Evidence map›Paper›PMID 40493621›Full record

ArticlePloS one2025

Stitching competition with digital threads: Unveiling the drivers of competitive success in the apparel sector.

Emmanuel Susitha, Amila Jayarathne, H M R P Herath

Abstract read
In one paragraph

Article in PloS one, 2025. 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.

Emmanuel SusithaFaculty of Graduate Studies, University of Kelaniya, Kelaniya, Sri Lanka.ORCID 0000-0001-7787-1590
Amila JayarathneFaculty of Management Studies and Commerce, University of Sri Jayawardanapura, Nugegoda, Sri Lanka.
H M R P HerathDepartment of Information Management, SLIIT Business School, Malabe, Sri Lanka.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores and validates the dimensions of digital capabilities and competitive performance within the apparel industry, aiming to develop a robust, multidimensional measurement instrument. Employing a sequential QUAL→QUAN exploratory design, the research began with in-depth interviews with key industry experts to identify critical constructs. These insights informed the subsequent quantitative phase, in which exploratory factor analysis and confirmatory factor analysis were applied to confirm the factor structure and validate the instrument. The study identifies four key dimensions of competitive performance in the apparel supply chain: customer satisfaction, better utilisation of resources, collaboration to compete, and strategic advantage. Process and technical digitalisation emerged as essential components of digital capabilities, reflecting the dual role of digital infrastructure and operational integration in enhancing performance. Theoretically, this research contributes by introducing validated instruments grounded in the Resource-based view, the Dynamic capabilities view, and the Extended resource-based view, offering an empirical framework that links digital capabilities with competitive performance. Practically, the instrument provides apparel manufacturers with a diagnostic tool to assess digital maturity and strategically align digital initiatives with performance goals. These findings are particularly relevant for firms navigating rapid technological change and seeking sustained global apparel supply chain competitiveness.

Indexed as

Economic CompetitionIndustryCompetitive BehaviorConsumer BehaviorHumans

Identifiers

PMID40493621
PMCPMC12151375

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.