Restricted accessResearch articleFirst published online 2026
Antecedents of mobile e-waste recycling behaviour: A study of planned intentions and direct behavioural triggers among adolescents and young Indian adults
This study investigates the psychological pathways influencing mobile e-waste recycling behaviour among adolescents and young Indian adults (AYAs), a critical issue given the nation’s rising volume of electronic waste. We examine the mediating role of recycling intention in the relationship between key psychological antecedents – environmental concerns (EC), normative interpersonal influence (NII), perceived psychological obsolescence (PPO) and information security risk perception (ISRP) – and actual recycling behaviour utilising the theory of planned behaviour (TPB). Using survey data from 368 AYAs, a generalized linear model mediation analysis with 5000 bootstrap resamples was conducted. The results revealed distinct operational pathways for different predictors. EC was found to significantly predict recycling intention in a partially mediated relationship that also included a direct effect on behaviour. Conversely, NII, PPO and ISRP did not significantly predict recycling intention; instead, they exerted a significant direct positive influence on subsequent recycling behaviour. Theoretically, this research reveals a dual-pathway mechanism within an extended TPB: Value-based constructs operate via planned intentions, while pragmatic factors (social cues, obsolescence, security risks) directly trigger behaviour. Practically, the findings explain the prevalent e-waste hoarding phenomenon in India as a rational, security-driven response. This identifies direct behavioural triggers, alongside intention, as key targets for future interventions.
AdhikariKRoyAS (2023) E-waste by mobile phones: A case study on the consumption, disposal behavior, and awareness of consumers in Kolkata, India. Bulletin of Science, Technology & Society43: 55–66.
2.
AhossouheMSAndrianisaHADambaDD, et al. (2025) Scenario-based extended cost–benefit analysis for e-waste metal recovery in low-income countries: Evidence from an integrated model in Burkina Faso. Sustainability17: 8351.
3.
AjzenI (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes50: 179–211.
4.
AryaSKumarS (2020) E-waste in India at a glance: Current trends, regulations, challenges and management strategies. Journal of Cleaner Production271: 122707.
5.
AwasthiAKWangMWangZ, et al. (2018) E-waste management in India: A mini-review. Waste Management & Research36: 408–414.
6.
BaldéC. P.KuehrR.YamamotoT.McDonaldR.D’AngeloE.AlthafS.BelG.DeubzerO.Fernandez-CubilloE.FortiV.GrayV.HeratS.HondaS.IattoniG.KhetriwalD. S.Luda di CortemigliaV.LobuntsovaY.NnoromI.PralatN.WagnerM. (2024). Global e-waste monitor 2024. International Telecommunication Union (ITU) & United Nations Institute for Training and Research (UNITAR).
7.
Ben YahyaTJamalNMSundarakaniB, et al. (2023) The effects of data security and perceived benefits on mobile phone recycling behaviour and the recycling intention mediation role. Recycling8: Article 1.
8.
BiliciFÖzdemirE (2024) Consumer perception of planned obsolescence: A research on smartphone owners. Journal of Business Research – Turk16: 1788.
9.
BorthakurASinghP (2022) Understanding consumers’ perspectives of electronic waste in an emerging economy: A case study of New Delhi, India. Energy, Ecology & Environment7: 199–212.
10.
BotetzagiasIGrigorakiEKokkorisGD (2024) Using the theory of planned behavior for explaining mobile phone recycling: The role of subjective norms. Sustainability16: 8773.
11.
BurnsB. (2010). Re-evaluating obsolescence and planning for it. In CooperT. (Ed.), Longer lasting products. London: Routledge.
12.
CanovaLManganelliAM (2020) Energy-saving behaviours in workplaces: Application of an extended model of the theory of planned behaviour. Europe’s Journal of Psychology16: 384–400.
13.
CenciMPEidelweinEMVeitHM (2023) Composition and recycling of smartphones: A mini-review on gaps and opportunities. Waste Management & Research41: 1512–1528.
14.
ChangY-SYueZQureshiM, et al. (2022) Residents’ waste mobile recycling planned behavior model: The role of environmental concern and risk perception. International Journal of Emerging Markets18: 6388–6406.
15.
ChaudharyKMathiyazhaganKVratP (2017) Analysis of barriers hindering the implementation of reverse supply chain of electronic waste in India. International Journal of Advanced Operations Management9: 143.
16.
ChenHTurelOYuanY (2021) E-waste information security protection motivation: The role of optimism bias. Information Technology & People35: 600–620.
17.
ChenM-F (2015) Self-efficacy or collective efficacy within the cognitive theory of stress model: Which more effectively explains people’s self-reported proenvironmental behavior?Journal of Environmental Psychology42: 66–75.
18.
CialdiniR. B.KallgrenC. A.RenoR. R. (1991). A Focus Theory of Normative Conduct: A Theoretical Refinement and Reevaluation of the Role of Norms in Human Behavior. Advances in Experimental Social Psychology, 24, 201-234. https://doi.org/10.1016/s0065-2601(08)60330-5
19.
ClarkRAGoldsmithRE (2006) Global innovativeness and consumer susceptibility to interpersonal influence. Journal of Marketing Theory and Practice14: 275–285.
20.
CooperT (2004) Inadequate life? Evidence of consumer attitudes to product obsolescence. Journal of Consumer Policy27: 421–449.
21.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly13: 319–340.
22.
DowlingGRStaelinR (1994) A model of perceived risk and intended risk-handling activity. Journal of Consumer Research21: 119–134.
23.
DunlapR. E.JonesR. E. (2002). Environmental concern: Conceptual and measurement issues. In DunlapR. E.MichelsonW. (Eds.), Handbook of environmental sociology (pp. 482–524). Westport, CT: Greenwood Press.
24.
FelsAFalkBSchmittR (2016) Social media analysis of perceived product obsolescence. Procedia CIRP50: 571–576.
25.
FishbeinM.AjzenI. (2010). Predicting and changing behavior: The reasoned action approach (1st ed.). New York: Psychology Press. https://doi.org/10.4324/9780203838020
26.
GautamSJainS (2022) Managing electronic waste: A qualitative inquiry into the behaviour of young Indian consumers. Global Business Review. Epub ahead of print 22 September 2022. DOI: 10.1177/09721509221121714.
27.
GawandeAJainNKBhattacharyyaSS (2024) Making the invisible count: Factors influencing small e-waste recycling intentions in the age of rapid electronics consumption. International Social Science Journal74: 1611–1631.
28.
GhimireHAriyaPA (2020) E-wastes: Bridging the knowledge gaps in global production budgets, composition, recycling and sustainability implications. Sustainable Chemistry1: 154–182.
29.
HaggerMSHamiltonK (2025) Progress on theory of planned behavior research: Advances in research synthesis and agenda for future research. Journal of Behavioral Medicine48: 43–56.
30.
HayesAF (2018) Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs85: 4–40.
31.
HoggMATerryDJ (2000) Social identity and self-categorization processes in organizational contexts. The Academy of Management Review25: 121–140.
32.
HunterJD (2007) Matplotlib: A 2D graphics environment. Computing in Science & Engineering9: 90–95.
33.
HuoXZouXZhangY, et al. (2025) Driving factors of pro-environmental behavior among rural tourism destination residents-considering the moderating effect of environmental policies. Scientific Reports15: 7663.
34.
IndrawatiIWidarmantiTRamantokoG, et al. (2024) Towards a unified model of planned obsolescence and innovation adoption in consumer behavior: A literature review and conceptual proposition using the stimulus-organism-response framework. Management and Production Engineering Review15: 25–41.
35.
IslamMHIslamMTHoqueMR, et al. (2025) Understanding young consumers’ e-waste recycling behaviour in Bangladesh: A developing country perspective. Recycling10: Article1.
36.
JoonVShahrawatRKapahiM (2017) The emerging environmental and public health problem of electronic waste in India. Journal of Health and Pollution7: 1–7.
37.
KeeganTHMAbrahãoRAlvarezE (2024) Survival trends among adolescent and young adults diagnosed with cancer in the United States: Comparisons to children and older adults. Journal of Clinical Oncology42: 630–641.
38.
KhetriwalDSKraeuchiPWidmerR (2009) Producer responsibility for e-waste management: Key issues for consideration – Learning from the Swiss experience. Journal of Environmental Management90: 153–165.
39.
KochanCGPourrezaSTranH, et al. (2016) Determinants and logistics of e-waste recycling. The International Journal of Logistics Management27: 52–70.
40.
KumarA (2019) Exploring young adults’ e-waste recycling behaviour using an extended theory of planned behaviour model: A cross-cultural study. Resources, Conservation and Recycling141: 378–389.
41.
LiBYangJSongX, et al. (2012) Survey on disposal behaviour and awareness of mobile phones in Chinese University students. Procedia Environmental Sciences16: 469–476.
42.
LiWLiangHWangW, et al. (2024) Global cancer statistics for adolescents and young adults: Population based study. Journal of Hematology & Oncology17: 1–14.
43.
LiuTWangPWuS, et al. (2023) Achieving a win-win situation by promoting internet recycling of waste electronics and preventing information leakage in a multi-party game. Heliyon9: e19903.
44.
LuCYiXZouY (2024) Does the new municipal solid waste management regulation promote residents’ waste separation behaviour? Evidence from survey data in Beijing, China. Waste Management & Research42: 556–568.
45.
LyuTChenHGuoY (2023) Investigating innovation diffusion, social influence, and personal inner forces to understand people’s participation in online e-waste recycling. Journal of Retailing and Consumer Services73: 103366.
46.
MahmudISultanaSRahmanA, et al. (2020) E-waste recycling intention paradigm of small and medium electronics store managers in Bangladesh: An S-O-R perspective. Waste Management & Research38: 1438–1449.
47.
MakovTFitzpatrickC (2021) Is repairability enough? Big data insights into smartphone obsolescence and consumer interest in repair. Journal of Cleaner Production313: 127561.
48.
McKinneyW (2010) Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp.51-56, 28 June-3 July 2010, Austin, TX, United States, scipy. https://doi.org/10.25080/Majora-92bf1922-00a
49.
NadarajanPVafaei-ZadehAHanifahH, et al. (2023) Sustaining the environment through e-waste recycling: An extended valence theory perspective. Aslib Journal of Information Management76: 1059–1087.
50.
NewazMSAppolloniA (2024) Evolution of behavioral research on E-waste management: Conceptual frameworks and future research directions. Business Strategy and the Environment33: 477–503.
51.
NieXZhouYPanS, et al. (2025) Peer influence on adolescents’ physical activity: A dual-process perspective. Applied Psychology: Health and Well-Being17: e70042.
52.
NowakowskiP (2016) The influence of residents’ behaviour on waste electrical and electronic equipment collection effectiveness. Waste Management & Research34: 1126–1135.
53.
OsibanjoONnoromIC (2007) The challenge of electronic waste (e-waste) management in developing countries. Waste Management & Research25: 489–501.
54.
PrabhuNSMajhiR (2024) An empirical investigation to understand mobile phone users’ behavioural intention to give their end-of-life mobile phones for formal recycling. Waste Management177: 34–45.
55.
R Core Team (2024) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
56.
RahmanAAi PingTMahmudI, et al. (2025) Investigating the factors affecting the intention to separate e-waste among mobile phone repairers in an emerging economy: A hybrid structural equation modelling and artificial neural network approach. Waste Management & Research43: 1559–1576.
57.
SheoranMDas GuptaD (2023) International best practices for e-waste take back and policy interventions for India. Facilities42: 376–404.
58.
Shreyas MadhavAVRajaramanRHariniS, et al. (2022) Application of artificial intelligence to enhance collection of E-waste: A potential solution for household WEEE collection and segregation in India. Waste Management & Research40: 1047–1053.
59.
SinghSDasguptaMSRoutroyS (2022) Evaluation of sustainable e-waste collection method for urban and rural region of India. Waste Management & Research40: 545–555.
60.
SternPC (2000) Toward a coherent theory of environmentally significant behavior. Journal of Social Issues56: 407–424.
61.
StoetG (2010) PsyToolkit: A software package for programming psychological experiments using Linux. Behavior Research Methods42: 1096–1104.
62.
StoetG (2017) PsyToolkit: A novel web-based method for running online questionnaires and reaction-time experiments. Teaching of Psychology44: 24–31.
63.
TaylorSToddPA (1995) Understanding information technology usage: A test of competing models. Information Systems Research6: 144–176.
64.
The jamovi project (2024) Jamovi (Version 2.6) [Computer software]. Available at: https://www.jamovi.org (accessed 17 April 2025).
65.
TorabiZ-ARezvaniMRHallCM, et al. (2025) Place identity and environmental conservation in heritage tourism: Extending the theory of planned behavior to iranian rural heritage villages. Tourism and Hospitality6: 150.
66.
TsushimaE (2022) Interpreting results from statistical hypothesis testing: Understanding the appropriate p-value. Physical Therapy Research25: 49–55.
67.
Van RossumGDrakeFL (2009) Python 3 Reference Manual. Scotts Valley, CA: CreateSpace.
68.
VenkateshVMorrisMGDavisGB, et al. (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27: 425–478.
69.
VermaSYadavRDe Souja AndradeH, et al. (2025) Understanding household hesitancy: Analysis Of E-waste recycling application adoption among indian recyclers. Cleaner Waste Systems11: 100265.
70.
WangMZhangCHuangM, et al. (2025) Green travel behaviour in China’s dual-carbon targets. Transportation Safety and Environment7: tdaf026.
71.
WaskomML (2021) Seaborn: statistical data visualization. Journal of Open Source Software6: 3021.
72.
YadavRKumar PandaDKumarS (2022) Understanding the individuals’ motivators and barriers of e-waste recycling: A mixed-method approach. Journal of Environmental Management324: 116303.
73.
ZhangYWuSRasheedMI (2020) Conscientiousness and smartphone recycling intention: The moderating effect of risk perception. Waste Management101: 116–125.
