• 한국IT서비스학회지
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• 제9권2호
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• pp.73-92
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• 2010

E-government refers to the delivery of government information and services online through the Internet or other digital means. Unlike traditional structures, e-government systems are two-way, and available 24 hours a day, seven days a week. The interactive aspects of e-government allow both citizens and bureaucrats to send and receive information. Criticism about the provision of e-Government services, however, has proposed a more user-oriented approach. The user needs to be placed at the center of the development and the provision of e-Government services. Furthermore, e-government literatures seldom explore acceptance issues among the aged. Attempting to address this gap, we take the approach based on a combination of Technology Acceptance Model and Theory of Planned Behavior, with data gathered via a questionnaire from service users over 50 years and older. The findings indicate that perceived usefulness, ease of use, subjective norms, trust, visibility, facilitating conditions and political efficacy are direct or indirect predictors of citizens‘ intention to use an e-government service. Implications of this study for research and practice are presented.

• International Journal of Knowledge Content Development & Technology
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• 제5권2호
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• pp.25-43
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• 2015

The purpose of this paper is to investigate the information needs and information seeking behavior of foreign students. A survey method was used for the undertaken study. The data were collected using a structured questionnaire, self-administered to 120 foreign students (60 males & 60 females) with 88 (47 males & 41 females) returns. The research is limited to post-graduate, M.Phil. and Ph.D. foreign students in University of Delhi. It was found that post-graduate students need information regarding their program of study while research scholars need information for writing research articles and for doing their research work. Most of them seek information through the internet. Research scholars used electronic resources such as databases, e-journals and e-theses and dissertations. 88.6% of the respondents also use books for seeking information. Their use of the library is limited with complaints about library staff and too few computer terminals. The present study will be helpful in designing new systems and services for the foreign students so that their information needs can be fulfilled easily. Further, findings of the study indicate that how the library professionals should assist foreign students to accomplish their information needs.

• Journal of Information Science Theory and Practice
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• 제9권1호
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• pp.54-67
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• 2021

This paper presents the ISRI (Information Systems Research Indicators) Web tool, publicly and freely available at isri.sciencesphere.org. Targeting Information Systems (IS) researchers, it compiles and organizes IS adoption and use theories/models, constructs, and indicators (measuring variables) available in the scientific literature. Aiming to support the IS theory development process, the purpose of ISRI is to gather and systematize information on research indicators to help researchers and practitioners' work. The tool currently covers eleven theories/models: DeLone and McLean's IS Success Model (D&M ISS); Diffusion of Innovations Theory (DOI); Motivational Model (MM); Social Cognitive Theory (SCT); Task-Technology Fit (TTF); Technology Acceptance Model (TAM); Technology-Organization-Environment Framework (TOE); Theory of Planned Behavior (TPB); Decomposed Theory of Planned Behavior (DTPB); Theory of Reasoned Action (TRA); and Unified Theory of Acceptance and Use of Technology (UTAUT). It also includes currently over 400 constructs, nearly 2,500 indicators, and about 60 application contexts related to the models. For the creation of the tool's database, nearly 580 references were used.

• 한국컴퓨터정보학회논문지
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• 제29권3호
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• pp.181-189
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• 2024

본 연구의 목적은 상대적으로 관심이 부족하였던 상호작용 프라이버시 관리행위(IPC 사용)의 역할을 규명하는 것이다. 이를 위해 본 연구는 프라이버시 계산모델의 주요 변수들과 상호작용 프라이버시 관리행위 사이의 관련성을 이론화한 통합 모델을 제안한다. 이 모델에 대한 실증 분석 결과, IPC 사용은 위험을 낮추고, 혜택을 높이며, 결과적으로 자기노출의 증가를 촉진하는 역할을 하는 것으로 나타났다. 이러한 결과는 사용자들의 자기노출이 계산모델에서 제안하는 제한적 노출뿐만 아니라 IPC 사용을 통한 비제한적 노출도 포함하는 것을 의미하기 때문에 계산모델의 이론적 논리를 확장시키는 의미를 갖는다.

• 한국IT서비스학회지
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• 제13권2호
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• pp.1-17
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• 2014

Social networking service (SNS) is a service that allows people to share information, manage relationships with others, and express themselves on the Internet. The number of SNS users have increased explosively with the growth of mobile devices such as smartphones. As the influence of SNS has grown extensively, potential threats to privacy have also become pervasive. The purpose of this study is to empirically examine the main factors that affect users' intentions to use security functions provided by their SNS. The main theories for this study include the rational choice theory and the theory of planned behavior. This study has identified the factors that affect intention to use security functions. In addition, security function awareness and information security awareness are found to be important antecedents for intention to use security functions. The results of this study implies that when SNS providers develop security policies, they should consider the ways to improve users information security awareness and security function awareness simultaneously.

• 시큐리티연구
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• 제56호
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• pp.145-163
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• 2018

최근 조직 구성원의 보안행동은 기업 차원의 정보보안에 중요한 부분으로 인식되고 있다. 정보유출 및 정보보안에 대한 연구는 보안 위협에 대한 개인행동이나 보안 기술을 사용하는 조직 구성원을 대상으로 연구가 활발히 진행되고 있다. 본 연구의 목적은 조직구성원들이 정보보안 활동을 촉진할 수 있는 효과적이고 효율적인 발전방안을 제시하고자 한다. 이를 위해 계획된 행동이론과 보호동기이론의 통합을 중심으로 주요 변수들을 적용한 연구모형을 제시하였다. 본 연구모형을 실증적으로 검증하기 위해 기업에서 보안 경험이 있는 조직원들을 대상으로 설문조사를 실시하였다. 이를 통해 조직구성원들이 정보보안 행동에 대해 긍정적인 구전을 유도하는 것이 중요하다. 이를 통해 기업에서는 조직구성원들이 정보보안 사고에 대해서 내 외부에서 발생 가능한 보안위험을 예방 및 대응하고 관리하기 위해 다양한 보안 솔루션 도입해야하며, 정보시스템에 대한 취약점 점검과 보인 패치 등의 보안 사항을 만족시키기 위한 행동을 실시해야 할 것이다.

• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.615-627
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• 2009

본 연구의 목적은 효과적인 마케팅전략 수립에 도움이 되는 정보를 제공하는 데 있다. 이를 위하여 화장품구매 자료로부터 고객 구매형태와 재구매 간의 관계를 분석하여 고객충성도 예측모형을 개발하였다. 고객충성도는 재구매 가능성으로 측정하였다. 본 연구에서 사용된 자료는 국내의 한 화장품회사 고객들의 2000년부터 2008년까지 9년간의 구매자료 (432,528명, 2,440,107건)이다. 예측모형의 목표변수는 재구매 유무이고, 설명변수는 구매수량, 구매액, 휴면기간 등의 기본변수와 구매횟수와 거래 일자를 이용한 가공변수들이다. 충성도 예측모형은 데이터마이닝 기법인 로지스틱회귀, 의사결정나무 및 신경망모형을 사용하였다. 예측모형평가의 측도로는 하이드게 점수를 사용하였으며, 최대의 하이드게 점수를 가지는 분계점을 선택하였다. 각예측모형에서 선택된 변수는 유사하며, 모형비교 결과 세 모형의 효율과 평가측도의 차이는 크지 않았다. 정분류율이 다소 높고 해석과 활용이 쉬운 의사결정나무모형을 최종모형으로 선택했다.

• 대한가정학회지
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• 제27권2호
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• pp.115-132
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• 1989

The household behavior of food, clothing, and shelter in Seoul area was analyzed to determine characteristics of consumer behavior in the industrial society. Questionaires were administered to 1095 housewives to find out the degrees and types of household labor substituted by the commodities and their buying behavior. Attitudes and values concerning clothing and housing were also measured. The results of the study indicated that the degree of labor substitution by commodities in clothing related area were high, while traditional food items were relatively low. Household labors related to clothing and housing maintenance also showed increased tendency to be substituted by the commercial services. The age and educational level of housewife, and household income were found to be the influencing factors to accelerated labor substitution, which is expected to increase as the industrialization progresses. Buying behavior varied in store selection and information sources according to commodities. Marketer dominated information sources according to commodities. Marketer dominated information sources were used for foods and clothing commodities, while interpersonal information sources were used for services. Shortened clothing life cycles, and rental housing were also found indicating consumer's change in housing concept from possession to use. Suggestion were made to consumers, industries, and government based on the results from the study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.