Advances in Economics, Business and Management Research, volume 105 1st International Scientific and Practical Conference on Digital Economy (ISCDE 2019) The index of virtual representation in the context of digital marketing

Nazarov A.D. Radkovskaya E.V. State University of Economics Ural State University of Economics , Yekaterinburg, Russia [email protected] [email protected]

Tovmasyan N.D. Ural State University of Economics Yekaterinburg, Russia [email protected]

Abstract — Websites and their effective optimization and According to the Ministry of Education and Science of the promotion through web analytics and internet marketing become Russian Federation requirements, specificity of education the integral part of educational services during industrialization activity and maintenance of the university site do not allow to and transformation of digital economy. Now, when most of analyze a site through the well-known tools. That is why we applicants chooses a university on the basis of information on the developed the weighting mathematical model by calculating the Internet, such the site should be easy-to-use and be continuously updated. However, the question is how “user-friendliness” and quantitative and qualitative criteria. “usefulness” of a website of an education institution can be II. MATERIAL AND METHODS estimated. The development of the weighting mathematical model of the university index of virtual representation which allows to The formation of the pool of criteria and indicators which estimate usability and completeness of information of one or the affect these processes is necessary for creation of the other site and to propose recommendations on its improvement on mathematical model of the cumulative university score. This the basis of obtained data is proposed for that in this article. This pool includes not only web-criteria but also psychosocial ones. model is successfully tested on the several websites of the Ural The model has to incorporate the largest possible number of region . both quantitative and qualitative indicators, including intangible Keywords — internet marketing, website effectiveness score, factors which are difficult to assess. model of website score, score of websites, website, digital marketing, Two main groups of criteria (indicators) were identified for web analytics. the formation of the cumulative website score model. I. INTRODUCTION • psychosocial criteria (they are measured in the Any organization should be on the Internet and have the range of 0 to 1); modern and optimized website, including educational • web-criteria (they are measured in the range institutions, during industrialization. Due to the information of 0 to 1). technologies development, applicants choose a university, building on representation and completeness of information of Each criterion has its own impact, therefore, this model is this university on the Web. The official website is a reflection quality-weighting. Figure 1 reflects a systematic assessment of of virtual representation of a university on the Internet [5]. original consolidated criteria of CUS (cumulative university score). The purpose of the site creating on the Internet is to get users to its content eventually. The question is how to determine This economic model can be presented as mathematical: “user-friendliness” and “usefulness” of one or the other site. The main tools of internet marketing can be used to deal with this 퐶푈푆 = 푥2푆 + 푥3푊 (1) issue [3]. where CUS –university index of virtual representation; Internet marketing is the practice of using all aspects of S – psychosocial criteria; traditional marketing on the Internet concerning the main W – web-criteria; elements of marketing-mix: price, point of sale and promotion [2]. The important aim of internet marketing is to attain the x1,2,3 – weighting rate of an indicator. maximum economy effect on the target audience of a site. In this case, the target audience is applicants who want to pursue higher The cumulative score of a university website is from 0 to an education and their parents, therefore, the main informational undefined indicator, the maximum value means completeness of source of a university is its website and groups on social media a university website and sufficient promotion of it on the [1, 4]. Internet.

Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). 117 Advances in Economics, Business and Management Research, volume 105

Fig 1. Author’s model

Next are “a number of requests a month related to a III. MODEL DESCRIPTION university title on Yandex (YZ)” and “a number of requests a In our model we identify psychosocial criteria as usability of month related to a university title on Google (GZ)”. They refer a site (S1, its design (S2), i.e. this is the group of criteria which is to the frequency of users requests of information about a impossible to determine in quantitative way, and therefore, the university on the Internet. The higher this indicator, the better pool of these criteria will be defined by the expert method. and more successful a site. It is necessary to use “Yandex.Wordstat” to identify this criterion. We consider web-criteria of the cumulative score of a university website as the main indicators of the website score: The last of them are “a place of an official site in requesting Yandex thematic index of citing (T), a number of pages indexed a university title on Yandex (YM)” and “a place of an official on Google at present (GC), a number of pages indexed on site in requesting a university title on Google (GM)”. These Yandex at present (YC), a number of requests a month related criteria indicate the popularity of a site, its optimization for users to a university title on Yandex (YZ), a number of requests a and search engines. The lower this indicator, the better and more month related to a university title on Google (GZ), a place of an successful a site. official site in requesting a university title on Yandex (YM), a The primary model (1) has the following form, taking into place of an official site in requesting a university title on Google account the detailed subparameters: (GM).

Let us consider each criterion of a website in more detail. Yandex thematic index of citing (T) is a way of calculating 푉푅퐼 = (푥1(푦1푆1 + 푦2푆2) + 푥2(푦3푇 + 푦4퐺퐶 + 푦5푌퐶 + credibility of a web-based facility, taking into account links to 푦6푌푍 + 푦7퐺푍 + 푦8푌푀 + 푦9퐺푀) (2) the other sites resources. The main subparameter of Yandex thematic index of citing is the similarity of sites theme. That is, where L – consumer’s loyalty; the cross-reference of a university with the other one is more S1,2 – subparameter of the group of psychosocial criterion; valuable than, for example, with a news site. Moreover, an indicator is calculated for each page of a site and then is W1,2,3,4,5,6,7 – subparameter of the group of web-criterion; summarized. The higher this indicator, the better and more x1,2 – weights of the criteria group; successful a site. y1,2,3,4,5,6,7,8,9 – weights of the subparameters group. It is necessary to use http://pr-cy.ru/ service to identify thematic index of citing of an official university site. It is necessary to identify the weights to finalize the model: x1,2 – the weights of the criteria group и y1,2,3,4,5,6,7,8,9 – the Let us turn to the following criteria such as “a number of weights of the subparameters group. sites indexed on Yandex at present (YC)” and “a number of sites indexed on Google at present (GC)”. These indicators refer to Let us use the expert method of the hierarchy analysis to the quantity of site pages which are indexed in search engines at identify the weight of one or the other criterion and its present. It is possible to use the above service to identify these importance. The analytical and intellectual service data of a particular site. The higher this indicator, the better and MakeItRational (https://makeitrational.com/demo/decision- more successful a site. making-software) can be used for ease of understanding.

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Eventually, the weights of criteria were distributed in the VALUE identifies the final weight of one or the other following way. Indicators EXP1, EXP2, EXP3, EXP4, EXP5 quantitative criterion (Table 1). correspond to 5 different experts’ opinions, and indicator TABLE I. Weights of criteria

EXP1 EXP2 EXP3 EXP4 EXP5 VALUE

x1 of W 0.5 0.55 0.6 0.5 0.45 0.52

x2 of S 0.5 0.45 0,4 0.5 0.55 0.48

y1 of S1 0.6 0.55 0.5 0.7 0.5 0.57

y2 of S2 0,4 0.45 0.5 0.3 0.5 0.43

y3 of T 0.05 0.05 0.05 0 0.05 0.04

y4 of YC 0.1 0.1 0.05 0.1 0.05 0.08

y5 of GC 0,15 0.1 0.05 0.1 0.05 0,09

y6 of YZ 0.05 0.2 0.1 0,15 0.2 0.14

y7 of GZ 0.1 0,15 0,15 0,15 0,25 0,16

y8 of YM 0,25 0,25 0,25 0,25 0,25 0,25

y9 of GM 0.3 0,15 0,35 0,25 0,15 0.24

IV. RESULT AND DISCUSSIONS Following the development of our model of the university index of virtual representation, it is important to test it. The Ural region universities websites will be the basis of testing this model. We choose 10 universities such as the , Ural State Economic University, Russian State Vocational Pedagogical University, Ural State University of Railway Transport, Ural State Forestry University, Ural State Medical University, Ural State Agrarian University, Ural State Pedagogical University, Ural State Law University, Ural State Mining University. The interface of 2 universities sites such as the Ural Federal University (Fig.1) and the Ural State Economic University (Fig.2) are presented. Fig 1. Home page of the Ural Federal University Several expert opinions were used to estimate the “usability of a website” and “design of a website” criterion. Every expert measured these criteria in the range of 0 to 1 and then the model calculated an arithmetic mean of the expert scores for each criterion. Table 2 shows the following results received through the mathematical model of the university website effectiveness score.

Fig 2. Home page of the Ural State Economic University

TABLE II. RESULTS OF THE DEVELOPED MODEL

UNIVERSITY T YC GC YZ GZ S1 S2 CUS Ural Federal University 4900 16 916 265 000 216 953 249496 0.9 0.8 49761 Ural State Pedagogical University 1200 8327 119000 52320 71005 0.8 0.8 15658 Ural State Law University 1100 223626 76500 9088 10451 0.7 0.6 14438 Ural State University of Railway Transport 1100 46046 93700 47472 52219 0.7 0.6 14125 Ural State Medical University 800 13216 104000 33320 34986 0.8 0.9 10771 Russian State Vocational Pedagogical University 850 16984 43200 41066 47226 0.8 0.7 9665,4 Ural State Forestry University 600 6500 150000 13369 15374 0.6 0,4 9555,8 Ural State Pedagogical University 2300 8253 45100 39883 43871 0.8 0.7 9056 Ural State Agrarian University 650 5061 4910 11200 11760 0.6 0.5 2248,2 Ural State Mining University 325 27476 17600 207 217 0.7 0.5 2007,1

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V. CONCLUSIONS Results show that the most effective site in terms of the internet marketing and analytics tools is the site of the Ural Federal University, next is the site of the Ural State Economic University, and next comes the Ural State Law University and Ural State University of Railway Transport sites. The official sites of the universities above have the competent inner and external SEO-optimization, as evidenced by the high indicators of thematic index of citing (Yandex TIC). Eventually, the developed model was successfully tested and identified the pros and cons of the Ural region universities sites. We intend to implement a negative point “risks” in this model and represent it as the intellectual system of the university website effectiveness score, designed to automatically scan a web-based facility, to make recommendation on the improvement of a website on the basis of the developed criteria, and to realize these recommendations in real-time through visual and structural change of a site. References [1] Seyfabad, M.K., Fard, M.J.S. (2019). An analysis of the impact of SEO on university website ranking. Iranian Journal of Information Processing and Management, 34 (4), pp. 1787-1810. [2] Jeyshankar, R. (2019). Webometric analysis of Deemed University Websites in India. Library Philosophy and Practice, 2019, № 2266, pp 423- 425. [3] Jati, H., Nurkhamid, Wardani, R. (2018). Quality Analysis of University Websites from Usability Side with Multicriteria Decision Analysis Method. Journal of Physics: Conference Series, 1140 (1), pp 42-45 [4] Svendsen, J.T., Svendsen, A.M. (2018). Social life for sale! A critical discourse analysis of the concept of student life on Danish university websites. Discourse, 39 (4), pp. 642-663. [5] Rădulescu, C., Hudea, O.S.C., Papuc, R.M. (2018). Analysis of websites belonging to public universities from the perspective of the democratic governance exigencies. A marketing research approach. Transylvanian Review of Administrative Sciences, 14 (54E), pp. 90-106. [6] Hasim, M.S., Hashim, A.E., Ariff, N.R.M., Sapeciay, Z., Abdullah, A.S. (2018). Commitment to sustainability: A content analysis of website for university organisations. IOP Conference Series: Earth and Environmental Science, 117 (1), № 012046, pp. 141-142 [7] Ezpeleta-Piorno, P., Albi, A.B. (2018). The multilingual university website (MUW) genre ecology Content analysis and translation processes. Revista Espanola de Linguistica Aplicada, 30 (2), pp. 636-661. [8] Shahzad, A., Nawi, N.M., Sutoyo, E., Naeem, M., Ullah, A., Naqeeb, S., Aamir, M. (2018). Search engine optimization techniques for Malaysian University websites: A comparative analysis on google and bing search engine. International Journal on Advanced Science, Engineering and Information Technology, 8 (4), pp. 1262-1269. [9] Olaleye, S.A., Sanusi, I.T., Ukpabi, D.C., Okunoye, A. (2018). Evaluation of Nigeria Universities websites quality: A comparative analysis. Library Philosophy and Practice, 2018, № 1717, pp. 66-68 [10] Barcellos, L.I., Inglesis Barcellos, E.E., Botura, G., Jr. (2018). Analysis of communicational design and information on university websites. Advances in Intelligent Systems and Computing, 609, pp. 64-72. [11] Napitupulu, D. (2017). Analysis of factors affecting the website quality based on WebQual approach (study case: XYZ University). International Journal on Advanced Science, Engineering and Information Technology, 7 (3), pp. 792-798.

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