World Conference on Waste Management...2019/04/03 · • Around 5.6 million tonnes per annum (TPA)...

©M. S. Ramaiah University of Applied Sciences

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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


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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


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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


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Reverse Vending Machine


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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


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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


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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


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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)

Reverse Vending Machine.mp4


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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


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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


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Conceptual Model (RO2)

Willingness

Awareness

Convenience

Incentives

Involvement

Dependent Independent


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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


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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


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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


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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


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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


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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


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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


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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


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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


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Structural Equation Modelling


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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


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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


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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)





The multi-group analysis allows to test if pre-defined data groups have significantdifferences in their group-specific parameter estimates


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Occupation p-Values (GROUP 1 - Student) p-Values (GROUP 2 - Others)





Marital Status p-Values (GROUP 1 - Married)p-Values (GROUP 2 -

Unmarried)





Multi Group Analysis


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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


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Limitations

• Conducted in the Bangalore North region only hence it

cannot be generalized to who of Bangalore or India


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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


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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].


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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.

World Conference on Waste Management...2019/04/03 · • Around 5.6 million tonnes per annum (TPA)...

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Transcript of World Conference on Waste Management...2019/04/03 · • Around 5.6 million tonnes per annum (TPA)...