CC BY-NC-ND
47
DOI: 10.17323/j.jcfr.2073-0438.15.1.2021.48-66 JEL classification: C30, Q13
(cc)
Impact of Digitalisation
on Corporate Finance
in the Agro-Industrial Complex
Mikhail Chernyakov
Dr. Sci. (Econ.), Professor ORCID
E-mail: [email protected]
Novosibirsk State Technical University, Novosibirsk, Russia
Olesya Usacheva ei
Cand. Sci. (Econ.) Associate professor ORCID
E-mail: [email protected]
Novosibirsk State Technical University, Novosibirsk, Russia
Maria Chernyakova
Cand. Sci. (Econ.), Associate professor ORCID
E-mail: [email protected]
RANEPA Siberian Institute of Management, 630102, Russia
The presented model allows us to forecast with a sufficient degree of confidence (deviation not exceeding 10%) a probable value of the digitalisation index of dairy cattle breeding for 10 prospective economic entities of the Novosibirsk Region (Table 7). Consequently, the organisations which plan 'chipping' of their dairy herd may consider the digitalisation index of dairy cattle breeding a reasonable reflection of an attractive business format for them.
Journal of Corporate Finance Research, Vol. 15, No. 1, pp. 48-66 (2021)
For citation: Chernyakov, M., Usacheva, O. и Chernyakova, M. (2021) «Impact of Digitalisation on Corporate Finance in the Agricultural Sector», Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438, 15(1), сс. 48-66. doi: 10.17323/j.jcfr.2073-0438.15.1.2021.48-66.
Received 20 December 2020 | Peer-reviewed 10 January 2021 | Accepted 20 January 2021
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Abstract
The purpose of our paper is to examine the interrelation between digitalisation indicators of dairy industry government regulation and economic efficiency, using large corporations of Novosibirsk Region as an example. We propose to identify an integrated system approach to evaluating the influence of state programs related to digitalisation of the dairy industry on industry performance.
A system-wide transition to digital technology in the infrastructure of dairy industry regulation is nearly totally absent from academic research. The existing literature considers the influence of state programs and policies on the industry and proposes various performance indicators. However, it is uncertain how industry digitalisation may affect these performance indicators.
To address this gap in the literature, we propose a hypothesis of dependency between digitalisation indicators and performance indicators of dairy corporations. The basis of the methodology is the calculation of a digitalisation index used to assess the efficiency of government support of the industry corporations. In order to substantiate the hypothesis, we apply a correlation and regression analysis and established interrelations between the offered criteria (digitalisation index and share) and operating performance of dairy industry economic entities.
Our results indicate general consistent patterns and interrelations between digitalisation of state regulatory programs and the performance of dairy industry corporations. Our statistical analysis reveals digital technology as a tool of government has a significant impact on business performance. The offered digitalisation criteria and patterns of performance efficiency are indicative of the possibility to manage the digitalisation process based upon preset parameters of business performance.
Our research will be of interest to specialists developing state programs and policies applying digital technology, directors of dairy companies, and scientists who conduct research in related fields, who may use our approach for evaluating and forecasting performance in the dairy industry, accounting for the impact of government regulation.
Keywords: financial stability, dairy industry, government regulation, digitalisation, correlation and regression analysis, forecasting
Introduction
Recent decades have been characterised by a global trend towards increased interest in food security and the government's role in providing such security. On the basis of a review of literature in the area, we identified the following important aspects of the authors' research: the influence of factors on food security via the interrelation between armed violence and food supply chain [1]; via evaluation of influence of extractive industries [2]; via assessment of quality of government regulation and government efficiency [3]; via influence of government regulation on development of dairy industry [4], and the influence of food security itself on key indicators of national security such as public health care [5]. One of the key spheres of food security is supplying food to public including dairy products [6]. Dairy products, as a nutritious source of protein, fat, micronutrient elements, prebiotics and probiotics, make a substantial contribution to food security and human health [7]. So, the primary objective of any government is the provision of sustainable development and proper functioning of companies engaged in this industry. In spite of the fact that the dairy industry exists in every state, its state and development level differ significantly from country to country. Developed economy countries such as European countries, USA, India, China, New Zealand, Australia are the leaders in this sphere. The main milk producer among them is the USA which accounts for 25% of the total output [8]. In this market segment Russia is 6th, and its share in the total milk output is approximately 8%. The state and further development of dairy subcomplex are subject to a significant government influence. Analysis of the scientometric database of the Russian Index of Science Citation (RISC) showed lack of attention to issues of dairy industry government regulation (less than 3%) and digitalisation in particular
(0.15%). Analysis of the scientometric database WoS yields similar results. It also gives little, although more than RISC, attention to issues of dairy industry regulation (a little over 5%) and digitalisation in particular (0.2%) while dairy sub-complex regulation under conditions of digitalisation is described in just four papers. Exponential growth of interest to publications dedicated to dairy subcomplex regulation against the background of digitalisation was revealed.
Heavy demands are placed on the system of government regulation, and one of them is its efficiency. In the scientific literature, a lot of research is related to evaluation of state programs and policies [8-11]. However, the interrelation between government influence and efficiency of industry development under the circumstances of digitalisation has been insufficiently studied [12]. In our opinion, there is no doubt that study of influence of the state digitalisation policy on the state and development of dairy corporations is of academic interest. In our research we tried to generalise the accumulated experience and offer a common approach to evaluation of digitalisation influence on performance of dairy corporations.
We presume that results of our research and the ones similar to it will be interesting to specialists developing state programs and policies applying digital technology, directors of dairy companies, and scientists who conduct research in this and related fields.
Literature Review
Assessment of the State of the Dairy Industry
Dairy industry development trends in Russia are dubious. So, in the past few decades milk production in Russia has showed a downward trend, while its efficiency has grown (Table 1).
Table 1. Main performance indicators of agricultural organisations for 2000-2018 (according to Rosstat)
Indicator Year
2000 2014 2015 2016 2017 2018
Number of cattle, million heads 16.5 8.5 8.4 8.4 8.3 8.1
Number of cows 6.5 3.4 3.4 3.4 3.3 3.3
Raw milk production, millions of tons - 30 29.9 39.8 30.2 30.6
Milk production (except raw milk), thousands of tons - - - 5430 5301 5382
Butter and butter pastes, thousands of tons - - - 251 270 267
Cheese, thousands of tons - - - 450 454 467
Condensed milk products, millions of conventional tins - - - 842 837 806
Milk products for infant food, thousands of tons
229 285 313 Higher School of Economics
Year
Indicator
2000 2014 2015 2016 2017 2018
Consumption of concentrated feed per 1 liter milk 0.31 39 0.4 0.4 0.4 0.41
Milk output per 1 cow, kg 2502 4021 4134 4218 4368 4492
Milk sales, millions of tons 16.1 19.4 19.8 20.3 21 21.5
Milk vendibility (share of sales of production) 81.6 93.7 94.2 94.5 94.7 94.8
Number of agricultural organizations, total, thousand, including: 27.7 5.9 5.2 5 5.2 5.2
Number of profit-making organisations, thousands 13.7 4.3 4 3.9 4 3.8
Share of profit-making organizations, % 49.3 73.6 77 77.7 75.6 73.8
Product profitability in cattle breeding, % 1.4 18.3 15.4 9.8 12 12.8
Profitability of milk and milk products, excluding budget subsidies, % - 23.7 19.5 18.5 25 14.5
Profitability of milk and milk products including budget subsidies, % - 33.3 26.6 28.2 32.3 23.9
A decrease in raw milk production of more than 40% is caused by cow livestock reduction in agriculture. Reduction in the size of dairy herds resulted in a decrease of cattle stock and, consequently, the decline of production in the meat industry. Research conducted by Russian scientists discovered a trend of outstripping rate of cattle head count decline in comparison to milk yield per head of livestock which they correctly transpose to all Russian regions [13]. Within the reviewed period milk output per cow increased by 1.8 while the cattle stock decreased more than twice. What is conspicuous, is the dramatic reduction in the number of agricultural organisations, from 28,000 in 2000 to 5,000 in 2018, but still the share of revenue-earning enterprises grows. The industry state may be evaluated as unstable. Some indicators (for example, milk and dairy products output, product profitability) show an ambiguous trend: growth periods are followed by declines and vice versa. According to Table 1, due to budget subsidies the product profitability is higher on average by 7-8%. Self-production of milk increases but at the same time the structure of agricultural production changes by way of decrease of the milk share.
The dairy industry in Russia, as well as in other countries, is susceptible to government regulation. But along with this, the problems the government solves are different. So, in China and Brazil intensive growth of milk production is encouraged by price control and capital indemnification, in EU, USA and Canada restraint policy is implemented to solve the problem of milk excessive production, and independent quality inspection is applied at all stages of the production process. In Russia, milk producers' subsidising policy is carried out at the federal and regional
levels in the form of concessional lending offered by PJSC SberBank and PJSC Russian Agricultural Bank, compensatory and stimulating subsidising (subsidies for development of genetic and pedigree infrastructure, recovery of a part of capital expenditure), and concessional leasing. The current policy of government support of dairy industry implemented in recent years resulted in increase of raw milk output while consumption of dairy products decreased, which is shown in Figure 1 [14].
Figure 1. Dynamics of production of per capita milk consumption in Russia for 2012-2019, %
Rate of change of average per capita consumption
Rate of change of milk production
Government support is based on a corresponding legal framework comprising state programs as well as laws and regulations which regulate the dairy industry. The problem in the system of industry government support
consists in nonconformance of the criteria for programs' efficiency evaluation to the indicators embodied in them. One such indicator is the level of milk and dairy products self-production, which is 84% at present, while the established key indicator amounts to at least 90%. The following key indicators of implementation of the program for development of agriculture and regulation of markets of agricultural products, raw materials and commodities for 2013-2020 were established: growth of livestock products output (in comparable prices) by 20.8%, growth of the index of physical volume of capital investments in agriculture by 36%, rise in profitability of agricultural organisations by at least 10-15% (taking into consideration subsidies). However, the above indicators are not performance indicators and, consequently, they cannot be used as criteria for evaluating the efficiency of state programs.
A. Panyshev and O. Katlishin specified in their paper the problem of assessing the influence of a certain state program on dairy industry development [8]. The approach-
es to evaluation of government influence on the dairy industry are studied in papers by Russian [12; 15; 16] and foreign authors [17-19]. The search for and substantiation of the optimal way of government regulation of producers and consumers of dairy products are described in the paper by E. Twine [20]. J. Tricarico et al. [11] speak of the possibility of public-private partnership in regulation of the dairy industry. Y. Chen and X. Yu assessed the influence of subsidies on competitiveness of the Chinese dairy industry [18]. The literature review is indicative of the problem of efficiency of state programs aimed at supporting and developing the dairy industry. On the basis of our review of academic papers [6; 8; 13] in this paper we make an attempt to systemise the problems of low efficiency of state programs regulating corporations in the dairy subcomplex. Unlike the existing research, we idnetify innovative problems which have to be addressed.
The problems of low efficiency of government programs intended to support and develop the dairy industry may be systematised as follows (Figure 2).
Figure 2. Key problems of low efficiency of state programs in the dairy subcomplex
• insufficient government support;
• problems of concessional lending; Economic • a large amount of counterfeit dairy products;
• input intensity of the industry;
• problems of effective coopertaion of dairy producers and distribution network
Political • monopolisation by foreign companies of the dairy industry
• low efficiency of use of modern feeds;
Technology-related • outdated technology in milking operation;
• high degree of manufacturing equipment wear;
• capacity bottleneck.
Investment-related • low attractiveness of the industry to private investors
• an extremely low use of modern technology for collection and processing of data on
Innovative the state of dairy herd;
• insufficient digitalisation of dairy production
Government Regulation of the Industry under the Conditions of Digitalisation
Academic literature has not yet accumulated a sufficient amount of research on dairy industry government regulation against the background of digitalisation. The majority of research is ad hoc and non-systemic. Absence of a consistent approach impedes evaluation of influence of government regulation digitalisation on operations of dairy entities and, as a consequence, the assessment of the efficiency of state programs and policies in this economic sector.
So, scientists study various aspects of digitalisation issues: investment-related [21], manufacturing [22], and financial [23]. Digitalisation of corporations is considered as an essential prerequisite for government regulation of the dairy industry [24].
A successful digitalisation of the dairy and other AIC industries depends to a great extent on the level of digital infrastructure built in a country. We presume that nowadays one of the components which characterise efficiency of state programs implemented in the industry should be the level of its digitalisation. The strategy of agricultur-
al-industrial and fisheries industry complex development for the period up to 2030 defines one of six goals - AIC digital transformation. This associates with the national goal to speed up implementation of digital technology in
the economy and the social sector. It is assumed that it may be achieved due to implementation of the AIC state program and the national project Digital Economy of the Russian Federation.
Figure 3. Problems hindering digitalisation of the agro-industrial complex
Lack of financial resources to implement ICT
a bipolar economy evolved in the agricultural sector:
one side is represented by highly profitable enterprises with a wide access to high performance technology (most often agroholdings);
the other side is represented by enterprises on the edge of payback which use outdated technology
Shortage of skilled personnel
In Russia there are half as many IT specialists engaged in agriculture than in the countries with a traditionally highly developed AIC;
The Russian agricultural sector needs approximately 90,000 IT specialists
Absence of digital infrastructure
underdevelopment of digital infrastructure in rural areas; digital inequality between town and countryside
Imperfection of legal regulation of ICT development
the issues of development of the system of government information support in agriculture are governed by art. 17 of Federal Law of December 29, 2006 No. 264-FZ On Agricultural Development which needs improvements and adaptation to the current situation
Consequences of imperfection of legal regulation of ICT development
a weak policy of agricultural protectionalism;
poor cooperation of milk and dairy product manufacturers;
difficulties in their cooperation with processing companies and distribution networks
Source: compiled by the authors on the basis of [25-28].
Solving the problems (Figure 3) which impede AIC digitalisation, including the dairy industry, is a part of the national goal of an integrated development of rural areas which comprises the necessity to develop (taking into consideration the spatial development of the country) the pattern of AIC industries' and organisations' arrangement and specialisation arrangement on the basis of a multilevel integrated information space applying current digital technology1.
In order to provide government support to AIC, an Analytical Center is established in the Ministry of Agriculture of the Russian Federation. It builds up a digital technology and a solutions portfolio for AIC, and provides a more efficient informing of farmers on new opportunities, technology, and existing practices. Russian academic literature offers the main areas of improvement of parameters of dairy industry regulation via its digitalisation. A.V. Glotko et. al [29] outlined the
methodological framework for dairy industry modeling, applying digital technology, and showed the possibility to define the necessary amount of government financing to achieve the targeted indicators of the dairy industry at any regulation level by means of inverse forecasting. S.E. Terentyev et. al [30] described the implementation of cross-platform technology into manufacturing processes, the building of new business models of enterprises' market interaction on the basis of add-on applications for solving various practical problems as a prerequisite for development of the innovative mechanisms of the dairy industry. E.V. Zakshevskaya et. al offer a series of government regulation measures to overcome the problems structured in Figure 4. However, the possible ways of solving the above problems fail to comprise an important modern area of dairy subcomplex digitalisa-tion which may mitigate and even eliminate the majority of identified problems.
1 Digital Transformation of Russian agriculture: official publication - M.: Federal State Funded Research Institution Rosinformagrotech, 2019. ISBN978-5-7367-1495-7
Figure 4. Problems of development of the dairy subcomplex and measures of state regulation to overcome them
• a weak policy of agricultural protectionism;
• poor cooperation of milk and dairy product manufacturers;
• difficulties in their cooperation with processing companies and distribution
Development networks;
problems • a long investment cycle;
• a low operating efficiency of manufacturing;
• no well-established approach to control of livestock breeding, quality of used
materials (bull semen, supplement feeds etc.) and manufactured milk
• restoration of stock breeding in cattle breeding;
• investments in construction of drying equipment to even out the seasonal factor;
• strengthening of protectionist measures and targeted government support of milk
Regulation producers;
measures • increase of state control of price volatility in the markets of feed, fuels, electricity and
other resource markets;
• development of the transport, social and engineering infrastructure in rural areas to
attract skilled personnel
Source: [27].
The majority of papers on the regulation of the dairy subcomplex are dedicated to indicators of dairy stock farming as the basic parameters which define its development level. In particular, papers by A. Voitko [31; 32] describe some aspects of dairy stock farming development in Russia using the Stupinsky District of the Moscow Region as an example. He considers the issues of modernisation and enhancement of the industry efficiency by means of providing government regulation of production and sales of agricultural products. Digital technology will provide an opportunity to forecast the necessary extent of government support, its target orientation and eliminate intermediaries which assist in selling it.
Papers by N.I. Strekozov et. al are dedicated to the study of the problems in the dairy sector of AIC. They emphasise
that [33] the existing situation in the Russian dairy market
raises certain difficulties for using competitive advantages of Russian corporations. It is mainly related to underper-formance of government regulation in solving the top-priority problems in this multicommodity system [34]. The existing model of economic relations between all players of the Russian dairy market does not provide an optimal accord of interests of the dairy subcomplex partners. A price imbalance between the agricultural and servicing sectors of the dairy subcomplex caused a conundrum: on the one hand, agricultural corporations find it very difficult to sell their products (milk vendibility for all categories of entities does not exceed 65%), and on the other hand, there is a milk deficiency in the retail market where demand is unsatisfied [28]. The end links of the product promotion chain - an agricultural producer and retail buyer - are either forced to agree to the dictated terms and suffer losses, or reduce their share in the internal food market, which is
the main cause for continuing reduction in livestock number and milk and dairy products consumption per capita. In terms of Russian cattle breeding the main impediment in development is low profitability of the industry [35]. Digitalisation of government regulation of price formation processes and product promotion from the producer to the end consumer is necessary in order to solve these problems of the dairy subcomplex. Consequently, we may identify the main aspects which need digitalisation of the dairy subcomplex in the first instance:
Sale of dairy products over the internet, applying electronic commerce systems [36].
Use of cloud technology for cooperation and integration of economic entities in the virtual environment [37].
Evaluation of Corporations Readiness for Digitalisation
Dairy stock farming develops according to the scenario of the industries with rising expenses [38]. Reduction in expenses is possible mainly due to efficient development of innovative technology in the areas of manufacture, management, marketing, and logistics. Improvement of the ways of government support implies an increase of agricultural output with a simultaneous decrease in customer prices, which will make food affordable to the general public.
After analysis of the Russian experience of government regulation of dairy subcomplex digitalisation, we made an attempt to structure the problems of the enterprises of this industry and to offer ways of their solving. The obtained results are systematised in Table 2.
Table 2. Problems of state regulation of digitalisation of the dairy subcomplex and ways to overcome them
Problems Ways to overcome the problems
Insufficient attention to the issues of government regulation of the dairy industry in scientometric bases Analysis of results applying digital technology of sciento-metric bases and statistics
Lack of financial resources to implement ICT Shortage of skilled personnel Absence of digital infrastructure Imperfection of legal regulation of ICT development Necessity to develop (taking into consideration spatial development of the country) the pattern of AIC industries' and organisations' arrangement and specialisation arrangement on the basis of a multilevel integrated information space applying current digital technology
Insufficient genetic potential of livestock's productive capabilities Noncontact remote measurements using digital technol- °gy
Assessment of personnel qualification, exterior and non-contact measurements Possibility to apply the comparative analysis, scientific classification, systematisation, theoretical generalisation and statistical methods
Evaluation of the state of a regional dairy market Possibility to use digital technology as the most important resource of government regulation
Assessment of automation and robot automation of economic entities Development of digital technology which improves accuracy of data analysis, automation not just for operational staff but for specialists as well
Assessment of the potential of dairy farming and the dairy industry Development of digital technology aimed at vendibility improvement of the produced milk
Development of economic entities Development of the mathematical apparatus of digital technology which defines prospective lines of development
Making a regional program for development of all areas of activities Development of the mathematical apparatus of influence of regulation on dairy subcomplex performance
Formation of state policy and regulation measures Development of the mathematical apparatus of forecasting the necessary extent of government support
Evaluation of government regulation efficiency Digitalisation of government regulation of the processes of price formation and product promotion from the manufacturer to the customer
Cost reduction Development of the mathematical apparatus of cost optimisation
Innovative modernisation Bank of the best available technology and mechanisms based on simulation modeling
As we see in Table 2, several key aspects of the problems of digitalisation of government regulation in the dairy subcomplex may be defined: information, financial, personnel-related, and selection aspects. Solving of the problems requires application of mathematical tooling and digital technology. So, according to the academic literature, problems in the digitalisation of the economy are studied in papers by Russian and foreign authors but in spite of the number of these papers some issues have not been covered in full. In particular, the economic science has not developed a consistent approach to study of influence of government regulation on performance of dairy subcomplex enterprises under the conditions of digitalisation. The performed research is based on the data concerning one of the largest constituent entities of the Russian Federation - the Novosibirsk Region. This constituent entity has been chosen for several reasons. First, the Novosibirsk Region ranks among top 10 regions of the Russian Federation according to the three key indicators: cow population, output and milk sale and consumption per capita. The Novosibirsk Region is the location of a large-scale livestock
industry, and overall, local enterprises manufacture 80% of milk and 83% of meat. Second, in 2018 the Novosibirsk Region was the 18th in the country by dairy cow production and its share in all-Russian milk output amounted to 2.4%. As long as our research is dedicated to dairy industry digitalisation we think it is necessary to confirm that the region chosen for analysis is ready for such transformation. Study of innovative development of the Novosibirsk Region on the basis of the Russian regional innovative index is indicative of moderate incremental dynamics: so, within the period of 2014 - 2019 the Novosibirsk Region went up in the rating from the 41st to the 8th position and became a part of the first group of constituent entities of the Russian Federation which index deviates from the leader's index (Moscow) less than 20%. Besides, it is necessary to emphasise that the region occupies the 3rd position in the quality of innovative policy. Affiliation to the first group, according to the Russian regional innovative index, is all the more important because this constituent entity lacks social and economic conditions for innovative activity (index of 38). On the basis of the results of the National Investment Climate Index, the Novosibirsk Region is steadily in the top 20 and is the 19th for the past two years. As for dynamics and current development of digital life the Novosibirsk Region is in the first of the four groups which is characterised by strengthening leadership with high current indicators and high dynamics, i.e. it develops quicker than the leader (Ekaterinburg) and its digital life index is above the average.
Figure 5. The criteria of digitalisation
Thus, the chosen constituent entity of the Russian Federation has several characteristics most important for research: a pronounced specialisation of cow population (milk production), a high level of productivity, and a high level of prerequisities for the implementation of digital technology in government regulation of the industry (as well as in the activity of the corporations which form this industry).
Research Methodology
A preliminary analysis revealed the following main fields of high-priority research.
1) Development of criteria for assessment of the digitalisation level of economic entities (organisations, districts, regions) of the dairy subcomplex.
2) Defining possible interrelations between the offered criteria and operating profit of economic entities.
3) Development of the methods of preliminary evaluation of efficiency of the procedure of economic entities' digitalisation depending on the offered evaluation criteria.
In order to study the offered fields of research, we suggested the following hypotheses.
1) As long as the academic community offered various criteria for assessment of the digitalisation level of countries and organisations (Figure 5) development of such criteria is possible for the milk industry as well.
BCG (Boston Consulting Group) [39; 40] • 11 - subindex Infrastructure development 12 - subindex Online expenses 13 - subindex User engagement
Country Digitalisation Index (E-Government Development Index) [41] 11 - subindex Web presence of government authorities 12 - subindex Telecommunication infrastructure 13 - subindex Human capital
Digital Spillover (Free goods of the digital economy) [42] 11 - subindex Speeding up of knowledge transfer 12 - subindex Innovation in business 13 - subindex Productivity improvement
N.A. Stefanova Evaluation of efficiency of • the digital economy [43] • 11 - subindex Readiness to networked economy 12 - subindex Readiness to electronic commerce 13 - subindex Readiness to e-government 14 - subindex Readiness to society informatisation
Small and medium business digitalization • index (Business Digitization Index, BDI) • [44] • 11 - subindex Information transfer channels 12 - subindex Information storage channels I3- subindex Use of Internet for sales 14 - subindex Information security 15 - subindex Digital training
Business Digitalisation Index (Institute of Statistic Studies and Economics of Knowledge) NRUHSE [45] 11 - subindex Broad Band Internet 12 - subindex Cloud services 13 - subindex RFID technology 14 - subindex ERP systems 15 - subindex Electronic sales using special forms on a site/ extranet, EDI systems
Source: developed by the authors on the basis of [39-45].
Figure 6. Statistical analysis algorithm with classification of methods
Statistical grouping method
Parameter matching Grouping Ranking Allocation
Dispersion analys is
Problem Statement Statistical Analysis Interpretation
definition of hypotheses modeling
>1
Correlation analysis
Correlation ratios calculation Grouping Ranking Allocation
Regression analysis
Defining factors Defining the model Defining interrelation of ratios Defining regression parameters Validity check
Source: developed by the authors.
Table 3. Characteristics of precision (precision) animal husbandry in the Maslyaninsky district of the Novosibirsk region, heads
Identification
Monitoring of Electronic database and monitoring Monitoring
livestock products of production of certain herd of herd
Company quality process individuals health
Sibirskaya Niva LLC 8391 18 699 17 025 17 025
Sibirskiy Pakhar, LLC 423
Head of KFH Gerasi-
mov A.I., Individual 160
entrepreneur
Gasimov Ch.R.O., Individual entrepreneur
20
If there exist criteria for evaluation of the digitalisation level of dairy subcomplex economic entities, there may be a functional relationship with performance indicators of economic entities and a possibility to define efficiency of the digitalisation process using them.
We used the data from the sites of Novosibirskstat2, Ministry of Agriculture of the Novosibirsk Region3 and related publications as sources of initial information. Statistical analysis was applied as methods of evaluation of the situation in AIC. Its algorithm is presented in Figure 6.
2 Territorial body of the Federal State Statistics Service for the Novosibirsk Region, Ministry of Agriculture of the Novosibirsk Region. URL: https:// novosibstat.gks.ru/
3 Ministry of Agriculture of the Novosibirsk Region. URL: https://mcx.nso.ru/
On the basis of the results of previous research (Figure 5) we offer to introduce criteria of evaluation of the informatisation level of dairy subcomplex economic entities in order to define their readiness to transformation into the digital economy. We accepted as analogues the last two criteria indicated in Figure 5. Due to a specific character of the industry it is problematic to apply the above indices to all economic entities of the dairy subcomplex because other digitalisation criteria are used (Table 3).
It should be noted that the characteristics listed in Table 3 may be applied in economic entities in their entirety as well as partially and also may differ or concur in number. Taking into consideration industry characteristics, we attampted to perform an integral evaluation of the level of expansion of digital technology in dairy cattle breeding using the following two parameters: digitalisation share and index. The first indicator characterises the share of an economic entity among all entities participating in digital-isation of dairy herd, while the second one characterises the four indicators of the rate of adaptation to digital transformation by the level of use.
In view of the necessity of defining the influence of the industry corporations' digitalisation established in state programs on corporations' performance, we used the correlation and regression analysis approach.
Research Results
Development of Criteria for Evaluation of the Corporations' Digitalisation Level
On the basis of the objective stating that it is necessary to develop criteria for evaluation of the digitalisation level of economic entities, conditions and limitations imposed when achieving this can assume hypothetically that there is an interrelation between digitalisation indicators and performance indicators of dairy industry corporations. We applied the correlation and regression analysis to verify this hypothesis (Figure 6).
For integral evaluation of the expansion level of digital technology in dairy cattle breeding we offer to use two parameters: digitalisation share and index of dairy cattle breeding.
Development of the Corporations Digitalisation Index
The first indicator characterises the share of an economic entity among all entities participaring in digitalisation of dairy herd, while the second one characterises the four indicators of the rate of adaptation to digital transformation by the level of use. See the examples of calculation of the offered digitalisation indicators for the districts and economic entities of the Novosibirsk Region in Figures 7-10.
Figure 7. Index of digitalisation of dairy cattle breeding in the Novosibirsk Region by districts
Novosibirsk Region Maslyaninsky Kargatsky Ordynsky Bagansky Novosibirsky Suzunsky Iskitimsky Cherepanovsky Krasnozersky Kuybyshevsky Karasuksky Tatarsky I 0.5 Source: developed by the authors
82.5
Digitalization index of dairy cattle breeding
The data from Figure 7 is indicative of a low rate of adaptation to digital transformation of the dairy industry in Novosibirsk Region. Thus, digitalisation covers less than 50% - just 12 districts of the region out of 29. The index in Figure 8 shows that in general in the Novosibirsk Region 15.6% of dairy herd administration has been digitalised, with Maslyaninsky district as the leader with 82.5%, and
Tatarsky district is an outsider with a digitalisation index of less than 1%. A wide distribution of the obtained index values (82%) is indicative of a significant differentiation of the digitalisation level even in the districts where it is conducted.
The digitalisation index of dairy cattle breeding is calculated in a similar way for corporations (Figure 8).
Figure 8. Index of digitalisation of dairy cattle breeding in the Novosibirsk Region by companies
CJSC n.a. Kirov
Sibirskaya Niva LLC JSC AF Lebedevskaya CJSC PZ Irmen CJSC Plamya
Agricultural Production Cooperative Kirzinsky Sibirsky Pakhar LLC JSC Ivanovskoe Peasant Farm Enterprise Russkoe Pole LLC JSC Instructional Farm Tulinskoe
Source: developed by the authors.
70.0
72.7
75.1 I 77.0
78.1 78.5 79.1
84.0
85.1
90.2
Digitalization index of dairy cattle breeding
The digitalisation index of dairy cattle breeding with a breakdown into corporations demonstrated in Figure 8 confirms the assertion expressed above on insufficient digitalisation of the industry. The figure shows the top 10, where over 70% of dairy herd of the entity is digitalised. Over half of these 10 largest corporations - milk producers - failed to achive the digitalisation index level of 80% and just one corporation - Instructional Farm Tulinskoe LLC has a digital index exceeding 90%.
Development of Digitalisation Share of Economic Entities of Dairy Cattle Breeding
The second indicator of the integral evaluation which characterises the share of an economic entity among all entities participating in digitalisation of dairy herd is shown in Figures 9-10.
The indicated data confirms the conclusions made earlier. So, by the digitalisation share, the top three is comprised of the same districts of the Novosibirsk Region as by the digitalisation index: Maslyaninsky, Kargatsky, Ordynsky. The digitalisation share in these districts exceeds 65%. As we see from Figure 9, only in five out of 12 districts is more than half of dairy cattle breeding digitalised. In the remaining seven districts the digitalisation share is less than 40% and in four districts out of these seven the share is below 10%, which is indicative of the districts' unpre-paredness to digital transformation.
Figure 9. Share in digitalisation of dairy cattle breeding in the districts of the Novosibirsk Region
2%
2% 1% 0%
Maslyaninsky Suzunsky Novosibirsky Cherepanovsky
Bagansky Kargatsky Karasuksky Kuybyshevsky
Ordynsky Iskitimsky Krasnozersky Tatarsky
Source: developed by the authors.
Figure 10. Share in digitalisation of dairy cattle breeding by corporations in the Novosibirsk Region
Digitalisation share
Individual Entrepreneur Gasymov Ch.R.O. Individual Entrepreneur Chief of Peasant Farm Gerasimov A.I.
Sibirsky Pakhar LLC CJSC Neudachino OJSC Novaya Zarya Chernakovo LLC CJSC Agricultural Enterprise Lukovskoe Federal State Unitary Enterprise Elitnoe CJSC Zaprudikhinskoe Sadovskoe+ LLC CJSC Shilovo-Kuryinskoe JSC Plemzavod Pashinsky CJSC Blagodatskoe Alians LLC JSC Molochny Dvor CJSC PZ Medvedsky JSC Instructional Farm Tulinskoe Agricultural Production Cooperative Kirzinsky CJSC Bobrovskoe CJSC Plamya OJSC Nadezhda OJSC Severo-Kulundinskoe JSC AF Lebedevskaya CJSC n.a. Kirov OJSC Voznesenskoe JSC Ivanovskoe Peasant Farm Enterprise Russkoe Pole LLC CJSC Plemzavod Irmen Sibirskaya Niva LLC
Source: developed by the authors.
29 economic entities implement digitalisation of dairy cattle breeding, including two individual entrepreneurs, out of 10 districts of the Novosibirsk Region. Moreover, the top three accounts for almost a half of the share in digitalisation of dairy cattle breeding.
Financial Standing of AIC Companies
An opportunity to establish relations between financial parameters and digitalisation indicators offered by the authors is of special interest. Comparative characteristics of financial indicators of ten economic entities in the Novosibirsk Region with digitalisation parameters are presented in Table 4.
The indicators listed in Table 4 in comparison to industry average values are declarative of an ambiguous character of financial standing of dairy corporations of the Novosibirsk Region. So, by financial soundness indicators, Novosibirsk corporations are less sound (the equity to total assets ratio is less than the industry average indica-
4.933 I 5.322 ■ 5.521
11.708 12.252
21.556
tor, while the leverage ratio is greater) which indicates a higher financial risk level. However, with profitability indicators the situation is reverse: return on assets and return on equity exceed the industry average value. The presented data shows a top five of corporations - leaders in the key financial indicators (their values exceed the industry average value). They comprise CJSC Plemzavod Irmen, CJSC n.a. Kirov, Agricultural Production Cooperative Kirzinsky, CJSC Plamya and Sibirsky Pakhar LLC. Such enterprises as Sibirskaya Niva LLC (GK EcoNi-va - Agro-Industrial Complex Holding), Peasant Farm Enterprise Russkoe Pole LLC, CJSC Agricultural Firm Lebedevskaya are in a difficult financial position due to a high financial dependence and insufficient working capital, but regardless, these companies are profitable. In spite of different financial situations all corporations are to some extent involved in digitalisation.
Let us conduct a correlation analysis of comparative characteristics of financial indicators in Table 5.
Table 4. Comparative characteristics of financial indicators of 10 economic entities of the Novosibirsk Region with digitalisation parameters
Parameter CJSC Plemzavod Irmen Sibirskaya Niva LLC (GK EcoNiva - AIC Holding) Peasant Farm Enterprise Russkoe Pole LLC JSC Agricultural Firm Lebedevskaya JSC Ivanovskoe CJSC n.a. Kirov APC Kirzinsky CJSC Plamya JSC Instructional Farm Tulinskoe Sibirsky Pakhar LLC Industry average values of indicators4
Y1 Revenue, rub 2 495 091 2 023 843 1 463 589 736 546 318 942 262 026 214 501 214 151 131 625 50 641 -
Y2 Cost of sales, rub 2 071 171 1 732 180 1 353 470 714 861 301 978 261 485 206 056 207 748 118 680 45 464 -
Y3 Profit on sales, rub 406 496 289 214 -1969 21 685 16 964 541 2759 1764 12 945 5177 -
Y4 Net profit, rub 486 133 62 982 26 975 1426 43 194 24 015 18 529 16 139 18 502 11 185 -
Y5 Equity capital, rub 3 286 493 445 131 43 654.5 166 726 498 340 317 857 218 268 318 205 17 044 82 064 -
Y6 Autonomy coefficient 0.95 0.04 0.01 0.10 0.72 0.88 0.78 0.83 0.34 0.97 0.56
Y7 Financial leverage ratio 0.06 21.68 172.01 9.07 0.40 0.14 0.29 0.20 1.96 0.03 0.31
Y8 Noncurrent assets, rub 1754369 6214146,5 5 531 414.5 1 105 065 392 536 160 567 143 958 129 053 785 33 812 -
Y9 Share of non-current assets,% 50,55 61,55 73.24 65.79 56.44 44.24 51.24 33.73 1.55 39.97 -
Y10 Current assets, rub 1716231 3882333 2 021 256 574 566 302 914 202 355 137 011 253 563 49 702.5 50 786 -
Y11 Share of current assets,% 49,45 38,45 26.76 34.21 43.56 55.76 48.76 66.27 98.45 60.03 -
Y12 Total asset value, rub 3470600 10096479,5 7 552 670.5 1 679 631 695 449 362 922 280 969 382 616 50 487.5 84 598 -
Y13 Ratio of own circulating assets 0,89 -1,49 -2.72 -1.63 0.35 0.78 0.54 0.75 0.33 0.95 0.37
Y14 Net profit sales margin,% 19,48 3,11 1.84 0.19 13.54 9.17 8.64 7.54 14.06 22.09 10.2
Y15 Return on equity,% 14,79 14,15 61.79 0.86 8.67 7.56 8.49 5.07 108.55 13.63 22
Y16 Return on assets,% 14,01 0,62 0.36 0.08 6.21 6.62 6.59 4.22 36.65 13.22 8.9
Y17 Digitalisation share,% 12,252 21,556 11.708 4.619 5.521 4.933 2.369 3.575 2.113 0.149 -
Y18 Digitalisation amount, heads 34752 61140 33 208 13 100 15 660 13 992 6720 10 140 5992 423 -
Y19 Digitalisation index of dairy cattle breeding, % 77,0 72,7 85.1 75.1 84.0 70.0 78.5 78.1 90.2 79.1 -
4 According to the site: https://www.testfirm.ru/otrasli/01/
Table 5. Correlation analysis of comparative characteristics of financial indicators of 10 economic entities of the Novosibirsk Region with digitalisation parameters
Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14 Y15 Y16 Y17 Y18 Y19
Y1 1.00 1.00 0.88 0.73 0.69 -0.32 0.33 0.74 0.49 0.85 -0.49 0.79 -0.41 -0.14 -0.05 -0.25 0.87 0.87 -0.23
Y2 1.00 1.00 0.85 0.70 0.66 -0.36 0.37 0.77 0.52 0.86 -0.52 0.81 -0.46 -0.18 -0.05 -0.28 0.88 0.88 -0.23
Y3 0.88 0.85 1.00 0.84 0.83 0.00 -0.13 0.44 0.18 0.69 -0.18 0.53 0.01 0.19 -0.15 -0.01 0.73 0.73 -0.31
Y4 0.73 0.70 0.84 1.00 0.99 0.31 -0.12 0.10 0.08 0.31 -0.08 0.18 0.24 0.44 -0.10 0.14 0.38 0.38 -0.13
Y5 0.69 0.66 0.83 0.99 1.00 0.37 -0.19 0.05 0.10 0.26 -0.10 0.12 0.29 0.43 -0.20 0.09 0.35 0.35 -0.19
Y6 -0.32 -0.36 0.00 0.31 0.37 1.00 -0.58 -0.71 -0.34 -0.57 0.34 -0.67 0.93 0.76 -0.39 0.20 -0.50 -0.50 -0.18
Y7 0.33 0.37 -0.13 -0.12 -0.19 -0.58 1.00 0.69 0.49 0.41 -0.49 0.60 -0.77 -0.46 0.37 -0.32 0.36 0.36 0.31
Y8 0.74 0.77 0.44 0.10 0.05 -0.71 0.69 1.00 0.59 0.94 -0.59 0.99 -0.80 -0.52 0.12 -0.44 0.89 0.89 -0.10
Y9 0.49 0.52 0.18 0.08 0.10 -0.34 0.49 0.59 1.00 0.51 -1.00 0.57 -0.62 -0.50 -0.53 -0.88 0.51 0.51 -0.40
Y10 0.85 0.86 0.69 0.31 0.26 -0.57 0.41 0.94 0.51 1.00 -0.51 0.97 -0.60 -0.38 -0.01 -0.38 0.98 0.98 -0.26
Y11 -0.49 -0.52 -0.18 -0.08 -0.10 0.34 -0.49 -0.59 -1.00 -0.51 1.00 -0.57 0.62 0.50 0.53 0.88 -0.51 -0.51 0.40
Y12 0.79 0.81 0.53 0.18 0.12 -0.67 0.60 0.99 0.57 0.97 -0.57 1.00 -0.74 -0.48 0.07 -0.42 0.94 0.94 -0.16
Y13 -0.41 -0.46 0.01 0.24 0.29 0.93 -0.77 -0.80 -0.62 -0.60 0.62 -0.74 1.00 0.78 -0.20 0. 47 -0.54 -0.54 -0.07
Y14 -0.14 -0.18 0.19 0.44 0.43 0.76 -0.46 -0.52 -0.50 -0.38 0.50 -0.48 0.78 1.00 0.06 0.60 -0.37 -0.37 0.22
Y15 -0.05 -0.05 -0.15 -0.10 -0.20 -0.39 0.37 0.12 -0.53 -0.01 0.53 0.07 -0.20 0.06 1.00 0.72 -0.05 -0.05 0.77
Y16 -0.25 -0.28 -0.01 0.14 0.09 0.20 -0.32 -0.44 -0.88 -0.38 0.88 -0.42 0.47 0.60 0.72 1.00 -0.38 -0.38 0.58
Y17 0.87 0.88 0.73 0.38 0.35 -0.50 0.36 0.89 0.51 0.98 -0.51 0.94 -0.54 -0.37 -0.05 -0.38 1.00 1.00 -0.29
Y18 0.87 0.88 0.73 0.38 0.35 -0.50 0.36 0.89 0.51 0.98 -0.51 0.94 -0.54 -0.37 -0.05 -0.38 1.00 1.00 -0.29
Y19 -0.23 -0.23 -0.31 -0.13 -0.19 -0.18 0.31 -0.10 -0.40 -0.26 0.40 -0.16 -0.07 0.22 0.77 0.58 -0.29 -0.29 1.00
Development of Methods for Preliminary Evaluation of Companies' Digitalisation Efficiency
The correlation analysis showed that financial indicators, except for net profit, equity, ratios, shares and profitability are closely correlated (correlation ratio R >0.7) with the extent of digitalisation, especially current assets (R = 0.98). The digitalisation index of dairy cattle breeding showed a strong relationship only with return on equity (R = 0.77) and no relationship at all with the extent of digitalisation (R = -0.29). We can assume that the offered digitalisation parameters do not duplicate, but rather complement each other. The strength of relationship between return on equity and digitalisation index is to a greater extent caused by dependency on the asset turnover ratio and leverage (over 0.5) than on return on sales (a little over 0.2). The obtained results confirm our hypothesis and suggest that there is a dependency of assets utilisation
efficiency and financial risk on the digitalisation index in dairy cattle breeding.
The interrelation of the digitalisation index of dairy cattle breeding with return on equity with a relative accuracy of less than 10% (Table 6) which is fewer than the admissible value of 15% may be presented as the following regression equation:
Id = 0.17 x ROE + 74.258. (1)
Discussion of Results
Analysis of mathematical model (1) showed that corporations of the Novosibirsk Region which chip their dairy herd have a minimum digitalisation index of dairy cattle breeding of 74%, which deviations with the ratio of 0.17 depend on return on equity, which in its turn, is related to the velocity of assets circulation and leverage.
Table 6. Checking the adequacy of the relationship between the dairy cattle digitalisation index and return on equity
S
и № TS
S4
s s?
!ч >
№ v < ^
U -û
Return on equity,% 14.79 14.15 61.79 0.86 8.67 7.56 8.49 5.07 108.55 13.63
Digitalisation index of dairy cattle breeding (estinated), % 76.77 76.67 84.77 74.40 75.73 75.54 75.70 75.12 92.73 76.58
Digitalisation index of dairy cattle breeding (actual), % 77.0 72.7 85.1 75.1 84.0 70.0 78,5 78.1 90.2 79.1
Absolute deviation, % -0.225 4.007 -0.373 -0.696 -8.295 5.583 -2.818 -3.002 2.570 -2.541
Relative deviation, % -0.29 5.,51 -0.44 -0.93 -9.87 7.98 -3.59 -3.84 2.85 -3.21
The presented model allows us to forecast with a sufficient degree of confidence (deviation not exceeding 10%) a probable value of the digitalisation index of dairy cattle breeding for 10 prospective economic entities of the Novo-
sibirsk Region (Table 7). Consequently, the organisations which plan 'chipping' of their dairy herd may consider the digitalisation index of dairy cattle breeding a reasonable reflection of an attractive business format for them.
Conclusion
In this paper we have considered the influence of government digitalisation policy on the state and development of corporations of the dairy industry. We have revealed an integrated system approach to evaluation of influence of state programs related to digitalisation of the dairy industry on corporations' performance, as exemplified by economic entities of the Novosibirsk Region. The research results are indicative of general consistent patterns and interrelations between components of digital technology provided for in state programs and performance of dairy industry corporations. The statistical analysis (Figure 7) allows to assert that digital technology which is a part of government regulation of the dairy industry implemented in corporations has a significant impact on business performance. The offered digitalisation criteria and revealed consistent patterns of their interrelation with performance and expected efficiency, in their turn, are indicative of the possibility to manage the digitalisation process based upon preset parameters of business performance and the possibility to forecast the key indicator - the digitalisation index on the basis of a derived regression equation.
The research makes a contribution to development of theoretical approaches to evaluation of influence of state programs on business performance in the dairy industry. This is performed under the conditions of the digital economy, by means of development of a common methodology of evaluation of influence of government regulation on the performance of the dairy industry. The basis of the methodology is the calculation of a digitalisation index used to assess the efficiency of government support of the industry corporations. The practical value of our presented research consists in the possibility to use the offered approach for evaluation and forecasting of performance of dairy industry corporations, taking into consideration the impact of government regulation via the offered digitali-sation parameters.
Acknowledgments
The paper has been written with financial support of Novosibirsk State Technical University (project C21-11).
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