• Journal of Service Research and Studies
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• v.13 no.3
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• pp.82-104
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• 2023

This study analyzes the effect of users' experiences using AI chatbot services on relationship quality and behavioral intention. For the study, a survey was conducted on users who experienced AI chatbot services, and the research hypothesis was verified by analyzing the final 299 copies of valid data. As a result of the analysis, it was confirmed that satisfaction and trust, which are the relationship quality dimensions of AI chatbot service, were formed in users through the cognitive experience, emotional experience, and relational experience. In addition, it was confirmed that satisfaction and trust have a positive effect on the intention to continue using and recommending AI chatbot services, which correspond to the level of consumers' behavioral intentions, respectively. In addition, in terms of relationship quality, it was significant in all paths of the road of behavior, but in satisfaction, the path coefficient of the road of continuous use of AI chatbot and recommended road was significantly higher than the path coefficient in trust. This study provided a theoretical foundation that the relationship with relationship quality that affects behavioral intention also affects AI chatbot services in the online environment, and it is significant in that it suggests that relationship quality is an important mediating factor in establishing long-term relationships with consumers.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.181-199
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• 2021

Recently, financial companies are promoting chatbot services in line with the reduction of branches and the expansion of non-face-to-face services. However, it is difficult to expand the chatbot services at once in the presence of technical limitations and constraints of internal and external environment. Therefore, it is necessary to analyze the various situations of chatbot service to preemptively identify problems that can occur in stages and seek solutions. This study conducted interviews with 12 field practitioners and researchers to examine the intentions and behaviors of financial chatbot service users and interpreted them using TPB. The study revealed the characteristics of 'feelings and attitudes' such as convenience or inconvenience from the chatbot experience, 'subjective norms' such as herd behavior or the yearning for empathy of others, and 'behavioral control' according to the recognition of difficulty or convenience of chatbot use process. This study shows that this characteristic can affect the intention and actual behavior of users to use chatbot service continuously. In the future research, it is necessary to empirically study specific intentions and influence factors for actual users.

• The Journal of Bigdata
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• v.7 no.2
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• pp.91-102
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• 2022

As artificial intelligence develops, AI chatbot systems are actively taking place. For example, in public institutions, the use of chatbots is expanding to work assistance and professional knowledge services in civil complaints and administration, and private companies are using chatbots for interactive customer response services. In this study, we propose a scenario-based AI voice chatbot system to reduce museum operating costs and provide interactive guidance services to visitors. The implemented voice chatbot system consists of a watcher object that detects the user's voice by monitoring a specific directory in real-time, and an event handler object that outputs AI's response voice by performing inference by model sequentially when a voice file is created. And Including a function to prevent duplication using thread and a deque, GPU operations are not duplicated during inference in a single GPU environment.

• Korean Journal of Clinical Pharmacy
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• v.30 no.4
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• pp.243-249
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• 2020

Background: Despite the increased use of direct-acting oral anticoagulants, warfarin is still recommended as first-line therapy in patients with mechanical valves or moderate to severe mitral stenosis. Anticoagulation management services (AMSs) are warranted for patients receiving warfarin therapy due to the complexity of warfarin dosing and large interpatient variability. To overcome limited health care resources, we developed a messenger app-based chatbot that provides information to patients taking warfarin. Methods: We developed "WafarinTalk" as an add-on to the open-source messenger app KakaoTalk. We developed the prototype chatbot after building a database containing seven categories: 1) dosage and indications, 2) drug-drug interactions, 3) drug-food interactions, 4) drug-diet supplement interactions, 5) monitoring, 6) adverse events, and 7) precautions. We then surveyed 30 pharmacists and 10 patients on chatbot reliability and on participant satisfaction. Results: We found that 80% of the pharmacists agreed on the consistency of chatbot responses and 44% agreed on the appropriateness of chatbot. Furthermore, 47% of pharmacists said that they were willing to recommend the chatbot to patients. Of the seven categories, information on drug-food interaction was the most useful; 90% of patients said they were satisfied with the chatbot and 100% of patients said they were willing to use it when they were unable to see a pharmacist. We updated the prototype chatbot with feedback from the survey. Conclusion: This study showed that warfarin-related information could be provided to patients through a messenger application-based chatbot.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.55-62
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• 2022

A smart tourism chatbot is needed as a user interface to efficiently provide smart tourism services such as recommended travel products, tourist information, my travel itinerary, and tour guide service to tourists. We have been developed a smart tourism app and a smart tourism information system that provide smart tourism services to tourists. We also developed a smart tourism chatbot service consisting of khaiii morpheme analyzer, rule-based intention classification, and tourism information knowledge base using Neo4j graph database. In this paper, we develop the Korean and English smart tourism Name Entity (NE) datasets required for the development of the NER model using the pre-trained language models (PLMs) for the smart tourism chatbot system. We create the tourism information NER datasets by collecting source data through smart tourism app, visitJeju web of Jeju Tourism Organization (JTO), and web search, and preprocessing it using Korean and English tourism information Name Entity dictionaries. We perform training on the KoBERT-CRF NER model using the developed Korean and English tourism information NER datasets. The weight-averaged precision, recall, and f1 scores are 0.94, 0.92 and 0.94 on Korean and English tourism information NER datasets.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.57-76
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• 2023

This study analyzes the factors and effects on the users' intention to switch from contact center-oriented to AI chatbot-oriented customer services by combining Push-Pull-Mooring Model and provides insights for companies considering the adoption of AI chatbots. To test the model, we surveyed users with experience using chatbots at least once across different age groups. Finally, we analyzed 176 cases for the analysis using IBM SPSS Statistics and SmartPLS 4.0. The results of hypotheses testing rejected the hypotheses for variables of inconsistent quality and low availability of push factors and low switching cost of mooring factor while accepting the hypotheses for the tardy response of push factors and all pull factors. Therefore, these findings provide important implications for researchers and practitioners who wish to conduct research or adopt AI chatbots. In conclusion, users do not feel inconvenienced by the contact center-oriented service but also perceive high trust and convenience with AI chatbot-oriented service. However, despite low switching costs, users consider chatbots a complementary tool rather than an alternative. So, companies adopting AI chatbots should consider what features the users expect from AI chatbots and facilitate these features when implementing AI chatbots.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.318-333
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• 2021

Smartphones are one of the most widely used mobile devices allowing users to communicate with each other. With the development of mobile apps, many companies now provide various services for their customers by studying interactive systems in the form of mobile messengers for business marketing and commercial promotion. Such interactive systems are called "chatbots." In this paper, we propose a method of avoiding the redundant responses of chatbots, according to the utterances entered by the user. In addition, the redundant patterns of chatbot responses are classified into three categories for the first time. In order to verify the proposed method, a chatbot is implemented using Telegram, an open source messenger. By comparing the proposed method with an existent method for each pattern, it is confirmed that the proposed method significantly improves the redundancy avoidance rate. Furthermore, response performance and variation analysis of the proposed method are investigated in our experiment.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.397-404
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• 2018

Various applications are widely developed for smartphones to meet customer's needs. In many companies, messenger's typed interactive systems have been studied for business marketing, advertising and promotion to provide useful services for the customers. Such interactive systems are usually called as "Chatbot". In Chatbot, duplicated responses from Chatbot could occur frequently, and these make one lose interest. In this paper, we define a case that the response of Chatbot is duplicated according to the user's input, and propose a method to reduce duplicated responses of Chatbot. In the proposed method, we try to reduce duplication responses through a new duplication avoidance algorithm by building multiple tables in a database and by making combinations of user's input and its response in each table. In our experiments, the proposed method shows that duplicated responses are reduced by an average of 70%, compared with the existing method.