Collaborative Writing Models for Enhancing Academic Publication Productivity: A Systematic Literature Review

DOI:

https://doi.org/10.58421/gehu.v5i3.1249

Authors

  • Pance Daniel Universitas Komputer Bandung
  • Umi Narimawati Universitas Komputer Bandung
  • Bobi Kurniawan Universitas Komputer Bandung

Keywords:

Digital Media, Canva, Science Learning, Elementary School

Abstract

Increasing publication pressure in academia has prompted universities and research institutions to adopt strategies to improve scholarly productivity and research competitiveness. The demand for measurable research performance, reflected through publication output, citations, and international visibility, has led institutions to explore collaborative approaches to academic writing. This study aims to analyze collaborative writing models that can improve academic publication productivity in higher education institutions. The research employed a systematic literature review (SLR) method to synthesize empirical evidence from previous studies. Data were collected from 25 empirical research articles published between 2015 and 2025 in reputable academic databases. The selected studies were analyzed using thematic synthesis to identify patterns, structures, and outcomes of collaborative writing practices in academic environments. The findings reveal four main collaborative mechanisms that significantly contribute to publication productivity: structured role allocation within writing teams, integration of digital collaboration technologies, mentoring-based writing systems, and interdisciplinary research collaboration. These mechanisms collectively enhance manuscript completion rates, improve publication quality, and increase research visibility. The results indicate that collaborative writing is not merely a shared authorship activity but a strategic organizational approach to strengthening research capacity. The study implies that academic institutions should institutionalize structured collaborative writing programs, provide digital infrastructure, and establish mentoring systems to sustainably improve publication output and global research competitiveness

Downloads

Download data is not yet available.

References

K. Emery and S. Matthews, “Integrating Digital Feedback To Enhance Sustainable And Equitable Peer Collaborative Skills, Feedback Literacy And Reflective Practice In Undergraduate Groups: Learnings From Australian Case Studies,” Community Notebook. People, Education and Welfare in the Society 5.0, Jul. 2025, doi: 10.61007/QdC.2025.2.379.

D. Zou, H. Xie, and F. L. Wang, “Effects of technology-enhanced peer, teacher and self feedback on students’ collaborative writing, critical thinking tendency and engagement in learning,” J. Comput. High. Educ., vol. 35, no. 1, pp. 166–185, Apr. 2023, doi: 10.1007/s12528 022 09337 y.

E. Er, Y. Dimitriadis, and D. Gašević, “A collaborative learning approach to dialogic peer feedback: a theoretical framework,” Assess. Eval. High. Educ., vol. 46, no. 4, pp. 586–600, May 2021, doi: 10.1080/02602938.2020.1786497.

G. Van Helden, V. Van Der Werf, G. N. Saunders Smits, and M. M. Specht, “The Use of Digital Peer Assessment in Higher Education—An Umbrella Review of Literature,” IEEE Access, vol. 11, pp. 22948–22960, 2023, doi: 10.1109/ACCESS.2023.3252914.

T. López Pellisa, N. Rotger, and F. Rodríguez Gallego, “Collaborative writing at work: Peer feedback in a blended learning environment,” Educ. Inf. Technol. (Dordr)., vol. 26, no. 1, pp. 1293–1310, Jan. 2021, doi: 10.1007/s10639 020 10312 2.

M. Kwiek, “What large-scale publication and citation data tell us about international research collaboration in Europe: changing national patterns in global contexts,” Studies in Higher Education, vol. 46, no. 12, pp. 2629–2649, Dec. 2021, doi: 10.1080/03075079.2020.1749254.

Y. Wang, Y. Long, L. Tu, and L. Liu, “Delivering Scientific Influence Analysis as a Service on Research Grants Repository,” IEEE Trans. Serv. Comput., vol. 15, no. 4, pp. 1896–1911, Jul. 2022, doi: 10.1109/TSC.2020.3025318.

D. D. Von Hoff et al., “A Grant-Based Experiment to Train Clinical Investigators: The AACR/ASCO Methods in Clinical Cancer Research Workshop,” Clinical Cancer Research, vol. 27, no. 20, pp. 5472–5481, Oct. 2021, doi: 10.1158/1078 0432.CCR 21 1799.

S. Zhuchkova, E. Terentev, A. Saniyazova, and S. Bekova, “Departmental academic support for doctoral students in Russia: Categorization and effects,” Higher Education Quarterly, vol. 77, no. 2, pp. 215–231, Apr. 2023, doi: 10.1111/hequ.12389.

L. A. Asante and Z. Abubakari, “Pursuing PhD by publication in geography: a collaborative autoethnography of two African doctoral researchers,” Journal of Geography in Higher Education, vol. 45, no. 1, pp. 87–107, Jan. 2021, doi: 10.1080/03098265.2020.1803817.

N. Veles and P. A. Danaher, “Transformative research collaboration as third space and creative understanding : learnings from higher education research and doctoral supervision,” Res. Pap. Educ., vol. 39, no. 1, pp. 50–66, Jan. 2024, doi: 10.1080/02671522.2022.2089212.

D. Lee, “Exploring the determinants of research performance for early career researchers: a literature review,” Scientometrics, vol. 129, no. 1, pp. 181–235, Jan. 2024, doi: 10.1007/s11192 023 04868 2.

H. Marshall and M. Fernandes, “Early‐career researchers shaping publishing strategy,” Learned Publishing, vol. 34, no. 4, pp. 675–678, Oct. 2021, doi: 10.1002/leap.1383.

G. Abramo, D. W. Aksnes, and C. A. D’Angelo, “Comparison of research performance of Italian and Norwegian professors and universities,” J. Informetr., vol. 14, no. 2, p. 101023, May 2020, doi: 10.1016/j.joi.2020.101023.

B. D. Lund, T. Wang, N. R. Mannuru, B. Nie, S. Shimray, and Z. Wang, “ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing,” J. Assoc. Inf. Sci. Technol., vol. 74, no. 5, pp. 570–581, May 2023, doi: 10.1002/asi.24750.

M. Bond et al., “A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour,” International Journal of Educational Technology in Higher Education, vol. 21, no. 1, p. 4, Jan. 2024, doi: 10.1186/s41239 023 00436 z.

M. Lee, P. Liang, and Q. Yang, “CoAuthor: Designing a Human AI Collaborative Writing Dataset for Exploring Language Model Capabilities,” in CHI Conference on Human Factors in Computing Systems, New York, NY, USA: ACM, Apr. 2022, pp. 1–19. doi: 10.1145/3491102.3502030.

C. Song and Y. Song, “Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students,” Front. Psychol., vol. 14, Dec. 2023, doi: 10.3389/fpsyg.2023.1260843.

E. Kasneci et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Learn. Individ. Differ., vol. 103, p. 102274, Apr. 2023, doi: 10.1016/j.lindif.2023.102274.

F. Ouyang, M. Wu, L. Zheng, L. Zhang, and P. Jiao, “Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course,” International Journal of Educational Technology in Higher Education, vol. 20, no. 1, p. 4, Jan. 2023, doi: 10.1186/s41239 022 00372 4.

H. M. Alwahoub, N. J. Jomaa, and M. N. L. Azmi, “The impact of synchronous collaborative writing and Google Docs collaborative features on enhancing students’ individual writing performance,” Indonesian Journal of Applied Linguistics, vol. 12, no. 1, pp. 111–123, May 2022, doi: 10.17509/ijal.v12i1.46541.

C. Veddayana, I. Suyitno, D. Widyartono, and F. Aldresti, “Systematic Review: How Technology Supports Collaborative Writing Learning in Higher Education,” Electronic Journal of e Learning, vol. 23, no. 3, pp. 64–78, Jul. 2025, doi: 10.34190/ejel.23.3.3974.

K. Jaskyte, A. Hunter, and A. C. Mell, “Predictors of Interdisciplinary Team Innovation in Higher Education Institutions,” Innov. High. Educ., vol. 49, no. 1, pp. 113–132, Feb. 2024, doi: 10.1007/s10755 023 09676 3.

D. McEwan, G. R. Ruissen, M. A. Eys, B. D. Zumbo, and M. R. Beauchamp, “The Effectiveness of Teamwork Training on Teamwork Behaviors and Team Performance: A Systematic Review and Meta Analysis of Controlled Interventions,” PLoS One, vol. 12, no. 1, p. e0169604, Jan. 2017, doi: 10.1371/journal.pone.0169604.

F. Liu, J. Du, D. Q. Zhou, and B. Huang, “Exploiting the potential of peer feedback: The combined use of face to face feedback and e feedback in doctoral writing groups,” Assessing writing, vol. 47, p. 100482, Jan. 2021, doi: 10.1016/j.asw.2020.100482.

V. P. H. Pham, “The Effects of Collaborative Writing on Students’ Writing Fluency: An Efficient Framework for Collaborative Writing,” Sage Open, vol. 11, no. 1, Jan. 2021, doi: 10.1177/2158244021998363.

I. Dergaa, K. Chamari, P. Zmijewski, and H. Ben Saad, “From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing,” Biol. Sport, vol. 40, no. 2, pp. 615–622, 2023, doi: 10.5114/biolsport.2023.125623.

A. Mughal, K. J. Wahlberg, Z. Li, J. N. Flyer, N. C. Olson, and M. Cushman, “Impact of an institutional grant award on early career investigator applicants and peer reviewers,” Res. Pract. Thromb. Haemost., vol. 5, no. 5, p. e12555, Jul. 2021, doi: 10.1002/rth2.12555.

M. M. Gonzales, “School technology leadership vision and challenges,” International Journal of Educational Management, vol. 34, no. 4, pp. 697–708, Nov. 2019, doi: 10.1108/IJEM 02 2019 0075.

J. Wang, C. Liardét, J. Lum, and M. Riazi, “Co authorship between doctoral students and supervisors: motivations, reservations, and challenges,” Higher Education Research & Development, vol. 43, no. 7, pp. 1615–1631, Oct. 2024, doi: 10.1080/07294360.2024.2354253.

Downloads

Additional Files

Published

2026-06-01

How to Cite

[1]
P. Daniel, U. Narimawati, and B. Kurniawan, “Collaborative Writing Models for Enhancing Academic Publication Productivity: A Systematic Literature Review”, J.Gen.Educ.Humanit., vol. 5, no. 3, pp. 3279–3286, Jun. 2026.

Issue

Section

Articles