**3. Development of research model and hypotheses**

Companies will switch to sources of their own generation when they are able to perceive them and are ready for their use. Therefore, the basis for studying the possibilities of using distributed generation technologies is their acceptability or perception on the part of industrial companies.

The use of factor analysis involves the study of factors that have the greatest influence on the industrial companies' decision to adopt new technology.

In the literature, there are a fairly limited number of studies on the adoption of new technologies by industrial companies. The most famous models are:


**Table 1** presents the intraorganizational and external factors affecting the adoption of new-generation technologies by companies.

Thus, we can formulate the first hypothesis of our study.

Hypothesis 1. Industrial companies' adoption of distributed energy technologies is influenced by intraorganizational factors: (a) technical feasibility, (b) availability of human resources, (c) perceived risks, (d) perceived advantage, (e) connection costs, (f) electricity costs and external costs, (g) market pressure, (h) pressure of the regulator (government), and (i) technological changes in the industry.

To identify the specific characteristics of distributed energy technologies that influence their adoption by companies, we used research results [15–21] and identified the most significant specific factors (**Table 2**).

The conducted analysis allowed us to formulate the second hypothesis of the study.

Hypothesis 2. The decision on the use of distributed energy technologies is influenced by specific factors: (a) the presence of by-products that can be used as

**149**

**3.1 Description of variables**

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

**Perceived organizational e-readiness (POER) model** Technical feasibility (integration, scalability, remote access,

**Perceived external e-readiness (PEER) model**

decisions to use new technologies

technologies

decisions

**Table 1.**

Market changes affecting the company's decision to use new

Decisions of regulators (authorities), affecting the company's

Technological changes in the industry affecting the company's

*Factors affecting the adoption of new technologies by companies.*

infrastructure, complexity, etc.)

fuel, (b) high efficiency, (c) lack of energy transmission costs, (d) lack payments for technological connection to electric networks, (e) the existing ratio of prices for electric energy and natural gas, (f) possibility of changing the volume of generated electricity and heat when economic situation changes, (g) reduced need for energy transmission over long distances, and (h) increased share of local energy resources. At the next stage, the index was calculated for the main factors influencing the decision on self-generation for intraorganizational factors (technical feasibility; availability of human resources; perceived risks; perceived benefits and the need for alternative energy sources; cost of electricity; costs for construction and installation of distributed sources generation) and external factors (changes in the market that affect the company's decision to use innovation; decisions of regulators (authorities), affecting the decisions of companies on the use of new technologies; technological changes in the industry, affecting decisions of company) by summing

**Adoption factors Studies confirming the** 

Availability of human resources Vorozikhin [8]

Perceived benefits and need for alternative energy sources Seo et al. [11]

Cost (transaction fee) Brandon et al. [12] Costs Seo et al. [11]

Perceived risks (safety, investment) Wu [6]

**importance of the relevant factor**

Wu [6] Trachuk [7]

Bhowmik et al. [9]

Trachuk et al. [10] Bhowmik et al. [9]

Brandon et al. [12]

Subhes [13] Seo et al. [11] Trachuk et al. [7] Trachuk et al. [10]

Subhes [13] Michael et al. [14] Brandon et al. [12] Trachuk et al. [7]

Subhes et al. [13] Michael et al. [14]

up the references to individual items from the questionnaire (**Table 3**).

The frequency of mentioning specific factors is calculated in the same way (**Table4**). Then, nonparametric Spearman correlation coefficients (ρs) were calculated for the ranked data. To recognize the relationship between the components of the model as significant, the correlation coefficient had to exceed a threshold value of 0.50.

For the quantitative phase of the study, questions were formulated, measuring the most significant factors. The questions were formulated as follows: "How much do you


#### **Table 1.**

*Intellectual Property Rights - Patent*

energy technologies.

and at the level of the economy as a whole.

**3. Development of research model and hypotheses**

perception on the part of industrial companies.

tion of new-generation technologies by companies.

fied the most significant specific factors (**Table 2**).

Thus, we can formulate the first hypothesis of our study.

the regulator (government), and (i) technological changes in the industry.

employees to innovation.

grid. The consumer of electricity begins to play an increasing role in the energy system, mastering new roles—generator and accumulator of electricity. Freedom of consumer choice is increasing. At the same time, there are many opportunities for demand management and energy efficiency both at the level of a specific household

In order to carry into effect these possibilities, the states are changing the models of electricity and capacity markets toward their liberalization. It can be said without exaggeration that a necessary basis is being formed for building a competitive environment at the retail level with the development of distributed energy. The entry of distributed energy into the Russian energy system became noticeable in the 2000s, but over the past 17 years, in fact, it was limited to only distributed generation. The development of this process in Russia takes place at a much lower rate, which requires a deep study of the spreading factors of distributed

Companies will switch to sources of their own generation when they are able to perceive them and are ready for their use. Therefore, the basis for studying the possibilities of using distributed generation technologies is their acceptability or

The use of factor analysis involves the study of factors that have the greatest

• Perceived organizational e-readiness (POER) model is used to measure the intraorganizational factors for the adoption of new technologies. This model was proposed by Molla and Licker in 2002 [5] for analyzing the intraorganizational environment factors, including personal characteristics of company's employees, system of internal assistance in the company, and attitude of

• Perceived external e-readiness (PEER) model is used to analyze external

factors. The PEER model [5] analyzes the factors of competitive pressure in the industry, influence of regulators, and technological changes in the industry.

**Table 1** presents the intraorganizational and external factors affecting the adop-

Hypothesis 1. Industrial companies' adoption of distributed energy technologies is influenced by intraorganizational factors: (a) technical feasibility, (b) availability of human resources, (c) perceived risks, (d) perceived advantage, (e) connection costs, (f) electricity costs and external costs, (g) market pressure, (h) pressure of

To identify the specific characteristics of distributed energy technologies that influence their adoption by companies, we used research results [15–21] and identi-

The conducted analysis allowed us to formulate the second hypothesis of the

Hypothesis 2. The decision on the use of distributed energy technologies is influenced by specific factors: (a) the presence of by-products that can be used as

In the literature, there are a fairly limited number of studies on the adoption of

influence on the industrial companies' decision to adopt new technology.

new technologies by industrial companies. The most famous models are:

**148**

study.

*Factors affecting the adoption of new technologies by companies.*

fuel, (b) high efficiency, (c) lack of energy transmission costs, (d) lack payments for technological connection to electric networks, (e) the existing ratio of prices for electric energy and natural gas, (f) possibility of changing the volume of generated electricity and heat when economic situation changes, (g) reduced need for energy transmission over long distances, and (h) increased share of local energy resources.

At the next stage, the index was calculated for the main factors influencing the decision on self-generation for intraorganizational factors (technical feasibility; availability of human resources; perceived risks; perceived benefits and the need for alternative energy sources; cost of electricity; costs for construction and installation of distributed sources generation) and external factors (changes in the market that affect the company's decision to use innovation; decisions of regulators (authorities), affecting the decisions of companies on the use of new technologies; technological changes in the industry, affecting decisions of company) by summing up the references to individual items from the questionnaire (**Table 3**).

The frequency of mentioning specific factors is calculated in the same way (**Table4**).

Then, nonparametric Spearman correlation coefficients (ρs) were calculated for the ranked data. To recognize the relationship between the components of the model as significant, the correlation coefficient had to exceed a threshold value of 0.50.

#### **3.1 Description of variables**

For the quantitative phase of the study, questions were formulated, measuring the most significant factors. The questions were formulated as follows: "How much do you agree with the statements below?". The 7-point Likert scale was used for answers (1 "I completely disagree," 4 "I do not know if I agree or disagree," 7 "I completely agree").

The "technical feasibility" factor was measured using a scale consisting of two questions that determine the company's ability to install distributed generation facilities taking into account the existing infrastructure. To assess the "perceived advantage" factor, questions were to evaluate higher rates of distributed generation efficiency than UNEG services. The factor "construction costs and installation of distributed


#### **Table 2.**

*The most significant specific factors for companies to adopt new technologies for distributed energy.*


#### **Table 3.**

*Frequency of mentioning internal and external factors of distributed energy technology acceptance by companies.*

**151**

generation.

**Table 4.**

distributed energy technology adoption.

**3.2 Description of the data analysis procedure**

ing 70% of the variation in structural models.

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

generation sources" was measured using two questions that characterize the need to pay back the construction of our own generation in the medium term or the absence of

*Frequency of mentioning specific factors of distributed energy technology adoption by industrial companies.*

8 Increasing proportion of local energy resources 55.6

1 Availability of by-products that can be used as fuel 41.5

3 No cost of power transmission 58.9

the needs of a specific industrial production in both electrical and thermal energy)

2 High efficiency (provided that the generating facility is designed to meet

4 No payment for technological connection to electric networks (if the object of generation is isolated from the power system)

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

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

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

Similarly, a questionnaire was formed to analyze the specific factors of the

In conducted analysis, the reliability factors (Cronbach's alpha) were first evaluated for all variables, measured on a scale of several questions. The calculated coefficients corresponded to the recommended minimum level of reliability −0.75. At the next stage, the factor analysis was carried out using the method of principal components for nine questions describing four aspects of intraorganizational fac-

The analysis of specific factors affecting the distributed generation technology

In total, four specific factors explained 73.8% of the variation in the answers to questions from companies, which corresponds to the recommendations for explain-

A factor analysis based on the method of principal components with orthogonal

adoption by distribution network companies was conducted for 15 questions.

rotation revealed the presence of four intraorganizational factors and two environmental factors that described a total of 72.8% of the variation in questions. The values of the factors obtained were used to form a final set of factors influencing

tors and six questions describing three aspects of external factors.

Measuring the external factors of adopting distributed generation technologies was based on three groups of questions. First, market pressure was measured in accordance with the answers to questions about competitive pressure, comparing the technologies used. Second, technological changes in the industry were evaluated. They were measured by assessing the possibility of equipment repair and equipment operation during peak hours of load. Third, decisions of regulators were measured in the absence of administrative obstacles and support for distributed

**Percentage of mentioning**

48.4

79.4

42.6

41.2

34.6

a significant impact of construction costs on the cost structure of the company.


#### **Table 4.**

*Intellectual Property Rights - Patent*

Specific factors

energy)

gas potential

distances

**Table 2.**

agree with the statements below?". The 7-point Likert scale was used for answers (1 "I completely disagree," 4 "I do not know if I agree or disagree," 7 "I completely agree"). The "technical feasibility" factor was measured using a scale consisting of two questions that determine the company's ability to install distributed generation facilities taking into account the existing infrastructure. To assess the "perceived advantage" factor, questions were to evaluate higher rates of distributed generation efficiency than UNEG services. The factor "construction costs and installation of distributed

**Adoption factors Studies confirming** 

Availability of by-products that can be used as fuel Juan et al. [16]

No cost for power transmission Berg et al. [15]

Increasing the share of local energy resources You et al. [20]

*The most significant specific factors for companies to adopt new technologies for distributed energy.*

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

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

The existing ratio of prices for electric energy and natural gas indicates a high

Energy production takes place in the immediate vicinity of the consumption points, which leads to less need for energy transmission over considerable

Ability to change the volume of generated electrical and thermal energy

of generation is isolated from the power system)

when economic situation changes

**Intraorganizational factors Percentage of** 

1 Technical feasibility (integration, scalability, remote access, infrastructure, complexity, etc.)

 Availability of human resources 19.3 Perceived risks (safety, investment) 45.9 Perceived benefits and need for alternative energy sources 76.3 Electricity cost 74.1 Costs of building and installing distributed generation sources 81.5

External factors

10 Decisions of regulators (authorities), affecting company's decisions to use new technologies

9 Market changes affecting company's decision to use innovation 62.7

11 Technological changes in the industry affecting company's decisions 73.5

*Frequency of mentioning internal and external factors of distributed energy technology acceptance by* 

**mentioning**

**the importance of the relevant factor**

Zhang [21]

Yingyuan et al. [19]

Berg et al. [15]

Juan et al. [16]

Li et al. [18]

Kazemi et al. [17]

61.6

96.3

**150**

**Table 3.**

*companies.*

*Frequency of mentioning specific factors of distributed energy technology adoption by industrial companies.*

generation sources" was measured using two questions that characterize the need to pay back the construction of our own generation in the medium term or the absence of a significant impact of construction costs on the cost structure of the company.

Measuring the external factors of adopting distributed generation technologies was based on three groups of questions. First, market pressure was measured in accordance with the answers to questions about competitive pressure, comparing the technologies used. Second, technological changes in the industry were evaluated. They were measured by assessing the possibility of equipment repair and equipment operation during peak hours of load. Third, decisions of regulators were measured in the absence of administrative obstacles and support for distributed generation.

Similarly, a questionnaire was formed to analyze the specific factors of the distributed energy technology adoption.

#### **3.2 Description of the data analysis procedure**

In conducted analysis, the reliability factors (Cronbach's alpha) were first evaluated for all variables, measured on a scale of several questions. The calculated coefficients corresponded to the recommended minimum level of reliability −0.75. At the next stage, the factor analysis was carried out using the method of principal components for nine questions describing four aspects of intraorganizational factors and six questions describing three aspects of external factors.

The analysis of specific factors affecting the distributed generation technology adoption by distribution network companies was conducted for 15 questions.

In total, four specific factors explained 73.8% of the variation in the answers to questions from companies, which corresponds to the recommendations for explaining 70% of the variation in structural models.

A factor analysis based on the method of principal components with orthogonal rotation revealed the presence of four intraorganizational factors and two environmental factors that described a total of 72.8% of the variation in questions. The values of the factors obtained were used to form a final set of factors influencing

the distributed generation technology adoption by companies, which were then included in the regression analysis.

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 influenced the distributed generation adoption by companies more.
