The fourth industrial revolution, characterized by the interaction of digital, physical and biological domains [Schwab, 2016], is fundamentally transforming the face of the electric power industry both around the world and in Russia. The introduction of technological innovations, in addition to increasing efficiency and effectiveness of companies, leads to a significant change in technical and economic paradigms, market environment, as well as business and organizational models of companies [Freeman et al., 1982; Technical change .., 1987].

Researchers have shown that one of the main technologies for the digital transition in the electric power industry is the technology of intelligent electricity metering systems (IMS)[Khovalova, Zholnerchik, 2018; Smart grids ..., 2013]. Such systems are based on electricity meters equipped with modules for collecting, processing, storing, sending and receiving data.

Unlike traditional electricity meters, smart metering systems have a wider range of functions:

However, despite the current extensive functionality of intelligent accounting systems, IMSs built on the basis of Non-Intrusive Load Monitoring (NILM) technology have even greater capabilities. NILM is a method for analyzing total electrical load data obtained by measuring current and voltage at one point, with the subsequent division of the total load into the loads of individual devices [Kuzmin, 2019].

The researchers note that non-intrusive load monitoring technology can play a key role in the digital transition in the power industry [Zoha et al., 2012]. It cannot only improve the current operating activities of power companies, but also form the basis for the formation of new relations between the subjects of energy markets [Bergman et al., 2011; Lin, Wang, 2016; Zhuang et al., 2018].

IMS based on non-intrusive load monitoring have two significant differences that determine the specificity of their application:

Based on their technological features, IMS, using nonintrusive load monitoring meters, are able to find application in many areas:

A number of researchers conclude that the impact of the digital transformation of the electric power industry, including the impact of the introduction of intelligent metering systems, will become the strongest for end consumers of electricity [Trachuk, 2010; Digital transition .., 2017; Khokhlov et al., 2018], while the most significant changes will be experienced by end consumers in the retail market. The introduction of intelligent electricity metering systems (including those based on NILM technology) generates a wide range of effects both for end consumers and for companies in the electric power industry [Trachuk, 2010; Kuzmin, 2019]. Comparison of the effects created by the IMS with the participants in the electricity and capacity market is presented in table. 1.

A large number of works have been devoted to the topic of implementing smart metering systems. However, they are mainly scientific and technical publications, the focus of which is on the technological aspects of the creation and implementation of intelligent electricity metering systems, articles exploring the positive effects of implementation and the possibility of commercializing data collected using the IMS [Devlin, Hayes, 2018; McKenna et al., 2012]. Other researchers have assessed the applicability of the IMS in the implementation of demand management and active consumer models [Bergman et al., 2011; Lin, Wang, 2016; Zhuang et al., 2018]. However, among scientific works there are not enough publications that investigate the nature of the perception of IMS technologies by potential users, which directly affects the speed of technology spread.

Thus, the task of this work is to identify and systematize factors that have both a positive and a negative impact on the perception of smart metering systems both among electric power companies and among end consumers - participants in the retail electricity markets.

The adoption of innovative technologies by industrial companies is considered by a small number of studies. The most widespread models of factors of adoption of innovations are described in the works [Molla, Licker, 2002; 2005]:

On the part of end consumers, the following methods of analyzing consumer acceptance of innovative technologies are most often used:

The process approach considers the adoption of an innovation as a series of successive stages replacing each other. Some of the most popular models for describing innovation adoption are Everett Rogers’ innovation diffusion model [Rogers, 2003] and Frank Davis's technology acceptance model [Davis, 1989].

Diffusion of innovations is described by Rogers as a combination of key components: innovation, person, time, communication channels and the social system, as well as a series of stages of adoption of an innovative technology: awareness, interest, evaluation, testing, adoption of technology (adoption) (Fig. 1).

First, the consumer receives initial information about the technology, then, based on this information, forms his attitude towards it. If the innovation interests the consumer, he assesses its applicability and, if a positive decision is made, proceeds to the stage of testing the innovation, defining its advantages and disadvantages for himself. The final stage is the acceptance stage. In the case of a positive assessment of the technology, a person makes a decision on its further use, and if it is negative, he refuses to accept it.

An important provision in Rogers' model is the segmentation of potential consumers based on individual predisposition to the adoption of innovative technologies:

1) innovators (2.5%): risk-oriented, inclined to accept innovations in the forefront, are able to absorb complex technical knowledge, have sufficient financial resources to compensate for risks;

2) early adopters (13.5%): make up the bulk of “opinion leaders”, it is them that relatively late followers are guided by;

3) early majority (34%): representatives of this segment may show some uncertainty before the adoption of the innovation, in the process of adopting the innovation they prefer to follow the early adopters;

4) late majority (34%): a skeptical group of consumers, their acceptance of innovations occurs either under increasing social pressure or due to economic necessity;

5) latecomers (laggards, 16%): consumers of a conservative orientation, accept an innovation only when it is fully integrated into the environment, and may refuse to accept it altogether.

Davis's model of technological acceptance (Fig. 2) was created to describe the factors involved in the activities of the employees of a computer equipment enterprise in the workplace and was based on the assumption that the use of a particular technological innovation depends on the attitude formed by a person towards it, as well as the ability and intention to use it. Later, the model was successfully used to study the innovative activities of companies [Rebyazina et al., 2011], as well as the spread of distributed generation [Trachuk, Linder, 2018].

The factorial approach is based on the analysis of the factors that have the greatest influence on the consumer's decision to adopt an innovation.

Rogers and Davis identified two groups of factors that influence the perception of technology: general and specific. Common characteristics of innovation were identified by Rogers and include compatibility, testability, comparative advantage, and acceptability. Davis formulated the specific characteristics of innovation: speed, reliability, pleasure in use, control of the process of use, perceived risks of use.

Table 2 presents general and specific characteristics that influence the adoption of innovations.

Adoption of intelligent electricity metering systems technology. For further research, it is necessary to identify the factors influencing the adoption of IMS technology within the companies in the electric power industry.

Table 3 collected intra-organizational factors and environmental factors that were identified in the relevant studies.

After the analysis of innovative technologies adoption models on the part of industrial companies and on the part of end users, a number of the most significant factors were identified that form the user's decision to adopt a technology.

After identifying the factors in the adoption of IMS technologies, it is necessary to proceed to the formation of a sample and conduct a study in order to obtain a quantitative assessment of the nature and degree of influence of the factors.

To study the previously identified factors in the adoption of IMS technologies, a qualitative stage of analysis was carried out in order to verify the factors affecting the adoption of smart metering technologies, and to develop a detailed questionnaire for subsequent quantitative analysis.

The qualitative stage consists of in-depth semi-structured interviews with eleven representatives of power companies, as well as companies - consumers of electricity in the retail markets.

Employees of electric power companies, such as retail, power grid and generating companies, companies of the commercial infrastructure of the wholesale electricity and capacity market were selected for the interview. The consumer side was represented by employers from large construction companies, owners of commercial real estate, retail trade, as well as a Russian company engaged in the research, development and production in the field of state emblems.

Each of the respondents holds a managerial position at the level of deputy director of the department and above and:

The author also managed to conduct an interview with the founder of a company that manufactures and supplies non-intrusive load monitoring systems in the European Union.

Based on the results of the analysis of the interviews, the factors influencing the adoption of technologies of intelligent energy metering systems were confirmed and updated both among energy companies (Table 5) and among end consumers in the retail markets (Table 6).

The next step is to calculate Spearman's nonparametric correlation coefficients (ρS) for ranked data to confirm relationships between model elements. The relationships between the components of the model are considered significant if the correlation coefficient exceeds the threshold value of 0.50.

In the course of the correlation analysis, it was found that the adoption of smart metering technologies by electricity companies is associated with technical feasibility (ρS= 0,76), perceived risks (ρS= 0,57), perceived benefits (ρS= 0,80), expected costs (ρS= 0,61), the influence of regulators (ρS= 0,53), pressure of the market environment (ρS= 0,81), technological changes in the industry (ρS= 0,52).

The results of the analysis of interviews conducted with representatives of companies in the electric power industry confirm the selected factors of the internal and external environment, as well as their interconnection, and serve as an additional justification for the questionnaire developed to quantify the factors influencing the adoption of IMS technologies.

Correlation analysis of general and specific factors for end consumers of electricity in retail markets revealed the connection between decision making on use with testing capability (ρS= 0,54), comparative advantage (ρS= 0,68),complexity of development (ρS= 0,52), innovative activity (ρS= 0,62), reliability (ρS= 0,60), risk of use (ρS= 0,64).

The results of the analysis of interviews conducted with representatives of companies - end consumers of electricity, confirm the selected general and specific factors, as well as their interconnection, and serve as an additional justification for the questionnaire developed to quantify the factors influencing the adoption of intelligent metering systems.

After substantiating the factors of adoption of technologies for intelligent electricity metering systems questionnaires were drawn up as well as a survey was conducted among representatives of electric power companies and end consumers of electricity (162 respondents, Table 7). The aim was to further build a regression analysis model designed to determine the degree of influence of the identified factors on the decision to implement IMS. To measure the identified factors, the statements of the questionnaire were formulated, the respondents were asked to choose the degree of agreement with them. To measure the degree of agreement, a seven-point Likert scale was used, where 1 - "completely disagree", 4 - "I do not know whether I agree or disagree", 7 - "completely agree". The survey questions are given in table 8, 9.

To measure the factor "Technical Feasibility", a scale with two questions was used to assess how the company's ability to collect data from consumers affects decision-making.

To assess the Perceived Risks factor, we used a scale with four questions, assessing the risks of data breaches and their consequences, as well as the risk of an increase in the amount of information disclosed at the request of regulatory authorities. The Perceived Benefit factor was measured using five questions describing a range of improvements to the company's current operations, as well as new sources of revenue. The “Expected Costs” factor was measured by four questions describing the costs of installing the IMS, as well as the sources of covering the costs. To measure the factor “Influence of regulatory bodies”, two questions were used that described the compliance of the IMS installation with government programs and the possibility of including costs in tariff sources. “Market pressure” was assessed using four questions focused on interest in smart metering technology from end users as well as competitors. The factor "Technological changes in the industry" was measured by two questions characterizing the need to implement IMS technologies if it becomes a time requirement or technologies with similar functionality develop.

Similarly, questions were formed to study general and specific factors affecting the adoption of IMS technologies by end users in the retail electricity markets. Testing ability was measured by two questions assessing respondents' attitudes toward the ability to test MIS for free or with a grace period. Comparative advantage was measured as an assessment of how significant the positive effects of the introduction of an IMS on end users are. Difficulty in mastering was measured with the help of two questions assessing how ready the respondents are to master the new technology. The factor "Innovative activity" was measured in accordance with  the questions regarding the importance for the respondent to be an innovator. The Reliability factor was measured in accordance with the respondents' assessment of the need for after-sales service and technical support. To assess the Perceived Risks factor, a scale with four questions was used, assessing the risks of data breaches and their consequences, as well as the risk of a negative impact of the IMS on the operation of production equipment.

At the beginning of the quantitative analysis, reliability factors (Cronbach's alpha) were calculated for all factors studied on the basis of multi-question scales. The obtained coefficients satisfy the requirement for the minimum level of reliability for field research - 0.65. Further, factor analysis was performed using the principal component method and orthogonal rotation that maximizes variation (varimax) for four groups of questions describing four aspects of intraorganizational factors, and three groups of questions describing three aspects of external factors.

Factor analysis based on the method of principal components with orthogonal rotation, which maximizes variation, revealed the presence of four intraorganizational characteristics and three environmental factors, which described a total of 87.2% of the variation in questions. The values of the obtained factors were used to form the final set of factors that influence the adoption of smart metering technologies by electric utilities and which were then included in the regression analysis model.

The results obtained at the end of the factorial analysis were used to determine the degree of influence of factors on the adoption of technologies of intelligent metering systems among energy companies:

Yi= β0+ β1 ·Ai+ β2 ·Ri+ β3 + Pi+ β4· Ci+ β5· Gi+ β6· Mi+ β7· Ti+ εi, (1)

Where Yi – is the indicator of the adoption of the technology of intelligent metering systems (binary variable, where 1 is the technology of intelligent metering systems for electricity adopted for use by the company; 0 - not accepted); Ai - the possibility of technical feasibility for using the IMS; Ri– risks perceived by companies associated with the use of IMS; Pi – the perceived advantages of using the IMS by the company; Ci– the expected costs of the installation of the IMS; Gi – pressure from the regulating bodies, affecting the installation of the IMS; Mi – market pressures affecting the adoption of smart metering technologies; Ti – technological changes in the industry, contributing to the adoption of smart energy metering technologies; εi – random error.

The study of general and specific factors influencing the adoption of smart energy metering technologies by end users in the retail electricity markets was carried out by means of eighteen questions.

Overall, three general and three specific factors explained 81.3% of the variation in answers to questions by respondents, which is in line with the recommendations to explain 70% of the variation in structural models.

The study of general and specific factors in the adoption of technologies of intelligent metering systems by end consumers of electricity was carried out according to the following model:

Yi= β0+ β1 ·TAi+ β2 ·CPi+ β3 ·DFi+ β4· SFi+ β5· SCi+ β6· RSi+ εi, (2)

where Yi – is the indicator of adoption of smart metering technologies (binary variable, where 1 - smart metering technology is adopted for use by the company; 0 - not accepted); TAi – the ability to test the IMS; CPi – the comparative advantage associated with the use of IMS; DFi– the complexity of mastering the use of IMS; SFi – innovative activity of consumers, influencing the decision to install the IMS; SCi – reliability of using the IMS; RSi – risks of use, affecting the adoption of technologies of intelligent accounting systems; εi – random error.

With the use of the maximum likelihood method, standardized and non-standardized regression coefficients were obtained, which were subsequently used to determine the degree of influence of factors on the adoption of intelligent accounting systems.

Regression analysis made it possible to identify the degree of influence of various factors on the adoption of smart metering technologies by companies in the electric power industry (internal organizational characteristics of the company and environmental factors), as well as the degree of influence of general and specific factors (Tables 10-11). The impact of these independent variables on the adoption of IMS was assessed via the maximum likelihood method.

Interpretation of the data obtained allows us to answer the first research question: "What factors influence the adoption of technologies for intelligent electricity metering systems, what is the degree of their influence?"

Table 10 shows the results of regression analysis of the influence of intra-organizational factors and environmental factors on the process of adopting technologies of intelligent accounting systems. All characteristics have a positive effect on the adoption of technology, but the degree of their influence is different, due to the specifics of the compiled questionnaire. The obtained regression coefficients can be used to compare the degree of significance of factors influencing the adoption of IMS by electric utilities.

Thus, when deciding on the implementation of the IMS, the most powerful influence is exerted by the pressure of the regulatory bodies (β = 0,538), as well as the low level of risk of using the technology (β = 0,471). The factor associated with the risk of data leakage is no less important: the IMS should be no more dangerous than the existing metering devices, and the risks of leakage should not lead to serious consequences. Market pressure (β = 0,423) and perceived advantages (β = 0,402). are of no less importance for the companies. The factor related to technical feasibility (β = 0,359), has a moderate impact, which may indicate that the company is ready to implement an IMS, even if it is not possible to collect data from all meters. Factors related to estimated costs (β = 0,261) and technological changes in the industry (β = 0,258) have relatively little influence.

Table 11 shows the results of a regression analysis of the influence of general and specific factors on the process of adopting intelligent metering systems by the end consumer of electricity in the retail markets. All characteristics have a positive effect on the adoption of technology, but the degree of their influence is different, due to the specifics of the compiled questionnaire. Similarly to the analysis of the results for electric power companies, the obtained regression coefficients are used to interpret the impact of factors influencing the adoption of the IMS by consumers in the retail markets.

The perceived comparative advantages (β = 0,417), as well as the low risk level of using the technology (β = 0,318)and its reliability (β = 0,347). influence end users when they make a decision about the implementation of IMS. This may be due to the desire of consumers to use their equipment as efficiently as possible and protect themselves from commercial losses. At the same time, consumers want to be sure that the installed intelligent electricity metering system is not capable of negatively affecting the operating modes of production equipment, and the system itself will be provided with a high level of technical support. Of no less importance is the factor connected with the risks of data leakage: IMS should be no more dangerous than existing metering devices, and the risks of leakage should not lead to serious consequences. The ability to preferentially test the system and see its benefits has a moderate impact (β = 0,291), as well as the difficulty of mastering (β = 0,295). Consumers are ready to start using the system, if its development does not involve significant difficulties, and the system interface is understandable. The factor associated with the company's innovative activities has a low degree of influence. The factor associated with the company's innovative activities has a low degree of influence (β = 0,096). Analyzing indepth interviews, the low degree of the influence of this factor can be explained by the fact that innovation activity is of little interest to the company in isolation from achieving any comparative advantages.

The quantitative stage of the study showed the degree of influence of various factors in the adoption of technologies of intelligent electricity metering systems in the context of electric power companies and companies that consume electricity in retail markets. It has been empirically shown that regulatory pressures and the level of use risk are most influential in adopting smart metering by utilities. For consumers, the decisive factors are related to the benefits from the introduction of IMS with low risks of use and a high level of reliability. Most of the respondents identified these aspects as the most important.

At the end of the work, a list of measures will be presented to accelerate the implementation of technologies for intelligent energy metering systems, as well as a number of solutions aimed at further developing of innovative energy practices.

Recommendations for electricity suppliers. The most significant effects from the introduction of technologies of intelligent electricity metering systems will be observed with a wide coverage of end consumers of electricity. Based on this, in the early stages of implementation, it makes sense to centrally install meters in order to provide the first consumers with the opportunity to test and visually study the benefits of the technology. As measures for centralized implementation, you can propose:

In addition to being able to test and understand comparative advantage for users (and for utilities), one of the most important factors is data security. To ensure an adequate level of security, it is recommended to implement a processing system on the territory of the Russian Federation and, if possible, avoid cross-border data transmission, as well as use secure transmission technologies. A significant help will be the availability of a developed IT infrastructure in the ownership of the energy company.

An analysis of the factors influencing the adoption of IMS technologies by its consumers showed that the reliability of the IMS operation and a high level of technical support are important to users. Thus, energy companies need to pay attention to the development of interaction with customers, both in terms of the reliability and intuitive clarity of the user application and online platform, and in the context of personal technological support.

Recommendations for demand aggregators

Subjects of the electric power industry, including guaranteeing suppliers and independent power supply companies or electricity consumers who have entered into an agreement for the provision of services to change the load with consumers of the retail electricity market, can qualify the competitive selection conducted by JSC System Operator of the Unified Power System and receive the status of a demand aggregator.

The market for demand management services is quite promising and, according to the estimates of the National Technology Initiative Energy Net, in the long term, it may amount to 50 to 70 billion rubles, which makes it especially important to gain leadership positions in this market.

To successfully gain leadership positions in the market, you must complete the following steps:

Recommendations for authorities and regulators

As noted earlier, governments and regulators have a critical impact on the rules and regulations for the operation of the wholesale and retail electricity and capacity markets.

The development of technologies for intelligent electricity metering systems can have a number of positive effects in the context of authorities:

As a result, the listed measures will lead to the establishment of a more fair and economically justified price for electricity and capacity for end consumers.

One of the key factors influencing the adoption of smart energy metering technologies by power companies is pressure from regulatory authorities and the ability to include the introduction of IMS into tariff sources. Based on this, accounting for the costs of implementing IMS electricity technologies in the required gross revenue of the organization can serve as a powerful incentive for implementation.

At the same time, despite the qualitative growth in the volume of analytical data collected using the IMS, a sharp increase in the load on energy companies in the context of information disclosure should not be allowed, as this can become a serious factor that reduces the motivation for implementation.

Thus, the well-coordinated work of the retail market entities, regulatory bodies, the practice of timely updating of the legal and regulatory framework in the field of the electric power industry and technical regulation, the updating of the rules and regulations of the market system of the electric power industry in Russia can accelerate the spread of smart metering systems. As a result, the positive effects from their implementation will be achieved.