Arthit Apichottanakul

Purpose: This study investigates how financial and entrepreneurial capability configurations differentiate internationalized from domestically oriented Thai SMEs. By adopting a decision-science perspective, the study identifies the capability patterns that shape firms’ strategic international orientation. Design/methodology/approach: This research, which utilizes survey and financial data from 179 Thai SMEs (2021–2023), employs an inductive machine learning approach based on Extreme Gradient Boosting (XGBoost). The analytical framework integrates profitability, liquidity, leverage, operational efficiency, international experience, team readiness, market knowledge, and institutional connectivity. Feature importance scores and confirmatory statistical tests are used to validate differentiating capability structures. Findings: Internationalized SMEs are characterized by stronger financial agility, higher profitability, disciplined leverage, and more efficient resource utilization, together with entrepreneurial preparedness reflected in international experience, risk tolerance, team coordination, and institutional embeddedness. Localized SMEs, by contrast, display liquidity-heavy but lower-dynamism profiles, weaker network engagement, and more limited organizational readiness. Practical implications: The findings suggest that SME support should move beyond finance-only assistance and address capability alignment. Programs that strengthen financial agility, team readiness, international market knowledge, digital channels, and institutional networks may improve SME readiness for international engagement. Originality/value: The study contributes to international business theory by reframing SME internationalization as a selective and configurational process rather than a linear or binary outcome. It extends the resource-based view by emphasizing capability bundles rather than isolated resources and contributes to the dynamic capabilities perspective by showing how financial, entrepreneurial, and institutional capabilities are associated with SMEs’ differentiated participation in international markets.

Purpose: This study investigates how financial and entrepreneurial capability configurations differentiate internationalized from domestically oriented Thai SMEs. By adopting a decision‐science perspective, the study identifies the capability patterns that shape firms’ strategic international orientation. Design/methodology/approach: This research, which utilizes survey and financial data from 179 Thai SMEs (2021–2023), employs an inductive machine learning approach based on Extreme Gradient Boosting (XGBoost). The analytical framework integrates profitability, liquidity, leverage, operational efficiency, international experience, team readiness, market knowledge, and institutional connectivity. Feature importance scores and confirmatory statistical tests are used to validate differentiating capability structures. Findings: Internationalized SMEs demonstrate stronger financial agility, higher profitability, disciplined leverage, and superior resource utilization combined with entrepreneurial preparedness, as evidenced by international experience, risk tolerance, effective team coordination, and institutional embeddedness. In contrast, localized SMEs exhibit liquidity-heavy but low-dynamism profiles, limited network engagement, and weaker organizational readiness. The machine learning model highlights the non-linear interactions among these capabilities, achieving high predictive accuracy (92.59%). Practical implications: The findings offer a capability-based diagnostic tool for managers and policymakers. Strengthening financial agility, team readiness, and institutional ties can enhance the global competitiveness of SMEs. Originality/value: This study introduces a multidimensional, data-driven capability configuration model for SME internationalization, advancing decision science, dynamic capabilities, and network-based theories in emerging-market contexts.

1. The agro-food sector plays a central role in the economies of many developing countries, often accounting for a substantial share of GDP – frequently exceeding 50% – and serving as a primary eng...

Logistics and supply chain management for the healthcare sector is concerned with activities related to procurement, manufacturing, storage, distribution, and transportation. Key stakeholders are from various parties inclusive of material supplier, manufacturer, distribution center, regional hospital, provincial hospital, as well as patients. In this research, we examine the delivery-route decisions for last-mile delivery using a selective case study of transporting healthcare products to downstream points of consumption. Initially, the Vehicle Routing Problem with Time Window (VRPTW) is developed. Then, in order to model the driver perspective to deliver healthcare products and to capture urgent needs of healthcare products under time constraints due to temperature control in the healthcare cold chain (HCC), the minimax objective is used as a surrogate objective function reflecting social perspective in this study. Then, a sensitivity analysis is performed to assess how changing the number of vehicles affects the model performance. Finally, the case study from the third-party logistics (3PL) company is used to verify and validate the model functionalities. Our initial study provides promising result and is subject to the on-going development of the healthcare cold chain optimization model in our research study.

The objective of this study was to develop a new method for freight movement in urban areas by combining a light rail transit system and a road transport system to distribute and deliver parcels. Utilisation of light rail transportation systems for parcel delivery can help reduce traffic congestion and air pollution in cities. This paper presents a location–allocation model to determine the optimum number and location of light rail stations suitable to establish rail distribution hubs, in a bid to minimise transportation costs. A case study is conducted to assess this approach in the city of Bangkok. The results show that the cost, time, and distance of transportation can be reduced by 10%, 16.67%, and 33.33%, respectively.

Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency–Frequency–Monetary (RFM) model has been commonly recognised as an effective approach to analyse customer behaviour. However, the traditional RFM approach is a coarse method for quantifying customer loyalty and contribution that can only provide a single lump-sum value of the recency (R), frequency (F), and monetary value (M); hence, it discards information regarding customers’ product preferences. Typically, different customers make different purchases. Subsequently, purchases are likely to be different across customers. This creates data sparsity, which affects the performance of conventional clustering methods. In this study, we integrated the group RFM analysis and probabilistic latent semantic analysis models to perform customer segmentation and customer analysis. The results indicated that the developed approach takes into account the product preference and provides insight into and captures a wide variety of the types of true ordering behaviour of the company’s customers. The information allows the manager to improve customer relationships and build a personalised purchasing management system for grouping customers with similar purchasing patterns.