• Asia pacific journal of information systems
• /
• 제18권3호
• /
• pp.123-145
• /
• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• 한국항해항만학회지
• /
• 제30권2호
• /
• pp.151-159
• /
• 2006

This study intends to surveyrequirements for port and logistics supply chain management with RFID {Radio Frequency IDentification)technology. Port and logistics supply chain management has become a critical issue due to the necessity of efficiency, visibility, trace-ability, etc. Since the introduction of RFID technology, its performance, reliability, validity, and safety have been a concern in most industries. Particularly, in port and logistics supply chain management, RFID has the potential to track the movement of containers and to provide in-transit visibility toward customers. In thispaper we consider some critical issues related to port and logistics supply chain management, which previously adopted RFID technology. In order to successfully design and adopt RFID technology and utilize it as optimally as is possible in the port and logistics industry, it is necessary to understand the potential of shipping companies and their requirements in adopting RFID technology in port and logistics.

• 품질경영학회지
• /
• 제44권4호
• /
• pp.813-831
• /
• 2016

Purpose: The purpose of this study is to assess the service quality attributes of Korean airlines service for Chinese passengers and suggest revised model to count potential improvement. Methods: Using the Kano and Timko models based on survey questionnaire to classify the quality attributes and to calculate the customer satisfaction index for each service attributes. And the revised potential customers satisfaction index(R-PCSI) are used to access the improvement possibilities by Kano model's attributes. Results: The attributes by Kano model, the relative importance, and the priorities for improvement for 30 airline service quality characteristics are identified. The most important item for improvement is 'Loses and delays compensations service'. Conclusion: According to the PCSI calculation results, this paper can help for Korean Airlines to improve customer satisfaction for Chinese passengers. And R-PCSI model suggested by this paper can be used for other service quality analysis.

• International journal of advanced smart convergence
• /
• 제12권3호
• /
• pp.175-185
• /
• 2023

As the era of ChatGPT and generative AI technologies unfolds, the marketing industry stands on the precipice of a paradigm shift. Innovations such as GPT-4, DALL-E 2, and Mid-journey Stable Diffusion possess the capacity to dramatically transform the methods by which advertisers reach and engage with customers. The potential applications of these advanced tools herald a new age for the marketing and advertising sectors, offering unprecedented opportunities for growth and optimization. Nevertheless, the rapid adoption of generative AI within these industries presents a unique set of challenges, particularly for organizations that lack the necessary technological infrastructure and human capital to effectively leverage these innovations. As a result, a competitive crisis may emerge, exacerbating existing disparities between well-equipped enterprises and their less technologically adept counterparts. In this article, we undertake a comprehensive exploration of the implications of generative AI for the future of marketing, examining both its potential benefits and drawbacks. We consider the possible impact of these developments on the advertising and marketing industries at large, as well as the ways in which professionals operating within these fields may need to adapt to remain competitive in an increasingly AI-driven landscape. By providing a holistic overview of the challenges and opportunities associated with generative AI, this study aims to elucidate the complex dynamics at play in the ongoing evolution of the marketing and advertising sectors.

• The Journal of Asian Finance, Economics and Business
• /
• 제7권3호
• /
• pp.201-210
• /
• 2020

The study aims to investigate determinants of performance indicator and perceptions of existing and potential customers in accommodation sharing. This study uses data of Airbnb in Busan and Jeju from January 1 to December 31 in 2018, provided by AirDNA. The total number of listed accommodation sharing were 5,109 accommodations in Busan and 11,502 accommodations in Jeju. More than 90 property types of registered accommodation are subcategorized and re-classified in this study. Study 1 examined current usage and effects of factors on performance indicator in tourism destinations by applying Airbnb data. Study 2 investigated effects of perceived factors on satisfaction, intention to use, loyalty, and tourism competitiveness by applying online survey data. This study applies statistical analyses such as factor and regression analyses, ANOVA, t-test, and MANOVA. Results of Study 1 showed that usage and effects of accommodation sharing differ from regulation that is related to sharing types. Effects also differ based on travel destinations. Results of Study 2 showed how customers perceive accommodation sharing differ from pure meaning of sharing. The results of Study 1 and 2 found significant effects of price and service factors on performance indicator and customer satisfaction. The findings of Study 2 showed significant effects on loyalty and tourism competitiveness.

• 한국정보시스템학회지:정보시스템연구
• /
• 제13권2호
• /
• pp.1-22
• /
• 2004

Internet commerce has the potential to propel a company to "break out" of existing strategic constraints and radically alter business processes, strengthen customer and supplier ties, and open up new markets. Therefore, many firms have rushed into internet commerce to conduct business more efficiently, create new business opportunities, and generate business value. Since internet shopping mall not only become a valuable channel for selling goods to customers, but also offer companies an important vehicle for attaining competitive advantages in the new digital economy, the design and content of internet shopping mall must reflect its business goals and customers' needs. However, as little empirical evidence on the effect of internet shopping mall contribution of firm performance exist, the functionality of a firm's internet shopping mall has been decided voluntarily from its business experience. The purpose of this study is to examine the relationship between the marketing functional characteristics of internet shopping mall and its performance. 125 questionnaires from internet shopping malls which sell physical goods direct to an individual end consumer were collected. The results showed that some factors(price, product recognition, reliability enhancement) affect positive effects on the performance of internet shopping mall.

• 한국정보시스템학회지:정보시스템연구
• /
• 제27권3호
• /
• pp.63-80
• /
• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

• 한국데이타베이스학회:학술대회논문집
• /
• 한국데이타베이스학회 2010년도 춘계국제학술대회
• /
• pp.249-264
• /
• 2010

Mobile banking is a subset of electronic banking which underlies not only the determinants of the banking business but also the special conditions of mobile commerce. Nowadays wireless networks are being evolved and diversified. In this situation The wireless e-commerce is in the limelight on new profits of Carriers. Because of this situation, the importance of mobile billing service is being emphasized. This paper searches the definition and service types of mobile banking, and suggests status and prospects of domestic mobile banking. We suggest the basic direction, the stage of development and functions of services by analyzing the cases of Nonghyup's. Finally we derive the critical factors from those and suggest the effect of introduction and the direction of development. From the customer perspective, mobile banking has many strengths. For example, it allows that all customers access banking service at anytime, anywhere more easily than telephone banking or pc banking. And it reduces the time and the effort for using the service. It enables the company to make a business against global customers. On the other hand, from the company perspective, it has a lot of potential that affect market share and reduce the costs of human and material resources which used to operate and support branches. However, it needs many efforts to reach at the stage of completion. And We will have to solve the problems that develop many contents, expend the range of services and raise the service convenience.

• 한국조리학회지
• /
• 제14권4호
• /
• pp.106-118
• /
• 2008

Even though the number of food courts increased in recent years, there were few studies about them. Considering the effects of a food court on the sales of a mall or other shopping spots, it was necessary to analyze what kind of attribute attracts customers. For this research, conjoint analysis was used to test which attribute was the most decisive factor, and the results of this study were as follows. First, price was the first factor that customers attached great importance to. Next were time required from order to eat and menu diversity. Second, cluster analysis used by the individual value of utilities derived through the conjoint analysis showed two clusters. Third, the most preferred food court form gained 35.4% potential market share from the simulation. The information gained from this analysis provided an important starting point for marketing and determined what kind of attribution was considered in being part of the malls or buildings. Also, it could be made full use of creating and executing the most effective marketing strategies.

• 한국식품조리과학회지
• /
• 제21권5호
• /
• pp.637-646
• /
• 2005

The objectives of this study were, 1) to investigate the perception and preference for Japanese chain restaurants due to demographics and consumption behaviors, and 2) to research improvements for Japanese chain restaurants according to the customers' dissatisfaction. Frequency analysis and one-way ANOVA were used to analyze the data. Three hundred questionnaires were distributed and 254 were returned(84.66%). The customers' perception and preference about Japanese chain restaurants were significantly related with each other. The differences of perception and preference due to demographics and consumption behaviors were significant. The most dissatisfied selection attributes were price, number of Korean dishes, number of branch offices, and advertisements, in order. From examining the progressive circmnstances of Korean food-service industry and the social trends toward a preference for healthy, special ethnic food and dishes for diet control and high protein-low fat, it is apparent that food-service businesses related to Japanese food have the potential for success. The results of this study should provide valuable information for administrators and managers in the hospitality industry.