• 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.

• 지능정보연구
• /
• 제22권3호
• /
• pp.143-163
• /
• 2016

인구통계학적 정보는 디지털 마케팅의 핵심이라 할 수 있는 인터넷 사용자에 대한 타겟 마케팅 및 개인화된 광고를 위해 고려되는 가장 기초적이고 중요한 정보이다. 하지만 인터넷 사용자의 온라인 활동은 익명으로 행해지는 경우가 많기 때문에 인구통계특성 정보를 수집하는 것은 쉬운 일이 아니다. 정기적인 설문 조사를 통해 사용자들의 인구통계특성 정보를 수집할 수도 있지만 많은 비용이 들며 허위 기재 등과 같은 위험성이 존재한다. 특히, 모바일 환경에서는 대부분의 사용자들이 익명으로 활동하기 때문에 인구통계특성 정보를 수집하는 것은 더욱 더 어려워지고 있다. 반면, 인터넷 사용자의 온라인 활동을 기록한 클릭스트림 데이터는 해당 사용자의 인구통계학적 정보에 활용될 수 있다. 특히, 인터넷 사용자의 온라인 행위 특성 중 하나인 페이지뷰는 인구통계학적 정보 예측에 있어서 중요한 요인이 된다. 본 연구에서는 기존 선행 연구를 토대로 클릭스트림 데이터 분석을 통해 인터넷 사용자의 온라인 행위 특성을 추출하고 이를 해당 사용자의 인구통계학적 정보 예측에 사용한다. 또한, 1)의사결정나무를 이용한 변수 축소, 2)주성분분석을 활용한 차원축소, 3)군집분석을 활용한 변수축소의 방법을 제안하고 실험에 적용함으로써 많은 설명변수를 이용하여 예측 모델 생성 시 발생하는 차원의 저주와 과적합 문제를 해결하고 예측 모델의 정확도를 높이고자 하였다. 실험 결과, 범주의 수가 많은 다분형 종속변수에 대한 예측 모델은 모든 설명변수를 사용하여 예측 모델을 생성했을 때보다 본 연구에서 제안한 방법론들을 적용했을 때 예측 모델에 대한 정확도가 향상됨을 알 수 있었다. 본 연구는 클릭스트림 분석을 통해 추출된 인터넷 사용자의 온라인 행위는 해당 사용자의 인구통계학적 정보 예측에 활용 가능하며, 예측된 익명의 인터넷 사용자들에 대한 인구통계학적 정보를 디지털 마케팅에 활용 할 수 있다는데 의의가 있다. 또한, 제안 방법론들을 통해 어느 종속변수에 대해 어떤 방법론들이 예측 모델의 정확도를 개선하는지 확인하였다. 이는 추후 클릭스트림 분석을 활용하여 인구통계학적 정보를 예측할 때, 본 연구에서 제안한 방법론을 사용하여 보다 높은 정확도를 가지는 예측 모델을 생성 할 수 있다는데 의의가 있다.

• 한국정보기술응용학회:학술대회논문집
• /
• 한국정보기술응용학회 2007년도 춘계학술대회
• /
• pp.234-245
• /
• 2007

The empirical studies on the implementation of data warehousing systems (DWS) are lacking while there exist a number of studies on the implementation of IS. This study intends to examine the factors affecting the implementation success of DWS. The study adopts the empirical analysis of the sample of 112 responses from DWS practitioners. The study results suggest several implications for researchers and practitioners. First, when the support from top management becomes great, the implementation success of DWS in organizational aspects is more likely. When the support from top management exists, users are more likely to be encouraged to use DWS, and organizational resistance to use DWS is well coped with increasing the possibility of implementation success of DWS. The support of resource increases the implementation success of DWS in project aspects while it is not significantly related to the implementation success of DWS in organizational aspects. The support of funds, human resources, and other efforts enhances the possibility of successful implementation of project; the project does not exceed the time and resource budgets and meet the functional requirements. The effect of resource support, however, is not significantly related to the organizational success. The user involvement in systems implementation affects the implementation success of DWS in organizational and project aspects. The success of DWS implementation is significantly related to the users' commitment to the project and the proactive involvement in the implementation tasks. users' task. The observation of the behaviors of competitors which possibly increases data quality does not affect the implementation success of DWS. This indicates that the quality of data such as data consistency and accuracy is not ensured through the understanding of the behaviors of competitors, and this does not affect the data integration and the successful implementation of DWS projects. The prototyping for the DWS implementation positively affects the implementation success of DWS. This indicates that the extent of understanding requirements and the communication among project members increases the implementation success of DWS. Developing the prototypes for DWS ensures the acquirement of accurate or integrated data, the flexible processing of data, and the adaptation into new organizational conditions. The extent of consulting activities in DWS projects increases the implementation success of DWS in project aspects. The continuous support for consulting activities and technology transfer enhances the adherence to the project schedule preventing the exceeding use of project budget and ensuring the implementation of intended system functions; this ultimately leads to the successful implementation of DWS projects. The research hypothesis that the capability of project teams affects the implementation success of DWS is rejected. The technical ability of team members and human relationship skills themselves do not affect the successful implementation of DWS projects. The quality of the system which provided data to DWS affects the implementation success of DWS in technical aspects. The standardization of data definition and the commitment to the technical standard increase the possibility of overcoming the technical problems of DWS. Further, the development technology of DWS affects the implementation success of DWS. The hardware, software, implementation methodology, and implementation tools contribute to effective integration and classification of data in various forms. In addition, the implementation success of DWS in organizational and project aspects increases the data quality and system quality of DWS while the implementation success of DWS in technical aspects does not affect the data quality and system quality of DWS. The data and systems quality increases the effective processing of individual tasks, and reduces the decision making times and efforts enhancing the perceived benefits of DWS.

• Asia pacific journal of information systems
• /
• 제20권3호
• /
• pp.139-166
• /
• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Asia pacific journal of information systems
• /
• 제22권4호
• /
• pp.7-29
• /
• 2012

Smartphone and its applications (i.e. apps) are increasingly penetrating consumer markets. According to a recent report from Korea Communications Commission, nearly 50% of mobile subscribers in South Korea are smartphone users that accounts for over 25 million people. In particular, the importance of smartphone has risen as a geospatially-aware device that provides various location-based services (LBS) equipped with GPS capability. The popular LBS include map and navigation, traffic and transportation updates, shopping and coupon services, and location-sensitive social network services. Overall, the emerging location-based smartphone apps (LBA) offer significant value by providing greater connectivity, personalization, and information and entertainment in a location-specific context. Conversely, the rapid growth of LBA and their benefits have been accompanied by concerns over the collection and dissemination of individual users' personal information through ongoing tracking of their location, identity, preferences, and social behaviors. The majority of LBA users tend to agree and consent to the LBA provider's terms and privacy policy on use of location data to get the immediate services. This tendency further increases the potential risks of unprotected exposure of personal information and serious invasion and breaches of individual privacy. To address the complex issues surrounding LBA particularly from the user's behavioral perspective, this study applied the privacy calculus model (PCM) to explore the factors that influence the adoption of LBA. According to PCM, consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. Consistent with the principal notion of PCM, we investigated how individual users make a risk-benefit assessment under which personalized service and locatability act as benefit-side factors and information privacy risks act as a risk-side factor accompanying LBA adoption. In addition, we consider the moderating role of trust on the service providers in the prohibiting effects of privacy risks on user intention to adopt LBA. Further we include perceived ease of use and usefulness as additional constructs to examine whether the technology acceptance model (TAM) can be applied in the context of LBA adoption. The research model with ten (10) hypotheses was tested using data gathered from 98 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a LBA allows the participant to purchase time-and-location sensitive discounted tickets for nearby stores. Structural equations modeling using partial least square validated the instrument and the proposed model. The results showed that six (6) out of ten (10) hypotheses were supported. On the subject of the core PCM, H2 (locatability ${\rightarrow}$ intention to use LBA) and H3 (privacy risks ${\rightarrow}$ intention to use LBA) were supported, while H1 (personalization ${\rightarrow}$ intention to use LBA) was not supported. Further, we could not any interaction effects (personalization X privacy risks, H4 & locatability X privacy risks, H5) on the intention to use LBA. In terms of privacy risks and trust, as mentioned above we found the significant negative influence from privacy risks on intention to use (H3), but positive influence from trust, which supported H6 (trust ${\rightarrow}$ intention to use LBA). The moderating effect of trust on the negative relationship between privacy risks and intention to use LBA was tested and confirmed by supporting H7 (privacy risks X trust ${\rightarrow}$ intention to use LBA). The two hypotheses regarding to the TAM, including H8 (perceived ease of use ${\rightarrow}$ perceived usefulness) and H9 (perceived ease of use ${\rightarrow}$ intention to use LBA) were supported; however, H10 (perceived effectiveness ${\rightarrow}$ intention to use LBA) was not supported. Results of this study offer the following key findings and implications. First the application of PCM was found to be a good analysis framework in the context of LBA adoption. Many of the hypotheses in the model were confirmed and the high value of $R^2$ (i.,e., 51%) indicated a good fit of the model. In particular, locatability and privacy risks are found to be the appropriate PCM-based antecedent variables. Second, the existence of moderating effect of trust on service provider suggests that the same marginal change in the level of privacy risks may differentially influence the intention to use LBA. That is, while the privacy risks increasingly become important social issues and will negatively influence the intention to use LBA, it is critical for LBA providers to build consumer trust and confidence to successfully mitigate this negative impact. Lastly, we could not find sufficient evidence that the intention to use LBA is influenced by perceived usefulness, which has been very well supported in most previous TAM research. This may suggest that more future research should examine the validity of applying TAM and further extend or modify it in the context of LBA or other similar smartphone apps.

• 한국ITS학회 논문지
• /
• 제21권6호
• /
• pp.21-35
• /
• 2022

최근 정부의 적극적인 지원정책과 함께 전기차 이용자들이 급증하고 있으며, 이로 인해 이용자 중심의 충전인프라 구축에도 많은 관심을 쏟아지고 있다. 다양한 정책의 수립과 함께 건물 특성에 기반한 총량적인 전기차 충전기 보급대수 기준은 마련되고 있으나, 장소별 특성에 기반한 완속과 급속충전기 적정 보급 비율에 대한 연구는 제한적이다. 이에 본 연구에서는 전기차 이용자들을 대상으로 진행한 설문조사를 통해 수집한 장소 유형별 공용 완속충전기 보급 비율 자료를 바탕으로 적정 보급비율을 도출하고, 개인별로 충전 환경 요구가 어떻게 차별적으로 유형화되고 이들이 어떠한 특성과 연관되는지 분석하였다. 분석 결과, 10% 이하의 완속 충전기가 필요한 유형, 40-60% 수준의 완속충전기가 필요하여 완속과 급속충전기의 균등 분배가 필요한 유형, 완속이 80% 이상 필요한 유형 등 총 세 가지 장소 유형을 도출할 수 있었다. 또한 잠재계층 군집분석을 통해 개인별로 서로 다른 장소유형별 완속충전기 필요 수준을 분류한 결과 5개 군집으로 유형화할 수 있었으며, 이들은 사회경제적 변수, 차량의 특성, 통행 및 충전행태와 연관된 것으로 나타났다. 특히, 충전행태와 주말 통행행태 그리고 성별, 소득과의 연관성이 높은 것으로 나타났다. 본 연구의 분석결과는 향후 충전인프라 정책 수립 및 전기차 시장의 변화에 따른 충전인프라 보급 기준 마련에 활용될 수 있을 것으로 사료된다.

• 한국ITS학회 논문지
• /
• 제23권2호
• /
• pp.132-141
• /
• 2024

본 연구에서는 수요응답 교통서비스를 이용한 통행목적을 통근·통학과 쇼핑·여가로 구분하고 통행목적별 만족도와 영향변수의 차이를 비교한다. 세종특별자치시 '셔클(Shucle)' 이용자를 대상으로 실시한 만족도 설문조사 자료를 활용하고, 다중선형모델의 과적합(overfitting) 문제점을 최소화하기 위해 LASSO 회귀분석을 적용한다. 분석 결과, 수요응답 교통서비스 도입으로 기존 대중교통 사각지역의 공백이 해소되고, 자가용 이용 감소로 저탄소 및 대중교통 활성화 정책을 유인할 수 있으며, 간헐적인 통행행태를 갖는 행위자(예컨대 고령자, 주부 등)에게 최적의 이동 서비스를 제공할 수 있는 가능성이 확인되었다. 또한, 차량 호출 후 대기시간, 탑승 후 이동시간, 앱이용 편리성, 예상 출/도착 시간의 정시성, 승·하차 지점의 위치 요인은 통근·통학과 쇼핑·여가 통행 시 수요응답 교통서비스 만족도에 긍정적인 영향을 미치는 공통요인으로 나타났다. 한편, 타 교통수단과의 환승은 통근·통학의 경우에만 만족도에 영향을 미치고 쇼핑·여가의 경우는 미치지 않는 것으로 나타났다. 수요응답 교통서비스를 활성화하기 위해서는 분석된 5개의 영향요인에 대한 고려뿐만 아니라 통근·통학과 쇼핑·여가의 차별화 요인 즉, 통근·통학의 경우 행위자는 시간가치를 중요하게 여기므로 총 통행시간을 줄이기 위한 타교통수단과의 환승 편의를 도모하고, 쇼핑·여가 통행의 경우 이용자가 승·하차 지점의 위치를 쉽고 편하게 지정하여 이용할 수 있는 이용편의 조성방안의 고려가 필요할 것으로 사료된다.

• 지능정보연구
• /
• 제17권4호
• /
• pp.131-142
• /
• 2011

최근 페이스북, 트위터 등 다양한 소셜 네트워크 서비스(SNS)가 등장하였으며, 많은 사용자들이 SNS를 이용하고 있다. 이러한 사용자의 증가로 인해 많은 조직들은 SNS에 관심을 가지게 되었다. 조직에서 SNS의 사용은 다양한 이점을 지니고 있다. SNS를 통해 조직들은 사용자들의 행위에 신속하고 지속적으로 반응할 수 있고, 다양한 특성을 지닌 사용자에게 쉽게 접근할 수 있으며, 타 매체에 비하여 사용자 특성이 반영된 차별화된 전략을 세울 수 있다. 또한 기업들은 SNS를 통해 상대적으로 저렴한 비용으로 활용이 가능하며, 사용자들과 양방향 소통이 가능하여 친근성과 신뢰성이 있는 관계 구축이 용이하다. 그러나 네트워크의 특성에 따라 SNS의 정보전달의 효과가 다르게 나타남에도 불구하고 조직들은 네트워크의 특성을 고려하지 않고 획일화된 방법으로 SNS를 활용하여 사용자들과 커뮤니케이션하고 있다. 따라서 본 연구에서는 네트워크에 따른 SNS의 정보전달의 효과 차이를 분석하였다. 즉 오프라인에서의 커뮤니케이션 기반으로 형성된 네트워크와 무작위로 형성된 네트워크를 생성하여, 각각의 네트워크들의 특징 차이를 분석하기 위하여 소셜 네트워크 분석을 하였다. 또한, 각각의 네트워크에서 SNS를 이용한 정보 전달 효과의 차이가 있는지 실증적으로 검증하였다. 실증 분석후 네트워크의 특성에 따라 네트워크 내 사용자들은 SNS를 받아들이는 반응이 달랐다. 따라서 조직이 효과적인 마케팅 수단으로 소셜 네트워크를 활용하기 위해서는 그 목적에 따라 네트워크의 특성을 고려하여 적절한 네트워크 형태를 구성해야 함을 도출하였다.

• 한국조경학회지
• /
• 제47권2호
• /
• pp.49-58
• /
• 2019

시대에 따라 변화하는 공원 이용자들의 다양한 활동을 반영하고, 공원의 특성에 따라 미래의 공원설계와 관리 방향성을 제시하기 위해 공원 만족도, 선호도, 이용 후 평가 등 다양한 연구가 수행되어 왔다. 이러한 선행 연구는 주로 설문 조사를 이용하였는데, 설문은 적절한 표본 설정을 통해서 이용자로부터 직접 의견을 청취할 수 있는 좋은 방법이지만, 비용과 시간이 많이 소요되는 단점이 있고, 나아가 빠르게 변화하는 공원의 이용 행태를 파악하기에는 부족하다. 본 연구는 다양한 분야에서 새롭게 활용되고 있는 제3세대 SNS 데이터를 활용하여 공원 유형별 이용 행태, 새로운 이용 행태, 만족도 등을 비교 분석하고 시사점을 논의하였다. 이를 위해서 제3세대 SNS로 대표되는 인스타그램과 구글 콘텐츠를 활용하였다. 인스타그램에서는 공원 이용자가 올린 키워드와 사진을 분류하여 정보를 추출하였으며, 구글에서는 이용 시간과 후기를 비교 분석하였다. 공원 간 비교 연구 결과, 주거지 인접형 공원은 가족과 나들이하거나 공원 내 시설에서 이루어지는 프로그램을 주로 활용하는 것으로 나타났다. 상업지 인접형 공원에서는 상업지역와 연계된 먹는 활동과 공원 내 오픈스페이스와 시설 내 프로그램이 적절하게 이용되는 현상을 확인하였다. 독립형 공원인 한강공원의 경우에는 다양한 운동, 풍경 감상 활동이 빈번히 이루어지고 있었다. 또한 각 공원의 유형별로 동반 유형이 다르게 나타났으며, 새로운 이용 행태가 나타나는 현상을 확인하였다. 이처럼 SNS 데이터는 공원의 이용 행태와 만족요인을 실시간으로 파악할 수 있는 근거를 마련해 주며, 새로운 기술과 정책 도입에 따른 공원의 이용 행태 변화를 파악하여 추후 공원설계와 공원관리의 방향성을 수립하는데 있어 중요한 정보를 제공하는 효과적인 방법으로 활용될 수 있다.

• 한국환경생태학회지
• /
• 제22권3호
• /
• pp.249-259
• /
• 2008

본 연구의 목적은 울진 금강송 생태 숲을 사례로 이용자 행태를 분석하고 개선방안을 제시하는 것이다. 연구방법은 2007년 8월 29일${\sim}$9월 3일에 걸쳐 금강송 숲 이용자의 122명을 대상으로 직접 설문조사를 통해 이루어졌다. 분석결과는 첫째, 남자와 $20{\sim}30$대, 울진의 거주자, 고학력자가 주류를 이루었다. 따라서 보다 다양한 계층과 다양한 지역민이 이용하도록 친환경무인 전동차(Tram)도입 및 생태관찰 탐방로의 개설이 필요하다. 둘째, 울진금강숲 이용의 신택한 동기는 금강송의 멋진 운치와 아름다움과 숲의 상태에 의해서이고 자연관찰 및 교육적인 면에서는 낮았다. 이용객의 동반구성은 가족이 주류를 이루었고 단체에서는 적었다. 따라서 학교 등 단체 그룹의 이용목적에 맞는 프로그램 서비스 제공이 필요하다. 셋째, 참여 동기는 대면 접촉의 비율이 높은 반면, 정보통신 및 여행관련 업체는 낮으며, 참여활동은 금강송 풍경 및 운치의 감상이고 승용차를 이용하였다. 따라서 인터넷이나 전문 여행업체를 통해 홍보효과를 높임과 함께 취미관련 프로그램을 개발하도록 한다. 넷째, 이용시설 및 운영관리에서는 만족도가 낮았고 생태 숲의 상태는 매우 만족한 젓으로 나타났다. 따라서 친환경적인 이용편익시설의 확충과 더불어 숲의 상태를 항구적으로 지키기 위해 세계문화유산으로 등재할 필요가 있다. 다섯째, 이용전 기대도와 이용 후 만족도에서는 금강송 수목의 가치에 비중이 두드려졌다. 울진을 상징하는 훌륭한 소재될 수 있는 금강송에 대해 보다 적극적인 마케팅 전략수립이 필요하다. 여섯째, 이용전과 이용후의 평가에서 만족도가 기대도보다 높게 나타났다. 만족도가 지속될 수 있도록 모니터링과 평가제를 도입하도록 한다. 결론적으로 국민들이 금강송 숲의 가치와 우수성을 직접 체득하고 느낄 수 있도록 금강송 생태숲을 항구적으로 할 수 있는 임업적 관리가 모색되어야 할 것이다.