Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 565

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Bridge the gap: Consumers’ purchase intention and behavior regarding sustainable clothing

Rausch, Theresa Maria, Kopplin, Cristopher Siegfried·2021·Journal of Cleaner Production
DOI 10.1016/j.jclepro.2020.123882
Open this paper in CoChat — free account

Abstract

Abstract With the textile industry satisfying steadily increasing consumption levels, excessive usage of valuable natural resources provokes a major environmental footprint: 118 billion cubic meters of water are expected to be utilized for global clothing production in 2030. Therefore, consumers' clothing consumption behavior needs to be shifted towards a more sustainable one. While green purchase behavior in general is well understood, research still lacks a comprehensive approach to explain consumers' purchase behavior of sustainable clothing. To provide a holistic framework which determines the main antecedents of purchase behavior of sustainable clothing and further, to shed light on the gap between purchase intention and subsequent purchase behavior of such clothes, we extended the Theory of Reasoned Action (TRA) approach with well-established constructs from green literature (i.e., perceived environmental knowledge and environmental concerns) and novel constructs derived from prior exploratory findings (i.e., greenwashing concerns, perceived economic risk, and perceived aesthetic risk). Four hundred sixty-four participants were inquired to assess these constructs in the context of sustainable clothing. Our findings indicate that attitude towards sustainable clothing has the highest impact on purchase intention. However, this relation is negatively influenced by consumers' greenwashing concerns. Moreover, we find evidence that consumers’ perceived aesthetic risk negatively impacts the intention-behavior relation, whereas perceived economic risk has no significant effect on this relation.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jclepro.2020.123882

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 565

Metadata

Authors
Rausch, T. M., & Kopplin, C. S.
Venue
Journal of Cleaner Production
Publisher
Elsevier BV
Published
2021-01
Volume
278
Pages
123882
ISSN
0959-6526 (ISSN-L)
DOI
10.1016/j.jclepro.2020.123882
OpenAlex
W3082203814
Cited by
565
References
21
FWCI
54.43
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.