Evaluating Generalized Alternatives Of Competition Regulation In Local Food Industry Market

General model to evaluate aggregated alternatives of regional competition regulation

Approaches to regulate, and as a consequence, to evaluate competition are connected with the ambiguity of “competition” interpretation.

Competitive situation is of interest to various market players: producers, consumers, and the state - because it sufficiently predetermines their market/competitive behavior. But end up consumers are interested in competition results, reflected in relative price reduction, diversity enhancement, and improvement of goods and services quality, while producers and the state are also interested in competition processes, and possibilities to influence state and nature of competition.

So it is natural to evaluate competition in the context of its various interpretations. Evaluation of competitive situation - either formal analytical or subjective – supplies market players with some basis for structuring their market behavior. Depending on the interpretation of competition, chosen by a market player, this behavior will have different emphasis. In particular, the leading form of competition appearance in economic sector: companies have no stable competitive advantages (functional interpretation), rivalry for market and resources (behavioral interpretation), quantity and market shares of producers (structural interpretation) defines direction of state influence, required for the market (Table 01 ).

The last column of the table shows major approaches to state market regulation, which are of interest to us. Depending on the evaluation of competition state in the sector and possible consequences of regulations, it is assumed that a primary decision will be made for each position of the last column: to intensify regional regulation, to loosen it, or leave without change. On top of that, there is a possibility, when regulations are required, but nationwide rather than regional.

Objects of such regulation are producing companies. So, we are talking not about regulation of competition in the consumer market itself (in our case on the food market) but rather about regulation of competition environment. This agrees with the interpretation of competition in the Federal Law of Russia “On competition development”, in which competition is understood as “rivalry of economic entities”. At the same time the “Standard of competition development in subjects of Russian Federation” adhere to the principle of customer priority (The Standard for the Development of Competition in the Russian Regions, 2015).

Therefore, it is possible to list several levels of decision-making (selecting aggregated alternatives) on state regulations of the market sector in the region (Figure 01 ).

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Analytic Hierarchy Process (AHP) as a method to evaluate aggregated alternatives of regional competition regulation

Saaty's (2008) Analytic Hierarchy Process is a simulation scheme for multiple criterion decision making problems, where factors are arranged in a hierarchic structure, which allows to numerically estimate the intensities of interaction between hierarchy elements, and produce an estimation of preference degree for each alternative with respect to a main goal.

A solution of decision-making problem using AHP consists of the following steps:

• analyzing problem and building a hierarchy (see Figure 01 );

• calculating local priorities and checking the consistency of the alternatives;

• synthesizing alternatives’ priorities with respect to a main goal and general evaluation of hierarchy consistency.

Calculating local priorities on each hierarchy level is conducted using paired-comparison method: elements of subsequent hierarchy level are compared in pairs with respect to the concept or property, expressed by elements of the higher level. A result of this comparison is the paired comparison matrix ω, whose elements are estimations of advantage of the element x_n over the element x_m with respect to elements of higher hierarchy. Estimation values in the following scale (Table 02 ) are taken from a dialog with an expert (or a group of experts with further estimate of opinion consistency).

Questions needed to construct the matrix ω are created in the following way, showing the essence of hierarchy analysis:

• ω01(i) – In which case regional authorities should make a decision to regulate competition in food market of Omsk region:

(i=1) when product diversity is decreasing;

(i=2) when product quality is deteriorating;

(i=3) when product prices are increasing?

• ω12(i,j) – What has a stronger impact on change of (i=1,j) diversity; (i=2,j) quality; (i=3,j) product prices on food market of Omsk region:

(j=1) change in product demand;

(j=2) companies from other regions entering (leaving) regional market;

(j=3) change in regional conditions and factors of production?

• ω23(j,k) – What may have a stronger impact on changes in (j=1,k) product demand; (j=2,k) activity of external producers; (j=3,k) business environment for local producers on food market of Omsk region:

(k=1) regional authorities support for innovation activities of companies;

(k=2) providing equal competition conditions for all companies of the market sector;

(k=3) regulating activities of monopolies in the market sector?

• ω34(k,l) – What has a higher value in regulating competition in the food market of Omsk region (k=1,l) in supporting innovation activities of companies in the market sector; (k=2,l) in providing equal competition conditions for producers; (k=3,l) in regulating activities of monopolies in the market sector:

(l=1) the problem cannot be solved by regulations on regional level;

(l=2) regional authorities should strengthen competition regulation of the sector;

(l=3) existing regulation terms should not be changed by regional authorities;

(l=4) regional authorities should lessen competition regulation of the sector?

As a result of hierarchy analysis, using matrices of local priorities defined above, we construct the row vector, whose elements are required values of alternative priorities (lowest hierarchy level) with respect to decision making goal (top hierarchy level):

A = ω01(i) * ω12(i,j) * ω23(j,k) * ω34(k,l)

Possible violations in answers logic, shown up as transitivity of judgments violations, are estimated using corresponding consistency measures (Saaty, 2008), which are calculated in our case using © Mpriority program.

Evaluating possible competitive behavior of produces

Evaluation of aggregated alternatives of competition regulation, showing the perception of need for state regulation of competition, should be complemented by evaluation of competition means, considered by producing companies for their own development. In our case, we use polls of companies as the method to collect data for such evaluation. Therefore questions, aimed at highlighting various aspects (interpretations) of competition, were included in the model, and correspondingly to surveys (Table 03 ).

We use factor analysis model to obtain numeric estimation for possible competitive behavior of companies, this allows us to detect latent characteristics of the investigated subject. They are determined as the result of generalization of elementary attributes and serve as integrated characteristics.

In our case elementary attributes are factors, influencing competition, as perceived by producing companies. The list of these factors is shown in the second column of the table (see Table 03 ). Further these factors will be called elementary factors, initial characterics, or just characteristics, due to factor analysis tradition. Integrated characteristics, which are detected in course of factor analysis, are further called latent factors (detected factors) or just factors.

Besides the questions about competition perception, the survey includes questions, describing respondents (for end up users we ask for: age, gender, social background, place of living, etc.; for companies we ask how long they are on the market, size, location, etc.). The answers allow to discuss competition perception of respondents by various consumer segments and industry groups.

Results of aggregated alternatives evaluation

Involving experts and implementing AHP (using geometric average for expert answers) we obtained the following results (Table 04 ).

Results show that regional regulation of food industry sector should be strengthened. From the state point of view (see Table 01 ) this means, first of all, support of innovation and providing equal competition conditions.

As was already stated, regulation is aimed at producing companies, therefore it is important to consider local priority estimation for regional production factors, provided in (Table 05 ).

Therefore, the nature of regional food markets is such that in making practical decisions it is reasonable to focus more on behavioral nature of competition rather than on structural and functional approaches for the competition interpretation, which are widely used at the moment (see also Dobson, Clarke, Davies, & Waterson, 2001; Kokovikhin, Ogorodnikova, Williams, & Plakhin, 2018). Now, in the framework of functional approach, market system provides natural operating mechanism for companies, pushing companies-outsiders off the market, which corresponds to current situation in the food market in Omsk region. The conclusion also relies on a notice that there are no monopolies in the sector, which may distort the market.

Quantity analysis of companies in the regional food market and their dynamics (Report “The state and development of the competitive environment in the markets of goods, works and services of the Omsk region by the result of 2016”, 2016), state support for agricultural producers, infrastructure for state support, the big city in Omsk region, show that there are enough market participants to consider the structural interpretation of the term “competition”. Therefore, using these approaches, we may state that there is competition as such on the market, since the market is built, competition mechanism is working, a number of entities is sufficient, and market infrastructure is in place in Omsk region.

Another argument for the behavioral approach is that decision making process on companies’ behavior in consumers market is connected mostly not with factors coming from objective statistical data for the sector but with subjective perception of market by buyers and producers.

Evaluation results of possible competitive behavior of producers

From behavioral point of view one of the most important competitive factors is a way of computation, i.e. a complex of economic, management, and marketing variables (technologies, methods, tools), which can be used by a company to gain competitive advantages.

Research results of producers’ possible competition behavior are obtained in the form of aggregated weighted estimates in numerical scale, showing the influence type of supply and demand on competition perception by end up customers; and also in the form of clustering tree using Clustering Method to detect competition factors in producers’ perception, and split them into groups (Mamontov & Chernobaeva, 2017).

Further we present the results of competition perception by producers, which were obtained using factor analysis and clustering methods.

In view of load values of latent factors in two-factor model (see Table 06 ), Factor1, in essence, shows readiness of companies to compete by extension/development (maximal positive load values for this factor are: increasing diversity (0.89); orientation on new geographical markets (0.76); and the factor which is close to them is readiness to product innovations (0.6). Positive values of these factors are in good standing with the factor, having the smallest absolute value – 0.77 (leaving the market). Therefore, positive direction of axis for Factor 1 (see Figure 02 ) may be interpreted as companies’ orientation on extensive expansion of product offer. Negative direction of this axis would be orientation on diversification and leaving the market.

Factor2 axis may be interpreted in the following way: positive direction shows orientation on quality competition. Note that load for these characteristics in latent factor (0.66) is not very high, even though much higher than other loads. Negative direction of this axis reflects marketing way of competition, including segmentation (correspondingly -0.80 и – 0.85).

Note that model factors explain no more than half (48%) of variance (Table 07 ), which is evidence of significant diversity of competition approaches, used by companies.

Detecting third latent factor (Table 06 ) in principal does not change the structure of factor loads for the first two factors (axis direction is not taken into account in this type of analysis) and allows to detect relatively weak factor of competition in the form of reducing production costs (load 0.68) with 13% variance explained by this factor (Table 07 ).

It can also be noted that price competition and competition based on engineering and manufacturing innovations are not priority types of competition for the whole sector in general.

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Group (complex) ways of competition, reflected as closeness of points in the space of factors (Figure 02 ), is confirmed and depicted as a result of clustering analysis of initial characteristics (Figure 03 ). From the resulting dendrogram we see that in conditional closeness scale (horizontal axis) the smallest distance (maximal closeness) is between two groups of characteristics: “product diversity” (2) and “new geographical markets” (9), also “marketing innovations” (7) and “segmentation” (8). The first group is also close to characteristics “new products” (6).

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It can also be noted that understanding, that the proper way of competition is quality improvement, is increasing with company’s age (Table 08 ), which is evidence, in our opinion, that such companies have long term orientation and understand deep needs of end up customers.