world conference on waste management...2019/04/03 · • around 5.6 million tonnes per annum (tpa)...
TRANSCRIPT
©M. S. Ramaiah University of Applied Sciences
1
World Conference on Waste Management“Towards a Sustainable Future Through Strategic Waste Management”
08th March 2019 | Colombo | Sri Lanka
A Study on Challenges for Adoption of Reverse
Vending Machine: A Case of North Bangalore, India
Authors:Pramita S.K (Presenter), Dr. Mamatha.S.V, Abhishek Gowda S,
Prathamesh Mhatre, Deeksha R, Srikanth .U
Ramaiah University of Applied Sciences,
Bangalore, India
©M. S. Ramaiah University of Applied Sciences
2
Introduction
• Around 5.6 million tonnes per annum (TPA) of plastic waste is generated
in India, which is about 15,342 tonnes per day (TPD)
• The amount of plastic
generated per million
tonnes in Bangalore is
84.83 KG
62.81
116.79
102.98
95.42
84.83
0 20 40 60 80 100 120 140
MUMBAI
KOLKATA
DELHI
CHENNAI
BANGALORE
Plastic waste generated in KG per million tonnes
©M. S. Ramaiah University of Applied Sciences
3
Literature Reviews
Author Year Variables Research Focus
Suchismita Satapathy 2017Driving Power Dependence
An analysis of barriers for plastic recycling in
the Indian plastic industry
Dr. Pawan Sikka 2007Government
InitiativesPlastic Waste
Management in India
Razali Tomari, AeslinaKadirb et al
2016Weight
Type Price
Development of RVM framework for
implementation to a standard recycle bin
Jayant Singh 2019PenaltiesIncentives
Waste Management Laws in India: Plastic & Biomedical Wastes
RVM = Reverse Vending Machine
©M. S. Ramaiah University of Applied Sciences
4
Reverse Vending Machine
©M. S. Ramaiah University of Applied Sciences
5
Literature Reviews
Author Year Variables Research Focus
Shaufique F. Sidiquea, Satish V. Joshi, Frank
Lupi2009
PricingRegulationsEducation
Factors influencing the rate of recycling: An analysis of Minnesota counties
John Momoh and Oladebeye D. H.
2010Awareness
AttitudeWillingness
Assessment people’s willingness to participate in
household solid waste recycling program
Tanya Domina and Kathryn Koch
2002Accessibility Convenience
Frequency Convenience and Frequency of Recycling
Yunchang Jeffrey Bora, Yu-Lan Chien
and Esher Hsu2004
Incentives Policy
The market-incentive recycling system for waste packaging containers in Taiwan
Patricia Valle, EfigenioRebelio, Elizabeth Reis
and Joao Menezes2005
Involvement Convenience
Behavior
Combining Behavioral Theories to Predict Recycling Involvement
©M. S. Ramaiah University of Applied Sciences
6
Identified Gaps
• Literature Gaps
– 60% of the papers studied concentrate on solid waste management and
segregation
– Literature has not focused on users’ perspective of efficient recycling
methods
– Geographically limited, no papers about RVMs in India
– Fails to explore reasons for lack of policies
©M. S. Ramaiah University of Applied Sciences
7
The existing RVM’s were visited and it was observed that the machines were either dysfunctional or inefficient
The locations of the RVM’s were : 1) Cantonment Railway
Station (Central)
2) Majestic Metro Station
(Central)
3) Yeshwanthpur Railway
Station (North)
4) KSR Railway Station (East)
Field Study
©M. S. Ramaiah University of Applied Sciences
8
Field Study Observations Factors Description
Inefficiency
• Difficult to find RVM due to lack of awareness• Lack of instructions on how to use machine• The machine was not maintained regularly by the respective
authorities
Dysfunctionality
• The slot for inserting the plastic bottles was filled to the brim,blocking the entry for more bottles(Yeshwanthpur)
• The machines were irresponsive and showed noacknowledgement of any transactionReverse Vending Machine.mp4
Partialdysfunctionality
• On entering the mobile number, a message is generated to therespective number, the cashback was still not received(Majestic Station)
©M. S. Ramaiah University of Applied Sciences
9
Objectives
• To understand the extent of adoption of Reverse Vending
Machines by citizens in North Bangalore
• To analyse the factors which influence the adoption of
Reverse Vending Machines for citizens
• To recommend the methods or practices which will
improve the adoption of Reverse Vending Machines
©M. S. Ramaiah University of Applied Sciences
10
Research Methodology (Survey based)
Research problem identified
Review of literature
Field study Gaps identified
Variables identified/
Conceptual model
Survey Based Method/
Questionnaire design
Pilot StudyQuestionnaire modification
Main Study/Hypothesis
TestingInterpretation Conclusions
Practical Implications/
Recommendations
©M. S. Ramaiah University of Applied Sciences
11
Conceptual Model (RO2)
Willingness
Awareness
Convenience
Incentives
Involvement
Dependent Independent
©M. S. Ramaiah University of Applied Sciences
12
Hypotheses
• H0= Willingness to adopt RVM is not affected by Awareness
• H1= Willingness to adopt RVM is affected by Awareness
• H0= Willingness to use RVM is not influenced by convenience
• H1= Willingness to use RVM is influenced by convenience
• H0= Willingness to use RVM is not affected by the incentives received
• H1= Willingness to use RVM is affected by the incentives received
• H0= Willingness to adopt RVM is not influenced by the user’s level of involvement
• H1= Willingness to adopt RVM is influenced by the user’s level of involvement
©M. S. Ramaiah University of Applied Sciences
13
Research Design
• Survey Design
• Part A – Demographics
• Part B - Questions on Each variable using 5point Likert Scale
• Total no of questions 26
• Sampling design – Convenience Sampling
• Target audience –Citizens of North Bangalore
©M. S. Ramaiah University of Applied Sciences
14
Reliability Test for Pilot Study
Variables Cronbach’s Alpha(Pilot Data S=30)
Awareness 0.589
Willingness 0.552
Incentives 0.217
Convenience 0.329
Involvement 0.826
Cronbach's alpha is a measure of internal consistency of questionsmeasuring a latent variableAcceptable value > 0.6
©M. S. Ramaiah University of Applied Sciences
15
Sample Description Main Study
Age Marital Status
Gender Occupation
• 46% of respondents between the age group of 19-28.
• Almost equal number of male and female respondents
• 60% of them were unmarried
• 77% of the respondents consisted of Students and Employed
©M. S. Ramaiah University of Applied Sciences
16
Construct Validation-Reliability Test
Variables Cronbach’s Alpha(Main Data S=247)
Awareness 0.754
Willingness 0.876
Incentives 0.575
Convenience 0.742
Involvement 0.731
©M. S. Ramaiah University of Applied Sciences
17
Structural Equation Modelling
• Second generation, multivariate method used to assess the reliability and validity of the model measures.
• To analyse the relationship between the dependent variables and the dependent variables
• Multiple regressions at the same time
• Consists of two parts – Structural model and Measurement model
©M. S. Ramaiah University of Applied Sciences
18
Measurement Model
• The measurement model is the part which relates
measured variables to latent variables
• Validity of questions
• If it isn’t holding good, correlation cannot be found
• To measure construct validity
©M. S. Ramaiah University of Applied Sciences
19
Factor LoadingsItems Awareness Convenience Incentives Involvement Willingness
AW02 0.810
AW03 0.917
CN01 0.794
CN02 0.622
CN03 0.775
CN04 0.602
CN05 0.733
IN01 0.732
IN04 0.642
IN05 0.729
IV01 0.738
IV02 0.581
IV04 0.829
IV05 0.738
IV06 0.702
WI01 0.858
WI02 0.807
WI03 0.828
WI04 0.901
WI05 0.684
©M. S. Ramaiah University of Applied Sciences
20
Structural Model
• The structural model is the part that relates latent
variables to one another
• Which independent variable contributes how
much towards the dependent variable
• Used to find the relation between latent variables
©M. S. Ramaiah University of Applied Sciences
21
Structural Equation Modelling
©M. S. Ramaiah University of Applied Sciences
22
Research hypothesisObjecti
vesHypothesis
Test done
Values (β,p Value and R square adjusted)
Inference
RO3
H0= Willingness to adopt RVM is not affected by awarenessH1= Willingness to adopt RVM is affected by Awareness
PLS SEM
β = 0.185p-value > 0.01R2 = 0.030
H0 is accepted, hence Awareness has no effect on Willingness
H0= Willingness to use RVM is not influenced by convenienceH1= Willingness to use RVM is influenced by convenience
PLS SEM
β = 0.452p-value < 0.01R2 = 0.201
H0 is rejected hence Convenience influences willingness
H0= Willingness to use RVM is not affected by the incentives receivedH1= Willingness to use RVM is affected by the incentives received
PLS SEM
β = 0.309p-value < 0.01R2 = 0.092
H0 is rejected hence Incentives influences willingness
H0= Willingness to adopt RVM is not influenced by the user’s level of involvementH1= Willingness to adopt RVM is influenced by the user’s level of involvement
PLS SEM
β = 0.606p-value < 0.01R2 = 0.365
H0 is rejected hence Involvement influences willingness
©M. S. Ramaiah University of Applied Sciences
23
Interpretation
Results show that convenience and involvement were the 2 primary
variables that affect a persons willingness to use a Reverse Vending
Machine because most variance in the data is explained by these
variables
Convenience Involvement
©M. S. Ramaiah University of Applied Sciences
24
Multi Group Analysis
Agep-Values (GROUP 1 – 28 years and
below)
p-Values (GROUP 2 – 29 years and
above)
Willingness -> Awareness 0.000 0.324
Willingness -> Convenience 0.000 0.000
Willingness -> Incentives 0.190 0.209
Willingness -> Involvement 0.000 0.000
Gender p-Values (GROUP 1 – Male) p-Values (GROUP 2 - Female)
Willingness -> Awareness 0.003 0.073
Willingness -> Convenience 0.000 0.000
Willingness -> Incentives 0.000 0.087
Willingness -> Involvement 0.000 0.000
The multi-group analysis allows to test if pre-defined data groups have significantdifferences in their group-specific parameter estimates
©M. S. Ramaiah University of Applied Sciences
25
Occupation p-Values (GROUP 1 - Student) p-Values (GROUP 2 - Others)
Willingness -> Awareness 0.000 0.200
Willingness -> Convenience 0.000 0.000
Willingness -> Incentives 0.000 0.049
Willingness -> Involvement 0.000 0.000
Marital Status p-Values (GROUP 1 - Married)p-Values (GROUP 2 -
Unmarried)
Willingness -> Awareness 0.282 0.000
Willingness -> Convenience 0.000 0.000
Willingness -> Incentives 0.299 0.000
Willingness -> Involvement 0.025 0.000
Multi Group Analysis
©M. S. Ramaiah University of Applied Sciences
26
Practical Applicability/Recommendations
Variables Recommendations
Convenience • Install RVM’s in educational institutions andoffices
Incentives • Beverage Vending Machine
Involvement • Green Campaigns• Workshops• Enforcement Laws
Policies • Deducted as part of GST
©M. S. Ramaiah University of Applied Sciences
27
Limitations
• Conducted in the Bangalore North region only hence it
cannot be generalized to who of Bangalore or India
©M. S. Ramaiah University of Applied Sciences
28
Future Research
• Extend the Study to other parts of India so that it can be
generalized
• Studies can focus of retailers and other sectors which
used plastics heavily
• Technical aspects, dysfunctionalities of RVM
• The same model can be expanded or altered
©M. S. Ramaiah University of Applied Sciences
29
References- Ghosh, S. (2018). India to galvanise greater action against plastic
waste on World Environment Day. [online] Mongabay-India. Available at: https://india.mongabay.com/2018/04/india-to-galvanise-greater-action-against-plastic-waste-on-world-environment-day/ [Accessed 8 Mar. 2019].
- Prasher, G. (2018). The plastic footprint of India. [online] Citizen Matters. Available at: http://citizenmatters.in/india-plastic-waste-world-environment-day-6800 [Accessed 8 Mar. 2019].
- Satapathy, S. (2017). An analysis of barriers for plastic recycling in the Indian plastic industry | Benchmarking: An International Journal | Vol 24, No 2. [online] Emeraldinsight.com. Available at: https://www.emeraldinsight.com/doi/abs/10.1108/BIJ-11-2014-0103 [Accessed 3 Mar. 2019].
©M. S. Ramaiah University of Applied Sciences
30
References- Sikka, P. (2007). Plastic waste management in India. [online]
Waste.com.br. Available at: http://waste.com.br/textos/PLASTIC%20WASTE%20MANAGEMENT%20IN%20INDIA.pdf [Accessed 3 Mar. 2019].
- Tomari, R., Kadir, A.A., Zakaria, W.N.W., Zakaria, M.F., Wahab, M.H.A. and Jabbar, M.H., 2017. Development of reverse vending machine (RVM) framework for implementation to a standard recycle bin. Procedia Computer Science, 105, pp.75-80.
- Singh, J., 2019. Waste Management Laws in India: Plastic & Biomedical Wastes. Available at SSRN 3311161.