USING THE BLOGOSPHERE FOR MONITORING SMALL AND MEDIUM ENTERPRISE PERFORMANCE
POLINA SHTERK, YEVGENIYA DRAGUNOVA,
NOVOSIBIRSK STATE TECHNICAL UNIVERSITY
Abstract. The present paper presents blogosphere analysis as an alternative method of monitoring small and medium enterprise performance in Russian federation subjects. The study, carried out using publicly available online tools, reveals the main challenges facing SMEs in the Siberian Federal District of Russia, with the most widely discussed topics being the tax system and enterprise support programs.
Keywords: monitoring, blogosphere, social networks, small and medium enterprise, federal subject classification.
SHTERK, POLINA; DRAGUNOVA, YEVGENIYA (2015) "USING THE BLOGOSPHERE FOR MONITORING SMALL AND MEDIUM ENTERPRISE PERFORMANCE". Journal of Russian Review (ISSN 2313-1578), VOL. 1(2), 20-29.
The goal of the present study is to set out a method of monitoring small and medium enterprise performance through both statistical (including correlation and cluster analysis) and alternative means (blogosphere analysis). The proposed method is expected to make analysing SME mesoscale performance analysis more effective.
Coined in 1999 as a joke by an Englishman named Brad L. Graham, the term blogosphere was subsequently re-introduced by William Quick. Initially used by bloggers reporting on the US war in Afghanistan, the term went on to spread outside the warblogging community. With the information society gaining prominence, since 2002 blogosphere analysis has become a staple of opinion surveys.
Recently, there has been an increased interest in the use of monitoring at different levels as a means of addressing various economic problems, with new organisations engaged in monitoring various aspects of life appearing almost daily. Using blogosphere analysis in addition to standard statistical tools to monitor SME performance allows one to instantly gauge user reaction to reforms, detect weak spots, and quickly address development issues.
2. Research into small and medium enterprise performance
Late 2000s saw SMEs becoming a point of interest for the government. With Russian SMEs responsible for just 15 to 25 per cent of the country’s GDP (compared to over 50% in most of the developed nations), President Putin tasked the government with bringing the figure up to 60-70%. The current figures indicate that small and medium enterprises still have a long way to go towards boosting the country’s economic growth .
According to World Bank Group’s 2014 Doing Business rating, Russia is a lowly 92nd out of 120 countries in terms of ease of doing business .
Using the Internet for doing business is beyond many Russian entrepreneurs’ understanding. According to the survey figures, less than 35% of Russian citizens use the Internet on a daily basis.
The causes and consequences of Russia’s geographically uneven SME development, identified with the help of the logical framework approach, are presented in Figure 1.
Figure 1. Uneven development of SMEs across Russia’s federation subjects: problems tree chart
Issues connected with taxaction, administrative burdens, real estate, bank loans, and support programs must be addressed by the local governments.
But the lack of a comprehensive monitoring system can be addressed by developing a system that would integrate both qualitative and quantitative indices of SME performance.
One of the causes of uneven regional SME development, detected by the authors of the present paper was the absence of a comprehensive regional-level SME monitoring system, which means no full picture of regional SME development is available.
It is possible to monitor small and medium enterprise performance at both federal (national) and regional levels. The main sources of information about business activity are as follows:
Most of the above assess SME performance using quantitative (statistical) data. To obtain a complete picture of conditions for small and medium enterprises, a monitoring system must include analysis of both quantitative and qualitative data. There are federal-level figures based on qualitative data, the so-called OPORA Index. However, reporting on only 30 of the regions, it does not provide full qualitative data for assessing mesoscale SME performance. There is, therefore, a lack of data on mesoscale SME performance.
Using the logical framework approach, Figure 2 features the above problems tree chart transformed into a goals tree chart.
Figure 2. Actions needed to even out development of SMEs and their results
An alternative means of detecting problems would be blogosphere analysis. The term, as was mentioned earlier, came into prominence after 2002. In many aspects of life, both in Russia and internationally, blogosphere analysis has become an instrument of choice. As argued by Inna Kouper in Conversations in the Blogosphere: An Analysis «From the Bottom Up», an article on blog interconnections, the blogoshpere constitutes a small world capable of influencing the international community .
Many major international and Russian companies place a lot of emphasis on monitoring their network traffic. Foreign companies stress the importance of this kind of control  . However, only a small number of companies engage in analysing the blogosphere to assess the appeal of their products.
Blogosphere analysis can also be used to assess SME performance. Some of the methods here are borrowed from the Chamber of Commerce and Industry’s Monitoring SME Performance in the Russian Federation by Analysing the Blogosphere report . The parameters used in the analysis were as follows:
Figure 3 presents online platforms most widely used for discussing SME issues, with their respective share of total SME-themed discussions online, according to total discussions figures per platform.
Figure 3. Blogs and social networks most widely used to discuss SME issues
The no.1 platform for discussing SME issues is the microblogging website Twitter, used for posting individual comments and suggestions regarding SMEs. Using the Topsy  online tool for assessing the content of microblog entries across a period of 30 days, 1500 comments were found to mention SMEs, with around 50% expressing a positive attitude. The no.1 issue discussed was the contributions SMEs have to make to minicipal budgets. Most Twitter users think it is bound to hinder SME development. Users were supportive of reports of regional programmes aimed at encouraging the lending to SME.
The analysis of the tone of the comments made on blogging and social networking websites gave the following results: 28% of comments were negative, 32% were positive, with the remaining 45% neutral in tone, meaning, as is the case with Twitter, the number of positive and negavite comments was roughly equal. Below are the SME-themes search query figures for the Siberian Federal District. The search queries fit the causes of uneven regional SME development referenced in Figure 1. The figures were obtained using the Socialmention online tool . Table 1 lists the top 10 queries.
Table 1. Search engine queries in the Siberian Federal District:
|Subject||No. of queries||Popularity|
|Quitting a business||5,171603||7|
The high number of queries indicates that users are not finding the information offered by official regional websites on SMEs adequate. In particular, analysing the related blogosphere search queries for the top 3 subjects (sole proprietorship, business support, and taxes) reveals that users are unable to find sufficient information on how to start a business, taxation, and business support programs.
Blogosphere analysis is incomplete without a study of trends. Below is a screenshot of Google Trends discussions dynamics for 2006-2014 for the Siberian Federal District.
Figure 4. Search engine queries trends
There is a noticeable rise in the number of people researching sole proprietorship. Taxes remain a popular subject although gradually less so.
The comparison of the goals tree chart (Figure 2) and the blogosphere analysis data shows that a system for monitoring SME development would facilitate access to up-to-date compresensive data on regional SME performance and a more complete implementation of the government’s SME development strategy.
Wrapping up the literary part of analysing the issues currently facing SMEs, it is clear that the goal undertaken by the present study is timely because of
A detailed description of the proposed monitoring method follows.
The proposed method of monitoring SME performance across federal subjects of Russia involves the following stages:
3.1. Researching the link between SME performance and economic growth
Despite the importance of researching the link between SME performance and economic growth stressed by international studies (Joce C. Farinas, Lourdes Moreno) , the existing monitoring projects overlook the aspect. The study cited above offers three possible variants of the link:
This methodology has previously been tested to assess the two-way relationship between economy and entrepreneurship in 21 country, though Russia was not among them. Also, the exercise involved entire countries, not their regions. It is appropriate to apply this method to the study of a federal subject of the Russian Federation.
The statistical analysis was performed using the scientific, analytical, and statistical data on the state of SMEs, SME development predictions, information on government programmes for supporting regional SMEs, as well as studies into the link between SME performance and economic growth.
Figure 5 represent the study process in IDEF3 notation (using the ERWIN Process Modeller software). The functional structure implies creating a software implementation of the proposed monitoring method with reports on its practical results.
Figure 5. Proposed monitoring system’s functional structure
To assess the correlation between economic and business performance two kinds of indices were used: the indices of economic performance and the indices of SME performance:
Table 2 presents correlation matrix of functionally independent indices relationship (compiled with the help of the SPSS statistical tool)
Table 2. Correlation matrix
The 60 federal subjects of Russia were broken down into clusters by the level of SME development (for complete results see Appendix 1). The regions selected were found to exhibit direct correlation between business development and SMEs.
3.2. Level of SME development in a region
The statistical measures used to assess the overall direction of SME development in a region were as follows:
Figure 6 presents a tree diagram of cluster distribution for regions of the Siberian Federal District (done using the Statistica software package).
Figure 6. Tree chart
Krasnoyarsk region, Novosibirsk region, Omsk region, Irkutsk region, Kemerovo region, Altai region, Republic of Tuva, Zabaykalsky region, Republic of Khakassia, Buryat Republic, Altai Republic.
The first group includes regions with the highest statistical indices, topped by the Novosibirsk and the Kemerovo regions.
The second group includes regions with the lowest number of SMEs and average workforce figures. This means SMEs in the Altai Republic, Zabaykalsky region, Buryat Republic, and Republics of Tuva and Khakassia are less well developed.
Appendix 1. Federal subjects of Russia by SME development
|Cluster 1 – High SME development regions|
|Altai region||Krasnoyarsk region|
|Republic of Bashkortostan||Perm region|
|Chelyabinsk region Nizhny||Novgorod region|
|Republic of Tatarstan||Novosibirsk region|
|Samara region||Moscow region|
|Rostov region||Sverdlovsk region|
|Krasnodar region||St. Petersburg|
|Cluster 2 – Medium SME development|
|Kaluga region||Kaliningrad region|
|Tver region||Vladimir region|
|Arkhangelsk region||Tula region|
|Penza region||Republic of Udmurtia|
|Ryazan region||Voronezh region|
|Smolensk region||Saratov region|
|Ulyanovsk region||Omsk region|
|Chuvash Republic||Irkutsk region|
|Leningrad region||Volgograd region|
|Tomsk region||Yaroslavl region|
|Khabarovsk region||Kemerovo region|
|Belgorod region||Primorsk region|
|Cluster 3 – Low SME development regions|
|Republic of Tuva||Kurgan region|
|Republic of Altai||Republic of Mordovia|
|Magadan region||Amur region|
|Kabardino-Balkar Republic||Republic of Mari El|
|Kamchatka region||Orel region|
|Republic of Dagestan|