Evidence map›Paper›PMID 36353161›Full record

ArticleHeliyon2022

Utilizing SEM-RFC to predict factors affecting online shopping cart abandonment during the COVID-19 pandemic.

Ardvin Kester S Ong, Marjorie Joy R Dejucos, Mary Anne F Rivera, John Vincent D J Muñoz, Miguel S Obed, Kirstien Paola E Robas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2022. 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

6 authors.

Ardvin Kester S OngSchool of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines.
Marjorie Joy R DejucosDepartment of Industrial Engineering, Faculty of Engineering, University of Santo Tomas, España Blvd, Manila 1015, Philippines.
Mary Anne F RiveraDepartment of Industrial Engineering, Faculty of Engineering, University of Santo Tomas, España Blvd, Manila 1015, Philippines.
John Vincent D J MuñozDepartment of Industrial Engineering, Faculty of Engineering, University of Santo Tomas, España Blvd, Manila 1015, Philippines.
Miguel S ObedDepartment of Industrial Engineering, Faculty of Engineering, University of Santo Tomas, España Blvd, Manila 1015, Philippines.
Kirstien Paola E RobasSchool of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Online shopping has accelerated during to the pandemic and an increase in online shopping cart abandonment (SCA) was also evident. The growth of online shopping is contributed by the rising middle class, high consumer spending, millennials, and a tech-savvy population which is valuable to the growth of e-commerce. This study aimed to predict the factors that affect SCA during the COVID-19 Pandemic utilizing the SEM-RFC hybrid. Several factors such as self-efficacy, attribute conflicts, hesitation at checkout, emotional ambivalence, choice process satisfaction, attitude, subjective norms, and perceived behavioral control were analyzed simultaneously. This study integrated the cognition-affect-behavior paradigm with the Theory of Planned Behavior to provide a conceptual framework measured through an online survey questionnaire answered by 1015 valid responses collected by convenience sampling. Results showed that Attitude, Attribute Conflict, Self-Efficacy, and Emotional Ambivalence are the primary significant factors affecting SCA. Amidst the pandemic, consumers still value the ease of use, convenience and safety of the mobile online shopping applications that they have, which they do not positively experience at this time. The findings of this study may be applied and extended by researchers, online retailers, and businesses to understand consumer's abandonment intentions. Moreover, the results and framework of this study may be capitalized on by the business sector to create marketing strategies and develop business models for a sustainable online shopping business worldwide.

Indexed as

Online shoppingRandom forest classifierShopping cart abandonmentStructural equation modeling

Identifiers

PMID36353161
PMCPMC9638758

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.