Corporate carbon abatement is commonly modeled as a direct and continuous response to environmental regulation or green innovation. Such representations cannot fully explain persistent differences among firms facing similar policy conditions or distinguish the pressure required to initiate a low-carbon transition from that needed to sustain it. This study develops a three-state continuous-time model linking corporate carbon abatement, accumulated green innovation, and effective regulatory pressure, with green market preference treated as an external demand-pull parameter. Green innovation lowers the marginal cost of abatement, abatement generates learning and commercial returns to innovation... More >

Artificial intelligence (AI) investment, environmental, social, and governance (ESG) disclosure, and green supply chain collaboration increasingly operate as an interconnected corporate sustainability system, yet their relationships are commonly examined through static or approximately linear models. This study develops a bounded three-dimensional discrete nonlinear framework in which firms periodically adjust AI-investment maturity, ESG disclosure quality, and green supply chain collaboration in response to cross-domain benefits and self-limiting organizational costs. The model contributes by separating structural complementarity from organizational responsiveness, allowing the same long-ru... More >

Generative artificial intelligence (GenAI) is increasingly embedded in enterprise workflows, yet the models that support these workflows are updated at discrete and sometimes frequent intervals. Each update can improve technical effectiveness while simultaneously invalidating prompts, interfaces, controls, and employee routines. This study develops an impulsive dynamical system to analyze the resulting tension between technical improvement and organizational disruption. The continuous subsystem tracks enterprise process stability, organizational adaptation, and effective model capability between updates. The impulsive subsystem represents the instantaneous effects of a model release on workf... More >

Artificial intelligence is moving from analytical support toward active participation in enterprise decisions, creating an organization-design problem: firms must decide which rights may be delegated to AI and how responsibility should follow the actors who can prevent, challenge, or remedy failure. Existing work explains automation, augmentation, delegation, human oversight, and responsible AI governance, but does not reveal how specific transfers of decision authority create responsibility gaps inside a focal enterprise decision. This conceptual paper develops a contingency governance framework through a transparent theory-synthesis procedure. A purposive corpus of 44 peer-reviewed studies... More >

Artificial intelligence is becoming a core engine of corporate digital transformation, but its value depends first on secure, reliable, and accountable data and model infrastructures. As firms combine cloud platforms, edge devices, IoT sensors, digital twins, platform data, and algorithmic decision systems, they also expand the attack surface, privacy exposure, model security risk, and compliance burden. This paper develops a security-aware data and AI governance framework for AI-driven corporate digital transformation. It positions the framework as a unified governance model rather than a narrow extension of data management: data governance controls data classification, provenance, access,... More >