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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