ArticlePloS one2024
Unveiling the affecting mechanism of digital transformation on total factor productivity of Chinese firms.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Article
- Article
- Unveiling the Nexus: Influence of learning motivation on organizational performance and innovative climate of Chinese firms.PloS one · 2024Article
- Role of social media to improve job performance with mediating role of transactive memory capability and employee creativity.PloS one · 2024Article
- Unveiling the economic potential of sports industry in China: A data driven analysis.PloS one · 2024Article
- Role of scientific and technological innovations on industrial upgradation in China: A spatial econometric analysis.PloS one · 2024Article
- Retraction: Unveiling the affecting mechanism of digital transformation on total factor productivity of Chinese firms.PloS one · 2024Article
Corrections and comments
- Retraction · 2024-11-11Concerns/Issues about Authorship/Affiliation · Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review · Compromised Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Data · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The importance of digital transformation (DGT) for increasing productivity cannot be negated and Chinese firms are rapidly embracing the digital transformation for their sustainability. But the mechanism and impact of digital transformation on total factor productivity (TFP) of firms is still unclear and this study is intended to fill this gap using the data of 3112 listed firms of China during 2011 to 2022. We applied various econometric techniques like stepwise regression analysis, instrumental variable approach, differences in difference approach, and mediating analysis to determine the relationship between digital transformation and TFP and robustness of estimated findings. The findings indicate that DGT has a positive impact on overall TFP of firms in China while operating efficiency, cheaper costs, and a stronger capacity for innovation mediates this relationship. Moreover, it is explored that conventional information and communication technologies have not significant impact on TFP of firms. The findings of the study remain valid even applying many robustness checks and attempts to control the issue of endogeneity. To fully leverage the potential benefits of digital transformation on TFP, it is essential to focus on enhancing digital literacy and skills among the workforce. Governments and relevant stakeholders should prioritize and invest in comprehensive digital literacy and skills training programs to empower the workforce with the knowledge and expertise needed to navigate the digital age effectively.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.