Identifying the Variables of Intellectual Capital and Its Dimensions with the Approach of Structural Equations in the Educational Technology of Iran

With the growth of the knowledge economy, organizations have found that the value of an organization is not just the financial capital of an organization but also its intellectual capital. Since intellectual capital is an intangible asset, identification of its indicators and its variables from the financial and cost perspective has become a challenge for managers. This has increased the importance of intellectual capital as research and economic categories. Due to the role of intellectual capital spending in the value added of organizations, this topic is the subject of new research. The purpose of this study is to identify intellectual capital variables based on cost indices in the Iranian educational technology. Therefore, firstly, cost indicators that are effective are found, then a questionnaire based on the Likert scale is developed. Exploratory factor analysis has been used to extract factors. After performing the factor analysis, the indicators are classified into nine categories. According to the extracted factors, 9 factors are classified into 3 groups. Confirmatory factor analysis has been used to confirm this categorization. For this purpose, all the statistical indices introduced by the experts in this field have been presented to confirm the model. The results of this research show that human capital dimension includes 4 factors, structural capital dimension includes 3 factors, and dimension of relational capital includes 2 factors in intellectual capital of Iranian educational technology.


INTRODUCTION
The concept of intellectual capital is introduced as a relatively new perspective that is an integration of resourceoriented and knowledge-oriented perspective (Stam & Andriessen, 2009).In the resource-oriented view, there was no clear distinction between varieties of organizational resources.On the other hand, in the knowledge-based view (knowledge management), all attention was focused on organizational knowledge and its explicit and hidden forms.But in terms of intellectual capital, emphasis is placed on the identification and management of all intangible resources and capital of the organization.In this view, knowledge and other intangible assets as valuable resources along with the resources of work, land, and capital (previously considered in the economy) are presented as assets that, unlike previous sources, their value is increased as they are being used (Afrazeh, 2005).With the advent of the information technology revolution, the information and networking society, and the rapid growth and development of superior technology, the pattern of global economic growth has changed.As a result, knowledge has become the most important alternative to financial and physical capital in today's global economy.In a knowledge-based organization, traditional accounting methods, based on tangible assets of the organization, are inadequate to value intellectual capital, the largest and most valuable intangible assets of organizations.Studies have shown that, contrary to the decline in the returns of traditional resources such as money, land, machinery, etc., knowledge is really a source of business performance improvement.Intellectual capital has been conceptualized by various disciplines.For example, accountants are interested in measuring it in balance sheets, information technology experts are trying to codify it in information systems, sociologists tend to balance power using it, psychologists tend to develop minds, human resource managers tend to calculate the return on investment through it, and education and development staff are keen to be sure they can put it into human resource development programs.With regard to the elements that comprise intellectual capital and its components, many comments and models have been presented by scientists.It seems that when looking at the literature of intellectual capital research, most intellectual capital models have tried to consider three components of human capital, structural capital, and relational capital for intellectual capital (Bontis, 2003).
Factor analysis is a general approach to some of the multivariate methods, the main purpose of which is to summarize the data, and it examines the internal correlation of a large number of variables and ultimately describes them in the form of finite general factors.This technique is a method in which all variables are simultaneously considered.Therefore, the value of factor analysis is that it derives a useful organizational design that can be used to explain a large amount of behavior with the most savings in structures (Hair Jr, Hult, Ringle, & Sarstedt, 2016).In this paper, exploratory and confirmatory factor analysis is used to identify and categorize the cost indices of intellectual capital.In this research, we first studied the literature of the subject and extracted items related to the cost indexes of intellectual capital.Then, a questionnaire has been developed to examine the relationship between these items and their categorization.The statistical population of the research consisted of researchers and experts in the educational technology of Iran.Finally, 300 people responded to the research questionnaires.The reliability of the Guttman test is appropriate for each case of reliability.To use the exploratory factor analysis, we examined the data normality.For this purpose, the KMO coefficient is used for data adequacy, the Bartlet test is used for spatial and symmetry, and principal component method is used to extract the factors, and the VARIMAX rotation have been used.After performing factor analysis, the indices are classified into nine categories.It was found out that the intellectual capital indices were classified into three categories of human capital, structural capital and relational capital, and, according to the factors extracted, the nine factors were classified into these three groups.Confirmatory factor analysis has been used to confirm this categorization.For this purpose, all the statistical indices introduced by the experts in this field have been presented to confirm the model.

SIGNIFICANCE OF THE STUDY
With the growth of knowledge economy, studies on intellectual capital and cost and its added value have been considered in Iranian companies in recent years.In a large amount of research, the relationship between intellectual capital and value added of organizations has been investigated.With this in mind, the importance of the role of cost indicators of intellectual capital in improving organizational performance and management is not at stake.Intellectual capital indexes and their priorities in each organization or industry are different and belong to the same organization.The educational industry in Iran is no exception.Given the importance of this industry in Iran and its role in economic development, it cannot be ignored.Therefore, paying attention to intellectual capital cost indicators leads to the optimal use of resources in order to increase productivity and value added in this industry.Therefore, the identification of intellectual capital indices in this industry requires careful examination.Accordingly, the categorization and presentation of appropriate cost variables would be helpful in order to plan for the use of those intangible resources.

Contribution of this paper to the literature
• This research, considers on studying the structures of intellectual capital factors in educational technology through exploratory factor analysis, based on indicators in subject literature and interviews with educational experts.
• Using statistical methods, it's been confirmed and categorized so as to 1. Choosing effective factors in educational industry, 2. Allocating costs of each indicator to the related dimension.
• EFA method considers on dimensions of intellectual capital and categorization of them into educational technology, also CFA method was used for confirmation of these categorizations.

OBJECTIVES
In this research, considering the importance of calculating the cost of intellectual capital and its role in calculating the value added of educational technology in Iran, in order to avoid overlapping of these costs, dimension reduction is attempted through exploratory and confirmatory factor analysis, so as to eventually present a model for calculating the impact of intellectual capital on the value added of Iran's educational technology with cost approach.The purpose of this study is to identify the intellectual capital variables in Iran's educational technology using exploratory factor analysis and identify its dimensions using confirmatory factor analysis.THEORETICAL FOUNDATIONS AND RESEARCH LITERATURE

Intellectual Capital
By entering the knowledge economy, the role of intangible assets has become more important than the past, so that future competitive advantage in all organizations, including sports organizations, is based on the effective and appropriate use of these types of intangible assets (Hofmann, Schneider, & Walter, 2005).Organizations often have limited resources and facilities; therefore, identifying, defining and prioritizing elements and indicators that are more important in their performance and productivity will lead to a guidance for decision making and planning and enables managers to understand the most important and effective indicators of intellectual capital in order to invest on them.As a result, taking advantage of the intellectual capital benefits and their management is the first step in identifying their dimensions and indicators (Bozbura, Beskese, & Kahraman, 2007).
The scholars have considered three types of intellectual capital for organizations, including human, structural and relational capital.Human capital refers to a set of knowledge, skills, competence, problem-solving capabilities, and decision-making in human resources (Curado, Henriques, & Bontis, 2011).Relational capital emphasizes the organization's ability to interact with the business environment.The third type of intellectual capital is structural (organizational) capital (María Viedma Marti, 2001).If we deduct human from an organization, what remains is structural capital (Baker, 2008).

RESEARCH LITERATURE
Table 1 presents researches and literature on the topic of identifying intellectual capital indicators.Initially, the dimensions of intellectual capital along with the indicators identified by the authors of each research are presented separately.

CONCEPTUAL UNDERSTANDING OF FACTOR ANALYSIS AND ITS APPLICATION
Factor analysis is a technique that enables reducing the number of dependent variables into a smaller number of hidden or latent dimensions.Its main purpose is to adhere to the principle of economics and saving through the use of the smallest explanatory concepts in order to explain the maximum amount of common variance in the correlation matrix.The basic assumption of factor analysis is that the underlying factors of variables can be used to explain complex phenomena and the observed correlations between variables are the result of their sharing in these factors.The objective of the factor analysis is to detect these unobservable factors based on a set of observable variables.The factor is a new variable that is estimated by linear combination of the main scores of observed variables on the basis of formula (1): where, W denotes factor coefficients and P represents the number of variables.These factors, in themselves, are hypothetical or theoretical structures that contribute to the interpretation of consistency and harmony in the data set.Therefore, the value of factor analysis is that it provides a useful organizational design that can be used to interpret a large amount of behavior with the greatest savings in explanatory constructions.
The hope is that a small number of these factors (that is, linear combinations of the main scores of observed variables) can cover almost all the information obtained by a larger set of variables and as a result, simplify describing the characteristics of the individual.Moreover, we hope that with the proper development of the factors, we create variables that imply a clear structure with a psychological meaning in such a way that our description of the person is not only simpler, but also clearer and more decisive (Hair Jr et al., 2016).
Smart PLS version 2.0 was used for data analysis.It is a second-generation tool, referred to as partial least squares structural equation modeling (PLS-SEM) (Hair Jr et al., 2016).

METHODOLOGIES
This research is a descriptive correlational and applied research study in which exploratory and confirmatory factor analysis of intellectual capital in the educational technology of Iran has been addressed.In this research, from the one hand, the analysis of the contents of articles related to the subject of research and, on the other hand, the questionnaire has been used for data collection.This questionnaire contains 57 questions that have been prepared for modeling and determining factors and indicators of intellectual capital.To achieve this goal and validate it, the most important factor is reliability and validity.In this research, content validity (confirmed by 8 experts of intellectual capital in the educational technology) and construct validity, and for determining the reliability, the Cronbach Alpha reliability coefficient were used.The statistical population of this research included: presidents, vice presidents, teachers, principals of educational centers, students and experts of educational technology.From this, 340 people were selected as a sample of research using random sampling.Of the whole sample, 32 questionnaires were not returned and 8 questionnaires lacked the required accuracy; therefore, 300 questionnaires had the necessary conditions for the analysis.Statistical analysis of exploratory and confirmatory factor analysis have been used to analyze the data.The findings of exploratory factor analysis led to identification of 9 factors of job satisfaction, human abilities and skills, job competency and level of personnel training on human capital dimensions, information technology systems, process and brand of structural capital dimensions, customer information and customer satisfaction of the dimensions of relational capital.By looking at the literature on intellectual capital, three general categories are considered for its variables.In this research, this has been addressed to explore and identify the dimensions of intellectual capital in this technology using confirmatory factor analysis.Findings of the confirmatory factor analysis showed that the intellectual capital model in the Iranian educational technology is fitted and applicable.

IDENTIFYING INTELLECTUAL CAPITAL INDICES IN IRAN'S EDUCATIONAL TECHNOLOGY
In the first step, by studying authoritative and scientifically valid sources, while familiarizing with the concepts and definitions of intellectual capital components and intellectual capital models in education, a preliminary list of suitable indicators for measuring intellectual capital in educational technologies was identified.Table 1 shows a number of these indicators.
In the second step, using the opinions of the experts working in the educational technology of Iran, a number of the most appropriate indicators of intellectual capital were extracted from the preliminary list, which is presented in Table 2.It should be noted that this step was aimed at identifying indices appropriate to the structure of the Iranian educational technology.
The third step is identifying the variables of intellectual capital using exploratory factor analysis.For this purpose, a questionnaire was prepared for the experts to identify the variables.Questionnaire questions were created based on the Likert scale.Subsequently, the questionnaires were gathered and observed.Only 300 questionnaires had the necessary conditions for exploratory factor analysis.In order to verify the reliability of the questionnaire, GUTTMAN method was used in this section.If the Lambda coefficient is higher than .7,then the reliability of the questionnaire is confirmed.The results of this test are presented in Table 3.The cost of identifying processes and analyzing the flow of information and operations along mechanization 11 The cost of treatment and psychology 40 The cost of maintenance of communication networks 12 The cost of festive and occasions 41 The cost of information retrieval 13 The cost of compassionate help in personal problems 42 The cost of implementing an information system in the field of CRM 14 The cost of bonus package (Stock) 43 The cost of implementing the information system in the field of communication and interaction with suppliers 15 The cost of purchasing employees' consumables 44 The cost of launching search engines 16 The cost of interest-free gratuitous loans 45 The cost of measuring process adaptation 17 The cost of receiving a car insurance benefit 46 The cost of process audit results 18 The cost of supervisory loan 47 The cost of implementation of management systems 19 The cost of recreational programs 48 The cost of process monitoring 20 The cost of cash or non-cash rewards of the organization to the person for motivation 49 The cost of measurement of Process Indicators (KPIs)

21
The cost of developing integrated information systems to improve the cost of processes and the effective interaction of staff 50 The cost of designing process models such as: Customer-oriented process reference model (CPRM) Event-driven process chain (EPCs) Medical process models (MoBimeP) 22 The cost of providing facilities to superior staff The cost of examining an international registration application as a source office 28 The cost of collecting potential customer profiles 56 The cost of logo design 29 The cost of attending exhibitions 57 The cost of brand research

Data Normality
One of the presumptions of factor analysis is data normality.For this purpose, Skewness must be between (3, -3) and Kurtosis must be between (5, -5).In Appendix 1, the results of this test are presented (Hair Jr et al., 2016).

Exploratory Factor Analysis
To this end, two tests are necessary before doing this analysis.The first test is the KMO, which is performed to verify the adequacy of the sample size, if the KMO value is greater than .7,this test is confirmed (Kaiser, 1974).But another test that expresses spatiality and symmetry of relationships is the Bartlett test.This test is based on observations and if it is meaningful, spatiality is confirmed and exploratory factor analysis is allowed (Bartlett, 1954).To perform exploratory factor analysis in this research, Principal component and VARIMAX rotation were used.In Table 4, the results of this test are presented.
In Table 5, the number of factors extracted by the exploratory factor analysis and the degree of variance explained by each of the factors before and after the rotation are shown.The results show that 57 surveyed indicators are classified into 9 factors.These 9 factors account for 64.010 percent of the total variance.

Naming Factors
Regarding the category in the Rotated Component Matrix, the names of the factors are shown in Table 6.

CONCLUSION
The new economic growth comes from knowledge and information.This has increased the importance of Intellectual capital as a research and economic category.In this paper, exploratory and confirmatory factor analyses were used to identify and categorize intellectual capital indices.In this research, we first studied the literature of the subject and extracted items related to intellectual capital indices.Then, a questionnaire was developed to examine the relationship between these items and their categorization.The statistical population of the research consisted of researchers and experts in the educational technology of Iran.Finally, 300 people responded to the research questionnaires.In this research, the validity of the questionnaire was confirmed by experts and the Guttman method was used.The number of statistical samples in the exploratory factor analysis method was determined based on the number of items (questions).The results of Factor Analysis showed that 57 indicators are suitable for the separation of questions and compliance with the basics.The reliability of the Guttman test for each case was higher than .7,indicating a good reliability.To use the exploratory factor analysis, data normality was examined.For this purpose, the KMO coefficient was used for data adequacy, the Bartlet test was used for spatial and symmetry, and principal component method was used to extract the factors, and the VARIMAX rotation was used.After performing factor analysis, the indices were classified into nine categories.It was found out that the intellectual capital indices were classified into three categories of human capital, structural capital and relational capital, and, according to the factors extracted, the nine factors were classified into these three groups.Confirmatory factor analysis was used to confirm this categorization.For this purpose, all the statistical indices introduced by the experts in this field were presented to confirm the model.The results of this research showed that human capital dimension included 4 factors of job satisfaction, human capabilities and skills, job competency and employees' training level, structural capital dimension included 3 factors of information technology systems, process and brand, and the dimension of relational capital included 2 factor of customer information and customer satisfaction in intellectual capital of Iran's educational technology.

1.
What are the factors of intellectual capital in Iran's educational technology? 2. How much do supposed factors explain observations?3. How much of the specific variance is covered by the observed variable?4. What are the factors associated with intellectual capital measurement in producing educational technologies? 5. What is the effect of each of the factors associated with intellectual capital assessment?

Figure 3 .
Figure 1.Dimensions of human capital

Table 2 .
Indices of intellectual capital proportional to research

Table 3 .
Reliability statistics

Table 5 .
Total Variance Explained