The Potential Determinants for Smartphone Recycling Behaviour Sustainability in UAE
Abstract
:1. Introduction
2. Literature Review
2.1. Sustainability of the Behaviour
2.2. Integrated Behavioural Model (IBM)
2.3. Knowledge and Skills
2.4. Salience of Behaviour
2.5. Environmental Constraints
2.6. Habit
3. Research Methodology
3.1. Survey Questionnaire Design
- Survey keywords and consent message.
- Questions related to the conceptual framework variables.
- Questions related to mobile phone recycling treatment options and respondents’ opinions.
- Respondents’ demographic information.
3.2. Data Collection
3.3. Data Analysis Method
4. Data Analysis and Results
4.1. Descriptive Statistics of the Respondents
4.2. Measurement Model Evaluation (the Outer Model)
4.3. Structural Model Evaluation (the Inner Model)
5. Discussion
Theoretical and Practical Implications
6. Conclusions
Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Indicator or Variables | Questions | Scale |
---|---|---|
Actual knowledge | I have the knowledge regarding what kinds of e-waste can be recycled or reused. | 1 = Fully disagree to 5 = Fully agree |
I have more of an idea about where I can return my mobile phone (locations and channels) for recycling. | ||
I have enough information about when I should return my end-of-life mobile phone. | ||
I have enough information about the recycling process and what will happen to my recycled mobile phone. | ||
Salience of behaviour | Having enough information about where to return an end-of-life mobile phone is important to me. | |
Having enough information about how I can return an end-of-life/end-of-use mobile phone to a producer or recycler is important to me. | ||
Having sufficient information about the end of life of my mobile phone is critical to me (e.g., date/time of handover to producer or collector). | ||
I know that end-of-life mobile phones may pollute the environment or endanger human health if not disposed properly. | ||
Environmental constraints | Electronic waste recycling is someone else’s responsibility. | |
The recycling collection sites are far and I do not have reliable transportation. | ||
I do not have the time to recycle my end-of-life/end-of-use mobile phone. | ||
I think that sending my end-of-life/end-of-use mobile phone for recycling is costly. | ||
Habit | I like what I know about mobile phone recycling rather than getting to know new things. | |
Recycling general waste is my daily routine. | ||
I always follow the same action regarding my end-of-life/end-of-use mobile phone (e.g., stockpiling, recycling, resale, etc.). | ||
I have previously engaged in the practice of recycling or returning my old mobile phone to the manufacturer. | ||
Behaviour of mobile users | I used to separate recyclable items from general waste. | 1 = Never to 5 = Always |
During the previous month, I have done more recycling than I usually do. | ||
During the last three months, I have recycled my old mobile phone at a specific collection point specific to electronic waste. | ||
During the last three months, I have recycled my old mobile phone after receiving cash incentives from the phone producer or municipality. | ||
I discarded my previous mobile phone three months ago after the phone manufacturer/municipality deleted my data. |
References
- Yahya, T.B.; Jamal, N.M.; Sundarakani, B.; Omain, S.Z. Factors Affecting Mobile Waste Recycling through RSCM: A Literature Review. Recycling 2021, 6, 30. [Google Scholar] [CrossRef]
- Doan, L.T.T.; Amer, Y.; Lee, S.H.; Phuc, P.N.K.; Dat, L.Q. E-Waste Reverse Supply Chain: A Review and Future Perspectives. Appl. Sci. 2019, 9, 5195. [Google Scholar] [CrossRef] [Green Version]
- UNEP. A New Circular Vision for Electronics Time for a Global Reboot; UNEP: Nairobi, Kenya, 2019. [Google Scholar]
- Attia, Y.; Soori, P.K.; Ghaith, F. Analysis of Households’ E-Waste Awareness, Disposal Behavior, and Estimation of Potential Waste Mobile Phones towards an Effective E-Waste Management System in Dubai. Toxics 2021, 9, 236. [Google Scholar] [CrossRef] [PubMed]
- Shi, J.; Liu, Z.; Tang, L.; Xiong, J. Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm. Appl. Math. Model. 2017, 45, 14–30. [Google Scholar] [CrossRef]
- Yin, J.; Gao, Y.; Xu, H. Survey and analysis of consumers’ behaviour of waste mobile phone recycling in China. J. Clean. Prod. 2014, 65, 517–525. [Google Scholar] [CrossRef]
- Statista—Radicati Group. Forecast Number of Mobile Users Worldwide from 2020 to 2025. Available online: https://www.statista.com/statistics/218984/number-of-global-mobile-users-since-2010/ (accessed on 5 February 2021 ).
- ITU―The World Bank Group. Mobile Cellular Subscriptions (per 100 People)—United Arab Emirates. Available online: https://data.worldbank.org/indicator/IT.CEL.SETS.P2?locations=AE (accessed on 18 December 2021).
- Veracity World. E-waste in UAE: Current Scenario, Issues, and Strategies. 2020. Available online: https://www.veracityworld.com/e-waste-management-dubai-uae/ (accessed on 18 December 2021).
- Aboelmaged, M. E-waste recycling behaviour: An integration of recycling habits into the theory of planned behaviour. J. Clean. Prod. 2021, 278, 124182. [Google Scholar] [CrossRef]
- Bovea, M.D.; Ibanez-Fores, V.; Perez-Belis, V.; Juan, P. A survey on consumers’ attitude towards storing and end of life strategies of small information and communication technology devices in Spain. Waste Manag. 2018, 71, 589–602. [Google Scholar] [CrossRef]
- Kianpour, K.; Jusoh, A.; Mardani, A.; Streimikiene, D.; Cavallaro, F.; Nor, K.M.; Zavadskas, E. Factors influencing consumers’ intention to return the end of life electronic products through reverse supply chain management for reuse, repair and recycling. Sustainability 2017, 9, 1657. [Google Scholar] [CrossRef] [Green Version]
- Zhang, L.; Ran, W.; Jiang, S.; Wu, H.; Yuan, Z. Understanding consumers’ behavior intention of recycling mobile phone through formal channels in China: The effect of privacy concern. Resour. Environ. Sustain. 2021, 5, 100027. [Google Scholar] [CrossRef]
- Kazancoglu, Y.; Ozkan-Ozen, Y.D.; Mangla, S.K.; Ram, M. Risk assessment for sustainability in e-waste recycling in circular economy. Clean. Technol. Environ. Policy 2020, 1–13. [Google Scholar] [CrossRef]
- Saldaña-Durán, C.E.; Messina-Fernández, S.R. E-waste recycling assessment at university campus: A strategy toward sustainability. Environ. Dev. Sustain. 2021, 23, 2493–2502. [Google Scholar] [CrossRef]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Montano, D.E.; Kasprzyk, D. Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health Behav. Theory Res. Pract. 2015, 70, 231. [Google Scholar]
- Pope, C.N.; Sezgin, E.; Lin, S.; Morris, N.L.; Zhu, M. Adolescents’ attitudes and intentions to use a smartphone app to promote safe driving. Transp. Res. Interdiscip. Perspect. 2020, 4, 100090. [Google Scholar] [CrossRef]
- Rahman, K.M.; Noor, N.A.M. In search of a model explaining organic food purchase behavior. Br. Food J. 2016, 118, 2911–2930. [Google Scholar] [CrossRef]
- Winterich, K.P.; Nenkov, G.Y.; Gonzales, G.E. Knowing What It Makes: How Product Transformation Salience Increases Recycling. J. Mark. 2019, 83, 21–37. [Google Scholar] [CrossRef]
- Islam, N.; Want, R. Smartphones: Past, present, and future. IEEE Pervasive Comput. 2014, 13, 89–92. [Google Scholar] [CrossRef]
- Sardar Donighi, S.; Yousefi, M. Impact of service quality and perceived value on post-purchase intention with mediation of customer satisfaction (Case Study: Pharmacies in Tehran, Iran). Eur. Online J. Nat. Soc. Sci. Proc. 2016, 4, 1472–1480. [Google Scholar]
- Kuo, Y.-F.; Wu, C.-M.; Deng, W.-J. The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Comput. Hum. Behav. 2009, 25, 887–896. [Google Scholar] [CrossRef]
- Min, Z.; Xin, G.; Mingxing, Z. Content, Enjoyment or Payment? Factors Influencing Consumers ‘Purchase Intention in Mobile Reading: An Empirical Study from China. Int. J. New Dev. Eng. Soc. 2018, 2. [Google Scholar] [CrossRef]
- Corsini, F.; Gusmerotti, N.M.; Frey, M. Consumer’s Circular Behaviors in Relation to the Purchase, Extension of Life, and End of Life Management of Electrical and Electronic Products: A Review. Sustainability 2020, 12, 10443. [Google Scholar] [CrossRef]
- Ylä-Mella, J.; Keiski, R.L.; Pongrácz, E. Electronic waste recovery in Finland: Consumers’ perceptions towards recycling and re-use of mobile phones. Waste Manage. 2015, 45, 374–384. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pember, S.E. A Qualitative Application of the Integrated Model of Behavioral Prediction to Graduate Student Eating Behaviors; University of Alabama Libraries: Tuscaloosa, AL, USA, 2017. [Google Scholar]
- Fishbein, M. The role of theory in HIV prevention. AIDS Care 2000, 12, 273–278. [Google Scholar] [CrossRef]
- Holbert, R.L.; Dias, N.C.; Hardy, B.W.; Jamieson, K.H.; Levendusky, M.S.; Renninger, A.S.; Romer, D.; Winneg, K.M.; Pasek, J. Exploring the Role of Media Use Within an Integrated Behavioral Model (IBM) Approach to Vote Likelihood. Am. Behav. Sci. 2021, 65, 412–431. [Google Scholar] [CrossRef]
- Rosenthal, S. Procedural Information and Behavioral Control: Longitudinal Analysis of the Intention-Behavior Gap in the Context of Recycling. Recycling 2018, 3, 5. [Google Scholar] [CrossRef] [Green Version]
- Nguyen, H.T.; Lee, C.H.; Hung, R.J. Willingness of end users to pay for e-waste recycling. Glob. J. Environ. Sci. Manag. Gjesm 2021, 7, 47–58. [Google Scholar]
- Taylor, C.Y.; Sheu, J.-J.; Chen, H.-S.; Glassman, T.; Dake, J. Predictors of Nursesʼ Intentions to Administer As-Needed Opioid Analgesics for Pain Relief to Postoperative Orthopaedic Patients in the Acute Care Setting. Orthop. Nurs. 2017, 36, 392–399. [Google Scholar] [CrossRef] [Green Version]
- Nantha, Y.S.; Haque, S.; Nantha, H.S. The development of an integrated behavioural model of patient compliance with diabetes medication: A mixed-method study protocol. Fam. Pract. 2019, 36, 581–586. [Google Scholar] [CrossRef]
- Siddique, Z.R.; Saha, G.; Kasem, A.R. Estimating green purchase behavior: An empirical study using integrated behavior model in Bangladesh. J. Asia Bus. Stud. 2020, 15, 319–344. [Google Scholar] [CrossRef]
- Welfens, M.J.; Nordmann, J.; Seibt, A. Drivers and barriers to return and recycling of mobile phones. Case studies of communication and collection campaigns. J. Clean. Prod. 2016, 132, 108–121. [Google Scholar] [CrossRef] [Green Version]
- Rauyruen, P.; Miller, K.E.; Groth, M. B2B services: Linking service loyalty and brand equity. J. Serv. Mark. 2009, 23, 175–186. [Google Scholar] [CrossRef]
- Kianpour, K.; Ahmad Jusoh, S.; Malaysia, U.T.; Management, F.O. Factors Influencing Customers’ Participation Intention in Reverse Supply Chain Management. 2017. [Google Scholar]
- Wang, Z.; Guo, D.; Wang, X.; Zhang, B.; Wang, B. How does information publicity influence residents’ behaviour intentions around e-waste recycling? Resour. Conserv. Recycl. 2018, 133, 1–9. [Google Scholar] [CrossRef]
- Echegaray, F.; Hansstein, F.V. Assessing the intention-behavior gap in electronic waste recycling: The case of Brazil. J. Clean. Prod. 2017, 142, 180–190. [Google Scholar] [CrossRef]
- Ersche, K.D.; Lim, T.-V.; Ward, L.H.; Robbins, T.W.; Stochl, J. Creature of Habit: A self-report measure of habitual routines and automatic tendencies in everyday life. Personal. Individ. Differ. 2017, 116, 73–85. [Google Scholar] [CrossRef]
- Lee, H.J. A Study on Customer Intention to Repurchase Smartphones; Temple University: Ann Arbor, MI, USA, 2020. [Google Scholar]
- Sujata, M.; Khor, K.-S.; Ramayah, T.; Teoh, A.P. The role of social media on recycling behaviour. Sustain. Prod. Consum. 2019, 20, 365–374. [Google Scholar] [CrossRef] [Green Version]
- Kumar, A. Extended TPB model to understand consumer "selling" behaviour Implications for reverse supply chain design of mobile phones. Asia Pac. J. Mark. Logist. 2017, 29, 721–742. [Google Scholar] [CrossRef]
- Tejada, J.J.; Punzalan, J.R. On the misuse of Slovin’s formula. Philipp. Stat. 2012, 61, 129–136. [Google Scholar]
- Barclay, D.; Higgins, C.; Thompson, R. The Partial Least Squares (PLS) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration; Technology Studies: Thousand Oaks, CA, USA, 1995; Volume 2, pp. 285–309. [Google Scholar]
- Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed.; Sage Publications: Thousand Oaks, CA, USA, 2021. [Google Scholar]
- Kock, N. Common method bias in PLS-SEM:A full collinearity assessment approach. Int. J. e-Collab. (IJeC) 2015, 11, 1–10. [Google Scholar]
- Ramayah, T.; Cheah, J.; Chuah, F.; Ting, H.; Memon, M.A. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using smartPLS 3.0; Pearson: Kuala Lumpur, Malaysia, 2018. [Google Scholar]
- SCAD. Population & Demographic Statistics. 2011. Available online: https://www.scad.gov.ae/en/pages/GeneralPublications.aspx (accessed on 15 January 2022).
- Souza, A.C.d.; Alexandre, N.M.C.; Guirardello, E.D.B. Psychometric properties in instruments evaluation of reliability and validity. Epidemiol. E Serviços De Saúde 2017, 26, 649–659. [Google Scholar] [CrossRef]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Khan, F.; Ahmed, W.; Najmi, A. Understanding consumers’ behavior intentions towards dealing with the plastic waste: Perspective of a developing country. Resour. Conserv. Recycl. 2018, 142, 49–58. [Google Scholar] [CrossRef]
- Jena, S.; Sarmah, S. Measurement of consumers’ return intention index towards returning the used products. J. Clean. Prod. 2015, 108, 818–829. [Google Scholar] [CrossRef]
- Zhang, Y.; Wu, S.; Rasheed, M.I. Conscientiousness and smartphone recycling intention: The moderating effect of risk perception. Waste Manag. 2020, 101, 116–125. [Google Scholar] [CrossRef]
- Park, J.; Ha, S. Understanding Consumer Recycling Behavior: Combining the Theory of Planned Behavior and the Norm Activation Model. Fam. Consum. Sci. Res. J. 2014, 42, 278–291. [Google Scholar] [CrossRef]
- Lee, H.G. A Study of Consumer Repurchase Behaviors of Smartphones Using Artificial Neural Network. Information 2020, 11, 400. [Google Scholar] [CrossRef]
- Tao, C.-C.; Fan, C.-C. A Modified Decomposed Theory of Planned Behaviour Model to Analyze User Intention towards Distance-Based Electronic Toll Collection Services. Promet-Traffic Transp. 2017, 29, 85–97. [Google Scholar] [CrossRef] [Green Version]
Construct | Source | No. of Items |
---|---|---|
Mobile users’ knowledge and skills to perform the behaviour (MU_KSP) | [12,37,38] | 4 |
Mobile users’ salience of behaviour (MU_SB) | [12,37,38] | 4 |
Mobile users’ environmental constraints (MU_EC) | [6,39] | 4 |
Mobile users’ habit (MU_H) | [30,40,41] | 4 |
Behaviour of mobile users (BMU) | [30,39,42,43] | 5 |
General information and questions related to mobile phone treatment options. | ||
General information about the users (gender, age, education, income, and city). |
Demographic Question | Options | What Is Your Gender? | |||
---|---|---|---|---|---|
Female | Male | ||||
Count | N% | Count | N% | ||
What is your age? | 18–24 | 82 | 29.30% | 59 | 18.40% |
25–35 | 116 | 41.40% | 108 | 33.60% | |
36–45 | 64 | 22.90% | 107 | 33.30% | |
46–60 | 16 | 5.70% | 43 | 13.40% | |
61 or older | 2 | 0.70% | 4 | 1.20% | |
What is the highest level of education that you have completed? | Less than high school | 9 | 3.20% | 12 | 3.70% |
High school graduate | 94 | 33.60% | 109 | 34.00% | |
Bachelor’s degree | 142 | 50.70% | 150 | 46.70% | |
Master’s degree | 33 | 11.80% | 43 | 13.40% | |
Doctorate | 2 | 0.70% | 7 | 2.20% | |
What is your approximate average household income in AED? | 0–4999 | 176 | 62.90% | 179 | 55.80% |
5000–9999 | 49 | 17.50% | 51 | 15.90% | |
10,000–29,999 | 44 | 15.70% | 43 | 13.40% | |
30,000–49,999 | 7 | 2.50% | 26 | 8.10% | |
50,000 or above | 4 | 1.40% | 22 | 6.90% | |
What is your occupation? | Student | 60 | 21.40% | 30 | 9.30% |
Employed for wages | 143 | 51.10% | 237 | 73.80% | |
Self-employed | 26 | 9.30% | 33 | 10.30% | |
Retired | 3 | 1.10% | 3 | 0.90% | |
Unemployed | 48 | 17.10% | 18 | 5.60% | |
What is your emirate? | Abu Dhabi | 99 | 35.40% | 109 | 34.00% |
Dubai | 114 | 40.70% | 133 | 41.40% | |
Sharjah | 31 | 11.10% | 41 | 12.80% | |
Ajman | 21 | 7.50% | 21 | 6.50% | |
Ras Al Khaimah | 7 | 2.50% | 6 | 1.90% | |
Umm Al Quwain | 3 | 1.10% | 3 | 0.90% | |
Fujairah | 5 | 1.80% | 8 | 2.50% |
Latent Variable | Indicators | Convergent Validity | Internal Consistency Reliability Validity | Discriminant Validity | ||
---|---|---|---|---|---|---|
Loading | AVE | Cronbach’s Alpha | Reliability | HTMT | ||
>0.70 | >0.50 | 0.60–0.90 | 0.60–0.90 | Significantly < 0.85 | ||
Mobile users’ knowledge and skills to perform the behaviour (MU_KSP) | KSP 1 | 0.832 | 0.740 | 0.883 | 0.919 | YES |
KSP 2 | 0.874 | |||||
KSP 3 | 0.870 | |||||
KSP 4 | 0.865 | |||||
Mobile users’ salience of behaviour (MU_SB) | SB 1 | 0.798 | 0.655 | 0.834 | 0.884 | YES |
SB 2 | 0.817 | |||||
SB 3 | 0.788 | |||||
SB 4 | 0.834 | |||||
Mobile users’ environmental constraints (MU_EC) | EC 1 | 0.683 | 0.518 | 0.757 | 0.805 | YES |
EC 2 | 0.495 | |||||
EC 3 | 0.811 | |||||
EC 4 | 0.838 | |||||
Mobile users’ habit (MU_H) | H1 | 0.798 | 0.614 | 0.801 | 0.863 | YES |
H2 | 0.770 | |||||
H3 | 0.711 | |||||
H4 | 0.848 | |||||
Behaviour of mobile users (BMU) | BMU1 | 0.257 | 0.611 | 0.783 | 0.860 | YES |
BMU2 | 0.581 | |||||
BMU3 | 0.876 | |||||
BMU4 | 0.832 | |||||
BMU5 | 0.805 |
BMU | MU_EC | MU_H | MU_KSP | MU_SB | |
---|---|---|---|---|---|
BMU | 0.782 | ||||
MU_EC | −0.137 | 0.719 | |||
MU_H | 0.251 | −0.470 | 0.783 | ||
MU_KSP | 0.201 | −0.400 | 0.713 | 0.860 | |
MU_SB | −0.126 | −0.239 | 0.383 | 0.440 | 0.809 |
BMU | MU_EC | MU_H | MU_KSP | MU_SB | |
---|---|---|---|---|---|
BMU | |||||
MU_EC | 0.132 | ||||
MU_H | 0.284 | 0.605 | |||
MU_KSP | 0.234 | 0.497 | 0.841 | ||
MU_SB | 0.167 | 0.355 | 0.511 | 0.520 |
Structural Path | (β) | t Value | p Value | 95% Confidence Interval | Conclusion |
---|---|---|---|---|---|
MU_KSP -> BMU | 0.138 | 2.246 | 0.025 | [0.017, 0.259] | H1, Supported |
MU_SB -> BMU | −0.289 | 4.374 | 0.000 | [−0.391, −0.136] | H2, Supported |
MU_EC -> BMU | −0.034 | 0.547 | 0.585 | [−0.147, 0.139] | H3, Not Supported |
MU_H -> BMU | 0.248 | 4.045 | 0.000 | [0.123, 0.363] | H4, Supported |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ben Yahya, T.; Jamal, N.M.; Sundarakani, B.; Omain, S.Z. The Potential Determinants for Smartphone Recycling Behaviour Sustainability in UAE. Sustainability 2022, 14, 2282. https://doi.org/10.3390/su14042282
Ben Yahya T, Jamal NM, Sundarakani B, Omain SZ. The Potential Determinants for Smartphone Recycling Behaviour Sustainability in UAE. Sustainability. 2022; 14(4):2282. https://doi.org/10.3390/su14042282
Chicago/Turabian StyleBen Yahya, Taher, Noriza Mohd Jamal, Balan Sundarakani, and Siti Zaleha Omain. 2022. "The Potential Determinants for Smartphone Recycling Behaviour Sustainability in UAE" Sustainability 14, no. 4: 2282. https://doi.org/10.3390/su14042282