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

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.

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

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

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

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

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

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

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

Life cycle of KM

Create

Store

Share

Disseminate

Utilize

KM Activities

Knowledge creation(KC)

Knowledge storing(KST)

Knowledge sharing(KSH)

Knowledge disseminating(KD)

Knowledge utilization(KU)

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

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

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

Data Viewer

Output Viewer

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.

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)

Reliability

Reliability Statistics:

Cronbach's AlphaCronbach's Alpha Based on Standardized Items

N of Items

.896 .903 42

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

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

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.

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

KMO and Bartlett’s Test

• Kaiser-Meyer-Olkin Sampling adequacy• Bartlett’s test Significance of data

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

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)

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.

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)

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

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/

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