ArticleHeliyon2024
Exploring the transactive relationships of influence factors for online asynchronous learning transactive memory system.
Article in Heliyon, 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 It has been retracted, and should not be counted. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Retraction · 2025-01-27Concerns/Issues about Referencing/Attributions · Error in Text · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transactive memory system (TMS) makes learning and transferring knowledge easy and efficient. This study has constructed a conceptual model of TMS to reveal the crucial factors influencing the formation of a TMS in online asynchronous learning to foster knowledge sharing and collaboration and enhance knowledge transfer effectiveness. The conceptual model of TMS is built upon theoretical foundations concerning the creation of TMS and the variables influencing them. The study has put forth a set of hypotheses to predict the expected effects of group factors, individual factors, task interdependence, the degree of intellectual silence, and knowledge management on the formation of TMS. In this study, a total of 229 questionnaire data were collected from undergraduate, master's, and doctoral students in Northeast China who had experience with an online asynchronous learning TMS. Structural equation modeling has been employed to identify the key indicators involved and their influence on the formation of TMS. The empirical study was carried out using statistical analysis of SPSS data, with the results indicating that each factor has varying impacts on knowledge management, ultimately affecting the formation of TMS. These findings provide a more nuanced understanding of how these factors shape online asynchronous learning environments.
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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.