The Effect of AIOps on R&D Efficiency in Chinese Technology Enterprises: The Moderating Role of Human-AI Integration

The Effect of AIOps on R&D Efficiency in Chinese Technology Enterprises: The Moderating Role of Human-AI Integration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of AIOps on R&D Efficiency in Chinese Technology Enterprises: The Moderating Role of Human-AI Integration Jun Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6556364/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the impact of Artificial Intelligence for IT Operations (AIOps) implementation on research and development (R&D) efficiency in Chinese technology enterprises. Using a structured survey approach, we examine how AIOps applications influence key performance metrics while considering Human-AI integration as a moderating variable. Data collected from 287 technology enterprises across China were analyzed using structural equation modeling. The results indicate that AIOps implementation has a significant positive effect on R&D efficiency (β = 0.423, p < 0.001). Moreover, the moderating effect of Human-AI integration strengthens this relationship (β = 0.218, p < 0.01), particularly in environments with high technical complexity. These findings contribute to the emerging literature on AI-driven operational transformations in technology enterprises and provide practical implications for optimizing R&D processes through strategic AIOps implementation and effective Human-AI collaborative frameworks. AIOps research and development efficiency Human-AI integration technology enterprises operational intelligence China technology sector Full Text Additional Declarations The authors declare no competing interests. Participant Consent Statement Participation in this study was entirely voluntary and anonymous. Completion of the online survey was considered as provision of informed consent, as stated in the introductory section of the questionnaire. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6556364","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449684454,"identity":"bdc6b324-6cc7-4025-886d-771c3b34a167","order_by":0,"name":"Jun Cui","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-9693-9145","institution":"solbridge international School of Business","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2025-04-29 12:20:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6556364/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6556364/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81794884,"identity":"6e7f194e-fd94-4e02-a239-6d94dbba4dd8","added_by":"auto","created_at":"2025-05-02 03:09:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":243808,"visible":true,"origin":"","legend":"","description":"","filename":"CscholarSampleformatstandardTemplatePaper5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6556364/v1_covered_3b456ac7-56df-452e-b9ec-04ba6a712737.pdf"}],"financialInterests":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eParticipant Consent Statement Participation in this study was entirely voluntary and anonymous. 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Data \u0026nbsp;collected from 287 technology enterprises across China were analyzed using structural equation \u0026nbsp;modeling. The results indicate that AIOps implementation has a significant positive effect on R\u0026amp;D \u0026nbsp;efficiency (β = 0.423, p \u0026lt; 0.001). Moreover, the moderating effect of Human-AI integration \u0026nbsp;strengthens this relationship (β = 0.218, p \u0026lt; 0.01), particularly in environments with high technical \u0026nbsp;complexity. 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