An Investigation of Factors and Measurements for Successful

e-Commerce Websites

Xiaosong Li, Wei Lian and Abdolhossein Sarrafzadeh

Department of Computing & Information Technology, Unitec Institute of Technology, Auckland, New Zealand

Keywords: e-Commerce, Website, Factor, Dimension, Measurement, Success.

Abstract: Business-to-Consumer (B2C) e-commerce is popular because of its convenience, speed and price. Although

there has been intense debate about quality dimensions of e-commerce websites, more research is needed to

find a well-established measurement. This empirical study identifies a set of measurements with 10 factors

and their corresponding dimensions, including software development attributes based on the literature and

the qualitative and quantitative data gathered from four different stakeholders. The survey results suggest

that security, smooth transaction processes and smooth shopping processes are the most important concerns

for online shoppers. The IS success model checking suggests that the proposed measurements are

comprehensive. This work is compared with the customized ISO 9126 quality model.

1 INTRODUCTION

Business-to-Consumer (B2C) e-commerce is

popular because of its convenience, speed and price

(Luo et al., 2008). Although there has been debate

about quality dimensions of e-commerce websites,

however, they are not well-established, and more

research is needed to find a measurement of e-

commerce website success (Balfagih et al., 2010).

The existing relevant works contain either a

broad framework which does not include specific

and detailed information for more accurate, effective

and practical evaluation of e-commerce websites, or

the measurements focus only on certain aspects of

the e-commerce websites, or the measurements are

applicable to a general information system not

specific for e-commerce websites. The existing

DeLone & McLean IS Success Model was updated

and extended by introducing new metrics for e-

commerce system success measurement (DeLone &

McLean, 2003). This framework was modified and

extended further to be more specific on measuring e-

commerce website success (DeLone & McLean,

2004). When these frameworks are applied to a

specific website, further interpretations or

refinements are required. An empirical study was

conducted to develop a set of instruments of B2C e-

commerce website quality (Cao et al., 2005). The

instruments covered some aspects of service quality,

such as responsiveness, information quality, and

information accuracy. However, they omitted other

aspects, such as page design and shopping process.

The 7C framework was developed (Rayport &

Jaworski, 2001) to evaluate e-commerce websites;

however, it emphasizes the specific role of interface

elements as a communication channel between

retailers and their customers. The 7Cs evaluates an

e-commerce website more from a user interface

design perspective than information quality and

usability perspectives. The 8C framework was

developed based on the 7Cs to cover the Web 2.0

features (Yang et al., 2008). However, the scope of

7Cs was extended for generic web application, and

the dimensions were interpreted more from

technique implementation perspectives. A B2B e-

commerce web application evaluation model was

proposed in 2009 (Behkamal et al., 2009), in which

a traditional software quality model, ISO 9126, was

customised by identifying the B2B quality

characteristics and adding the necessary attributes.

The customisation was carried out in five steps. A

numeric value was assigned to each of the quality

characteristics from both the B2B application users’

and developers’ viewpoints. So the result

emphasizes the user and development issues.

The long-term success is determined by their

ability to generate positive net revenues (DeLone &

McLean, 2004). Attracting Internet users,

encouraging customer loyalty and improving service

are aiming to increase profits ultimately (Bezes,

354

Li X., Lian W. and Sarrafzadeh A..

An Investigation of Factors and Measurements for Successful e-Commerce Websites.

DOI: 10.5220/0004933903540360

In Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST-2014), pages 354-360

ISBN: 978-989-758-023-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2009). These suggest that the ultimate measurement

for e-commerce website success is profit. To help

the business owners improve the quality of their e-

commerce websites, there is a need to identify the

factors that critically influence the quality of the

websites and the measurements which can reflect the

profit closely.

This empirical study focuses on B2C e-

commerce websites that support shopping

functionalities. It aims to find a set of

comprehensive, specific and objective measurements

based on the information possibly obtained from

different sources for successful e-commerce

websites. It uses the existing research findings as a

starting point and captures the critical factors that

contribute to the success of an e-commerce website

from design, content, shopping process, security,

management and technique perspectives. Ten

important factors and their dimensions were

identified initially (Lian, 2012). To verify and refine

these factors, data were collected from an online

survey of e-commerce website users, interviews of

relevant professionals and online tool testing. The

data were interpreted and analyzed with the help of

two simple quantitative instruments: mean and

standard deviation; and the factors as well as their

dimensions were refined. The refined factors were

examined against the six dimensions of the updated

DeLone and McLean (2004) information systems

success model. The refined factors were also

examined against the conceptual model proposed by

Ghandour group (Ghandour et al., 2008).

In the following sections, the initial

measurements are described first, that is, followed

by the data analysis and the measurement

refinement, the proposed measurements are

validated, and finally the result is discussed and the

paper is summarized.

2 INITIAL FACTORS

On the basis of a study of the existing publications,

this research identifies 10 factors that may contribute

to the success of e-commerce websites. The detailed

dimensions for each factor were also identified at the

same time. Following are the 10 factors, their

dimensions, their literature sources and their codes

which will be referred to in the rest of this paper:

 Factor 1. Site Popularity (F1) (Ghandour et

al., 2008): Keeping high daily reach (F1D1),

A large number of unique users (F1D2), A

larger number of visitors (F1D3);

 Factor 2. Website Profit (F2) (Carney, 2005;

Barnes, 2002);

 Factor 3. Usability and Performance (F3)

(Haenlein, 2004; Kuballa, 2007; May, 2000;

Chen, 2005): Speed of site load (F3D1),

Website consistence for the leading browsers

(F3D2), The home page is well-organized and

easy to read and understand (F3D3); Logical

and clear navigation choices with consistence

and availability (F3D4); Easy to use

navigation (F3D5); Easy to use search boxes

(F3D6); Functionality (F3D7); Error message

resolution (F3D8);

 Factor 4. Security and Privacy (F4) (Gehling

& Stankard, 2005): Escrow service (F4D1);

Personal information privacy (F4D2); Secure

server (F4D3); Secure payment mechanism

(F4D4); Antivirus software (F4D5); Firewall

(F4D6); Authentication (F4D7);

 Factor 5. Website Design (F5) (Lau, 2007;

Blanchard & Fabrycky, 1990): Use of

animated gifts and ticker tapes (F5D1); A

balance between design and functionality

(F5D2); Page fitness for scrolling entire page

(F5D3); Simple and logical web layout

(F5D4); Use of colors (F5D5); Expression of

company image (F5D6); Easy to understand

product categories (F5D7); Appropriate

typefaces and font sizes (F5D8); Ease in

finding checkout button (F5D9); Shopping

cart location (F5D10);

 Factor 6. Website Content (F6) (Leea &

Kozar, 2006): Quality information (F6D1);

Good service (F6D2); Easy to understand

(F6D3); Catalog information organized

logically (F6D4);

 Factor 7. Transaction Process and Volume

(F7) (John, 2004): The site supports secure

credit card transactions (F7D1); Different

payment methods available (F7D2); User-

friendly rules and policy for sellers and buyers

(F7D3); Good trade volumes (F7D4);

 Factor 8. Shopping Process (F8)

(Spiekermann et al., 2001): Reliable delivery

process (F8D1); Easy to obtain refunds

(F8D2); Easy to use the shopping cart (F8D3);

Easy to add or delete items from the shopping

cart (F8D4); Easy to view the contents of the

shopping cart (F8D5); An obvious click path

to the cash register (F8D6); Accessible

product inventory and availability of

information (F8D7); Customer

rating/recording system (F8D8);

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 Factor 9. Technical Support (F9) (Citera &

Beauregard, 2005): Availability of site map

(F9D1); Customer online help (F9D2);

Discussion board (F9D3); User guide (F9D4);

FAQs (F9D5); Contact details (F9D6);

 Factor 10. System Development (F10) (Anil

et al., 2003).

3 THE DATA AND ANALYSIS

Customer experience could influence customers'

perceptions of value and service quality, therefore,

affecting customer loyalty. This is critical for the

success of an e-commerce website. An online survey

(Lian, 2012) was used to solicit customers’ opinions

on most of the proposed factors, except F2 and F10,

which are more appropriate for professionals’

opinions. 52 e-commerce website users from a local

technology institute participated in the survey, of

which the majority are aged between 18 and 30

(Lian, 2012). These young tertiary students are

active on the internet and have good computer skills.

The results of the survey may reflect this situation.

Eight selected professionals participated in the

interviews. The chosen participants needed to

understand and have an interest in the research area,

as well as have certain knowledge of e-commerce

websites. In addition, the participants needed to have

the ability and insight to perceive critical success

factors for e-commerce websites with a technical

and business focus. Two academic researchers, three

business managers and three e-commerce website

developers were interviewed. The authors believe

that they possessed the required characteristics and

experiences.

3.1 Online Survey Data

The participants were asked to choose which

particular factors are critical for e-commerce website

success and how they weight each of the dimensions

of a particular factor (Lian, 2012). The questions

were presented using a five-point Likert-type scale,

5 being the most desirable, and 1 being the least. A

dimension score is the average of all the scores for

that dimension from all the participants. A factor

score is the average of all the scores for that factor

from all the participants. Table 1 is a summary of the

results.

The standard deviation of the dimension scores

for a factor was calculated to check the diversity of

the dimensions in the participants’ opinions. A small

standard deviation may indicate that the dimensions

Table 1: Online survey results.

Factors Dimensions

Dimension

Score

STDEV

Factor

Score

F1D1 3.79

0.093 4.21 F1D2 3.84

F1D3 3.97

F3D1 4.03

0.139 4.22

F3D2 3.94

F3D3 4.14

F3D4 4.09

F3D5 4.14

F3D6 4.39

F3D7 4.14

F3D8 3.97

F4D1 3.69

0.359 4.34

F4D2 4.29

F4D3 4.26

F4D4 4.54

F4D5 4.23

F4D6 4.09

F4D7 4.37

F5D1 3.00

0.359 4.22

F5D2 4.00

F5D3 3.57

F5D4 3.91

F5D5 3.56

F5D6 3.43

F5D7 4.17

F5D8 3.71

F5D9 4.00

F5D10 3.78

F6D1 3.97

0.075 4.29

F6D2 4.11

F6D3 3.97

F6D4 4.09

F7D1 4.20

0.109 4.32

F7D2 4.20

F7D3 4.14

F7D4 3.97

F8D1 4.09

0.135 4.29

F8D2 4.40

F8D3 4.00

F8D4 4.23

F8D5 4.14

F8D6 4.11

F8D7 4.31

F8D8 4.06

F9D1 3.44

0.278 4.17

F9D2 3.85

F9D3 3.56

F9D4 3.56

F9D5 3.71

F9D6 4.21

proposed for that particular factor are highly

correlated; a large one may indicate that certain

dimensions are inappropriate.

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The survey results show that F4 and F7 got the

highest factor scores of 4.34 and 4.32, respectively.

This suggests that security and smooth transaction

processes are the two most important concerns for

online shoppers.

System security becomes a more significant

system-quality issue as e-commerce is typically

conducted over the Internet (DeLone & McLean.

2004). Good protection on confidential information

should help to gain the consumer's trust

(Yazdanifard et al., 2011). An e-commerce website

must provide a secure user interface system and an

easy-to-use transaction process. F9 got the lowest

score of 4.17, suggesting that customers prefer a

convenient and easy-to-use shopping system than

using a help system. This may also reflect that the

survey samples are highly computing skilled. F8

scored 4.29 which is higher than F5 at 4.22. This

suggests that the shopping process and functionality

is more important to the customers than the site’s

visual appearance.

Table 2: The difference between the dimension average of

a factor and its factor score.

Factors

Dimension

Average

Factor

Score

Difference

F1 3.87 4.21 0.34

F3 4.11 4.22 0.11

F4 4.21 4.34 0.13

F5 3.71 4.22 0.51

F6 4.04 4.29 0.25

F7 4.13 4.32 0.19

F8 4.17 4.29 0.12

F9 3.72 4.17 0.45

The scores of all the dimensions of a particular

factor were averaged to compare with that factor’s

score to gauge the validity of the dimensions for that

particular factor. Table 2 shows the dimension

averages and the differences between the dimension

average of a factor and its factor score. A large

difference may indicate the omission of certain

dimensions for that factor. For example, the largest

difference between the two scores is 0.51 for F5,

which suggests that the identified dimensions for

this factor may not be suitable or complete.

Customization defined in the 7Cs (Rayport &

Jaworski, 2001) was missed out in the dimensions of

F5. With customization, a user is provided with

functions that enable him/her to enter personal

details or customize the look of a banner. So, the

dimension “Be able to customize the look of the user

interface” with code F5D11 is added to F5. F3 got a

small average score difference of 0.11 and a

relatively small standard deviation of 0.139, which

suggests that the dimensions for this factor are well

defined. For example, “Search facility” was

highlighted in (Cao et al., 2005) and it was included

in F3D6. The difference for F9 is 0.45, which is the

second largest. There may be serious omissions for

this factor. Service quality can be measured by the

effectiveness of on-line support capabilities, such as

answers to frequently asked questions or

responsiveness in the e-commerce environment

(DeLone & McLean, 2004). This is confirmed by

(Cao et al., 2005), where responsiveness is a

construct with 5 items. So “Responsive to

customers’ inquiries” with code F9D7 is added to

F9. The difference for F6, 0.251, is large as well.

This suggests omissions. The small standard

deviation 0.075 suggests that the proposed

dimensions may only focus on certain aspects of this

factor. The dimensions for F6 show that some

popular dimensions like “use video demonstration”

(Cao et al., 2005) are missing, thus, it is added to

code F6D5, and, similarly, “use customer

testimonial” with F6D6 is added. For F7, the

average score of F7D4 is much lower than the

others, suggesting that F7D4 may not belong to this

factor. Removing F7D4 from the factor reduces the

difference to 0.14, which is more appropriate. The

name of F7 has been changed, therefore, to

Transaction Process.

3.2 Interview Data

Each group of professionals was given a slightly

different set of questions to reflect their expertise

(Lian, 2012). While they shared some opinions with

the online survey participants, there were a few

differences that need to be highlighted.

Only the professionals’ opinions were solicited

for F2 and F10. For F2, six out of eight professionals

thought that website profit is a success factor and no

meaningful comments were given. On the other

hand, Transaction Volume was fully supported with

strongly supportive comments, such as, “there must

be a problem if the trading volume is too small”,

“transaction volume and website success are

positively correlated”, and “transaction volume

reflects the commercial value of a website”. In many

cases, commercial transaction volume is positively

related to commercial profit. It is reasonable to add

“Good transaction volume” with code F2D1 as a

dimension of F2.

F10 received full support as well, with many

meaningful comments. The comments added

desirable attributes for e-commerce websites from a

software development perspective. Here is a

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summary of the comments, which define the

dimensions for this factor: System Requirements-

Reliability (F10D1); System Requirements-Usability

(F10D2); System Requirements-Data Integrity

(F10D3); Resources-Technologies and Tools

(F10D4); Resources-Network & Internet Access

(F10D5); Resources-Relevant Knowledge (F10D6);

Process-Back up (F10D7); Process-Documentation

(F10D8); Process-Methodology (F10D9).

3.3 Online Testing Data

The online testing tool Alexa was used to test 30 e-

commerce websites, including three very successful

sites, TaoBao, eBay and Amazon (Lian, 2012). The

results showed that TaoBao, eBay and Amazon got a

much higher Alexa Traffic Rank than the others.

They scored 15, 24 and 17, respectively, while the

highest rank for the others was 387. It is reasonable

to use Alexa Traffic Rank (Global) as a quantitative

measurement for e-commerce website success. The

average score difference for F1 is quite large, 0.34,

with a small standard deviation, 0.093. This suggests

omissions again. So “Traffic rank” with code F1D4

is added to F1. Table 3 is a summary of the revised

factors and their dimensions.

Table 3: Revised factors and their dimensions.

Factors Dimensions

F1 F1D1, F1D2, F1D3, F1D4

F2 F2D1

F3D1, F3D2, F3D3, F3D4, F3D5, F3D6,

F3D7, F3D8

F4D1, F4D2, F4D3, F4D4, F4D5, F4D6,

F4D7

F5D1, F5D2, F5D3, F5D4, F5D5, F5D6,

F5D7, F5D8, F5D9, F5D10, F5D11

F6 F6D1, F6D2, F6D3, F6D4, F6D5, F6D6

F7 F7D1, F7D2, F7D3

F8D1, F8D2, F8D3, F8D4, F8D5, F8D6,

F8D7, F8D8

F9D1, F9D2, F9D3, F9D4, F9D5, F9D6,

F9D7

F10

F10D1, F10D2, F10D3, F10D4, F10D5,

F10D6, F10D7, F10D8, F10D9

4 INFORMATION SYSTEM

SUCCESS MODEL

According to DeLone and McLean (2004), all of the

proposed measures for e-commerce website could be

classified under six dimensions. To validate the

proposed measurements in Table 3, it is helpful to

check them against the six dimensions of the

DeLone & McLean Model. Table 4 shows detailed

information for each dimension.

For B2C e-commerce websites that support

shopping functions, the main stakeholders are the

customers and the business owners. For the net

benefits, we should consider the benefits for both of

them. When the customers find the site convenient

or are happy with the quality/price of the products

they purchase, so the website can make a profit.

Website Profit (F2) can be used to measure this. Site

Popularity (F1) can be used to measure customer

satisfaction.

Table 4 shows the proposed factors’

classification in the DeLone & McLean Model. The

factors are distributed into all six dimensions, so

they should be comprehensive; however, they are

not necessarily accurate. For example, F1 can reflect

customers’ satisfaction to a certain extent, however,

possibly not completely.

Table 4: Checking with DeLone & McLean Model.

Dimensions Measures Factors

System

quality

Measures the desired

characteristics such as

usability and response time

F3,

F10

Information

quality

Captures the content issue

such as personalization,

complete, relevant, easy to

understand and secure

F4, F5,

Service

quality

The overall support delivered F9

Usage

Measures everything from a

visit to a website and

navigation to information

retrieval

F7, F8

User

satisfaction

Measures customers’ opinions F1

Net benefits

Captures the balance of the

positive and negative impacts

In the DeLone & McLean Model, the role of

computing was not strongly suggested nor

recommended. This was considered in an extended

model proposed by Ghandour et al. (2008), where

there were three main phases of e-commerce website

success: development, use and consequences of the

website. This study categorized the factors proposed

in Table 3 by collaborating with the interviewed

professionals. They were asked to classify the ten

factors in the three given categories (Lian, 2012).

The results showed that 6 factors (F3, F4, F5, F6, F9

and F10) belong to the development; 2 factors (F7

and F8) belong to the use; and 2 factors (F1 and F2)

belong to the impact. Each category got more than

half of the experts’ support. So the proposed

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measurements fit in the extended model. It also

provided empirical evidence support for the

extended model.

5 DISCUSSION AND SUMMARY

A set of measurements with 10 factors and their

corresponding dimensions was identified in Table 3

based on the literature and the qualitative and

quantitative data gathered from four different e-

commerce website stakeholders. It is the

combination of these factors that measures the

success of an e-commerce website rather than an

individual factor. The survey results suggested that

security, smooth transaction processes and smooth

shopping processes are the most important concerns

for online shoppers. Good protection on confidential

information should help to gain the consumer's trust

(Yazdanifard et al., 2011) and maintain customer

loyalty. The IS success model (DeLone & McLean,

2004) checking suggested that the proposed

measurements are comprehensive; however, they are

not necessarily accurate. The proposed

measurements fit in the extended model (Ghandour

et al., 2008), which also provides empirical evidence

support for this model. To refine the measurements

further, empirical tests involving a set of e-

commerce websites and their customers/owners are

required.

Comparing the proposed measurements with the

existing work, the customized ISO 9126 quality

model (Behkamal et al., 2009) seems the closest.

They both define the quality factors in a hierarchical

structure and assign numerical values to the quality

factors. However, one is for B2B e-commerce

websites and the other is for B2C. In terms of quality

attributes, the customized ISO 9126 quality model

emphasizes the user and development factors, such

as “Functionality”, “Reliability” and “Usability”; the

proposed measurements emphasize the customer

and business-related factors more, such as “Website

Design”, “Website Content”, “Shopping Process”

and “Transaction Process and Volume”. In terms of

process, five steps were defined in the customized

ISO 9126 quality model, particularly in step 4, a

widely-used method, analytical hierarchy process

(AHP) is carefully followed. This systematic

approach makes it easy to generalise the five steps

for other types of e-commerce websites such as

B2C. However, the factors should be revised to

include more customer-related features. On the other

hand, this work used the scores given by the users

and adjusts the quality factors by means of simpe

statistics instruments. These were further improved

by considering the opinions of the other

stakeholders. In terms of data, only users’

viewpoints and developers’ viewpoints were

considered in the customized ISO 9126 quality

model; in the proposed measurements, users’,

developers’, business owners’ and researchers’

viewpoints were considered, therefore, the resulting

quality factors should be more complete.

The small sample size of the online survey

limited the use of rigorous statistics in the data

analysis, instead, a mixed approach using combined

qualitative and quantitative analysis was used on

these data. A large sample with a more general

population should improve the accuracy of the

results.

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