Virach Sornlertlamvanich

arXiv2026

Artificial Intelligence (AI) Maturity in Small and Medium-Sized Enterprises: A Framework of Internalized and Ecosystem-Embedded Capabilities

Artificial intelligence (AI) maturity models have proliferated, yet prevailing frameworks remain largely enterprise-centric, linear, and weakly aligned with the organizational realities of small and medium-sized enterprises (SMEs). This study develops a conceptual AI maturity framework explicitly grounded in SME contexts. Drawing on organizational capability theory, maturity model research, and the SME digital transformation literature, the framework reconceptualizes AI maturity as a multidimensional, non-linear, and ecosystem-embedded capability. It comprises eight interrelated capability dimensions, five maturity levels, and four archetypal development pathways, capturing heterogeneity in SME AI adoption trajectories. By foregrounding resource constraints, informal governance, owner-manager dominance, and external ecosystem dependence, the framework extends existing AI maturity theory and responds to calls for context-sensitive conceptualization of AI capability development. The study provides a foundation for future empirical validation and comparative research on AI maturity in SMEs, to measure their competitiveness, potential in self-development, and driving force in the SME context.

cs.CY↗

arXiv1995

Classifier Assignment by Corpus-based Approach

This paper presents an algorithm for selecting an appropriate classifier word for a noun. In Thai language, it frequently happens that there is fluctuation in the choice of classifier for a given concrete noun, both from the point of view of the whole spe ech community and individual speakers. Basically, there is no exect rule for classifier selection. As far as we can do in the rule-based approach is to give a default rule to pick up a corresponding classifier of each noun. Registration of classifier for each noun is limited to the type of unit classifier because other types are open due to the meaning of representation. We propose a corpus-based method (Biber, 1993; Nagao, 1993; Smadja, 1993) which generates Noun Classifier Associations (NCA) to overcome the problems in classifier assignment and semantic construction of noun phrase. The NCA is created statistically from a large corpus and recomposed under concept hierarchy constraints and frequency of occurrences.

cmp-lg↗