5 Mihai Covaci Hyperion University mihaicovaci@gmail.com PERSONAL VALUES AND UNIVERSITY MANAGEMENT Keywords: Organization, personal values, regression ________________________________________________ Abstract Personal and organizational values can create the difference between an organization excellence and one in which disorganization dominates. The current research topic is in the direction of studying the way in which organizations survive in an unfavourable context through the personal and organizational values they adopt. In a synthetic expression, the organizational culture is reduced to the adopted values, ways of doing things and the atmosphere promoted among the people within the organization. But often organizational values are made up of the sum of individual values. The organization chosen as a case study is a private university type. For this study, a personal values questionnaire was applied to 30 persons most representative for organizational management. The following values were questioned: order, learning, power, people, network, safety, personal, success and survival. _________________________________________________ mailto:mihaicovaci@gmail.com 6 1. Review of literature All systems move in the direction of increasing disorganization (Attard, 2002, p. 1). Some open systems generate disorganization which is subsequently exported outside the system. This aspect leads to the growth of its own organization, with the cost of increasing the disorder in the environment (Scarlat & Mitruț). Such systems are present in an appreciable number within the biological environment which, by developing such a mechanism, manages to maintain or even increase their degree of organization by transmitting in excess (therefore to other systems) the excess of disorganization (Scarlat & Mitruț). The management systems can be subjected to the same dissipative structure of disorganization as the systems that present dynamic self-organization. Open systems generate disorganization but it is a disorganization that is dissipated, or exported outside the system. Such behavior is commonly encountered in living organisms that take energy and matter from the environment in the form of light and food and then give it up as residual products that have a greater disruption than initially received (Scarlat & Mitruț). Or, as Lotka believes, each species can be regarded as another type of transformer for capturing and using available energy. Each transformer or body is equipped with a series of devices that it uses to retrieve energy from its surroundings (Lotka, 1925, p. 325). With all these known variables, the export of disorganization does not explain why and how the self-organization takes place (Scarlat & Mitruț). In addition, the paradigm of the axiological organization was proposed by P. Iluț in the context in which the problem of the values-attitudes-behaviors-situation relation, whereby he asserts that individuals and groups are differentiated also by the resistance of the individual and group axiological structures against the tendencies of disorganization of the environment social (Iluț, 2013, p. 5). For the social life, the law or the principle of the organization that postulates the degradation of the systems, their travelling 7 towards the maximum disorder is very important. Through information theory, the organizing principle was launched, that is, the phenomenon against degradation (Iluț, 2013, p. 5). Organizing through values can be understood as an organizational ability to: survive; regenerate or recover; to prevent disparity; to understand in time the situations that could lead to the first three issues mentioned. Some organizations may rely only on one of the mentioned aspects, in response / reaction to external factors and may have different intensities. The balance of the social macro-system implies a legality that includes the organization as an area of resistance or counter-action. Thus, organizational culture can be considered as a set of cognitions shared by the members of a social unit (O'Reilly III, Chatman, & Caldwell, 1991, p. 491); a system of shared values and beliefs that produce behavioral norms and establish a way of organizing life (Koberg & Chusmir, 1987, p. 397); the corporate culture is defined as constituting the main beliefs and values expressed by the management team and which provides the members of the organization with a reference frame for action (Goll & Zeitz, 1991, p. 191); for newcomers, the organizational culture is reflected in concrete aspects such as norms and behavioral models and subsequently, they are internalized in the form of culture-based assumptions and in time, concrete experiences and interpretations, constitute the basis for deeper unconscious processes (Gundry & Rousseau, 1994, p. 1064). To the details mentioned, some authors also added those aspects that “feel” in the organization (Warrick, 2005, p. 296). Organizational culture consists of two broad categories of factors: visible (started from explicit premises) and less visible, or as an extension of the visible ones (started from implicit premises). Visible factors are translated into: behavioral protocols, clothing and language rules, promoted principles and values, assumed identities, etc. Invisible factors often start from the aspects that must be visible and extend to personal or extra-organizational life. 8 Here visible factors can be re-included, along with aspects of attitude, reporting, the whole spectrum of social thinking and interpretation, and thus organizational culture is an expressive and non-expressible (externally conditioned) value and moral product. The sum of the beliefs and values adopted within an organization together with the sum of the premises of the respective organization. 2. Methodology Objectives The research objective was to highlight the personal and organizational values that can contribute to better organizational stability. Hypotheses The following hypotheses have been proposed and assumed: 1. We admit the existence of statistically significant differences between the eight types of personal values from the perspective of the seven independent variables; 2. We assume that the variables level of education, seniority, age and position cannot be a good predictor for the eight types of personal values specific to the organizational culture. Variables The independent variables were: seniority in the organization (with response variants up to 10 years, 11-15 years and over 16 years); the nature of the job occupied, having as variants a superior manager (rector, vice-rector, etc, middle level manager (dean, deputy dean), front line manager (chair, etc.), others ...; age (with variants of answer 24-40 years, 41-50 years and 51-65 years); gender; level of completed studies (with variants of doctoral, post- doctoral and other answers); completed studies (with mathematical and natural science answer variants, engineering 9 sciences, social sciences and humanities and arts) and consider that I have innate managerial qualities or have become manager through training and experience. Methods For the research in question, a private university from Bucharest was chosen considering from an external perspective that it is a higher education institution with multiple personal values. A priori arguments or factors can be used to demonstrate this. Finally, the data obtained were processed in SPSS v. 25 for parametric and nonparametric analysis as well as for regression analysis. Tools A questionnaire was used to identify the hidden set of eight personal values: orientation of subjects towards order, learning, power, people, network, safety, personal success and orientation to survival. The questionnaire contains 96 statements (12 statements for each of the eight values mentioned) to which the subjects could respond by marking an x next to the statement they agreed with. For each value 12 statements are assigned and therefore 12 possibilities to mark with x (Stoicescu, Păcurari, & Călineci, pp. 36-40). Population The research was carried out within a single institution of higher education in Bucharest in the form of an institutional case study. Within the research were involved a number of 30 people who are part of the hierarchical structures of the institution: rector, the president of the senate, rectors, deans, deans, heads of department, the economic director, the director of human resources or other persons in key positions. Or with administrative responsibility within the institution. 10 3. Results and discussions Parametric and nonparametric data analysis According to the average of the answers, the classification of the eight values was as follows: Order; learning; Power; People; Network; Safety; Personal; Success and Survival (see Figure 1). Thus, for the order value the scores obtained vary between 0 and 10 points, the average level being 5.2 points and the standard deviation of 0.44. The distribution in the S-W normality test is not significant compared to a normal distribution (p = 0.815). The quartile variation is asymmetrically negative, the median level (of the scores) being 5 points. According to the values, the 50% quarters of the central subjects have scores between 3.75 and 7 points. For the learning value, the scores obtained range from 0 to 10 points, the average level being 4.03 points and the standard deviation 0.48. The distribution in the S-W normality test is not significant compared to a normal distribution (p = 0.248). The quartile variation is asymmetrically positive with a median score of 4 points. According to the values of the quarters 50% of the central subjects with the scores between 1.75 and 6 points. For the power value the scores obtained range from 0 to 8 points, the average level being 3.4 points and the standard deviation of 0.39. The distribution in the S-W normality test is significant compared to the normal distribution (p = 0.005). The quartile variation is asymmetrically positive with a median score of 3 points. The values of the quartiles indicating that 50% of the central subjects had scores between 2 and 6 points. For the value of people, the scores obtained range from 0 to 8 points, the average level being 2.93 points and the standard deviation of 0.37. The distribution in the S-W normality test was significantly different from a normal distribution (p = 0.001). The quartile variation was asymmetrically positive and the median level of the scores was two points. According to the quarters, 50% of the central subjects have scores between 1.75 and 3.25 points. For the network value the scores 11 obtained vary between 0 and 8 points, the average level being 3 points and the standard deviation of 0.39. The distribution in the S-W normality test is different from a normal distribution (p = 0.044). The quartile variation is asymmetrically positive, the median level being 3 points. According to the quartile values, 50% of the central subjects had scores between 1 and 4.25 points. For the safety value the scores obtained range from 0 to 6 points and the average level is 2.17 points with a standard deviation of 0.37 points. In the S-W normality test, the distribution was significantly different from a normal distribution (p = 0.001). The quartile variation is asymmetrically positive, the median level of the scores being one point. According to the quarters, 50% of the central subjects have scores between 1 and 4 points. For the personal success value, the scores obtained range from 0 to 9 points with the average level of 3.17 points and a standard deviation of 0.4 points. The distribution in the S-W normality test is not significant compared to a normal distribution (p = 0.078). The quartile variation is asymmetrically positive with the median level of 3- point scores. According to the values of the quarters 50% of the subjects having scores between 1 and 5 points. For survival value the scores obtained range from 0 to 7 points with the average level of 2.87 points and the standard deviation of 0.32 points. The distribution in the S-W normality test is not significant compared to a normal distribution (p = 0.088). The quartile variation is asymmetrically positive with the median level of 3-point scores. According to the values, 50% of the subjects (central) had scores between 1 and 4 points. In the quintessence, the S-W test for the eight types of personal values obtained statistical significance of the normality distributions for the following values: - Orientation of subjects towards power (p = 0.005); - Orientation of subjects towards people (p = 0.001); - Orientation of the subjects towards the network (p = 0.044); 12 - Orientation of subjects towards safety (p = 0.001). Subsequent testing with the Mann-Whitney test for independent samples revealed that in the orientation of the subjects to the network there is a statistically significant difference between types of managers (self-consideration of their own managerial qualities). Thus, those who chose in response I have innate managerial qualities had an average of lower ranks (8.93) compared to those who opted for I became manager through training and experience (17.5). Therefore, we can deduce that the second group of subjects is much better oriented towards the network value compared to the first group. The Kruskal-Wallis nonparametric test comparing the ranks of more than two independent samples (or to determine the statistical significance of the differences between the distributions of the analyzed variables) was used to establish (in) the existence of differences and their significance. Kruskal-Wallis analysis revealed statistically significant differences in subjects’ orientation to the network (df = 2, p = 0.035). From the average ranks it was found that the difference is between people aged 41-50 years (19.22) and people aged 24-40 years (10.17). We conclude that people between the ages of 41-50 are significantly better oriented towards the network value as compared to people between the ages of 24-40. From the perspective of the value of learning, the Jonckheere- Terpstra test for comparing the levels of ranks, highlighted statistical significance (JT = -2,146, p = 0,032) for the group with studies completed at the doctoral level (18.42) and the group with other types of studies (11). Therefore, the persons who have completed the doctoral studies are significantly better oriented towards the value of learning compared to the persons who have completed other types of studies. The same test, for the value of power, showed statistical significance (J-T = -1,987, p = 0.047) for the group with studies completed at the doctoral level (17.83) and the group with other types of studies (7.83). As with the previous 13 situation, the persons who have completed the doctoral studies are significantly better oriented towards the value of power compared to the persons who have completed other types of studies. Regression analysis and existing correlations Order Based on the correlative analysis indicating a directly linear and statistically significant relationship between the personal value scores “order” and seniority (r = 0.044; p = 0.409), job (r = -0.089; p = 0.321), age (r = - 0.152; p = 0.211) and study level (r = 0.207; p = 0.136) independent variables were introduced into a multiple linear regression model. The introduction of the variables in the model was of the backward type, stepwise regression with ante-degree selection. Model 1, which contains all four variables, has an explanatory capacity of 9.8% of the experimental distribution of the order value scores (R square = 0.098) and increases the explanatory capacity to 9.8% (R square change = 0.098) and statistically insignificant p (F change) = 0.614. Model 2, obtained by removing the age variable, has an explanatory capacity of 9.8% (R square = 0.098) and an explanatory capacity of 0% (R square change = 0.000) and without statistical significance for p (F change) = 0.974. Model 3, obtained by removing variables age and age, has an explanatory capacity of 8.7% (R square = 0.087) and a reduction of explanatory capacity of -1.1% (R square change = -0.011). Not statistically significant for p (F change) = 0.576. Model 4, obtained by removing the variables age, seniority and position and reducing the explanatory capacity by -4.4% (R square change = -0.044). Not statistically significant for p (F change) = 0.267. Based on the regression equation determined for models 1 and 2, the highest explanations of the variation of the score obtained for the personal value of the order R2 = 0.098 (9.8%) are obtained. 14 Subsequently, the overall correlation between predictors and criterion was evaluated. The main problem we have to answer is whether the predictor variables significantly correlate with the criterion variable. For this, the ANOVA table was inspected. The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of valid models. It is observed that the R2 modifications for the models obtained by eliminating the independent variables are statistically insignificant (Sig. F change). In turn, the significance of the individual regression coefficients must be analysed to see if they truly describe a relationship between the predictor variables and the criterion. The null hypothesis in this case is that, the regression coefficients are equal to 0, the alternative hypothesis stating that they are different from 0. The test result is displayed in the columns of the Coefficients table as a test t. The values of t (t = coefficient B / standard error B) expresses the significance of the difference between the respective coefficients and 0. For all four models, the coefficients have statistically insignificant values (Sig. is greater than 0.05), which allows us to conclude that all four coefficients are not statistically significant to differ of 0 and consequently the predictor variables are not important enough to estimate the criterion variable. The lack of statistical significance for the F test can be accounted for by the small number of subjects. Regression equation: Model 1. Order score = 3.067 + 0.33 * seniority-0.6 * post-0.03 * age + 1.33 * education level. Model 2. Order score = 3.015 + 0.33 * seniority-0.61 * post + 1.35 * education level. Model 3. Order score = 3.912-0.58 * post + 1.25 * education level. Model 4. Order score = 3.407 + 0.82 * education level. 15 It is observed that the R2 modifications for the models obtained by eliminating the independent variables are statistically insignificant (Sig. F change). Learn Based on the correlative analysis indicating a directly linear and statistically significant relationship between the personal value scores “learning” and seniority (r = -0.004; p = 0.491), post (r = - 0.101; p = 0.297), age (r = 0.003; p = 0.495) and level of studies (r = 0.168; p = 0.187) were introduced independent variables into a multiple linear regression model. Model 1, which contains all four variables, has an explanatory capacity of 10.1% of the experimental distribution of learning value scores (R square = 0.101) and increases the explanatory capacity to 10.1% (R square change = 0.101) and statistically insignificant p (F change) = 0.596. Model 2, obtained by removing the old variable, has an explanatory capacity of 10.1% (R square = 0.101) and a reduction of the explanatory capacity of -0.1% (R square change = 0.001) and without statistical significance for p (F change) = 0.890. Model 3, obtained by removing the age and age variables, has an explanatory capacity of 6.9% (R square = 0.069) and a reduction of the explanatory capacity of -3.1% (R square change = -0.031). Not statistically significant for p (F change) = 0.350. Model 4, obtained by removing the variables age, age and position and has an explanatory capacity of 2.8% (R square = 0.028) and a reduction of explanatory capacity of -4.1% (R square change = -0.041). Not statistically significant for p (F change) = 0.285. Based on the regression equation determined for models 1 and 2, the highest explanations of the variation of the score obtained for the personal learning value R2 = 0.101 (10.1%) are obtained. The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of a valid model. In the table Coefficients for all models’ coefficients have statistically insignificant values. 16 Regression equation: Model 1. Learning score = 0.979 + 0.091 * seniority-0.97 * post + 0.8 * age + 1.68 * education level. Model 2. Learning score = 1.193-0.93 * post + 0.82 * age + 1.66 * education level. Model 3. Learning score = 2.980-0.62 * post + 1.18 * education level. Model 4. Learning score = 2.444 + 0.72 * education level. The lack of significance was also present in the R2 modifications for the models obtained by eliminating the independent variables. Power Based on the correlative analysis indicating a directly linear and statistically significant relationship between the scores of personal values “power” and seniority (r = -0.004; p = 0.419), post (r = - 0.017; p = 0.465), age (r = 0.158; p = 0.202) and study level (r = 0.177; p = 0.175) independent variables were introduced into a multiple linear regression model. Model 1, which contains all four variables, has an explanatory capacity of 14.2% of the experimental distribution of power value scores (R square = 0.142) and increases the explanatory capacity to 14.2% (R square change = 0.142) and statistically insignificant p (F change) = 0.410. Model 2, obtained by removing the old variable, has an explanatory capacity of 14.2% (R square = 0.142) and an explanatory capacity of 0% (R square change = 0.000) and without statistical significance for p (F change) = 0.991. Model 3, obtained by removing the old and post variables, has an explanatory capacity of 7.6% (R square = 0.076) and a reduction of the explanatory capacity of -6.5% (R square change = -0.065). Not statistically significant for p (F change) = 0.171. Model 4, obtained by removing the age, post and age variables has an explanatory capacity of 3.1% (R square = 0.031) and a reduction of the explanatory capacity of -4.5% (R square change = -0.045). Not statistically significant for p (F change) = 0.260. Based on the 17 regression equation determined for models 1 and 2, the highest explanations of the variation of the score obtained for the personal power value R2 = 0.142 (14.2%) are obtained. The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of a valid model. In the table Coefficients for all models’ coefficients have statistically insignificant values. Regression equation: Model 1. Power score = -0.271 + 0.01 * seniority-0.73 * post + 1.17 * age + 1.5 * education level. Model 2. Power score = -0.257-0.73 * post + 1.17 * age + 1.5 * education level. Model 3. Power score = 0.334 + 0.68 * age + 0.81 * education level. Model 4. Power score = 2.056 + 0.61 * education level. The lack of significance was also present in the R2 modifications for the models obtained by eliminating the independent variables. People Based on the correlative analysis indicating a directly linear and statistically significant relationship between the scores of personal values “people” and seniority (r = -0.097; p = 0.306), post (r = - 0.209; p = 0.134), age (r = 0.156; p = 0.205) and level of studies (r = 0.150; p = 0.214) were introduced independent variables into a multiple linear regression model. Model 1, which contains all four variables, has an explanatory capacity of 12.6% of the experimental distribution of “people” value scores (R square = 0.126) and increases the explanatory capacity to 12.6% (R square change = 0.126) and statistically insignificant p (F change) = 0.480. Model 2, obtained by removing the old variable, has an explanatory capacity of 12.4% (R square = 0.124) and a reduction of the explanatory capacity of -0.2% (R square change = -0.002) and without statistical significance for p (F change) = 0.814. Model 3, obtained by removing the age and age 18 variables, has an explanatory capacity of 12.2% (R square = 0.122) and a reduction of the explanatory capacity of -0.2% (R square change = -0.002). Not statistically significant for p (F change) = 0.803. Model 4, obtained by removing the variables old, age and level of studies, has an explanatory capacity of 4.4% (R square = 0.044) and a reduction of the explanatory capacity of -7.8% (R square change = -0.078). Not statistically significant for p (F change) = 0.134. Based on the regression equation determined for model 1, the highest explanation of the variation of the score obtained for the “people” value R2 = 0.126 (12.6%) is obtained. The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of a valid model. In the table Coefficients for all models’ coefficients have statistically insignificant values. Regression equation: Model 1. Score “people” = 2.396-0.12 * seniority – 0.8 * post + 0.19 * age + 1.12 * education level. Model 2. Score “people” = 2.12-0.81 * post + 0.17 * age + 1.17 * education level. Model 3. Score “people” = 2.48-0.74 * post + 1.05 * education level. Model 4. Score “people” = 4.02-0.44 * post. The lack of significance was also present in the R2 modifications for the models obtained by eliminating the independent variables. Network Based on the correlative analysis indicating a directly linear and statistically significant relationship between the personal “network” value scores and seniority (r = 0.166; p = 0.190), post (r = -0.117; p = 0.269), age (r = - 0.096; p = 0.308) level studies (r = 0.133; p = 0.241), the independent variables were introduced in a multiple linear regression model. Model 1, which contains all four variables, has an explanatory capacity of 10.6% of the experimental distribution of “network” 19 value scores (R square = 0.106) and increases the explanatory capacity to 10.6% (R square change = 0.106) and statistically insignificant p (F change) = 0.575. Model 2, obtained by removing the age variable, has an explanatory capacity of 10.5% (R square = 0.105) and an explanatory capacity of 0% (R square change = 0.000) and without statistical significance for p (F change) = 0.914. Model 3, obtained by removing variables age and seniority, has an explanatory capacity of 5.9% (R square = 0.059) and a reduction of explanatory capacity of -4.7% (R square change = -0.047). Not statistically significant for p (F change) = 0.256. Model 4, obtained by removing the variables age, seniority and position has an explanatory capacity of 1.8% (R square = 0.018) and a reduction of explanatory capacity of -4.1% (R square change = -0.041). Not statistically significant for p (F change) = 0.289. Based on the regression equation determined for model 1, the highest explanation of the variation of the score obtained for the “network” value R2 = 0.106 (10.6%) is obtained. The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of a valid model. In the table Coefficients for all models’ coefficients have statistically insignificant values. Regression equation: Model 1. Score “network” = 0.644 + 0.59 * age-0.58 * post + 0.078 * age + 1.06 * education level. Model 2. Score “network” = 0.795 + 0.6 * seniority-0.55 * post + 1.01 * education level. Model 3. Score “network” = 2,413-0.5 * post + 0.83 * education level. Model 4. Score “network” = 1,981 + 0.46 * education level. The lack of significance was also present in the R2 modifications for the models obtained by eliminating the independent variables. 20 Safety Based on the correlative analysis indicating a directly proportional linear relationship and statistically significant between the personal value scores “safety” and seniority (r = - 0.029; p = 0.440), post (r = -0.009; p = 0.482), age (r = -0.184; p = 0.165) and level of studies (r = 0.196; p = 0.150) were introduced independent variables into a multiple linear regression model. Model 1, which contains all four variables, has an explanatory capacity of 6.1% of the experimental distribution of “safety” value scores (R square = 0.061) and increases the explanatory capacity to 6.1% (R square change = 0.061) and statistically insignificant p (F change) = 0.805. Model 2, obtained by removing the old variable, has an explanatory capacity of 6% (R square = 0.060) and a reduction of explanatory capacity of -0.1% (R square change = - 0.001) and without statistical significance for p (F change) = 0.875. Model 3 obtained by removing the old and post variables has an explanatory capacity of 5.7% (R square = 0.057) and a reduction of the explanatory capacity of -0.2% (R square change = -0.002). Not statistically significant for p (F change) = 0.795. Model 4, obtained by removing the age, post and age variables has an explanatory capacity of 3.8% (R square = 0.038) and a reduction of the explanatory capacity of -1.9% (R square change = -0.019). Not statistically significant for p (F change) = 0.471. Based on the regression equation determined for model 1, the highest explanation of the variation of the obtained score for the safety value R2 = 0.061 (6.1%) is obtained. The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of a valid model. In the table Coefficients for all models’ coefficients have statistically insignificant values. Regression equation: Model 1. “Safety” score = 1.497 + 0.08 * seniority-0.14 * post- 0.34 * age + 0.67 * education level. 21 Model 2. “Safety” score = 1.688-0.14 * post-0.33 * age + 0.65 * education level. Model 3. “Safety” score = 1,799-0.42 * age + 0.52 * education level Model 4. “Safety” score = 0.741 + 0.65 * education level. The lack of significance was also present in the R2 modifications for the models obtained by eliminating the independent variables. Success Based on the correlative analysis indicating a directly linear and statistically significant relationship between the scores of personal values “success” and seniority (r = 0.152; p = 0.211), post (r = - 0.008; p = 0.483), age (r = 0.122; p = 0.261) and study level (r = 0.153; p = 0.210) independent variables were introduced into a multiple linear regression model. Model 1, which contains all four variables, has an explanatory capacity of 7.4% of the experimental distribution of “success” value scores (R square = 0.074) and increases the explanatory capacity to 7.4% (R square change = 0.074) and statistically insignificant p (F change) = 0.737. Model 2, obtained by removing the variable post has an explanatory capacity of 7% (R square = 0.070) and a reduction of the explanatory capacity of -0.3% (R square change = -0,003) and without statistical significance for p (F change) = 0.763. Model 3, obtained by removing the variables post and age has an explanatory capacity of 5.7% (R square = 0.057) and a reduction of the explanatory capacity of -1.3% (R square change = -0.013). Not statistically significant for p (F change) = 0.550. Model 4, obtained by removing the variables post, age and seniority has an explanatory capacity of 2.3% (R square = 0.023) and a reduction of explanatory capacity of -3.4% (R square change = -0.034). Not statistically significant for p (F change) = 0.333. Based on the regression equation determined for model 1, the highest explanation of the variation of the score obtained for the “success” value R2 = 0.074 (7.4%) is obtained. 22 The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of a valid model. In the table Coefficients for all models’ coefficients have statistically insignificant values. Regression equation: Model 1. “Success” score = 1.153 + 0.59 * age-0.18 * post-0.27 * age + 0.75 * education level. Model 2. “Success” score = 1.305 + 0.58 * seniority-0.39 * age + 0.58 * education level. Model 3. “Success” score = 0.467 + 0.53 * seniority + 0.68 * education level. Model 4. “Success” score = 1.944 + 0.56 * level of education. The lack of significance was also present in the R2 modifications for the models obtained by eliminating the independent variables. Survival Based on the correlative analysis indicating a directly linear and statistically significant relationship between the scores of personal “survival” and seniority (r = 0.255; p = 0.087), post (r = -0.143; p = 0.226), age (r = - 0.103; p = 0.294) and level of studies (r = 0.156; p = 0.205) were introduced as independent variables in a multiple linear regression model. Model 1, which contains all four variables, has an explanatory capacity of 18.4% of the experimental distribution of “survival” scores (R square = 0.184) and increases the explanatory capacity to 18.4% (R square change = 0.184) and statistically insignificant p (F change) = 0.261. Model 2, obtained by removing the age variable, has an explanatory capacity of 18.3% (R square = 0.183) and a reduction of explanatory capacity of -0.1% (R square change = 0.001) and without statistical significance for p (F change). = 0.865. Model 3, obtained by removing the age and position variables having an explanatory capacity of 10.8% (R square = 0.108) and a reduction of the explanatory capacity of -7.5% (R square change = 0.075). Not statistically significant for p (F change) = 0.135. Model 23 4, obtained by removing the variables age, position and level of studies and has an explanatory capacity of 6.5% (R square = 0.065) and a reduction of the explanatory capacity of -4.3% (R square change = 0.043). Not statistically significant for p (F change) = 0.264. Based on the regression equation determined for model 1, the highest explanation of the variation of the score obtained for the “survival” value R2 = 0.184 (18.4%) is obtained. The lack of statistical significance obtained from the ANOVA analysis for all models indicates that the observed data do not allow the identification of a valid model. In the table Coefficients for all models’ coefficients have statistically insignificant values. Regression equation: Model 1. “Survival” score = 0.191 + 0.7 * seniority-0.59 * post + 0.1 * age + 1.08 * education level. Model 2. “Survival” score = 0.377 + 0.71 * seniority-0.55 * post + 1.03 * education level. Model 3. “Survival” score = 0.069 + 0.65 * seniority + 0.6 * education level. Model 4. “Survival” score = 1,590 + 0.56 * old. The lack of significance was also present in the R2 modifications for the models obtained by eliminating the independent variables. 4. Conclusions Hypothesis 1 was confirmed by the fact that the Mann-Whitney test revealed that in the orientation of the subjects to the network there is a statistically significant difference between types of managers (those who chose in response I have innate managerial qualities had an average of lower ranks compared to those who opted for I became a manager through training and experience). The Kruskal-Wallis test revealed statistically significant differences in the orientation of the subjects to the network (persons aged 41- 50 years had a higher average rank compared to persons aged 24- 40). The Jonckheere-Terpstra test from the perspective of the 24 value of learning highlighted statistical significance for the group with studies completed at the doctoral level (which had a higher average) and the group with other types of studies. The same test, for the value of power, revealed statistical significance for the group with studies completed at the doctoral level (with a higher average) and the group with other types of studies. The second hypothesis was validated. No value generated a statistically significant model in relation to the four independent variables. References Attard, P. (2002). Thermodynamics and Statistical Mechanics: Equilibrium by Entropy Maximisation. Academic Press. Goll, I., & Zeitz, G. (1991). Conceptualizing and measuring corporate ideology. Organization Studies, 191-207. Gundry, L. K., & Rousseau, D. M. (1994). Critical incidents in communicating culture to newcomers: The meaning is the message. Human Relations, 1063-1088. Iluț, P. (2013). În Căutare de Principii. Epistemologie și Metodologie Socială Aplicată. Iași: Polirom. Koberg, C. S., & Chusmir, L. H. (1987). Organizational culture relationships with creativity and other job-related variables. Journal of Business Research, 397-409. Lotka, A. J. (1925). Elements of Physical Biology. Baltimore: Williams & Wilkins Company. O'Reilly III, C. A., Chatman, J., & Caldwell, D. (1991). People and organizational culture: A profile comparaison approach to assessing person-organization fit. Academy of Management Journal, 487-516. Scarlat, E., & Mitruț, D. (n.d.). Curs Bazele ciberneticii economice. Retrieved from http://www.asecib.ase.ro/BCIB/bcib.html Stoicescu, D., Păcurari, Ş. O., & Călineci, M. C. (n.d.). Valori comportamentale şi reducerea violenţei în şcoală. Retrieved 03 25 06, 2017, from http://www.scoala- danielacuciuc.com/Mentori/2013/Programe /Violenta%20in%20scoala.pdf Warrick, D. (2005). Launch: Assessment and Action Planning. In W. J. Rothwell, & R. Sullivan, Practicing Organization Development: A Guide for Consultants (2nd edition ed., pp. 271-312). Pfeiffer. 26 Tables, Figures and Appendices Fig. 1. Distribution of personal values 2,17 2,87 2,93 3,00 3,17 3,40 4,03 5,20 0,00 2,00 4,00 6,00 Safety Survival People Network Personal Success Power Learning Order