Academic Journal
Workflow Automation in Open-Source Software Development: Accelerating Innovation Through Mechanization and Orchestration.
| Τίτλος: | Workflow Automation in Open-Source Software Development: Accelerating Innovation Through Mechanization and Orchestration. |
|---|---|
| Συγγραφείς: | Huang, Ao, Huang, Ni, Hong, Yili |
| Πηγή: | Information Systems Research (INFORMS); Sep2026, Vol. 37 Issue 3, p1643-1662, 20p |
| Θεματικοί όροι: | Mechanization, Open source software, Orchestra, Originality, Continuous improvement process, Workflow management systems |
| Εταιρία/Οντότητα: | Github Inc. |
| Περίληψη: | This study develops a conceptual framework distinguishing two mechanisms of workflow automation: mechanization and orchestration. Mechanization automates discrete, self-contained, repeatable tasks through standardized execution to enhance consistency, reliability, and efficiency, while orchestration automates the communication between tasks, workers, and stages, which facilitates information flow and coordination. We theorize that these mechanisms differentially affect incremental versus substantive innovation. Using a multimethod approach integrating machine learning and econometrics, we analyze the effects of workflow automation in open-source software development, demonstrating that mechanization accelerates maintenance-oriented exploitative innovation, whereas orchestration accelerates development-oriented explorative innovation. This mechanization-orchestration distinction extends beyond software contexts. For practitioners, aligning automation strategies with innovation goals is essential: deploy mechanization to enhance operational efficiency and support incremental improvements in stable environments; and implement orchestration to enable adaptive coordination in exploratory, high-velocity development requiring creativity and flexibility. For policymakers, understanding this distinction informs workforce development and technology adoption policies, as automation reshapes work by shifting human contribution from routine execution toward coordinated problem solving and strategic decision making. This study develops a conceptual framework distinguishing two foundational mechanisms of workflow automation, mechanization and orchestration, and empirically examines how workflow automation accelerates innovation in the context of open-source software (OSS) development. Drawing on the process automation, innovation, and OSS development literature, we propose that (a) workflow automation accelerates OSS innovation and (b) distinct workflow automation mechanisms—mechanization and orchestration—differentially affect incremental innovation (i.e., maintenance) and substantive innovation (i.e., new development), respectively. Based on longitudinal observations of more than 4,500 GitHub repositories and 280,000 issues from 2019 to 2020, we combine machine learning with econometrics in an embedded interlayering multimethod design. To test our hypotheses, our econometric analyses further employ look-ahead rolling entry matching (LA-REM) in tandem with different estimation strategies, including difference-in-differences (DiD) and instrumental variables (IVs). Our analyses first estimate that workflow automation (specifically GitHub Actions) accelerates issue resolution by 10.1%, translating to 4.3 days saved per issue, demonstrating significant improvement in innovation speed. Second, particularly notably, we distinguish between mechanization and orchestration to further investigate how these workflow automation mechanisms differentially affect two types of innovation in software development. Our analyses uncover that mechanization significantly accelerates the incremental innovation of maintenance by 7.8%, saving an average of 3.0 days per issue; however, orchestration shows an inconsequential effect. In contrast, for the substantive innovation of new development, mechanization shows a limited effect, whereas orchestration accelerates issue resolution speed by 16.6%, saving an average of 9.1 days per issue. We further leverage counterfactual estimations to analyze the downstream effects of workflow automation. We observe that the speed gain does not compromise quality, as workflow automation significantly increases the contribution volume (closed issues) and project quality (stars, forks, and releases). This study unpacks the distinct mechanisms of workflow automation, showing that mechanization accelerates routine maintenance-oriented incremental innovation, whereas orchestration catalyzes creative development-oriented substantive innovation. Our study contributes to research and practice on automation, innovation, and software development by demonstrating that workflow automation not only accelerates innovation but also reshapes the underlying processes through which innovation unfolds in OSS development. History: Gal Oestreicher-Singer, Senior Editor; Min-Seok Pang, Associate Editor. Funding: This work was supported by OpenAI's Researcher Access Program and the Google Cloud Researcher Award. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2024.1551. [ABSTRACT FROM AUTHOR] |
| Copyright of Information Systems Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Workflow Automation in Open-Source Software Development: Accelerating Innovation Through Mechanization and Orchestration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Ao%22">Huang, Ao</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Ni%22">Huang, Ni</searchLink><br /><searchLink fieldCode="AR" term="%22Hong%2C+Yili%22">Hong, Yili</searchLink> – Name: TitleSource Label: Source Group: Src Data: Information Systems Research (INFORMS); Sep2026, Vol. 37 Issue 3, p1643-1662, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Mechanization%22">Mechanization</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Orchestra%22">Orchestra</searchLink><br /><searchLink fieldCode="DE" term="%22Originality%22">Originality</searchLink><br /><searchLink fieldCode="DE" term="%22Continuous+improvement+process%22">Continuous improvement process</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow+management+systems%22">Workflow management systems</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="CO" term="%22Github+Inc%2E%22">Github Inc.</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study develops a conceptual framework distinguishing two mechanisms of workflow automation: mechanization and orchestration. Mechanization automates discrete, self-contained, repeatable tasks through standardized execution to enhance consistency, reliability, and efficiency, while orchestration automates the communication between tasks, workers, and stages, which facilitates information flow and coordination. We theorize that these mechanisms differentially affect incremental versus substantive innovation. Using a multimethod approach integrating machine learning and econometrics, we analyze the effects of workflow automation in open-source software development, demonstrating that mechanization accelerates maintenance-oriented exploitative innovation, whereas orchestration accelerates development-oriented explorative innovation. This mechanization-orchestration distinction extends beyond software contexts. For practitioners, aligning automation strategies with innovation goals is essential: deploy mechanization to enhance operational efficiency and support incremental improvements in stable environments; and implement orchestration to enable adaptive coordination in exploratory, high-velocity development requiring creativity and flexibility. For policymakers, understanding this distinction informs workforce development and technology adoption policies, as automation reshapes work by shifting human contribution from routine execution toward coordinated problem solving and strategic decision making. This study develops a conceptual framework distinguishing two foundational mechanisms of workflow automation, mechanization and orchestration, and empirically examines how workflow automation accelerates innovation in the context of open-source software (OSS) development. Drawing on the process automation, innovation, and OSS development literature, we propose that (a) workflow automation accelerates OSS innovation and (b) distinct workflow automation mechanisms—mechanization and orchestration—differentially affect incremental innovation (i.e., maintenance) and substantive innovation (i.e., new development), respectively. Based on longitudinal observations of more than 4,500 GitHub repositories and 280,000 issues from 2019 to 2020, we combine machine learning with econometrics in an embedded interlayering multimethod design. To test our hypotheses, our econometric analyses further employ look-ahead rolling entry matching (LA-REM) in tandem with different estimation strategies, including difference-in-differences (DiD) and instrumental variables (IVs). Our analyses first estimate that workflow automation (specifically GitHub Actions) accelerates issue resolution by 10.1%, translating to 4.3 days saved per issue, demonstrating significant improvement in innovation speed. Second, particularly notably, we distinguish between mechanization and orchestration to further investigate how these workflow automation mechanisms differentially affect two types of innovation in software development. Our analyses uncover that mechanization significantly accelerates the incremental innovation of maintenance by 7.8%, saving an average of 3.0 days per issue; however, orchestration shows an inconsequential effect. In contrast, for the substantive innovation of new development, mechanization shows a limited effect, whereas orchestration accelerates issue resolution speed by 16.6%, saving an average of 9.1 days per issue. We further leverage counterfactual estimations to analyze the downstream effects of workflow automation. We observe that the speed gain does not compromise quality, as workflow automation significantly increases the contribution volume (closed issues) and project quality (stars, forks, and releases). This study unpacks the distinct mechanisms of workflow automation, showing that mechanization accelerates routine maintenance-oriented incremental innovation, whereas orchestration catalyzes creative development-oriented substantive innovation. Our study contributes to research and practice on automation, innovation, and software development by demonstrating that workflow automation not only accelerates innovation but also reshapes the underlying processes through which innovation unfolds in OSS development. History: Gal Oestreicher-Singer, Senior Editor; Min-Seok Pang, Associate Editor. Funding: This work was supported by OpenAI's Researcher Access Program and the Google Cloud Researcher Award. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2024.1551. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Information Systems Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/isre.2024.1551 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1643 Subjects: – SubjectFull: Github Inc. Type: general – SubjectFull: Mechanization Type: general – SubjectFull: Open source software Type: general – SubjectFull: Orchestra Type: general – SubjectFull: Originality Type: general – SubjectFull: Continuous improvement process Type: general – SubjectFull: Workflow management systems Type: general Titles: – TitleFull: Workflow Automation in Open-Source Software Development: Accelerating Innovation Through Mechanization and Orchestration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Ao – PersonEntity: Name: NameFull: Huang, Ni – PersonEntity: Name: NameFull: Hong, Yili IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10477047 Numbering: – Type: volume Value: 37 – Type: issue Value: 3 Titles: – TitleFull: Information Systems Research (INFORMS) Type: main |
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