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 (KMMMâ). 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 |