online rankings and reviews; an influence on consumers’ decision making
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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
albert@abnpranger.nl
student 1629980
Supervisor: M.C. Non
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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
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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
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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
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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.
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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
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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).
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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
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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)
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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).
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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
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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.
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H3; The relation between positive valence and one‟s buying decision is influenced by
whether the source is a professional or not.
H4; The relation between positive valence and one‟s buying decision is influenced by
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.
H6; The relation between positive valence and one‟s buying decision is influenced by the
readers age.
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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
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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.
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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.
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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
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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.
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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.
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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.
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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.
H2; The relation between positive valence and one‟s buying decision is influenced by
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.
H3; The relation between positive valence and one‟s buying decision is influenced by
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.
H4; The relation between positive valence and one‟s buying decision is influenced by
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.
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H5; The relation between positive valence and one‟s buying decision is influenced by the
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.
H6; The relation between positive valence and one‟s buying decision is influenced by the
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.
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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.
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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.
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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
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