• Journal of the East Asian Society of Dietary Life
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• v.20 no.4
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• pp.606-618
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• 2010

The study investigated how 'word of mouth' originates in the chain restaurant industry. It has long been acknowledged that 'word of mouth' is a critical factor for the success of a restaurant business due to its targetability and cost effectiveness. A review of the literature revealed four antecedents of 'word of mouth': service quality, perceived value, satisfaction, and relationship quality. Based on the theoretical/empirical relationships between those constructs, a structural model composed of the hypotheses was proposed. The structural model was tested with data collected from 471 chain restaurant patrons. The structural equation modeling analysis revealed that five constructs in the proposed model are interrelated, and during this process, word of mouth is formed in the chain restaurant industry. A positive relationship between service quality and satisfaction (0.265, p<0.05), service quality and perceived value (0.831, p<0.05), service quality and relationship quality (0.465, p< 0.05), and service quality and WOM (0.263, p< 0.05) were found, indicating that service quality is a key prerequisite for word of mouth and other constructs proposed in the model. It was revealed that perceived value doe not have a direct impact on WOM formation (t=1.275, p=0.202), but a positive relationship between perceived value and satisfaction (0.293, p<0.05) and between satisfaction and WOM (0.627, p< 0.05) were found. Therefore, it was concluded that patrons' perceived value influences word of mouth formation, but that impact is mediated by satisfaction. During this process, relationship quality also plays a mediating role in generating word of mouth. Based on data analysis, theoretical/managerial implications are discussed.

• Journal of KIISE:Information Networking
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• v.30 no.2
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• pp.199-206
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• 2003

one of the representative micropayment protocols. The original PayWord system is designed for a user who generates paywords by performing hash chain operation for payment to an only designated vendor. In other words, a user has to create new hash chain values in order to establish commercial transactions with different vendors on the Internet. Therefore, we suggest an efficient scheme that is able to deal with business to different vendors by using only one hash chain operation to supplement this drawback. In this proposed system, a broker creates a new series of hash chain values along with a certificate for the user's certificate request. This certificate is signed by a broker to give authority enabling a user to generate hash chain values. hew hash chain values generated by a broker provide means to a user to do business with multiple vendors.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.163-176
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• 2022

As the business ecosystem has become more uncertain, the sources of supply chain risk have also been becoming more diverse. In particular, due to the development of informational technology in recent years, firms need to consider the emerging supply chain risk sources as well as traditional supply chain risk sources. A typical example is negative word-of-mouth by social media. Therefore, we investigated the virality of negative word-of-mouth on manufacturing firms by using YouTube as a representative social media. More specifically, we investigated how the social capital of the video creator influences the virality of negative word-of-mouth and how the emotional tone of the video affects the virality of negative word-of-mouth. In conclusion, the social capital of the video creator influenced the scale and speed of negative word-of-mouth. Furthermore, negative emotion words moderated the relation between the social capital of the video creator and the scale of negative word-of-mouth.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.274-284
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• 2002

Word segmentation errors occurring in text preprocessing often insert incorrect words into recognition vocabulary and cause poor language models for Korean large vocabulary continuous speech recognition. We propose an automatic word segmentation algorithm using Markov chains and syllable-based n-gram language models in order to correct word segmentation error in teat corpora. We assume that a sentence is generated from a Markov chain. Spaces and non-space characters are generated on self-transitions and other transitions of the Markov chain, respectively Then word segmentation of the sentence is obtained by finding the maximum likelihood path using syllable n-gram scores. In experimental results, the algorithm showed 91.58% word accuracy and 96.69% syllable accuracy for word segmentation of 254 sentence newspaper columns without any spaces. The algorithm improved the word accuracy from 91.00% to 96.27% for word segmentation correction at line breaks and yielded the decomposition accuracy of 96.22% for compound-noun decomposition.

• Journal of Distribution Science
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• v.21 no.1
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• pp.73-83
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• 2023

Purpose: This study analyzed the influence of restaurant service perception on customer satisfaction and its implications on customers' attitude towards, intention to return to, and word-of-mouth (WOM) regarding a restaurant chain. Research design, data and methodology: Data from 421 respondents were collected using the convenience sampling method. After analyzing the data normality and removing responses with missing data and outliers, 342 responses were selected for further analysis, and the hypotheses were tested using Structural Equation Modeling (SEM). Results: We found that service perception affected customer satisfaction and customer satisfaction affected the customers' attitude toward the restaurant chain, which affected customers' intention to return and WOM about the restaurant chain. Conclusions: This paper provides one of the most important empirical results for managers in the restaurant sector, especially in Indonesia. Restaurant managers should thus provide training to their employees to improve the quality of the interaction with the customers and thereby increase customer satisfaction. The limitations listed in this study include the exclusion of respondents' income. For future research, we suggest investigating models of customer participation or consumer value co-creation for restaurant marketing success. Consumers are generic actors in the service ecosystem engaged in the value co-creation process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4568-4584
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• 2016

Steganography based on text generation has become a hot research topic in recent years. However, current text-generation methods which generate texts of normal style have either semantic or syntactic flaws. Note that texts of special genre, such as poem, have much simpler language model, less grammar rules, and lower demand for naturalness. Motivated by this observation, in this paper, we propose a text steganography that utilizes Markov chain model to generate Ci-poetry, a classic Chinese poem style. Since all Ci poems have fixed tone patterns, the generation process is to select proper words based on a chosen tone pattern. Markov chain model can obtain a state transfer matrix which simulates the language model of Ci-poetry by learning from a given corpus. To begin with an initial word, we can hide secret message when we use the state transfer matrix to choose a next word, and iterating until the end of the whole Ci poem. Extensive experiments are conducted and both machine and human evaluation results show that our method can generate Ci-poetry with higher naturalness than former researches and achieve competitive embedding rate.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.441-448
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• 2019

Previous researches on automatic spacing of Korean sentences has been researched to correct spacing errors by using n-gram based statistical techniques or morpheme analyzer to insert blanks in the word boundary. In this paper, we propose an end-to-end automatic word spacing by using deep neural network. Automatic word spacing problem could be defined as a tag classification problem in unit of syllable other than word. For contextual representation between syllables, Bi-LSTM encodes the dependency relationship between syllables into a fixed-length vector of continuous vector space using forward and backward LSTM cell. In order to conduct automatic word spacing of Korean sentences, after a fixed-length contextual vector by Bi-LSTM is classified into auto-spacing tag(B or I), the blank is inserted in the front of B tag. For tag classification method, we compose three types of classification neural networks. One is feedforward neural network, another is neural network language model and the other is linear-chain CRF. To compare our models, we measure the performance of automatic word spacing depending on the three of classification networks. linear-chain CRF of them used as classification neural network shows better performance than other models. We used KCC150 corpus as a training and testing data.

• School Mathematics
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• v.9 no.1
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• pp.141-160
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• 2007

The purpose of this paper was to evaluate how students apply prior knowledge or experience in solving algebra word problems from the chain of signification-based perspective. Three middle school students were evaluated in this case study. The results showed that the subjects formed similarities in the process of applying knowledge needed for solving a problem. The student A and C used semi-open-end formulas and closed formulas as solutions. They then formed concrete shape for each solution using the chain of signification that was applied for solution by forming procedural similarity. At this time, the chain of signification could be the combination of numbers, words, and pictures (such as diagrams or graphs) or just numbers or words. On the other hand, the student C who recognized closed formulas and her own rule as a solution method could not formulate completely procedural similarity due to many errors arising from number information. Nonetheless, all of the subjects showed something in common in the process of coming up with a algorithm that was semi-open-end formula or closed formula.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.49-57
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• 1997

In this paper, we propose the stochastic pronunciation lexicon model for large vocabulary continuous speech recognition system. We can regard stochastic lexicon as HMM. This HMM is a stochastic finite state automata consisting of a Markov chain of subword states and each subword state in the baseform has a probability distribution of subword units. In this method, an acoustic representation of a word can be derived automatically from sample sentence utterances and subword unit models. Additionally, the stochastic lexicon is further optimized to the subword model and recognizer. From the experimental result on 3000 word continuous speech recognition, the proposed method reduces word error rate by 23.6% and sentence error rate by 10% compare to methods based on standard phonetic representations of words.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.899-902
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• 2001

공개키 연산을 이용하는 고액 지불 시스템(Macro Payment System)은 높은 수수료로 인해 경제성이 맞지 않아 소액 지불(Micro Payment)에는 적합하지 않다. 해쉬 연산을 이용한 PayWord 프로토콜은 저렴한 메커니즘 비용과 신속한 트랜잭션 처리, 거래과정에서 브로커의 오프라인 참여로 소액 대금 결재에 적합하다. 그러나 특정 상점에만 사용 가능한 화폐가치를 포함하여, 사용자가 거래하는 상점이 많아지면 관리 저장해야 하는 해쉬 체인의 수가 늘어나는 단점이 있다. 본 논문에서는 전자화폐에 해당하는 해쉬 체인을 하나만 생성하여 여러 상점들에 안전한 지불을 수행하는 개선된 소액 지불 프로토콜을 제안한다. 제안한 방법은 지불과정에 MAC(Message Authentication Code)을 이용한 해쉬 간을 추가하여, 상점들의 공모 및 악의적인 수정을 방지한다. 따라서 사용자는 하나의 해쉬 체인만을 생성함으로써 기존의 PayWord보다 계산부담이 줄고, 여러 상점들과의 일시적인 거래관계에서도 효율적인 지불을 수행한다.