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In the rapidly evolving business landscape of Thailand, the design and facilities of office buildings play a crucial role in enhancing employee satisfaction and productivity. This study seeks to answer the question: "How can office building facilities be optimized to meet the diverse preferences of occupants in Thailand, thereby improving their satisfaction and productivity"? This study employs a copula-based ordinal regression model combined with machine learning techniques to investigate the determinants of facility preferences in office buildings in Thailand. By analyzing data from 372 office workers in Bangkok, we identify the factors influencing facility needs and preferences, and measure the correlation between these preferences. Our findings reveal that safety and security are the highest-rated amenities, indicating their importance in the workplace. The findings reveal distinct preferences across demographic groups: age negatively influences the demand for certain amenities like lounges, while higher education levels increase the preference for cafeteria services. Employees in smaller firms show a higher preference for lounges and fitness centers but lower for restaurants and cafeterias. Interestingly, the size of the enterprise does not significantly affect preferences for fundamental facilities like security and cleaning. The study also uncovers the significant role of gender and income in shaping preferences for certain facilities. These results suggest that while basic amenities are universally valued, luxury or leisure-oriented facilities are more appreciated in smaller, possibly more community-focused work environments. This study highlights the need for tailored facility management in office buildings, considering the diverse needs of different employee groups, which has significant implications for enhancing workplace satisfaction and productivity. [ABSTRACT FROM AUTHOR]

Thai jasmine rice holds immense economic significance both domestically and as a major export, making it a vital contributor to the Thai economy. Traditional and modern agricultural practices, centered around rice cultivation, play a pivotal role in the culture and economy of the Northeast region. However, concerns have arisen regarding rice production efficiency in recent times due to capital and labor availability challenges. Motivated by these concerns, this study employs a copula-based stochastic frontier modeling (copula-SFM) framework to investigate production efficiency empirically. The research is based on a sample of 397 farmers in the Northeast of Thailand. The Akaike Information Criterion (AIC) is utilized to select the most appropriate model. The results indicate that the Gaussian copula-SFM outperforms other copula models. Notably, the study identifies total area, capital, and labor as critical factors significantly contributing to the positive impact on jasmine rice production. Furthermore, the research ranks provinces based on average technical efficiency (TE) scores, revealing Khon Kaen Province, Yasothon Province, and Roi Et Province as the top-performing regions. [ABSTRACT FROM AUTHOR]

This study examines the efficiency of rice production in Thailand, especially major rice, which is the main crop of farmers in all regions of Thailand and is still a pressing issue. Analyzing technical efficiency by using the appropriate analytical tools inevitably brings about determining the correct production efficiency measures. In this study, we applied the K-Means algorithm and copula-based stochastic frontier model to cluster farmer groups in order to find the different factors that impact the group, and to relax the assumption of the two components of random error, which is that they are independent to each other; the correlation of the two components of random error is also represented by the estimation of copula. The findings from the K-Means clustering algorithms applied in this study indicate that the production frontiers can be divided into two frontiers, with the number of farmers under the frontiers of such production differing from the number of farmers collected in each area. The production frontiers were obtained with 591 farmers under the first production frontier and 65 farmers under the second. In addition, the results reflected a correlation between the two error components U and V. This suggests inefficiencies and zero-mean, and that the symmetric error is not independent of each other. The findings from the application of the copula-based stochastic frontier production function models indicate that land, cost of chemicals, and labor inputs have significant positive effects on the mean output of major rice in both groups of farmers. Therefore, the results of this study indicate that the financial services in rural areas should be continuously promoted by governmental policy, particularly via agricultural loans, to rural people since the utilization of inputs affects the quantity of rice produced. Timely loans should be encouraged. [ABSTRACT FROM AUTHOR]

The traditional Stochastic Frontier Model (SFM) suffers from a very restrictive assumption of independence of its error components and also limited ability to address heterogeneity (inefficiency effects) satisfactorily, thereby leading to potential biases in the estimation of model parameters, identification of inefficiency effect variables influencing efficiency and, ultimately, efficiency scores. This paper aims to investigate the consequences of ignoring any dependency in error components and heterogeneity in the stochastic frontier model, and proposes a copula-based SFM with heterogeneity to resolve such weaknesses based on a simulation study to prove its superiority over the traditional SFM, followed by an empirical application on a sample of rice producers from northern Thailand. We demonstrate that the proposed model, i.e., copula-based SFM with dependent error components and heterogeneity, is unbiased and robust. The simulation experiments show that the traditional SFM can cause biases in parameter estimation and severe overestimation of technical efficiency. The traditional SFM with heterogeneity also has similar consequences. However, just ignoring heterogeneity does not have a great impact on parameter estimation and technical efficiency compared to the consequence of ignoring dependency in error components. The empirical application of the proposed model results show that land, labor and material inputs are all significant drivers of rice production in our copula-based SFM with dependent error components and heterogeneity, whereas in the traditional SFM model only the land variable seems to be a significant driver of rice production. The mean technical efficiency (MTE) score was also overestimated by two points in the traditional SFM, i.e., MTE = 0.88 versus 0.86. Finally, results of the copula-based SFM with dependent error components and heterogeneity reveals that both subsistence pressure and the use of hired labor are significantly associated with technical inefficiency, whereas the traditional SFM could identify the effect of hired labor use only. Therefore, caution is necessary when interpreting results from the conventional SFM as the results may be biased, incomplete and/or inadequate. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the impact of trade openness and the factors based on the gravity model on the bilateral trade flows between Thailand and Japan. The factors consist of GDP, distance, trade openness, and exchange rate. Bilateral trade is composed of two flows: Thailand's export flow to Japan, and Thailand's import flow from Japan. The specified gravity equations are estimated by Copula-based Markov switching seemingly unrelated regression approach. The best-fitting model is chosen based on the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC). The Normal and Student's t distributions are for Thailand's export equation and Thailand's import equation, respectively. The Student's t copula is applied for joint distribution. Analyzing the bilateral trade flow is separated into two situations, namely the high and the low growth markets. Empirical results show that distance provides a positive effect on the export in a high growth regime, but a negative impact on the export in a low growth regime. As for Thailand's import flow, all variables, but especially trade openness, provide strong evidence supporting significance for both regimes. For the GDPs of both Thailand and Japan, trade openness and the exchange rate increase import flow in a high growth market. Meanwhile, the exchange rate decreases import flow in a low growth market. The Markov Switching Probability Estimation notes that Thailand's trading with Japan is mostly in the fast-growing market. [ABSTRACT FROM AUTHOR]