MODEL OF TEAMWORK-BASED IMPROVEMENT OF TEAM PERFORMANCE AT REgIONAL SECRETARIAT OF SEMARANG REGENCY
DOI:
https://doi.org/10.30659/ijibe.2.4.225-233Abstrak
This study aims to examine the effect of communication and trust in teamwork and its impact on team performance. The research problem is how to improve team performance through communication and trust by mediated teamwork. The sample of this study is the employees of the Regional Secretariat of Ungaran as much as 115 respondents. Regression analysis done with Statistical Package of Social Science (SPSS) software, was used to analyze data. The analysis showed that communication and trust significantly influenced teamwork in improving team performance. The empirical findings indicated that communication significantly affected teamwork; trust had a significant effect on team performance; communication had significant influence on teamwork; trust had a significant effect on team performance; and teamwork had a significant effect on team performance .Keywords: communication, trust, teamwork and team performance
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
License and Copyright AgreementIn submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Authors who publish with IJIBE agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
- Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/
- Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.
This work is licensed under a Creative Commons Attribution 4.0 International License.



