This research paper investigates the potential of FinTech solutions in promoting sustainable finance in Mongolia, an emerging market economy with a burgeoning FinTech landscape. This study explores how Mongolia leverages FinTech innovations to advance sustainable development goals. Utilizing secondary data sourced from Mongolian government agencies, global financial organizations, academic institutions, market research firms, and industry associations, we conducted an integrated SWOT-MLP analysis of Mongolia’s FinTech industry, which includes the FinTech business models, ecosystem, regulatory frameworks, traditional financial institutions, and stakeholders. This study presents a comprehensive analysis of the opportunities and challenges facing Mongolia in integrating FinTech into its sustainable finance agenda, and SWOT-based strategies.

AI recommendation agents increasingly mediate consumer decision-making in electronic commerce, yet algorithm-based agents often suffer credibility deficits relative to human sources. This research examines how recommendation agent type (human vs. AI) influences behavioral intentions through perceived credibility and how psychological ownership moderates this process. Across two controlled online experiments, consumers evaluated recommendations delivered by human or AI agents. Study 1 shows that baseline AI agents are perceived as less credible than human agents, while AI agents receiving minimal user involvement (naming) exhibit partially improved credibility, and that credibility mediates the effects of agent type on intention to use the system, recommendation acceptance, and purchase intention. Study 2 introduces a stronger psychological ownership manipulation through AI agent customization. Results indicate that customization strengthens psychological ownership, which reduces the credibility gap between AI and human agents and, when ownership is high, even allows AI agents to be evaluated as more credible. Conditional process analyses confirm that psychological ownership moderates both the effect of agent type on credibility and the indirect effects on behavioral intentions. Overall, the findings demonstrate that credibility toward AI recommendation agents is dynamically shaped by user–agent relational experiences. By integrating algorithm aversion, source credibility, and psychological ownership perspectives, this research advances understanding of consumer–AI interaction and provides design insights for AI-enabled recommendation systems in electronic commerce.

Artificial intelligence is increasingly embedded in service interactions, requiring users to form rapid social judgments about AI communicators based on limited linguistic and contextual cues. This research examines how AI communication tone shapes behavioral intentions through social cognitive processes of role construal and agency attribution. Drawing on politeness theory, formality research, and social cognition perspectives, two scenario-based experiments test whether formal versus casual tone influences responses via attitudes toward the tone and the AI, and how these effects depend on perceptions of AI as a servant-like social actor. Study 1 shows that tone effects are moderated by servant perception and that economic framing, specifically paid versus free access, functions as an antecedent of hierarchical role construal. Study 2 replicates these effects and demonstrates that interaction structure, one-way versus two-way communication, similarly shapes servant perception by signaling differential autonomy. Across both studies, formal tone is more effective when AI is construed as subordinate, whereas casual tone is less effective under hierarchical role frames. By identifying servant perception as a central social cognitive mechanism, this research advances understanding of human judgment and decision making in technology-mediated interactions and offers implications for AI communication design aligned with role expectations. Because both studies rely on U.S. consumers, the findings should be interpreted within cultural contexts characterized by relatively low power distance, where role expectations and hierarchy norms may differ from other cultural settings.

This study explores the effects of algorithmic governance on consumer responses, with a particular focus on the role of territorial psychological ownership (TPO) as a moderator. In increasingly automated digital environments, consumers interact with algorithms that govern content, services, and personalized experiences. This research demonstrates that TPO influences consumer reactions to algorithmic actions, such as the perceived intrusiveness of automated systems, which subsequently affects brand attitude and purchase intention. The findings highlight the importance of understanding how algorithmic governance can be designed to optimize consumer interactions while maintaining consumer satisfaction. By balancing the need for personalized experiences with respect for consumer autonomy, businesses can create more effective and efficient governance systems that avoid negative consumer perceptions. This study offers key insights for companies looking to navigate the delicate balance between providing personalized services and respecting consumer preferences, ultimately ensuring positive interactions with automated systems.

Digital retailing increasingly relies on short-form video advertising where human and AI presenters coexist, requiring consumers to form rapid social judgments based on minimal perceptual cues. This research examines how presentation format shapes consumer responses through perceived creator responsibility and effort, and how viewer-inferred privacy concern moderates these effects. Drawing on social cognition, deindividuation, and heuristic-cue perspectives, two online experiments (N = 656; N = 769) compared a human presenter with captions-only and with an AI avatar in retail-product video scenarios. Across both studies, the presence of a human presenter enhanced attitudes toward the video, perceived usefulness, trust, and purchase intention by sequentially increasing perceived responsibility and effort, reflecting viewers’ attributions of agency and motivational investment. Viewer-inferred privacy concern weakened these effects by attenuating responsibility attributions, demonstrating how contextual explanations recalibrate social judgments. The findings show that minimal human cues function as social cognitive signals of accountability in digital retail advertising. This research advances understanding of human judgment and decision making in consumer contexts and offers guidance for balancing human and AI communication under privacy-sensitive conditions.