Examples of Applications of the Sknowinnov Model in Creating an Innovative Company

Abstract

The success of a company lies more in its IC than in its physical assets. The capacity to manage knowledge and convert it into useful products and services is fast becoming the current primary executive skill. As a result, there has been a flurry of interest in IC, creativity, innovation, and learning within an organization. However, surprisingly little attention has been given to the management of dependence on the value of IC and innovation in a company.

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The success of a company lies more in its IC than in its physical assets. The capacity to manage knowledge and convert it into useful products and services is fast becoming the current primary executive skill. As a result, there has been a flurry of interest in IC, creativity, innovation, and learning within an organization. However, surprisingly little attention has been given to the management of dependence on the value of IC and innovation in a company.

The Sknowinnov method (Chap. 4) allows the construction of a decision model that involves all the elements of Sknowinnov, including an assessment of the method’s implementation efficiency. The modeling object consists of a pair of values: the values of the personnel usefulness function for the m-th knowledge worker and the values of innovation characteristics. The application of the Sknowinnov model makes it possible to forecast the value of knowledge workers. The solution, defined in terms of predictive indicators for the efficiency in knowledge worker selection, will be shown using the consulting software. Only the employment of appropriate knowledge workers can guarantee a company’s enduring competitive edge in the market.

This chapter presents my system for assessing knowledge workers in relation to increasing innovation in a company (Sknowinnov system). Through the research studies, I will show how forecasting the values of strategic knowledge resources (values of the personnel usefulness function for the m-th knowledge worker) are carried out. Two medium-sized companies that fulfill the qualifying criteria of innovative companies, were chosen as test subjects for the effectiveness of the Sknowinnov method. The research questions included the following. Is it possible to forecast the values of the personnel usefulness function for the m-th knowledge worker when given the values of the characteristics of innovation in a company? Is it possible to identify knowledge workers who can become innovative workers?

5.1 Sknowinnov System

5.1.1 Selection of Appropriate Knowledge Workers

The decision regarding the selection of appropriate employees requires that the company management assess the efficiency of the investment. The application of this model allows a forecast to be made about the value of the strategic knowledge resources within a given organization (Patalas-Maliszewska 2009). The decision-making situation, in which an innovative company is considering the employment of the m-th knowledge worker, is presented in Fig. 5.1.

The decision-making situation of the company has been presented; this determines whether the new knowledge worker should be employed in sales. In addition, I will describe the example of company A2, which is looking for an employee to fill the position of regional assistant. That company expects to retain its current level of innovation.

Fig. 5.1

The decision-making situation in which an innovative company is considering the employment of a new m-th knowledge worker

The decision situation is as follows.

Stage 1

Using the computer-based Sknowinnov system, it is possible to check whether the company complies with the specified reference model. A potential new employee selects actions that will be performed in the company. The developed reference model will help companies determine the work place for a new employee.

Fig. 5.2

Example of the Sknowinnov system in use

Fig. 5.3

Example of the Sknowinnov system in use

Fig. 5.4

Example of the Sknowinnov system in use

Fig. 5.5

Example of the Sknowinnov system in use

Fig. 5.6

Example of the Sknowinnov system in use

Fig. 5.7

Example of the Sknowinnov system in use

Stage 2

A tool in the Sknowinnov method supports decision making at the strategic level for assessing knowledge in an innovative company. The following information is produced:

• For m₄—regional assistant:

$$ {{\mathbf{m}}_{\mathbf{4}}}^{*} = -\mathbf{0},\mathbf{6490}+\mathbf{3},\mathbf{4592}{{\mathbf{X}}_{\mathbf{7}}}-\mathbf{2},\mathbf{2539}{{\mathbf{X}}_{\mathbf{13}}}-\mathbf{2},\mathbf{0984}{{\mathbf{X}}_{\mathbf{7}}}^{\mathbf{2}}-\mathbf{1},\mathbf{7486}{{\mathbf{X}}_{\mathbf{13}}}^{\mathbf{2}}+\mathbf{3},\mathbf{8323}{{\mathbf{X}}_{\mathbf{7}}}{{\mathbf{X}}_{\mathbf{13}}}, $$

where

X₇—number of employees with science degrees,

X₁₃—number of purchased and used licenses.

Stage 3

For a new potential knowledge worker as a regional assistant, by using the decision model we obtain the following forecast of the personnel usefulness function. This is the company’s request for the sample of A2’s values for knowledge worker:

$$ {{\mathbf{m}}_{\mathbf{4}}}^{*} = -\mathbf{0},\mathbf{6490}+\mathbf{3},\mathbf{4592}{{\mathbf{X}}_{\mathbf{7}}}-\mathbf{2},\mathbf{2539}{{\mathbf{X}}_{\mathbf{13}}}-\mathbf{2},\mathbf{0984}{{\mathbf{X}}_{\mathbf{7}}}^{\mathbf{2}}-\mathbf{1},\mathbf{7486}{{\mathbf{X}}_{\mathbf{13}}}^{\mathbf{2}}+\mathbf{3},\mathbf{8323}{{\mathbf{X}}_{\mathbf{7}}}{{\mathbf{X}}_{\mathbf{13}}}, $$

where

X₇—number of employees with scientific degrees, and

X₁₃—number of purchased and used licenses.

Stage 4

Using the Sknowinnov system, the actual value of the personnel usefulness function for a new employee is checked (see Appendix 1).

Fig. 5.8

Example of the Sknowinnov system in use

Fig. 5.9

Example of the Sknowinnov system in use

Stage 5

We then compare the actual value of the personnel usefulness function with the expected value for the new employee. If these values are similar, it is assumed that the employment of the employee will allow the current level of innovation to be maintained within the company.

The actual value of the function: Wm ₄  = 19

The forecast value of the function:

$$ \mathbf{W}{{\mathbf{m}}_{\mathbf{4}}}^{*} = -\mathbf{0},\mathbf{6490}+\mathbf{3},\mathbf{4592}{{\mathbf{X}}_{\mathbf{7}}}-\mathbf{2},\mathbf{2539}{{\mathbf{X}}_{\mathbf{13}}}+\mathbf{2},\mathbf{0984}{{\mathbf{X}}_{\mathbf{7}}}^{\mathbf{2}}-\mathbf{1},\mathbf{7486}{{\mathbf{X}}_{\mathbf{13}}}^{\mathbf{2}}+\mathbf{3},\mathbf{8323}{{\mathbf{X}}_{\mathbf{7}}}{{\mathbf{X}}_{\mathbf{13}}} = \mathbf{19},\mathbf{6231} $$

for X₇—number of employees with science degrees, X₇ = 2,

X₁₃—number of purchased and used licenses, X₁₃ = 1.

The company may decide to recruit new employees for the position of regional assistant. This is because the predicted value of the personnel usefulness function for the new employee is in line with the actual value of the function, which would allow the company to maintain a certain level of innovation.

The resulting decision-making models may take different forms if changes are made to the databases (database of values for the personnel usefulness function, database of values for the characteristics of innovation). The larger the database is (based on experiments and research results), the more accurate the defined decision-making models will be.

The following section presents the decision-making situation in which an innovative company is considering the employment of a new m-th knowledge worker.

5.1.2 Designing a Decision-Making Model for Assessing the Value of a Knowledge Worker

Based on information found in the database for the values of strategic knowledge resources and the qualification criteria for an innovative company, the variants of the GMDH algorithm available in the computer program are examined.

Because of the possibility of using the GMDH algorithm only for nonsingular matrices, the decision-making model with the following characteristics of innovation is obtained:

• X₂—number of new products implemented in a given year (for the last 5 years),

• X₄—number of completed research topics in a given year (for the last 5 years),

• X₇—number of employees with science degrees,

• X₈—number of employees with higher education in relation to other staff, and

• X₁₃—number of purchased and used licenses.

For m₁—sales director:

Fig. 5.10

Example of the Sknowinnov system in use

Fig. 5.11

Example of the Sknowinnov system in use

The GMDH algorithm uses the best possible polynomial, which is characterized by the lowest-value criteria for regularity assigned to the pair object (the values of the characteristics of innovation in a company and the values of the personnel usefulness function for the sales area). The algorithm evolution process is completed on the second iteration. It should be noted that the second-degree polynomial is obtained as a result of implementing the defined database. Thus, it can be different from the value of characteristics of innovation.

Fig. 5.12

Example of the Sknowinnov system in use

Fig. 5.13

Example of the Sknowinnov system in use

In this way, the best polynomial is chosen, which is the one with the smallest error of modeling.

$$ {{\mathbf{m}}_{\mathbf{1}}}^{*} = \mathbf{20},\mathbf{07759}+\mathbf{0},\mathbf{6842}{{\mathbf{X}}_{\mathbf{2}}}-\mathbf{2},\mathbf{1282}{{\mathbf{X}}_{\mathbf{4}}}+\mathbf{0},\mathbf{0909}{{\mathbf{X}}_{\mathbf{2}}}^{\mathbf{2}}+\mathbf{0},\mathbf{1610}{{\mathbf{X}}_{\mathbf{4}}}^{\mathbf{2}}-\mathbf{0},\mathbf{1818}{{\mathbf{X}}_{\mathbf{2}}}{{\mathbf{X}}_{\mathbf{4}}}, $$

where

X₂—number of new products implemented in a given year (for the last 5 years), and

X₄—number of completed research topics in a given year (for the last 5 years).

For m₂—sales specialist:

Fig. 5.14

Example of the Sknowinnov system in use

Fig. 5.15

Example of the Sknowinnov system in use

Fig. 5.16

Example of the Sknowinnov system in use

Fig. 5.17

Example of the Sknowinnov system in use

In this way, the best polynomial is chosen, which is the one with the smallest error of modeling.

$$ {{\mathbf{m}}_{\mathbf{2}}}^{*} = -\mathbf{34},\mathbf{1402}+\mathbf{10},\mathbf{12823}{{\mathbf{X}}_{\mathbf{4}}}-\mathbf{4},\mathbf{3094}{{\mathbf{X}}_{\mathbf{13}}}+\mathbf{0},\mathbf{0861}{{\mathbf{X}}_{\mathbf{4}}}^{\mathbf{2}}+\mathbf{0},\mathbf{8112}{{\mathbf{X}}_{\mathbf{13}}}^{\mathbf{2}}-\mathbf{1},\mathbf{0611}{{\mathbf{X}}_{\mathbf{4}}}{{\mathbf{X}}_{\mathbf{13}}}, $$

where

X₄—number of completed research topics in a given year (for the last 5 years),

X₁₃—number of purchased and used licenses.

For m_3—marketing specialist:

Fig. 5.18

Example of the Sknowinnov system in use

Fig. 5.19

Example of the Sknowinnov system in use

Fig. 5.20

Example of the Sknowinnov system in use

Fig. 5.21

Example of the Sknowinnov system in use

In this way, the best polynomial is chosen, which is the one with the smallest error of modeling.

$$ {{\mathbf{m}}_{\mathbf{3}}}^{*} = -\mathbf{1},\mathbf{0920}+\mathbf{6},\mathbf{0274}{{\mathbf{X}}_{\mathbf{2}}}-\mathbf{5},\mathbf{3324}{{\mathbf{X}}_{\mathbf{4}}}+\mathbf{0},\mathbf{3174}{{\mathbf{X}}_{\mathbf{2}}}^{\mathbf{2}}+\mathbf{0},\mathbf{5490}{{\mathbf{X}}_{\mathbf{4}}}^{\mathbf{2}}-\mathbf{0},\mathbf{8606}{{\mathbf{X}}_{\mathbf{2}}}{{\mathbf{X}}_{\mathbf{4}}}, $$

where

X₂—number of new products implemented in a given year (for the last 5 years), and

X₄—number of completed research topics in a given year (for the last 5 years).

For m₄—regional assistant:

Fig. 5.22

Example of the Sknowinnov system in use

Fig. 5.23

Example of the Sknowinnov system in use

Fig. 5.24

Example of the Sknowinnov system in use

Fig. 5.25

Example of the Sknowinnov system in use

In this way, the best polynomial is chosen, which is the one with the smallest error of modeling.

$$ {{\mathbf{m}}_{\mathbf{4}}}^{*} = -\mathbf{0},\mathbf{6490}+\mathbf{3},\mathbf{4592}{{\mathbf{X}}_{\mathbf{7}}}-\mathbf{2},\mathbf{2539}{{\mathbf{X}}_{\mathbf{13}}}+\mathbf{2},\mathbf{0984}{{\mathbf{X}}_{\mathbf{7}}}^{\mathbf{2}}-\mathbf{1},\mathbf{7486}{{\mathbf{X}}_{\mathbf{13}}}^{\mathbf{2}}+\mathbf{3},\mathbf{8323}{{\mathbf{X}}_{\mathbf{7}}}{{\mathbf{X}}_{\mathbf{13}}}, $$

where

X₇—number of employees with science degrees,

X₁₃—number of purchased and used licenses.

For m₅—product manager:

Fig. 5.26

Example of the Sknowinnov system in use

Fig. 5.27

Example of the Sknowinnov system in use

Fig. 5.28

Example of the Sknowinnov system in use

Fig. 5.29

Example of the Sknowinnov system in use

In this way, the best polynomial is chosen, which is the one with the smallest error of modeling.

$$ {{\mathbf{m}}_{\mathbf{5}}}^{*} = -\mathbf{10},\mathbf{0552}+\mathbf{3},\mathbf{4124}{{\mathbf{X}}_{\mathbf{4}}}-\mathbf{0},\mathbf{2434}{{\mathbf{X}}_{\mathbf{8}}}-\mathbf{0},\mathbf{2298}{{\mathbf{X}}_{\mathbf{4}}}^{\mathbf{2}}-\mathbf{0},\mathbf{0193}{{\mathbf{X}}_{\mathbf{8}}}^{\mathbf{2}}+\mathbf{0},\mathbf{1261}{{\mathbf{X}}_{\mathbf{4}}}{{\mathbf{X}}_{\mathbf{8}}}, $$

where

X₄—number of employees with science degrees,

X₈—number of purchased and used licenses.

Polynomial models of decision making (Figs. 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 5.10, 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.20, 5.21, 5.22, 5.23, 5.24, 5.25, 5.26, 5.27, 5.28, 5.29, and 5.30) are constructed from the four groups in the Sknowinnov method (Chap. 4). The Sknowinnov model allows the determination of the value of the personnel usefulness function for a new employee, including the value of innovation characteristics. Based on the projected value of these indicators, the company management can decide on the selection of a new knowledge worker (Fig. 5.1).

5.2 Case Studies Using the Sknowinnov System

5.2.1 Selection of Appropriate Knowledge Workers in an IT Company

The decision about selection appropriate knowledge workers requires the company management to assess the efficiency of the investment. The application of the Sknowinnov model allows a forecast to be made about the value of knowledge workers. The decision-making situation for a company considering the employment of the m-th knowledge worker is presented below.

To illustrate the use of the Sknowinnov model, I will consider an IT company that provides services in the form of projects for both organizations and individual customers (Fig. 5.31). The company decides that it needs to find a new employee to fill the position of sales specialist. It is assumed that in hiring the new employee, the company wishes to maintain its level of innovation.

Fig. 5.30

Business processes in an IT company for a knowledge worker, m₂—sales specialist

The Sknowinnov model was used to assess the following employment decisions:

$$ \mathrm{ Wm}{*_2} = - 34,14+10,13{{\mathrm{ x}}_4}-4,31{{\mathrm{ x}}_{13}}+0,09{{\mathrm{ x}}_4}^2+0,811{{\mathrm{ x}}_{13}}^2+1,06{{\mathrm{ x}}_4}{{\mathrm{ x}}_{13, }}\mathrm{ where} $$

Wm*₂—value of the personnel usefulness function for the sales specialist, x₄—number of completed research topics in a given year (for the last 5 years—at the IT company this was four research topics), x₁₃—number of purchased and used licenses (at the IT company this was three licenses).

The model compiles all groups of the elements of the Sknowinnov method. A decision-making model for a selection of the knowledge (Sknowinnov model) was built for each of five knowledge workers based on empirical research and using GMDH. It allows a forecast to be made about the future value of the decision about the selecting an employee to increase a company’s innovation capacity.

With the Sknowinnov model, the estimated value of the personnel usefulness function (Wm*₂) for the new knowledge worker to fill the position of sales specialist was determined as: Wm*₂ = 14,86. The prospective knowledge worker then completed the test for the Sknowinnov system to obtain the value of the personnel usefulness function (Wm₂). The actual value of the personnel usefulness function for the prospective employee was Wm₂ = 11. Examples of using the Sknowinnov system to obtain actual values for the personnel usefulness function Wm₂ are presented in Figs. 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 5.10, 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.20, 5.21, 5.22, 5.23, 5.24, 5.25, 5.26, 5.27, 5.28, and 5.29. The managing director of the IT company should not select this person since his personnel usefulness function was unsatisfactory compared with the projected value of this function at a given level of innovation.

In addition to being a calculation of the profitability of investment, this approach would appear to be an excellent tool for an “economic” quantitative knowledge analysis. The Sknowinnov model (based on collected data) connects selected determinants described for an innovative company with the value of the personnel usefulness function. It thus allows an assessment of the rationality of hiring knowledge workers and their potential effectiveness. In consequence, this model permits a quantitative evaluation of knowledge workers in a company to be made.

5.2.2 Selection of Appropriate Knowledge Workers by a Service Company

The main purpose of the next experiment was to determine and compare the forecasts of the value of the personnel usefulness function for a new m-th knowledge worker; this depends on the defined values of the characteristics of innovation. The object of this experiment for examining the effectiveness of the Sknowinnov method consists of two features—a service company faced with choosing a new employee and the defined innovation characteristics.

A service company decided that it needed to find a new employee to fill the position of sales specialist. It was assumed that following the hiring of the new employee, the company would maintain its current level of innovation. The Sknowinnov model was used to assess the employment decisions:

$$ \mathrm{ Wm}{*_{12}} = - 34,14+10,13{{\mathrm{ x}}_4}-4,31{{\mathrm{ x}}_{13}}+0,09{{\mathrm{ x}}_4}^2+0,811{{\mathrm{ x}}_{13}}^2+1,06{{\mathrm{ x}}_4}{{\mathrm{ x}}_{13, }}\mathrm{ where:} $$

where Wm*₂—value of the personnel usefulness function for the sales specialist, X₄—number of completed research topics in a given year (for the last 5 years—at the this was 5 completed research topics), X₁₃—number of purchased and used licenses (at the company this was one license).

The estimated value of the personnel usefulness function (Wm₁₂) for the new employee to fill the sales specialist position was W₁₂ ^* = 20,46. The prospective employee then completed the test to obtain the value of the personnel usefulness function (Wm2) according to the employee personnel evaluation sheet (described in detail in Appendix 2). The actual value of the personnel usefulness function for the prospective employee was W₁₂ = 21 (Figs. 5.32, 5.33, and 5.34).

Fig. 5.31

Examples of the Sknowinnov system in use

Fig. 5.32

Examples of the Sknowinnov system in use

Fig. 5.33

Examples of the Sknowinnov system in use

Fig. 5.34

Examples of the Sknowinnov system in use

Since there was similarity in the values—the actual personnel usefulness function and the predicted values based on the answer sheet—it was decided that this company should hire the employee as sales specialist.

This monograph examines the usefulness and the applicability of my decision-making model for selecting knowledge workers from a group of specialists in selling. The information presented is based on a real case study. The sections above presented a review of the appropriate research.