Knowledge Dynamics Modeling Using Analytic Hierarchy Process (AHP)

The purpose of this paper is to present a new perspective in modeling knowledge dynamics of the human capital by using the Analytic Hierarchy Process (AHP). The theoretical approach is based on the dynamic equilibrium equation of the level of organizational knowledge, and on the AHP mathematical model developed by Saaty. The dynamic equilibrium equation is derived for a time interval Delta T, and contains the following terms: the level of total organizational knowledge variation Delta K in the time interval Delta T, the knowledge creation variation Delta Cr in the time interval Delta T, the knowledge acquisition Delta A variation in the time interval Delta T, and the knowledge loss variation Delta L in the time interval Delta T. Since each of these terms has a different relative importance in the organizational knowledge balance, it is necessary to find a way of evaluating their weighting factors. For this purpose we use the AHP mathematical model since it allows us a structuring of knowledge dynamics on the main components of the equilibrium equation. For the present research we considered a structure composed of three levels: (1) the goal level - increasing the level of organizational knowledge; (2) the criteria or strategies level the strategy for increasing knowledge creation (C1), the strategy for increasing acquisition of new knowledge (C2), and the strategy for reducing knowledge loss (C3); (3) the activities level - hiring new valuable human resources (A1), developing training programs (A2), creating a performing motivation of employees (A3), and purchasing books, journals, software programs, and other information materials (A4). This structured model of AHP has been applied as an empirical research within a large company. We sent questionnaire to a number of 500 employees, and received valid answers from 173 respondents. The method is based on paired comparisons of strategies with respect to the goal of increasing the level organizational knowledge, and then on paired comparisons of activities with respect to each strategy we defined. These paired comparisons yield matrices that lead to systems of linear equations. Actually we get an eigenvalue problem whose solution is the vector of priorities for strategies, and then for activities with respect to each strategy. Values of the vector of priorities for strategies are the weighting factors for the equilibrium equation components. The new model we propose proved valuable in understanding much better the knowledge dynamics, as the main component of the intellectual capital of a company.