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.
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
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;
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
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’sinfluential 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
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
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 2 1 1 1 100% n n n n ij ij n i j ij t t n t
= 2.252% < 5%, where n denotes the number of sample
and n ij
Table 5 The total influence matrix TD 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 40 50 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
(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
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
1 A S 1 A Q 1 1 * * max j A j 1,..., 0.587 p A A f f Q d j n 1 1 1 * 6 1 * 1 10 8.867 10 7.467 10 7.067 0.178 0.190 0.185 10 0 10 0 10 0 10 4.133 10 7.400 10 8.667 0.160 0.143 0.144 0.273 10 0 10 0 10 0 j A j p A A j j j j f f S 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 3 1 1 10 8.867 10 7.467 10 7.067 0.322 0.344 0.335 10 0 10 0 10 0 =0.222 D D j kj D p D D j D D j j j f f S d w f f
1 1 0.293 p D D Q d 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.
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
A via Equations (1) and (2). =m K A (1) 1 1 1 1 min , max | | max | | n n ij ij i j j i m a a
(2)Thirdly, Derive the total influence matrix T. T can be derived by using the formula, T K + K2K3+ Kq= ( - )K I K 1, 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 ri represents the sum of the ith row meaning the total impacts of criterion i on another criteria. Also, dj 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 (ridi), it shows the degree of influences given and received; i.e.,(ridi) exhibits the intensity of the cardinal role that factor i performs in the
problem. Other factors are affected by factor i, when (ridi) is positive. Yet, if
(ridi) 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 11 1 1 1 11 1 1 1 1 c c c c c c c c c c c c cm ci ci cim cn cn cnmn j n m j jmj n nmn i i n D D D c c c c c c j n D i ij in D n nj nn D T T T T T T T T T T (5)
Use the total degree of influence to normalize every level of T for acquiring C C
T
based on Equation (6). 11 12 1 1 1 2 1 2 1 11 11 1 1 1 ... 11 1 1 1 1 c c c c c c c c j n c ij in C n nj nn c c c m ci ci cim cn cn cnmn j n m j jmj n nmn 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 α11 cT can be calculated via Equation (7) and (8), and we can obtain Tcαnn by the same way.
1 11 11 1 C m i ij j d t , i1, 2,...,m1 (7) 1 1 1 1 1 1 1 1 1 1 1 11 11 11 11 11 11 11 1 1 1 1 11 11 11 11 11 11 11 1 11 11 11 11 11 11 / / / / / / / / / T m C C C im C C C C m m j m m C 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 1 1 1 1 1 11 11 11 11 1 11 11 11 1 11 11 11 m C C C im C C C m mj m m C C C j i ij t t t t t t t t t (8)
According to the interdependent relationship in group to array TC , the
unweighted supermatrix can then be obtained by Equation (9).
11 12 1 1 1 2 1 2 1 11 11 1 1 1 ... 11 1 1 ' 1 1 ( c ) c c c m c c j c jm cn cn cnmn i n m i imi n nmn j j j n D D D c c c c c c D i n D j ij nj n in nn D T W W W W W W W W W W (9) where 11
W 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.
1 1 1 1 1 1 1 1 11 1 1 11 11 11 11 1 1 11 11 11 11 11 11 1 1 11 11 11 1 1 ( ) m c ci cm c j cij cm j m c m cim cm m t t t t t t t t t i j c c c c T c c W (10)
The step 3 is to derive the weighted supermatrix. The total influence matrix of dimensions
T
D is counted by Equation (11). Utilize the total degree of influence tonormalize every level of
T
D for obtainingT
D according to Equation (12).1 n ij i D j d t , i1, 2,...,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
T
D 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 nj D 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
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 A A1, 2,...,Ak,...,Am. Also, fkj
denotes the performance score of alternative Ak under consideration of the jth
criterion; the weight (relative importance) of the jth criterion is wj , where 1, 2,...,
j n, and n is the number of criteria. VIKOR begin with the following form of L metricp : * * 1/ 1 { [ (| |) / (| |)] } n p p p k j j kj j j j L w f f f f
,where 1 p ; k1, 2,...,m; weight w comes from DANP. VIKOR are thus j
utilized to formulate the ranking and gap measure Lpk1 (as Sk) and Lkp(as Qk ).
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 j j Q L f f f f j n .The compromise solution min pk
k L presents the minimum integrated gap and will be chose in that its value is the closest to the aspired level. Besides, when pis small (such as p1), the group utility is stressed; on the contrary, if ptends 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 k
k S accents the maximum group utility; on the other hand, min k
k Q stresses on the selection of the minimum gap from the maximum individual gaps.
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