Received: May 31, 2011 / Accepted: June 21, 2011 / Published: January 25, 2012.
　　Abstract: For a modern organization, KM (Knowledge Management) plays a critical role in terms of strategy development. Key determinants of KM lead to better understanding of various influences that enable and organisation to face competitors. For this reason, sharing and managing knowledge in an organization involves a series of activities that are related to the specific functional aspects of that organisation. In order to foster KM in an organisation, these functional aspects must be understood properly, and within the context of a given organisation, its geographical location and the cultural aspects of the given organisation. This was the premise on which this study was conducted with Indian organisations. A mixed method approach was used to understand the views of Indian region towards KM in this study, be selecting 400 participants in four major each cities. A second order regression model was built using Structural Equation Model to arrive at nine constructs that are relevant to KM in an organisation.
　　Key words: Knowledge management, knowledge management systems, organisational performance.
　　 1. Introduction
　　Knowledge management (KM) plays an important role for organisations. While involves activities such as the process of creating, acquiring, sharing and managing knowledge at individual and organizational level [1], how these are facilitated depend on the organisation, its culture, the context in which the organisation is performing and mainly its people. Researchers have provided definitions to better understand the concepts of knowledge and knowledge management. For example, knowledge management has been defined as the process of capturing, storing, sharing, and using knowledge [2]. KM is also the systematic and explicit management of knowledge-related activities, practices, programs and policies within the enterprise [3], or the art of creating value to organisations by leveraging intangible assets[4]. Accordingly, knowledge is defined as a justified belief that increases an entity’s capacity for effective action [1, 5]. Knowledge can be further viewed as a state of mind; an object; a process; a condition of having access to information; or a capability [1]. Thus, knowledge and knowledge management are both multi-faceted concepts and activities, and strongly related to cultural background [6]. In this context, Srinivas [7] indicates that the theories of knowledge management generated—based on western cultural background—are not necessarily applicable to eastern cultures such as India, because the way in which an organisation operates is quite different in India. As a result of reviewing existing literature, in this study, the nine constructs (Collaboration (C), Mutual Trust (MT), Learning (L), Leadership (LS), Incentives & Rewards(IR), Non-Centralisation (NC), and T-shaped Skills(TSS), to the dependent variable Information Communications Technologies (ICT)) of knowledge management are analysed for the four major cities(Chennai, Coimbatore, Madurai, and Villupuram) in India to understand business views towards these constructs.
　　 2. Literature Review
　　Previous studies have indicated that when organisations implement their knowledge management systems, some obstacles and enablers exist in the process. For example, many firms actively limit knowledge sharing because of the threats associated with industrial espionage, as well as concerns about diverting or overloading employees’ work-related attention [8]. Once knowledge sharing is limited across an organisation, the likelihood increases that knowledge gaps will arise, and these gaps are likely to produce less-than-desirable work outcomes [6]. Prior studies have attempted to provide guidelines and successful experiences to reduce obstacles encountered in implementing a KM system. For instance, there are four areas that need to be focused on when implementing knowledge management systems. These areas include understanding who the knowledge sources are, measuring where and how knowledge flows, getting knowledge to flow more rapidly and freely, and reinforcing knowledge with supportive relationships [9]. Additionally, a review of the literature reveals that there are many enablers that are known to influence knowledge management practices[10] and these enablers can be broadly classified into either a social or technical perspective. This study is concerned with social perspective of KM and the technical perspective is beyond the scope of this study.
　　The social perspective of knowledge management enablers plays an important role and has been widely acknowledged [11]. These enablers are further discussed below.
　　One of the enablers is collaboration. Collaboration is an important feature in knowledge management adoption. It is defined as the degree to which people in a group actively assist one another in their tasks [12]. A collaborative culture in the workplace influences knowledge management as it allows for increased levels of knowledge exchange—a prerequisite for knowledge creation. Another enabler is mutual trust. It exists in an organisation when its members believe in the integrity, character and ability of each other [13]. Trust has been an important factor in high performance teams as explained in organizational behaviour literature. The existence of mutual trust in an organisation facilitates open, substantive and influential knowledge exchange.
　　A further important enabler is learning. It is defined as any relatively permanent change in behavior that occurs as a result of experience [13]. In addition to the above, leadership is often stated to be a driver for effective knowledge management in organizations [14]. Leadership is defined as the ability to influence and develop individuals and teams to achieve goals that have been set by the organization [13]. Adequate leadership can exert substantial influence on organisational members’ knowledge creation activities.
　　Organisational incentives and rewards that encourage knowledge management activities amongst employees play an important role as an enabler [15]. Incentives are something that have the ability to incite determination or action in employees within an organization [13]. Rewards, on the other hand, can be broadly categorised as being either extrinsic or intrinsic. Extrinsic rewards are positively valued work outcomes that are given to the employee in the work setting, whilst intrinsic rewards are positively valued work outcomes that are received by the employee directly as a result of task performance [16].
　　Organisational structure plays an important role as it may either encourage or inhibit knowledge management. The structure of the organisation impacts the way in which organisations conduct their operations, and in doing so, affects how knowledge is created and shared amongst employees [12]. One enabler to KM is the level of non-centralisation. This refers to the degree to which decision making is non-concentrated at a single point, normally at higher levels of management in the organisation (Robbins et al. 2001; Wood et al. 1998). The concept of centralisation includes only formal authority, that is, rights inherent in one’s position.
　　Another structural enabler is the level of non-formalisation. It refers to the written documentation of rules, procedures and policies to guide behaviour and decision making in organizations[16]. When an organisation is highly formalised, employees would then have little discretion over what is to be done, when it is to be done and how they should do it, resulting in consistent and uniform output [13]. However, formalisation impedes knowledge management activities. Most teams are composed of individuals who operate from a base of deeply specialised knowledge [17]. These individuals need mechanisms to translate across the different“languages” that exists in organizations [18]. This brings rise to the need for employees with T-shaped skills—that is, skills that are both deep and broad [19]. Employees who possess T-shaped skills not only have a deep knowledge of a particular discipline (e.g., financial auditing), but also about how their discipline interacts with other disciplines (e.g., risk analysis, investment analysis and derivatives). Iansiti (1993) states that the deep knowledge in a particular discipline is aptly represented by the vertical stroke of the ‘T’, whilst knowledge of how this discipline interacts with other disciplines is represented by the horizontal top stroke of the “T” [20].
　　Lastly, but not less important as an enabler, is IT infrastructure. It plays an important role in knowledge management. Technology infrastructure includes information technology and its capabilities which are considered to assist organisations to get work done, and to effectively manage knowledge that the organisation possesses [21]. It has been found that information technology infrastructure plays a crucial role in knowledge management as it allows for easy knowledge acquisition and facilitates timely communication amongst employees. These aspects were investigated in this study for their applicability in the Indian context.
　　 3. Research Methodology
　　A multiple case study was conducted to identify the possible enablers for organisations when implementing their KMS. Twenty organisations were chosen in each of the Indian cities: Chennai; Coimbatore; Madurai; and Villupuram. A total number of 80 local and international organisations were interviewed with focus given to the exploration of factors that influence KMS implementation. Hence, the unit of analysis is“organization”.
　　The four cities can then be grouped into two main categories for further analysis: metropolitan and regional cities. The metropolitan group includes Chennai and Coimbatore, and the regional group includes Madurai and Villupuram. In later sections of this study, it is found that even in the same nation, the results of data analysis can significantly vary from one group to another. Subsequent to the findings of the qualitative1 data gathered through multiple case study and model building, a survey was administered in the same Indian cities to further examine and confirm the results of the case study. The survey either adapted measures that had been validated by other researchers, or converted the definitions of constructs into a questionnaire. A five-point Likert scale was used to measure the extent that each factor influenced the respondents’ organisations. Opinions from 400 respondents (100 in each city) in the domain of KMS implementation, with a focus on what the enablers of KMS were collected and analysed.
　　The nine KM constructs (Collaboration (C), Mutual Trust (MT), Learning (L), Leadership (LS), Incentives& Rewards (IR), Non-Centralisation (NC), and T-shaped Skills (TSS), to the dependent variable Information Communications Technologies (ICT) are based on a review of the literature and a multiple case study with 80 organisations in four Indian cities. These cities are located in metropolitan and regional areas with various population sizes, social structures and history.
　　 4. Data Analysis and Discussions
　　A multiple case study was conducted to identify the possible enablers for organisations when implementing 1Finding of qualitative analysis is already published previously by the authors and not reported here. their KMS. Twenty organisations were chosen in each of the Indian cities: Chennai; Coimbatore; Madurai; and Villupuram. A total number of 80 local and international organisations were interviewed with focus given to the exploration of factors that influence KMS implementation. Hence, the unit of analysis is“organization”.
　　Basic information of the interviewees is summarised in Tables 1-2. Table 1 indicates that the interviewees cover three main job levels: senior executives; middle managers; and operational staff. Table 2 summarises the seniority of interviewees. The percentage of interviewees who worked in the organisations for more than two years is over 90%. This assists the interviewers in better understanding the organisational environment and its working culture.
　　The distribution of industry for the 80 organisations that the interviewees worked at was adopted a classification scheme of industries from the Australian Bureau of Statistics [22]. The dominating industries included manufacturing (22.50%), finance and insurance (20%), and information technology (10%). The frequency of distribution represents the economic and social structure of the four Indian cities.
　　Table 3 builds the linkages between the body of literature and the case study. The enablers of KMS that have been discussed in previous literature are summarised in this table. The enablers were all identified throughout the multiple case studies. The results are illustrated in Table 4.
　　Before using the AMOS to conduct the confirmatory factor analysis, data were analyzed through descriptive analysis to provide the reader better understanding of the data. The results of the descriptive analysis are presented in this section. Table 4 illustrates the demographic information of the survey respondents.
　　


　　


　　Prior to conducting the higher level statistical analysis to understand the relationship of the independent determinants Collaboration (C), Mutual Trust (MT), Learning (L), Leadership (LS), Incentives& Rewards (IR), Non-Centralisation (NC), and T-shaped Skills (TSS), to the dependent variable Information Communications Technologies (ICT), a reliability analysis was conducted on the instrument. Theinstrument value of Cronbach’s Alpha was above 0.9, and according to Hair (2006), such a value for the Cronbach’s Alpha corresponds to a very high value of reliability. A summary analysis for the composite variable is displayed in Table 5.
　　A model is considered to be a good fit if the difference between the sample variances and covariances, and the implied variances and covariances derived from the parameter estimates, is small(Holmes-Smith, 2000). The number of “fit” statistics has been used by researchers to assess how well the model fits the data (Byrne, 2001; Hair et al., 2006).
　　AMOS version 18 was used to establish the confirmatory factor analysis. Table 6 provides the summary of factor loadings and their respective values of the Indices.
　　


　　


　　In Table 6, the analysis was conducted in the context of nine constructs of knowledge management Collaboration, Mutual Trust, Learning, Leadership, Incentives & Rewards, Centralisation, Formalisation, T-shaped Skills, and Information Technology Infrastructure. Confirmatory factor analysis to understand the views of participants towards nine constructs of the knowledge management in the Indian region. Results show that square multiple correlations(SMC) for all the three cities to individual constructs of knowledge management in some cases are not measuring the same constructs. For example, SMC for all three cities (Chennai, Coimbatore, and Villupuram) for the construct Collaboration is very high (values range from 0.7 to 0.9). Whereas SMC for Madurai is only 0.4, this suggests that participants from the Madurai city see Collaboration differently as compare to other three cities. However, in the case of construct“Mutual Trust”, two cities Chennai and Coimbatore are highly correlated. This suggests that participants in these cities see the construct “Mutual Trust” quite
　　Table 6 Summary of confirmatory factor analysis for the enabler of knowledge management.
　　


　　 identical. Therefore, city Coimbatore does not really uniquely contribute to the construct “Mutual Trust”. This outcome was also supported by the SMC values, for cities Chennai, Madurai, and Vilupuram SMC values range from 0.5 to 0.9 and for city Coimbatore the value of SMC is less than 0.01.
　　According to Ref. [23], if the value of SMC is greater than 0.5, this indicates that the item is a good measure of the construct. Therefore, we conclude for city Maduraithe constructs Collaboration, Incentive & Rewards; for city Coimbatorethe constructs Mutual Trust and Learning, Centralisation, and Leadership; and for city Chennai the constructs Mutual Trust; are not good measures of the constructs respectively.
　　From the above confirmatory factor analysis, it can be concluded that Villupuram city views all the nine constructs as strong measures of knowledge management. Madurai city participants view constructs Collaborations, and Incentive & Rewards are not strong representative of knowledge management. Coimbatore city participants considered constructs such as Mutual Trust, Learning, Leadership and Centralization do not provide strong measure of the respective constructs. Chennai city participants, view Mutual Trust construct are not strong representation. Therefore, Coimbatore participants have quite different views about the constructs of knowledge management as compare to their counter parts in Villupuram, Madurai, and Chennai. However, the reason behind this difference was not in the scope of the research. One of the implications to the business community in Indian region is that there is consistent understanding and recognitions in the value of knowledge management across the four cities participated in the research study. Participants also provided clear evidences that top and middle m management should not try to implement same policies and procedures across the subcontinent as study clearly demonstrate some differences in the views and perception of role of knowledge management among various business processes and models.
　　


　　 5. Conclusions
　　For most of the nine constructs for the knowledge management in the three Indian cities represent good measurements for the constructs. The implications of such a finding would be that all the three cities view nine knowledge management constructs similarly. Future research can concentrate on finding out the reasons behind this and to be able to explain why there were a few differences exist as mentioned above. The limitation, to the researchers’ best knowledge, this study is the first study of this nature and needs further investigation before generalization of the finding of the study.
　　 References
　　[1] M. Alavi, D.E. Leidner, Review: knowledge management and knowledge management systems: Conceptual foundations and research issues, MIS Quarterly 25 (2001) 107-136.
　　[2] T.H. Davenport, L. Prusak, Working Knowledge, Harvard Business School Press, Boston, 1998.
　　[3] K.M. Wiig, Knowledge management: An introduction and perspective, Journal of Knowledge Management 1 (1997) 6-14.
　　[4] K. Sveiby, The New Organisational Wealth: Managing and Measuring Knowledge-Based Assets, Berret-Koehler Publishers, San Francisco, 1997.
　　[5] G.P. Huber, Transfer of knowledge in knowledge management systems: Unexplored issues and suggested studies, European Journal of Information Systems 10(2001) 72-79.
　　[6] G.-W. Bock, et al., Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organisational climate, MIS Quarterly 29 (2005) 87-111.
　　[7] N. Srinivas, Mimicry and revival: the transfer and transformation of management knowledge to India 1959-1990, International Studies of Management and Organisation 38 (2009) 38-57.
　　[8] D. Constant, et al., The kindness of strangers, Organization Science 7 (1996) 119-135.
　　[9] R. Emelo, The future of knowledge management, Chief Learning Officer, 44-47, May 2009.
　　[10] G.G.G. Gan, Knowledge management practices in multimedia super corridor status companies in Malaysia, Master of Business Information Technology, Business Faculty, University of Southern Queensland, 2006.
　　[11] P. Smith, Knowledge management: people are important, Journal of Knowledge Management Practice, 2004.
　　[12] H. Lee, B. Choi, Knowledge management enablers, processes and organisational knowledge: An integrative view and empirical investigation, Journal of Management Information Systems 20 (2003) 179-228.
　　[13] S. Robbins, et al., Organisational behaviour: leading and managing in Australia and New Zealand, 3rd ed., Prentice Hall, Malaysia, 2001.
　　[14] M. Khalifa, V. Liu, Determinants of sccessful knowledge management programs, Electronic Journal of Knowledge Management 1 (2003) 103-112.
　　[15] S.-H. Yu, et al., Linking organisational knowledge management drivers to knowledge management performance: An exploratory study, in: 37th Hawaii International Conference on System Sciences, Hawaii, USA, 2004.
　　[16] J. Wood, et al., Organisational Behaviour: An Asia-Pacfic Perspective, John Wiley, Australia, 1998.
　　[17] C. Davvy, Recipients: the key to information transfer, Knowledge Management Research and Practice 4 (2006) 17-25.
　　[18] D. Ford, D. Staples, Perceived value of knowledge: The potential informer’s perception, Knowledge Management Research and Practice 4 (2006) 3-16.
　　[19] D. Leonard-Barton, Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Harvard Business School Press, Boston, 1995.
　　[20] M. Iansiti, Real-world R&D: jumping the product generation gap, Harvard Business Review, 1993, pp. 138-147.
　　[21] C. Holsapple, The inseparability of modern knowledge management and computer-based technology, Journal of Knowledge Management 9 (2005) 45-52.
　　[22] ABS, Australian and New Zealand standard industrial classification, available online at: http://www.abs.gov.au/AUSSTATS/[email protected]/66f306f50 3e529a5ca25697e0017661f/acc2f9615290d8eeca25697e0 018faf6!OpenDocument, October 02, 1993.
　　[23] P. Holmes-Smith, Structural Equation Modeling from the Fundamentas to Advanced Topics: ACSPRI Course Notes, Scsool Research, Evaluation and Measurement Services, Victoria, Australia, 2009.