Vol. 1 No. 1 (2026): January

Editorial Introduction Journal of Thai-Chinese Social Science (JTCSS) Vol. 1 No. 1 (2026): January

By Dr. Jiang Songyu, Editor-in-Chief

It is my great pleasure to introduce Vol. 1 No. 1 (2026) of the Journal of Thai-Chinese Social Science (JTCSS). This inaugural issue of 2026 brings together four studies that converge on a timely social-science concern: how data, algorithms, and experience design are reshaping organizational governance and consumer decision-making across Thai–Chinese–connected contexts. From the fast-evolving market of Xiaomi Auto and new energy vehicles to the operational realities of public hospitals, the authors in this issue address a shared question—how can institutions translate “digital capability” into real improvements in value, efficiency, and trust?

A defining feature of this issue is its dual attention to market innovation and public-service governance. On one hand, we see how brand experience can be engineered and measured to strengthen consumers’ purchase intentions in the emerging NEV ecosystem. On the other hand, three complementary papers focus on healthcare management, illustrating how AI can move hospitals from experience-driven administration toward process-oriented, data-driven performance governance—while also confronting the organizational and data-governance challenges that accompany intelligent transformation.

The opening study examines how brand experience translates into purchase intention for new energy vehicles, using a survey of 396 potential consumers in Chengdu and testing a dual-mediation mechanism through perceived value and brand identification within an S–O–R framework. The findings demonstrate that experience is not merely an “added extra” in technology-intensive consumption; rather, it becomes a conversion mechanism that simultaneously strengthens value-based evaluation and identity-based connection, both of which significantly predict intention. This paper offers actionable guidance for ecosystem brands: orchestrating coherent multi-touchpoint experiences can turn attention into intention by making value legible and identity meaningful.

Shifting from consumer markets to public services, the second contribution tackles a familiar pressure point in healthcare: hospital queuing. Rather than treating waiting time as a problem solvable only by workforce expansion, the paper conceptualizes queuing as a system-level outcome shaped by information asymmetry, decision delays, and capacity–flow misalignment. Through scenario analysis, it proposes three AI-enabled pathways—true bottleneck identification, medical decision front-loading, and dynamic capacity scheduling—to support proactive patient flow management and reduce waiting-time variability without relying solely on fixed-capacity increases. The paper’s core contribution is its reframing of AI’s value from “speeding up steps” to optimizing decision structures and coordinating the entire care pathway.

The third paper approaches hospital transformation from the perspective of management logic. It argues that traditional KPI systems struggle with data integration, dynamic monitoring, and decision support, and it builds a normative framework showing how AI can strengthen medical quality control, operational efficiency, cost management, and patient experience. Importantly, the study highlights a key governance shift: AI enables KPI management to move from outcome-oriented assessment to process-oriented governance, and from experience-driven judgment to data-driven decision-making, offering a staged pathway for hospitals to integrate data, analytics, and closed-loop performance improvement.

Completing the healthcare cluster, the fourth study focuses on a foundational yet often under-discussed step—how KPIs are formulated. By comparing traditional manual KPI formulation with AI-assisted approaches, the paper argues that intelligent KPI formulation demonstrates advantages in accuracy, timeliness, and predictive capability, while also acknowledging real-world constraints such as data governance and organizational adaptation. By clarifying differences in formulation logic, data sources, and dynamic adjustment capacity, this paper provides a practical lens for hospitals seeking not only to “use AI,” but to rebuild performance systems that are responsive to complexity and uncertainty.

On behalf of the editorial team, I extend sincere thanks to our authors, reviewers, and readers for supporting JTCSS as a platform for rigorous and relevant Thai–Chinese social science. We warmly welcome new submissions that engage with Thai–Chinese themes and broader regional questions through interdisciplinary perspectives—especially research that combines clear theory, transparent methods, and practical implications for institutions navigating digital transformation.

With best wishes,
Dr. Jiang Songyu
Editor-in-Chief, Journal of Thai-Chinese Social Science (JTCSS)
Vol. 1 No. 1 (2026): January