CoChat
Sign Up
Cited by 112Digital Marketing and Social MediaConsumer Behavior and Marketing InfluenceTechnology Adoption and User Behaviour

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

The impact of positive valence and negative valence on social commerce purchase intention

Hajli, Nick·2019·Information Technology & People
DOI 10.1108/itp-02-2018-0099
Open this paper in CoChat — free account

Abstract

Purpose New research emphasizes the importance of social communications in e-commerce purchase decision-making processes but there are many technical and social challenges such as multi-faceted trust concerns. How consumers view and value referent’s online testimonials, ratings, rants and raves, and product usage experiences remain an important factor that needs to be better understood. Social commerce as a relatively new stream in e-commerce yet is growing fast and gaining the attention of scholars and practitioners, especially due to recent revenue developments. Consistent with e-commerce websites that do not enable consumer feedback, trust is a challenging matter for consumers to consider when they visit social commerce websites. Researching trust models and influences is increasingly important especially with the proliferation of online word of mouth (WOM) strongly effecting many consumers at many different phases of social commerce purchase decision making and transacting. The paper aims to discuss this issue. Design/methodology/approach This study examines the effects and importance of institution-based trust and WOM within a model of consumer behaviour on social commerce websites. This research examines how trust and consumer feedback may affect consumers’ purchase intentions. This study collects data from the little-understood market of urban Iran and develops a research model to examine consumers’ purchase intentions on social commerce websites. A robust data set fro…

Overview

Integrity signals

Registry

Crossref · 10.1108/itp-02-2018-0099

Cites

89 of 89 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 89 references

Research works printed but not linked: 4

  1. 2013, Social marketing transformed: Kotler, Polonsky and Hastings reflect on social marketing in a period of social change, European Journal of Marketing, 47, 1525
  2. 1981, Work Stress and Social Support
  3. 1954, Motivation and Personality
  4. 1997, Global word of – mouth: service bashing on the internet is a thorny issue, Marketing Management, 6, 28

Cited by

Sorted by citations

Items 101–109 of 109

Related

See all

Metadata

Authors
Hajli, N.
Affiliations
Swansea University (GB)
Corresponding author
Hajli, Nick
ORCID
Hajli, Nick
Venue
Information Technology & People
Publisher
Emerald
Published
2019-10-02
Volume
33
Issue
2
Pages
774-791
ISSN
0959-3845 (ISSN-L)
DOI
10.1108/itp-02-2018-0099
OpenAlex
W2978349044
Cited by
112
References
89
Citations by year
2026: 7 · 2025: 17 · 2024: 21 · 2023: 24 · 2022: 21 · 2021: 16 · 2020: 6
FWCI
21.65
Type
journal article
Language
en
Open access
closed
Licence
www.emerald.com/insight/site-policies (tdm)
Locations
Information Technology and People (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
social commerce, purchase intention, positive valence, negative valence, institutional trust, electronic word of mouth, consumer trust, social media communication, customer feedback, purchase decision

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