Factors influencing the behavior in recycling of e-waste using integrated TPB and NAM model

Figures & data

Table 1. Sample distribution (n = 415).

Variable	Category	Total	Percentage
Gender	Male	244	58.79
	Female	171	41.21
Age group	18–25	219	90.36
	26–35	196	9.64
Qualification	UG	219	52.77
	PG	157	37.83
	PhD	39	9.40

Table 1 indicates descriptive data that shows majority of respondents were male (58.79%), and were of age group 18–25 (90.36%) pursuing their undergraduate, postgraduate, and Ph.D. programs. It was also found that the majority of the respondents were pursuing their undergraduate program (52.77%)

Table 2. Description of the measurement items in the questionnaire.

Variables	Indicators	Loading factors	Cronbach's alpha	Composite reliability	Average variance extracted (AVE)
Disposal Behaviour	DB3	0.829	0.734	0.832	0.564
(DB)	DB4	0.854
	DB5	0.788
Convenience	CON1	0.776	0.607	0.782	0.501
(Conv)	CON2	0.791
	CON3	0.840
Data Security	DS1	0.592	0.669	0.801	0.504
(DS)	DS2	0.703
	DS4	0.745
	DS5	0.784
Environmental Knowledge	EK2	0.720	0.780	0.850	0.532
(EK)	EK3	0.785
	EK4	0.704
	EK5	0.693
	EK6	0.742
Intention	INT1	0.791	0.838	0.885	0.607
(Int)	INT2	0.760
	INT3	0.777
	INT4	0.760
	INT5	0.805
Personal Norms	PN1	0.812	0.713	0.839	0.635
(PN)	PN2	0.806
	PN3	0.773
Public Awareness	PA1	0.688	0.790	0.855	0.541
(PA)	PA2	0.699
	PA3	0.722
	PA4	0.777
	PA5	0.786
Publicity	PUB1	0.677	0.774	0.847	0.528
(Pub)	PUB2	0.638
	PUB4	0.823
	PUB5	0.769
	PUB6	0.710
Willingness to pay	WTP1	0.762	0.713	0.823	0.538
(WTP)	WTP2	0.645
	WTP3	0.753
	WTP4	0.767
Infrastructure	INFR1	0.700	0.691	0.811	0.519
(Infra)	INFR2	0.719
	INFR3	0.653
	INFR4	0.802

The components DB1, DB2, DS3, CON 4, CON 5, EK1, PUB 3, WTP5, and INFRA5 have been removed as their factor loadings were less than 0.5.

Table 3. Discriminant validity test results.

Variables	Item indicators	Correlations
		DB	Conv	DS	EK	Int	PN	PA	Pub	WTP	Infra
DB	4	0.751
Conv	4	0.316	0.708
DS	4	0.229	0.417	0.710
EK	5	0.500	0.214	0.048	0.730
Int	5	0.662	0.342	0.236	0.547	0.779
PN	3	0.596	0.281	0.216	0.569	0.651	0.797
PA	5	0.479	0.123	0.051	0.650	0.501	0.557	0.736
Pub	5	0.433	0.315	0.212	0.482	0.500	0.461	0.375	0.726
WTP	4	0.441	0.312	0.168	0.572	0.558	0.508	0.494	0.398	0.734
Infra	4	0.414	0.132	−0.015	0.594	0.461	0.467	0.544	0.349	0.503	0.721

Note: Bold diagonals indicate the square root of AVE.

Table 4. Heterotrait–Monotrait ratio (HTMT).

	DB	Conv	DS	EK	Int	PN	PA	Pub	WTP	Infra
DB
Conv	0.472
DS	0.334	0.641
EK	0.662	0.328	0.158
Int	0.817	0.470	0.309	0.675
PN	0.783	0.430	0.302	0.760	0.841
PA	0.602	0.165	0.102	0.815	0.597	0.723
Pub	0.569	0.454	0.295	0.622	0.612	0.622	0.467
WTP	0.571	0.455	0.231	0.770	0.716	0.708	0.643	0.529
Infra	0.590	0.223	0.125	0.805	0.599	0.658	0.730	0.482	0.715

Table 5. Inner VIF values.

	Beh	Conv	DS	EA	INT	PN	PA	PUBs	WTP	Infra
Behavior
Convenience					1.355
Data Security					1.251
Environmental Knowledge					2.373
Intention	1.736
Personal Norms	1.736
Public Awareness					1.912
Publicity					1.441
Willingness to pay					1.748
infrastructure					1.754

Figure 1. Integrated TPB-NAM Conceptual model showing the research hypothesis presented in the study.

Figure 2. Outer model with factor loading value.

The structural model in Figure 2 demonstrates strong predictive relevance, as indicated by a Q² value surpassing 0. The values exceed 0.350, suggesting a high level of robustness in the model (Alexander et al., 2012).

Table 6. Predictive relevance using Q² and R² values.

	Q²_predict	R²	Effect
Behavior	0.414	0.485	High
Intention	0.448	0.482	High

Table 7. Path coefficients for the structural model.

Hypothesis	Paths	Estimate (Beta)	t-Stat	Hypothesis result
H1	Public Awareness -> Intention	0.156	3.282**	Supported
H2	Environmental Knowledge -> Intention	0.133	2.112*	Supported
H3	Willingness to pay -> Intention	0.225	3.877***	Supported
H4	infrastructure -> Intention	0.102	2.170*	Supported
H5	Publicity -> Intention	0.197	4.049***	Supported
H6	Convenience -> Intention	0.107	2.483*	Supported
H7	Data Security -> Intention	0.099	2.259*	Supported
H8	Personal Norms -> Behaviour	0.287	5.816***	Supported
H9	Intention -> Behaviour	0.474	9.436***	Supported

Note: +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001.

Table 8. Each variable's indirect, direct, and total effects on the e-waste behavior disposal system.

Variables	Indirect effects	Direct effect	Total effects	p-Values
Convenience	0.051	0.107	0.158*	0.013
Data Security	0.047	0.099	0.146*	0.024
Environmental Knowledge	0.063	0.133	0.196*	0.035
Intention	0.474	0.000	0.474***	0.000
Personal Norms	0.287	0.000	0.287***	0.000
Public Awareness	0.074	0.156	0.230**	0.001
Publicity	0.094	0.197	0.291***	0.000
Willingness to pay	0.107	0.225	0.331***	0.000
infrastructure	0.049	0.102	0.151*	0.031

Note: +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001.

Table A1. Constructs with their items and questions.

Constructs	Items	Questions	Ref.	Items reversed	Items deleted
Public Awareness	PA1	I am often concerned about environmental issues	Z. Wang et al., 2016	No	No
	PA2	I think everyone should contribute to environmental protection	Thi Thu Nguyen et al., 2019	No	No
	PA3	It's wrong to dispose electronic waste and regular waste together		Yes	No
	PA4	Disposing e-waste in a proper manner is my responsibility.		No	No
	PA5	I feel very satisfied when recycling e-waste.		No	No
Environmental Knowledge	EK1	When I buy electrical and electronic equipment I consider the harmful effects towards the environment.	Islam et al., 2016	No	No
	EK2	I know that recycling preserves natural resources for the benefit of present and future generations.	Afroz et al., 2013	No	No
	EK3	Recycling my old electrical or electronic appliances is good for the environment.	Nduneseokwu et al., 2017	No	No
	EK4	I know that e-waste is rich in recyclable components.		No	No
	EK5	Recycling my old electronic appliances is good for my health and my family’s health.		No	No
	EK6	E-waste can be a resource if properly managed.		No	No
Willingness to Pay	WTP1	I am Willing to share a part of recycling cost, only if there is recycling facility/collection center nearby	Kwatra et al., 2014	No	No
	WTP2	Willing to share a part of recycling cost, is not consumer’s responsibility alone	Shaikh et al., 2020	Yes	No
	WTP3	Willingness to take responsibility for recycling of E-waste		No	No
	WTP4	Willingness to pay a charge for disposal of e-waste		No	No
	WTP5	I want the government to pay for the disposal and recycling of e-waste		Yes	Yes
Infrastructure	Infra1	The e-waste collection centers have to be properly managed.	Nduneseokwu et al., 2017	No	No
	Infra2	I will drop-off my e-waste if the government provides adequate infrastructure.		No	No
	Infra3	E-waste collection centers have to be situated close to the community/residential area.		No	No
	Infra4	The collection centers ought to be a sustainable establishment.		No	No
	Infra5	There are many e-waste collection facilities near my house.		No	No
Advertising and Promotions - Publicity	Pub1	Publicity will help me to know that, e-waste is the reason to pollute the environment or endanger human health.	Z. Wang et al., 2018	No	No
	Pub2	Publicity will help me to know there are still many recyclable components available in e-waste		No	No
	Pub3	I think the relevant information about e-waste recycling publicity is important.		No	No
	Pub4	Because of publicity I know the shortcomings of the informal e-waste disposal.		No	No
	Pub5	Publicity will help me to know what household e-waste can be recycled.		No	No
	Pub6	Because of publicity, I know where to dispose my household e-waste for recycling.		No	No
Convenience	Conv1	It is easy for me to go to an E-waste recycling centre	Sidique et al. (2010),	No	No
	Conv2	It is easy for me to find information on E-waste recycling	Saphores et al. (2012)	No	No
	Conv3	It is easy for me to find time to recycle E-waste		No	No
	Conv4	I have no convenient transport to send E-waste to the collection point.		Yes	Yes
	Conv5	I think surrounding E-waste recycling channels are incomplete.		Yes	Yes
Data Security	DS1	I don't know how to back-up my data before disposing.	Liu et al., 2019,	Yes	No
	DS2	I believe the data from my mobile/laptop/desktop won't be breached/leaked.		No	No
	DS3	I am concerned about my data theft.		Yes	Yes
	DS4	I believe after the disposal of my mobile/Laptop/ Desktop my recycler will destroy the available data.		No	No
	DS5	I trust my recycler		No	No
Personal Norms	PN1	I would feel proud if I actively recycled my household e-waste.	Z. Wang et al., 2018	No	No
	PN2	I would feel guilty if I did not recycle my household e-waste.		Yes	No
	PN3	Not separating waste would violate my moral principles		Yes	No
Intention	Int1	In the future when dealing with e-waste, I would like to try to contact the factory or professional recycling agencies.	Y. Zhang et al., 2020	No	No
	Int2	When I buy electronic products in the future, I tend to choose products that promise to be recycled or disposed of in a proper manner		No	No
	Int3	I intend to put extra effort into recycling E-waste		No	No
	Int4	In the future, I intend to recycle my e-waste by formal channels.		No	No
	Int5	In the future, I will actively participate in e-waste recycling and tell surrounding people about the recovery experience.		No	No
Disposal	DB1	I keep e-waste stored at home	Borthakur & Govind, 2019	No	No
	DB2	I prefer to exchange dead/used electronic waste at the store when buying a new one		No	No
	DB3	I will ensure that I dispose e-waste properly in the future		No	No
	DB4	I would feel guilty if I did not recycle my household e-waste		Yes	No
	DB5	When I buy electronic products in the future, I tend to choose products that promise to be recycled		No	No

Alexander, M., MacLaren, A., O’Gorman, K., & Taheri, B. (2012). “He just didn’t seem to understand the banter”: Bullying or simply establishing social cohesion? Tourism Management, 33, 1245–1255. https://doi.org/10.1016/j.tourman.2011.11.001

 Web of Science ®Google Scholar

Wang, Z., Guo, D., & Wang, X. (2016). Determinants of residents’ e-waste recycling behaviour intentions: evidence from China. Journal of Cleaner Production, 137, 850–860. https://doi.org/10.1016/j.jclepro.2016.07.155

 Web of Science ®Google Scholar

Thi Thu Nguyen, H., Hung, R. J., Lee, C. H., & Thi Thu Nguyen, H. (2019). Determinants of residents’ E-waste recycling behavioral intention: A case study from Vietnam. Sustainability, 11(1), 164. https://doi.org/10.3390/su11010164

 Web of Science ®Google Scholar

Islam, M. T., Abdullah, A. B., Shahir, S. A., Kalam, M. A., Masjuki, H. H., Shumon, R., & Rashid, M. H. (2016). A public survey on knowledge, awareness, attitude and willingness to pay for WEEE management: Case study in Bangladesh. Journal of Cleaner Production, 137, 728–740. https://doi.org/10.1016/j.jclepro.2016.07.111

 Web of Science ®Google Scholar

Afroz, R., Masud, M. M., Akhtar, R., & Duasa, J. B. (2013). Survey and analysis of public knowledge, awareness and willingness to pay in Kuala Lumpur, Malaysia–A case study on household WEEE management. Journal of Cleaner Production, 52, 185–19s3. https://doi.org/10.1016/j.jclepro.2013.02.004

 Web of Science ®Google Scholar

Nduneseokwu, C. K., Qu, Y., & Appolloni, A. (2017). Factors influencing consumers’ intentions to participate in a formal e-waste collection system: A case study of Onitsha, Nigeria. Sustainability, 9(6), 881. https://doi.org/10.3390/su9060881

 Web of Science ®Google Scholar

Kwatra, S., Pandey, S., & Sharma, S. (2014). Understanding public knowledge and awareness on e-waste in an urban setting in India. Management of Environmental Quality: An International Journal, 25(6), 752–765. https://doi.org/10.1108/MEQ-12-2013-0139

 Google Scholar

Shaikh, S., Thomas, K., & Zuhair, S. (2020). An exploratory study of e-waste creation and disposal: Upstream considerations. Resources, Conservation and Recycling, 155, 104662. https://doi.org/10.1016/j.resconrec.2019.104662

 Web of Science ®Google Scholar

Wang, Z., Guo, D., Wang, X., Zhang, B., & Wang, B. (2018). How does information publicity influence residents’ behaviour intentions around e-waste recycling? Resources, Conservation and Recycling, 133, 1–9. https://doi.org/10.1016/j.resconrec.2018.01.014

 Web of Science ®Google Scholar

Sidique, S. F., Lupi, F., & Joshi, S. V. (2010). The effects of behavior and attitudes on drop-off recycling activities. Resources, Conservation and Recycling, 54(3), 163–170. https://doi.org/10.1016/j.resconrec.2009.07.012

 Web of Science ®Google Scholar

Saphores, J. D. M., Ogunseitan, O. A., & Shapiro, A. A. (2012). Willingness to engage in a pro-environmental behavior: An analysis of e-waste recycling based on a national survey of US households. Resources, Conservation and Recycling, 60, 49–63. https://doi.org/10.1016/j.resconrec.2011.12.003

 Web of Science ®Google Scholar

Liu, J., Bai, H., Zhang, Q., Jing, Q., & Xu, H. (2019). Why are obsolete mobile phones difficult to recycle in China? Resources, Conservation and Recycling, 141, 200–210. https://doi.org/10.1016/j.resconrec.2018.10.030

 Web of Science ®Google Scholar

Zhang, Y., Wu, S., & Rasheed, M. I. (2020). Conscientiousness and smartphone recycling intention: The moderating effect of risk perception. Waste Management, 101, 116–125. https://doi.org/10.1016/j.wasman.2019.09.040

 PubMed Web of Science ®Google Scholar

Borthakur, A., & Govind, M. (2019). Computer and mobile phone waste in urban India: An analysis from the perspectives of public perception, consumption and disposal behaviour. Journal of Environmental Planning and Management, 62(4), 717–740. https://doi.org/10.1080/09640568.2018.1429254

 Web of Science ®Google Scholar