**4. Results of the study**

**Tables 5** and **6** demonstrate the regression analysis results showing the influence of various factors on the distributed energy technology adoption by industrial companies (the company's internal characteristics and environmental factors), as well as the influence of specific factors. We evaluated the impact of these independent variables on the adoption of distributed energy technologies using the maximum likelihood method.


*\*\*Significance of the coefficient p < 0.05.*

*\*\*\*Significance of the coefficient p < 0.01.*

*Standard errors are given in brackets.*

#### **Table 5.**

*Acceptance of distributed energy technology by industrial companies: the impact of internal organizational characteristics and environmental factors.*

**153**

companies.

energy technologies.

*Dissemination of Distributed Energy Technologies DOI: http://dx.doi.org/10.5772/intechopen.88604*

Availability of by-products that can be used as

High efficiency (provided that the generating facility is designed to meet the needs of a specific industrial production in both electrical and

No payment for technological connection to electric networks (if the object of generation is

Existing ratio of prices for electric energy and natural gas indicates a high gas potential

Energy production takes place in the immediate vicinity of consumption points, which leads to a reduction of needed energy transmission over

Ability to change the volume of generated electrical and thermal energy when economic

isolated from the power system)

No cost for power transmission Hypothesis

Specific factors

thermal energy)

situation changes

considerable distances

*Significance of the coefficient p < 0.10. \*\*Significance of the coefficient p < 0.05. \*\*\*Significance of the coefficient p < 0.01. Standard errors are given in brackets.*

*†*

*\**

**Table 6.**

fuel

**Independent factors Hypotheses Non-**

Invariable (β0) 0.216 (0.031)

Hypothesis 2(b)†

Hypothesis 2(c)

2(e)

Hypothesis 2(h)

Hypothesis 2(g)

Hypothesis 2(i)

Hypothesis 2(j)

**standardized coefficients**

> 0.421\*\*\* (0.023)

0.324\*\*\* (0.127)

0.016\*\*\* (0.091)

0.211\*\*\* (0.009)

0.163\*

0.378\*\* (0.212) 0.381\*\*\*

0.321\*\* (0.041) 0.323

(0.037) 0.168\*

**Standardized coefficients**

0.419\*\*\*

0.327\*

0.009\*\*\*

0.209\*\*\*

In general, the results of the regression analysis confirmed the hypotheses of the study. The models based on the regression equations were able to explain 63% of the variation of internal organizational and external factors in the distributed energy

When modeling the distributed energy technology adoption by companies (**Table 5**), it turned out that technical feasibility (β = 0.264; *p* < 0.05), comparative advantage of using distributed generation (β = 0.451; *p* < 0.10), and cost of electricity (β = 0.598; *p* < 0.10) positively affect the adoption of distributed energy technologies. The factor "perceived risks" (β = 0.166; *p* = 0.01) does not have a significant impact on the growth in the number of distributed energy users. And, the factor "costs for construction and installation of distributed generation sources" (β = −0.387; *p* < 0.10) has a negative influence on the decision to use distributed

Adjusted R-square 0.628 Number of observations 69

Among the external factors, regulators' decisions have a significant impact on the distributed energy technology adoption by companies (β = 0.393; *p* < 0.05). Market pressure and technological changes in the industry do not have a significant negative impact on the rate of distributed energy technology adoption by

technology adoption by companies and 57% of specific factors.

*Adoption of distributed generation technologies: the impact of specific factors.*

*Hereinafter, the designation of the hypothesis corresponds to its formulation in the text.*

*Intellectual Property Rights - Patent*

**4. Results of the study**

likelihood method.

Intraorganizational characteristics

sources of generation (URi)

generation sources (Ci)

Environmental factors

technologies (GRi)

*Significance of the coefficient p < 0.10. \*\*Significance of the coefficient p < 0.05. \*\*\*Significance of the coefficient p < 0.01. Standard errors are given in brackets.*

*characteristics and environmental factors.*

Technical feasibility (integration, scalability, infrastructure, complexity, etc.) (Ti)

Perceived advantages and need for alternative

Costs of building and installing distributed

Decisions of regulators (authorities) affecting decisions of companies on the use of new

Perceived risks (safety, investment) (RKi) Hypothesis

Electricity cost (COSTi) Hypothesis

Market Pressure (EASEi) Hypothesis

Technological changes in the industry (TRi) Hypothesis

included in the regression analysis.

the distributed generation technology adoption by companies, which were then

influenced the distributed generation adoption by companies more.

**Independent factors Hypotheses Non-**

Invariable (β0) 0.191 (0.0134)

Using the maximum likelihood method, standardized and non-standardized regression coefficients were determined. Non-standardized coefficients were used to test hypotheses, and standardized factors were used to determine factors that

**Tables 5** and **6** demonstrate the regression analysis results showing the influence of various factors on the distributed energy technology adoption by industrial companies (the company's internal characteristics and environmental factors), as well as the influence of specific factors. We evaluated the impact of these independent variables on the adoption of distributed energy technologies using the maximum

> Hypothesis 1(а) †

> > 1(c)

Hypothesis 1(d)

1(e)

Hypothesis 1(f)

1(g)

1(h)

Hypothesis 1(i)

Adjusted R-square 0.709 Number of observations 69

*Acceptance of distributed energy technology by industrial companies: the impact of internal organizational* 

*Hereinafter, the designation of the hypothesis corresponds to its formulation in the text.*

**standardized coefficients**

0.264\*\*\*(0.098) 0.281\*\*\*

0.166\*\*\* (0.015) 0.185

0.451\*\* (0.104) 0.454\*\*

0.598\*\*\*(0.062) 0.599\*\*\*

−0.387\*\*\*(0.209) −0.385\*\*\*

−0.196\*\* (0.118) −0.394\*\*

0.153 \*\*\*(0.201) 0.254\*\*\*

−0.393 \*\*\*(0.023) −0.194\*\*\*

**Standardized coefficients**

**152**

**Table 5.**

*†*

*\**


*\* Significance of the coefficient p < 0.10.*

*\*\*Significance of the coefficient p < 0.05.*

*\*\*\*Significance of the coefficient p < 0.01. Standard errors are given in brackets.*

#### **Table 6.**

*Adoption of distributed generation technologies: the impact of specific factors.*

In general, the results of the regression analysis confirmed the hypotheses of the study. The models based on the regression equations were able to explain 63% of the variation of internal organizational and external factors in the distributed energy technology adoption by companies and 57% of specific factors.

When modeling the distributed energy technology adoption by companies (**Table 5**), it turned out that technical feasibility (β = 0.264; *p* < 0.05), comparative advantage of using distributed generation (β = 0.451; *p* < 0.10), and cost of electricity (β = 0.598; *p* < 0.10) positively affect the adoption of distributed energy technologies. The factor "perceived risks" (β = 0.166; *p* = 0.01) does not have a significant impact on the growth in the number of distributed energy users. And, the factor "costs for construction and installation of distributed generation sources" (β = −0.387; *p* < 0.10) has a negative influence on the decision to use distributed energy technologies.

Among the external factors, regulators' decisions have a significant impact on the distributed energy technology adoption by companies (β = 0.393; *p* < 0.05).

Market pressure and technological changes in the industry do not have a significant negative impact on the rate of distributed energy technology adoption by companies.

Thus, the technical feasibility, comparative advantage, and cost of electricity are the main factors for the growth in the number of distributed energy technology users in the studied sample.

**Table 6** shows the regression analysis results of the specific factors that have influence on the distributed energy technology adoption.

All specific factors had a positive effect on the distributed generation technology adoption by companies with a probability of error *p* of no more than 0.05. The coefficient β with the variable "efficiency" was 0.324 (*p* < 0.01); for the factor "no energy transfer costs" was β = 0.378 (*p* < 0.05); and for the factor "absence of payment for technological connection to electric networks" was β = 0.321 (*p* < 0.05). At the same time, the factors "the existing price ratio for electric energy" (β = 0.016; *p* > 0.10) and "the possibility of changing the volumes of generated electric and thermal energy when economic situation changes" (β = 0.163; *p* > 0.10) did not have a significant impact.

The results of testing hypotheses are the following. According to Hypothesis 1, which described the factors influencing the distributed energy technology perception by companies, it was partially confirmed for intraorganizational factors, (a) technical feasibility (β = 0.264; *p* < 0.05); (d) perceived benefits (β = 0.451; *p* < 0.01), and (e) electricity cost (β = 0.598; *p* < 0.05), and environmental factor, (i) the regulator's decision (β = 0.396; *p* < 0.05). The factors (f) costs of building and installing distributed generation sources (β = −0.387; *p* < 0.01) and (g) market pressure (β = −0.196; p < 0.01) have a negative impact on the distributed energy technology adoption. For factors (c) perceived risks (β = 0.166; *p* < 0.01) and (h) possibility of changing the volumes of generated electrical and thermal energy (β = 0.153; *p* < 0.01), the hypothesis was not confirmed.

According to Hypothesis 2, companies' perception of distributed energy technologies is influenced by specific factors. This hypothesis is partially confirmed for common factors: (b) the presence of by-products that can be used as fuel (β = 0.421; *p* < 0.01); (d) high efficiency (β = 0.324; *p* < 0.10); (e) no costs for energy transfer (β = 0.316; *p* < 0.01); and (h) lack of payment for technological connection to electric networks (β = 0.363; *p* < 0.01). The influence of factors (g) existing ratio of prices for electric energy and natural gas (β = 0.016; *p* < 0.01); (i) possibility of changing the volume of generated electrical and thermal energy (β = 0.163; *p* = 0.45); and (j) a reduction in need for energy transmission over considerable distances (β = 0.211; *p* < 0.01) has not been confirmed.

Thus, the proposed model of analysis is successful, describing various factors of adoption of technologies of distributed energy by industrial companies. Standardized coefficients not only allow testing hypotheses but can also be used to compare the influence of various characteristics of distributed energy facilities on the likelihood of their acceptance by industrial companies.

#### **5. Conclusions**

Thus, according to the obtained results, when deciding on the company's own generation, the main factors are technical feasibility (β = 0.421), perceived advantages (β = 0.363), electricity cost (β = 0.324), and the decision of regulators (β = − 0.309). It can be concluded that for analyzed companies, technical feasibility, cost of electricity, and perceived benefits are critical factors in deciding on the use of distributed generation technologies. The risk factor turned out to be insignificant (β = 0.209), which, when conducting in-depth interviews, the companies explained by the fact that distributed generation systems reduce the occurrence of the listed adverse effects to a minimum. Obtaining cheap electric and thermal energy, a

**155**

**Author details**

important.

**Conflict of interest**

Arkady Trachuk\* and Natalia Linder

There is no conflict of interest.

the Russian Federation, Moscow, Russia

provided the original work is properly cited.

\*Address all correspondence to: atrachuk@fa.ru

Department of Management, The Financial University under the Government of

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Dissemination of Distributed Energy Technologies DOI: http://dx.doi.org/10.5772/intechopen.88604*

**5.1 Limitations of the study**

sample of companies.

technology adoption by companies.

gradual increase in energy capacities and evenness of investment with fast energy for industrial and household needs are possible today due to the use of energy-

It is necessary to note some of the limitations of this study. It was not possible for us to interview the entire totality of Russian companies due to limited data collection opportunities. However, our sample of companies covers a representative part by sector, sales revenue, and company size. In the future, researchers would be able to analyze the factors of distributed generation technology adoption in a larger

The results of a sample of 69 companies confirm the practicability of a comprehensive assessment of the distributed generation technology adoption factors. Within the framework of this study, the selected internal, external, and specific factors were measured empirically and used to analyze the distributed generation

The qualitative stage of research allowed us to draw initial conclusions about the significance of certain aspects of distributed generation technology adoption. Thus, in accordance with the results of the theoretical base analysis, it was empirically confirmed that when companies adopted distributed generation, the cost of electricity and technical compatibility were of greatest importance. At the qualitative stage, the majority of respondents named these aspects of adoption as the most

efficient solutions based on distributed generation technologies.

gradual increase in energy capacities and evenness of investment with fast energy for industrial and household needs are possible today due to the use of energyefficient solutions based on distributed generation technologies.
