However, information quality
dimensions and textual analysis can only reveal the tip of the iceberg of information quality
criteria that are likely to contribute to perceived information helpfulness. Therefore, we have
used Churchill’s (1979) approach to identify additional information quality dimensions for
perceived helpful reviews.
The new dimensions identified through interviews included: review factuality, relevance, two-
sided information, and credibility. Information factuality is the degree to which a comment in
a review is logical; is based on specific facts related to experiencing a product; and is free
from emotional, subjective, and vacuous comments. Information relevance refers to the extent
to which a review message is applicable to and helpful for the task at hand and depends on a
specific customer need in a specific situation (Wang & Strong, 1996). Two-sided information
refers to a review message that discusses both the positive and negative sides of a product
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(Kamins et al., 1989). Information credibility is defined as the extent to which a user
perceives a message as believable or true (Cheung et al., 2009).
In this study, we hypothesize that if a review is perceived to be of high quality, it will affect
consumers’ perceptions of the level of diagnosticity of the review. The more an online review
is detailed, long, based on facts, contains both positive and negative comments, and is
relevant to consumer needs, the more consumers will find such information helpful.
H1: Information quality significantly and positively influences perceived info diagnosticity
In addition, scholars have suggested that information quality influences consumer purchase
intentions in an e-WOM context (Park et al., 2007; Lee et al., 2008). In fact, the more
informative the review is, the more favorable associations consumers may have, resulting in
an increase in behavioral intention. Thus, we hypothesize:
H1a: Information quality significantly and positively influences purchase intentions.
4.1.2 Source credibility
Credible sources are among the most persuasive sources of influence (e.g., Hovland et al.,
1953). e-WOM research shows that source expertise and trustworthiness do not influence
perceived information usefulness (Cheung et al., 2008). Based on dual-process theory
(Deutsch & Gerard, 1955), we argue that credible sources are more likely to provide
diagnostic information than non-credible ones. Thus, we hypothesize:
H2: Source credibility significantly and positively influences info diagnosticity.
In addition, marketing scholars have proved that source expertise and trustworthiness
positively influence consumer purchase intentions and purchase behavior (Gilly, Graham,
Wolfinbarger, & Yale, 1998). Zhang and Watts (2008) show that source credibility has a
positive and significant influence on information adoption for online travel websites. Thus:
H2a: Source credibility significantly and positively influences purchase intentions.
4.1.3 Source homophily
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Perceptual homophily represents the result of the ‘textual interaction’ between a reader and a
source of communication in e-WOM. In e-WOM communications people have to retrieve
profile information or read the content of reviews to make inferences about their similarity
with a reviewer. Perceptual homophily concerns the similarities among people regarding their
likes, dislikes, values, and experiences (Bruyn & de Lilien, 2008). Research has suggested
that consumers tend to have greater levels of interaction, trust and understanding with people
who are similar to them (Ruef, Aldrich, & Carter, 2003). In e-WOM homophily predicts
trust (Tang et al., 2013) as well as source trustworthiness and expertise (Ayeh et al., 2013). In
this study, we argue that consumers will find reviews from other consumers who are similar
to them in terms of their viewpoints, experiences and preferences to be more diagnostic. For
example, a backpacker traveller will find the opinion and reviews of people who share the
same style of travelling more useful while a young couple with kids will look for reviews
from people travelling with their family members. Thus:
H3: Perceived source homophily significantly and positively influences info diagnosticity.
Additionally, scholars have attempted to prove the role of homophilous ties on consumer
decisions. For instance, Brown and Reingen (1987) suggest that homophilous sources of
information will be perceived as more credible than heterophilous ones, which should result
in greater influence. Thus, we hypothesize:
H3a: Perceived source homophily significantly and positively influences purchase intentions.
4.1.4 Product quality marks
The technological environment limits e-retailers’ capabilities for providing specific product
attributes information such as smell, taste, touch, feel and the like (Grewal et al., 2004). It
follows that e-retailers must leverage signals that facilitate a consumer’s ability to make
accurate quality assessments about products being sold (Pavlou et al., 2007). Marketing
scholars have investigated how e-retailers use different trustmarks including third-party
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marks, symbols or logos such as the VeriSign logo to reduce perceptions of the potential risks
involved in an online transaction (Aiken & Boush, 2006). Similarly, many e-retailers provide
quality marks as signals to communicate product quality and facilitate consumers’ choices.
Quality marks can be defined as any symbol, icon, signal that is presented by an e-retailer in
an effort to reduce ambiguity and uncertainty about the quality of a product or service. Many
third-party e-retailers provide quality marks. For example Booking.com uses an ok-hand icon
to signal their preferred hotels which they believe offer the best value for money and achieve
high satisfaction scores from previous customers. In this study, we hypothesize that quality
marks can help consumers in assessing the quality and performance of a product that they are
interested in. Additionally, we also expect that quality marks can also influence consumers’
purchase intentions. Thus, we hypothesize:
H4: Website quality marks significantly and positively influence info diagnosticity.
H4a: Website quality marks significantly and positively influence purchase intentions.
4.2 Normative influences
4.2.1 Overall Ranking
Overall ranking is a summary statistic of how all customers have rated (reviewers’ average
evaluation) a product or service in a specific category, such as the ranking of hotels available
in a particular destination. Overall ranking is what social psychologists refer to as base-rate
information and defined as ‘general information, usually factual and statistical, about an entire
class of events’ (Hogg & Vaughan, 2014, p.70). For example, when using Agoda.com, every
reviewer can rate the overall quality of a hotel using a scale from one to ten (superb). Such a
summary statistic is a unique feature of e-WOM communications and indicates how all
customers have evaluated a product or service. Research on the role of summary statistics in
e-WOM is still scant. Scholars have studied the role of individual ratings on the perceived
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trustworthiness of retailers (Benedicktus et al., 2011) or focused on review rating consistency
(Baek et al. 2012). Chevalier and Mayzlin (2006) conclude that consumers read review text
rather than rely solely on summary statistics for books, while Qiu et al. (2012) focus on
conflicting aggregated ratings and their influence on the diagnosticity of single reviews.
In this study, we argue that consumers benefit from access to summary statistics (rankings).
By classifying the products in a category through the use of average ratings (from best to
worst), a crowd of customers communicate how a product or service is performing relative to
competitors. Accordingly:
H5: Overall ranking significantly and positively influences perceived info diagnosticity.
H5a: Overall ranking significantly and positively influences consumer intentions.
4.2.2 Product popularity
A product is considered popular when many people talk about it or purchase it. Online, e-
retailers and online communities provide signals that communicate a product’s popularity. For
example, the number of download counts indicates the quality and reliability of software
products (Hanson & Putler, 1996). The volume of consumer reviews is perceived by
consumers as an indicator of the market performance of a product (Chevalier & Mayzlin,
2006; Huang & Chen, 2006) as it is associated to the number of consumers who have bought
a product (Chatterjee, 2001). Social influence scholars observe that when individuals are
uncertain about a situation they observe what other people do and imitate their behavior (e.g.
Asch, 1951). Such imitative behavior can occur also in e-WOM communications. For
example, when consumers are unsure about which product to buy they may look at the
number of reviews per product, which communicates how many people are buying the
product, to help their purchase decisions. To this extent, consumers think that the more people
choose a specific product, the higher will be its quality; thus product popularity can be helpful
information for consumers.
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H6: Product popularity significantly and positively influences perceived info diagnosticity.
In addition, researchers have found that popularity signals such as best-seller lists and down-
load counts affect consumer’s decisions (Hanson & Putler, 1996; Bonabeau, 2004). In e-
WOM research, the perceived popularity of products has been shown to affect consumer’s
purchasing intention (Park et al., 2007; Huang & Chen 2006), while sales volume predict the
sales (Dellarocas et al., 2007; Liu, 2006). Following this literature:
H6a: Product popularity significantly and positively influences purchase intentions.
4.3 Perceived information diagnosticity and customer purchase intentions
Information helpfulness is a key construct in adoption behavior (Sussman & Siegal, 2003),
which displays significant correlations with both current and future self-reported technology
usage (Davis, 1989). If users believe that the information retrieved is helpful to evaluate a
product’s quality and performance, then they will be more likely to purchase the
recommended product from an e-retailer. Existing studies in e-WOM have focused more on
the antecedents of review helpfulness (e.g. Mudanbi & Schuff, 2010) and no studies have
analysed the links between information diagnosticity and purchase intentions. To fill this gap:
H7: Information diagnosticity significantly and positively influences purchase intentions.
5. Methodology
5.1 Data Collection and measures, scale development, and sample profile
An online questionnaire was created using professional survey design software and was
primarily composed of closed-ended questions that were measured using a 7-point Likert
scale ranging from strongly disagree (1) to strongly agree (7). The questionnaire was available
both in English and in Chinese Cantonese and was pilot-tested three times. The final pilot test
was carried out with a sample of 104 users of online OCRs.
The main data collection was carried out at Hong Kong International Airport. Travellers in
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who have had recent experiences with travel consumer reviews before booking
accommodation for holiday were asked to fill an online questionnaire using an Ipad provided
by the researchers. During a period of two months, approximately 1.100 people were
approached and a total of 432 responses were collected. However, 31 questionnaires were
excluded because not completed properly, which yielded a total of 401 usable questionnaires.
Some of the items and scales used in this study had shown high reliability in previous studies
and therefore were adopted in this study, too (see Table 2). Source credibility and
trustworthiness were measured using a scale developed by Ohanian (1990). Information
diagnosticity was measured using three items developed by Jiang and Benbasat (2007) and
purchase intentions using Dodds, Monroe and Grewal (1991) scale. Perceptual homophily
was measured by we used the widely adopted using a scale recently used by Ayeh, Au, Law
(2013) in e-WOM research. Four scales for measuring overall product ranking, product
popularity, information quality, and e-retailer’s quality marks were developed for this study
following Churchill (1979)’s approach.
The socio-demographic characteristics of the sample are presented in Table 2. The sample
was primarily composed of individuals aged 18-35 (91% of the sample) and all respondents
were Chinese. The age range can be considered a limitation; however, individuals in this age
group use consumer reviews the most (Nielsen, 2013).
Table 1 Socio-Demographic Characteristics of the Respondents
Dimension Items PercentageGender F 61
M 39Age 18 – 25 74
26 – 35 1836 – 45 446 – 54 2
>55 2Educational Level Elementary school 1
High-school 15Undergraduate 78Postgraduate 7
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6. Analysis and Findings
Convergent Validity was assessed through average variance extracted (AVE) and Composite
Reliability (CR). All of the constructs’ AVE values were above the recommended level of 0.5
and CR values were well above the threshold of 0.6 (Fornell & Larcker, 1981) (Table 2).
Reliability was also assessed for each construct with Cronbach’s α. All items had an overall
Cronbach’s α value of 0.906, which indicates an excellent level of reliability for the items and
scale that were used in this study (see Table 2). Table 2 and 3 shows that discriminant validity
is supported (Hair et al., 2010).
In terms of model fit, the x2/df = 1.901 is below the recommended threshold of 3 (Kline,
2011) and the Chi-Square was 899.311 with 473 Degrees of freedom. The goodness-of-fit
index (GFI) was 0.906, and the comparative fit index (CFI) was 0.979; thus, both were above
the suggested cut-off of 0.9 (Hu & Bentler, 1999). The standardised root mean square residual
yielded a favourable value in relation to the accepted threshold of 0.08 (Hu & Bentler, 1999).
The root mean square error of approximation (RMSEA) was below the recommended cut-off
of 0.06 (Hu & Bentler, 1999; Kline, 2011). Thus, the SEM shows a good fit (Table 4).
We tested our hypotheses using structural equation modelling (SEM) and the results are
presented in Table 4.
Table 2 Items used in the study, Cronbach’s α, CR and Factor Loadings.
Construct Items α CR Factor Loadings*
Information Quality (INFOQUAL)
1.Credible2.Relevant to my needs3.Long4.Factual5.Detailed6.Two-sided
.908 .922 .845.822.827.854.802.814
Source Credibility (SC)
The reviewers were…1.Credible2. Experienced3. Trustworthy4. Reliable
.924 .924 .858.842.880.891
Source Homophily (HOMO)
1. Like and dislike the same things as I do2. Have the same travel experiences as I do3. Have the same values as I do4. Have the same viewpoints as I do5. Have the same preferences in travel-related products as I do
.940 .940 .760.865.941.931.845
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(e-retailer) Quality Marks (QUALMARK)
1. I look at the recommendations provided by this website to make up my mind2. The recommendations provided by this website are helpful because they allow me to identify the best products/services3.This website’s recommendations facilitate my choice of the product/service I’m going to buy4. I trust the recommendations of this website5. I rely on the product/service recommended by this website a lot
.921 .904 .846
.836
.834
.833
.845
Overall Ranking Score (RANK)
The overall ranking…1. … Has helped me to rapidly identify the best products/services2. … Has guided my purchase decision to a specific product/service3. … Has facilitated my purchase decision
.888 .878 .816
.856
.848
Product Popularity (POP)
1. The higher the number of reviews the more popular the product/service is2. The more the reviews the easier is to evaluate product/service’s quality3. It makes feel more confident about the product/service’s quality when many people have reviewed it
.843 .853
.697
.862
.870
Information Diagnosticity (DIA)
1. The information provided in online reviews provided valuable tips on products/services2. Was helpful for me to evaluate the product/service I was planning to buy3. Was helpful to familiarize myself with the product/service I was planning to buy4. Was helpful for me to understand the performance of the product/service I was planning to buy
.913 .913.818
.854
.876
.857Purchase Intentions (PUR)
1. If I was going to purchase a product, I would consider buying the product/servicerecommended (by other reviewers) on this review website2. If I was shopping for product/service, the likelihood I would purchase the recommended product/service is high3. My willingness to buy a product/service recommended by consumer reviews would be high if I was shopping for such a product/service4. The probability I would consider buying the recommended product/service is high
.911 .911 .755
.825
.899
.909*Factor Loadings of Rotated Component Matrix. Extraction Method: Maximum Likelihood Rotation Method: Varimax with Kaiser Normalization.
Table 3Means, SD, correlations, and average variance extracted (AVE).Variable Mea
n
SD 1 2 3 4 5 6 7 8
1. INFOQUAL 5.05 1.02
4.68
4
- - - - - - -
2. SC 4.97 1.02
8.
502.753
- - - - - -
3.HOMO 4.58 1.10
4.315
.681
.758 - - - - -
4. RANK 5.04 .959 .533
.632
.480
.706 - - - -
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5. POP 5.13 1.13
0.
605.
497.282
.583
.662 - - -
6.
QUALMARK
4.53 1.17
5.
327.529
.538
.428
.253
.701 - -
7. DIA 5.22 1.02
9.63
6.568
.340
.612
.639
.324
.725 -
8. PUR 5.05 0.98
7.
369.587
.501
.560
.485
.459
.530
.721
Note. Off-diagonal values are squared correlations and on-diagonal values are AVEs.Note. All correlations are significant at p < 0.001 Table 4
Structural equation modelling results.
Goodness of Fit of the Model
Hypotheses Relationship Standardised regression weight (β)
t Supported vs. non supported
x2/df 1.901 H1 INFOQUAL > DIA .430*** 6.457 Supported
GFI 0.906 H1a INFOQUAL > PUR -.166* -1.897 Rejected
NFI 0.930 H2 SC > DIA .173* 2.248 Supported
CFI 0.979 H2a SC > PUR .274*** 4.040 Supported
RMSEA 0.055 H3 HOMO > DIA -.005n.s. -.073 Rejected
SRMR 0.070 H3a HOMO > PUR .058 n.s. .755 Rejected
Chi-Square 899.311 H4 QUALMARK > DIA -.107* -2.135 Rejected
H4a QUALMARK > PUR .124* 1.953 Supported
H5 RANK > DIA .257*** 4.236 Supported
H5a RANK > PUR .220** 2.513 Supported
H6 POP > DIA .251*** 4.762 Supported
H6a POP > PUR .011n.s. .178 Rejected
H7 DIA > PUR .280*** 4.078 Supported
7. Discussion
In the paper, we have provided evidence of the antecedents of information diagnosticity and
consumer’s purchase intentions in e-WOM. Previous studies that investigated review helpful-
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ness mostly used databases (e.g., Mudanbi & Schuff, 2010; Pan & Zhang, 2011; Baek et al.,
2012). Instead we analysed consumers’ perceptions and adopted the dual-process theory
(Deutsch & Gerard, 1955) to test the hypotheses we developed. The tested model agrees with
Deutsch and Gerard (1955) who state that informational and normative influences commonly
are found together. The results of our research advance social cognition and behavioral influ-
ence theories by showing that informational and normative influence in online environments
both occur even in absence of group influence, namely people conform and are influenced by
‘anonymous’ crowds when they are uncertain about a situation or a product to buy. Contrary
to social psychology paradigms (e.g., Asch, 1951), we have demonstrated that the normative
influences in the online environment operate in private settings and people conform and ac-
cept the recommendations of anonymous people.
We found information quality to be the most important determinant of information helpful-
ness and therefore we agree with previous findings on the role of information depth and
length as predictors of review helpfulness (Mudanbi & Schuff, 2010; Pan & Zhang, 2011;
Baek et al., 2012). In addition, this study has identified additional information quality dimen-
sions which are associated to review helpfulness, namely information factuality, relevance,
credibility, two-sided reviews. These information quality dimensions cannot be inferred from
textual analysis because they involve user’s perceptions of online review messages and the
newly developed scale showed a high level of reliability.
Contrary to previous findings in studies on online communities (Park et al., 2007; Lee et al.,
2008), we our results suggest that information quality is negatively related to consumer pur-
chase intentions. Information diagnosticity appears to mediate the relationship between in-
formation quality and purchase intentions which can be explained by the fact that consumers
in the purchase decision stage stages will not go through all high-quality reviews available.
Instead, they will only consider the reviews from the most credible sources that are more dia-
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gnostic in anticipating the quality and performance of a product. To summarise, it is not the
informational quality of reviews that directly influences an individual’s purchasing intentions;
rather it is the capacity of the reviews to disclose the quality and performance of a product
that will ultimately affect consumer purchase intentions.
Interestingly, our results suggest that source credibility influences both information diagnosti-
city and purchase intentions. This finding contrasts with Cheung et al. (2008)’ findings on a
food community. This is consistent with the dual process theory and can be explained by the
nature of an e-retailers website. E-retailers allow only customers who have actually purchased
a product to publish a review, while in some online travel communities (e.g. Tripadvisor.com)
anyone is allowed to post a comment without exhibiting a proof of purchase. Therefore, the
credibility of the source is probably more prominent in e-retailer websites rather than in on-
line communities.
Homophily did not predict information diagnosticity. It is possible that when consumers scru-
tinize information from online reviews, they focus on the content of the review or on the ex-
pertise of the reviewer, rather than on how similar the reviewer is to the receiver in terms of
personality, viewpoints.
In this study we have developed and tested a scale for measuring product quality marks
provided by e-retailers, which has showed a high level of reliability. Interestingly, our find-
ings show that the quality marks provided by an e-retailer are not perceived to be diagnostic
by consumers seeking to familiarise themselves with a product and learn about its quality.
Nevertheless, they still influence consumers’ purchase decisions. This result may be explained
by the fact that consumers in the alternatives evaluation stage rely more on the recommenda-
tions coming from fellow customers in the form of reviews and ranking scores than reviews
originating from commercial sources. However, in the purchase decision stage, website re-
commendation signals are influential but their influence is lower than other factors. We can
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therefore infer that if the shortlisted products are also recommended in perceived diagnostic
reviews from credible sources such recommendations will influence consumers’ purchase in-
tentions.
In this study we have developed and tested a scale for measuring overall ranking scores,
which showed a high level of reliability. The influence of summary statistics is higher on in-
formation helpfulness than on consumer’s purchase intentions. A tourist visiting a new destin-
ation has a lot of accommodation options to choose from. However, scrolling all alternatives
that match a consumer’s requirements and reading the related customer reviews is not a viable
option as this would take a long time. From this result we can infer that overall ranking helps
consumers rapidly identify the best value for money options and reduces the number of altern-
atives that they consider. This result contrasts with recent e-WOM research showing that con-
sumers ignore aggregate ratings (Qiu et al., 2012). The influence documented influence of
overall ranking contrasts with social psychologists finding, who conclude that while making
judgements, consumers tend to underuse base-rate information because they are not relevant
(Bar-Hillel, 1980). In e-WOM, base-rate information seems to be particularly helpful because
helps reduce the number of options available by focusing only on the ones that are ranked
highly by the crowd. Deutsch & Gerard (1955) state that people tend to believe what most
others believe, even though these beliefs may not be true. By adopting a ranking score con-
sumers accept the evaluation provided by the majority as reality even if this may not be true.
The influence of summary statistics on purchase intentions is less significant and less strong
than other factors (i.e. information helpfulness and source credibility) but still important. We
can infer after having shortlisted options with similar (and probably high) ranking scores, at
the purchase decision stage consumers will reflect more about the arguments contained in the
most diagnostic reviews coming from the most credible sources and that a ranking score will
be possibly used as a support to the final decision.
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Information on the popularity of products is particularly important to consumers seeking to
assess product quality. This is because popularity signals may reduce the perceived risk asso-
ciated with a purchase and increase consumers’ confidence about the quality of the option.
Nevertheless, in contrast to the results of previous research (Park et al., 2007) our findings
suggest that product popularity did not influence consumers’ final purchase decisions. This
may because in the purchase decision stage, consumers may not consider how popular a
product if the alternatives shortlisted have a sufficient/equal number of reviews.
Finally, perceived information diagnosticity was shown to affect consumers’ purchase inten-
tions, which advances our understanding of the links between information diagnosticity and
consumer behavior in e-WOM communications. This result indicates that when the informa-
tion provided in online reviews is judged to be helpful, consumers are more likely to purchase
a product or service.
8. Managerial implications
Important managerial implications can be drawn from the results of this study. First, e-retail-
ers must ensure that the reviews hosted on their website are helpful to customers to motivate
them to purchase products or services. To be diagnostic, information in reviews must be de-
tailed, two-sided, and long enough to provide relevant and factual details, that are ultimately
perceived as believable by readers. Thus, E-retailers can adopt the information quality criteria
identified in this study when building the forms/templates that reviewers fill in when submit-
ting a review.
Additionally, e-retailers managers could use a rating system to rate the helpfulness of reviews
and the expertise of reviewers. Websites that host reviews should be enabled so that it is easy
to provide more information about reviewers, including their experience, number of reviews
posted, and helpful votes awarded. By doing so, e-retailers will more clearly communicate a
reviewer’s credibility in terms of expertise and knowledge. A system similar to the one adop-
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ted by Amazon (Amazon Vine Programme) could be adopted by major e-retailers like Book-
ing.com or Agoda.com where reviewers are ranked based on the number of helpful votes
awarded to them. By integrating information about the credibility of reviewers and the help-
fulness of reviews, marketing managers can facilitate consumers to retrieve helpful reviews
from credible sources, thereby enhancing purchase intentions.
In this study summary statistics emerged as strong antecedents of information diagnosticity
and as influencers of consumers’ purchase intentions. In order to facilitate consumers’
product evaluations, e-retailers should make product overall ranking metrics (based on
customer evaluations) visible and easy to locate. Additionally, we also recommend e-retailers
adopt a wider range of summary statistics to evaluate, for example, how the different product
attributes are ranked in comparison to competitors.
9. Limitations and future research
Like all studies, this research has limitations. First, the sample was primarily composed of
Asian respondents from Hong-Kong. Therefore, it would be valuable to replicate the study in
other contexts. Moreover, this study was based on e-retailers in the travel and tourism sector.
Future research could test the model for different types of websites such as online forums or
communities and with different product categories (e.g., search products). Additionally, the
results of our research suggest that homophily does not predict information diagnosticity.
Future studies could also consider if homophily affects information quality or source
credibility perceptions. Finally, an in-depth qualitative study of consumer perceptions of
review helpfulness is still lacking but would be valuable.
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