HIERARCHICAL MODEL

HIERARCHICAL MODEL FOR SUCCESSFUL KNOWLEDGE MANAGEMENT: A STUDY OF SMALL AND MEDIUM ENTERPRISES

DEEPANKAR CHAKRABARTI
Apeejay School of Management
Sector 8, Dwarka Institutional Area, New Delhi 100 077, India
E-mail:dchakrabarti.asm@gmail.com

P.K. GUPTA
Center for Management Studies, Jamia Millia Islamia,
Maulana Mohammed Ali Marg, New Delhi 110 025, India
E-mail: pkgcms@rediffmail.com
Enabling organizations to capture, share, and apply collective experience and know-how of people is emerging as fundamental to competing in the knowledge economy. There is a growing recognition in the business community about the importance of Knowledge Management (KM). As a result, there is growing enthusiasm and activity centered on Knowledge Management. Some organizations have taken initiatives to understand and manage this critical resource. But, small and medium enterprises (SMEs) still have not approached Knowledge Management activity formally or deliberately. The cause for this indifference towards Knowledge Management could be that most organizations are still struggling to comprehend the Knowledge Management concept. The reason for this confusion may be attributed to a gap between the emerging concept of Knowledge Management and the lack of understanding about it particularly in terms of awareness and perception. Awareness and perception about Knowledge Management is associated with risks and rewards. To bridge the gap, the fundamental issue of identifying salient characteristics of Knowledge Management phenomena i.e. awareness and perception needs to be addressed. This paper presents a hierarchical model that identifies attributes necessary for a successful Knowledge Management initiative. This paper uses the Analytic Hierarchy Process (AHP) method to ascertain the relative importance of the attributes towards a successful Knowledge Management implementation.

1. Introduction

Knowledge Management is based on the strategic principle that an organization’s most valued resource is the ‘knowledge’ of its people. As concept, Knowledge Management is new, but it essentially flows from the principle of ‘human resources’, an idea that exists in history from the times of the industrial revolution in Europe. Today, as businesses all over the world have changed forms, managers’ ways of handling human resource in organizations have also changed. The most critical factor in this ‘change’ is that we have added a new dimension to human resource by focusing not just on the people of an organization, but also on the knowledge that these people possess.
This change in focus from people to their knowledge is a result of the changing nature of the global world that is witnessing radical changes in organizations and in the society as a whole. Knowledge Management acknowledges the fact that most of the jobs today involve several levels of “knowledge work”. Hence, the people working for an organization are all “knowledge workers” to some degree or the other.
This paper evolves a model for successful Knowledge Management using the Analytic Hierarchy Process (AHP). AHP is an effective quantitative tool that helps to prioritize problems, issues or variables based on relevant criteria and alternatives. The applicability and usefulness of the AHP approach as a multi-criteria decision-making tool is well acknowledged in the management literature. The present work has adopted this tool for segregating a few critical aspects of Knowledge Management from the inconsequential many, so that organizations depending on their goal orientation of a successful Knowledge Management could focus only on those dimensions that are crucial for their success instead of spending a large quantity of time, effort and resources in mindlessly concentrating on peripheral issues.
Hence the objectives of this paper are two-fold:
• To identify the criteria for the AHP model with respect to issues relating to successful Knowledge Management within SMEs.
• To present an AHP framework for absolute measurement of priorities in order to critically evaluate the issues relating to successful Knowledge Management within SMEs.

2. Literature Review

2.1 Need for Knowledge Management

Since organizations today are hiring ‘minds’ more than ‘hands’, the need for leveraging the value of knowledge has increased. As a result, knowledge is being treated systematically much like other tangible resources. Several organizations are exploring the field of Knowledge Management in order to improve and sustain their competitiveness. Organizations need to be cognizant and aware of the factors that will influence the success of a Knowledge Management initiative. Ignorance and oversight of the necessary important factors will most likely hinder an organization’s efforts to realize its full benefit (Wong 2005).
The basic assumption of Knowledge Management is that organizations that manage their knowledge better will deal more successfully with the challenges of the new business environment. Knowledge Management is considered to be central to achieving process and product improvement through innovation, competitive advantage, executive decision-making and organizational adaptation and renewal (Earl, 2001).
In the fast changing business world of today, innovation has become the mainstay of every organization. The complexity of innovation has also been increased by growth in the amount of knowledge available to organizations as basis for innovation. Innovation is extremely dependent on the availability of knowledge and therefore the complexity created by the explosion of richness and reach of knowledge has to be identified and managed to ensure successful innovation (Adams and Lamont, 2003; Cardinal et al., 2001; Darroch and McNaughton, 2002; Pyka, 2002; Shani et al., 2003).
Further, Knowledge Management is considered to be central to achieving process and product improvement, executive decision-making and organizational adaptation and renewal (Earl, 2001), However, while there is widespread agreement on the importance of knowledge with respect to the struggle for economic success, there are differences among researchers and practitioners alike in what constitutes useful knowledge and the ways in which it should be managed (Handzic et.al, 2008). This is because Knowledge Management theory is relatively new, prone to misconceptions and misappropriations and there are many unresolved issues that need to be dealt with before Knowledge Management evolves into a mature discipline.
As organizations strive to improve their business performance and capacity for innovation, their attention is increasingly focused on how they manage knowledge. As per the CEN workshop Agreement, (2004) successful Knowledge Management implementations in business settings prioritize attention on soft issues - including human and cultural aspects, personal motivations, change management methodologies, new and improved business processes enabling multidisciplinary knowledge sharing, communication and collaboration - and see technology as an enabler.

2.2 Successful Knowledge Management

Knowledge Management is a complex discipline with many factors contributing to successful implementation. The creation of understanding of the organization’s knowledge resources should be addressed in the first place. Assessing knowledge resources leads to shaping of knowledge agenda to achieve sustainable results in alignment with the business strategy. It is critical that the Knowledge Management strategy should be tied to the business strategy. Knowledge Management should never be implemented as an end in itself (Plessis, 2007).
Knowledge Management implementation is a multi-faceted approach, comprising many organizational elements like technology, human resource practices, organizational structure and culture (Plessis, 2007). It is essential to align culture, technology, infrastructure and measurement. Thus it requires a holistic approach. Plessis mentions the following as critical elements towards a successful Knowledge Management implementation: an effective technological infrastructure; integrating the technology infrastructure into everyday processes; having an enterprise-wide knowledge structure or taxonomy; a Knowledge Management strategy; Knowledge Management metrics of success and identification of inhibitors of knowledge usage. Plessis suggest that the solutions to Knowledge Management implementations have to be a mix of cultural, organizational, process, management and technology initiatives. The challenge is to select and combine the methods and approaches available, and harness them to address the organization’s business needs.
Recently (He and Wei, 2009) discussed that Knowledge Management System users' beliefs are contextually differentiated, and a distinction between knowledge contribution and knowledge-seeking behaviors and an adequate emphasis on their variance in terms of user belief is needed. Wen (2009) developed an AHP model for the measurement of the effectiveness of Knowledge Management in Taiwanese high-tech enterprises. Yang et al. (2009) identified crucial Knowledge Management enablers and examined their impacts on organizational performance.
Chen et al. (2009) proposed an approach of measuring a technology university’s Knowledge Management performance from competitive perspective. Their approach integrates analytical network process with balanced scorecard that contains four perspectives, including customer perspective, internal business perspective, innovation and learning perspective, and financial perspective. Cheng et al. (2009) investigates the key factors for Knowledge Management in the national government of Taiwan. Their study relied on two distinctive dimensions: core KM processes (organizational missions and values, IT applications, documentation, process management, and human resource) and KM performance (knowledge capture and transformation, business performance, and knowledge sharing and value addition).
Lai et al (2009) research concluded that Knowledge Management Systems with a higher level of knowledge map fit and personalization will satisfy employees directly or indirectly through the mediation effects of increased perceptions of ease of use and usefulness of Knowledge Management Systems.

2.3 Risks & rewards of Knowledge Management in SMEs

It is true that (Dawson, 2000) organizations struggle to define their Knowledge Management initiatives. Many Knowledge Management initiatives are born from a desire to "find out what knowledge we have out there" or to "break down traditional barriers (e.g. regional, departmental) that prevent sharing knowledge".
Knowledge Management initiatives often deliver significant benefits yet fail to meet expectations (Boisot, 1998). There are several risks arising from unrealistic expectations. Perceived failure despite material successes is a substantial risk. Perhaps more severe (e.g. cost and time) is the risk that the initiative becomes over-engineered in a futile attempt to meet unrealistic expectations.
Even when the initiative is well-defined, expectations are reasonable and ownership of various aspects of the initiative clearly set out, it is often still fiendishly hard to set tangible success measures (Boisot, 1998). Boisot describes that success is largely intangible and hard to measure. He states as a minimum, it should be possible to set tangible milestones on the initiative itself and measure whether or not the activities of the initiative are being achieved on time and on budget without reducing scope.
Marshall, Prusak & Shpilberg (1996) define risk as the potential for (financial) loss due to the reduction in value of a tangible or intangible asset, or an increase in the value of a liability, due to the crystallization of an event. In this definition of ‘risk’, they say that (financial) is in brackets because not all assets are shown on the balance sheet. Not all of them are reflected in the way organizations record how viable they are. However, whilst a lot of the maintenance of knowledge and information in organizations, if lost, does not have a financial impact in an overt way clearly still has a significant financial impact.
There are also, of course, the intangible assets of reputation, brand, intellectual property, intellectual assets and know-how, most of which are not recorded on the balance sheet, (though they may be reflected in the market value). The loss of these assets will not go directly through the profit and loss, and hit the bottom line, but will have great impact on the organization as a going concern (Marshall, Prusak & Shpilberg 1996).
Davenport & Prusak (1998) argued that people create knowledge through a kind of social knowledge production system also referred to as knowledge processing. Knowledge processing includes things like problem detection, problem solving, learning, training, innovation; as well as forming, testing and evaluating new ideas. Once produced in these ways, shared knowledge in organizations eventually shows up in use in the form of behaviors. Outcomes of all kinds then follow, some good and some bad. If knowledge processing system in place is dysfunctional, closed, or hidden from view, bad ideas might survive longer than they should – even at stakeholders’ expense.
Navarro (2005) says that not all knowledge-processing systems are the same, and some fetch better results than others. In this regard, then, managing corporate knowledge processing should be seen as a form of risk management, very much akin to the treatment of concerns related to social responsibility, sustainability and the environment, employee and product safety, quality, regulatory compliance, audits, governance, and ethics (Feng, Chen, & Liou 2005).
Chatzoglou and Diamantidis (2009) conducted research that focused on the IT impact on firm’s non-.financial IT risk. Their results indicate that IT risk factors affect mainly coordination and partially information ability but not productivity. Furthermore, the most significant risk factors affecting business performance are management ability, information integrity, controllability and exclusivity.
We argue that SMEs may face the following risks to Knowledge Management:
• Inability to earmark well-defined areas that provide knowledge for management
• Unrealistic expectations of stakeholders
• Inappropriate measures for tangible success
• Loss of knowledge without visible financial impact
• Absence of financial reflections of intangible assets like know-how
• Presence of dysfunctional knowledge processing systems
• Inertia or lack of motivation on part of employees to knowledge sharing
From the above list it can only be implied that Knowledge Management in SMEs is evolving into a form of risk processing which can be considered of three types – technical, human, and financial.
Knowledge sharing is a critical step for successful Knowledge Management. However, sharing knowledge not only requires time and effort of a knowledge worker but also reduces the unique value or power that the worker enjoys in the organization. Therefore, for successful knowledge sharing, a reward system is needed to compensate knowledge-sharing activities (Ahn & Chang, 2004).
Rewards and incentives are crucial to the success of Knowledge Management (Plessis, 2007). It creates a climate of co-operation, learning and innovation. Incentives and rewards create and support positive behaviors required for Knowledge Management. Plessis (2007) suggests that knowledge creation, sharing, harvesting and leveraging can be encouraged by tying it to job evaluations and performance measurement. In general, recognition for participation is essential. Some organizations are wary of monetary rewards, and rather embed Knowledge Management activities as a cultural norm that has its own intrinsic value. Plessis (2007) is of the opinion in many organizations a culture of knowledge hoarding, or ‘‘knowledge is power’’ prevails. The reward and incentive system for Knowledge Management should consist of push and pull rewards, e.g. rewarding people as part of their performance appraisals according to participation in the program (push) and incentivizing people to use the knowledge base to provide a platform for their innovative ideas, i.e. providing them and their ideas with visibility in the organization (pull). Thus, rewards are also of three types – technical, human, and financial.

2.4 Knowledge Management in SMEs

A study of the involvement of Knowledge Management in businesses also reflects another crucial aspect, i.e. it’s being valued not just in large organizations, but also in small and medium enterprises (SMEs) (Groom & David 2001). The small business, by its very nature, usually demonstrates a high degree of informal sharing of tacit knowledge. Unlike large businesses, the stakes are high for a handful of people who may have preferred a start-up venture to a comfortable job in a multi-national company. Most people who contribute to the growth of such enterprises exhibit a high level of assimilation and integration of knowledge from diverse sources. Even within the set-up, a lot of sharing happens as far as knowledge is concerned. In exceptional cases where knowledge is not openly shared in the enterprise, that becomes one of the primary objectives of a Knowledge Management strategy (Groom & David 2001).
Through the generation of new knowledge in the form of lessons learned, small entrepreneurs can avoid potentially costly future mistakes (Carlsen & Skaret 1999; Groom & David 2001). Creating new knowledge, within small teams whose members share a mutual context of experience and collaborate on a joint task bonded by a common sense of purpose and the need to know what the other ‘community members’ know, can lead to profitable product and service innovations.
The less involved SMEs are not fully convinced of the advantages of absorbing new knowledge for innovative purposes. For them, the absorption of new knowledge is interesting only if it can be easily obtained and will lead to more efficiency, a higher turnover or to competitive advantages. These advantages should be clear and easy to attain, otherwise, these SMEs are likely to focus on their traditional way of working. There are differing degrees of awareness and perception amongst the owner/managers of the small businesses.
To stimulate knowledge absorption in SMEs, the focus should hence be on the bottlenecks and resistance perceived and demonstrated by less involved SMEs. Thus, there are risks which are intrinsically linked to Knowledge Management which looks at how people know what they know, how they can learn from past experiences and how innovative approaches can help uncover and transmit knowledge in a rapidly changing environment.
If risk, its management, and knowledge and its management are viewed together, the potential rewards could be enormous in terms of cultural cohesion, harnessing know how, sharing skills and experiences, and this could mean getting tangible benefits from intellectual capital (Davenport & Prusak, 1998).

3. Hierarchical Model to Successful Knowledge Management
Any successful managerial implementation requires being aware of and having the information about the issues/problem. Similarly, in case of Knowledge Management it is important to have the information about the influential factors for the successful implementation of Knowledge Management. Not all of the influential factors are equally important for the successful Knowledge Management. For this reason we have used the AHP frame work for finding the importance of the influential factors. AHP has been widely used as an analytical tool for decisions related to Knowledge Management. Recent work by Wen (2009) in presenting an effectiveness measurement model for Knowledge Management using AHP is a contribution in this direction.
In AHP the complex decision is structured into a hierarchy descending from an overall objective to various influential ‘factors’, ‘sub-factors’, and so on, until the lowest level. The objective or the overall goal of the decision is represented at the top level of the hierarchy. The factors and sub-factors contributing to the decision are represented at the intermediate levels. Finally, the decision alternatives or selection choices are laid down at the last level of the hierarchy. According to Saaty (2000), a hierarchy can be constructed by creative thinking, recollection, and using people's perspectives. It should be noted that there is no set procedures for generating the levels to be included in the hierarchy. The structure of the hierarchy depends upon the nature or type of managerial decisions. Also, the number of the levels in a hierarchy depends on the complexity of the problem being analyzed and the degree of detail of the problem that an analyst requires to solve. As such, the hierarchy representation of a system may vary from one person to another.
In the present study the influential factors are determined via widespread investigations and consultations with various experts, and owner/managers of SMEs. Synthesizing the literature review from (Chang et al., 2009; Chatzoglou, and Diamantidis, 2009; Chen et al. 2009; He and Wei, 2009; Lai et al., 2009; Wen, 2009; Yang and Marlow, 2009), the opinions of the experts and owner/managers are employed to obtain the two main factors: awareness and perception. From these factors, 16 influential sub-factors for the successful implementation of Knowledge Management are briefly described as follows (refer to Figure 1 for complete hierarchical structure):
• Awareness (C1). This factor includes two sub-factors, C11: risks; C12: rewards. The sub-factor C11 further includes three sub-factors, C111: technical: C112: human; C113: financial. Similarly sub-factor C12 includes three sub-factors, C121: technical: C122: human; C123: financial
• Perception (C2). This factor includes two sub-factors, C21: risks; C22: rewards. The sub-factor C21 further includes three sub-factors, C211: technical: C212: human; C213: financial. Similarly sub-factor C22 includes three sub-factors, C221: technical: C222: human; C223: financial
According to the AHP methodology, weights (priorities) can be determined using a pair-wise comparison within each pair of factors. To determine the relative weights, owner/managers can be asked to make pair-wise comparisons using a 1–9 preference scale (Saaty, 2000). However, in the present study for the pair-wise comparison, we have relied on actual data, that is, the data extracted from the questionnaire survey. The advantage of using actual data (quantitative data) over preference scale for pair-wise comparison eliminates the need for consistency checks (Saaty, 2000).











Figure 1: Hierarchical Model to Successful Knowledge Management

4. Methodology
Data Source: The research used both secondary and primary data. An extensive literature survey, opinion from experts and owner/managers was undertaken, which helped in framing the questionnaire for the primary data collection. The focus of the study was on primary data.
Research approach: The survey method was used for the study. Our primary data has been gathered using questionnaire technique. Our target population is all small firms in the National Capital Territory of Delhi (India) with turnover ranging from Rs. 50 million to Rs. 250 million and employment levels between 15 and 50 employees. Specifically, we are targeting the owners or top managers at these firms. The reason for targeting owners/top managers lies in the fact that for successful implementation of Knowledge Management the first step is to gain the support and commitment of top management to the initiative (Chang et al., 2009). Yeh et al. (2006) also suggests that for the successful implementation of Knowledge Management the most important part is to obtain the support of the top managers.
For the purposes of this research, we used a questionnaire survey. The questionnaire included 60 questions in four sections such as:
(i) Awareness of Knowledge Management
(ii) Perception about Knowledge Management
(iii) Risk associated with Knowledge Management
(iv) Rewards of Knowledge Management
Contact Method: The questionnaires were sent via email and were telephonically followed up.
Sample Size: Amongst the 4263 companies (as per Centre for Monitoring Indian Economy Prowess database) that belonged to the criteria in the entire country, 1039 such companies were located in the National Capital Region of Delhi, which included New Delhi, Delhi, Faridabad, Gurgaon, Ghaziabad and NOIDA. Due care has been taken to include only those companies that made the sample more representative thus, e-mail questionnaires were sent to 500 amongst these 1039 companies. 119 responses were received that formed the sample for the study. This is a 23.8% response rate, which is acceptable.
Data Analysis: The data so collected were analyzed with the AHP techniques to arrive at weights (priorities).
The following procedure has been adopted on the collected questionnaire survey data for pair-wise comparison of AHP. Firstly, we calculated the average value of 119 responses (preferences based on 5- point Likert scale) obtained for each question. These average values were calculated to describe the central location of an entire distribution of responses. Then for every said category we calculated the Composite Preference Value (out of 5) using the following relation:
Composite Preference Value (CPF) = (Corrected Value ∕ Maximum Value) x 5
where, Calculated value = sum of the average values for the questions considered in a category.
Maximum value = sum of the highest possible values that a respondent can choose for the questions considered in a category.

5. Research Findings

The comparison matrices showing the measure of each attributes’ relative importance with respect to the overall objective is summarized in Table 1. For the pair wise comparison of the attributes and sub attributes, we relied on inputs obtained from the survey.
We consider two attributes as important for successful Knowledge Management: awareness and perception. The picture emerges from the pair-wise comparison suggest for successful Knowledge Management, awareness (53.12%) is more important over perception (46.88%). Thus it is important to generate awareness about Knowledge Management and its benefits amongst owner/managers.
According to the hierarchical model considered in the present study, awareness and perception has been further decomposed into risks and rewards for capturing reality. On pair-wise comparison of risks and rewards corresponding to awareness; rewards (52.14%) contribute more than risk (47.86%) to enhance awareness. It may be noted that when the same risks and rewards are compared pair-wise corresponding to perception, both risks (49.76%) and rewards (50.24%) weigh almost equally. That means with better understanding about Knowledge Management, one can distinctly assess the risks and rewards.
As we have discussed earlier risks and rewards corresponding to awareness and perception are of three types, viz. technical, human and financial. The picture that emerges in pair wise comparison of the said types corresponding to risks (awareness); technical (35.87%) is the most important component of risk in comparison to human (30.16%) and financial (33.97%). This means that owner/managers of SMEs are more concerned with technical implementations involved in Knowledge Management over the fear of financial losses and human attritions.
Similarly, in comparison of the said types corresponding to rewards (awareness); technical (39.06%) dominates the other types, i.e. human (36.29%) and financial (24.65%). It implies that owner/managers of SMEs consider investments in technology as the ultimate solution to all problems.
Further, in pair wise comparison of the said types corresponding to risks (perception); technical (34.93%) and financial (34.93%) are almost equally important component of risk and dominates the other type, i.e. human (30.24%). This means that owner/managers of SMEs are more concerned with technical implementations and corresponding financial investments involved in Knowledge Management over the loss of manpower.
On the contrary, it is interesting to note that when the same said types are compared pair wise corresponding to rewards (perception); human (37.28%) is the most important component of reward in comparison to technical (34.68%) and financial (28.04%). This means that owner/managers of SMEs are prepared to reward employees for sharing knowledge for the benefit of the organization.
In what follows next, we use the bottom up approach to get the global relevance of technical, human and financial aspects towards Successful Knowledge Management. Towards this we multiply the local relevance of technical, human and financial corresponding to sub-attributes with the local relevance of the sub-attributes corresponding to its parent attribute. This is further multiplied with the local relevance of the parent attribute corresponding to the overall objective. Finally, the obtained relevance’s of technical, human and financial aspects corresponding to the main attributes, i.e. awareness and perception are added to get the global relevance. Finally, the picture emerges for the global relevance of technical, human and financial aspects incorporating relevance of the main attributes i.e., awareness and perception as well as their sub-attributes risk and rewards; technical (36.23%) is most important followed by human (33.55%) and financial (30.22%). Thus, the key to Successful Knowledge Management rests on appropriate awareness and perception about technical risks and rewards. In other words, owner/managers of SMEs need to prioritize their efforts towards Successful Knowledge Management in terms of technical, human and financial aspects necessarily in that order.




Table 1: Pair wise Comparisons

Successful Knowledge Management
Awareness Perception Weights
Awareness 1 1.1332172 0.5312
Perception 0.882443366 1 0.4688
1.882443366 2.1332172

Awareness
Risk Reward Weights
Risk 1 0.918024835 0.4786
Reward 1.08929515 1 0.5214
2.08929515 1.918024835
Perception
Risk Reward Weights
Risk 1 0.990430753 0.4976
Reward 1.009661702 1 0.5024
2.009661702 1.990430753
Risk Awareness
Human Technical Financial Weights
Human 1 0.840921979 0.888020008 0.3016
Technical 1.189170964 1 1.056007609 0.3587
Financial 1.126100754 0.946962874 1 0.3397
3.315271718 2.787884853 2.944027616
Reward Awareness
Human Technical Financial Weights
Human 1 0.928970312 1.472474371 0.3629
Technical 1.076460666 1 1.585060742 0.3906
Financial 0.679128968 0.630890649 1 0.2465
2.755589633 2.559860961 4.057535113
Risk Perception
Human Technical Financial Weights
Human 1 0.868131293 0.865595744 0.3024
Technical 1.151899497 1 0.997079302 0.3483
Financial 1.155273702 1.002929253 1 0.3493
3.307173199 2.871060547 2.862675046
Reward Perception
Human Technical Financial Weights
Human 1 1.074940766 1.329185595 0.3728
Technical 0.930283818 1 1.23651985 0.3468
Financial 0.752340383 0.808721348 1 0.2804
2.682624201 2.883662114 3.565705445
Successful Knowledge Management
Awareness Perception Final weight
Human 0.177203615 0.15832443 0.3355
Technical 0.19939688 0.16291761 0.3623
Financial 0.154623986 0.147533479 0.3022

6. Conclusion

We have developed a hierarchical model for the implementation of Successful Knowledge Management. In the proposed model, first we identified the influencing factors and sub-factors for the implementation of Successful Knowledge Management. For this we relied on critical literature review and opinion of experts, and owner/managers of SMEs. Survey has been conducted for getting responses of owner/managers towards the influential factors and sub-factors with a view to successfully implement Knowledge Management. Finally, these responses have been collated to find the composite preference value (CPF) used as weights for the pair-wise comparison of the factors and sub-factors in AHP.
Based on the AHP results, we conclude the following for Successful Knowledge Management in SMEs:
• It is important to generate awareness about Knowledge Management and its benefits.
• Appropriate perception about Knowledge Management helps in assessing risks and rewards distinctly.
• Owner/managers are concerned with technical implementations involved in Knowledge Management over the fear of financial losses and human attritions.
• Owner/managers consider investments in technology as the ultimate solution to all problems.
• There is willingness on the part of owner/managers to reward employees for sharing knowledge for the benefit of the organization.
To make Knowledge Management successful in SMEs owner/managers need to prioritize their efforts in terms of technical, human and financial aspects necessarily in that order.
This study is an attempt towards building an implementation model for Successful Knowledge Management in SMEs. Although, there is growing enthusiasm and activity centered on Knowledge Management, several organizations have taken initiatives to understand and manage this critical resource of knowledge within their organizations. But, in spite of these initiatives, several other organizations particularly the SMEs still have not approached Knowledge Management activity formally or deliberately. The cause for this sluggishness towards Knowledge Management could be that most organizations are still struggling to comprehend the Knowledge Management concept. This study can act as an implementation road map for owner/managers wanting to embark into Knowledge Management.
In the present study the model considered influential factors such as awareness, perception, risks and rewards. The subject of Knowledge Management being vast, many other factors may influence Knowledge Management besides the ones considered in the present study. Future research may be directed towards identifying several other influential factors with a view to make the implementation road map for SMEs comprehensive. Also the present work has considered only the top down approach. Clear identification of influencing factors would need to consider a bottom up approach as well.

References
Adams, G.L. and Lamont, B.T. (2003), ‘‘Knowledge management systems and developing sustainable competitive advantage’’, Journal of Knowledge Management, 7 (2): 142-54.
Ahn, J.H. and Chang, S.G. (2004), “Assessing the contribution of knowledge to business performance: The KP3 methodology”, Decision Support Systems 36 (4), 403-416.
Boisot, M. (1998) Knowledge Assets: Securing Competitive Advantage in the Information Economy, Oxford University Press, London.
Cardinal, L.B., Allessandri, T.M. and Turner, S.F. (2001), ‘‘Knowledge codifiability, resources, and science based innovation’’, Journal of Knowledge Management, 5 (2): 195-204.
Carlsen, A. and Skaret, M. (1999) “Practicing Knowledge Management: Lessons from Processes in Small Firms”, In: Schreinermakers, J. & Barthes J. (eds) Knowledge Management: Enterprise, Network and Learning, Wurzburg: Ergon Verlag.
CEN Workshop Agreement, (2004), European Guide to good Practice in Knowledge Management - Part 3: SME Implementation (CWA 14924-3), European Committee for Standardization, Brussels
Chang, Mong-Yuan, Hung, Yu-Chung, Yen D. C. and Tseng P. T.Y. (2009), “The research on the critical success factors of knowledge management and classification framework project in the Executive Yuan of Taiwan Government”, Expert Systems with Applications, 36: 5376–5386.
Chatzoglou, P. D. and Diamantidis, A. D. (2009), “IT/IS implementation risks and their impact on firm performance”, International Journal of Information Management, 29: 119–128.
Chen, Mu-Yen, Huang, Mu-Jung and Cheng, Yu-Chen (2009), “Measuring knowledge management performance using a competitive perspective: An empirical study”, Expert Systems with Applications, 36: 8449–8459
Chong, Chee Wen, Holden, Tony, Wilhelmij, Paul, and Schmidt, Ruth A, (2000) “Where does knowledge management add value?”, Journal of Intellectual Capital, 1 (4): 366-380.
Darroch, J. and McNaughton, R. (2002), ‘‘Examining the link between knowledge management practices and types of innovation’’, Journal of Intellectual Capital, 3 (3): 210-222.
Davenport, T. H., and Prusak, L., (1998) Working knowledge: How organizations manage what they know, Boston: Harvard Business School Press.
Dawson, Ross (2000) Developing Knowledge-Based Client Relationships = The Future of Professional Services, Butterworth-Heinemann.
Earl, M (2001) Knowledge management strategies: toward a taxonomy, Journal of Management Information Systems, 18(1), 215–233.
Feng, K., Chen, E.T., and Liou, W., (2005) “Implementation of Knowledge Management Systems and Firm Performance: An Empirical Investigation,” Journal of Computer Information Systems, Spring: 92-104.
Ghobadian, A. and Gallear, D.(1997) “TQM and organisation size”, International Journal of Operations and Production Management, 17 (2).
Groom, J. and David, F. (2001) “Competitive Intelligence Activity Among Small Firms“, SAM Advanced Management Journal, 66(1): 12-20.
Handzic, Meliha, Lagumdzija, Amila and Celjo, Amer (2008), Auditing knowledge management practices: model and application, Knowledge Management Research & Practice, 2008(6), 90–99.
He, W. and Wei, Kwok-Kee (2009), “What drives continued knowledge sharing? An investigation of knowledge-contribution and -seeking beliefs”, Decision Support Systems, 46: 826–838.
Lai, Jung-Yu, Wang, Chao-Te and Chou, Chun-Yi (2009), “How knowledge map fit and personalization affect success of KMS in high-tech firms”, Technovation, 29: 313–324.
Marshall, C., Prusak, L. and Shpilberg, D (1996) “Financial Risk and the need for superior Knowledge Management” California Management Review, 38:3.
Navarro, J.G. C. (2005) “An empirical investigation of organizational learning through strategic alliances between SMEs”, Journal of Strategic Marketing, 13: 3-16
Plessis, Marina du, (2007), “Knowledge management: what makes complex implementations successful?” Journal of Knowledge Management, 11 (2), 91-101
Pyka, A. (2002), ‘‘Innovation networks in economics: from the incentive-based to the knowledge based approaches’’, European Journal of Innovation Management, 5 (3): 152-163.
Saaty T. L. (2000), Fundamentals of decision making and priority theory with the AHP, 2nd edn. RWS, Pittsburg PA
Shani, A.B., Sena, J.A. and Olin, T. (2003), ‘‘Knowledge management and new product development: a study of two companies’’, European Journal of Innovation Management, 6 (3): 137-149.
Sureshchandar G.S. and Leisten Rainer “A framework for evaluating the criticality of software metrics: an analytic hierarchy process (AHP) approach”, Measuring Business Excellence, 10 (4): 22-33.
Wen, Yuan-Feng (2009), “An effectiveness measurement model for knowledge management”, Knowledge-Based Systems, 22: 363–367.
Wong, Kuan Yew, (2005), “Critical success factors for implementing knowledge management in small and medium enterprises”, Industrial Management & Data Systems, 105 (3): 261-279.
Wong, K, Y. and Aspinwall, E. (2004), “Characterizing knowledge management in the small business environment”. Journal of Knowledge Management, 8 (3).
Yang, Ching-Chiao and Marlow, P. B. (2009), “Chin-Shan Lu (2009) Knowledge management enablers in liner shipping”, Transportation Research Part E, doi:10.1016/j.tre.2009.05.003.
Yeh, Ying-Jung, Lai, Sun-Quae, Ho, Chin-Tsang, (2006), “Knowledge management enablers: a case study”, Industrial Management & Data Systems Volume 106 Number 6 pp. 793-810