Engineering, Technology & Applied Science Research Vol. 7, No. 3, 2017, 1725-1731 1725 www.etasr.com Fashami and Babaei: A Behavioral Maturity Model to Establish Knowledge Management in an … A Behavioral Maturity Model to Establish Knowledge Management in an Organization Camellia Salehi Fashami Department of Information Technology Management College of Management and Economics Science and Research Branch, Islamic Azad University Tehran, Iran Mohammadreza Babaei Department of Industrial Management College of Management and Accounting Yadegar-e-Imam Khomeini (RAH) ShahreRey Branch Islamic Azad University Tehran, Iran Abstract—Modern organizations need intangible assets such as organizational knowledge and human resources to gain competitive advantage in the market. Organizations can provide opportunities for behavioral maturity of managers to establish knowledge management. This study tries to develop a behavioral maturity model for managements to examine effectiveness of knowledge management. The study is conducted in Iran Insurance Company as an empirical case study. Twenty academic and organizational experts are selected for the study. Employees and managers of Iran Insurance Company are used to measure and test conceptual model (behavioral maturity of managers to establish knowledge management). Both interview and questionnaire tools are used to collect data. Fuzzy AHP and PLS methods are used to analyze the collected data. Fuzzy AHP results show that transformational leadership, human and social skills, knowledge orientation, emotional intelligence, trustful climate are identified as highly effective priorities. Keywords-behavioral maturity model; knowledge management; managers; organization I. INTRODUCTION Knowledge organization is considered one of the organizational capitals. Organizational knowledge means choosing the right science in the appropriate time and conditions [1]. In fact, information and knowledge management has become a strong position to survive in dynamic and innovative organizations; even market and business competitiveness requires acquisition, development and updating of individual and organizational knowledge to the extent that knowledge is considered as an essential part of organizational capitals [2]. Hence, intelligent management is to use knowledge to face and deal with uncertainty factors, maintain opportunities and innovate to expand competition [3]. This requires organizations to value and prioritize knowledge management and related steps as an essential requirement for pioneering in the competition [4]. Organizations with a high degree of creativity and work performance manage their knowledge more effectively [5]. In current business environment which is characterized by market globalization, increased competition and high rate of technological changes, tangible assets (such as capital, land, raw materials, etc.) do not create sustainable competitive advantages for the organization. Recognition of knowledge management as the spirit of organizational actions is essential for its implementation [6]. Despite strong reasons for strategic use of knowledge management to improve organizational performance, there are various obstacles in implementing knowledge management in organizations [7]. These obstacles can be summarized as: 1) unfamiliarity of managers with knowledge management (senior and middle managers are not fully familiar with knowledge management and do not understand its advantages for the organization); 2) employees consider knowledge as a source of power (employees do not tend to share their knowledge) [7]. Currently, managers realize that machinery, equipment and buildings are not the most important organizational assets and and that a proper management of organizational knowledge lead to a competitive advantage for the organization [8]. Following significance of knowledge assets, organizations are increasingly required to manage their knowledge assets. Organizations need to share organizational knowledge between different sectors to accelerate individual and organizational performance [9]. It seems essential that leadership establish knowledge in the organization properly. Considering the essential role of knowledge management, numerous methods have been presented for deployment of knowledge management in the organization; this confuses managers who tend to implement knowledge management. The purpose of this study is to develop a behavioral maturity model of managers for effectiveness of knowledge management establishment in Iran Insurance Company. II. LITERATURE REVIEW In [10], authors identified the effective factors on establishment of knowledge management systems in Taiwanese SMEs. They used LISREL to analyze the data gathered. Factor analysis model showed that structural (organizational), infrastructural and human factors were very important and effective in establishing knowledge management systems. In [11], authors developed a model for effective factors on establishment of knowledge management. Their findings showed that employee participation and organizational Engineering, Technology & Applied Science Research Vol. 7, No. 3, 2017, 1725-1731 1726 www.etasr.com Fashami and Babaei: A Behavioral Maturity Model to Establish Knowledge Management in an … factors including culture had the highest effect on successful establishment of knowledge management. In [12], authors experimentally evaluated knowledge sharing, knowledge leakage and innovative performance. Their results indicated that knowledge sharing positively influenced innovative performance; however, high rate of knowledge leakage negatively moderated the relationship between knowledge sharing and innovative performance. In [13], authors examined the role of knowledge-based leadership on actions of knowledge management and innovation. They found that actions of knowledge management mediated the relationship between knowledge-based leadership and innovative performance. Moreover, they found that actions of knowledge management were effective on innovative performance. In [14], authors addressed the barriers and solutions of knowledge management establishment in Iranian public organizations. They noted that intellectual assets and knowledge are as important as other physical and financial assets. They considered human, technological and structural barriers as important barriers to establishment of knowledge management. Moreover, their solutions included the increased leadership support, promoted learning and restructure of organizations. In [15], authors studied the infrastructures required for establishing knowledge management in universities. As they noted, knowledge management was considered as a strategic need of education and higher education institutions in the third millennium. In [16] the effect of knowledge management establishment on organizational excellence was determined. It was noted that proper knowledge management leads to competitive advantage and ultimately organizational excellence. The study was conducted in Payam Nour University of West Azarbaijan using descriptive-survey approach. Considering the great responsibility of Payam Nour University, it has established knowledge management in the organizational structure through a knowledge network including top and middle management, executives and employees. Collaboration, learning culture and knowledge sharing has improved capabilities of employees; it is suggested to repeat this study in other organizations. In [17], authors concluded that knowledge management directly influenced organizational innovation in manufacturing firms. In [18], authors analyzed the effect knowledge management project and EFQM for improving the key results of the business. In [19], authors defined a model to properly evaluate knowledge management value. III. RESEARCH METHODOLOGY The participants in this study included 20 experts in knowledge management establishment and organizational behavior as well as 800 employees of Iran Insurance Company. Twenty experts were used to identify effective factors on the role of behavioral maturity model in establishing knowledge management in the Iran Insurance Company. A series of criteria were determined for inclusion of experts; these criteria included practical and working experience, major and important positions in the company, enough experience in establishing knowledge management, and at least a bachelor's degree. Based on Cochran’s formula, at least 260 questionnaires should be handed out, however, in an effort to increase the validity of the study, a total of 280 questionnaires were distributed. A. Fuzzy Analytic Hierarchy Process (AHP) Triangular fuzzy numbers are used to avoid ambiguity caused by uncertainty in decision-making. Table I shows pairwise comparisons in AHP. A triangular fuzzy number denoted by Ã=(l,m,u) has the following membership function. Figure 1 shows the membership function selected for fuzzy numbers. Two indexes are used for triangular fuzzy numbers: confidence and optimism. Confidence index (α) indicates confidence of the decision maker in his prioritization and judgement. By defining α, triangular fuzzy number is defined as follows: 0 , 1 1 / 1, 1( ) / , 0 , F x x m x mx u x u m m x u x u               (1) [0,1] [ , ] [( 1) 1, ( ) ]M l u m u m u             (2) Optimism index (μ) can be used to estimate success rate. Optimism index as shown in the formula (3) is a linear convex combination. (1 ) , [0,1]ij iju ijua a a            (3) Accordingly, the following matrix can be formed from pairwise comparisons. 12 1 21 2 1 2 1 1 1 n a n n n a a a aA a a                                  (4) By completing pairwise comparisons, weight vector of indexes is calculated by using the following formula where λmax is the largest eigenvalue of the matrix: maxAw w (5) Once all matrices of pairwise comparisons are formed, consistency rate (CR) is calculated for each matrix using the following formula: C I C R R I  (6) CI indicates deviation from consistency and is calculated by: m a x 1 n C I n     (7) where, n denotes the size of pairwise comparisons matrix and RI represents random consistency index or average weights produced randomly, which can be extracted from the relevant Table 1. CR<0.01 indicates acceptable comparisons; otherwise, Engineering, Technology & Applied Science Research Vol. 7, No. 3, 2017, 1725-1731 1727 www.etasr.com Fashami and Babaei: A Behavioral Maturity Model to Establish Knowledge Management in an … comparisons should be repeated with more accurate information by more experienced people. IV. MEASUREMENT MODEL EVALUATION Convergent validity and discriminant validity are used to evaluate reliability of the measurement model using confirmatory factor analysis (CFA) and average variance extracted (AVE). CFA is in fact an extension of ordinary factor analysis and an important aspect of structural equations in which certain hypotheses are tested on the structure of factor loadings. According to [20], factor loadings which are larger than 0.5 are acceptably valid. Moreover, AVE≥0.5 is acceptable. As shown in Table I, all factor loadings are at least 0.5; thus, convergent validity of data is fully confirmed. Moreover, t-values listed in the left column indicate effectiveness of this variable for its corresponding construct. T- values≥1.96 indicate that the variable is effective for the considered construct at 95% confidence interval; otherwise, the variable is not effective. Obviously, t-values are >1.96 for all variables, indicating their effectiveness on the corresponding construct. Therefore, convergent validity of the constructs is confirmed. Moreover, composite reliability and Cronbach’s alpha obtained for all constructs indicate acceptable internal consistency of the measurement models. V. FINDING AND RESULTS Following criteria were extracted for the evaluation of behavioral maturity (Table II). These criteria were confirmed by experts and professors for their effect on behavioral maturity of managers for establishment of knowledge management. The second questionnaire developed as a matrix of these variables was distributed among experts. A. Prioritization of Criteria Using Fuzzy AHP Since 20 experts were used in this study, there are 20 different matrixes for comparison of criteria. Fuzzy AHP initially converts these matrixes to a single matrix. Let ãkij be the element related to the k-th respondent for comparison of the criterion i with criterion j; geometric mean for corresponding elements is calculated by: 1 1 1 1 2 10 10 1 10 12 ( ) ((1,2,3) (1,1,1) (1.67,0.2,0.25) (4,5,6) (2,3,4) (0.2,0.33,0.5) (6,7,8) (2,3,4) (1,2,3) (2,3,4)) (1.23,1.73,2.26) n n k ij ij k ij ij ij ij a a a a a a a                                (8) B. Calculation of Fuzzy Weights for Behavioral Maturity Criteria Considering fuzzy AHP, information of the integrated matrix of criteria is analyzed as follows. First, geometric mean of value of the j-th criterion is determined to other criteria: 1 13 1 11 12 13 14 15 16 17 18 19 110 111 112 113( )r a a a a a a a a a a a a a                          (9) For example, value of the first criterion is calculated as follows: TABLE I. FACTOR LOADINGS OF THE OBSERVED VARIABLES Constructs Item Factor loading t- statistic AVE CR Cronbach’s α Personnel empowerment 2 0.862 26.348 0.665 0.855 0.747 3 0.833 17.926 1 0.749 13.226 Training courses for managers 4 0.905 40.111 0.813 0.929 0.885 6 0.904 32.683 5 0.897 40.228 Teamwork spirit 8 0.869 22.148 0.754 0.902 0.838 7 0.876 31.277 9 0.861 22.938 Decision-making power 10 0.931 70.387 0.773 0.902 0.838 12 0.887 36.741 11 0.816 17.972 Human and social skills 15 0.929 64.505 0.822 0.932 0.892 14 901 55.742 13 0.884 31.577 Trustful climate 17 0.898 47.325 0.729 0.889 0.813 18 0.857 26.824 16 0.804 12.578 Professional commitment and responsibility 21 0.871 13.004 0.633 0.837 0.714 20 0.798 8.814 19 0.711 10.524 Knowledge orientation and organizational knowledge 22 0.897 28.144 0.775 0.912 0.856 23 0.879 29.775 24 0.867 28.803 Quantitative management 26 0.948 85.920 0.820 0.932 0.890 27 0.885 27.977 25 0.884 28.201 Supportive behavior 29 0.898 25.511 0.770 0.909 0.851 30 0.872 22.505 28 0.863 27.600 Transformational leadership 31 0.867 15.689 0.737 0.893 0.824 32 0.855 21.862 33 0.854 25.956 Emotional Intelligence 34 0.890 26.452 0.740 0.895 0.825 36 866 30.496 35 0.825 18.827 Motivation 38 0.840 14.796 0.571 0.798 0.814 37 0.760 7.659 39 0.657 6.128 TABLE II. EFFECTIVE CRITERIA ON BEHAVIORAL MATURITY OF MANAGERS EXTRACTED FROM LITERATURE AND INTERVIEWS Symbol Criteria C1 Transformational leadership C2 Emotional intelligence of managers C3 Training courses for managers C4 Knowledge orientation and organizational knowledge C5 Quantitative management C6 Supportive behavior C7 Personnel empowerment C8 Trustful climate C9 Teamwork spirit C10 human and social skills C11 Decision-making power C12 Stimulating and motivating people in the organization C13 Professional commitment and responsibility 1 (1,1,1) (1.23,1.73,2.26) (2.08,2.59,3.14) (1.23,1.66,2.08) (2.26,2.94,3.56) (3.65,4.47,5.81) (2.81,3.44,4.09)(1.89,2.51,3.2) (3.23,4.37,5.37) (1.15,1.58,2.05) (4.44,5.43,6.35) (5.85,6.69,7.47) (3.2 r             (2.272,2.876,3.456) 3,4.36,5.4)          (10) Engineering, Technology & Applied Science Research Vol. 7, No. 3, 2017, 1725-1731 1728 www.etasr.com Fashami and Babaei: A Behavioral Maturity Model to Establish Knowledge Management in an … where, the triangular fuzzy number (1.23, 1.73, 2.26) is fuzzy value of the first criterion versus the second criterion and the triangular fuzzy number (2.2727, 2.876, 3.456) is fuzzy value of the first criterion versus the other twelve criteria (Table III). Then, fuzzy weights of criteria are determined as follows: 1 1 2 3 4 5 6 7 8 9 10 11 12 13( )i iw r r r r r r r r r r r r r r                             (11) Value of each criterion is multiplied by the inverse fuzzy sum of values. For example, fuzzy weight of the first criterion is determined as follows: 1 1 1 1 2 3 4 5 6 7 8 9 10 11 12 13( ) (12)                           w r r r r r r r r r r r r r r Fuzzy weight of the first criterion is (0.118, 0.185, 0.28). Fuzzy weights are listed in Table IV. TABLE III. FUZZY VALUE OF PAIRWISE COMPARISONS OF BEHAVIORAL MATURITY CRITERIA ir ilr imr iur r 2.272 2.876 3.456 r 1.454 1.847 2.271 r 0.879 1.091 1.355 r 01.455 1.878 2.376 r 0.864 1.094 1.364 r 0.511 0.641 0.816 r 0.608 0.752 0.935 r 0.943 1.183 1.492 r 0.582 0.721 0.894 r 1.589 1.993 2.431 r 0.383 0.472 0.607 r 0.271 0.326 0.405 r 0.526 0.654 0.836 TABLE IV. FUZZY WEIGHTS OF BEHAVIORAL MATURITY CRITERIA jW ~ j lw j mw j uw Defuzzified weight Rank 1 ~ W 0.118 0.185 0.28 0.189 1 2 ~ W 0.076 0.119 0.184 0.12.26 4 3 ~ W 0.046 0.07 0.11 0.072 6 4 ~ W 0.076 0.121 0.192 0.12.53 3 5 ~ W 0.045 0.07 0.11 0.071 7 6 ~ W 0.026 0.041 0.066 0.042 11 7 ~ W 0.032 0.048 0.076 0.049 8 8 ~ W 0.049 0.076 0.121 0.079 5 9 ~ W 0.03 0.046 0.072 0.047 9 10 ~ W 0.083 0.128 0.197 0.132 2 11 ~ W 0.02 0.03 0.049 0.028 12 12 ~ W 0.014 0.021 0.033 0.021 13 13 ~ W 0.027 0.042 0.068 0.043 10 As shown, the top five priorities include transformational leadership (0.189), human and social skills (0.132), knowledge orientation and organizational knowledge (0.12.53), emotional intelligence of managers (0.12.26) and trustful climate (0.079), respectively. C. Conceptual Model Testing Structural equation modeling (SEM) was used to analyze the conceptual model using the software Smart PLS. The structural model is reported below. T-values were used to analyze significance of the relationships; the Figures 1 and Figure 2 report t-values for structural relationships and measurement. D. Hypothesis Testing This section tests hypotheses by β-values and t-values. T- values>1.96 indicate significant path; thus, the hypothesis is accepted (α=0.05). Table 5 reports t-test results. Based on PLS outputs, hypotheses are tested as follows:  Hypothesis 1: Personnel empowerment as an aspect of behavioral maturity is effective on KM establishment. T=5.533 (>|1.96|) indicates that hypothesis 1 is confirmed at 0.99 confidence. Thus, personnel empowerment as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.199 indicates direct and positive effect of personnel empowerment on KM establishment.word “data”  Hypothesis 2: training courses for managers as an aspect of behavioral maturity is effective on KM establishment. T=4.155 (>|1.96|) indicates that hypothesis 2 is confirmed at 0.99 confidence. Thus, training courses for managers as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.356 indicates direct and positive effect of training courses for managers on KM establishment.  Hypothesis 3: teamwork spirit as an aspect of behavioral maturity is effective on KM establishment. T=3.181 (>|1.96|) indicates that hypothesis 3 is confirmed at 0.99 confidence. Thus, teamwork spirit as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.194 indicates direct and positive effect of teamwork spirit on KM establishment.  Hypothesis 4: decision-making power as an aspect of behavioral maturity is effective on KM establishment. T=2.009 (>|1.96|) indicates that hypothesis 4 is confirmed at 0.99 confidence. Thus, decision-making power as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.122 indicates direct and positive effect of decision-making power on KM establishment.  Hypothesis 5: human and social skill as an aspect of behavioral maturity is effective on KM establishment. T=5.390 (>|1.96|) indicates that hypothesis 5 is confirmed at 0.99 confidence. Thus, human and social skill as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.288 indicates direct and positive effect of human and social skills on KM establishment. Engineering, Technology & Applied Science Research Vol. 7, No. 3, 2017, 1725-1731 1729 www.etasr.com Fashami and Babaei: A Behavioral Maturity Model to Establish Knowledge Management in an … Fig. 1. PLS model for estimates of significance Fig. 2. PLS model for standardized estimates  Hypothesis 6: trustful climate as an aspect of behavioral maturity is effective on KM establishment. T=3.549 (>|1.96|) indicates that hypothesis 6 is confirmed at 0.99 confidence. Thus, trustful climate as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.156 indicates direct and positive effect of trustful climate on KM establishment.  Hypothesis 7: professional commitment and responsibility as an aspect of behavioral maturity is effective on KM establishment. T=4.309 (>|1.96|) indicates that hypothesis 7 is confirmed at 0.99 confidence. Thus, professional commitment and responsibility as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.137 indicates direct and positive effect of professional commitment and responsibility on KM establishment.  Hypothesis 8: knowledge orientation and organizational knowledge as an aspect of behavioral maturity is effective on KM establishment. T=4.827 (>|1.96|) indicates that hypothesis 8 is confirmed at 0.99 confidence. Thus, knowledge orientation and organizational knowledge as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.231 indicates direct and positive effect of knowledge orientation and organizational knowledge on KM establishment.  Hypothesis 9: quantitative management as an aspect of behavioral maturity is effective on KM establishment. T=2.407 (>|1.96|) indicates that hypothesis 9 is confirmed at 0.95 confidence. Thus, quantitative management as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.158 indicates direct and positive effect of quantitative management on KM establishment.  Hypothesis 10: supportive behavior as an aspect of behavioral maturity is effective on KM establishment. T=3.508 (>|1.96|) indicates that hypothesis 10 is confirmed at 0.99 confidence. Thus, supportive behavior as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.189 indicates direct and positive effect of supportive behavior on KM establishment.  Hypothesis 11: transformational leadership as an aspect of behavioral maturity is effective on KM establishment. T=6.770 (>|1.96|) indicates that hypothesis 11 is confirmed at 0.99 confidence. Thus, transformational leadership as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.045 indicates direct and positive effect of transformational leadership on KM establishment.  Hypothesis 12: emotional intelligence as an aspect of behavioral maturity is effective on KM establishment. T=2.613 (>|1.96|) indicates that hypothesis 12 is confirmed at 0.99 confidence. Thus, emotional intelligence as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.166 indicates direct and positive effect of emotional intelligence on KM establishment.  Hypothesis 13: stimulation and motivation of organizational people as an aspect of behavioral maturity is effective on KM establishment. T=4.607 (>|1.96|) indicates that hypothesis 13 is confirmed at 0.99 confidence. Thus, stimulation and motivation of organizational people as an aspect of behavioral maturity is effective on KM establishment. Moreover, β=0.176 indicates direct and Engineering, Technology & Applied Science Research Vol. 7, No. 3, 2017, 1725-1731 1730 www.etasr.com Fashami and Babaei: A Behavioral Maturity Model to Establish Knowledge Management in an … positive effect of stimulation and motivation of organizational people on KM establishment. TABLE V. T-TEST RESULTS; HYPOTHESIS TESTING Hypothesis Variable - value t- value Result Independent Dependent 1 Personnel empowerment KM establishment 0.199 5.533 Accepted 2 Training courses for managers KM establishment 0.356 4.155 Accepted 3 Teamwork spirit KM establishment 0.194 3.181 Accepted 4 Decision making power KM establishment 0.122 2.009 Accepted 5 Human and social skills KM establishment 0.288 5.390 Accepted 6 Trustful climate KM establishment 0.156 3.549 Accepted 7 Commitment and responsibility KM establishment 0.137 4.309 Accepted 8 Knowledge orientation KM establishment 0.231 4.827 Accepted 9 Quantitative management KM establishment 0.158 2.407 Accepted 10 Supportive environment KM establishment 0.189 3.508 Accepted 11 Transformational leadership KM establishment 0.045 6.77 Accepted 12 Emotional intelligence KM establishment 0.166 2.613 Accepted 13 Stimulation and motivation KM establishment 0.176 4.607 Accepted These findings are consistent with [8, 21-26] who identified a part of effective variables on behavioral maturity. For example, in [21] authors identified feedback and participatory culture as effective factors on maturity of human resources. In [22], authors noted personnel attitude as an important factor in maturity of employees and managers in knowledge management, while they identified pessimism and organizational silence as destructive factors in KM establishment; this study identified these factors in a wider framework. Moreover, in [23], the author identified participatory culture, trustful climate, feedback, transformational leadership, organizational intelligence and spirituality as effective factors on behavioral maturity of employees and managers, which is partially consistent with the current study. In [26], the author identified objective trainings, performance evaluation and meritocracy as the most important measures in maturity of human resources in establishing knowledge management. VI. CONCLUSION Three steps were used to identify the effective factors on behavioral maturity of managers in establishing knowledge management. In the first step, the author identified a number of criteria based on theoretical background and literature review. Then, the author used interviews with experts to identify some other factors effective on behavioral maturity of managers in establishing knowledge management. Thirty-four criteria were identified in this step. The author used screening for consensus of experts. The identified criteria were weighted by 20 experts. According to the experts, the importance of 13 criteria was higher than average. Fuzzy AHP was used to obtain importance of variables. By pairwise comparison of variables by insurance and academic experts, the author weighted the variables. According to the results, the top five priorities included transformational leadership (0.189), human and social skills (0.132), knowledge orientation and organizational knowledge (0.12.53), emotional intelligence of managers (0.12.26) and trustful climate (0.079), respectively. As the results showed, behavioral maturity of managers was higher than average for KM establishment; this indicates good maturity of managers in KM establishment. T-test results showed that the mean behavioral maturity was higher than 3 (3.415-3.882), indicating relatively high importance of behavioral maturity among managers of Iran Insurance Company. This suggests that managers of Iran Insurance Company are well aware of the importance of KM establishment in their organization and support this effectively. 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