Evidence map›Paper›PMID 37976309›Full record

ArticlePloS one2023

Online reviews, customer Q&As, and product sales: A PVAR approach.

Miao Feng, Yituo Feng, Yang Li

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Miao FengBusiness School, Shandong Management University, Jinan, China.
Yituo FengManagement Information Systems, Chungbuk National University, Cheongju, South Korea.ORCID 0000-0001-7489-982X
Yang LiBusiness School, Shandong Normal University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Online reviews and customer Q&As have emerged as two vital forms of electronic word-of-mouth (eWOM) that significantly influence consumer decisions in e-commerce. Yet, a comprehensive understanding of the individual and combined roles of these eWOM types in shaping market dynamics remains elusive. This study addresses this research gap by tracking and analyzing three months of eWOM and sales data for 120 laptops on Amazon, comprising 7,205 online reviews, 6,365 customer Q&A questions, and 7,419 answers. Leveraging the Panel Vector Autoregression (PVAR) model and STATA16.0 software, we unravel the intricate dynamics between online reviews, customer Q&As, and laptop sales. The empirical results reveal distinctive influence mechanisms of online reviews and customer Q&As on product sales, with review volume and answer valence positively affecting sales. Importantly, answer volume was found to stimulate online reviews and enhance their valence. Our study elucidates the interplay among online reviews, customer Q&As, and product sales, underscoring the need for future research on multi-type eWOM. Further, the insights gleaned offer valuable guidance for online platforms and retailers to strategize their eWOM management.

Indexed as

CommerceSoftware

Identifiers

PMID37976309
PMCPMC10655965

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.