• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.221-238
• /
• 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 Internet Computing and Services
• /
• v.6 no.2
• /
• pp.141-152
• /
• 2005

Many organizations have been recognizing the necessity of workflow automation technologies according to the rapid expansion of business process oriented applications, such as enterprise resource pianning, customer relationship management, electronic approval management, and so on, Thus, they have started adopting workflow management systems as an essential technological solution for their workflow processes, However, we need some technological extensions and improvements on them in order to accommodate a new type of workflow processes, which is called cross-organizational global workflow processes that require a certain level of collaborations between the organizations engaged in the global workflow processes, Fragment-driven workflow modeling methodology is a Bottom-Up methodology composing a global workflow by defining each organization's own activities, which is called a fragment through a realtime cooperative system. The approach is able to not only simplify the modeling work but also keep each organization's independence in modeling a global workflow, In this paper, we describe the fragment-driven workflow modeling methodology and realize the methodology through the implementation of a cooperative swimlane workflow modeling system.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.93-107
• /
• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of dental hygiene science
• /
• v.9 no.4
• /
• pp.443-451
• /
• 2009

The purpose of this study was to examine the views of dental hygiene students about the possible countermeasure of domestic hospitals for the opening of the medical market. The subjects in this study were 269 dental hygiene sophomores, juniors and seniors at two different colleges in the region of Busan. The findings of the study were as follows: The opening of the medical market and views of possible countermove, whether they agreed to that or not made a statistically significant difference to their opinions on the necessity of customer-oriented marketing strategy(p=0.023), analysis of foreign medical markets/attempt to make inroads into the markets(p<0.000) and the improvement of the quality of medical services/the diversification of the services(p=0.025). As to an intention of going to a foreign hospital, they had a statistically significantly different intention about whether to go to a foreign hospital regardless of medical bills(p<0.000), whether to consult a doctor in a foreign hospital after going to a domestic hospital first (p<0.000), whether to consider the distance between their houses and a foreign hospital(p=0.05) and whether to take considerations on the assistance of an interpreter(p=0.023). In regard to preference for foreign hospitals, American hospitals ranked first(41.9), followed by Australian hospitals(19.9) and Canadian ones(14.2).

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.71-90
• /
• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.1
• /
• pp.55-69
• /
• 2021

For the national economic development, the participation of women in the social and economic activities is crucial. The popularization of start-ups, digital transformation, and WEconomy trends have lowered the barriers to opportunities for women to start a business and provide an environment in which women can grow faster. This paper examines the significance and process of success of women entrepreneurs and the characteristics of innovation strategies and achievements by linking the recently changing business environment of a company, factors influencing the success of women entrepreneurship, and innovation activities. To this end, four companies' cases were analyzed in the fields of distribution/service and consumer products/services, which are areas of large investment among female startups. The result shows that women entrepreneurs recognize the meaning of success as creating and continuing to create a 'corporate value through establishing a trust relationship with customers' within the 'balance between personal life and work.' In terms of the business ecosystem, women entrepreneurs strive for 'business activities based on the win-win growth of consumers, producers and sellers' for success, and rather 'focus on the process with a problem-solving approach' rather than achieving performance-oriented goals. Also through excellent power of observation, flexibility, and execution power, women entrepreneurs conduct business by adapting to changing trends. In terms of innovation activities, the innovation strategy of women-led companies puts priority on 'creating the value customers want' and focuses on innovation in the 'customer-centric business model' rather than technological innovation. As such, women-led companies show several differentiated characteristics, which enable them to create corporate value and achieve sustainable growth. The barriers to challenges and opportunities for women to start a business have been lowered, and an ecosystem has been created for female startups to grow. But why are there still so few women entrepreneurs, and the answer to where we need to close these gaps is ultimately a close analysis and investigation of the field. We must present milestones for growth steps through the accumulation of case studies of women startups that have exited. In addition, women can stand as economic agents only when the policy targets are subdivided and specific approaches to child-rearing and childcare for women entrepreneurs must be taken. This paper expects to serve as basic data for follow-up studies and become the basis of research for women entrepreneurs to grow as economic agents.