improving website performance for search engine …...1 improving website performance for search...

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Improving website performance for search engine optimization by

using a new hybrid MCDM model

Yen-Chang Chen

Institute of China and Asia-Pacific Studies, National Sun Yat-sen University, Taiwan,

R.O.C.

[email protected]

Yu-Sheng Liu

Department of Business Management, National Sun Yat-sen University, Taiwan,

R.O.C.

[email protected]

ABSTRACT

The purpose of this study is to establish a decision model of search engine

ranking for providing administrators to improve the performances of websites for

satisfying the needs of users. To probe into the interrelationship and influential

weights among criteria of SEO, and evaluate gaps to achieve the aspiration level of

implementation in real world, this research utilizes a new hybrid MCDM model,

including decision-making trial and evaluation laboratory (DEMATEL),

DEMATEL-based analytic network process (DANP), and VlseKriterijumska

Optimizacija I Kompromisno Resenje (VIKOR). This study investigates the websites

of leading technologic companies, including laser system, energy-efficient lighting,

and control system. The empirical findings discover that the criteria of SEO possessed

a self-effect relationship based on DEMATEL technique. According to the network

relation map, the dimension that administrators should improve first when

implementing SEO is internal website optimization. In the six criteria for evaluation,

meta tags is the most significant criterion influencing search engine ranking, followed

by keywords and website design. The evaluation of search engine ranking reveals that

website in laser system outperforms those in energy-efficient lighting and control

system, becoming the optimal example for administrators of websites to make website

high ranked during the time that this study is executed.

Keyword: search engine optimization, search engine ranking, multiple criteria

decision-making, decision making trial and evaluation laboratory, DEMATEL-based

analytical network process.

ISS & MLB―September 24-26, 2013

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INTRODUCTION

In modern times, one of the main search engines is the initial step to search for

information when people make decisions. Accordingly, for administrators of websites,

the forward appearance of the search results is fairly important. However, it is

extremely competitive to make a website appear on the foremost page (Dye, 2008).

Search engine optimization (SEO) is the procedure to advance the ranking of websites

on search engines for particular searching terms by managing incoming links and

characteristics of websites (Malaga, 2010). Nevertheless, administrators of websites

are much interested how to improve the factors of SEO in turn, the level of

importance of each factor, and reducing the gaps to achieve aspiration level under the

consideration of SEO’s factors.

Consequently, to provide the solution to these issues, the purpose of this research

is to establish a decision model of carrying out SEO for administrators of websites to

improve the performances of websites for satisfying the users’ needs. This paper will

indicate, by the proposed new hybrid MCDM (multiple criteria decision-making)

model, the practical sequence of implementing SEO, the influential weights of criteria,

and how to evaluate the gaps of a website to reach aspiration level. Amin and

Emrouznejad (2011), according to metasearch engine, proposed a linear programming

mathematical model for optimizing the ranked list result. Many previous researches

assumed the criteria/factors for exploring problems were independent and linear;

however, they are interdependent and feedback in real world. Moreover, there are lots

of factors influencing SEO; hence, the contributions to administrators of websites are

limited. Other preceding researches on SEO were mainly focused on introducing SEO

(Yalçın & Köse, 2010) and investigating influential factors of SEO (Bar-Ilan et al.,

2006; Dye, 2008; Xiang & Gretzel, 2010; Zhang & Dimitroff, 2005). Yet, the

messages conveyed, for administrators of websites, are merely what factors have

impact on SEO and whether the influence were positive or negative. Therefore, these

findings to construct a decision model of search engine ranking have little

contribution to it. In addition, researches on the interrelationship and influential

weights among factors were inadequate.

To supplement previous findings on SEO for establishing a decision model of

search engine ranking for administrators of websites to improve website performance

for achieving the greatest benefit of internet marketing, this study utilizes a new

hybrid MCDM model comprising DEMATEL (decision making trial and evaluation

laboratory), DANP (DEMATEL-based analytical network process), and VIKOR

(VlseKriterijumska Optimizacija I Kompromisno Resenje) for exploring search

engine ranking based on SEO. We recognize the criteria of SEO for building a

decision model of search engine ranking by reviewing literatures. According to the


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survey of experts, this paper uses DEMATEL technique to assess the interdependent

and feedback problems among criteria to form a network relation map (NRM). By

employing DANP to overcome the problems of dependence and feedback among

criteria based on basic concept of ANP (Saaty, 1996), the influential weights of each

criterion for high ranking on the search engines can be obtained; subsequently, we

rank the data to identify the important criteria. Eventually, VIKOR is adopted to

evaluate the performance of websites and to reduce gaps based on NRM for achieving

the aspiration level. Published work connecting such a new hybrid MCDM model

with improvement strategy of SEO is very few. This study, to bridge the breach,

utilizes an empirical case of technologic companies’ websites in Taiwan, and provides

the management of search engine ranking with a valuable decision model to improve

websites performances.

The remainder of this study is organized as follows: in Section 2, the criteria of

SEO can be identified based on literature review. In Section 3, a new hybrid MCDM

model for establishing a decision model of search engine ranking is illustrated.

Section 4 reveals an empirical study of improvement strategy for high search engine

ranking to demonstrate the usefulness of proposed model. Finally, conclusions and

remarks are presented in Section 5.

CRITERIA OF SEO FOR EXPLORING SEARCH ENGINE RANKING

SEO is the procedure to improve the ranking of websites for particular searching

terms on search engines by managing incoming links and characteristics of websites

(Malaga, 2010). Based on past literatures, the purpose of this section is to identify the

criteria of SEO’s two main dimensions: internal and external website optimization

(Yalçın & Köse, 2010).

Website design, meta tags, and keywords, for internal website optimization, are

necessary for the website, page names, photos, links, content texts in every page and

styles that used for the site map, RSS (really simple syndication) feeds, related texts,

pages in different languages. Besides, for external website optimization, joining

website to the site guide, utilizing social media factors, and employing links from

other optimized websites to the related webpage are included (Yalçın & Köse, 2010).

On the dimension of internal website optimization, Bar-Ilan et al. (2006)

mentioned that websites can obtain high rankings for specific search terms within

specific search engines by designing and redesigning webpages (ex. Taiwan SEO Inc.,

www.ITOutsourcing.com.tw). In the related literatures of meta tags, lots of

researchers have proved that it can greatly improve the search effectiveness by

associating the results of multiple search engines in the form of a metasearch engines

(Amin & Emrouznejad, 2011; Bar-Ilan et al., 2006; Spink et al., 2006; Spoerri, 2007;


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Vaughan, 2004). As for keywords, Zhang and Dimitroff (2005)found that by

increasing the frequency of keywords in the title, in the full-text and in both the title

and full-text, webpage visibility can be improved.

On the other dimension of external website optimization, Zhang and Dimitroff

(2005) suggested that after the test webpages were ready, they were posted in the

public domain so that search engines could crawl and index them for advancing their

visibility. In the associated literatures of social media, Xiang and Gretzel (2010)

discovered that social media play an important role when using a search engine. With

respect to linkage, Zhang and Dimitroff (2005) stated that webpages with high

hyperlink were regarded as more significant or influential than those with low

hyperlink. Therefore, it was taken into account by some search engine ranking

algorithms to let the search results more connected. Namely, webpages with better

hyperlink can get higher ranking than other pages (Dye, 2008).

According to SEO, two dimensions have impact on search engine ranking: (1)

internal website optimization, and (2) external website optimization. Moreover, by

reviewing literature, internal website optimization is affected by three criteria: website

design, meta tags, and keywords; external website optimization, on the other hand, is

affected by three criteria: public domain, social media, and linkage, which are all

arranged in Table 1.

Table 1 Explanation of criteria

Dimensions Evaluation Criteria

Descriptions

Internal website optimization (D1)

Website design (C1) A collection of online content comprising applications and documents

Meta tags (C2) A method for webmasters to supply search engines with information about their websites

Keywords (C3) A term utilized as a keyword to retrieve documents on a search engine

External website optimization (D2)

Site guide (C4) A human-edited directory of the Web Social media (C5) The use of web-based and mobile

technologies for interactive dialogue Linkage (C6) Links from other optimized websites to the

related webpage

ESTABLISHING A NEW HYBRID MCDM MODEL FOR SEARCH

ENGINE RANKING

MCDM is utilized to consider multiple criteria simultaneously for providing

decision makers with a valuable decision model to make the optimal decisions (Tzeng

& Huang, 2011). This study employs the technique of DEMATEL to build the

network relation map (NRM) for problems of MCDM. Subsequently, by using DANP

the influential weights of criteria of the structure can be obtained. The method of

VIKOR, eventually, is utilized to evaluate compromise ranking and gaps of the


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alternatives. These principal stages are included in the framework of the new hybrid

MCDM model.

Constructing the NRM by DEMATEL

To build a NRM, the DEMATEL technique was utilized to explore the

interdependent and feedback problems among criteria (Chen & Tzeng, 2011; Fontela

& Gabus, 1976; Liu et al., in press; Ou Yang et al., 2008). The method has been used

in many fields, such as portfolio selection, design service, development strategies,

knowledge management, and vendor selection (Ho et al., 2011; Huang et al., 2011;

Lin & Tzeng, 2009; Tzeng et al., 2007; Yang & Tzeng, 2011).

The DANP for calculating criteria’s influential weights based on the NRM

Saaty (1996) developed ANP to solve problems with dependence or feedback

between criteria. However, Chen et al. (2011) addressed that the traditional survey

questionnaire of ANP was too complicated and hard to comprehend. This research,

according to Ou Yang et al. (2008), adopts a new method of DANP to conquer the

obstruction of carrying out ANP for calculating the influence weights based on the

NRM of DEMATEL.

Calculating gaps for improving the alternatives by VIKOR

The compromise ranking method (VIKOR) as one applicable technique to

implement within MCDM model was proposed to determine the compromise solution

(Opricovic, 1998; Opricovic & Tzeng, 2002; Opricovic & Tzeng, 2003; Opricovic &

Tzeng, 2004; Opricovic & Tzeng, 2007; Tzeng et al., 2002a; Tzeng et al., 2002b;

Tzeng et al., 2005). In addition, the solution is feasible for decision-makers, because it

supplies a maximum group utility of the majority, and a maximal gap of minimum

individuals of the opponent. This new hybrid MCDM model employs the DEMATEL

and DANP processes in Sections 3.1 and 3.2 to obtain the weights of criteria with

dependence and feedback, and utilizes VIKOR for resolving the compromise solution.

EMPIRICAL CASE – USING SEMICONDUCTOR PORTFOLIO AS AN

EXAMPLE

In this section, an empirical study is exhibited to demonstrate the application of

the presented model for evaluating and selecting the best website of search engine

ranking.

Background and problem descriptions

Web users browse the few and forward searching results (Jansen & Spink, 2005);

therefore, the issue of search engine ranking for administrators of websites is very

significant. Taiwan Network Information Center (TWNIC) reported that the


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population of using internet in Taiwan has exceeded 16.95 million (73.57% of

population), and the intensity of accessing internet comes out on top of the world.

Looking for information by internet has been a very important tool; however, it is a

critical topic for administrators of websites to make websites forward listed on search

engine results in the times of information explosion.

Moreover, there are numerous factors influencing search engine ranking;

therefore, it is a hard problem for administrators of websites to evaluate and advance

search engine ranking. In order to assist administrators of websites in comprehending

what the factors are, this research investigates the criteria in the experts’ perspective

on SEO and establishes a decision model of search engine ranking.

Data collection

The experts with specialty of SEO and professional knowledge of internet

marketing are the objects of this study, including consultants of SEO, scholars of

computer science, and managers of internet marketing. Moreover, the experiences of

experts are depicted as follows: consultants of SEO are highly skilled in various

aspects of technology and web design, scholars of computer science are those who

have the specialty of information engineering and the experience of teaching

information technology, and managers of internet marketing specialize in marketing

and advertising of websites. Experts’ point of view on the criteria and the

performances under consideration of criteria of the websites mentioned below are

acquired by interviewing and filling in questionnaires. An amount of 15 objects

consist of 5 consultants of SEO, 5 scholars of computer science, and 5 managers of

internet marketing. The inquisition is implemented in August 2011.

Furthermore, this study takes samples from websites of technologic companies

including Laser Tools & Technics Corp. (LLT, www.lttcorp.com), AMKO SOLARA

Lighting Co., Ltd. (AMKO, www.amko.com.tw), and Taiwan Jantek Electronics Ltd.

(Jantek, www.jantek.com.tw) for administrators of websites to improve search engine

ranking.

Estimating the relationships among SEO for establishing NRM

DEMATEL technique proposed in Section 3.1 will be used to explore the

interdependent and feedback problems among six criteria summarized by literatures.

At first, the influence matrix A for criteria is exhibited (see Table 2). Secondly, the

normalized influence matrix K for criteria can then be derived by Eq. (1) (see Table 3).

Thirdly, based on Eq. (3), the total influence matrix T for criteria and dimensions is

calculated (see Table 4 and Table 5). Finally, the NRM of influential relationship is


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http://www.amko.com.tw/

http://www.jantek.com.tw/

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built by the vector r and d from the total influence matrix T (see Table 6) as shown in

Figure 1.

Table 2 The initial influence matrix A for criteria

Criteria C1 C2 C3 C4 C5 C6

C1 0.000 3.733 3.667 2.600 1.667 1.600

C2 3.733 0.000 3.867 2.933 1.867 1.933

C3 3.667 3.933 0.000 2.800 1.733 1.800

C4 2.600 2.933 2.800 0.000 1.733 1.800

C5 1.667 1.867 1.733 1.733 0.000 3.867

C6 1.600 1.933 1.800 1.800 3.867 0.000

Table 3 The normalized direct-influence matrix K for criteria


C1 0.000 0.259 0.255 0.181 0.116 0.111

C2 0.259 0.000 0.269 0.204 0.130 0.134

C3 0.255 0.273 0.000 0.194 0.120 0.125

C4 0.181 0.204 0.194 0.000 0.120 0.125

C5 0.116 0.130 0.120 0.120 0.000 0.269

C6 0.111 0.134 0.125 0.125 0.269 0.000

Table 4 The total influence matrix T for criteria


C1 1.282 1.567 1.527 1.301 1.107 1.115

C2 1.561 1.440 1.612 1.383 1.177 1.192

C3 1.533 1.627 1.374 1.354 1.149 1.163

C4 1.301 1.388 1.349 1.030 1.012 1.025

C5 1.107 1.182 1.145 1.012 0.820 1.040

C6 1.115 1.196 1.159 1.025 1.040 0.837

Note: 1

21 1

1100%

n nn n ij ij

ni j

ij

t t

n t

= 2.252% < 5%, where n denotes the number of sample

and n

ijt is the average influence of i criterion on j .


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Table 5 The total influence matrix DT for dimensions

Dimensions D1 D2

D1 13.524 10.941

D2 10.940 8.841

Table 6 The sum of influences given and received on criteria

Dimensions/Criteria ri di ri+di ri- di

Internal website optimization (D1) 24.465 24.464 48.928 0.001

Website design (C1) 7.899 7.899 15.797 -0.00004

Meta tags (C2) 8.365 8.399 16.764 -0.03337

Keywords (C3) 8.201 8.166 16.367 0.03427

External website optimization (D2) 19.782 19.782 39.564 -0.001

Site guide (C4) 7.104 7.105 14.209 -0.00022

Social media (C5) 6.306 6.307 12.613 -0.00032

Linkage (C6) 6.371 6.371 12.742 -0.00031

30 5040

D2 (39.564, -0.001),

External website

optimization (C4, C5, C6)

D1 (48.928, 0.001),

External website

optimization (C1, C2, C3)

0

-0.001

0.001

0

0.002

-0.002

-0.03

-0.04

0.03

0.04

0

15 16 17

C1 (15.797, -0.00004),

Website design

C2 (16.764, -0.03337),

Meta tags

C3 (16.367, 0.03427),

Keywords

-0.00032

-0.00031

0

0.00031

0.00032

C6 (12.742, -0.00031),

Linkage

12.6 13.0 13.4 13.8 14.2

C5 (12.613, -0.00032),

Social media

C4 (14.209, -0.00022),

Site guild

0

0

ri+di

ri- di

ri- di

ri- di

ri+di

ri+di

Figure 1 The NRM of influential relationships within SEO.

Finding influential weights of criteria by DANP

According to the construction of the influence network based on DEMATEL


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(see Figure 2), this study utilizes DANP to calculate the level of importance (global

weights) of six criteria shown as Table 7~9. The empirical results reveal that experts

are much concerned with meta tags and keywords, yet less concerned with linkage

and social media. The findings display that the level of importance is much higher in

meta tags, keywords, and website design. Concretely, A gains the highest point of

0.190, followed by keywords (0.185) and website design (0.178). Moreover, the level

of importance of linkage and social media is relatively lower averaging 0.144. If

comparison is made among dimension, experts regard meta tags as the most important

criterion in the dimension of internal website optimization (D1). On the other hand,

site guide is considered by experts as the most significant criterion in the dimension of

external website optimization (D2). The finding shows that experts suggest meta tags

is the last criterion for administrators of websites should neglect when implementing

search engine ranking. As far as dimensions are concerned, experts are much

concerned with dimension of internal website optimization (D1) because the mean of

it is much higher than the other. In addition, the integrated values are calculated to

obtain the total performance as presented in Table 10. The results show the total

performance is highest in website of LLT, followed by websites of AMKO and Jantek.

Consequently, according to the decision model of search engine ranking provided by

this paper, administrators of websites are suggested to take the website of LLT as an

example when advancing search engine ranking based on SEO.

Search engine optimization

Internal website optimization (D1) External website optimization (D2)

Website of LLT

(A1)

Website of AMKO

(A2)

Website of Jantek

(A3)

goal

dimensions

criteria

alteratives

Website design (C1)

Meta tags (C2)

Keywords (C3)

Site guild (C4)

Social media (C5)

Linkage (C6)

Outer-dependent

Inner-dependent Inner-dependent

Figure 2 Analytic framework of influence network of SEO.

Table 7 The unweighted supermatrix based on DANP


C1 0.293 0.338 0.338 0.322 0.322 0.321


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C2 0.358 0.312 0.359 0.344 0.344 0.345

C3 0.349 0.349 0.303 0.334 0.334 0.334

C4 0.369 0.369 0.369 0.336 0.352 0.353

C5 0.314 0.314 0.314 0.330 0.286 0.359

C6 0.317 0.318 0.317 0.334 0.362 0.288

Table 8 The weighted supermatrix


C1 0.162 0.187 0.187 0.178 0.178 0.177

C2 0.198 0.173 0.198 0.190 0.190 0.191

C3 0.193 0.193 0.168 0.185 0.185 0.185

C4 0.165 0.165 0.165 0.150 0.157 0.158

C5 0.140 0.140 0.140 0.148 0.128 0.160

C6 0.142 0.142 0.142 0.149 0.162 0.129

Table 9 The stable matrix of DANP when power limit z


C1 0.178 0.178 0.178 0.178 0.178 0.178

C2 0.190 0.190 0.190 0.190 0.190 0.190

C3 0.185 0.185 0.185 0.185 0.185 0.185

C4 0.160 0.160 0.160 0.160 0.160 0.160

C5 0.143 0.143 0.143 0.143 0.143 0.143

C6 0.144 0.144 0.144 0.144 0.144 0.144

Table 10 The weights of criteria for evaluating website and total performance by SAW

Dimensions/

criteria

Local

Weight

Global

Weight

Website of

LLT (A1)

Website of

AMKO (A2)

Website of

Jantek (A3)

Internal website optimization (D1) 0.553 7.784 7.891 7.087

Website design (C1) 0.322 0.178 (3) 8.867 9.133 8.600

Meta tags (C2) 0.344 0.190 (1) 7.467 7.400 6.467

Keywords (C3) 0.335 0.185 (2) 7.067 7.200 6.267

External website optimization (D2) 0.447 6.639 6.309 6.130

Site guide (C4) 0.358 0.160 (4) 4.133 3.933 2.533

Social media (C5) 0.320 0.143 (6) 7.400 7.533 7.800

Linkage (C6) 0.322 0.144 (5) 8.667 7.733 8.467


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1AS

1AQ

1

1 1

*

*max 1,..., 0.587

j A jpA A

j j

f fQ d j n

f f

1

1 1

*61

*1

10 8.867 10 7.467 10 7.0670.178 0.190 0.185

10 0 10 0 10 0

10 4.133 10 7.400 10 8.6670.160 0.143 0.144 0.273

10 0 10 0 10 0

j A jpA jA

j j j

f fS d w

f f

Total Performance 7.272 (1) 7.184 (2) 6.659 (3)

Example:

Calculating Total Performance by global weights:

0.178*8.867+0.190*7.467+0.185*7.067+0.160*4.133+0.143*7.400+0.144*8.667=7.272

Calculating Total Performance by local weights:

0.553*7.784+0.447*6.639=7.272

Compromise Ranking by VIKOR

After the weights of criteria are obtained by DANP in Section 4.4, VIKOR

technique is employed for compromise ranking. The results of calculation (Table 11)

illustrates that the total gap is highest in website of Jantek, followed by websites of

AMKO and LLT. Therefore, both VIKOR and DANP come to the same conclusion

that the decision model of search engine ranking demonstrates the website of LTT is

an optimal guide to be high ranked on search engine.

Table 11 The relative gaps from aspired value by VIKOR

Dimensions/

criteria

Local

Weigh

t

Global

Weight

Website

of LLT

(A1)

Website

of

AMKO

( A2)

Website

of

Jantek

(A3) Internal website optimization (D1) 0.553 0.222 0.211 0.291

Website design (C1) 0.322 0.178 (3) 0.113 0.087 0.140

Meta tags (C2) 0.344 0.190 (1) 0.253 0.260 0.353

Keywords (C3) 0.335 0.185 (2) 0.293 0.280 0.373

External website optimization (D2) 0.447 0.336 0.369 0.387

Site guide (C4) 0.358 0.160 (4) 0.587 0.607 0.747

Social media (C5) 0.320 0.143 (6) 0.260 0.247 0.220

Linkage (C6) 0.322 0.144 (5) 0.133 0.227 0.153

Total gaps 0.273 (1) 0.282 (2) 0.334 (3)

Maximal gaps 0.587 (1) 0.607 (2) 0.747 (3)

Example:

Calculating dimension gap by dimensions of local weights: 1 1

1

1 1 1 1

31

1

10 8.867 10 7.467 10 7.0670.322 0.344 0.335

10 0 10 0 10 0

=0.222

D D

j kjDp

D D j D Dj j j

f fS d w

f f

1 10.293p

D DQ d

Calculating total gap by criteria of global weights:


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IMPLICATIONS AND DISCUSSIONS

The empirical findings are discussed as follows. In the first place, NRM (Figure

1) of SEO constructed by DEMATEL reveals that administrators of websites should

improve first is internal website optimization (D1), if the search engine ranking of

websites going down. When brainstorm for the right keywords (C3) of specific fields,

website design (C1) satisfies the needs of users, and meta tags (C2) get suitable

description for search engines, external website optimization (D2) can get following

influences: the site guides (C4) of search engines will consider these websites as

significant websites and put them into index; users of these websites will share

valuable information to their friends by social media (C5); other websites will make

linkage (C6) to these websites for providing users with complete information.

Secondly, the most significant criterion found by DANP when implementing SEO is

meta tags (C2), which weights 0.190. When it comes to search engine ranking, an

essential procedure for administrators of websites to consider is that websites should

be found by search engines. If meta tags are not described properly, search engines

cannot access to the information provided by websites including videos, audios,

pictures, webpages, and so forth. Therefore, administrators of websites should give

every information appropriate descriptions not only for search engine to find, but also

let users easily look for the information that they need to make decisions. Thirdly, the

influential weight of keywords (C3) is 0.185 ranked second among the six criteria of

SEO. Once search engines can find the websites, the next cardinal issue for

administrators of websites is to let users have the opportunities to search for

information by utilizing keywords. Many administrators of websites may set up

keywords according to their businesses; however, these websites can be regarded as

not existed by decision makers, if they do not show up on the search engine results by

the decision makers’ keywords, though the businesses are operated well. Therefore,

administrators of websites should brainstorm for the optimal keywords from the

standpoint of decision makers to have their websites appeared on target users. At the

last point, the compromise ranking by VIKOR reveals that the website of LLT (A1) is

the optimal example among the three technologic industries for administrators of

websites to improve performances of websites for achieving aspiration level.

The proposed new hybrid MCDM model based on SEO can be utilized in

worldwide websites. Administrators of websites can adjust the influential weights of

the six criteria according to the situations of different countries to obtain valuable

information of decision making when improving performances of websites. Moreover,

they can select the websites of their industries to evaluate and reduce their gaps for

advancing search engine ranking.


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CONCLUSIONS AND REMARKS

SEO is utilized in the field of internet marketing as a significant reference for

high ranking on search engines. It has been developed for decades and examined that

two dimensions of internal and external website optimization have influences on

search engine ranking. Nevertheless, it is unclear how the criteria impact on the two

dimensions. Although the comprehension of the importance of the criteria can be

useful for administrators of websites when implementing SEO, the weights of criteria

are seldom investigated.

By utilizing DEMATEL, the criteria are demonstrated having interrelations and

self-feedback relationships. Moreover, DANP is employed to obtain weights of the

six criteria. Empirical findings show that meta tags is the most important criterion,

followed by keywords, website design, site guide, linkage, and social media. Experts

suggest that administrators of websites put the most emphasis on meta tags, though

they must comprehensively take criteria into consideration when making decisions of

SEO. As for evaluating SEO, the highest integrated scores of criteria is the website of

LLT, followed by websites of AMKO and Jantek, and the result is the same with

VIKOR method. Therefore, experts indicate that SEO of LLT’s website is an optimal

example when implementing SEO for providing administrators of websites to achieve

the greatest benefit of internet marketing.

Preceding studies pay most attention to introducing SEO and indentifying the

criteria that influence it. However, little researches are concerned about the

interrelationship among criteria, the weights of criteria, and the evaluation of

website’s SEO. This paper thus proposes a new hybrid MCDM model and

investigates the perspectives of experts for exploring these issues. Associating

previous theoretical researches with the experts of practical experience lets SEO more

beneficial for administrators of websites to improve search engine ranking of websites,

which is not offered by earlier studies. In brief, this research utilizes a new hybrid

MCDM model based on SEO to explore the subject for improving and evaluating

search engine ranking, and further studies can consider more comprehensive factors

or sub-factors to make this field more mature.

Appendix A. A new hybrid MCDM model combined with DEMATEL, DANP

and VIKOR

A.1. DEMATEL

The method is illustrated as follows: first, we acquire the influence matrix by

scores. The experts are required to point out the degree of influence among criteria:

indicating criterion i impacts on criterion j as aij. The influence matrix, A, can thus be

received. Second, the normalized influence matrix K can be calculated by normalizing


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A via Equations (1) and (2).

=m K A (1)

1 1

1 1min ,

max | | max | |n n

ij iji j

j i

m

a a

(2)

Thirdly, Derive the total influence matrix T. T can be derived by using the

formula, 2 3 1+ = ( - )q T K + K K K K I K , where I denotes the identity matrix.

In the fourth step: construct the NRM based on the vectors r and d. The vectors r and

d of matrix T stand for the sums of rows and columns respectively, which are shown

as Equations (3) and (4).

1

1 1

[ ]n

i n ij

j n

r t

r (3)

1

1 1

[ ]n

j n ij

i n

d t

d (4)

where ir represents the sum of the i th row meaning the total impacts of criterion i

on another criteria. Also, jd denotes the sum of the j th column of matrix T and

displaying the entire effects that criterion j receives from another criteria. Moreover,

when i j ( )i ir d , it shows the degree of influences given and received; i.e.,

( )i ir d exhibits the intensity of the cardinal role that factor i performs in the

problem. Other factors are affected by factor i , when ( )i ir d is positive. Yet, if

( )i ir d is negative, other factors impact on factor i and thus the NRM can be built

(Liou et al., 2007; Tzeng et al., 2007).

A.2. Based on DEMATEL technique to find ANP weights

DANP consists of four steps (Lee et al., 2011), and the first step is to build the

construction of the influence network based on DEMATEL. In the second step, the

unweighted supermatrix is calculated. The total influence matrix T is derived from

DEMATEL, as shown in Equation (5).

11

12

1 1

1

2

1

2

1

11 1 1 11

111 1 1

1

1

c cc

c c c

c

c

c c

c

c

cm

ci

ci

cim

cn

cn

cnmn

j n

m j jm n nmnj

i

i

n

D D D

c c c c c c

j nD

i ij inD

n nj nnD

T T T

T T T T

T T T

(5)


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Use the total degree of influence to normalize every level of CT for acquiring

C

T based on Equation (6).

11

12

1 1

1

2

1

2

1

11 1 1 11

1

...

11 1 1

1

1

cc

cc c

c c c

j n

c

ij in

C

n nj nn

c

c

c m

cici

cim

cncn

cnmn

j n

m j jm n nmj n

i

i

n

D D D

c c c c c c

D

D i

D

T

T T T

T T T

T T T

(6)

where 11α

cT can be calculated via Equation (7) and (8), and we can obtain Tαnn

c by

the same way.

111 11

1

C

m

i ijj

d t , 1

1,2,...,i m (7)

1 1

1

11 1 1 11 1 1

11 11 1111 11 11

11 1 1 1 1

11 11 1111 11 1111

1

11 11 1111 11 11

/ / /

/ / /

/ / /

T

mC C C

imC CC C

m m j m mC C C

j

i i ij i i

m m m

t d t d t d

t d t d t d

t d t d t d

1 1

1

11 1 1 1

11 11 11

11 1

11 11 11

1

11 11 11

mC C C

imC C C

m m m mjC CC

j

i ij

t t t

t t t

t t t

(8)

According to the interdependent relationship in group to array C

T , the

unweighted supermatrix can then be obtained by Equation (9).

11

12

1 1

1

2

1

2

1

11 1 1 111

...

11 1 1

' 1

1

( )c

c

c

c m

c

c j

c jm

cncn

cnmn

i n

m i im n nmi n

jj

j

n

D D D

c c c c c cD

i n

D j ij nj

n in nn

D

T

W W W

W W W W

W W W

(9)

where 11W can be derived by Equation (10), and so does nn

W . Besides, the group or

criterion is independent, if a blank space or 0 shows up in the matrix.


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1

1

1

1

1 1 1 1

11 1 1

11 11 11

11 1 111

11 11

11 11 111 1

11 11 1111

( )

m

c ci cm

c j cij cm j

mc m cim cm m

t t t

t t t

t t t

i

j

c c c

c

Tc

c

W (10)

The step 3 is to derive the weighted supermatrix. The total influence matrix of

dimensions DT is counted by Equation (11). Utilize the total degree of influence to

normalize every level of DT for obtaining D

T according to Equation (12).

1

nij

i Dj

d t

, 1,2,...,i n

11 1 1

1

1

=T

D D D

D D D

D D D

j n

i ij in

D

n nj nn

t t t

t t t

t t t

(11)

11 1 1 11 1 1

1 1 1

1 1

2 2 2

1 1

/ / /

/ / /

/ / /

T

D D D D D D

D D D D D D

D D D D D D

j n j n

i ij in i ij in

D

n nj nn n nj nn

n n n

t d t d t d t t t

t d t d t d t t t

t d t d t d t t t

(12)

The weighted supermatrix can thus be calculated by normalizing D

T into the

unweighted supermatrix shown in Equation (13).

11 11 1 1 1 1

1 1

1 1

i i n n

D D D

j j ij ij nj njD D D D

n n in in nn nn

D D D

t t t

t t t

t t t

W W W

W T W W W W

W W W

(13)

In the fourth step, the limit supermatrix is calculated. The weighted supermatrix

multiplies by itself many times, based on the concept of Markov Chain, to acquire the

limit supermatrix. Therefore, the influential weights of every criterion is calculated by


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lim z

zW . Specifically, by the limit supermatrix W with power z, denoting any

figure for power, the influential weights of DANP are acquired.

Assume the alternatives are represented by 1 2, ,..., ,...,k mA A A A . Also, kjf

denotes the performance score of alternative kA under consideration of the jth

criterion; the weight (relative importance) of the jth criterion

is jw , where

1,2,...,j n , and n is the number of criteria. VIKOR begin with the following form

of pL metric :

* * 1/

1

{ [ (| |) / (| |)] }n

p p p

k j j kj j j

j

L w f f f f

,

where 1 ;p 1,2,...,k m ; weight jw comes from DANP. VIKOR are thus

utilized to formulate the ranking and gap measure 1p

kL (as kS ) and p

kL (as kQ ).

1 * *

1

[ ( | |) / (| |)]n

p

k k j j kj j j

j

S L w f f f f

,

* *max{(| |) /(| |) | 1,2, , }p

k k j kj j jj

Q L f f f f j n .

The compromise solution min p

kk

L presents the minimum integrated gap and will

be chose in that its value is the closest to the aspired level. Besides, when p is small

(such as 1p ), the group utility is stressed; on the contrary, if p tends to be infinite,

the individual maximal gap should be improve prior to others, for it is much important

(Freimer & Yu, 1976; Yu, 1973). Therefore, min kk

S accents the maximum group

utility; on the other hand, min kk

Q stresses on the selection of the minimum gap from

the maximum individual gaps.

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