Online rankings and reviews; an influence on consumers’ decision making.

Bachelor thesis – Social influence

Online rankings and reviews; an influence on

consumers’ decision making.

ABN Pranger

University of Groningen – Faculty of economics and business

Bsc International Business and Management

June 25, 2011

Po. Box 1302

9701 BH Groningen

+316 34 06 25 78

[email protected]

student 1629980

Supervisor: M.C. Non

- 2 -

ABSTRACT

Online rankings and reviews are a large pool of information, information which is widely

used in consumers‟ decision making process. Currently 69 percent of consumers make

use of these forms of online WOM before making on- or offline final purchase decisions.

This research focuses on the influence of positive valence towards buying decisions. Our

empirical analysis suggests that consumers do not pay attention to what form of online

WOM they review, but of more attention is paid to the source of the information;

professional or non-professional.

Key words: online reviews, rankings and ratings, buying decisions, word of mouth, bias,

manipulation and fraud.

Research theme: The potential relationship of positiveness of online professional and

consumer reviews and rankings with regard to buying decision.

Tutorial supervisor: Ms. M.C. Non

Word count: 5647

- 3 -

TABLE OF CONTENT

1. INTRODUCTION PAGE 4

2. RELATED LITERATURE PAGE 6

3. RESEARCH METHODOLOGY PAGE 14

3.1. Subjects and Data Collection Page 14

3.2. Measurement Page 14

3.3. Model explanation Page 15

3.4. Reliability Page 15

3.5. Data limitations Page 16

4. EMPIRICAL ANALYSIS PAGE 17

4.1. Descriptives and frequencies Page 17

4.2. Findings Page 20

4.3. Hypotheses testing Page 21

5. CONCLUSION PAGE 23

5.1. Conclusions Page 23

5.2. Managerial implications Page 24

5.3. Limitations and Further Research Page 24

REFERENCES PAGE 25

APPENDIX A PAGE 27

- 4 -

1. INTRODUCTION

Today‟s era of digitalization and continuous growth of online shopping offers a lot of

possibilities. For consumers as well as for organizations. There is an emerging ongoing

trend of posting reviews concerning products and organizations, on online forums,

product review sites, web shops and comparison websites. As more and more consumers

are contributing in the process of sharing experiences online, does this than in turn imply

that there are also more and more consumers using these reviews in their decision making

process when shopping? Along with the increasing number of reviews and rankings, what

can we accordingly state about their respective quality? Furthermore, and that is where

the main problem is to be indicated, how do these reviews and rankings influence

organizations‟ sales figures?

In the last decade more and more research with regard to the topic has been conducted.

Recent studies have shown us various insights in this ongoing process of reviews,

rankings and word of mouth and also stipulated the influence which they in turn have on

organizations. Chevalier and Mayzlin (2006) for example studied the extent to which

book reviews, and interconnected rankings influenced the sales of those respective books

on amazon.com and barnesandnoble.com. On their turn Zhu and Zhang (2010)

investigated the impact which online consumer reviews could have on sales of video

games in particular. Overall both researches have shown that there is a positive

relationship between those reviews and rankings and the related sales figures. Both

researches were executed from an organizational and a rather technical point of view,

merely focusing on how to predict sales figures based on the number of reviews. The

current mindset of consumers however, is attaching more and more value to reviews and

considers them more credible and trustworthy; research of Bickart and Schindler (2001)

confirms this assumption. Even though Hu et al. (2006) indicate that there is evidence

that online reviews possibly are not representative, an increasing number of people is

using online reviews. Multiple surveys have been conducted and results are showing that,

according to Deloitte (2007), almost two-thirds of consumers read consumer-written

product reviews online, 69 percent of them share these reviews with family and friends

- 5 -

which extends their reach even more. The problem which lies ahead is not how many

people are using reviews and connected rankings, yet important is the way in which

people use and value them in respect to their buying decisions. In the end these decisions

are directly connected to profit and sales figures of online and offline companies and

organizations, therefore a proper understanding of how reviews and rankings „work‟ is

considered to be key in order to be able to use reviews and rankings as a marketing tool

in order to stimulate the sales figures referred to. In current practice, research is using

reviews and rankings in order to estimate or even predict future sales figures (Li and Hitt,

2010). Organizations on the other hand are trying to use reviews, or the possibility of

posting them, as a sales and profit enhancing tool.

This research will therefore turn perspectives and will, in retrospect to previous research,

focus on the way in which consumers use and value online reviews and rankings when

finding themselves in the process of buying. Accordingly with the goal to create more

understanding and awareness concerning customers‟ perception and, according to Cao,

Duan and Gan (2011), the role of online user reviews. This study will therefore be based

on the following research question;

In which way do consumers value and use, reviews, rankings, worth of mouth and do they

result in positive buying decisions?

The structure of this paper will be as follows. First, in section two, a discussion

concerning existing literature, previous research and the concepts of online reviews and

rankings will be provided together with the presentation of the conceptual model and

related hypothesis. Second, the research methodology will be explaining the data analysis

and the model used. Continuously the gathered data will be presented and analyzed

accordingly. Afterwards, based on the outcomes of previous sections conclusions,

recommendations and limitations will be discussed.

- 6 -

2. RELATED LITERATURE

Reviews and rankings, what are they, what makes them helpful and what kind of role do

they have? For a proper understanding of their role and when they are helpful or not,

understanding of what reviews are, is essential. Defined, online customer reviews are

„peer-generated product evaluations posted on company or third party websites‟

(Mudambi & Schuff, 2010:186). As mentioned before one can find reviews all over the

web, even complete review communities have risen, see for an example

http://reviews.cnet.com/. Next to the reviewing part, reviewers frequently have the option

to „rate‟ the product in question. Poston and Speier (2005) identified thise as numerical

content ratings. Please note that such ratings often are used to draw up rankings, thus

showing interconnectedness with the written reviews.

In addition to the definition of what a review actually is a notion has to be made

concerning word of mouth (WOM). The internet enabled an online form of WOM, and

online reviews are considered to be part of that. It is even suggested by Zhu and Zhang

(2010) that online consumer reviews are a good indicator for overall WOM. Li and Hitt

(2010) tend to go even further with their assumption, according to them the networks

facilitated by product review-websites, discussion forums, blogs, and virtual communities

posses many similar functions as traditional WOM. Multiple researches tend to suggest

that „many consumers make offline purchase decisions based on online information and

that some aspects of online WOM are proxies for overall WOM‟ (Zhu and Zhang,

2010:P.133). In line with this assumption, research of Dellarocas (2006) indeed indicated

that firms should manage WOM actively.

When referring to the role of reviews an economical approach can be identified to

elucidate this particular role. According to Mudambi and Schuff, (2010:P187), „the

economics of information provides a relevant foundation to address the role of online

customer reviews in the consumer decision process‟. When consumers are in the process

of buying they tend to orientate themselves and gather information. The issue is that

becoming fully informed is a state which is rarely met, „they have to make purchase

- 7 -

decisions as they lack full information on product quality, seller quality, and the available

alternatives‟ (Mudambi and Schuff, 2010:P187). In their research it is identified that

customers are seeking to reduce that uncertainty. Traditional theory makes a distinction

between different types of goods, search and experience goods (Huang, Lurie and Mitra,

2009). In short, search goods are goods of which all information is relatively easy to

obtain and experience goods are goods that do not qualify as such, real experience with

the product is necessary in order to state something concerning its quality, music or wine

are examples of such products. With the internet as a medium, people able to share those

experiences with one and another and thus gather information before actually buying a

product. Traditionally models concerning the economics of gathering information assume

that consumers search for information until the marginal cost of search equals its

marginal benefit (Ratchford, Lee, and Talukdar, 2003). According to Huang, Lurie and

Mitra (2009), the internet will lower the cost of gathering and sharing information, and

offers new ways to learn about products before purchase. Therefore in short, one is able

to state that the role of a review is to provide consumers with experience specific

information in order to decrease uncertainty and the cost of search. According to Huang,

Lurie and Mitra (2009) several authors found that, because of this sharing experience

through internet the differences between search and experience goods are being erased.

Mudambi and Schuff‟s (2010) research identified another important aspect of reviews.

Helpfulness. Given that consumers have access to many sources of information they need

a way in order to distinguish the better from the worse. Therefore a lot of review websites

ask the reader to which extent the review was helpful, also called as helpfulness voting

systems. The study shows that there are two dimensions that determine the degree of

helpfulness of a review. The study found that customers tend to take two factors into

account when assessing the degree to which a review is helpful. These are, as Mudambi

and Schuff (2010) indicate, review extremity and review depth. Review extremity

indicates to which extent a review is either positive, neutral or negative. The depth of the

review indicates how extensive the review is written. According to them a helpful

customer review is „a peer-generated product evaluation that facilitates the consumer‟s

purchase decision process.‟(Mudambi and Schuff, 2010:P186).

- 8 -

However, organizations should be aware of a problem which can arise; Cao et al. (2011)

indicate problems related to the helpfulness voting systems. If there is a system put in

place, it should be clear that it is a prerequisite that people are voting. On the other hand,

if votes are entered than the most helpful reviews tend to attract more people and

accordingly receive more helpfulness votes. In the meantime reviews with less

helpfulness votes attract less people and are less likely to receive more helpfulness votes,

the probable result is that these reviews are ignored. Therefore their investigation

searches for other methods of determining helpfulness.

Furthermore it is debated that reviews may not be completely informative. Li, Hitt and

Zhang (2011) state that there are three probable causes. First of all it may be clear that

reviewers make mistakes. Also reviews may be influenced by firms themselves, e.g.

paying reviewers in order to leave favorable reviews. Even complete and accurate

reviews may not be entirely informative due to preferences between writer and reader of

the review. Thus, „there is always a chance that the consumer will receive an inaccurate

signal‟ (Li, Hitt and Zhang, 2011:P11).

Reading reviews, in order to decrease uncertainty (Mudambi and Schuff, 2010) makes

sense. However what motivates people to write online reviews? Anderson (1998) states

that consumers are more likely to contribute to WOM when they have extreme opinions.

Accordingly, Li and Hitt (2010) state that brand loyalty is also a driver of engaging in

WOM. More specific is the work of Hennig-Thureau, Gwinner, Walsh and Gremler

(2004), their study finds that “consumers desire for social interaction, desire for

economic incentives, their concern for other consumers , and the potential to enhance

their own self-worth are the primary factors leading to eWOM behavior” (Hennig-

Thureau et al. 2004:P.39). Additional motivation is found thanks to the research of

Dellarocas, Gao and Narayan (2010). It is found that internet, as new media, enabled the

discussion concerning niche products that mainstream media previously ignored. The

trend is that people feel like engaging in WOM about niche products because discussing

the latter makes them appear more helpful and intelligent in the eyes of those that read

their comments. On the contrary it is argued that consumers are, in addition to

- 9 -

contributing to niche products, eager to contribute to products that are already heavily

reviewed. These opposite forces are what Dellarocas et al (2010) have indicated as a U-

shaped relation.

As already stated before people are more likely to engage in WOM if they have an

extreme opinion. Evidence indeed states that reviews and ratings overall are extremely

positive (Hu, Pavlou and Zhang 2009) which is indeed one of the extremes. Chevalier

and Mayzlin (2006) also show that reviews on barnesandble.com as on amazon.com are

extremely positive as well. The research of Hu et al. (2009) finds the existence of a J-

shaped distribution in product reviews. As we can see in figure 1 below, the J-shaped

distribution is caused due to the earlier mentioned fact of extremes. There are some one-

star ratings, many five-star ratings, however barely any in between. The cause of this

change distribution is caused, according to Hu et al. (2009), thanks to two reasons. The

purchasing bias and the under-reporting bias. The purchasing bias states that people with

higher product valuations are likely to make the final purchase and thus review the

product positively. On their turn people who have a negative product evaluation are less

likely to purchase the product and thus negative reviews will not be written. The

underreporting is directly related to extreme opinions. People tend to “brag or moan” (Hu

et al. 2009, p.145), and conclusively it is found that people with moderate opinions are

less eager to share their opinion.

FIGURE 1

The J-shaped distribution

Figure 1. (Hu et al. 2009, p.145)

- 10 -

In relation to the reporting bias, Li and Hitt (2010) find that, because of the fact that early

adopters of products have different preferences for the products they immediately

purchase when available. Thus therein lays the explanation that reviews from early

adopters are not representative for the market as a whole. Kapoor and Piramuthu (2009)

take an even broader point of view by stating that online product reviews by their very

nature are biased. Another cause of reviews to be biased is stakeholder interference.

Dellarocas (2006) confirms this, according to his research; firms are tempted to

manipulate consumer perceptions in order to praise their own products. The large scale of

the internet and the degree of anonymity facilitate this manipulation process. It is found

to be relatively simple “for interested parties to manipulate the information propagated

through online forums by anonymously adding their own, strategically biased, message to

the total mix of posted opinions” (Dellarocas 2006, p. 1577). That this is part of daily

practice was revealed in February 2004 when a mistake at the Canadian website of

amazon.com revealed true identities of reviewers. Amongst were authors, publishers and

competitors (Harmon 2004). According to Dellarocas (2006) there are firms that

consistently examine online forums and review sites in order to find leading reviewers.

Pampering and sampling activities will be conducted, most likely with the result that

positive writings will appear. Research of Hu, Liu and Sambamrthy (2011) investigated

fraud in online reviews. The study suggests that it is possible to influence sales by

manipulating online reviews, with the unbeneficial consumer side effect that the degree

of informativeness of online reviews decreases. The study states that “WOM

communication is a valuable marketing resource for consumers and marketers with

critical implications for a products success” (Hu et al. 2011, P. 615). Herein, the large

motivation for manipulating reviews, ratings and thus rankings can be understood. When

thinking out of a business perspective, it would be even naïve not to participate in this

process of manipulation. “John Rechy, author of the best-selling 1963 novel ''City of

Night'' and winner of the PEN-USA West lifetime achievement award, is one of several

prominent authors who have apparently pseudonymously written themselves five-star

reviews on amazon.com, sees this form of manipulation as a means to survival when

online stars mean sales” (adapted from: Harmon 2004).

- 11 -

That indeed reviews, ratings and thus rankings are related to sales outcomes may be clear.

Multiple studies have indicated this relation, for example the study of Chevalier and

Mayzlin (2006) shows that the differences in reviews on barnesandnoble.com and on

amazon.com are indeed positively related to the distinction in sales on the to websites.

Other studies focused on the movie industry. Liu (2006) investigated how WOM helps to

explain and predict box office revenue. The research showed that WOM information

provides significant clarifying power for box office revenues. It also indicated that most

power is derived from the volume of WOM and not from its valence. Duan, Gu and

Whinston (2008) confirm this findings in their investigation concerning the relation of

online reviews and box office sales figures. The contrary is found by Chintagunta,

Gopinath and Venkataraman (2010), their study showed that it was not the volume, but

the valence of the reviews that imposed an influence on sales while also including a

geographical element.

As previous researches show, there is a positive relation to be found between online

WOM, which entails reviews, ratings and its connected rankings, blogs and forums, and

respective sales figures. This research will contribute to previous research by changing

the perspective; forgoing researches all connected the volume or valence of reviews to

sales figures and tried to find if there were any relations. This study changes that top

down perspective and tries to identify if there is, from a consumer perspective, any

relation to be found between the valence (positiveness or negativeness) of a review or

ranking and the buying decision of consumers.

Li and Hitt (2010, P.810) indicated that “for some consumers and products, consumer-

generated reviews are more valuable than expert reviews and that they have a greater

influence on purchase decisions than traditional media”. This reasoning is to some degree

related to this research. This research will investigate in which way consumers deal with

the valance of an either professional or non-professional review or ranking. The

conceptual model explains these expected relations. In addition, the valance of a review

is, considering extremes, either positive or negative. The study assumes that positive

valence influences consumers buying decision positively. However, expectations are that

- 12 -

it matters whether the element in question is a review or a ranking, whether its source is

professional or biased (non-professional) and age and the degree to which consumers

have internet experience in combination with how safe they feel themselves online are

moderators of the final buying decision.

FIGURE 2

Conceptual model

In turn this conceptual model results into the following main hypothesis:

H1; Positive valence has a positive influence on a consumers buying decision.

H2; The relation between positive valence and one‟s buying decision is influenced by

whether the source is a review or a ranking.

- 13 -


whether the source is a professional or not.


whether the reader has a high degree of internet experience.

H5; The relation between positive valence and one‟s buying decision is influenced by the

degree of internet confidence.


readers age.

- 14 -

3. RESEARCH METHODOLOGY

3.1. Subjects and Data Collection

As already stated before, the key element under investigation in this research is the use of

online reviews and rankings by randomly selected consumers. Liu (2006) indicates that

67 percent of the sales of consumer goods are based on WOM. In this case modern,

online WOM as it is seen as a good proxy of overall WOM (Zhu and Zhang 2010), is

expected to be the determinant of a consumers‟ buying decision.

The investigation has been conducted with help of a survey. The data resulting from the

survey was collected by using an online survey tool. The surveys have been spread by an

available amount of randomly selected e-mail addresses, and participants have been

invited through use of an online social network. The online survey tool explained the

participant the goal of the study, where after the survey started with questions concerning

the moderating variables. The survey continued with more specific questions concerning

the various independent variables. Lastly the participants were thanked for their efforts.

The total exposure, e-mail addresses and people in the online social network, has been

around 350 people, in the end 76 results have been recorded. The approximate response

rate is therefore found to be 22 percent.

3.2. Measurement

The survey was held using a questionnaire, the questionnaire was developed solely for

use in this study and based on the authors assumptions. Two questions were answered by

numbers, indicating „age‟ and „years of internet experience‟. Further questions were

measured by using a five point Likert scale. The Likert scale was set using the following

extremes; 1 = „I don‟t agree at all‟, which represents the poorest value, and 5 = „I

completely agree‟ which in turn represents the best achievable value, also the extremes

„Definitely wouldn‟t buy‟ and „Definitely would buy‟ were used. All indicators of the

independent variables and four control questions were measured using these scales. All

independent variables have been measured by providing examples of a positive and a

negative case. Participants were asked whether they, based on the example, would make

- 15 -

the decision to purchase the presented product. In all cases the product suggested was a

television, this in order to eliminate a bias based on product knowledge or preference.

3.3. Model explanation

The relationship between the independent variables, the dependent variable and the

various moderating variables will be calculated statistically by use of regression. The

regression model used to explain the dependent variable „Buying decision‟ (BD) will be;

BDx = αo + β1PVx + β2RRx + β3Sx + β4IEx + β5ICx + β6AGEx + β7(PV*RR)x + β8(PV*S)x

+ β9(PV*IE)x + β10(PV*IC)x + β11(PV*AGE)x + Σ

As already noted before, BD is the dependent variable „Buying decision‟. The element

αo is included and represents the residual outcome of the model. The independent

variable positive valence is included by „PV‟. Accordingly, „RR‟, „S‟, „IE‟, „IC‟ and Age

are respectively; Review or Ranking, Source, Internet experience, Internet confidence

and of course age speaks for itself. Foregoing are included as moderating variables which

are expected to have an influence on the relation between the independent and the

dependent variable. The following arguments of the model are included in order to depict

the influence of the moderating variables and the influence they impose on the relation of

the independent and the dependent variable. Furthermore, x includes the participants and

last Σ, which represents the degree of error between the predicted value of BD, as

dependent variable, and its actual value (Hair et al. 2009).

3.4. Reliability

The reliability of the independent variables is measured using the Cronbach alpha test,

this test indicates the degree of internal consistency or reliability. The threshold of the

Cronbach alpha test is, for social sciences, > .65. In this study‟s case, please refer to

appendix A - table 1, the value of the Cronbach alpha tests are for the positive

variables >.65. Unfortunately this does not hold for the negative group of variables.

Overall the Cronbach alpha is .627, as > .60 is a bare minimum in social sciences the

difference can be ignored, and the data can still be considered as reliable.

- 16 -

3.5. Data Limitations

Data limitations, a bias can arise thanks to the simple fact that the data is gathered with

the use of internet. This fact may indicate that it is likely that all respondents at least are

able to obtain easy access to online reviews and rakings. Therefore it is debatable

whether the data represents the opinion of the population as a whole.

- 17 -

4. EMPIRICAL ANALYSIS

As already specified above the research focused on the relationship between online

rankings and reviews and the influence they might have over people their buying

decisions. The model used to examine this relationship is regression analysis according to

the linear standard. The selected method performs exactly the analysis required. It

examines the relation between the independent variables and the dependent variable. The

final equation which is derived after analysis results in the final prediction of the

dependent variable. In our study this implies that a positive ranking or review should

result in a positive buying decision and vice versa for the negative cases. The overall

expectation is there will be a significant influence of online rankings and reviews, either

positive or negative, on people‟s buying decisions.

4.1. Descriptives and frequencies

For this research the descriptive statistics did allow us to obtain some information about

the selected sample of participants. As can be seen in table 1, below, we can see that

TABLE 1

Participant information

Measure Items Frequency Percentage

Age 16-25 36 47,4

26-30 13 17,1

31-40 9 11,8

41-50 6 7,9

51+ 12 15,8

Years of 1-5 5 6,6

Experience 6-10 37 48,7

10+ 34 44,7

Internet Not confident 3 3,9

Confidence Uncertain 16 21,1

Uncertain / confident 10 13,2

Pretty confident 25 32,9

Totally confident 22 28,9

- 18 -

in total 76 people participated in the survey, which automatically leads to a sample size

of the same order. Any larger sample would have been heavily appreciated, however the

current sample is considered to be sufficient. In total 49 or 64,5 percent of the people

that filled out the questionnaire are aged 30 or younger. They all have at least moderate

till long online experience, 93,4 percent has more than 6 years experience online. And

also 61,8 percent of the people is at least pretty confident in being online and actually

not afraid to engage actively online. Table to indicates the average of these three

moderators, it is found that the average age of respondents was 31,75 years. Overall

years of internet experience was high, 10,38 years online. In addition it was found that

the average degree of to which people feel themselves safe online is high, a score of

3.62.

TABLE 2

Average of three moderating variables

Variable Average score Scale Round

Age 31.75 Years - 32

Internet experience 10.38 Years - 10

Internet confidence 3.62 1 – 5 4

This leads to the examination of the other eight determinants of the independent variable,

table 3 shows preliminary results and descriptives. The percentage in the table indicates

which percentage of the sample would consider buying the case of for example a

positive consumer review. Theory suggests that consumers tend to rely more on

consumer than on professional reviews. The results from this investigation can indeed

confirm this in the „positive‟ case. In case of a positive review 47,3 percent of the

people relies on a consumer review and only 42,1 percent would rely on the professional

review. In the negative case this assumption is confirmed again, people are less likely to

buy when a consumer review is negative than when a professional review is negative,

score 6,6 percent against the opposing 15,8 percent.

- 19 -

TABLE 3

Preliminary results and descriptives

In a case when consumers want a summary of information, such as represented in a

ranking, the study results show something different. In the positive case consumers tend

to rely more on a professional ranking than they would do on a (biased) non-

professional ranking. The difference here is even bigger, the 59,5 professional percent

against the 42,2 non professional percent. Interesting to see is however that in the

negative case, theory again holds.

The reason behind these decision making patterns could be that, probably due to the fact

that professional reviews and also rankings are less extreme in their opinion. That

people are, for example in the case of negative reviews, less convince by professionals

and thus stile more willing to buy when compared to consumer reviews. Although this

does not hold for the professional ranking, a sound reasoning for this fact might be the

factor of objectivity. When searching for a summary of information, consumers are

more likely to rely on completely independent rankings than they would rely on non

professional colleagues.

- 20 -

4.2. Findings

Testing of the hypotheses was, as already indicated earlier, conducted by regression

analysis. The regression analysis showed some revealing outcomes. An overview of

these figures is provided in table 4.

TABLE 4

Outcomes regression analysis – Hypotheses testing

Model R² Adj. R² Std. β B Sig.

Positive valence on buying decision .295 .282

Posneg -.156 -.429 .391

Revra -.110 -.303 .024

ProfNprof .183 .500 .000

Internet confidence -.121 -.136 .030

Years online .080 .034 .134

Age -.136 -.015 .009

PosnegREVRA .098 .309 .101

PosnegProfNprof -.123 -.388 .040

PosnegIC .803 .549 .000

PosnegIE -.553 -.134 .000

PosnegAGE .458 .035 .000

First, when considering the model as a whole it is found that 29.5 percent of the

variance within buying decisions is explained by the independent and its moderating

variables. The standardized beta indicates the relative importance of each and every

individual variable in explaining „buying decision‟ as dependent variable. In this case it

is the moderator Internet confidence, represented as „PosnegIC‟, with a value of .803.

For testing the hypothesis the significance found in the regression analysis will

determine whether to reject the hypotheses or not. For this research a error of α .05 is

allowed, thus every value below α .05 will result in not rejecting the specific hypotheses.

- 21 -

When not rejected, the regression coefficient will indicate the strength of the relation

found.

4.3. Hypotheses testing

H1; Positive valence has a positive influence on a consumers buying decision.

The first hypothesis should not be accepted, due to a p-value of .391 which is >

α .05. Rejection of this hypothesis directly indicates that valence, as only predictor of

consumers buying decision, is not sufficient in explaining that buying decision.


whether the source is a review or a ranking.

The research shows that, according to the sample, the source being either a review

or a ranking does not influence the relation of positive valence and buying decision. As

indicated with a p-value of .101 which is > α .05, therefore the second hypotheses should

not be accepted as well.


whether the source is a professional or not.

This third hypotheses should be accepted, the found p-value of .040 is < α .05.

Therefore the study showed that the relation between positive valence and one‟s buying

decision is influenced by whether the source is a professional or not.


whether the reader has a high degree of internet experience.

The degree to which a consumer has internet experience has been found to

moderate the relationship between de independent and dependent variable. The

regression model indicates a p-value of .000, this is < α .05 and thus should the

hypotheses not be rejected.

- 22 -


degree of internet confidence.

With a p-value of .000 and a regression coefficient of .549, the degree of internet

confidence is found to be the most influential moderator in the model. This implies that

the more internet confidence a consumer „has‟ the merrier it is with regard to the relation

of positive valence and one‟s buying decision. The p-value of .000 is < α .05 and

therefore the hypotheses will not be rejected.


readers age.

Last the regression analysis shows that consumers‟ age will impose a small

influence on the predicted relation. The p-value again meets the < α .05 criterion, the

regression again finds a value of .000. The hypotheses will not be rejected.

- 23 -

5. CONCLUSIONS

5.1. Conclusions

After preceding chapters it is time to answer the main research question of the research,

In which way do consumers value and use, reviews, rankings, worth of mouth and do they

result in positive buying decisions?

The research indicated that consumers, when referring to H1, are not more eager to buy

or not to buy solely on the relationship between the degree of positive valence and their

buying decision. It is found however that consumers tend to let them influence by the

source of which the information comes from (see H3). The samples‟ behavior is nearly

completely consistent with preceding literature. The research indicates that the consumers

from our sample are more willing to base their decision upon consumer reviews and in

positive cases on professional rankings. Theory indeed states that consumers are more

and more focusing on the information provided by other consumers than they would rely

on professionals. The research also indicated that a large number of consumers use the

online available material in making their final buying decision. Furthermore the study

shows that there are three elements that impose a large effect on the buying process as a

whole. The regression analysis shows perfect significance levels for the moderators IE

(internet experience), IC (internet confidence), and Age. It is found that, most important,

IC has a large positive contribution to the relation of positive valence and consumers‟

buying decision. On the contrary it has shown that the number of years online does not

positively contribute to this prediction. On the contrary, a negative relationship has been

found. Overall it has been made clear that consumers indeed use rankings and reviews as

information source in their buying process; however a clear preference has not become

clear. Furthermore consumers do attach value to the source of which the information

originates. Professional reviews are less appreciated than their non-professional versions

and overall the degree to which consumers feel confident online plays a large role in

consumers‟ final buying decision.

- 24 -

5.2. Managerial implications

The research has found a number of significant contributing and influencing factors

towards consumer‟s decision making process. In the current internet era, the information

providing role of the former cannot be ignored. Online rankings and reviews as a part-

and indicator of WOM are not to be ignored. The contrary is key, if organizations want

maintain their competitiveness they acknowledge the existence and start actively

managing these forms of online WOM. The research has indicated which elements play a

significant role in consumers buying decision making. For managers it is crucial to

understand that it is not a professional review that results in the largest likeliness of a

purchase but a consumer review is. If an organizations goal is to maximize sales, this is a

way in which consumers can be stimulated. The research indicated that consumers are

aware of the bias in reviews and rankings, but that they nevertheless ignore that existence

in the process of buying. Therefore it is even indicated that „active management‟ of

online reviews and rankings is possible and maybe even necessary. As John Rechy

indicated, in times where stars mean sales, manipulation is a means to survival (Harmon

2004).

5.3. Limitations and Further Research

Even though this research indicated significant factors that impose an influence upon the

relationship between positive valence and consumers buying decisions it has to be noted

that the research knows some shortcomings. First of all the research is based on the

results of an online survey. It is possible that thanks to this fact a bias has been imposed

to the research, people without internet experience and or confidence were not likely to

respond to the questionnaire. Furthermore the correct measurement of the questions could

have been improved. A larger Likert scale could lead to other results, recommended for

further research is to use at least a seven-point scale. Lastly, the results of the study may

not be a 100 % reliable because of a change in the conceptual model during the

investigation. Area‟s which are not yet being investigated are the determinants of what

makes a review or a ranking positive. This will provide an even better insight of which

elements of a review or ranking are crucial in order to result in a positive buying decision.

- 25 -

REFERENCES

Anderson, E.W., 1998, Customer satisfaction and word of mouth, Journal of service

research, vol. 1, 1, p. 5-17.

Bickart, B., and Schindler, R. M., 2001, Internet forums as influential sources of

consumer information, Journal of Interactive Marketing, vol. 15, 3, p. 31-40.

Cao, Q., Duan, W., Gan, Q., 2011, Exploring determinants of voting for the “helpfulness”

of online user reviews: A text mining approach, Decision support systems, vol. 50, 2, p.

511-521.

Chevalier, J., and Mayzlin, D., 2006, The effect of word of mouth on sales: online book

reviews, Journal of Marketing Research, vol. 43, p. 345-354.

Chintagunta, P.K., Gopinath, S., Venkataraman, S., 2010, The effects of online user

reviews on movie box office performance: Accounting for sequential rollout and

Aggregation across local markets, Marketing Science, vol. 29, 5, p.944-957.

Dellarocas, C., 2006, Strategic Manipulation of internet opinion forums: Implications for

consumers and firms, Management Science, Vol. 52, 10, p. 1577-1593.

Dellarocas, C., Gao, G., Narayan, R., 2010, Are consumers more likely to contribute

online reviews for hit or niche products, Journal of management information systems,

vol. 27, 2, p.127-157.

Deloitte, New Deloitte study shows inflection point for consumer products industry;

companies must learn to compete in a more transparent age, Press release Deloitte, 2007,

http://www.prnewswire.com/news-releases/new-deloitte-study-shows-inflection-point-

for-consumer-products-industry-companies-must-learn-to-compete-in-a-more-

transparent-age-58235327.html, accessed 28-05-2011.

Duan, W., Gu. B., Whinston, A.B., 2008, The dynamics of online word-of-mouth and

product sales – an empirical investigation of the movie industry, Journal of Retailing,

vol. 84, 2, p. 233-242.

Hair et al., 2009, Statistics II for IB&M Course number EBB682A05 – University of

Groningen , Pearson Custom Publishing.

Harmon, A., Amazon glitch unmasks war of reviewers, New York Times, February 2004,

http://www.nytimes.com/2004/02/14/us/amazon-glitch-unmasks-war-of-

reviewers.html?pagewanted=2&src=pm, accessed 07-06-2011.

- 26 -

Hennig-Thureau, T., Gwinner, K.P., Walsh, G., and Gremler, D.D., 2004, Electronic

word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate

themselves on the internet?, Journal of interactive marketing, vol. 18, 1, p. 38-52.

Huang, P., Lurie, N.H., Mitrs, S., 2009, Searching for experience on the web: An

empirical examination of consumer behavior for search and experience goods, Journal of

Marketing, vol. 73, p. 55-69.

Hu, N., Liu, L., Sambamurthy, V., 2011, Fraud detection in online consumer reviews,

Decision support systems, vol. 50, 3, p. 614-626.

Hu, N., Pavlou, P.A., and Zhang, J., 2006, Can online reviews reveal a product‟s true

quality? Empirical findings and analytical modeling of online word of mouth

communication., Proceedings of the 7th

ACM Conference on Electronic Commerce, p.

324-330.

Hu, N., Pavlou, P.A., and Zhang, J., 2009, Overcoming the J-shaped distribution of

product reviews, Communications of the ACM, vol 52, 10, p. 144-147.

Kapoor, G., and Piramuthu, S., 2009, Sequential Bias in online product reviews, Journal

of Organizational computing and Electronic Commerce, vol. 19, 2, p.85-95.

Li, X., and Hitt, L.M., 2010, Price effects in online product reviews: An analytical model

and empirical analysis, MIS Quarterly¸ vol. 34, 4, p.809-831.

Li, X., Hitt, L.M. and Zhang, X., 2011, Product reviews and competition in markets for

repeat purchase products, Journal of management Information systems, vol. 27, 4, p. 9-

41.

Liu, Y., 2006, Word of mouth for movies; Its dynamics and impact on box office revenue,

Journal of marketing, vol. 70, 6, p.74-89.

Mudambi, S.M., Schuff, D., 2010, What Makes a Helpful Online Review? A Study Of

Customer Reviews on Amazon.com, MIS Quarterly, vol. 34, 1, p.185-200.

Poston, R.S., Speier, C., 2005, Effective use of knowledge management systems: A

process model of content ratings and credibility indicators, MIS Quarterly, vol. 29, 2, p.

221-244.

Ratchford, B.T., Lee, M., Talukdar, D., 2003, The impact of the internet on information

search for automobiles, Journal of marketing research, vol. 40, 2, p.193-209.

Zhu, F., and Zhang, X., 2010, Impact of Online Consumer Reviews on Sales; The

Moderating Role of Product and Consumer Characteristics, Journal of Marketing, vol.

74, p. 133-148.

Word count: 5647

- 27 -

APPENDIX A

TABLE 1

Measurement of reliability – Internal Correlation

Variable Items Cronbach

Alpha

(Definitely wouldn’t buy – Definitely would buy)

Positive Group PPRE .859

PNPRE

PPRA

PNPRA

Negative Group NPRE .512

NNPRE

NPRA

NNPRA

Overall All .627

Online rankings and reviews; an influence on consumers’ decision making.

Documents

Transcript of Online rankings and reviews; an influence on consumers’ decision making.