GSAS: Dissertations

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    Relationships between psychological factors, innovative performance, marketing capability, and entrepreneurial success among Thai fruit and vegetable processing and preservation SMEs in Thailand
    Natthawat Wiwatkitbhuwadol; Arnond Sakworawich (National Institute of Development Administration, 2019)
    An entrepreneur starts and runs a business through the pursuit of opportunities with the determination to use his/her knowledge, abilities, and experiences to effectively run his/her organization and with the resources at hand. He/she is a creative person who finds new approaches to market existing merchandise or better ways to improve and develop existing production processes to maximize the organization’s benefits. He/she is willing to undertake a business venture in exchange for profits and satisfaction. These are the characteristics of a potentially successful entrepreneur. Entrepreneurial success is the primary goal of every entrepreneur, in the pursuit of which he/she must endure different kinds of problems to achieve this goal, and there are many ways to measure business success. The aim of this study is to analyze the relationships between the psychological factors, innovative performance, marketing capability, and entrepreneurial success among Thai Fruits and Vegetables Processing and Preservation SMEs in Thailand. This is one of the first empirical studies to adopt the Giessen-Amsterdam Model of Entrepreneurial Success as the main research model with some added variables that may affect entrepreneurial success identified from a literature review. Another research interest is the impact of the rising number of Thai Fruit and Vegetable Processing and Preservation SMEs entrepreneurs on global businesses due to increased quantities of imitation goods and services. The results of the study show that innovative performance, and marketing capability are highly related to entrepreneurial success. The developed strategies using innovative performance, and marketing capability drivers could help Thailand’s SMEs entrepreneurs to be successful in a variety of industries.
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    Closed-loop supply chain model analysis
    Jariya Seksan; Kannapha Amaruchkul (National Institute of Development Administration, 2019)
    In this dissertation, we analyzed the closed-loop supply chain with buyback contract. We considered the model with one manufacturer and one retailer. First, we derived the optimal order quantity, the optimal contract parameter and consider the condition that buyback contract can coordinate supply chain. Next, we extended the first part by considering for the return policy. We considered when customers return follows the customer’s willingness to return function which depends on return price. We derived the optimal return price and the optimal order quantity follows that price. We found that the optimal return price depends on the buyback price offered by the manufacturer. Then, we considered when the retailer engages in secondary market. The retailer sells the returned product at discount price of the new product in the secondary market. We derived for the optimal resale unit and secondary product price. We found that the retailer can gain more profit when selling returned product in secondary market. In addition, we found that buyback contract can coordinate closedloop supply chain. Using numerical results, some of the relationships between parameters and the expected profits were shown. The results showed that when the wholesale price increases, the order quantity and retailer’s expected profit decrease, but the manufacturer’s expected profit increases. Besides, when the buyback price increases, the order quantity and the retailer’s expected profit increase, but manufacturer’s expected profit decreases. The results also show that the return price 𝑟 and the buyback contract parameters (𝑤, 𝑏) affect the performances of closed-loop supply chain and both manufacturer and retailer receive more profit when the retailer sets the optimal return price. Finally, we present the special topic of using buyback contract in closed-loop supply chain to achieve sustainability: case of electrical and electronic equipment (EEE) and show that supply chain sustainability can be achieved through a buyback contract.
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    A provably group authentication protocol for various LTE networks
    Boriphat Kijjabuncha; Pipat Hiranvanichakorn (National Institute of Development Administration, 2018)
    Group authentication is beneficial for group work in the Long Term Evolution (LTE) networks because it reduces the traffic of networks. For practical use, members of a group should be able to come from different network providers. In addition, while some group members use a network service, others may use other network services. Although the group members are on different networks, they should be able to work together. To fulfill these needs, we propose a secure group authentication protocol (SEGA) in which each group member uses his/her long-term private key and public key to create shared secret (keys) with network devices, such as Home and mobile management entity (MME). These shared keys are computed by using the DiffieHellman key exchange and are utilized in the authentication process. By using this technique instead of pre-shared keys between mobile devices and network devices, SEGA is flexible and scalable. In SE-GA, only the first member in an MME’s area has to authenticate himself/herself with the Home, while the remaining members in the area can authenticate directly with the MME. Thus the protocol reduces the amount of network usage. In this research, authentication proof is also given using the well-known BAN logic. Security analysis of the proposed protocol is also given and a comparison of our protocol with SE-AKA and GLARM was demonstrated. According to the comparison, we can see that the proposed protocol outperforms the former ones.
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    Multi-objective genetic algorithm for supervised clustering
    Vipa Thananant; Surapong Auwatanamongkol (National Institute of Development Administration, 2018)
    Supervised clustering organizes data instances into clusters on the basis of similarities between the data instances as well as class labels for the data instances. Supervised clustering seeks to meet multiple objectives, such as compactness of clusters, homogeneity of data in clusters with respect to their class labels, and separateness of clusters. With these objectives in mind, a new supervised clustering algorithm based on a multi-objective crowding genetic algorithm, named SC-MOGA, is proposed in this thesis. The algorithm searches for the optimal clustering solution that simultaneously achieves the three objectives mentioned above. The SC-MOGA performs very well on a small dataset, but for a large dataset it may not be able to converge to an optimal solution or can take a very long running time to converge to a solution. Hence, a data sampling method based on the Bisecting K-Means algorithm is also introduced, to find representatives for supervised clustering. This method groups the data instances of a dataset into small clusters, each containing data instances with the same class label. Data representatives are then randomly selected from each cluster. The experimental results show that SC-MOGA with the proposed data sampling method is very effective. It outperforms three previously proposed supervised clustering algorithms, namely SRIDHCR, LK-Means and SCEC, in terms of four cluster validity indexes. The experimental results show that the proposed data sampling method not only helps to reduce the number of data instances to be clustered by the SC-MOGA, but also enhances the quality of the data clustering results. Moreover, the biased initial approach is proposed in this thesis to find a good initial population to bias. The experimental results show that biased initial population of SCMOGA will improve clustering quality and the more percentage of biased initial population the better clustering quality.
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    Human emotion recognition in Thai short text
    Jirawan Charoensuk; Ohm Sornil (National Institute of Development Administration, 2018)
    Emotion classification is one of the topics in effective computing applicable in various research areas such as speech synthesis, image processing, and especially, text processing. Emotion classification is aimed at identifying a suitable emotion label for each review. In this research, a hierarchical classification framework to identify emotions (objective opinion and anger, disgust, fear, sadness, happiness, and surprise) is proposed for actual customer reviews written in Thai. The hierarchical classification framework consists of three levels: opinion, sentiment, and emotion. First, the opinion level distinguishes customers’ reviews into two types, namely objective and subjective opinions. Second, the sentiment level is used to categorize the subjective opinions as either positive or negative. Last, in the emotion level, an emotion label is assigned to an opinion as either anger, disgust, fear, happiness, sadness, or surprise. The proposed method consists of three main processes: (1) text preprocessing, (2) feature extraction, and (3) emotion classification. Text preprocessing provides necessary information and normalization of words in the reviews and comprises word segmentation, part-ofspeech (POS) tagging, word replacement, and stop-word elimination. Feature extraction is a process to construct a vector space model (VSM) for opinion classification. Five feature sets for generating the VSM are created by using a corpusand lexicon-based approach: the term frequency-inverse document frequency (Tf-Idf) of unigram words (TUW), bigram words (TBW), unigram POS (TUP), and bigram POS (TBP), and a Thai sentiment lexicon (TSL). Furthermore, a decision tree, multinomial naïve Bayes, and a support vector machine (SVM) are used as classifiers in the emotion classification process. The experimental results show that for the hierarchical approach where the subjectivity of a review is first determined, the polarity of an opinion is identified, and then the emotional label is calculated yielded the highest performance with an accuracy of 69.60%. Overall, TBW was the most effective feature subset used for filtering opinions, determining polarity, and classifying negative emotions. Lexicon resources such as TSL and the POS tag sets in the morphology level improved the accuracy of opinion filtering in two- and three-level hierarchical classification. SVM achieved a high performance in identifying contrasting opinions such as objective versus subjective opinions and positive versus negative sentiment. Meanwhile, multinomial naïve Bayes performed the best when identifying closely related emotions such as happiness versus surprise in positive emotion classification.
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    A penalty function in binary logistic regression
    Piyada Phrueksawatnon; Jirawan Jitthavech (National Institute of Development Administration, 2018)
    An algorithm is proposed to determine the logistic ridge parameter minimizing the MSE of the estimated parameter estimators, together with a theorem on the upperbound of the optimal logistic ridge parameter to facilitate the nonlinear optimization. A simulation is used to evaluate the relative efficiencies of the proposed estimator and other six well-known ridge estimators with respect to the maximum likelihood estimator. The simulation results confirm that the relative efficiency of the proposed estimator is highest among other well-known estimators. Finally, a real-life data set is used to repeat the evaluation and the conclusion is the same as in the simulation