Evidence map›Paper›PMID 38635589›Full record

ArticlePloS one2024

Research on collaborative innovation optimization strategies for digitally enabled higher education ecosystems.

Yan Zhao, Zheng Yang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2024. 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
2.5field-weighted citation impact, top 11% of its field
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, 5 citations in OpenAlex.

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

2 authors at 1 institution in 1 country.

Yan ZhaoPersonnel Department, Shenyang Institute of Engineering, Shenyang, China.ORCID 0009-0009-8697-2292
Zheng YangFaculty Affairs Office of the Shenyang Institute of Engineering Committee, Shenyang Institute of Engineering, Shenyang, China.
Shenyang Institute of Engineering · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digitally enabled higher education involves the in-depth use of new-generation digital technology, which has subverted and innovated the traditional teaching mode, driven the development of high-quality teaching and learning, and improved teachers' teaching experience, and increased efficiency. Based on ecosystem theory, this paper constructs a higher education ecosystem with the government, enterprises, and universities as the core participating subjects. It considers the participating subjects' effort level and the ecosystem's overall benefits under the three scenarios of noncooperative research and development (R&D), cost sharing, and cooperative R&D. The results show that (1) the service innovation effort level of the three parties increases with increasing human resource level and technology maturity, and the government's benefit decreases with increasing cost of fulfilling social responsibility. (2) The government's cost subsidies to universities and enterprises can enhance the service innovation level of both parties and increase the optimal returns of the three parties and the ecosystem as a whole. (3) In the cooperative R&D game scenario, the effort level of the three parties and the total ecosystem returns are greater than those in the noncollaborative R&D scenario, and after determining the subsidy coefficients of the government, Pareto optimality of the three parties and the ecosystem as a whole can be achieved. The conclusions of this study can aid in understanding the dynamic evolution mechanism of digitally enabled higher education and provide a realistic decision-making reference for higher education ecosystem managers.

Indexed as

Cost SharingEcosystemChinaDigital TechnologyGovernmentHumansLearning

Identifiers

PMID38635589
PMCPMC11025831
OpenAlexW4394933375

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.