1. An opportunity to choose the language. It provides a company with high attractiveness for a potential partner. Moreover, multi-language web site is orientated toward cooperation with potential foreign partner. It is especially relevant in global economy. Shaw and Holland stated there is a high probability that a company, which provides information about its product in different languages in its web site, repays these expenditures in the form of cooperation with foreign firms.(Shaw and Holland 2010)

2. Presence of the section group “Investors”. This web site section group covers the information about company's performance, financial results and competitive advantages. It helps to other market players to take decision about cooperation with a company, which is ready to release information of this type.

3. Access to information, which is presented on more than ten webpages. Web site of this type is considered to provide potential partners with full company's characteristics. It also increases company's attractiveness for other firms.

4. Using flash animation elements. It is an indirect indicator of company's willingness to use completely new and innovative web technology. In the framework of cooperation, this technology is connected with creation of joint Internet products (web sites), which task is to facilitate cooperation with the help of exchange of ideas and suggestions. (Chen, Zhang, and Zhou 2007)

If two or more options characterize a company's web site, it is of a high quality. If a company, which is a participant of association, has subsidiaries and a web site of a high quality simultaneously, it is considered to cooperate with other market players. By applying this system of proxy-indicators, we get the following results:

Table 4

Classification of companies by participation in cooperation

Two-way ANOVA test for statistical difference of firms that cooperate and do not cooperate

At this stage of the research, we present the results of three models, analyze their quality and interpret estimation results. The range of the factors in the model has already been discussed in the Methodology. Variables definition is presented in Appendix 5.The data in all models is presented in the cross-sectional form due to specific features of MLE method.

4.1 Model Including All Sectors of Russian Economy

This model includes all sectors of Russian economy. It covers 5163 observations: 5% of trade companies, 9% - finance, 8% - services, 46% - manufactory, 19% - construction, 12% - energy. Among them there are 1721 observations of cooperation participant: 87 in trade industry, 151 - services, 192 - finance sector, 256 - energy, 283 - construction and 753 - manufactory. The mean EVA of companies that take part in cooperation is positive; it is much higher than of organizations, which prefer not to cooperate. The mean EVA of the second group is negative.

At this stage, we discuss the result of the model, presented in Table 6. Table 7 demonstrates the quality of the model: the number of correct and incorrect predictions. Area under ROC curve equals to 0.74: the quality of this model is high. (Appendix 6)

Table 7

Number of correct and incorrect forecasted results in model including all sectors of Russian economy

Location in the city with more than 1 million citizens, employment of foreign capital, competition (1% level of significance) and operating in high technology intensive industry (10% level of significance) are external factors that influence cooperation significantly. There are much more significant factors among internal characteristics: size of the company, company's experience, some financial indicators (fixed assets, net capital expenses, invested capital and book value) (1% level of significance); implementation of a strategy and intellectual capital or knowledge management strategy (5% level of significance); qualification of boards of directors (10% level of significance). Consequently, the influence of internal factors exceeds the influence of external environmental characteristics on participation in interfirm relationships. The hypothesis 1 is accepted.

Table 8

Results of model including all sectors of Russian economy estimation

Forecasted probability to cooperate is higher for companies that operate in high and medium technology intensive industries by 20% (10% level of significance) Companies operating in the real sector tend to cooperate with the high probability, because they participate in resource-based and knowledge-based cooperation more often than other companies do. Operation in these industries is associated with high-quality production, which is inseparably associated with high risk and costs. (Miotti and Sachwald 2003; Estanyol 2010; Edwards-Schachter et al. 2012) Cooperation helps companies to minimize and sometimes avoid these consequences of operation. The link is illustrated on the Figure 1. This fact confirms the hypothesis 2.

Figure 1 Predicted probability of cooperation for companies from different sectors; y-axis of a plot - size of the company

The negative influence on cooperation has the volume of fixed assets, net capital expenses, book value and invested capital (1% level of significance). It is associated with resource-based approach. Companies with low meaning of these factors tend to cooperate, because participation in interfirm relationships lets them use assets of other firms. It, in turn, let them reduce risks and minimize costs.(Estanyol 2010) This link also reflects the tendency of large Russian companies to disintegration because of current economic situation in Russia. (Kusch, 2006)

The age has nonlinear effect on cooperation. Analyzing of polynomial terms of higher orders in the model and an attempt to approximate the link nonparametrically does not change the obtained results. Company's tendency to cooperate increases as a firm becomes more mature until the certain moment. Until this age, company's probability to cooperate increases, after this age it equals to one, that is associated with the concept of company's lifecycle. Moreover, it is connected with the tendency of Russian companies to cooperate as they become mature, that means that they have enough materiel resources to find potential partners and to invest in interfirm relationships. This connection corresponds with the idea that many Russian companies prefer not to cooperate with newcomers, which can be explained by the tendency of Russian market players not to trust to other entities. (Sheresheva and Peresvetov 2012)

4.2 Model Including Manufacturing, Energy & Chemical and Construction & Real Estate

The model includes 1979 observation; 892 of them are participants of cooperation: 195 firms in construction; 177 - in energy sphere and 520 in manufactory. Participants of cooperation are innovative, because they implement IC and KM more often than other organization. Probably, it is one of the components that provide participants of interfirm cooperation with the high EVA.

At this stage, we discuss the results of the second model, which are covered in Table 10. Table 9 presents the quality of this model.

Table 9

Number of correct and incorrect forecasted results in model including Manufacturing, Energy & Chemical and Construction & Real Estate

Area under ROC curve equals to 0.71 The Figure is presented in Appendix 6.

Table 10

Results of model including Manufacturing, Energy & Chemical and Construction & Real Estate

External factors (location in the city with more than 1 million citizens, employment of foreign capital and location near top-10 universities) have positive influence on cooperation (1% level of significance). Cooperation in Energy & Chemical industry is more popular than cooperation in construction and manufacturing industries in Russia by 26% (10% level of significance). Companies from this industry are export-oriented that provides them with cooperation with foreign partners. In addition, energy organizations tend to cooperate with government. (Buchaev 2013) This link implies the idea about the influence of sectoral affiliation on cooperation.

Such internal factors as company's experience, size, fixed assets and invested capital (1% level of significance); qualification of boards of directors (5% level of significance); intangible assets, implementation of IC or KM and number of patents(10% level of significance) influence company's probability to cooperate. The experience of the company has nonlinear effect as in the previous model.

Companies with high patent activity have the higher forecasted probability to take part in interfirm relationships that those companies that have low patent activity (Table 11). The link between components of intellectual capital and cooperation in real sector of economy is explained by an incentive to cooperation - access to new resources and knowledge, which leads to learning and sharing experience, increasing flexibility and access to new markets.(Roijakkers 2003) The relationships between patents and cooperation depend on the characteristics of firms' industrial environments: this effect is much stronger in technology intensive sector. (Janne and Frenz 2007)

Table 11

Comparison of forecasted probability of cooperation for companies with high and low patent activity

Using information about the factors' influence on cooperation in technology intensive sector, we compare forecasted probability to cooperate of two different types of firms (Table 12).

Table 12

Comparing forecasted probability to cooperate of two types of Russian companies

The first company is characterized by low patent activity; members of boards of directors in this firm has 0 point; this company does not implement IC or KM strategy and it is not located near top-10 universities. The second company is located near top-10 universities; it has many patents, implement IC or KM and its boards of directors is of good quality (2 points). Probability of cooperation for the second company is much greater that for the first one.

According to the developed model, components of intellectual capital encourage cooperation; it means that Russian companies from high and medium technology intensive industries are ready to share some components of intellectual capital. This fact corresponds to the tendency of companies all over the world, which cooperation is positively associated with their innovation performance.(Miotti and Sachwald 2003; Janne and Frenz 2007; Dachs, Ebersberger, and Pyka 2008) However, such internal characteristics as intangible assets, invested capital and fixed assets discourage cooperation, because Russian companies with high volume of different assets have a tendency to information hiding and disintegration because of uncertainty of business environment and relationships and relation to other market players as to competitors. (Michailova and Husted 2003)

4.3 Model Including Trade & Related Services, Services and Finance & Insurance

The third model is devoted to trade, services and finance industries. The model is developed for 716 observations, 235 of them participate in interfirm relationships (20% from trade industry, 35% from services industry and 45% from finance sector). Participants of cooperation in this sector are more successful at value creation than other firms that operate in this sector. We cannot call this sector innovative, because only 7% of all companies implement intellectual capital or knowledge management strategy. Table 13 contains the quality of this model. Area under ROC curve is 0.73. (Appendix 6)

Table 13

Number of correct and incorrect forecasted results in model including Trade & Related Services, Services and Finance & Insurance

According to the estimation of this model (Table 13), such external factors as location in the city with more than 1 million citizens, employment of foreign capital, location near top-10 universities and citation have an impact on cooperation (5% level of significance). Brand name reputation has negative influence on cooperation. Brand name reputation is a critical factor in the restaurant industry, because it is a key determinant of whether or not potential customers patronize an establishment. Customers often make first-time purchases based on brand name reputation. Thus, chains with unknown brand names should engage in cooperation with well-known entities to become more famous. (Combs and Ketchen 1999)

Cooperation in finance sphere is more popular than cooperation in trade industry (forecasted probability of cooperation decreases by 31%) and service industry (forecasted probability decreases by 29%) (5% level of significance). This tendency reflects the popularity of financial industrial group in Russia. While discussing the specific features of cooperation in interfirm relationships, we noticed that cooperation in trade and services industries is not popular among small companies, which present these industries in Russia.

The internal characteristics like book value, fixed assets, qualification of boards of directors (1% level of significance) and size, company's experience (10% level of significance) influence cooperation significantly. The components of intellectual capital do not influence on cooperation in finance, trade and services industries, because operation in this sector do not depend on technology and production directly, that is why companies can ignore to some degree incentives connected with intellectual capital. (Dumont and Meeusen 2000; Audretsch and Feldman 2004).

Table 14

Results of model including Trade & Related Services, Services and Finance & Insurance

Overall, we can draw a conclusion that companies from finance, trade and services industry cooperate with high probability if they are mature, because in this way they have enough power and resources to find potential partners. However, potential participant of interfirm relationships understand that they not famous enough to improve their financial position by functioning along; or they have not enough assets or investments to be flexible and to increase their market position.

Conclusion

The aim of this paper attempts to provide information for a better understanding of interfirm cooperation in the country, on which the literature and empirical evidence is scarce. In this sense, the study aims to provide a major knowledge about different motives of cooperation and contributes with empirical evidence on the identification of company's probability to cooperate in different sectors of Russian economy.

This paper presents quantitative research of interfirm relationships. Contribution of this paper is developed criteria for identifying company's probability to cooperate with the help of its internal and external features. These factors, in turn, demonstrate motives that encourage Russian companies to cooperate and reflect special features of interfirm relationships in a certain country in different industries. It is known, that current economic turbulence prevents Russian markets from the development of relationships in Russia. However, according to the obtained results, the influence of internal factors exceeds the influence of external characteristics of environment on cooperation in Russia. Consequently, Russian market players can disregard some environmental characteristics and develop their activity connected with interfirm relationships. It has significant implication for companies operating on Russian markets. Nowadays it is essential for market agents to recognize potential participants of interfirm relationships and their motives for cooperation, because participating in cooperation is an indicator of a firm, which optimizes its operation, maintains costs and conforms to current market conditions. This information is valuable for potential investors, managers, boards of directors' members and owners of a company in the Russian current market in actual practice.

In addition, it was found that cooperation in Russia depends on industrial affiliation: interfirm relationships in different sectors are associated with various factors. Furthermore, forecasted probability of participation in interfirm relationships is different for various industries. Despite the fact that, we cannot carry out comparative analysis of the results of models developed for various samples, this aspect has significant implications for governmental policies. Probably, the forecasted probability of cooperation in various sectors of Russian economy is not equal to each other because of different supported policy and financial mechanism of supporting interfirm relationships. In turn, it can be an important barrier to cooperation in such sectors as Trade and Services. It means the necessity of Russian economy in the policy of fostering interfirm relationships in these industries to correspond to the new market conditions.

Although this study has attempted to provide an in-depth view on interfirm relationships in Russia, factors influencing such collaborative arrangement are not exhaustive. Given the diversity of current business environment, future study should therefore consider other elements that challenge the interfirm linkages. We noticed a severe problem of developing interfirm relationships in Russia connected with company's tendency to disintegration because of lack of confidence to other market players. Thus, including such social factor as trust to other partners in the study with the help of questionnaire method can improve the quality of the model. Moreover, this method can help us to eliminate the main constraint of the paper: using proxy-indicators as a dependent variable in the estimated model. Another way to remove this limitation is the usage of specialized databases like SDC Platinum. Special databases provide researchers with specific information about the number of companies, with which a certain firm cooperates. It will maximize the reliability of the study and minimize the probability of obtaining biased results.

Another constraint of the study is associated with factors' classification. According to the existed papers, there is no standard classification of motives to cooperation. Thus, it is hard to separate external features from internal characteristics because of their interdependency. Probably, the definition of some motives and factors is misguided.

Finally, future research should analyze the influence of the presence and effectiveness of government programs and the presence of private or public funding organizations on cooperation. The most appropriate way to do it is analysis of dynamic panel. In spite of these limitations, the research can be relevant. It provides with information about the development of interfirm relationships between Russian market players and point out the specific features of this phenomenon in various sectors and industries. Furthermore, it has significant implications for governmental policies in Russia and Russian market players who can use the results of this paper, while taking strategic decisions and determine a potential partner for cooperation.

Acknowledgments

This study comprises research findings from the «The Changing Role of Companies' Intangibles over the Crisis» carried out within The National Research University Higher School of Economics' Academic Fund Program in 2013, grant No 13-05-0021.

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