Research Paper Performance Of Knowledge

Performance of knowledge management practices: a causal analysis Halil Zaim, Ekrem Tatoglu and Selim Zaim

Abstract Purpose – The main purpose of this paper is to investigate the effects of knowledge management (KM) infrastructure and KM processes on the performance of KM practices. Design/methodology/approach – The paper is based on personal interviews, data were collected from 83 managers from a single case study of a Global System for Mobile Communications (GSM) operator in Turkey. Findings – The paper finds that hypotheses were tested using structural equation modeling. Test of hypotheses revealed that both KM processes and KM infrastructure positively and significantly influenced the performance of KM practices. These findings tend to corroborate our conceptual model and are also in line with the existing literature. KM infrastructure was found to be more significantly affecting KM performance than KM processes, indicating that the context and background of KM is more important than the application aspects of KM. Halil Zaim is based at Fatih University, Faculty of Economics and Administrative Sciences, Buyukcekmece, Istanbul, Turkey, Ekrem Tatoglu is based at Bahcesehir University, Faculty of Business Administration, Besiktas, Istanbul, Turkey, and Selim Zaim is also based at Fatih University, Faculty of Economics and Administrative Sciences, Buyukcekmece, Istanbul, Turkey.

Research limitations/implications – The findings in this paper cannot be generalized due to the case study approach. It may not be claimed that both KM processes and KM infrastructure solely determine the performance of KM practices. Instead, there are many other factors that may influence KM performance, which are somewhat beyond the scope of this research. Practical implications – The paper shows that the evaluation of KM performance is expected to increase the effectiveness, efficiency and adaptability of KM efforts so as to add more value to the overall performance of the organization. Originality/value – In this paper there is little or no empirical evidence investigating the influence of KM infrastructure and KM processes on KM performance. This paper rectifies this imbalance by clarifying the link between KM infrastructure, processes and performance. Keywords Knowledge management, Intellectual capital, Turkey Paper type Research paper

Introduction Knowledge and intellectual capital (IC) are considered as organizations’ primary sources of production and value, while tangible assets such as land, machinery and equipment are rarely their most valuable competitive assets (Nonaka and Takeuchi, 1995; Davenport and Prusak, 1998). Knowledge management (KM) has recently emerged as a discrete area in the study of organizations and frequently cited as an antecedent of organizational performance. With successful implementation of KM practices that organizations are able to perform intelligently to sustain their competitive advantage by developing their knowledge assets (Wiig, 1999). There is a wide range of studies on the process-related issues such as creation, development, codification, storage, distribution, sharing and utilization of knowledge. A great deal of research attention has been given to the efforts for developing a comprehensive model of KM in recent years. There exist, however, relatively rare empirical evidences investigating the influences of KM infrastructure and KM processes on

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DOI 10.1108/13673270710832163

KM performance. Relying on a case study, this paper attempts to rectify this imbalance by clarifying the link between KM infrastructure, processes and performance. The paper is organized into five sections. The next section provides a brief review of the relevant literature and sets out the research model. The third section presents the methodology followed by the analyses and results. Conclusions are in the final section.

Literature review The area of knowledge management is still in its early stages in terms of developing its theoretical base with contemporary KM approaches representing largely extensions of either organizational learning or business information systems. It has been widely accepted among scholars and practitioners that KM infrastructure and processes have considerable influences on the performance of KM practices. There is a rich array of research on the technological, cultural and organizational issues, which can be considered as the components of KM infrastructure. IC has also a unique place in the KM literature where it can be viewed as the most valuable competitive asset in contemporary business world (Wang and Chang, 2005). While the existing literature defines KM in a number of ways (see, for example, Wiig, 1997; Cortada and Woods, 2000; Scarbrough et al., 1999; Malhotra, 2000a; Darroch, 2005), the focus of KM is on the integration and coordination of individuals’ knowledge, i.e. the appropriate management of current organizational knowledge and the creation of knowledge (Diakoulakis et al., 2004). The following subsections briefly review the previous literature on the related issues including KM infrastructure, KM processes and KM performance. KM Infrastructure Knowledge management infrastructure is considered as the backbone of KM (Davenport and Vo¨lpel, 2001). Almost every successful organization that applies KM realizes the need and importance of an explicit and supportive infrastructure to assist KM practices (O’Dell and Grayson, 1998). Hence, it is acknowledged that the efficient and effective application of KM requires a strong and appropriate KM infrastructure (Tiwana, 2000), which is composed of four components: technology, organizational culture, organizational structure and intellectual capital. Apparently, KM is more than a technological toolkit, though technology is clearly an integrated part of KM (Thierauf, 1999) and availability of certain technologies plays an instrumental role in catalyzing the KM movement (Davenport and Prusak, 1998). Indeed the phenomenal growth of new technologies makes it easier to implement KM systems (Binney, 2001). Accordingly, the technological issues are cited as one of the most exciting and promising aspects of KM projects (Gottschalk and Khandelwal, 2003; Reyes and Raisinghani, 2002). KM practices take advantage of a large spectrum of technologies (Lindvall et al., 2003). Nevertheless KM technologies in general can be classified into two main categories, namely ‘‘the core technologies’’ and ‘‘the supporting technologies’’. The core technologies are the ones, which are specifically designed and developed for sophisticated KM requirements, whereas the supporting technologies are those, which are not specifically designed for KM but are useful for KM implementations. One of the most important and challenging aspects of KM is to enhance the development of a collaborative, trustworthy, emphatic and helpful organizational culture. The executives and scholars agree on the importance of a knowledge-friendly culture for the success of KM (Hauschild et al., 2002; Skyrme, 1999). It is because knowledge is a context-dependent social concept (Lang, 2001) and a large part of organizational knowledge is embodied in social processes, institutional practices, traditions and values (Fayard, 2003; Boisot, 1998). Therefore, no matter how powerful the tools and functions of KM are, it is of no use without willing participants and a supportive social and cultural environment (Koulopoulos and Frappaolo, 1999).

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While the cultural resistance is generally cited as one of the most important barriers to an effective implementation of KM (Sveiby and Simons, 2002), it is still contemplated as the neglected or underestimated side of KM applications. Therefore, it is strictly recommended for organizations to place a special emphasis on the social and cultural issues for the successful implementation of KM practices (Bhatt, 2001). The dimensions discussed in this paper pose some specific organizational design challenges if knowledge is to be managed effectively (Narasimha, 2001). The appropriate organizational structures and guidelines as well as technical and non-technical expedients of which the organization has disposal constitute another building blocks of KM infrastructure (Beijerse, 1999). Nonetheless, there is no single appropriate organizational structure for KM. Some scholars suggest a radical re-design for KM (Malhotra, 2000b), while others think that it is not necessary. However, instead of highly centralized, control-based and rigid hierarchies, more flexible, decentralized and trust-based organizational structures with empowered workers are highly recommended in the KM literature (Maier and Hadrich, 2006; Malhotra, 2005). Finally, the KM literature clearly exposes that knowledge resources have been increasingly seen as an integral part of organizations’ value creating processes. In a similar vein, companies have become aware of the importance of IC of their own (Guthrie et al., 2003). IC can be defined as ‘the sum of all the intellectual material of a company’’ – knowledge, information, intellectual property including trademarks, patents and licenses, experience and integrity, personnel competencies, collective brainpower, etc. – that is captured and leveraged to create value and that can be converted to wealth and profit (Stewart, 2001; Harrison and Sullivan, 2000; Bontis et al., 2000). Though there are a variety of different components that constitute IC, an increasingly popular classification divides intellectual assets into three categories: human capital, structural capital and customer capital (Skyrme, 2002). KM process In our conceptual framework, KM is composed of four main processes, which are namely: knowledge generation and development; knowledge codification and storage; knowledge transfer and sharing; and knowledge utilization. The ability to generate knowledge and diffuse it throughout the organization has been recognized as a major strategic capability for gaining sustainable competitive advantage (Roth, 2003; Beveren, 2002). Thus, knowledge generation that is considered as the major focus of KM includes all the activities that aim to originate novel and useful ideas and solutions by which new knowledge is generated for the organization’s benefit (Abou-Seid, 2002). It can be defined as the process of conscious and intentional generation of knowledge under specific activities and initiatives firms undertake to increase their stock of corporate knowledge (Davenport and Prusak, 1998). Knowledge development, on the other hand, is the process of either converting the innovative and creative ideas into actions, goods and services or the development of goods and services for a higher customer value (Shani et al., 2003). Knowledge is meaningful when it is codified, classified, given a shape, put in a useful format and stored. Only then, it can be used by the right person, at the right time, in the right way (Nemati and Barko, 2002). That is why one of the core processes of KM is the codification of knowledge according to the type, purpose of knowledge – in favor of the organizational objectives and priorities – and storage of knowledge for the access of the employees at present and in the future (Davenport and Prusak, 1998). However, it is also vital to remember that organizational knowledge is dispersed and scattered throughout the organization. It is found in different locations, in people’s mind, in organizational processes, in corporate culture; embedded into different artifacts and procedures and stored into different mediums such as print, disks and optical media (Bhatt, 2001). Therefore capturing, codifying and storing of knowledge are suggested as the most challenging aspects of KM.

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‘‘ One of the most important and challenging aspects of KM is to enhance the development of collaborative, trustworthy, emphatic and help organizational culture. ’’

Transfer of knowledge and benefits of sharing it effectively within the organization have been given a great deal of attention among the scholars and practitioners (Kwok and Gao, 2004). Therefore, one of the most important objectives of KM is to bring together intellectual resources and make them available across organizational boundaries (Robertson, 2002). It has been argued that only those organizations that methodically, passionately and proactively find out how to organize generation of new knowledge and transfer of existing knowledge in the organization will not only survive but also excel (O’Dell and Grayson, 1998). One of the most important objectives of KM is to create value from organization’s knowledge resources so that the knowledge held by the company will be transformed to fields of application and action (Ordaz et al., 2004). Thus, KM activities should lead to changes in behavior, changes in practices and policies and the development of new ideas, processes, practices and policies (Bender and Fish, 2000). This implies the effective and efficient use of knowledge for the organization’s competitive edge. For that reason, it has been argued that the success of KM activities mostly depends on how efficient and effective the knowledge has been used and the level of action based on it (Wilhelmij and Schmidt, 2000). KM performance and research model A vast number of studies and surveys indicate that there is a positive relationship between an efficient and effective application of KM and organizational performance (Hasan and Al-Hawari, 2003; Claycomb et al., 2002). There is, however, a dearth of studies undertaken to empirically examine this relationship. The main objective of KM performance evaluation is to increase the effectiveness, efficiency and adaptability of KM efforts so as to add more value to the overall performance of the organization (Toften and Olsen, 2003). Given the general rule about performance evaluation that performance improves through evaluation (Tarim, 2003), it is reasonable to argue that measuring the outcomes and evaluating the contribution of KM applications are important to ensure the sustainability and success of KM efforts over time. Without assembling the link between desired outcomes and KM practices continuously and demonstrating tangible or quantifiable intangible results, it is not possible for the top management to keep on investing and for the workers to preserve their concentration and motivation (O’Dell and Grayson, 1998). Apparently, KM performance evaluation also shows to what extent the intellectual resources of a firm have been utilized (Firer and Williams, 2003; Marr et al., 2003) as well as the degree of the conversion of the organizational knowledge into improved performance (Kalling, 2003). KM performance can be evaluated in four stages. The first stage involves identifying the goals and objectives (Catska et al., 2003), which is characterized as a predominant and important part of KM performance evaluation process (Yeo, 2003). The second stage is to identify the key knowledge capabilities and intellectual resources of the organization and visualize the value creation pathways for an effective performance evaluation (Marr et al., 2003). At this stage the management should decide the policies to be undertaken in order to achieve the desired performance outcomes. The third stage involves collecting and processing the data for the unique metrics of KM performance evaluation. The metrics used in this analysis can be tangible or intangible, local or global, financial or non-financial but in all circumstances have to be appropriate with the specific needs of the KM project applied in the organization (Catska et al., 2003). The last stage is analyzing the KM performance and

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determining the gap between the desired and actual performance (Carpenter and Rudge, 2003). At this stage the management decides what has to be done for the future. KM performance can be evaluated at three different levels, which involve strategic level, functional/operational level and employee/performer level. KM performance evaluation at the strategic level seeks to measure the contribution of KM solutions to overall performance and also involves gauging the results from a top management point of view according to the mission and the strategic objectives of the organization (del-Rey-Chamorro et al., 2003). In contrast, KM performance evaluation at functional or operational level aims to assess the contribution of KM applications on functional departments, working groups, operational processes and daily routines (del-Rey-Chamorro et al., 2003). The evaluation at the performer level focuses on assessing the contribution of KM applications on employees’ decisions, actions and behaviors. In fact, KM performance depends heavily on the workers’ performance and improving the knowledge worker’s performance constitutes one of the main objectives of KM applications. There is already a substantial body of research on the evaluation of the knowledge workers’ performance; the models and criteria of this evaluation; and the role of human resource management on this issue (Gooijer, 2000; Hislop, 2003). Based on the discussion of the above constructs, we propose a conceptual model of KM, which is composed of two main dimensions: the KM infrastructure and the KM processes. As noted earlier, each underlying dimension of KM is in turn explained by four sub-dimensions. We suggest that these factors have direct and indirect effects on the performance of KM practices and are also likely to determine to a great extent the success or the failure of KM applications. Another important point is that the elements of both KM infrastructure and KM processes are all interrelated so it is not easy to visualize the effects of these factors on KM performance as independent from each other. The research model adopted in this study is shown in Figure 1. The following two hypotheses are then proposed to more formally state the underlying impact of KM infrastructure and KM processes on the performance of KM practices. Figure 1 Path model

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H1.

Knowledge management process directly and positively affects performance of knowledge management practices.

H2.

Knowledge management infrastructure directly and positively affects performance of knowledge management practices.

Research methodology Survey setting A case study method was used to collect the required data on the underlying dimensions of the research model. The GSM industry was chosen as an ideal research setting in Turkey. The main rationale for selecting GSM industry is that the GSM operators are relatively large and they require the existence of some processes to facilitate knowledge management and are also heavily involved in KM applications. There are at present three GSM operators in Turkey. All three companies with some experience in KM applications were initially contacted. Of these companies, AVEA was identified to be the most successful and experienced in KM practices as well as being the most cooperative in securing the required data. AVEA (TT&TIM Iletisim Hizmetleri A.S) is Turkey’s fast growing mobile communications company and was officially formed in 2004 with the merger of Turk Telekom’s GSM operator Aycell with Aria (Is-TIM), joint venture of Is Bank (51 per cent) and Telecom Italia and Mobile (TIM) (49 per cent). The merger of Aycell and Aria gave birth to a new and strong entity that contributes to the development of the Turkish telecommunications sector. The integration of the experience and the know-how of the two companies created operational and financial strength. Being the youngest, dynamic and the alternative operator, AVEA has triggered the competition in the Turkish GSM sector. With approximately 7 million customers AVEA represents 17 per cent of the total GSM subscriptions in Turkey. In a relatively short span of time it reached 315 international roaming partners in 150 countries and 102 GPRS roaming partners in 64 countries. Having 6,610 base stations scattered throughout the country and employing more than 1200 personnel AVEA keeps on its investments. Cuneyt Turkkan, the CEO of AVEA states that their investment plan for 2006 is not going to be less than 300 million dollars (AVEA, 2006). Survey instrument and respondents The survey questionnaire was devised drawing on an extensive literature review and a series of discussions with a number of academicians on the relevant subject. The survey instrument was essentially composed of questions relating to KM processes, KM infrastructure and performance of KM practices. Respondents were asked to indicate the level of agreement based on five-point Likert scales ranging from 1 ‘‘strongly disagree’’ to 5 ‘‘strongly agree’’ on each of the 11 items measuring various aspects of KM processes including knowledge generation, knowledge transfer and sharing, knowledge utilization and codification. Similarly, the respondents identified their level of agreement on each of the 15 items related to KM infrastructure, which covers organizational and technological aspects of knowledge management. With respect to the performance of KM applications, respondents were asked to rate to what extent KM applications have led to improvement on each of the following four performance criteria over the last three years: overall organizational performance (perf1), usability of KM applications (perf2), overall employee performance (perf3) and having a common sense of corporate mission (perf4). KM performance was measured using five-point scales ranging from ‘‘definitely better’’ through ‘‘about the same’’ to ‘‘definitely worse’’ or ‘‘don’t know’’. Then initial developments of the questionnaire were piloted on a set of experienced managers in KM applications. Following refinement and retesting, the final questionnaire was subjected to 83 managers from various ranks based on personal interviews. As the focus of the study was on the KM practices and their performance, the respondents were

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identified to be the most knowledgeable in KM applications and have some capacity to comment on the flow of knowledge within the organization.

Analyses and results The data analysis essentially comprised the following steps: 1. Exploratory factor analysis (EFA) with varimax rotation to determine the underlying dimensions of KM process and KM infrastructure. 2. Measuring internal consistency of constructs at both individual and composite levels. 3. Measuring direct effect of the KM process and infrastructure on the performance of KM practices using partial least squares analysis. These steps are discussed in more detail in the following subsections. Exploratory Factor Analysis (EFA) Owing to potential conceptual and statistical overlap (Spearman correlation coefficients between the constituent items of KM process and KM infrastructure revealed a number of low to moderate inter-correlations) an attempt was made to produce parsimonious set of distinct non-overlapping variables from the full set of items underlying each construct. Exploratory factor analysis with varimax rotation was performed separately on the KM process and KM infrastructure criteria in order to extract the dimensions of each construct. Tables I and II show the results of EFA. The EFA using varimax rotation on a set of 11 items comprising KM process initially produced four factors. A content analysis was conducted to purify the uncovered factors since items measuring the same factor must have consistent substantive meanings. Thus items that have inconsistent substantive meanings with the factor or that have low factor loadings were removed from further analysis. This procedure has been widely applied in the EFA applications (Deshpande, 1982; Cavusgil and Zou, 1994), recognizing that a ‘‘blind’’ EFA can produce factors that lack substantive meanings and are inappropriate for theory development. This purification process resulted in the elimination of two items. The remaining nine items were again factor analyzed and produced four factors, which make good conceptual sense and explained 84.4 per cent of observed variance, as shown in Table I. Based on the item loadings, these factors were labeled as knowledge generation (KG), knowledge transfer and sharing (KT&S), knowledge utilization (KU) and knowledge codification (CODE). An internal reliability test showed strong Cronbach alphas for the purified multi-item factors ranging from 0.80 to 0.82 with all values being well over 0.70, suggesting satisfactory level of construct reliability (Nunnally, 1978). Table I EFA of the KM processes Factors

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Variables

KG

Level of support for R&D activities Organizational knowledge generation Adequacy of informal procedures for an effective knowledge sharing Transferability of organizational knowledge resources Efficiency of knowledge sharing throughout the organization Ability to leverage organizational knowledge resources Easiness of information accessibility Utilization of personal knowledge Codifiability of knowledge resources

0.88 0.80

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KT&S

KU

CODE

0.87 0.75 0.56 0.91 0.77 0.73 0.98

Table II EFA of the KM infrastructure Variables

CUL

Organizational culture supportive of knowledge transfer Organizational culture supportive of knowledge sharing Organizational culture supportive of inter-organizational knowledge transfer Organizational culture supportive of knowledge sharing Organizational culture supportive of team work Existence of knowledge-based organizational culture Adequacy of technological infrastructure Ability of transferring new technology applications Employee motivation to learn new technologies Organizational structure supportive of KM applications Top management support for KM applications Significance of tangible and intangible intellectual resources for KM applications Organization’s use of its intellectual capital

Factors TECH ORG

IC

0.90 0.89 0.85 0.82 0.74 0.68 0.88 0.86 0.67 0.80 0.67 0.69 0.65

Similarly, EFA was undertaken to produce a set of parsimonious distinct non-overlapping dimensions of KM infrastructure from the full set of 15 items. Following the purification process, two items were dropped from the analysis. The remaining 13 items were again factor analyzed and yielded four factors which explained a total of 81 per cent of the observed variance, as shown in Table II. Cronbach alphas for the underlying factors range from 0.69 through 0.93 exhibiting highly satisfactory level of construct reliability. These factors were labeled as organizational culture (CUL), technology (TECH), organizational structure (ORG) and intellectual capital (IC).

Unidimensionality tests of constructs in the path model A causal modeling approach represented the constructs and was used to test the hypotheses. The key premises of the testable hypotheses in this study depend on the validity of the measurement properties of the three constructs. In the research framework, since all manifest variables reflect their related latent variables, a reflective representation is more appropriate than a formative one. The validity and reliability of three reflective constructs were assessed by checking unidimensionality of each construct using three tools: Principal component analysis, Cronbach’s alpha and Dillon-Goldstein’s r (Chin, 1998). As shown in Table III, all of the Cronbach alpha values met the minimum threshold value of 0.70. According to the principal component analysis, since the first eigenvalues of the manifest variables of each construct is more than one along with the second eigenvalues being smaller than one, each construct was considered as unidimensional. Similarly, Dillon-Goldstein’s r analysis provides r values that are well above 0.70 for each construct supporting unidimensionality. Table III Unidimensionality check of the factors Factors KM process KM infrastructure KM performance

Number of indicators

Cronbach Alpha

Dillon-Goldstein’s r

First eigenvalue

Second eigenvalue

4 4 4

0.79 0.89 0.73

0.87 0.89 0.83

2.17 2.23 1.81

0.49 0.63 0.54

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Structural equation modeling In order to avoid the multi-collinearity and measurement errors, while addressing the cause-effect relationships among the research constructs, we utilized partial least squares (PLS) method, which is a variance-based structural equation modeling approach. The PLS procedure, developed by Wold (1985), uses two stage estimation algorithms to obtain weights, loadings and path estimates. In the first stage, an iterative scheme of simple and/or multiple regressions contingent on the particular model was performed until a solution converges on a set of weights used for estimating the latent variables scores. The second stage involves the non-iterative application of PLS regression for obtaining loadings, path coefficients, mean scores and location parameters for the latent and manifest variables. For calculating the PLS procedure Spad Decisia V56 statistical data analysis software was employed (Fornell and Cha, 1994; Tenenhaus et al., 2005). Outer model estimation Outer model, also known as measurement model, links the manifest variables to their latent variables. The outer model estimation results are shown in Table IV. The correlations between the manifest variables and their related latent variables were found to be very satisfactory. A communality measure, which is also R 2 value, is the squared correlation between the manifest variable and its own related latent variable. It measures the capacity of the manifest variables to describe the related latent variables. Communality measure is expected to be higher than 0.60 for each manifest variable. In this application with the exception of organization structure, intellectual capital and perf1, all of the communality scores indicate that the manifest variables are very capable of estimating the change in related latent variables. Inner model estimation The hypothesized relationships as shown in Figure 1 were tested. Table V shows the estimation results for the inner model. Following the parameter estimation, bootstrapping was also undertaken to confirm the robustness of the findings. To do this, 1,000 Bootstrap samples were built by re-sampling with replacement from the original sample. The summary

Table IV Outer model estimation results Latent variables

Manifest variables

KM process

KG KT&S KU CODE CUL TECH ORG IC Perf1 Perf2 Perf3 Perf4

KM infrastructure

KM performance

Outer weight

Correlation

Communality

0.42 0.45 0.28 0.21 0.55 0.41 0.16 0.29 0.15 0.37 0.32 0.45

0.85 0.87 0.83 0.70 0.87 0.83 0.39 0.66 0.56 0.86 0.77 0.87

0.73 0.75 0.69 0.50 0.76 0.70 0.15 0.44 0.31 0.74 0.60 0.76

Table V Inner model estimation results Model

R2

t-value

p-value

Bootstrap estimated coefficients

h1 ¼ 0:1565 þ 0:2573 j1 þ 0:6080j2 þ z1

0.68

1.9479 (for j1 ) 4.6024 (for j2 )

0.05 (for j1 ) 0.000 (for j2 )

0.2834 (for j1 ) 0.6344 (for j2 )

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results for bootstrapping were provided in the last column of Table V. The bootstrap estimated coefficients of inner model are very close to those estimated by PLS. Figure 2 presents the results of the structural model related to both hypotheses. The model has one endogenous variable (dependent variable), which is labeled as KM performance and two exogenous variables (independent variables), which are labeled as KM process and KM infrastructure. This model evaluates the impact of KM process and KM infrastructure on KM performance. Based on the test results of the overall model, KM process and KM infrastructure explain approximately 68 percent of the variation in KM performance. Of the KM process factors, knowledge transfer and sharing was found to be the most important criterion with the value of its standardized regression weight being 0.45 (p , 0:01) followed by knowledge generation that has also a significant effect (b ¼ 0:42; p , 0:01) on KM process. In contrast, knowledge utilization (b ¼ 0:28; p , 0:05) and knowledge codification and storage (b ¼ 0:21; p , 0:05) have comparatively less impact on KM process. This finding is not particularly surprising in that KM studies and applications have been primarily focused on knowledge generation and sharing rather than knowledge codification and utilization. However, it should be recognized that the factors comprising the KM process are interrelated. That is, in order to improve KM process the constituent factors should be considered as a whole. As for KM infrastructure, organizational culture (b ¼ 0:55; p , 0:01) appeared to be the leading factor, which is also consistent with the existing KM literature. Similarly, technology (b ¼ 0:41; p , 0:01) was found to be the second most critical factor affecting the KM infrastructure. Both intellectual capital (b ¼ 0:29; p , 0:05) and organizational structure (b ¼ 0:15; p , 0:05) also featured as important though they had relatively less impact on KM infrastructure.

Figure 2 Path model

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Test of H1 indicated that KM process had a positive impact on KM performance. The standardized regression weight for KM process was found to be significant (p , 0:05), which tends to support H1 that KM process has a positive and moderate direct effect on KM performance. A good deal of support has been found for H2 that KM infrastructure had a positive and significant impact on KM performance (b ¼ 0:608; p , 0:01), indicating that KM infrastructure has a direct and strong impact on KM performance.

Conclusion Despite lack of confirming empirical evidence, it has been widely accepted in the KM literature that KM processes and infrastructure have significant influences on the performance of KM applications. This study aims to rectify this imbalance by investigating the relationship among KM infrastructure, processes and performance. Exploratory factor analysis was employed to identify the underlying dimensions of KM processes and KM infrastructure. EFA yielded four distinct and non-overlapping factors of KM process, which explained 84.4 per cent of the observed variance in the sample data. Similarly, EFA produced four non-overlapping factors of KM infrastructure, which explained almost 81 per cent of the observed variance. Test of hypotheses H1 and H2 revealed that both KM processes and KM infrastructure positively and significantly influenced the performance of KM practices. These findings tend to corroborate our conceptual model and are also in line with the existing literature. Somewhat surprisingly, KM infrastructure was found to be more significantly affecting KM performance than KM processes, which indicates that the context and background of KM is more important than the application aspects of KM. The study, however, is subject to some limitations. While the findings of this study confirm the direct and positive relationships among KM processes, infrastructure and performance, they cannot be generalized to the whole population of GSM companies in Turkey due to the case study approach. The sensitive nature of the subject and the availability of personal connections, however, have made the selection of case study methodology mandatory over other large-scale quantitative surveys. Another limitation is that while there is a general assent on the likely impacts of KM infrastructure and KM processes on KM performance in the extant literature, it may not be claimed that these two factors solely determine the performance of KM practices. Instead, there are several other factors that may influence KM performance, which is beyond the scope of this research. Also, future research is called for to firmly establish a link between the KM performance and the firm performance.

References Abou-Seid, S. (2002), ‘‘A knowledge management reference model’’, Journal of Knowledge Management, Vol. 6 No. 5, pp. 486-99. AVEA (2006), AVEA, available at: www.avea.com.tr (accessed 3 February). Beijerse, R. (1999), ‘‘Questions in knowledge management: defining and conceptualizing a phenomenon’’, Journal of Knowledge Management, Vol. 3 No. 2, pp. 94-110. Bender, S. and Fish, A. (2000), ‘‘The transfer of knowledge and the retention of expertise: a continuing need for global assignments’’, Journal of Knowledge Management, Vol. 4 No. 2, pp. 125-37. Beveren, J. (2002), ‘‘A model of knowledge acquisition that focuses knowledge management’’, Journal of Knowledge Management, Vol. 6 No. 1, pp. 18-22. Bhatt, G. (2001), ‘‘Knowledge management in organizations: examining the interaction between technologies, techniques and people’’, Journal of Knowledge Management, Vol. 5 No. 1, pp. 68-75. Binney, D. (2001), ‘‘The knowledge management spectrum – understanding the KM landscape’’, Journal of Knowledge Management, Vol. 5 No. 1, pp. 33-42. Boisot, M. (1998), Knowledge Assets, Oxford University Press, New York, NY.

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Further reading Chua, A. (2002), ‘‘The influence of social interaction on knowledge creation’’, Journal of Intellectual Capital, Vol. 3 No. 4, pp. 375-92.

About the authors Halil Zaim is based at Faculty of Economics and Administrative Sciences, Buyukcekmece, Fatih University, Istanbul, Turkey. Ekrem Tatoglu is based at Faculty of Business Administration, Bahcesehir University, Besiktas, Istanbul, Turkey. Ekrem Tatoglu is the corresponding author and can be contacted at: [email protected] Selim Zaim is also based at Faculty of Economics and Administrative Sciences, Fatih University, Buyukcekmece, Istanbul, Turkey.

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