Given the uneven financial resources, digital capabilities, technical support, and production environments characterizing Thai smallholder agriculture, Internet of Things (IoT) adoption readiness requires both willingness to adopt and confidence in using IoT under actual farming conditions. This study examines the determinants of IoT adoption readiness using an extended UTAUT2-based framework that distinguishes behavioral intention from technological self-efficacy. The framework was extended by incorporating government support and financial support as two context-specific factors, while technological self-efficacy was positioned as a capability mechanism connecting enabling conditions with behavioral intention. Data were collected through face-to-face questionnaires from 475 smallholder farmers across four regions of Thailand and analyzed using covariance-based structural equation modeling. The structural model demonstrated acceptable fit (CFI = 0.934, RMSEA = 0.059, SRMR = 0.050) and explained 54.7% of the variance in behavioral intention and 72.7% in technological self-efficacy. Technological self-efficacy was the strongest predictor of behavioral intention (β = 0.393, p < 0.001), followed by habit and price value. Environmental suitability was the strongest predictor of technological self-efficacy (β = 0.493, p < 0.001), followed by financial support and facilitating conditions. In contrast, effort expectancy did not significantly influence technological self-efficacy. The findings demonstrate that agricultural IoT readiness depends not only on perceived benefits and economic value but also on farmers’ confidence that IoT technologies can be applied within their actual production environments. The study highlights the importance of locally compatible technologies, capability development, financial accessibility, and coordinated institutional support for promoting agricultural IoT adoption among Thai smallholders.
Business intelligence technologies have evolved from retrospective reporting tools into strategic assets integrating artificial intelligence, cloud computing, and big-data infrastructure. However, systematic, evidence-based roadmaps for forecasting BI innovation trajectories remain limited. This study investigates principal technology clusters within BI innovation, their lifecycle stages, and maturation timelines relevant to strategic planning and open innovation decision-making. Using a multi-stage patent analytics framework, 14,072 BI patents retrieved from Lens.org and spanning 1928–2026 were analyzed. Four transformer-based sentence-embedding models were evaluated to capture semantic relationships, and ensemble validation selected architectures suited to the patent corpus. Uniform Manifold Approximation and Projection reduced embedding dimensionality, while K-means clustering was assessed across 4–25 clusters using the Silhouette coefficient, Davies–Bouldin index, Calinski–Harabasz score, and Gap statistic. The analysis identified 20 technology clusters representing infrastructure platforms, analytical methods, and vertical applications. Cluster themes were characterized through keyword extraction combining class-based TF–IDF with BM25 Okapi scoring, revealing technologies across healthcare, telecommunications, retail, financial services, and smart cities. Logistic S-curves fitted to cumulative patent counts estimated lifecycle stages and projected saturation years, with partial-year data treated explicitly to avoid truncation bias. All clusters were classified as emerging or growing based on patent activity trajectories; two are projected to reach saturation by 2033, whereas eighteen extend beyond 2034. The resulting multi-horizon roadmap supports research prioritization, investment allocation, competitive positioning, external technology search, ecosystem mapping, and partner identification for organizations pursuing open innovation strategies. Methodologically, the study advances patent analytics through comparative transformer evaluation, multi-metric cluster validation, and fused keyword extraction.
