Document Type : Original Article
Authors
1
M.A. in Psychology and B.A. in Business Administration, University of Applied Science and Technology, Ilam Branch, Ilam, Iran
2
B.Sc. in Applied Chemistry, Islamic Azad University, South Tehran Branch, Tehran, Iran, CEO of Ravan Gostaran Tavan Gharb Company
3
Ph.D. in Industrial Management (Systems Management), Islamic Azad University, Sanandaj Branch, Sanandaj, Iran; Deputy CEO of Ravan Gostaran Tavan Gharb Company
10.5281/zenodo.23441177
Abstract
Purpose—Generative artificial intelligence (GenAI) is being integrated into knowledge work, organizational decision support, and sustainability-related operations, yet evidence regarding governance, algorithmic trust, and durable organizational value remains fragmented. This article develops an integrated explanation of how responsible governance can enable, rather than merely constrain, the creation of sustainable innovation and organizational performance. Design/methodology/approach—A critical, structured integrative synthesis draws on recent peer-reviewed research and authoritative policy and standards publications issued between 2023 and October 2026. Sources compared through thematic coding across governance architecture, calibrated algorithmic trust, human–AI collaboration, sustainable innovation, and organizational performance. The article proposes constructs, testable propositions, an implementation framework, and a future empirical research design; it does not claim to report original survey or experimental findings. Findings—Governance quality theorized to improve organizational outcomes through calibrated trust and responsible human–AI practices. The effects are contingent on decision criticality, employee AI literacy, accountability arrangements, institutional pressure, and the environmental costs of computation. Trust appropriately calibrated to system reliability rather than maximized. Practical implications—Organizations should combine documented use-case inventories, risk-tiered oversight, evaluation, traceability, worker participation, and multidimensional scorecards. Originality/value—the framework connects governance mechanisms and behavioral trust calibration to an explicitly triple-bottom-line account of innovation and performance, while separating established empirical evidence from propositions requiring testing.
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