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Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen

Continuously creating new knowledge is a vital business strategy for a company to sustain competitive advantage. In a Lean Six Sigma organization, Kaizen is performed as a project, with common underpinning of methodology such as PDCA and DMAIC. As a team-based and goal-driven activity, Kaizen also p...

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Main Author: Muhammad, Nur Amalina
Format: Thesis
Language:English
Published: 2019
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T Technology
TJ1-1570 Mechanical engineering and machinery
Online Access:http://eprints.usm.my/47848/1/Systematic%20Approach%20To%20Knowledge%20Routines%20And%20Knowledge%20Creation%20In%20Kaizen.pdf
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spelling my.usm.eprints.47848 http://eprints.usm.my/47848/ Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen Muhammad, Nur Amalina T Technology TJ1-1570 Mechanical engineering and machinery Continuously creating new knowledge is a vital business strategy for a company to sustain competitive advantage. In a Lean Six Sigma organization, Kaizen is performed as a project, with common underpinning of methodology such as PDCA and DMAIC. As a team-based and goal-driven activity, Kaizen also provides a regular base to actively create knowledge. Specifically, different knowledge routines would take place to create knowledge in Kaizen. Five knowledge routines of interest to Kaizen are meeting (KR1), Gemba walk (KR2), mentoring (KR3), coaching (KR4) and referencing (KR5). An extensive literature review has shown that mainstream research often focused on incorporation of selective knowledge routines in Kaizen, presuming the value of these routines and with little disclosure on their deployments. In this sense, research aims to distinguish the forms of these routines and then, to measure their significances to knowledge creation through several case studies. Specifically, the research defines three different systems of knowledge routines: Basic knowledge routine system (S1), refined knowledge routine system (S2) and SECI-Ba knowledge routine system (S3). S1 represents knowledge routines deployed in a crude form, often informally and with little planning and predetermined structure. S2 represents knowledge routines running in a defined and feedback-loop system. S3 is an extended system of S2 by making explicit the elements of two related knowledge creation models, SECI and Ba. Five performance measures appertaining knowledge creation are considered: percentage of goals met (TSO1), impact on area (TSO2), overall business success (TSO3), knowledge gain in Kaizen and LSS (SSO1) and skill of idea generation and decision making (SSO2). Study was performed by using case studies, questionnaire survey and statistical analysis. Twenty Kaizen case studies were collected and questionnaires were conducted with knowledge leaders involving in these Kaizens, over three years period. In statistical analysis, a multiple regression analysis was used to determine the relationship between knowledge routines to knowledge creation. Two critical findings were gained from the study. Firstly, the survey showed that S2 achieved 52.76% higher mean rating of effectiveness of knowledge creation compared to S1. S3 achieved higher mean ratings of effectiveness of knowledge creation than S2 and S1, with increment of 25% and 64.57%, respectively. These provide strong evidences that system harnessing SECI and Ba consistently outperformed its counterparts. Secondly, while statistical analysis showed that five knowledge routines are significantly related to knowledge creation in all systems, their individual significances vary to measurement items of knowledge creation. S1-KR2, S2-KR1 and S3- KR1 have the highest significance to TSO1. In terms of significance to TSO2, KR2 is the highest among all systems. KR1 has the highest significance to TSO3 and SSO2 in all systems. S1-KR4, S2-KR3 and S3-KR3 have the highest significance to SSO1. KR5 is the least significant in all systems. The research contribution is the system development and empirical evidence underscoring SECI-BA enabling conditions in knowledge routines to facilitate knowledge creation in Kaizen. The main research limitation is case studies with relatively small sample size and based on a single organization, despite characteristically heterogeneous and diverse. 2019-12-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/47848/1/Systematic%20Approach%20To%20Knowledge%20Routines%20And%20Knowledge%20Creation%20In%20Kaizen.pdf Muhammad, Nur Amalina (2019) Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TJ1-1570 Mechanical engineering and machinery
spellingShingle T Technology
TJ1-1570 Mechanical engineering and machinery
Muhammad, Nur Amalina
Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen
description Continuously creating new knowledge is a vital business strategy for a company to sustain competitive advantage. In a Lean Six Sigma organization, Kaizen is performed as a project, with common underpinning of methodology such as PDCA and DMAIC. As a team-based and goal-driven activity, Kaizen also provides a regular base to actively create knowledge. Specifically, different knowledge routines would take place to create knowledge in Kaizen. Five knowledge routines of interest to Kaizen are meeting (KR1), Gemba walk (KR2), mentoring (KR3), coaching (KR4) and referencing (KR5). An extensive literature review has shown that mainstream research often focused on incorporation of selective knowledge routines in Kaizen, presuming the value of these routines and with little disclosure on their deployments. In this sense, research aims to distinguish the forms of these routines and then, to measure their significances to knowledge creation through several case studies. Specifically, the research defines three different systems of knowledge routines: Basic knowledge routine system (S1), refined knowledge routine system (S2) and SECI-Ba knowledge routine system (S3). S1 represents knowledge routines deployed in a crude form, often informally and with little planning and predetermined structure. S2 represents knowledge routines running in a defined and feedback-loop system. S3 is an extended system of S2 by making explicit the elements of two related knowledge creation models, SECI and Ba. Five performance measures appertaining knowledge creation are considered: percentage of goals met (TSO1), impact on area (TSO2), overall business success (TSO3), knowledge gain in Kaizen and LSS (SSO1) and skill of idea generation and decision making (SSO2). Study was performed by using case studies, questionnaire survey and statistical analysis. Twenty Kaizen case studies were collected and questionnaires were conducted with knowledge leaders involving in these Kaizens, over three years period. In statistical analysis, a multiple regression analysis was used to determine the relationship between knowledge routines to knowledge creation. Two critical findings were gained from the study. Firstly, the survey showed that S2 achieved 52.76% higher mean rating of effectiveness of knowledge creation compared to S1. S3 achieved higher mean ratings of effectiveness of knowledge creation than S2 and S1, with increment of 25% and 64.57%, respectively. These provide strong evidences that system harnessing SECI and Ba consistently outperformed its counterparts. Secondly, while statistical analysis showed that five knowledge routines are significantly related to knowledge creation in all systems, their individual significances vary to measurement items of knowledge creation. S1-KR2, S2-KR1 and S3- KR1 have the highest significance to TSO1. In terms of significance to TSO2, KR2 is the highest among all systems. KR1 has the highest significance to TSO3 and SSO2 in all systems. S1-KR4, S2-KR3 and S3-KR3 have the highest significance to SSO1. KR5 is the least significant in all systems. The research contribution is the system development and empirical evidence underscoring SECI-BA enabling conditions in knowledge routines to facilitate knowledge creation in Kaizen. The main research limitation is case studies with relatively small sample size and based on a single organization, despite characteristically heterogeneous and diverse.
format Thesis
author Muhammad, Nur Amalina
author_facet Muhammad, Nur Amalina
author_sort Muhammad, Nur Amalina
title Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen
title_short Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen
title_full Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen
title_fullStr Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen
title_full_unstemmed Systematic Approach To Knowledge Routines And Knowledge Creation In Kaizen
title_sort systematic approach to knowledge routines and knowledge creation in kaizen
publishDate 2019
url http://eprints.usm.my/47848/1/Systematic%20Approach%20To%20Knowledge%20Routines%20And%20Knowledge%20Creation%20In%20Kaizen.pdf
http://eprints.usm.my/47848/
_version_ 1717094497907638272
score 13.211869

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