CiteNScope — Scholarly Index & Analytics
Discover / IJRBSPUBLICATION RECORD

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

Research reach

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.

CROSSREF CITATIONS0

Observed 21 Oct 2026, 07:22 UTC

OpenAlex · Citations0Cached provider observation

Observed 21 Oct 2026 07:23 UTC

Mendeley · Readers—No verified count available

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.

OpenAlex / Citation year

Annual citations to this article

View provider totals ↑

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

OpenAlex

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.

Overview

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.