Consumer perspectives on reverse logistics in e-commerce in Gauteng, South Africa
Babra Duri ORCID ↗ · Blessing Takawira ORCID ↗ · Xihluke Maluleke ORCID ↗
International Journal of Research in Business and Social Science · 2026 · Vol. 14 / Issue 10 · 22-33
Publication metrics
External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.
Dimensions
Live provider badge. If unavailable, use the Dimensions link above.
Observed 21 Oct 2026, 07:22 UTC
Observed 21 Oct 2026 07:23 UTC
OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.
Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.
2017–2026 Annual history last updated:
Citations received
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
View data & coverage
2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.
Abstract
The rapid growth of e-commerce has significantly increased product returns, making reverse logistics a critical component of sustainable supply chain management. The purpose of this study is to examine consumer perspectives on reverse logistics in Gauteng, South Africa, where infrastructural constraints and diverse consumer expectations pose unique challenges. A quantitative research method was employed, with data collected from 250 online shoppers through a self-administered questionnaire. Exploratory factor analysis confirmed five underlying constructs, which are Return policy transparency, Efficiency of return process, Customer support, Customer satisfaction and Trust. Multiple regression analysis tested the construct's predictive power. The findings reveal that Return policy transparency was the strongest predictor of consumer satisfaction, while efficiency and customer support were less influential in this context. The findings highlight that clear, accessible return policies improve consumer trust and satisfaction, even in the presence of operational inefficiencies. This study highlights that the strategic role of transparent communication instils positive return experiences in emerging markets. The study contributes to reverse logistics literature by integrating behavioral dimensions into operational frameworks and offers practical implications for retailers aiming to build consumer trust without heavily investing in infrastructural investments.
Keywords: {'name': 'Reverse logistics'}, {'name': 'Consumer satisfaction'}, {'name': 'Return policy'}, {'name': 'E-commerce'}, {'name': 'Consumer trust'}
Authors & affiliations
Babra Duri
University of Johannesburg
Blessing Takawira
University of Johannesburg
Xihluke Maluleke
University of Johannesburg
Deposited references
Supplied references, not a list of publications citing this article.
DOI: 10.1016/j.ijpe.2012.08.003
DOI: 10.3390/logistics7040084
DOI: 10.1016/j.eswa.2025.128203
DOI: 10.33168/JLISS.2024.1217
DOI: 10.1080/09718923.2017.1359908
DOI: 10.5901/mjss.2014.v5n9p23
DOI: 10.3846/transport.2021.14956
DOI: 10.1002/sd.2504
DOI: 10.1080/23311975.2024.2390213
DOI: 10.55214/25768484.v9i3.5251
DOI: 10.1109/IEEM55944.2022.9989833
DOI: 10.4102/jtscm.v16i0.777
DOI: 10.3390/systems13030158
DOI: 10.4102/jtscm.v18i0.1025
DOI: 10.1002/tqem.22229
Kashalikar, U., & Rangarajan, A. (2017, April 11–13). Returns management and value recovery through systemized orchestration and inventory tagging. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 399–400). Rabat, Morocco.
DOI: 10.13169/workorgalaboglob.19.2.0005
DOI: 10.1016/j.resconrec.2018.10.013
DOI: 10.28991/esj-2021-01291
DOI: 10.1109/SIEDS.2019.8735643
DOI: 10.1016/j.procs.2022.12.296
DOI: 10.1080/20421338.2016.1222752
DOI: 10.4018/978-1-5225-2599-8.ch005
DOI: 10.4102/jtscm.v18i0.1023
DOI: 10.1007/s11356-020-11675-2
Pallant, J. (2011). SPSS survival manual: A step by step guide to data analysis using SPSS (4th ed.). Allen & Unwin.
DOI: 10.1080/1331677X.2011.11517472
DOI: 10.1002/bse.4020
Raosoft, Inc. (n.d.). Sample size calculator. Retrieved September 12, 2025, from http://www.raosoft.com/samplesize.html
DOI: 10.1108/IJPDLM-10-2019-0302
DOI: 10.3390/su12031121
DOI: 10.4018/979-8-3373-2434-0.ch014
DOI: 10.3390/tourhosp6020095
Statistics South Africa. (2024). General household survey (GHS), 2023. Statistics South Africa.
Wang, H., & Tu, M. (2010). Reverse logistics outsourcing model based on principal-agent theory. In Proceedings of the 2010 International Conference on Optoelectronics and Image Processing (ICOIP). IEEE.
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2000). Business research methods (6th ed.). The Dryden Press.