Volume 3 Issue 1 INTERNATIONAL JOURNAL OF HUMANITIES AND June 2016 CULTURAL STUDIES ISSN 2356-5926

Improvement of Competitive Advantage through Knowledge Management Capabilities in ’s Industry: A Conceptual Model

Faranak Nadi Ph.D. Candidate in Technology Management, Department of Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran [email protected]

*Reza Radfar Associate Professor in Industrial Management, Department of Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran *Corresponding Author Email: [email protected]

Abstract

The purpose of this research was to investigate the role of knowledge management capabilities in improvement of competitive advantage. 370 experts and managers of steel companies who were familiar with knowledge management systems were randomly selected. Data were collected using a questionnaire that measured 5 components, i.e. structure, culture, skills, technology, and knowledge management process. Data were analyzed in AMOS using structural equation modeling. The propose model consisted of 10 first-order constructs and 2 second-order structures, and confirmatory factor analysis was performed to examine model fit and construct validity. Survey results supported the literature and showed that there are 3 key KM capabilities: technology infrastructure, social infrastructure, and KM process. Technology infrastructure refers to information technology (IT), which is used to support KM activities. Social infrastructure included cultural infrastructure, structural infrastructure, and people infrastructure (t-shaped skills). Finally, KM processes included discovery, acquisition, development, sharing, application, and protection of knowledge.

Keywords: Knowledge management, knowledge management capabilities, competitive advantage, structural equation modeling, technology infrastructure, social infrastructure.

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Introduction

In the not so distant past, power and competitive advantage of an organization or a society was determined by access to financial resources (Ansari et al., 2012). However, with the transition from the industrial age to the new millennium, financial resources and capital are no longer the sole drivers of growth, and knowledge has become an integral business function for many organizations (Grover and Davenport, 2001). Knowledge is considered one of the most important strategic resources and knowledge creation is now essential to competitive advantage and success of organizations (Nonaka, 1994). Today, knowledge management provides the key competitive edge (Youshanlouei, 2011).

Knowledge is defined as a mix of framed experience, values, contextual information, and expert insight (Davenport and Prusak, 1998). Knowledge exists in tacit and explicit forms and can be effective only if it is properly managed. Knowledge management (KM) is the process of capturing, developing, sharing, and effectively using organizational knowledge. Businesses make little benefit from the isolated knowledge of individuals, and to effectively use knowledge, it must be captured, developed, and shared (Pearlson and Saunders, 2006).

Today, organizations that will succeed in the global information society are those that can identify, value, create, and evolve their knowledge assets. Effective management of knowledge, change, and innovation are central or “core competencies” that must be mastered for organizations to succeed. (Rowley, 2000).

In Iranian Steel Industry, many activities are interrelated and highly complex, while the knowledge within the industry is mostly tacit and depends on people’s experiences. As a result, capturing and reusing knowledge has become very challenging. Under such circumstances, knowledge management can be effective for improving performance and achieving competitive advantage. Although managers of steel companies have realized the key role of knowledge management, they are still faced with challenges for adopting and implementing it. The purpose of the present research is to structurally examine and model the effect of knowledge management, i.e. knowledge management processes and infrastructure capabilities, in the competitive advantage of Iranian steel companies.

Methodology

This research was a descriptive survey. The population consisted of all the experts and managers in Iran’s Steel Industry who were familiar with KM systems (N = 41921). 370 experts and managers were randomly selected as the sample. To ensure better responses and prevent low response rate, correspondence and negotiations were done with IMIDRO1 and the Department of Planning and Empowerment to provide a list of steel industry representatives and help in the process of sending and completing the questionnaires. In March 2014, meetings were held with steel industry representatives during the “Conference on Knowledge Management, Value Creation, Innovation, and Intellectual Capital”, where top Iranian companies received MAKE2 rewards. Interviews were conducted with representatives of different companies such as Steel Company, Esfahan Steel Company, and

1 Iranian Mines and Mining Industries Development and Renovation 2 Most Admired Knowledge Enterprises

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Khorasan Steel Company. The interviews centered on the current state of knowledge management in these companies, and the topic and purpose of the research were also explained. Finally, with the cooperation of the Department of Education, Research and Technology and the Ministry of Industries and Mines, the questionnaires were mailed along with an official letter from the said ministry. To encourage the participants to complete the questionnaires, they were promised to receive a summary of the results of the study. Moreover, to ensure high response rate, further reminders were sent through e-mails and phone calls. The questionnaire measured 11 latent constructs and was rated on a 7-point Likert scale.

Information Technology (Technology Infrastructure) Gold et al.’s (2001) measures of IT infrastructure were used in this study (Table 1).

Table 1. Item measures of technology infrastructure

Code Item My organization uses technology that allows … TI1 It to monitor its competition and business partners TI2 Employees to collaborate with other persons inside the organization TI3 Employees to collaborate with other persons outside the organization TI4 People in multiple locations to learn as a group from a single source or at a single point in time TI5 People in multiple locations to learn as a group from multiple sources or at multiple points in time TI6 It to search for new knowledge TI7 It to map the location (i.e. an individual, specific system, or database) of specific types of knowledge Structural Infrastructure A 9-item measure of structural infrastructure, developed by Gold et al. (2001), is used in this research (Table 2). Table 2. Item measures of structural infrastructure

Code Item My organization('s) … SI1 Structure promotes collective rather than individualistic behavior SI2 Bases our performance on knowledge creation SI3 Has a standardized reward system for sharing knowledge SI4 Designs processes to facilitate knowledge exchange across functional boundaries SI5 Has a large number of strategic alliances with other firms SI6 Encourages employees to go where they need for knowledge regardless of structure SI7 Managers frequently examine knowledge for errors/mistakes SI8 Structure facilitates the transfer of new knowledge across structural Boundaries SI9 Employees are readily accessible

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Cultural Infrastructure

The 10-item measure of cultural infrastructure, developed by Gold et al. (2001), is used in this research (Table 3). Table 3. Item measures of cultural infrastructure

Code Item In my organization … CI1 Employees understand the importance of knowledge to corporate success CI2 High levels of participation are expected in capturing and transferring knowledge CI3 Employees are encouraged to explore and experiment CI4 On-the-job training and learning are valued CI5 Employees are valued for their individual expertise CI6 Employees are encouraged to interact with other groups CI7 Overall organizational vision and objectives are clearly stated CI8 Shares its knowledge with other organizations CI9 The benefits of sharing knowledge outweigh the costs CI10 Senior management clearly supports the role of knowledge in our firm’s success

People Infrastructure (T-Shaped Skills) While the role of human resources in knowledge creation has been extensively studied (Chuang, 2004), this study focuses on T-shaped skills which refers to workers’ degree of understanding of their own and other’s task areas which is both deep (the vertical part of the ‘T’) and broad (the horizontal part of the ‘T’) (Lee and Choi, 2003). This concept was developed by Lee and Choi (2003), expanded by Chuang (2004) and Migdadi (2005), and used in the present research (Table 4).

Table 4. Item measures of people infrastructure (T-shaped skills)

Code Item My organization’s members … PI1 Can understand not only their own tasks but also others’ tasks PI2 Can make suggestions about others’ tasks PI3 Can communicate well not only with their department members but also with other department members PI4 Are specialists in their own field of expertise PI5 Can perform their own task effectively without regard to environmental changes

Knowledge Discovery Process

Knowledge discovery is the first stage of the KM process, which involves identification of tacit, explicit, and embedded knowledge within the organization or external sources (Abolghasemi, 2010). This study uses four measures of knowledge discovery developed by Khamseh et al. (2014) (Table 5).

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Table 5. Item measures of knowledge discovery process

Code Item My organization … DP1 Identifies internal databases and documents DP2 Identifies internal knowledge and expertise DP3 Identifies external databases and documents DP4 Identifies external knowledge and expertise

Knowledge Acquisition Process Knowledge acquisition involves obtaining knowledge from internal and external sources (Abolghasemi et al., 2010). Six adjusted measures of knowledge acquisition are used in this research (Table 6). Table 6. Item measures of knowledge acquisition process

Code Item My organization … AP1 Uses internal and external experts and advisors for knowledge acquisition AP2 Conducts joint research projects with other organizations and research centers AP3 Participates in national and international conferences AP4 Holds training courses and workshops AP5 Has processes for accessing information sources (articles, books, journals, research projects, etc.) AP6 Provides access to information superhighway through internet and intranet

Knowledge Development Process Knowledge development involves converting the knowledge acquired from internal and external sources into the design of new products and services (Abolghasemi et al., 2010). In this research, knowledge development is measured using three items (Table 7).

Table 7. Item measures of knowledge development process

Code Item My organization … KDP1 Synchronizes and updates knowledge based on latest changes in the environment KDP2 Acquires knowledge that contributes to the changes in organizational goals, procedures, and processes KDP3 Attempts to attract individuals that possess the required knowledge

Knowledge Sharing Process

This stage of knowledge management deals with sharing and transfer of knowledge and making it accessible to everyone within the organization. In other words, it attempts to transfer knowledge from individual to group and organizational levels (Abolghasemi et al.,

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2010). Table 8 provides the items used to measure knowledge sharing process (Khamseh et al., 2014).

Table 8. Item measures of knowledge sharing process

Code Item In my organization … SP1 Senior members participate in academic conferences to share their knowledge SP2 The use of internet forums for discussion is stressed SP3 Job rotation is effectively used SP4 Many tasks are performed by teams

Knowledge Application Process At this stage, all KM efforts focus on applying the knowledge that exists within the organization to developing new products and services, improving efficiency, and solving problems (Abolghasemi et al., 2010). Here, knowledge application process is measured using three items from Khamseh et al. (2014) (Table 9).

Table 9. Item measures of knowledge application process

Code Item My organization … KAP1 Has processes for using knowledge in development of new products or services KAP2 Has processes for using knowledge to solve new problems KAP3 Uses knowledge to improve efficiency

Knowledge Protection Process This stage of the KM process involves is processes designed to protect the organizational knowledge from theft and improper or illegal uses (Abolghasemi et al., 2010). This study uses four item measures of knowledge protection process adopted from Khamseh et al. (2014) (Table 10).

Table 10. Items measures of knowledge protection process

Code Item My organization … PP1 Has processes for preserving knowledge from inappropriate use inside and outside the organization PP2 Has processes for preserving knowledge from theft from within or outside the organization PP3 Has policies and procedures for protecting confidential information PP4 Has technology to restrict access to some sources of knowledge

Competitive Advantage Competitive advantage is considered to be the objective of strategy (Porter, 1985). It is defined as the unique position that an organization develops over its competitors by

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employing its resources. The multi-dimensions of the construct developed by Chuang (2004), including innovativeness, market position, mass customization, and difficulty in duplicating, is adopted in this study (Table 11).

Table 11. Item measures of competitive advantage

Code Item My organization('s) … CA1 Often uses knowledge-based innovation CA2 Market position can provide strong barriers to entry for other firms CA3 Uses knowledge management to widen the array of products without increasing costs CA4 Has a knowledge management capability that is difficult and expensive for rivals to duplicate

The results were analyzed using confirmatory factor analysis in SPSS 22 and structural equation modeling in SPSS Amos 22.

Results Structural Infrastructure Confirmatory factor analysis (CFA) was used to assess the construct validity of structural infrastructure. The results showed that the initial model does not fit the data and needs to be modified (Tables 12, 13, and 14).

Table 12. Goodness of fit indices for the initial model

Index Value Standard Result Chi-square 10.41 2-3 Poor fit p-value 0.00 > 0.05 Poor fit GFI 0.83 > 0.9 Poor fit NFI 0.86 > 0.9 Poor fit CFI 0.87 > 0.9 Poor fit RMSE 0.16 < 0.1 Poor fit IFI 0.87 Close to 1 Poor fit

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Figure 1. Results of CFA for the initial structural infrastructure model

Model diagnostics such as standardized residuals, path analysis, and adjusted indices can be used to refine the structural infrastructure model. Factor loading or regression weight of all the indices is between 0.91 and 0.25. The higher the factor loading, the higher is the correlation between the items and the construct. Therefore, factor loadings greater than 0.5 are considered significant. The results showed that S5 and S9 have a low factor loading (0.25 and 0.33 respectively). In terms of standardized residuals, items 8 and 9 had higher values. Adjustment of indices showed that removing error covariance of items 1 and 3 improves model fit. Therefore, the first step was to remove item S8. Re-estimating the model showed that the fit is still poor. By implementing four changes and applying CFA, it was found that items S1, S3, S5, S8, and S9 must be removed. The results of CFA for the refined model are provided in the tables below. As the data show, all the factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.92).

Table 13. Goodness of fit indices for the refined model

Index Value Standard Result Chi-square 0.75 2-3 Good fit p-value 0.47 > 0.05 Good fit GFI 0.99 > 0.9 Good fit NFI 0.99 > 0.9 Good fit CFI 1 > 0.9 Good fit RMSE 0.000 < 0.1 Good fit IFI 1 Close to 1 Good fit

Table 14. Factor loadings in the refined model

Item Factor Loading S2 0.83 S4 0.92 S6 0.90 S7 0.78

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Figure 2. Results of CFA for the refined structural infrastructure model

The results show that the goodness of fit indices are satisfactory and the refined model is suitable for other analyses.

Cultural Infrastructure Cultural infrastructure was measured using 10 items. The results of CFA for the initial model showed that the model fit is not satisfactory (Table 15).

Table 15. Goodness of fit indices for the initial cultural infrastructure model

Index Value Standard Result Chi-square 13.29 2-3 Poor fit p-value 0.00 > 0.05 Poor fit GFI 0.78 > 0.9 Poor fit NFI 0.82 > 0.9 Poor fit CFI 0.84 > 0.9 Poor fit RMSE 0.18 < 0.1 Poor fit IFI 0.84 Close to 1 Poor fit

Figure 3. Results of CFA for the initial cultural infrastructure model

By examining regression weights (factor loadings) and error residuals and using adjusted indices, changes were made to refine the model and improve its fit. The results showed that items CI1, CI4, CI5, CI7, and CI9 must be removed. The goodness of fit indices of the refined model indicate good fit. All the factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.93).

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Table 15. Goodness of fit indices for the initial cultural infrastructure model

Index Value Standard Result Chi-square 0.38 2-3 Good fit p-value 0.86 > 0.05 Good fit GFI 0.99 > 0.9 Good fit NFI 0.99 > 0.9 Good fit CFI 1 > 0.9 Good fit RMSE 0.00 < 0.1 Good fit IFI 1 Close to 1 Good fit

Table 7. Factor loadings in the refined model

Item Factor Loading CI2 0.94 CI3 0.63 CI6 0.96 CI8 0.92 CI10 0.73

Figure 4. Results of CFA for the refined cultural infrastructure model

People Infrastructure People infrastructure was measured using 5 items. The results of CFA for the initial model showed that the model fit is not satisfactory and needs to be modified (Table 18).

Table 18. Goodness of fit indices for the initial model

Index Value Standard Result Chi-square 24.68 2-3 Poor fit p-value 0.00 > 0.05 Poor fit GFI 0.89 > 0.9 Poor fit NFI 0.85 > 0.9 Poor fit

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CFI 0.86 > 0.9 Poor fit RMSE 0.25 < 0.1 Poor fit IFI 0.86 Close to 1 Poor fit

Figure 5. Results of CFA for the initial people infrastructure model

A few changes were made to improve the model fit, and items PI2 and PI5 were removed. The refined model has a reasonable fit, with all the factor loadings being greater than 0.5 and an appropriate construct reliability (CR = 0.90).

Table 19. Goodness of fit indices for the refined model

Index Value Standard Result Chi-square 0.04 2-3 Good fit p-value 0.85 > 0.05 Good fit GFI 1 > 0.9 Good fit NFI 1 > 0.9 Good fit CFI 1 > 0.9 Good fit RMSE 0.000 < 0.1 Good fit IFI 1 Close to 1 Good fit

Table 20. Factor loadings in the refined model

Item Factor Loading PI1 0.85 PI3 0.88 PI4 0.86

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Figure 6. Results of CFA for the refined people infrastructure model

Social Infrastructure Social KM infrastructure capability is assumed to be a second-order latent construct that is determined by three first-order latent variables, i.e. structural infrastructure, cultural infrastructure, and people infrastructure. The construct validity test shows an acceptable model fit. The following figure provides a summary of the outputs. Factor loadings are greater than 0.5 and the second-order construct is reliable (CR = 0.95) (Tables 22 and 23).

Table 21. Goodness of fit indices for the social infrastructure model

Index Value Standard Result Chi-square 2.71 2-3 Good fit p-value 0.344 > 0.05 Good fit GFI 0.96 > 0.9 Good fit NFI 0.97 > 0.9 Good fit CFI 0.98 > 0.9 Good fit RMSE 0.068 < 0.1 Good fit IFI 0.98 Close to 1 Good fit

Figure 7. Construct validity of the social infrastructure model

Table 22. Correlations between social infrastructure capabilities

Item Correlation SI ↔ CI 0.91 CI ↔ PI 0.86 PI ↔ SI 0.83

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Technology Infrastructure Technology infrastructure (information technology) was measured using 7 items. The results of CFA for the initial mode indices poor fit (Table 23).

Table 23. Goodness of fit indices for the social infrastructure model

Index Value Standard Result Chi-square 11.96 2-3 Poor fit p-value 0.00 > 0.05 Poor fit GFI 0.89 > 0.9 Poor fit NFI 0.85 > 0.9 Poor fit CFI 0.86 > 0.9 Poor fit RMSE 0.17 < 0.1 Poor fit IFI 0.86 Close to 1 Poor fit

Figure 8. Results of CFA for the initial technology infrastructure model Further analysis showed that removing items TI1, TI4, and TI6 can improve the model fit. As shown in the following figure, the refined model reasonably fits the data. All the factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.89).

Table 24. Goodness of fitness indices for the refined technology infrastructure model

Index Value Standard Result Chi-square 0.06 2-3 Good fit p-value 0.94 > 0.05 Good fit GFI 1 > 0.9 Good fit NFI 1 > 0.9 Good fit CFI 1 > 0.9 Good fit RMSE 0.000 < 0.1 Good fit IFI 1 Close to 1 Good fit

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Table 25. Factor loadings in the refined technology infrastructure model

Item Factor Loading TI2 0.95 TI3 0.78 TI5 0.83 TI7 0.71

Figure 9. Results of CFA for the refined technology infrastructure model

Knowledge Discovery Process Knowledge discovery process was measured using 4 items. The results of CFA for the initial model indicates that model fit is not satisfactory (Table 27).

Table 26. Goodness of fit indices for the initial discovery process model

Index Value Standard Result Chi-square 26.89 2-3 Poor fit p-value 0.00 > 0.05 Poor fit GFI 0.936 > 0.9 Good fit NFI 0.909 > 0.9 Good fit CFI 0.911 > 0.9 Good fit RMSE 0.265 < 0.1 Poor fit IFI 0.912 Close to 1 Good fit

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Figure 10. Results of CFA for the initial discovery process model

Further analysis showed that removing item DP3 improves model fit. The refined model fit is acceptable. All the factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.85).

Table 27. Goodness of fit indices for the refined discovery process model

Index Value Standard Result Chi-square 0.001 2-3 Good fit p-value 0.98 > 0.05 Good fit GFI 1 > 0.9 Good fit NFI 1 > 0.9 Good fit CFI 1 > 0.9 Good fit RMSE 0.000 < 0.1 Good fit IFI 1 Close to 1 Good fit

Table 28. Factor loadings in the refined discovery process model

Item Factor Loading DP1 0.92 DP2 0.60 DP4 0.87

Figure 11. Results of CFA for the refined discovery process model

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Knowledge Acquisition Process Knowledge acquisition process was measured using 6 items. The results of CFA for the initial acquisition process model indicated that model fit is somewhat acceptable (Table 9). However, the model can be improved based on the results.

Table 29. Goodness of fit indices for the initial acquisition process model

Index Value Standard Result Chi-square 47.29 2-3 Poor fit p-value 0.000 > 0.05 Poor fit GFI 0.797 > 0.9 Poor fit NFI 0.682 > 0.9 Poor fit CFI 0.586 > 0.9 Poor fit RMSE 0.354 < 0.1 Poor fit IFI 0.686 Close to 1 Poor fit

Figure 12. Results of CFA for the initial acquisition process model

Further analysis showed that removing item AP2 can improve model fit. The refined model fit is reasonable, with factor loadings greater than 0.5 and an appropriate construct reliability (CR = 0.85) (Tables 31 and 32). Table 30. Goodness of fit indices for the refined acquisition process model

Index Value Standard Result Chi-square 2.79 2-3 Good fit p-value 0.04 > 0.05 Good fit GFI 0.99 > 0.9 Good fit NFI 0.99 > 0.9 Good fit CFI 0.99 > 0.9 Good fit RMSE 0.07 < 0.1 Good fit IFI 0.99 Close to 1 Good fit

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Table 31. Factor loadings in the refined acquisition process model

Item Factor Loading AP1 0.78 AP3 0.57 AP4 0.88 AP5 0.67 AP6 0.78

Figure 13. Results of CFA for the refined acquisition process model

Knowledge Development Process Knowledge development process was measured using 3 items. The results of CFA showed that the initial model fit the data well. All factor loadings are greater than 0.5 and construct validity is appropriate (CR = 0.847). Therefore, all the items are kept for measuring the knowledge development process (Tables 32 and 33).

Table 32. Goodness of fit indices for the development process model

Index Value Standard Result Chi-square 2.81 2-3 Good fit p-value 0.09 > 0.05 Good fit GFI 0.99 > 0.9 Good fit NFI 0.94 > 0.9 Good fit CFI 0.96 > 0.9 Good fit RMSE 0.07 < 0.1 Good fit IFI 0.96 Close to 1 Good fit

Table 33. Factor loadings for the development process model

Item Factor Loading KDP1 0.82 KDP2 0.87 KDP3 0.72

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Figure 14. Results of CFA for the development process model Knowledge Sharing Process Knowledge sharing process was measured using four items. The results of CFA for the initial model indicate poor fit.

Table 34. Goodness of fit indices for the initial sharing process model

Index Value Standard Result Chi-square 11.79 2-3 Poor fit p-value 0.000 > 0.05 Poor fit GFI 0.97 > 0.9 Good fit NFI 0.95 > 0.9 Good fit CFI 0.96 > 0.9 Good fit RMSE 0.171 < 0.1 Good fit IFI 0.96 Close to 1 Good fit

Figure 15. Results of CFA for the initial sharing process model

Further analysis showed that removing item SP3 can improve model fit. The refined model fits the data well. All factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.79).

Table 35. Goodness of fit indices for the refined sharing process model

Index Value Standard Result Chi-square 0.919 2-3 Good fit p-value 0.338 > 0.05 Good fit GFI 1 > 0.9 Good fit

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NFI 1 > 0.9 Good fit CFI 1 > 0.9 Good fit RMSE 0.000 < 0.1 Good fit IFI 1 Close to 1 Good fit

Table 36. Factor loadings in for the refined sharing process model

Item Factor Loading SP1 0.67 SP2 0.77 SP4 0.80

Figure 16. Results of CFA for the refined sharing process model Knowledge Application Process Knowledge application process is measured using 3 items. The results of CFA for the initial model indicates acceptable fit. All factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.79). Therefore, all the items are kept for the measurement of knowledge application process (Tables 36 and 37).

Table 36. Goodness of fit indices for the application process model

Index Value Standard Result Chi-square 2.15 2-3 Good fit p-value 0.142 > 0.05 Good fit GFI 0.996 > 0.9 Good fit NFI 0.994 > 0.9 Good fit CFI 0.997 > 0.9 Good fit RMSE 0.056 < 0.1 Good fit IFI 0.997 Close to 1 Good fit

Table 37. Factor loadings in the application process model

Item Factor Loading KAP1 0.91 KAP2 0.69 KAP3 0.61

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Figure 17. The results of CFA for the application process model

Knowledge Protection Process Knowledge protection process is measured using 3 items. The results of CFA for the initial protection process model indicates poor fit (Table 38).

Table 38. Goodness of fit indices for the initial protection process model

Index Value Standard Result Chi-square 37.912 2-3 Poor fit p-value 0.00 > 0.05 Poor fit GFI 0.91 > 0.9 Good fit NFI 0.92 > 0.9 Good fit CFI 0.92 > 0.9 Good fit RMSE 0.07 < 0.1 Good fit IFI 0.92 Close to 1 Good fit

Figure 18. Results of CFA for the initial protection process model

Further analysis show that removing item PP4 improves model fit. The refined model fits the data well. All factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.90). Therefore, items PP1, PP2, and PP3 are kept for the measurement of knowledge protection process (Tables 39 and 40).

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Table 39. Goodness of fit indices for the refined protection process model Index Value Standard Result Chi-square 2.42 2-3 Good fit p-value 0.119 > 0.05 Good fit GFI 0.99 > 0.9 Good fit NFI 0.99 > 0.9 Good fit CFI 0.99 > 0.9 Good fit RMSE 0.06 < 0.1 Good fit IFI 0.99 Close to 1 Good fit

Table 40. Factor loadings in the refined protection process model

Item Factor Loading PP1 0.76 PP2 1.02 PP3 0.82

Figure 19. Results of CFA for the refined protection process model Knowledge Management Process Capability Knowledge management process capability is assumed to be a second-order latent construct that is determined by 6 first-order latent variables, i.e. knowledge discovery, knowledge, development, sharing, application, and protection. The results of CFA show acceptable model fit. Factor loadings are greater than 0.5 and the second-order construct has appropriate reliability (CR = 0.92). The correlation between the 6 components of the knowledge management process capability are shown in the following figure.

Table 41. Goodness of fit indices for knowledge management process capability

Index Value Standard Result Chi-square 2.44 2-3 Good fit p-value 0.16 > 0.05 Good fit GFI 0.91 > 0.9 Good fit NFI 0.90 > 0.9 Good fit CFI 0.93 > 0.9 Good fit RMSE 0.06 < 0.1 Good fit IFI 0.94 Close to 1 Good fit

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Figure 20. Results of CFA for knowledge management process capability

Figure 21. Construct validity of knowledge management process capability

Competitive Advantage Competitive advantage is measured using 4 items. The results of CFA for the initial model indicates poor fit (Table 42).

Index Value Standard Result Chi-square 0.13 2-3 Poor fit p-value 0.00 > 0.05 Poor fit GFI 0.96 > 0.9 Good fit NFI 0.91 > 0.9 Good fit CFI 0.91 > 0.9 Good fit RMSE 0.18 < 0.1 Good fit IFI 0.91 Close to 1 Good fit

Figure 22. Results of CFA for the competitive advantage model

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Further analysis showed that removing item CA3 improves model fit. The refined model fits the data well. As the data show, all factor loadings are greater than 0.5 and construct reliability is appropriate (CR = 0.84). Therefore, items CA1, CA2, and CA4 are kept for the measurement of competitive advantage (Tables 43 and 44).

Table 43. Goodness of fit indices for the refined competitive advantage model

Index Value Standard Result Chi-square 0.12 2-3 Good fit p-value 0.72 > 0.05 Good fit GFI 1 > 0.9 Good fit NFI 1 > 0.9 Good fit CFI 1 > 0.9 Good fit RMSE 0.000 < 0.1 Good fit IFI 1 Close to 1 Good fit

Table 44. Factor loadings of the refined competitive advantage model

Item Factor Loading CA2 0.75 CA3 0.91 CA4 0.84

Figure 23. Results of CFA for the refined competitive advantage model The Overall Model After evaluating the validity of the first- and second-order latent constructs, the overall model is evaluated using CFA. The results show that the model fit is satisfactory. All factor loadings are greater than 0.5 and construct reliability is appropriate (Table 45; Figure 14).

Table 45. Goodness of fit indices for the overall model

Index Value Standard Result Chi-square 2.26 2-3 Good fit p-value 0.00 > 0.05 Good fit GFI 0.96 > 0.9 Good fit NFI 0.90 > 0.9 Good fit CFI 0.94 > 0.9 Good fit RMSE 0.05 < 0.1 Good fit IFI 0.94 Close to 1 Good fit

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Figure 24. Results of CFA for the overall model

Table 46. Second-order factor analysis

t-value KMPC SI CI 16.33 0.80 SI 15.88 0.99 PI 0.99 AP 17.39 0.80 DP 0.82 PP 16.128 0.82 SP 13.488 0.76 KDP 21.516 0.96 KAP 17.389 0.99 CA 0.945 0.951

Structural Equation Modeling The overall model consists of four key constructs: technology infrastructure capability and competitive advantage are first-order latent constructs, while social infrastructure capability and knowledge management process are second-order latent constructs. The results of CFA show that the model fit is satisfactory. The proposed model is shown in the following figure (Figure 15).

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Figure 25. Results of structural equation modeling

Discussion and Conclusion

The purpose of the present research was to examine the role of knowledge management capabilities in competitive advantage of organizations. The results of confirmatory factor analysis showed that the overall model fits the data well. The validity and reliability of the components of knowledge management capability (social infrastructure, technology infrastructure, and knowledge management process) were also acceptable. The significant relationship between capabilities supports these findings and is consistent with the results of Grant (1991) and Smith (2006), who argue that KM capabilities are in integrated and interrelated. Two of the three KM capabilities, i.e. social infrastructure and technology infrastructure, were second-order latent constructs. The results supported the important role of these constructs in competitive advantage by highlighting that organizations must: (1) design processes that facilitate the creation, exchange, and transfer of knowledge across functional boundaries; (2) have a culture that acknowledges the importance of knowledge to corporate success, values on-the-job training and learning, and encourages employees to share their knowledge; and (3) have members who understand not only their own tasks but also others’ tasks and are specialists in their area of expertise. The correlation between the components of social infrastructure supports the theory of their interrelationships and is consistent with the extant literature (Lee and Lee, 2007; Zheng, 2005). Knowledge management process capability is a second-order latent construct that consists of knowledge discovery, acquisition, development, sharing, application, and protection. The results showed the importance of each

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of these stages, and the order of importance was as follows: (1) knowledge application, (2) knowledge development, (3) knowledge protection, (4) knowledge discovery, (5) knowledge acquisition, and (6) knowledge sharing. The correlation between the components of knowledge management process capability supports their interrelationships and is consistent with the results of previous studies (e.g. Gold et al., 2011; Smith, 2006). Overall, it can be concluded that knowledge management capability is a multi-dimensional construct consisting of social infrastructure, technology infrastructure, and knowledge management process.

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References

Abolghasemi, M., Rashid, S., Ahmadi, M., 2010. The role of organizational enablers in embeddedness of knowledge management processes. Iranian Journal of Cultural Strategy, 12, 183-200.

Ansari, M., Youshanlouei, H., Mood, M., 2012. A conceptual model for success in implementing knowledge management: A case study in Tehran municipality. Journal of Service Science and Management, 5, 212-222.

Chuang, S. H., 2004. A resource-based perspective on knowledge management capability and competitive advantage: An empirical investigation. Expert Systems with Applications, 27, 459-465.

Davenport, T., Grover, V., 2001. Special Issue: Knowledge Management. Journal of Management Information Systems, 18, 3-4.

Davenport, T. Prusak, L., 1998. Working Knowledge: How Organizations Manage What They Know. Boston, Ma: Harvard Business School Press.

Gold, A. H., Malhotra, A., Segars, A. H., 2001. Knowledge management: an organizational capabilities perspective. Journal of Management Information Systems, 18, 185-214.

Grant, R. M., 1991. The resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33, 114-135.

Khamseh, A., Ghozati, A., Eskandari, F., Mahmoodabadi, M., 2014. Evaluating the performance of knowledge management in the heavy equipment industry, providing solutions for improvement. Case study: HEPCO Factory. Indian Journal of Fundamental and Applied Life Sciences, 4, 446-454.

Lee, H., Choi, B., 2003. Knowledge management enablers, processes, and organizational performance: An integrative view and empirical examination. Journal of Management Information Systems, 20, 179-228.

Lee, Y. C., Lee, S. K., 2007. Capability, processes, and performance of knowledge management: A structural approach. Human Factors and Ergonomics in Manufacturing & Service Industries, 17, 21-41.

Migdadi, M. M., 2005. An integrative view and empirical examination of the relationships among knowledge management enablers, processes, and organizational performance in Australian enterprises. PhD thesis, School of Economics and Information Systems, University of Wollongong.

Nonaka, I., 1994. A dynamic theory of organizational knowledge creation. Organization Science, 5, 14-37.

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Pearlson K. E., Saunders C. S., 2006. Managing and Using Information Systems: A Strategic Approach. Third Edition, John Wiley & Sons, USA.

Porter, M. E., 1985. The Competitive Advantage: Creating and Sustaining Superior Performance. NY: Free Press.

Rowley, J., 2000. Is higher education ready for knowledge management? The International Journal of Educational Management, 14, 325–333.

Smith, T. A., 2006. Knowledge management and its capabilities linked to the business strategy for organisational effectiveness. DBA thesis, Nova Southeastern University.

Youshanlouei, H., 2011. Developing a model of knowledge management success: A multi- level approach. Master’s Thesis, Faculty of Management, University of Tehran.

Zheng, W., 2005. The impact of organizational culture, structure, and strategy on knowledge management effectiveness and organizational effectiveness. PhD thesis, University of Minnesota.

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