Intellectual Capital (IC); new asset in the knowledge economy

The concept of IC has been dealt with by various authors, and there is no generally accepted definition as such, but common elements referring to value creation, intangible assets (without physical substance), strategic assets, knowledge, and skills are evident in almost all definitions, mainly the following.

The University Institute Euroforum Escorial (1998), cited by ^{Ochoa, M (2010)} defines IC “as the set of intangible assets of an organization, which, although not reflected in traditional financial statements, is currently perceived by the market to generate value or to have the potential to generate value in the future” p. 35.

For ^{Edvinsson and Malone (1998)} IC refers to the relationships with customers and suppliers, the knowledge that the company possesses, technological capacities, and expertise of its employees, among others, capable of generating a competitive advantage in the market.

^{Ordoñez, P. (2004)} citing Bueno, E. (1998) defines IC as “the set of distinctive basic competencies: some of technological origin, which also include all the elements of knowledge and experience accumulated by the company; others of organizational origin or those from the ‘action processes’ of the organization, and others of a personal nature, concerning the attitudes, aptitudes, and skills of the members of the organization”. p. 134

For this research, IC shall be understood as all those assets of an intangible type that the company owns, that do not appear in the financial statements, that originate from knowledge, and that offer possibilities of generating value if they are appropriately managed.

Components of the Intellectual Capital (IC)

Several classification proposals presented by different authors were reviewed for this research, and it was decided to take the classification proposed by ^{Sveiby (2007)}, which approximates the classifications given by ^{Edvinsson and Malone (1998)}; ^{Kaplan and Norton (2008)}; ^{Nevado and López (2013)}; ^{Brooking (1996)}; Modelo Meritum, ^{Camisón, Lapiedra, Segarra, and Boronat (2003)} (Modelo Nova); and Modelo Intelect. Similarly, said classification was validated with the characteristics of the companies that were the object of study. The classification was also validated with the characteristics of the companies under study. Therefore, the IC is made up of the following components:

Technology-Based Companies (TBC) - Parquesoft Colombia

TBC defines as those organizations that generate value through the systematic application of technological and scientific knowledge, which design, develop, and make products, services, processes, and procedures with high innovation components. (^{Colciencias, 2008}).

For Fariñas and López (2006), cited by ^{Merritt, H. (2012)}, the creation of TBC presents excellent economic and social advantages due to its potential to achieve high growth rates in a relatively short period of time, to generate innovative items, to create high quality jobs, and its proclivity to generate products with higher added value and better rates of profit. (Lanzas, A. 2015)

The companies that are the object of study belong to a Colombian non-profit foundation founded in 1999, whose purpose is to facilitate the creation and development, for young entrepreneurs, of TBC that provide products and services of information technology to the market; it is called Parquesoft Colombia. The companies in the park have the following characteristics (Lanzas, A. 2015):

Value generation

The traditional objective of the companies has been to obtain the maximum benefit to achieve their survival. Nowadays, organizations have as a new aim to create or to generate value, not only to survive, but also to grow with time.

Generating value is a process that consists of identifying, creating, and maintaining opportunities and knowledge in order to allow companies to gain advantages to sustain and grow in the short, medium, and especially long term, this requires that the corporate objectives are integrated in a systemic manner with the strategies and resources necessary to achieve them. Under the protection of this process, companies can be oriented, managed, and measured, always to create value.

The creation of value involves identifying and exploiting the valuable resources the company has to improve its effectiveness, and this is translated into benefits for their clients, their employees, and especially their owners. It also means that the company can exceed initial expectations and that this enables it to have differentiating elements that generate advantages in the market.

“As value, we understand that conventional quality of the object not attributed from a calculation or an expert appraisal. Value is not a fact but an opinion” ^{Álvarez (2010)} citing Brilman (1990).

Traditionally, when speaking of value results and value creation in companies, this is associated with economic and financial indicators such as profitability, liquidity, risk, and financial structure. To measure the management of the company in its value creation process, financial tools are used such as return on investment (ROI), return on equity (ROE), economic value added (EVA), cash value added (CVA), discounted cash flows, among others.

It is relevant to point out that given the conditions of the current economy (knowledge economy), another series of non-financial indicators are included that help to identify whether organizations are generating value with future perspectives. When speaking of value with future perspectives, the fact that the company builds value with the capacity to generate even more value is referenced, that is to say, a value that allows it to sustain itself and grow over time, always with the aim of generating benefits higher than those expected by all the members of an organization (owners, employees, clients, and others).

^{Edvinsson et al. (1998)}, in the Skandia Navigator Model, propose a series of indicators in addition to the financial ones, which they call indices and that allow measuring the management of the company in the process of value creation. However, from the approach of proper IC use, these indices are related to customer satisfaction, employee training, motivation, leadership, market share, employee retention, administrative efficiency, among others.

^{Kaplan et al. (2008)} in the Balanced Scored Card Model also propose a system that includes non-financial indicators that aim to measure business management through value generation. These are grouped under the perspective of customers, internal processes, and learning. These perspectives are closely related to relational capital, structural capital, and human capital, respectively.

In this research, non-financial indicators will be used to measure the value created in the companies of the park, a situation that is theoretically supported by the authors mentioned above.

Intellectual capital and value creation

The resources that affect the financial result of the companies can be classified as circulating assets (current), not circulating assets (fixed), intangible assets, and IC. Each resource participates in the generation of benefits for the organizations to a certain extent, which together would give the total benefits of the same. Lately, it has become indispensable to identify how the intangibles and the IC generate added value and increase the efficiency in the organizations to make strategic decisions. Therefore, intangible assets and IC have become an essential part of the market value of companies and organizations in general.

According to ^{Ross et al. (1997)}, the total value of a company has two major components: Financial capital and IC. Financial capital refers to the components of value that are obtained by traditional valuation methodologies (multiples methods, accounting methods, based on value creation, mainly), and that can be represented in monetary values. The IC is in charge of valuing the intangible components of the organizations that generate the support of the financial results. The difficulty concerning this second component lies in the valuation of its component elements.

The analysis and specialized study of intangible assets (IC) arise from the limitations of traditional accounting since it does not provide sufficient information about their measurement and valuation. With traditional accounting, which only measures tangible assets and reports historical performance results, it is difficult to achieve full valuation of intangibles and thus cannot achieve a real vision of revenue potential for the company.

Since financial measures are not sufficient to determine the value status of an enterprise, other types of measures show a balanced value scheme between financial and intangible resources. “The development of a new measurement architecture requires defining what types of data a company needs to measure according to its strategies, how this data can be generated, and what rules must be followed to regulate its flow” (Santa y Salas 2012, p. 25).

It is possible to state that at this moment the IC is providing greater efficiency and proper sources of value creation, which add to the real value that a company has in the market (^{Casanueva, Palacios and Val, 2007}). “The value of future profits lies in intangible values such as key people, networks and relationships, alliances, culture, and knowledge.” (^{Edvinsson et al. 1998, p. 49}).

The valuation of intangibles and IC seeks to determine-through the use of mathematical and statistical models and as accurately as possible-the value of those company resources that are not material and that contribute to the generation of wealth and value.

Usually, when the term general asset valuation appears, reference is made to the economic and financial value of the assets that a company owns, that is, to their monetary value and, usually, not to their subjective value. For this research, it is necessary to clarify that the concept of value is not considered a monetary term, but rather a term of perception between the relations that exist between the variables to be analyzed since intangible assets are basically non-financial assets that contribute significantly to value generation in organizations.

To create value, intangible assets and IC must be transformed into products or services from which some value is derived. Intangible assets are generally called performance drivers, suggesting causal relationships between these resources and the creation of organizational value (^{Meljem et al., 2010, p. 82}).

^{Estrada and Dutrénit (2007)}, citing Dierickx and Cool (1989), point out that: “In value creation processes, intangible resources such as creativity, talent, or an innovative perspective, can generate sustainable competitive advantages since they are unique, difficult to imitate, tacit in nature, and complex” (p. 131). This is how competitive success is associated with the availability and accumulation of intangible resources.

Measuring the impact of IC on value generation requires “transforming the way it is measured from a traditional activity-based approach (measuring what is done) to a new results-centered approach (measuring what is achieved and the effect it generates)” (Torres, 2005, p. 52). Valuing intangible assets and IC makes sense only to the extent that they can be translated into business results such as productivity, quality, customer service, competitiveness, profitability, value generation for the shareholder, among others (Torres, 2005, p. 53).

For ^{Cañibano, García -Ayuso, and Sánchez (1999)} the IC is generally considered as a fundamental determinant of the value of the company, and as an element closely associated with the existence of competitive advantages; this is supported as well by the Nova Model developed by ^{Camisón et al. (2003)}.

Therefore, it is possible to state that an improvement in IC, understanding that its components (human capital, structural capital, and relational capital) are improved, generates a positive impact on value generation.

Proposed Model

As mentioned above, the IC is made up of three components: HC, SC, and RC. This conformation is the result not only of the differentiated accumulation of each one, but also of the connection and interrelation between them; that is to say that the components are not isolated elements, but somewhat connected and closely linked elements. Citing Bontis (1998), ^{Sánchez, Melián, and Hormiga (2007)} “certify the existence of the mentioned connections” (p. 108). The components of intellectual capital are not completely useful independently, and their combination offers more advantages for the benefit of companies. For ^{Edvinsson et al. (1998)}, intellectual capital is the result of the joint management of HC, SC, and RC; the combination of these three components is what provides the source of value creation for the organizations.

For ^{Sveiby (2007)}, ^{Bontis, Know, and Richardson (2000)}, and the West Ontario University Model, there is a causality between the IC components, with the human capital component being the basis for the development of the relational and structural components; this, in turn, influences the results of the organizations.

^{Nonaka (2007)} and ^{Bueno, Del Real, Fernández, Longo, Merino, Murcia et al. (2011)} in their Intellectus Model affirm that the IC base is in the HC and the RC, and these give rise to the EC. Nonaka (2007) expresses that the knowledge that the employees have (HC) and the relations that the company has (RC) must be conserved through the documentation and normalization of the same, generating the SC and protecting in this way the knowledge that leaves the company with the departure of the employees.

Taking into account the theoretical foundation presented so far, and according to the characteristics and needs identified in Parquesoft, a model is needed to determine if the IC that these companies currently have is contributing with value generation. Therefore, a model is proposed that identifies the impact that the IC has on value generation. Articulating the different pieces of the model that have been presented through the document, the following is obtained:

Source: own elaboration based on the concepts of the authors cited in the section

Figure 1 Value Generation Model through IC in Parquesoft Companies 

Construction of data collection instruments

To construct the data collection instrument-in this case, the survey-the elements proposed in the previous model were determined as variables. To measure the research variables, given that they are latent (non-observable) variables, several observable indicators are used. To determine the indicators to be used, a bibliographic review was carried out, through which the following indicators were defined (see Tables 1, 2, 3, and 4):

Population and Sample

As mentioned above, the companies under study are the companies of Parquesoft Colombia. The technical datasheet of the sample appears in Table 5.

Methodology for the analysis of the information

When reviewing the existing literature on the theoretical models that analyze the IC, it is evident that each one presents different structures, offers different results (financial results, indicators, value), uses different analysis variables, and depends on the specific conditions of the company to be valued. Therefore, it is possible to conclude that there is no single method of valuation or analysis of the IC.

Similarly, the literature review identified the existence of a series of authors who use other methodological tools to identify the relationships between IC components and the variables to be studied. The tools identified are multivalent statistical analysis techniques (second generation statistical techniques), including Structural Equation Modeling (SEM). ^{Barletta, Pereira, Yoguel, and Robert (2013)}; ^{Ospina, Berrio, Bedoya, Cárdenas, and Muñoz (2013)}; ^{Motta, Zavaleta, Llinás, and Luque (2013)}.

To analyze the research data and validate the proposed model, SEMs will be used. SEMs are multivariate techniques that combine aspects of multiple regression (examining dependency relationships) and factor analysis (representing immeasurable concepts - factors - with multiple variables) to estimate a series of simultaneously interrelated dependency relationships. ^{Cepeda, G. (2004). Pp.4}

SEM can be analyzed through two statistical techniques. For this research, it was chosen to use the analysis technique based on components or Partial Least Squares (PLS), due to being considering the appropriate one according to the characteristics of the variables of the research. The SmartPLS 2.0 software was used for the analysis.

Evaluation of the results of the research

The analyzed data come from the surveys carried out to the companies; these were organized and tabulated according to the requirements of the SmartPLS 2.0 software to be analyzed. To validate the model through the PLS technique of SEM in the first stage, it is necessary to evaluate the measurement models (indicators), which consists of determining whether the theoretical concepts are correctly measured through the observed variables. This evaluation has the purpose of demonstrating the solidity of the measurement instrument (survey) used.

The evaluation of the structural model is made in the second stage. This evaluation consists of determining how the empirical information obtained with the research supports the theoretical concepts, defining if it is possible to confirm the theory to examine the predictive capacities of the model and the relation between its constructs.

Table 6 presents the evaluations made in each of the stages and the expected results.

Figure 2 presents the nomogram of the model:

Source: own elaboration using SmartPLS 2.0. ^{Ringle, Wende, and Will (2005)}

Figure 2 Nomogram with individual loads 

Assessment of the validity and reliability of the model

Reliability of internal consistency: As can be seen in Table 7, the compound reliability of each of the constructs had a value greater than 0.7, which shows that the indicators that make up each of these factors are related to each other. In Table 7, the constructs SC, HC, and VG show Cronbach’s alpha values higher than the accepted minimum value, meaning that the indicators of each of them are related to their respective construct and provide validity to each of them. The RC variable had a value of 0.68, less than the expected minimum; for research purposes and given that in the analysis of internal reliability it obtained an acceptable value, it is not eliminated from the research, which also ensures preserving the validity of content.

Convergent validity: In Table 7, all constructs have an AVE value greater than 0.5, which means that the different items intended to measure a concept or construct indeed measure the same. So, the adjustment of these items is significant, and they are highly correlated.

Individual reliability of the item: In Figure 2, the values in the arrows pointing to the indicators (tables) are the loads λ, which give values greater than 0.5. The indicators in Figure 2 are less than the indicators presented in Tables 1, 2, 3, and 4, since a filtering of indicators was made, eliminating those that had a load λ of less than 0.5. A load greater than 0.5 means that this item contributes significantly to the construction of the latent variable.

Discriminant validity: Analyzing the cross-loads result (see Table 8 and 9) it was obtained that each indicator presents a higher correlation with its latent variable than with others; this indicates the extent that a given construct is different from other constructs. The square root of AVE of each construct is higher than the correlation between them; therefore, it is possible to conclude that the model meets the criterion of discriminant validity and that the latent variables are unmistakably differentiated.

From the results shown in this section, it is possible to conclude that the scales analyzed to measure the dimensions of human capital, structural capital, relational capital, and value generation are valid and reliable.

Validation of the structural model

R² coefficient: The values that appear within the circles (endogenous latent variables) (Figure 2) are the R² value, and as can be observed they have values greater than 0.1, which indicates that the model has predictive capacity on the endogenous variables of it.

Model path coefficients are the values that are in the arrows that join the latent variables (circles Figure 2), the values below 0.2 mean that the exogenous variable does not have a significant impact on the endogenous variable.

Table 9 displays the results of the Bootstrapping analysis.

As seen in the table above, the bootstrapping analysis for the model confirms the result given by the path ß coefficients, where the relationship between the structural capital construct and value generation is not significant.

Q² value: this value measures the predictive capacity of the model, Table 9 shows that the Q² values of endogenous constructs are higher than zero, which indicates the predictive capacity of the model.

With the results presented in this section, it can be stated that the empirical information obtained with the research supports the theoretical concepts, the model has predictive capacity, and there is a relationship between its constructs, in other words, they have statistical significance.

Qualitative analysis of results

The results of the evaluation of the model were presented in the previous section. In this section, a qualitative interpretative analysis of each of these results will be made to validate the proposed model.

Human Capital: To measure human capital, given that it is an exogenous latent variable, it was initially proposed to measure it using 14 indicators distributed in four dimensions (see Table 1). However, after evaluating the measurement models in which the robustness of the measurement instrument used is demonstrated, six indicators were eliminated. It is important to point out that all the indicators related to the education and training dimension were eliminated, a situation that raises concern regarding the strength of the HC since the training and knowledge of the employees are fundamental elements in this construct. As a result, it is possible to infer either that the companies are not allocating resources and efforts, or that the resources allocated are not generating the necessary impact.

The indicators that contribute most to the elaboration of the indicator are those related to the culture of innovation dimension, as they have the highest loads, all higher than 0.7 (see Graph 2). These factors reaffirm the nature of the companies in the Park, companies based on innovation in their products, processes, and services.

Second in importance, as regards the contribution of the HC construction, is the dimension of remuneration and incentives, highlighting the degree of satisfaction on the part of the company employees.

As for the third and last component, teamwork, all indicators have loads higher than 0.6, being acceptable values, but not optimal ones. It leads to the idea that teamwork spaces should be encouraged and improved because it is there that knowledge is created and transferred, and where innovation processes take place.

Structural Capital: Structural capital is an endogenous latent variable, which depends on the human capital construct and in turn, feeds the value generation construct, as defined in the model.

To measure the structural capital, nine indicators were initially proposed; however, after carrying out an analysis of the individual reliability of each item, three indicators were eliminated, two belonging to the information and communication technologies (ICT) dimension and one to the flexible organizational structure dimension.

The dimension that contributes most to this construct is “research and development”, which ratifies the basis of the companies of the park, highlighting mainly the existence of spaces, processes, and procedures for researching and developing ideas. This dimension is reinforced and ratified with the “culture of innovation” dimension of the human capital construct, in which it also plays an important role.

This construct, as shown by the SEM analysis presented, does not help explain value generation within the companies of the park.

Relational Capital: It is the component of the IC that contributes the most and explains the value generation construct. For this construct, 12 indicators were initially proposed, which were left in model 4 (indicators).

In this case, the dimension that stands out most is that of “participation in networks”, as it presents the highest burdens in terms of its contribution to the construction, confirming that the number of links that companies have is positively related to the level of value generation; this increases the possibilities of opening new markets and improving the competitive performance of companies.

While the “relationship with customers” dimension contributes, this is done weakly, a situation that is corroborated by the fact that companies have mostly business models focused on developing their products and services.

Value Generation: For this construct, eight indicators were initially proposed, leaving four indicators of the dimensions of innovation and employees. It is important to emphasize that these results validate one of the fundamental characteristics of the TBC software, which is precisely innovation.