promoting re through km
TRANSCRIPT
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PROMOTING RENEWABLE ENERGYTECHNOLOGIES THROUGH
KNOWLEDGE MANAGEMENT
Jeykishan Kumar .K(2014JES2631)
Under the supervision ofProf D.K. Sharma
Centre for Energy StudiesIndian Institute of Technology Delhi
February 2016
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Objectives of the study
1
• To study the perception of top management about the knowledge management as a crucial economic resource for the promotion of RET’s.
2• To identify the critical factors affecting the promotion of
RET’s
3• To identify the contribution of KM system towards the
promotion in RET’s
4• To Model a topology for KM implementation and
strategic formulation.
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Literature Review
• KM in SME’s- Financial and skilled labor• KM in energy sector- exchange best practices• KM in power sector- better performance• KM in hospitality sector- web portal• KM helps in promoting R&D in business
organizations
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Research methodology of the study
Find out the critical factors affecting RE
Classify the factors by KM activities
Create hypothesis for the study
Prepare the questionnaire
Interview experts on RE
Survey the questionnaire
Analysis of response
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Work Plan
Description Jan Feb Mar Apr May
Questionnaire Preparation
Survey implementation
Analysis using SPSS
Development of Model or suitable alternative
Conclusion and Thesis writing
2016
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Interviews
• Dr. Kalyan Bhattacharjee- IITD• Ashish Rathore- IITD• Dr. Seema Sharma- IITD• Dr. Richa Sharma- JSS• Dr. Dinesh Kumar- IIMB• Dr. D.M.R. Panda- NTPC• Dr. R.D. Sathish Kumar- CSIR• Dr. P.C. Pant- MNRE
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Factors
• Advertisement• Awareness• E-portal• Participation• Capturing ideas• Storing the happenings• Ease of access• Investor interaction• Training and workshops
• Confidentiality issue• Attrition management• PPP model• Innovation• Skill development• Capacity building• Collaboration• Investment
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Questionnaire
• Total of 23 questions• 5 point Likert scale1. Strongly Agree2. Agree3. Undecided4. Disagree5. Strongly Disagree• Online based
• Name• Age• Designation• Qualification• Years of experience• Organization’s Name
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Responses
• Need for high quality data
• More number of responses
• 1:5 ratioExample: 23 questions should minimum expect 115 responses
• 135 RE companies list• 64 MNRE officials list
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Life cycle of KM
Create
Store
Share
Disseminate
Utilize
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KM Activities
Knowledge creation(KC)
Knowledge storing(KST)
Knowledge sharing(KSH)
Knowledge disseminating(KD)
Knowledge utilization(KU)
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Null Hypothesis• H01: KM does not help in capture extensive tacit knowledge and make it
explicit
• H02: KM does not help in tracking the learning events in RE technologies
• H03: KM does not help in creating a community to share ideas of the best practices on RE industries
• H04: Lack of KM activities cannot be directly attributed to lack of skilled engineers
• H05: Implementing KM system in RE sector cannot boost innovation
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HypothesisKM helps to capture extensive tacit knowledge by making it explicit
KM helps to track learning events on RE technologies
KM helps in creating a community to share ideas of the best practices on RE industries
Lack of KM activity can be directly attributed to lack of skilled engineers
Implementing KM system in RE sector can boost innovation
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SPSS
• Statistical package for social sciences• Factor analysis- Rotated component matrix• Cronbach’s alpha- consistency• Linear regression analysis- • ANOVA- Significance value• KMO and Bartlett’s Test- Adequacy and
significance
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Data Viewer
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Output Viewer
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Cronbach’s Alpha
• expression for the standardized Cronbach's α value:
• α =
where N is equal to the number of items, c is the average co-variance among the items and vindicates the average variance. One can see from this formula that if you increase the number ofitems, you increase Cronbach's α.
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Cronbach’s Reliability
Range of α Internal Consistency
Less than 0.7 Less reliability(good)
Greater than 0.7 but less than 0.9
Optimal Reliability(better)
More than 0.9 Better reliability(best)
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Reliability
Reliability Statistics:
Cronbach's AlphaCronbach's Alpha Based on Standardized Items
N of Items
.896 .903 42
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Linear Regression Analysis
• R-Square - This is the proportion of variance in the dependent variable which can be explained by the independent variables .This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable
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Regression table
Model RR
Square
Change StatisticsDurbin-WatsonR Square
ChangeF
Change Df1 Df2Sig. F
Change1 .826a .683 .683 36.134 5 84 .000 1.537
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ANOVA
Sig.- This value indicates the exact significance ofANOVA and explains how much the survey can effect on the dependent variable or the objective of the study. The exact significance is 0.000, so that effect would be significant statistically. The range of values it can be for effective significance is less than 0.005. If the value is more than 0.005, then the data will not be significant to the study and the solution would be to change the questionnaire.
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ANOVA table
ANOVA
ModelSum of Squares Df
Mean Square F Sig.
1 Regression 428.269 5 85.654 36.134 .000a
Residual 199.120 84 2.370
Total 627.389 89
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KMO and Bartlett’s Test
• Kaiser-Meyer-Olkin Sampling adequacy• Bartlett’s test Significance of data
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KMO Test Table
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .542
Bartlett's Test of Sphericity Approx. Chi-Square 2691.969
Df 496
Sig. .000
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Performance IndexKMPI involves five steps:• Knowledge creation(KC)• Knowledge storing(KST)• Knowledge sharing(KSH)• Knowledge disseminating(KD)• Knowledge utilization(KU)
• RKC = F (RWV, APFV)= Renewable Energy Technology Knowledge Circulation• RWV = relative weight value• AFV = Average factor value• RKC = (RWVKC *AFVKC) + (RWVKST * AFVKST) + (RWVKSH * AFVKSH) + (RWVKD
*AFVKD) + (RWVKU * AFVKU)
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Calculation
• KMPI value is in term of percentage • If the value of KMPI is high, it means the
percent of support given by KM in achieving the objective of the study which is promoting renewable energy technologies through KM in our case.
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Initiatives done
• Mobile science labs• PTC and IFC• The India Innovation Lab for Green finance• Atal Innovation Mission• Ideas- IEA• MNRE- Biomass Knowledge Portal(in progress)
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References
• Alavi M, Leidner D.E (2001) Review: Knowledge management and knowledge management systems, Conceptual foundations and research issues. MIS quarterly, 107-36.
• Edwards. J.S.(2008) Knowledge management in energy industries, International Journal of Knowledge Management in Energy Sector, 2 (2), 197-217
• El Fadel M, Rachid G, El-Samra R, Boutros GB, Hashisho J. (2013) Knowledge management mapping and gap analysis in renewable energy: Towards a sustainable framework in developing countries, Renewable and sustainable energy reviews, 20, 576-84
• Lee K.C, Lee S, Kang IW. (2005) KMPI: measuring knowledge management performance, Information & Management, 42(3), 469-82
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References• Nonaka I. (1994) A dynamic theory of organizational knowledge creation,
Organization science. 5(1), 14-37• Pandey K.N. (2014) Knowledge Management Processes: A Case Study of
NTPC and POWERGRID, Global Business Review, 15(1), 151-74• Rathore A.K, Ilavarasan P.V (2014) Mobile Adoption in Collaborating
Supply Chains: A Study of Indian Auto SMEs, In Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, 55-57
• Sharma R, (2014) Role of knowledge management in promoting research and development in business organizations, International Journal of Business and Globalization, 13(4), 423-38
• http://climatepolicyinitiative.org/publication/solving-indias-renewable-energy-financing-challenge-which-federal-policies-can-be-most-effective/
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Appendix-IFACTOR WEV SET OF
QUESTIONS
AFV RWV
KC 4 4 0.1739
KST 2 2 0.0869
KSH 5 5 0.21739
KD 4 4 0.1739
KU 5 5 0.21739
TOTAL 23 23 Total average = ?
0.243456
RKC = AFV * RWV = AFV * 0.243456