1
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.
Yu-Sheng Liu
Department of Business Management, National Sun Yat-sen University, Taiwan,
R.O.C.
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.
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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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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
Criteria C1 C2 C3 C4 C5 C6
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
Criteria C1 C2 C3 C4 C5 C6
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
Criteria C1 C2 C3 C4 C5 C6
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
Criteria C1 C2 C3 C4 C5 C6
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
Criteria C1 C2 C3 C4 C5 C6
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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