1. PROMOTING RENEWABLE ENERGY
TECHNOLOGIES THROUGH
KNOWLEDGE MANAGEMENT
Jeykishan Kumar .K
(2014JES2631)
Under the supervision of
Prof D.K. Sharma
Centre for Energy Studies
Indian Institute of Technology Delhi
February 2016

2. 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.

3. 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

4. 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

5. 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

6. 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

7. 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

8. Questionnaire
• Total of 23 questions
• 5 point Likert scale
1. Strongly Agree
2. Agree
3. Undecided
4. Disagree
5. Strongly Disagree
• Online based
• Name
• Age
• Designation
• Qualification
• Years of experience
• Organization’s Name

9. Responses
• Need for high quality
data
• More number of
responses
• 1:5 ratio
Example: 23 questions
should minimum expect
115 responses
• 135 RE companies list
• 64 MNRE officials list

10. Life cycle of KM
Create
Store
Share
Disseminate
Utilize

11. KM Activities
Knowledge creation(KC)
Knowledge storing(KST)
Knowledge sharing(KSH)
Knowledge disseminating(KD)
Knowledge utilization(KU)

12. 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

13. Hypothesis
KM 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

14. SPSS
• Statistical package for social sciences
• Factor analysis- Rotated component matrix
• Cronbach’s alpha- consistency
• Linear regression analysis- 𝑟2
• ANOVA- Significance value
• KMO and Bartlett’s Test- Adequacy and
significance

15. Data Viewer

16. Output Viewer

17. Cronbach’s Alpha
• expression for the standardized Cronbach's α value:
• α =
𝑁 . 𝑐
𝑣+ 𝑁−1 .𝑐
where N is equal to the number of items, c is the average
co-variance among the items and v
indicates the average variance. One can see from this
formula that if you increase the number of
items, you increase Cronbach's α.

18. 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)

19. Reliability
Reliability Statistics:
Cronbach's Alpha
Cronbach's Alpha Based on
Standardized Items
N of Items
.896 .903 42

20. 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

21. Regression table
Model R
R
Square
Change Statistics
Durbin-
WatsonR Square
Change
F
Change Df1 Df2
Sig. F
Change
1 .826a .683 .683 36.134 5 84 .000 1.537

22. ANOVA
Sig.- This value indicates the exact significance of
ANOVA 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.

23. ANOVA table
ANOVA
Model
Sum 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

24. KMO and Bartlett’s Test
• Kaiser-Meyer-Olkin  Sampling adequacy
• Bartlett’s test  Significance of data

25. 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

26. Performance Index
KMPI 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)

27. 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.

28. 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)

29. 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

30. 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/

31. Appendix-I
FACTOR 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