THE INFLUENCE OF THE NUMBER OF OPINIONS ON THE

ATTRACTIVENESS OF RETAILERS OFFER IN ONLINE

SHOPPING

Tomasz Wanat

The Poznan University of Economics

Niepodleglosci St. 10, 61-875 Poznan, Poland

Aleksandra Grzesiuk

The West Pomeranian Business School

Zolnierska St. 53, 71-210 Szczecin, Poland

Keywords: Online shopping, Perceived risk, Retailer reputation, e-Store choice.

Abstract: The research investigates consumers’ online shopping behaviour. The Authors suggest that due to high

perceived risk of online shopping consumers are prompted to use cues of shoppers’ reputation. One of such

cues can be a number of opinions expressed by the previous customers. In the series of two experiments a

number of other consumers’ opinions has been manipulated to influence choice of product supplier. The

results suggest that other consumers’ opinions could be used as a shoppers’ quality cue. The subjects didn’t

choose the cheapest version of product but they made a trade-off between price and number of opinions.

1 PERCEIVED RISK

Risk is perceived to be a factor in most purchase

decisions. In buying process, risk emerges from any

of the following factors: 1/ uncertainty as to buying

goals, 2/ which of several purchases (product, brand,

model, etc.) best matches the buying goals, 3/

possible adverse consequences if the purchase is

made (or not made) (Cox, 1967). The concept of

perceived risk often used by researchers defines risk

in terms of the consumer’s perception of the

uncertainty and adverse consequences of buying a

product (or service) (Dowling and Staelin, 1994).

In generally, perceived risk is defined as

comprising the following components: financial,

psychological, performance, time, social and time-

related risk (Stone and Gronhaug, 1993). Consumers

are credited with the capacity to receive and handle

considerable quantities of information and undertake

extensive pre-purchase searches and evaluation.

Financial risk is the perception that a certain

amount of money may be lost or required to make a

product work properly. Performance risk is the

perception that a product purchased may fail to

function as originally expected. Time risk is the

perception that time, convenience, or effort may be

wasted when a product purchased is repaired or

replaced. Psychological risk is the perception that a

negative effect on a consumer’s peace of mind may

be caused by a defective product. And finally, social

risk is refers to the perception that a product

purchased may result in disapproval by family or

friends.

However the majority of research on perceived

risk has been focused on the choice of product. The

product class has been found to contribute to the risk

inherent in the purchase decision (Dowling and

Staelin, 1994). Therefore two product groups could

be distinguished: class risk (the riskiness of buying

an average product) and specific product risk

(determined by the particular product under

consideration and other factors such as the purchase

situation). There are also some empirical studies

explicitly addressing perceived risk and the selection

of retailers. Dash, Schiffman and Berenson observe

differences in the level of perceived risk between

specialty store and department store shoppers (Dash,

Schiffman and Berenson, 1976), while Mattson

202

Wanat T. and Grzesiuk A. (2009).

THE INFLUENCE OF THE NUMBER OF OPINIONS ON THE ATTRACTIVENESS OF RETAILERS OFFER IN ONLINE SHOPPING.

In Proceedings of the International Conference on e-Business, pages 202-208

DOI: 10.5220/0002186602020208

 SciTePress

(1982) shows that the selection of store type varies

with social and financial risk inherent in the

purchase situation. In the literature on e-commerce,

the risk associated with online shopping in general

(Garbarino and Strahilevitz, 2004) and with specific

retailer (Jarvenpaa, Tractinsky and Vitale, 2000) has

been found to be adversely related to intention to

buy. Biswas and Biswas (2004) demonstrate that

signals such as reputation matter more in e-

commerce because of higher risks associated with

buying online.

1.1 Perceived Risk in Online Shopping

Risk perception regarding the Internet is identified

as a primary obstacle to the future growth of e-

commerce and is one of the main predictors of

consumers’ decisions to shop online or a

conventional store. To understand online customer

behaviour the nature of perceived risk in online

shopping should be taken into account and studies

should be focused on prevalent motivators and

drives of customers once they enter online

environment. Noort, Kerkhof and Fennis (2007)

argued that because of the inherent risk on online

shopping, avoiding losses rather than achieving

gains may become the consumers prime goal.

Motivational factors are important in decision

making process. Higgins’ regulatory focus theory

states that a different psychological system operates

when the goal is loss-avoidance rather than

achieving gains (Higgins, 1997). Regulatory focus

theory posits two separate and independent self-

regulatory orientations: prevention and promotion. A

prevention focus emphasizes safety, responsibility,

and security needs. Goals are viewed as oughts and

there is a strategic concern with approaching non-

losses (the absence of negatives) and avoiding losses

(the presence of negatives). A promotion focus

emphasizes hopes, accomplishments, and

advancement needs. Goals are viewed as ideals, and

there is a strategic concern with approaching gains

(the presence of positives) and avoiding non-gains

(the absence of positives). Accordingly, we expect

online shopping (vs. conventional), by its risky

nature, to induce a prevention-focus self-regulation

among customers.

In spite of the wide advantages of online

shopping (e.g. vast selection, screening, product

comparison, comfortable shopping, time saving

shopping), there are also numerous factors that still

make consumers uncertain about online shopping.

The Internet, just like any type of non-store

shopping, makes it difficult to examine physical

goods; consumers must rely upon somewhat limited

information and pictures shown on the computer

screen (Jarvenpaa and Tractinsky, 1999). Moreover,

there is bound to be much uncertainty regarding

security, reliability, standards and some

communication protocols (Turban, 1999).

To identify perceived risk in e-commerce, Tan

(1999) examined costs and benefits of in-store

versus online shopping. The results showed that

perceived risk is higher when purchasing products

through the Internet than when purchasing by in-

store means. With the respect to specific types of

perceived risk in online shopping, Bhatnagar (2000)

emphasized two types of risk in online shopping:

product category risk and financial risk. Product

category risk is associated with performance risk,

which refers to a negative perception about quality

of a product. Therefore the risk is higher when the

product is technologically complex or the price is

high. Financial risk includes both tangible and

intangible assets of consumers. That means

consumers are quite apprehensive, not only losing

certain amounts of money, but also about losing

private information required in the transaction

(Szymanski and Hise, 2000).

1.2 Retailer Reputation as a Selection

Factor

Word-of mouth communication plays an important

role in reducing consumer risk perception of product

performance to a greater extent that any other

information sources in e-commerce (Tan, 1999).

With respect to reducing consumer perceived risk

and uncertainty, word-of-mouth is more relied on by

consumers than any other information, because it is

based on consumer experience and is especially

vivid. Vividity (as opposed to colourlessly)

presented information tends to have a stronger

influence on product judgement and risk reduction.

Consequently, marketers must participate actively in

creating positive word-of-mouth, because

dissatisfied customers will disseminate news of their

bad experiences with the retailer (Harrison-Walker,

2001). That is, dissatisfied customers participate in

negative word-of-mouth communication, and this

means that new and existing customers become

aware of a perceived risk or uncertainty for future

purchase opportunities. Thus, marketers must

effectively maintain and develop their website

communities, forum, and feedbacks sites in order to

retain satisfied customers and reduce their perceive

risk.

THE INFLUENCE OF THE NUMBER OF OPINIONS ON THE ATTRACTIVENESS OF RETAILERS OFFER IN

ONLINE SHOPPING

203

On the Web, there are concerns about seller

credibility due to the availability of an wide number

of retailers, which is partially due to the perceived

low entry and setup costs for sellers on the Web than

in the conventional economy. As a result, it is

perceived that almost anyone can set up a retail

presence on the Internet at a very low cost. It is also

difficult for consumers to distinguish a high-image

store from a low-image store on the Web, just from

appearance. On the other hand, conventional stores

would have to undergo high monetary expenses to

open up a high-image store in terms of location,

appearance and layout. Therefore, it becomes

difficult for the consumers to distinguish between

“fly-by-night operators” and regular “honest” sellers

in the online markets. (Biswas, Biswas, 2004).

As the result of the higher level of uncertainty

associated with online shopping, customers’ actions

towards collecting information on online retailer

reputation would be stronger than in conventional

retailing.

1.3 Quality Cues

The marketing literature has brought numerous

studies on quality signals and cues used by

customers. The most frequently analyzed cues are

price (Rao, 2005), brand equity (Erdem and Swait,

1998), retailer reputation (Biswas and Biswas, 2004;

Dawar and Parker, 1994), product ingredients

(Richardson, Dick and Jain, 1994), warranties or

guarantees (Purohit and Srivastava, 2001).

Researchers have shown that the most important

cues are brand, then price and retailer reputation.

The total effect of different cues has not been the

sum of their singly effects. This is because the role

of particular cues (excluding brand) have been

abating in multi-cue situation (Rao and Monroe,

1989). There have been some interactions between

cues as well. While the respondents have been

presented expensive tires, the products have been

evaluated positively or negatively depending on

additional information on the manufacturer

reputation (Miyazaki, Grewal and Goodstein, 2005).

Analogically to product evaluation, customers have

been used cues to evaluate unknown quality

(credibility, reliability) of retailer. The number and

character of customers opinions about the retailer

have been likely to play the role of the cues in online

context. If the customer has not acquire other

possibilities to evaluate retailer reputation (retailer

quality), the number of opinions have been of great

consequence.

2 ONLINE SHOPPING IN

POLAND

The e-commerce market in Poland is full of

potential. It is growing faster than Western European

markets and is overtaking Southern European

countries such as Spain and Italy. The value of

Polish e-commerce was estimated to be ca. €2.4

billion online in 2008. However the market value is

expected to grow quickly as Polish consumers

become wealthier. Polish online consumers use the

Internet to gather information about products and

services but still head to a high-street store for the

actual purchase. It's essential that e-retailers in

Poland find ways to turn these "lookers" into

"buyers”.

There is a lot of different research and studies on

attitudes of Polish customers towards online

shopping. Most of them is focused on the barriers of

growing interest of online shopping as well as

disadvantages that Polish customers perceive while

shopping online.

As The National Online Shopping Test showed

Polish customers preferred most attractive offers

from price perspective (The National Online

Shopping Test, 2008). There is the basic argument

for great popularity of online auctions. Over 61% of

the respondents declared to spend money on online

auctions compared to 34% of respondents shopping

in online stores.

In value terms, 60% of e-commerce market is

generated by online auction platforms and 40% of

the market by online shops (Digital Landscape

Report, 2008)

As D-Link Technology Trend research has

revealed, Polish online customers have becoming

more and more interested in the opinions of other

customers published on the Web (e.g. feedback

opinions, customer’s comments on retailer’s

reputation). In 2008 over 77% of online shoppers

was looking for other customers’ opinions compared

to 69% in 2007. The research showed, that there was

a slight decrease in the customers interests to use

price comparisons (53% respondents compared to

77% in 2007) (D-Link Technology Trend, 2008).

The mentioned facts facilitate introductory

research hypotheses that the number and quality of

other customers’ opinion on online seller’s

reputation are more important for Polish online

customers than price level.

The hypotheses could be supported by Polish

customers’ preferences towards terms of payment in

online shopping. According to D-Link Technology

Trend Report, most customers prefer bank transfer

ICE-B 2009 - International Conference on E-business

204

(66%) or cash on delivery (48%). The other

payments (credit cards, special cards for Internet

payments) are used occasionally by 1-2% of

respondents (D-Link Technology Trend, 2008).

According to Digital Landscape Report, 70% of

payments is on cash on delivery basis, 23% -

payment card and 7% - bank transfer. The

mentioned Polish customers’ preferences towards

online payment methods are significantly different

from other countries. In general, 70% of world

online shopping payments is on payment cards basis

and only 5% - cash on delivery ((Digital Landscape

Report, 2008). Poles rarely used payment or credit

cards because they are concerned about security or

they do not have a card allowing them to pay other

the Internet. Polish customers prefer cash on

delivery to secure the transaction, to avoid possible

adverse consequences and to protect themselves

against dishonest online retailer.

3 RESEARCH HYPOTHESES

Regarding previous notes on perceived risk in online

shopping it could be stated that online customers

would take strong actions towards collecting

information on online retailer reputation.

Concerning customers’ buying process, information

on the reputation of online retailer would be as

important as other elements of retailer’s offer (e.g.

price, product). Customers will rely upon other

consumers' opinions and experiences to make

decisions about which merchants (online shop) are

reliable and trustworthy to do business with.

Customers could use the number of opinions as

quality index and trade-off between quality of store

and price level. Here, the customers’ opinions would

play a role of quality cue for online retailer. Based

on the preceding research, this study presents the

following hypothesis:

H1: The higher number of opinions on online

retailers (in comparison with other online shops) the

higher the perceived online shop’s attractiveness.

H2: The lower the price differences the higher

the perceived online shop’s attractiveness.

The number of customers’ opinions published on

the Web would be a critical determinant of purchase

intention. Customers would be influenced by the

number of opinions and by its character (positive or

negative). Customers would be influenced by both

positive and negative opinions. Although it seems

that a number of negative opinions should exercise

more impact on consumers’ decisions comparing to

a number of positive opinions. People willingness to

give greater weight to negative entities (events,

objects) is well recognized in the literature

(Kahneman, Tversky, 1979; Rozin, Royzmann,

2001).

H3: Negative opinions have a greater influence

on perceived online shop’s attractiveness in

comparison to the total number of opinions.

4 STUDY 1

4.1 Design and Procedure

In order to verify the research hypotheses, the study

has been divided into two sub-research: The Study 1

and The Study 2. The empirical study was conducted

through laboratory experiment in January-February

2009.

One admitted shortcoming was that the

collection or participation rate was lower than it

might have been using other methodologies.

However the research method was advantageous in

that it offered preliminary data for future research

projects at reasonable cost.

For The Study 1, they were 59 participating

respondents who were students of one of the top

university of economics in Poland. Two

questionnaires were excluded due to missing data.

The hypotheses were tasted using a 2 (price

differences: high vs. low) x 2 (number of customers’

opinions: high vs. low) between-subjects design.

All the participants were provided with a

scenario. The scenario explained that the participants

should select and evaluate online shops while

purchasing a relatively expensive cellular phone.

The product itself (cellular phone) has not been the

object of customers’ choice and it was stated in

advance of the research. The participants has

received description of seven online shops similar to

those known from price comparison Web sites.

It has included shop’s number (instead if the

shop’s name), the number of other customers’

opinions, the product name (exactly the same for

each shop) and the product price (in increasing

order).

The participants has been asked to answer the

questions in which online shop would they do

shopping and to evaluate the attractiveness of online

shop No 3.

Two independent variables, price difference and

the number of opinions, were manipulated to change

the potential attractiveness of online shop No 3. The

lower, in comparison to the higher, price difference,

was manipulated through reducing the price in

THE INFLUENCE OF THE NUMBER OF OPINIONS ON THE ATTRACTIVENESS OF RETAILERS OFFER IN

ONLINE SHOPPING

205

online shop No 1 from 719 PLN to 689 PLN. The

price level in online shop No 3 was 724 PLN and it

was constant. The higher, in comparison to the lower

number of opinions was manipulated through

changing the number of opinion on shop No 3 from

974 opinions to 94 opinions. The number of

opinions in other shops was constant and presented

as 22 opinions for the shop No 1 and 2763 opinions

for the shop No 5.

The dependent variable has been the

attractiveness of online shop No 3. It has been

presented in point scale from 0 to 100 points (100 -

the most attractive). The additional dependent

variable has constituted the choice of the most

attractive online shop.

4.2 Research Results of Study 1

Supporting the Hypothesis 1 and Hypothesis 2,

analysis of variance (ANOVA) with the

attractiveness of online shop No 3 as a dependent

variable revealed significant main effects for both

price difference, F (1,53)=10.09, p<.001, and the

number of opinions on online shop F(1,53)=5.16,

p<.05. The respondents perceived online shop No 3

as more attractive in comparison to online shop No 1

while price difference was lower (M=78.8) than

while price difference was higher (M=66.1).

Simultaneously, respondents perceived online shop

No 3 as more attractive while the number of

opinions from other customers was high (M=76.6)

then while the number of opinions from other

customers was lower (M=68.1). The findings

support the general research hypothesis that the

customers pay attention to both the price of the

product and the number of opinions on online

retailer.

The selection of online shop as a second

dependent variable revealed much stronger influence

of customers’ opinions on the selection of online

shop. The respondents decided on the online shop

No 5 (58% of respondents) which was characterized

by relatively high price level and simultaneously

with distinctly higher number of customers’

opinions in comparison to other online shops.

Online shop No 3 was selected by 26% of

respondents whereas the cheapest online shop by 9%

of respondents. It allows to make a conclusion that

the price did not play an important role while

selection of online retailer.

The observed relatively low importance of price

factor while selecting online retailer (shop) seems to

be somehow surprising. Polish customers are

perceived to be relatively price sensitive. It should

result in higher share of online shop No 1 in

customers’ decision making process. But the

research revealed a different results. The Authors

suppose that it is the result of the research structure.

The research experimental plan did not force the

respondents to spend their own money. It is easier

then to accept a higher price level. However the

mentioned comments did not shake the research

hypothesis that in decision-making process in online

retailing, the important decision-making factor is the

number of opinions about online shop as well as the

price. The number of customers’ opinions performs

a role of quality signal of online shop.

The number of opinions itself does not reveal

their the character: positive or negative. As the

review of customers’ opinions on several Web sites

shows the published opinions are generally positive

and schematic. Negative opinions appear just

occasionally. But the existence of negative opinions,

even very few, should affect customer’s selection of

online shop. That was the point of interest for The

Study 2.

5 STUDY 2

5.1 Design and Procedure

The Study 2 was similar to The Study 1. The way of

information presentation was similar but the number

of negative customer opinions about online shop was

attached. Table 1 presents the information delivered

to respondents.

Table 1: Stimuli used in the Study 2.

Shop

number

Number of

consumers’

opinions

Number of

negative

consumers’

opinions

Price

172 1 699

723.9

864* 5* 724

33 0 769

2763

769

659 4 794

811

*The number was manipulated

A total of 66 students participated in the Study 2

and three questionnaires were excluded due to the

lack of answers.

The hypotheses were tasted through 2 (the

number of customers’ opinions: high vs. low) x 2

(number of negative customers’ opinions: high vs.

low) between-subjects design.

ICE-B 2009 - International Conference on E-business

206

Leaving out of account online shop No 3, the

relation of total number of opinions to the number of

negative opinions was as 172 to 1. It corresponds to

5 negative opinions out of total 864 and 3 negative

opinions out of total 518. The used numbers of

customers opinions were relevant; the reduction of

total number of opinions by 40% (from 864 to 518)

resulted in the reduction of total number of negative

opinions by 40% (from 5 to 3). If it is supposed that

the total number of negative opinions deeply

affected purchase intention then the general number

of customers’ opinions, online shop No 3 should

have better customers’ evaluation while having 518

customers’ opinions including 3 negative than while

having 864 opinions including 5 negative. In both

situations the relation of total number of customers’

opinions to the number of negative opinions was at

the same level.

5.2 Research Results of Study 2

For the Study 2, both the manipulation of the total

number of opinions and the number of negative

opinions did not bring satisfactory outcomes (F<1).

The respondents gave a higher evaluation to online

shop with total number of 518 customers’ opinions

and three negative opinions (M=83.6) than to shop

with total number of 864 customers’ opinions and

five negative opinions (M=76.7), the difference

between two options was not statistically valid

(t(31)=1.44, ns.).

The interesting findings were observed between

The Study No 1 and 2 in the context of the general

customers’ preferences towards online shop. As the

Study 2 showed, online shop No 1 was preferred by

48% of respondents, shop No 3 – 22% and shop No

5 – 20% of respondents.

The results and outcomes of The Study 2 are not

statistically valid. However some interesting

comments could be formulated on the above

findings. It implies that respondents were influenced

by the number of negative opinions rather than the

relation of the total number of customers’ opinions

to the number of negative opinions. Hence the online

shops with the lower number of negative opinions

were preferred.

There were no introductory assumption that the

lower number of negative opinions the better. The

assumption could be formulated provided suitably

high number of total customers’ opinions.

Therefore there is a nonlinear relation in

applying the number of opinions as quality signal for

online shop. It is necessary to cross the minimal

threshold of opinions for online shop to be

acceptable. Below the minimal threshold, the shop is

not acceptable. Above the minimal threshold, the

total number of opinions is of relatively low

importance. As The Study 1 has indicated, the

number of opinions levelled at 22 has not executed

the minimal level. Hence the low scores for shop 1

has been observed. For the Study 2, the number of

172 opinions for the shop 1 has executed the

minimal level and therefore more customers has

selected that online shop.

The general deduction derived fro the studies is

that customers are interested in shopping with the

lowest possible price but while minimal safety

conditions are fulfilled. The factors reducing

perceived risk are important in decision making

process and online shop selection.

6 CONCLUSIONS

The goal of the study reported here was to examine

the relation between the number of customers’

opinions on online shop and the evaluation criteria

of online shops’ decision-making process where to

shop. With respect to research outcomes, the number

of customers’ opinions could play an advisory role

in evaluation of online shop attractiveness.

However the results did not authorise the statement

that there is a linear correlation between the number

of opinions and perceived shop’s attractiveness. It

could be presumed that there is a nonlinear

correlation. There is a minimal level of opinions and

if the number cross the minimal threshold, the online

shop becomes acceptable. Further research should

focus on defining the minimal threshold. Has it got

an absolute character or it depends on the number of

opinions for other online shops included into

consideration set?

The research indicated that respondents did not

include the relation of the number of negative

opinions to the total number of opinion while

evaluation shop attractiveness. It could not be

excluded that other order rules or heuristic methods

are implemented. The future research could be

designed to examine and comment on those

procedures.

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