Journal of Knowledge Management Practice, Vol. 7, No. 3, September 2006

Empirical Study Based Evaluation Of KM Models In The IT Sectors:

Implications For Quality Outcomes

Lewlyn L.R. Rodrigues¹, R.S. Gayathri², Shrinivasa Rao³ , Manipal Institute of Technology¹, IBM India Pvt Ltd², NMAM Institute of Technology³

---

ABSTRACT:

This study was undertaken to evaluate the Knowledge Management (KM) practices processes & systems in relation to Nonaka’s spiral model and Knowledge Management Maturity Model (KMMM^â). The metrics used had 88 variables, classified as four dimensions of Nonaka’s Model and eight dimensions of KMMM Model. A sample size of 114 was selected from the IT sector on proportionate random sampling basis. The instrument was validated for content, criterion & construct validity, which includes factor analysis. Hypothesis testing that was undertaken to study the perceptional differences in the KM performance revealed that there is no significant difference in the perception of KM dimensions w.r.t. KMMM and Nonaka’s Model. The positive effect of infrastructure capabilities and process capabilities on KM success has also been studied to get a better understanding of the benefits of KM implementation. The study has revealed that w.r.t. Nonaka’s Model as well as KMMM model, the Knowledge Workers are moderately satisfied with the KM practices. Further, the Dimensions – ‘People Competencies’ and ‘Environment & Partnerships’ based on KMMM, and ‘Externalization’ and ‘Combination’ based on Nonaka’s Model were found to be the dimensions which were highly practiced. Through the empirical study results and findings, suggestions have been made to enhance the KM practices in the IT sector.

Keywords: Knowledge Management, KM Maturity Model, KM Measurement, Information Technology

---

1. Introduction

The relevance & importance of knowledge is becoming increasingly critical in business as we evolve from ‘industrial’ into ‘information & knowledge’ era. Drucker (1993/1994) argues that the world is witnessing a great transformation, which he calls the “post-capitalist” society, in which the basic economic resources will no longer be the traditional production input factors, but that the primary resource for both the organizations and the economy will be ‘knowledge’.

Organizational Knowledge Management (KM) as a source of competitive advantage is now widely recognized (Nonaka, 1991; Davis & Botkins, 1994; Bohn, 1994). KM holds key implications, virtually for both service and production sectors. Research indicates that knowledge and knowledge work has infiltrated deep into the value chain of most business (Quinn, 1992). The reason (such as product differentiation, creating “best in class” capabilities, setting high entry barriers, etc.) for this infiltration provides important insights into the area of organizational knowledge and its impact on core business processes & functions. According to Quinn (1992), the majority of all public & private organizations are rapidly shifting to become repositories & coordinators of knowledge based activities.

As we move from an industrial/manufacturing economy to a more service driven economy, we see the emergence of knowledge intensive service organizations emerging along in the more traditional capital-intensive & Labor- intensive organizations (Bonara & Revang, 1993). Hence, it is imperative that efficient transformation of data into information, and then into knowledge is the critical factor contributing to the success of a service/production sector. Both the manufacturing and service sectors have responded to this call of ‘Knowledge Management’ positively and many have already started reaping benefits from the same. During the growth period of KM popularity, many frameworks and models have also been developed and tested successfully in the knowledge intensive sectors. Comparative evaluation of these models is now required for the unification of various theories and this paper is an attempt in that direction.

2. Literature Review

The definition of KM may be contextual (Neef, 1999; Bhatt, 2001; Raub and Rulling, 2001), but it basically exists to identify, select, organize, disseminate, and transfer important information and expertise that are part of the organizational memory: typically residing within the organisation, in an unstructured manner (Turban & Aronson, 2002). All knowledge intensive service sectors possess ‘explicit’ and ‘tacit’ knowledge (Nonaka and Takeuchi, 1995) and the KM typically deals with the conversion of tacit knowledge in to explicit form so that every employee in the organization could be empowered to use it. Companies must innovate or die, and their ability to learn, adapt and change becomes a core competency for survival. The forces of technology, globalization and the emerging knowledge economy are creating a revolution that is forcing organizations to seek new ways to reinvent themselves. The current technological revolution is not characterised by the centrality of knowledge and information, but by the application of such knowledge and information to knowledge generation and information processing/communication devices, in a cumulative feedback loop between innovation and by the uses of innovation (Castells, 1996). Petrides and Nodine (2003) opine that KM brings together people, process & technology. These three core organizational resources enable the organization to use and share information more effectively. Ultimately, the purpose of KM practices is to store the Organizational Knowledge in a form that could be used by all the stake holders to enhance productivity, maintain quality and improve customer satisfaction.

One of the major beneficiaries of KM is IT based firms. There has been extensive research undertaken to study: the efficacy of KM implementation, Knowledge Diversity, Management of Knowledge, KM on Organizational Learning, TQM in KM, etc. Ramkrishnan & Boland, 1998-1999; Taylor et al, 2001; Rampersad, 2002; Fernandez et al, 2006). The two most widely used models in these studies are KMMM and Nonaka’s Model. Nevertheless, very few studies have been undertaken for a comparative evaluation of these models.

3. Research Methodology

The empirical study in this research is in line with Kerlinger’s (1977; pp. 185) procedure:

“…the theory and method of analyzing quantitative data obtained from samples of observations in order to study and compare sources of variance of phenomena, to help make decisions to accept or reject hypothesized relations between the phenomena, and to aid in making reliable inferences from empirical observations”.

A self-administered questionnaire consisting of three parts was used in this study. Part A had 43 variables pertaining to Nonaka’s model, and Part B comprised 44 items pertaining to KMMM model. Part C provided data to classify employees by their demographics. The key dimensions of the two models with description and sample item are given in Appendix 1. The statements were presented in a cyclic manner without references to scale or indicator identity.

The population of the selected industry is 250 out of which a sample size of 114 is used for this study. The target sample consists of Directors, Project Managers, Team Leaders, Delivery Heads, Senior Software Engineers, Senior Operational Specialists, Senior Technical Architects, Program Analysts, Module Leaders, Application Engineers, Software Engineers, Technical Writer, Operational Specialists, and Associate Operational Specialists who had at least five years of experience in the current IT-sector. The rationale for this sample selection is to ensure that employees who answered the questionnaires have at least some experience with the current industry and are in a position to assess knowledge management practices.

The data was processed through Microsoft Excel 2000 and SPSS software for statistical analysis. The empirical study consists of standard statistical measurements and independent sample t-test to study the correlation of perception of the professionals in the two models of the service sectors.

4. Findings

4.1. Reliability

Cronbach Alpha reliability of the analysis is 0.94 (Nonaka’s Model) & 0.95 (KMMM model), which indicates very high internal consistency based on average inter-item correlation. The item-wise reliability is given in Table1 & Table 2.

Table 1: Reliability Analysis (Nonaka’s Model)

Variable	Scale Mean if Deleted	Scale Variance if Item deleted	Corrected item correlation	Alpha if Item deleted
1	651.17	370.79	0.46	0.94
2	165.48	378.72	0.17	0.94
3	165.65	366.24	0.58	0.94
4	165.22	364.45	0.70	0.94
5	165.74	349.20	0.74	0.94
6	165.61	360.43	0.65	0.94
7	165.74	355.57	0.64	0.94
8	165.57	356.89	0.77	0.94
9	165.48	362.72	0.67	0.94
10	165.22	366.09	0.54	0.94
11	165.74	359.57	0.57	0.94
12	165.66	367.59	0.62	0.94
13	165.70	370.04	0.41	0.94
14	165.17	368.51	0.59	0.94
15	165.65	364.87	0.75	0.94
16	165.30	368.95	0.48	0.94
17	165.78	356.18	0.70	0.94
18	165.96	372.68	0.50	0.94
19	165.13	374.48	0.35	0.94
20	165.00	382.36	0.10	0.94
21	165.35	370.33	0.53	0.94
22	165.22	371.09	0.50	0.94
23	165.30	369.31	0.46	0.94
24	165.57	368.89	0.57	0.94
25	165.13	375.03	0.33	0.94
26	165.26	375.47	0.31	0.94
27	165.43	371.89	0.53	0.94
28	165.87	358.21	0.57	0.94
29	165.61	355.25	0.76	0.94
30	165.39	346.75	0.71	0.94
31	165.48	360.99	0.68	0.94
32	165.43	365.44	0.66	0.94
33	165.39	364.43	0.67	0.94
34	165.43	364.44	0.76	0.94
35	165.78	382.27	0.04	0.94
36	165.17	376.33	0.29	0.94
37	165.83	365.66	0.53	0.94
38	165.65	360.96	0.63	0.94
39	165.78	359.72	0.72	0.94
40	165.26	368.02	0.58	0.94
41	165.65	387.78	-0.10	0.95
42	165.65	377.60	0.20	0.94
43	165.61	365.98	0.49	0.94
Total Items = 43 Alpha = 0.9425 Standardized Item Alpha = 0.9457

Table 2: Reliability Analysis (KMMM Model)

Variable	Scale Mean if Deleted	Scale Variance if Item deleted	Corrected item correlation	Alpha if Item deleted
45	168.65	367.96	0.67	0.94
46	168.78	375.81	0.49	0.95
47	168.83	370.51	0.57	0.94
48	168.52	365.35	0.66	0.94
49	168.78	361.18	0.77	0.94
50	168.69	365.49	0.77	0.94
51	168.86	364.21	0.74	0.94
52	168.60	367.98	0.69	0.94
53	168.56	375.08	0.72	0.94
54	168.78	388.91	0.06	0.95
55	168.78	384.45	0.41	0.95
56	168.69	370.59	0.65	0.94
57	168.73	367.93	0.72	0.94
58	168.73	372.75	0.61	0.94
59	169.00	375.55	0.58	0.94
60	168.82	368.79	0.66	0.94
61	168.47	383.44	0.29	0.95
62	168.65	383.33	0.22	0.95
63	168.34	395.33	-0.10	0.95
64	168.26	392.20	-0.03	0.95
65	168.47	373.90	0.71	0.94
66	168.47	368.99	0.75	0.94
67	168.56	367.53	0.85	0.94
68	168.60	367.89	0.69	0.94
69	168.60	365.61	0.82	0.94
70	168.56	374.08	0.62	0.94
71	168.69	379.40	0.44	0.95
72	168.43	371.98	0.76	0.94
73	168.78	366.00	0.81	0.94
74	168.60	372.79	0.69	0.94
75	168.48	369.08	0.75	0.94
76	168.65	367.60	0.78	0.94
77	168.83	372.15	0.60	0.94
78	168.52	369.72	0.69	0.94
79	168.83	380.88	0.38	0.95
80	168.65	374.60	0.65	0.94
81	168.91	376.08	0.53	0.95
82	169.00	393.27	0.06	0.95
83	168.78	386.00	0.23	0.95
84	168.17	386.33	0.14	0.95
85	168.70	386.95	0.15	0.95
86	168.43	386.80	0.21	0.95
87	168.83	375.06	0.54	0.94
88	168.83	366.33	0.69	0.94
Alpha = 0.9462 Total items = 44 Standardized Alpha = 0.9501

4.2. Indices Of Perception Of KM Practices

In order to assess the perception of the knowledge workers in the IT Sectors the scores of the individual variables were aggregated and then classified into ‘High’, ‘Medium’, & ‘Low’ satisfaction Categories. The results reveal that (Table 3) a majority (74% in Nonaka’s Model; 80% in KMMM Model) of the knowledge workers felt ‘moderately satisfied’ with the KM practices. However, as KM practices are well established and practiced in the industry, comparatively a higher percentage of knowledge workers were satisfied to a ‘high’ degree in comparison to ‘low’ degree of satisfaction.

Table 3: KM Satisfaction Cross-Tabulation

IT Sector		Satisfaction			Total
		Low	Medium	High
Nonaka’s Model	Count % Satisfaction	3 3%	74 74%	23 23%	100 100%
KMMM Model	Count % Satisfaction	3 3%	80 80%	17 17%	100 100%
Total	Count % Satisfaction	6 3	154 77%	40 20%	200 200%

4.3. Validity Of The Instrument

The Factor Analysis using Principal Component Analysis (PCA) method with varimax rotation through Kaiser Variation was used to generate factors. The results of the factor analysis for the two instruments viz, KMMM & Nonaka’s model explaining the percentage variance and Eigen values is given in Tables 4 & 5. The required number of factors has been forced and only factor loadings above 0.4 were considered. The percentage variance extracted by the given number of factors in KMMM Model& Nonaka’s model is 81.2% & 86.93% respectively. Thus, with a reasonable degree of confidence, it could be concluded that the instruments used have measured what they were expected to measure.

Variables	Factors (Dimensions of KMMM model)
	1	2	3	4	5	6	7	8
VAR48	0.879
VAR46	0.861
VAR45	0.849				0.32
VAR49	0.792	0.547
VAR47	0.759		0.374
VAR76	0.685	0.387		-0.325
VAR52	0.675		0.359					0.402
VAR50	0.601	0.306	0.592
VAR72	0.583	0.446	0.428			-0.331
VAR51	0.552	0.42	0.439
VAR75	0.533	0.316	0.358		0.361
VAR87		0.863
VAR58		0.829
VAR53	0.344	0.704	0.348
VAR88		0.662	0.347
VAR73	0.525	0.638	0.361
VAR78		0.632		0.302	0.432
VAR70		0.577			0.332		-0.371	-0.327
VAR59		0.572	0.395			-0.384
VAR74		0.53	0.317		0.36		-0.347
VAR69	0.491	0.495	0.459
VAR77	0.414	0.494					-0.409
VAR60	0.365		0.777
VAR66	0.365		0.721
VAR68		0.374	0.707
VAR67	0.375	0.329	0.69		0.337
VAR57	0.471		0.664	-0.32
VAR83			0.601	0.522
VAR81		0.393	0.597	0.395
VAR65			0.593		0.584
VAR56	0.422	0.314	0.47				-0.324
VAR63				0.868
VAR64				0.847
VAR79				0.484	0.438	-0.48
VAR55					0.762
VAR71					0.72
VAR80			0.447	0.497	0.5
VAR82						0.769	0.32
VAR62		0.433				0.745
VAR86			0.326	0.366		0.716
VAR84							0.88
VAR85							0.689
VAR61	0.32							0.766
VAR54				-0.485				0.638
Eigen Values	17.06	4.67	3.53	2.83	2.36	2.01	1.71	1.56
% Variance	38.78	10.61	8.01	6.42	5.37	4.57	3.88	3.56

Table 4: Factor Loading (KMMM Model)

Table 5: Factor Loading (Nonaka’s Model)

Variables	Factors (Dimensions of Nonaka’s Model)
	1	2	3	4
VAR29	0.835
VAR43	0.794
VAR32	0.782
VAR31	0.773			-0.306
VAR08	0.745	0.384
VAR11	0.739
VAR07	0.736		0.336	-0.381
VAR30	0.732	0.46
VAR27	0.712
VAR28	0.699
VAR38	0.641			0.552
VAR15	0.632	0.329	0.3
VAR39	0.619			0.606
VAR05	0.524		0.469	0.31
VAR33	0.429	0.384	0.379
VAR18	0.32	0.771
VAR22		0.754
VAR25	0.376	0.717	-0.346
VAR41		-0.709
VAR04	0.429	0.669
VAR26		0.642
VAR20		0.627		0.367
VAR16	0.302	0.605
VAR36		0.57
VAR10		0.54		0.429
VAR24	0.491	0.514
VAR23		0.46	0.413
VAR02			0.744
VAR06			0.717
VAR13			0.702
VAR03		0.337	0.668
VAR12	0.503		0.660
VAR09	0.577		0.614
VAR40		0.306	0.604	0.434
VAR14		0.544	0.597
VAR34	0.51		0.569
VAR17	0.455		0.557
VAR37			0.544	0.543
VAR21		0.517	0.528
VAR42			0.411
VAR01		0.318	0.387
VAR35				0.663
VAR19				0.346
Eigen Values	14.82	4.98	4.05	2.76
% Variance	34.46	11.58	34.46	6.43

4.4. Factor Analysis Of KM Variables

Factor analysis is the generic name for one of the multivariate techniques that is used to ascertain the underlying structure in a data matrix (Hair et al, 1995). Principal Component analysis (PCA) method with Varimax Rotation has been used to generate factors. PCA is appropriate when the main concern is to predict the minimum number of factors that are required to account for the maximum proportion of the variance when there is a priori set of variables (Ghauri et al, 1995). The factor extraction was forced to yield four factors in Nonaka’s Model and eight factors in KMMM model (Tables 4 & 5). These factors were a set of variables grouped to be the dimensions, which represent the key performance indicators of the two models.

4.5. Ranking Of KM Dimensions

Table 6 gives the preferential ranking of KM dimensions of both the models in the IT industry. In relation to the KMMM Model the knowledge workers ranked People competencies, Environment & partnerships, Leadership support, Structure, and Knowledge objective as high. However, Knowledge structure/forms, Collaboration & culture, Technology & infrastructure, and Process, roles, organisation were poorly rated. On the contrary in the Nonaka’s model Externalization was ranked the highest and Combination was ranked the second best. The dimensions - Internalization and Socialization were poorly rated.

Table 6: Ranking Of KM Dimensions

KMMM Model				Nonaka’s Model
Dimensions	Mean	% Mean	Rank	Dimension	Mean	% Mean	Rank
People Competencies	3.93	78.68	1	Externalization	3.92	78.48	1
Environment & Partnerships	3.80	76.05	2	Combination	3.73	74.65	2
Leadership Support	3.60	71.99	3	Internalization	3.44	68.86	3
Strategy, Knowledge Objective	3.55	70.91	4	Socialization	3.35	66.90	4
Knowledge Structure/Forms	3.54	70.70	5
Collaboration & Culture	3.35	66.96	6
Technology & Infrastructure	3.26	65.26	7
Process, Roles, Organisation	3.24	64.76	8

4.6. Rank Order Correlations

The rank order correlation between the perceptions of the knowledge workers in the two models is shown in Table 7. It is clear from the table that there is a significant relationship between the perceptions of the knowledge workers in both the models. For the sake of comparison the KMMM model was reduced to equivalent Nonaka’s model and the results are depicted in the Radar Diagram in Figure 1.

Fig. 1: Gaps in KM Dimensions

The Radar Diagram indicates that there is no difference in the dimensions: Internalization and Externalization. Whereas, a wide gap exists between the dimensions: Socialization & Combination. Further, the mean score indicated that these dimension also received the least ranking by the senior and junior executives. This indicates the lack of periodic augmentation of technology and infrastructure to keep in pace with the rapid strides in technological changes.

The second largest gap observed from the Radar Diagram is on the dimension - Leadership Support. This calls the senior executives & the top management to provide clear understanding of the strategic objectives of the organization, and align the KM initiatives and practices with it so as to gain the competitive edge through innovation and research. The HRD initiatives such as Participative Management may also be considered as an option.

People Competency is another strong area of KM identified in this study. The knowledge workers have highly perceived this dimension (rank 1). This being one of the important performance indicators, measures to enhance knowledge, skill, and attitude on a regular basis would pave the way for success.

Table 7: Rank Order Correlation

Dimensions of Nonaka’s Model	Scores	Equivalent Dimensions of KMMM Model	Scores
Externalization	3.92	People competencies		3.93
Combination	3.73	Technology & Infrastructure Collaboration & Culture Environment & Partnerships	3.26 3.35 3.80	3.47
Internalization	3.44	Strategy, Knowledge objectives Process, Roles, Organization Knowledge Structure/Forms	3.55 3.24 3.54	3.44
Socialization	3.35	Leadership support		3.60

4.7. Hypothesis

There is a significant difference in the perception of Nonaka’s model & KMMM Model as perceived by the knowledge workers in the IT sector

Paired sample t-test (Table 8) indicates that the result has failed to reject the null hypothesis, and hence, there is no significant difference in the perceptions of the knowledge workers with reference to the two models. This is also justified by the fact that the majority (77%) of the people are in the medium level of satisfaction of KM practices as indicated in Table 3.

Table 8: Paired Sample t-test (p<0.05)

Paired Differences
	Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the		t	df	Sig. (2-tailed)
				Lower	Upper
Pair 1 IT Sector	.000	.2083	.1042	-.3315	.3315	.00	3	1.000

5. Conclusions

This empirical study has delineated the fact that mere systems, processes and technology cannot produce results without people involvement. This is the reason why People competency (KMMM) and Externalization (Nonaka’s Model) have been considered to be the most significant dimensions by the knowledge workers in the IT sectors. Personal interviews with the knowledge workers also revealed that ‘knowledge hoarding’ still exists, as people are not fully aware of the importance of KM. Hypothesis testing indicated that, as such, there is no significant difference between the perceptions of KMMM and Nonaka’s model in these organizations. However, they significantly differed in the dimensions Socialization and Combination. Socialization involves the transfer of tacit knowledge among the knowledge workers, and Combination involves conversion of explicit knowledge into more complex sets of explicit knowledge. As both of these dimensions involve ‘human aspects’, effective knowledge transfer across the cross-functional work teams, creating interdependence across multi-disciplinary branches, and informal systems such as induction, mentoring, experience sharing etc. will have to be given due importance to develop coherence with respect to these two dimensions.

Knowledge Management is a dynamic field and many new models are likely to emerge out in the nearest future. Nevertheless, KMMM and Nonaka’s Models have a proved validity across the IT Sectors and this paper has empirically studied the perceptions of the Knowledge Workers based on these two models. Furthermore, KM in different organizations may be required to serve different organizational objectives. Though limited in its sample size, this research has proved that by and large the two models are perceived similarly by the employees despite the fact that KMMM Model is ‘strategy based’ whereas the Nonaka’s model ‘structure based’. Future researchers in this area may thus formulate a hybrid model by combining these two models and empirically study the KM performance.

6. References

Bhatt, C. (2001), “KM in organizations: examining the interaction between people, processes and technology”, Journal of Knowledge Management, Vol. 5, No. 1; pp. 68-75.

Bohn R (1994), “Measuring& Managing Technological Knowledge”, Sloan Management Review, Vol. 36, No. 1; pp 61-72.

Bonora, E.A., Revang, P. (1993), “A framework for analyzing the storage & protection of knowledge in organisation: Strategic implication & Structural Arrangements”, in Implementing Strategic Process; Change, Learning & Cooperation, Blackwell, Cambridge; pp 190-212.

Castells, M. (1996), The Rise of Network Society, Blackwell, Oxford.

Davis, S., Botkins, J. (1994), “The coming Of Knowledge–based Business”, Harvard Business Review, Sept-Oct; pp 165-170

Drucker, P.E. (1993/1994), Post-capitalist Society, Harper Business, New York.

Fernandez, T. J., Segura1, S.L., Salmeron J.L., Moreno, J.R. (2006), “Operational Knowledge Management System Design in Total Quality Management: Small and Medium Size Companies”, Journal of KM Practice, Vol. 7, No. 1; Retrieved August 2006: www.tlainc.com/jkmpv7n106.htm

Ghauri, P., Gronhaug, K., Kristianslund, I. (1995), Research Methods in Business Studies, Prentice Hall, Hemel Hampstead.

Hair, J.F.(Jr.), Anderson, R.E., Tatham,R.L., Black,W.C. (1995), Multivariate Data Analysis, (4^th Ed.), Prentice Hall, Englewood Cliffs, N.J.

Kerlinger, F. N. (1977), Foundations of Behavioural Research (3rd Edn.), Holt, Renehart & Winston, New York; pp. 185.

Neef, D. (1999), “Making the case for knowledge management: The bigger picture”, Management Decisions, Vol. 37, No. 1; pp. 72-78.

Nonaka, I. (1991), “The Knowledge creating company”, Harvard Business Review, Nov-Dec; pp. 96-104.

Nonaka, I., Takeuchi, H. (1995), The Knowledge Creating Company: How Japanese Companies Create Dynamic Innovation, Oxford University Press, New York.

Petrides, L. A., Nodine, T. R. (2003), “Knowledge Management in Education: Defining the Landscape”, Monograph published by the Institute for the Study of Knowledge Management in Education, sponsored by SUN MICROSYSTEMS; Retrieved August 2006: http://eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/22/19/e4.pdf

Quinn, J.B. (1992), Intelligent Enterprise; A Knowledge & Service Based Paradigm For Industry, Maxwell Macmillan, New York.

Raub, S., Rulling, C. C. (2001), “The knowledge management tussle – speech communication and rhetorical strategies in the development of KM”, Journal of Information Technology, Vol. 16, No. 2; pp. 113-130.

Ramkrishnan, V.T., Boland Jr., R.J. (1998-1999), “Exploring Knowledge Diversity in Knowledge Intensive Firms: A New Role for Information Systems”, Journal of KM Practice, Vol. 1; Retrieved August 2006: www.tlainc.com/jkmpv1.htm

Rampersad, H. (2002), “Increasing Organizational Learning Ability Based on A Knowledge Management Quick Scan”, Journal of KM Practice, Vol. 3; Retrieved August 2006: www.tlainc.com/jkmpv3.htm

Taylor, D. W., Yamamura, J., Stedham, Y. (2001), “Managing Knowledge-Workers in Accounting Firms: The Role of Nutrient Information and Organisational Information Consciousness”, Journal of KM Practice, Vol. 2; Retrieved August 2006: www.tlainc.com/jkmpv2.htm

Turban, E., Aronson, J.,E. (2002), (Eds), Knowledge Management, Decision Support Systems and Intelligent Systems, Pearson Education; http://www.pearsoned.co.uk/Bookshop/

Appendix 1: KM Dimensions, Description and Sample Items

Dimension	Description	Sample Items
Strategy, knowledge objective	Organization considers innovation of knowledge as a primary objective. It is planning through experimentation, standardization, diffusion, and refinement.	Top management clarifies their mutual expectations with the employees.
Process, roles, organisation	It enables the creation of knowledge and its sharing within the organization.	There are formal & informal, policies, procedures, for workers in the organization.
Technology & infrastructure	It deals with information retrieval, collaboration support, decision support, and information security.	There is a knowledge documentation system in the organization.
Knowledge structure/forms	It is often imposed by systems whether those systems are communication systems or information systems.	Organization creates and contributes its own knowledge to the system.
Leadership support	The practice of creating, replacing & protecting identity.	Provides lots of opportunities for group discussion, especially informal ones to allow cross- fertilization of ideas.
Collaboration culture	Collective learning becomes useful within firms. Self-managed teams coordinate their functions & activities through learning negotiation & readjustment.	Learning networks are well supported.
People competencies	Employees are the custodians and developers of intellectual capital. When they work together or collaborate, they constitute a strategic asset.	People’s attitude plays a major role in sharing and transferring knowledge.
Environment partnerships	Organization gains knowledge from customer contact. It maintains contact with the important stakeholders. Knowledge is shared & exchanged on a regular basis.	There are collaboration programs to tap knowledge from other sources.
Externalization	It is characterized by dialogue through which individual knowledge is converted into shared terms & concepts. It is marked by extensive use of metaphors; analogies, hypothesis & models.	The knowledge dissemination processes are formalized.
Internalization	It facilitates the conversion of explicit to tacit knowledge for the individual, which is enhanced primarily through the use of explicit knowledge in the real-life or simulated application.	The knowledge transfer processes are formalized.
Combination	It is a place of interaction in the virtual world facilitated by the use of online networks, team room, learning space, and knowledge sharing.	Technology is coupled with human mind to create synergy.

---

Meet the Authors:

Dr. Lewlyn L. R. Rodrigues is Reader in the Department of Mechanical and Manufacturing Engineering at Manipal Institute of technology, Manipal. He can be contacted at Department of Mechanical & Manufacturing Engineering, Manipal Institute of Technology, Manipal 576104, Karnataka, India; Email: rodrigusr@rediffmail.com; Tel: +91-0820-2571061-70 Ext. 24042; Mob: 0971-50-6257818; FAX: +91-0820-2571061-71.

Ms. R.S. Gayathri is System Operations Senior Specialist, Global Service Delivery Centre, IBM India Pvt  Ltd, Bangalore, India; Email: gayathri.rshenoy@gmail.com

Dr. Shrinivasa Rao B.R is Associate Professor in the Department of Mechanical Engineering at the NMAM Institute of Technology, Nitte, Karnataka, Nitte- 574 110, India; Email: sam_nmamit@yahoo.com; Tel: 091-9448107800; FAX: +91-08258-281265.

---