ArticleScientific reports2025
Relational embeddedness, organizational learning ambidexterity, and competitive advantage in digital healthcare.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Article
- Digital health technology empowering China's community full life cycle health management.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Over the past few decades, research on organizational embeddedness and organizational learning has grown dramatically. In the organizational embeddedness and organizational learning context, relational embeddedness provides the resource foundation for organizational learning, ambidextrous learning converts resources into capabilities using exploration and exploitation approaches, and environmental dynamism determines the optimal combination of relational embeddedness and ambidextrous learning by moderating the uncertainty and changes in the external environment. Relational embeddedness and learning ambidexterity are becoming more widely acknowledged as sources of organizational performance. By analyzing the roles of relational embeddedness, ambidextrous learning, and environmental dynamism, this research aims to improve knowledge of the elements influencing competitive advantage in digital healthcare. Although numerous research studies have examined the effects of relational embeddedness on performance, little is known about the mechanisms by which relational embeddedness confers competitive advantages in a digital healthcare environment. Clarifying the role of relational embeddedness in driving competitive advantage is critical for delineating the intricate relationship between relational embeddedness and competitive advantage in digital healthcare. To fill this research gap, this study examines how ambidextrous learning and environmental dynamism impact the relationship between relational embeddedness and competitive advantage in digital healthcare. This paper analyzes the data using SPSS 26.0 and its macro software, PROCESS, with a focus on 356 middle and senior managers of enterprises surveyed between July 2024 and November 2024. Our findings support the notion that relational embeddedness enhances competitive advantage in digital healthcare. Additionally, explorative and exploitative learning partially mediates the relationship between relational embeddedness and competitive advantage. Furthermore, environmental dynamism significantly and differentially moderates the influence of relational embeddedness on exploitative and explorative learning. This study contributes to the existing literature by offering a more detailed examination of the relationship between relational embeddedness and competitive advantage in digital healthcare. It also has significant practical implications for healthcare improvement and socio-economic development.
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.