The rapid digitalisation of financial services has fundamentally transformed the regulatory landscape, compelling regulators and regulated entities to rethink how compliance, supervision, and risk governance are designed and operationalised. Regulatory Technology (RegTech) has emerged at the intersection of law, finance, and data science as a response to mounting regulatory complexity, accelerated innovation, and heightened societal expectations regarding transparency, fairness, and accountability. Drawing exclusively on the provided scholarly and institutional references, this article develops an integrated, theory-driven examination of RegTech as both a technological and normative project. It situates RegTech within broader traditions of principles-based regulation, risk-based supervision, and data-driven governance, while critically analysing the implications of artificial intelligence and machine learning for regulatory compliance, financial stability, and fundamental rights. The article adopts a qualitative doctrinal and conceptual methodology, synthesising insights from legal scholarship, financial regulation, political philosophy, and information systems research. The findings suggest that RegTech is not merely a tool for efficiency gains, but a transformative infrastructure reshaping the epistemic foundations of regulation itself. However, this transformation introduces new challenges, including algorithmic opacity, fairness concerns, data protection tensions under regimes such as the GDPR, and asymmetries between large incumbents and smaller organisations. The discussion highlights the need for a recalibrated regulatory imagination that aligns technological capability with principles-based norms, democratic accountability, and institutional resilience. The article concludes by outlining future research and policy directions aimed at steering RegTech development toward socially legitimate and systemically robust outcomes.

Initial public offerings represent one of the most information‑dense and uncertainty‑laden stages in corporate finance. Although regulatory frameworks require firms to disclose extensive risk information, long‑standing questions remain about whether investors genuinely understand these disclosures or simply navigate around them. This study examines the relationship between investor awareness of IPO risk factors and the risks that issuing firms formally disclose. Grounded in behavioural finance and disclosure theory, the research explores whether a meaningful gap exists between what companies communicate and what investors actually comprehend.

The study draws on survey data from a multi‑country sample of IPO investors and uses structured Likert‑scale analysis to assess awareness across seventeen categories of macroeconomic, regulatory, financial, and firm‑specific risk. These categories reflect the breadth of risks typically highlighted in prospectuses and

provide a detailed basis for evaluating how investors interpret disclosed information.

The findings reveal a high degree of alignment between investor awareness and the risk categories disclosed by firms. This challenges the common assumption that retail investors routinely misunderstand IPO risks or are overwhelmed by disclosure complexity. Instead, the results suggest that investors engage selectively but meaningfully with the information they consider most relevant.

The study contributes empirical evidence to ongoing debates about disclosure effectiveness, investor cognition, and market transparency. It also offers practical implications for regulators seeking to refine disclosure requirements, for issuing firms aiming to communicate risk more effectively, and for investor education initiatives focused on strengthening informed participation in IPO markets.

The rapid diffusion of generative artificial intelligence (AI) in marketing has transformed how firms design, personalize, and disseminate persuasive messages across global markets. However, limited research has theorized how consumers develop trust in AI as an active source of marketing communication rather than as a background analytical infrastructure. This study introduces algorithmic trust in interactive marketing as a multidimensional construct that captures cognitive, emotional, ethical, and institutional confidence in AI-generated marketing messages and the socio-technical systems that produce and govern them. Drawing on trust theory, technology acceptance research, source credibility theory, and cross-cultural institutional frameworks, we developed a conceptual model explaining how cultural values, regulatory environments, and transparency strategies shape the psychological mechanisms underlying trust formation and its behavioral outcomes. The framework advances interactive marketing theory by repositioning AI as a relational actor in consumer–brand communication. We derive six theoretically grounded propositions and outline a research agenda to guide empirical investigations into transparency, governance, and human–AI interaction in marketing contexts.