Exploring AI-powered Digital Innovations from A Transnational Governance Perspective: Implications for Market Acceptance and Digital Accountability Accountability

AI adoption requires more than just technical capability: The study finds that organizations adopting AI innovations must consider regulatory compliance, ethical standards, and cultural acceptance alongside traditional factors like perceived usefulness and ease of use—suggesting that responsible AI development demands attention to governance frameworks, not just technology performance.

Accountability cannot be assigned to a single actor: Rather than placing responsibility solely on developers or individual organizations, the research demonstrates that AI accountability is "co-constructed among organisations, regulators, and societal actors," indicating that responsible AI requires coordinated effort across multiple stakeholders including government bodies and society.

Organizations need internal governance restructuring: The paper identifies "internal resource reconfiguration" as essential, where companies must restructure their governance and compliance mechanisms to align with global standards—a practical step suggesting that responsible AI requires institutional changes beyond technical safeguards.

Engagement with external stakeholders improves accountability: The study proposes that organizations should reshape boundaries through "actor-network management," actively engaging with regulators, external stakeholders, and transnational governance institutions, indicating that transparency and collaboration with outside parties strengthens AI accountability and market trust.

Global standards matter for market acceptance: The research suggests that organizations aligning with transnational governance frameworks and global standards achieve better market acceptance, implying that responsible AI practices and regulatory compliance are not obstacles to adoption but rather drivers of consumer and institutional trust.