• Journal of Digital Convergence
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• v.19 no.9
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• pp.125-133
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• 2021

This study investigated to explore the broadened concept of the subscription service market and categorize of the subscription market and its consumer behavior. We examined the satisfaction of the service users and the reasons for terminating the subscription. Survey respondents were 443 people in their 20s and 30s, who actively use subscription services. As a result of the survey it was found that users in their 20s were more satisfied with the overall subscription service than those in their 30s, and that user's residential areas were evenly distributed regardless of metropolitan area or non-metropolitan area. As a reason for the cancellation of subscription service: the lower the novelty of subscription, the less personalization tailored to consumer, the lack of feeling self-growth while using the service, and the more termination is made. Our findings have magnified the understanding of consumers behaviors in the age of 20s and 30s of using and terminating subscription service and hopefully be used for future studies of subscription services.

• Asia pacific journal of information systems
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• v.14 no.3
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• pp.121-144
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• 2004

Developing a personalized system on a user's behalf which is working around the Internet-based marketplace is one of the challenging issues in intelligent e-business, especially mobile commenrce. It has been highly recommended that such a mobile personalized system has to perceive the user's needs a priori by tracking user's current context such as location with activity and then to identify the current needs dynamically and proactively. Automatically and unobtrusively getting user's context is an inevitable feature for the development of autonomous mobile commenrce. However, personalization methodologies and their feasible architectures for context-aware mobile commerce have been so far very rare. Hence, this paper aims to propose a context-aware mobile commerce development methodology by applying agent and semantic web technologies for personalized reminder system, which is one of the mobile commerce support system. We revisited associationism to understand a buyer's need identification process and adopt the process as 'purchase based on association' to implement a personalized reminder system. Based on this approach, we have showed how the agent-based semantic web service system can be used to realize need-aware reminder system. NAMA(Need-Aware Multi-Agent), a prototype system, has been implemented to show the feasibility of the methodology and framework under mobile setting proposed in this paper. NAMA embeds bluetooth-based location tracking module and identify what a user is currently looking at through her/his mobile device such as PDA. Based on these capabilities, NAMA considers the context, user profile with preferences, and information about currently available services, to aware user's current needs and then link her/him to a set of services, which are implemented as web services.

• Information Systems Review
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• v.11 no.2
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• pp.91-111
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• 2009

Collection and use of personal information have enabled firms to improve their value propositions by offering personalization. On the other hand, they have caused widespread concerns by consumers that their privacy is invaded. Because previously distributed personal information can be integrated and utilized to offer more personalized services, those concerns may be intensified with the progress of the digital convergence environment. In this study, factors for privacy invasion and their impact on the privacy concerns are suggested. Then, an empirical study is conducted to examine some of the suggestions. From the analysis, several results are derived. First, as more personal data are collected, secrecy and autonomy concerns increase exponentially rather than steadily. Second, as targeting accuracy is improved, greater secrecy and autonomy concerns are expressed by consumers, but less seclusion concerns. Finally, monetary reward such as coupons can reduce seclusion concerns when targeting accuracy is high. Based on the results, implications for managing consumer privacy concerns are provided under digital convergence environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.95-100
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• 2011

Due to rapid growth and development of telecommunication information technology, interest has been amplified regarding ubiquitous network computing and user-oriented service. Also, the rapid development of related technologies has been a big spotlight. Smart phone, with features such as a PC with advanced features is a mobile phone. According to environment and infrastructure development, a variety of mobile-based application software to provide various kinds of information and services has been released. However, most of them are provider-driven information systems and aim to provide large amounts of information simply to an unspecified number of users. Therefore, customized or personalized provision of information and service explained earlier for individual users has been hardly come true. According to background and need, this study wants to design and implement recommendations system for personalization and customization in mobile environments. To acquire more accurate recommendation results, recommendation system shall be composed using the Hybrid Filtering. Effective information recommendation according to user's situation by using user's context-information of purpose and location that are available in mobile devices before running the filtering of the information to improve the quality of recommendations.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.313-318
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• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.159-167
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• 2019

This study investigated the factors affecting the satisfaction of augmented reality (AR) fashion application focusing on user experience. The survey was conducted on 280 Chinese users who had used AR fashion applications. The results are summarized as follows. User experience had a significant effect on presence, flow, and satisfaction. Personalization service also increased user satisfaction. Presence influenced flow which enhanced satisfaction, indicating that presence had an indirect effect on satisfaction through flow. The results are expected to provide strategic implications by identifying factors that should be considered to help consumers experience products and improve satisfaction with applications in various industries focusing on augmented reality technology.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.109-118
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• 2021

Different from previous studies, this study focuses on accommodation providers' personalization services and platform convenience variables, identifying how these prior factors affect perceived value and trust in accommodation services on a shared homestay platform, and how consumers' innovation plays a role in the process. Through this, we would like to identify the mechanism of interaction between accommodation service providers and consumers mediated by the shared homestay platform and present implications for a more customer-centered platform operation strategy. This study has an extended meaning for prior research, empirically confirming that the increase in personalized offline service quality of personalized hosts in shared economic models has a positive impact on perceived value and platform trust of consumers. At the same time, we confirm that under the shared economy model, consumers' innovation propensity plays an important positive role in regulating their perceived value aspects as well as their confidence in the platform.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.115-124
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• 2021

The outbreak of covid-19 has brought the characteristics and advantages of non-contact services to increased prominence, and the development of smart hotels"has accelerated. This study aims to identify, categorize and define the smart service experience at different touch points of the customer experience. The concept and characteristics of the smart hotel were examined based on existing research and literature. An analytical framework was designed using smart experience factors and customer touch points of smart hotels. Selected Chinese smart hotels were then examined under this framework. The case analysis results show that the customer experience design of smart hotels has developed to different degrees, in terms of interactivity, personalization, accessibility, information and privacy security. Based on the above findings, this article suggests that the design of smart hotels should use integrated data to further enhance personalized service experience.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.1-6
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• 2022

Lifelog is a digital record of an individual collected from various digital sensors, and includes activity amount, sleep information, weight change, body mass, muscle mass, fat mass, etc. Recently, as wearable devices have become common, a lot of high-quality lifelog data is being produced. Lifelog data shows the state of an individual's body, and can be used not only for individual health care, but also for causes and treatment of diseases. However, at present, AI/ML-based correlation analysis and personalization are not reflected. It is only at the level of presenting simple records or fragmentary statistics. Therefore, in this paper, the correlation/relationship between lifelog data and disease, and AI/ML technology inside lifelog data are examined, and furthermore, a lifelog data analysis process based on AI/ML is proposed. The analysis process is demonstrated with the data collected in the actual Galaxy Watch. Finally, we propose a future convergence service roadmap including lifelog data, diet, health information, and disease information.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.307-316
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• 2016

A user usually gets the same suggesting results as everyone else in most of the multimedia social services, nowadays. To address the challenging problem of personalization in the social network, we propose a method which exploits user's activities, user's moods, and user's friend relationships from the social network to build a decision-making system. Depending on a current state of the user's mood, this system infers the most appropriated video for the user. In the system, the user evaluates a set of the given recommendation methods which extract from the user's database social network and assigns a vague value to each method by a weight. Then, we find the fuzzy collection solution for the system and classify the set of methods into subsets, and order the subsets based on its local dominance to choose the best appropriate method. Finally, we conduct an experiment using the YouTube API with a lot of video types. The experiment result shows that the channel recommendation system appropriately affords the user's character, it is more satisfying than the current YouTube based on an evaluation of several users.