• Asia pacific journal of information systems
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• 제16권3호
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• pp.95-116
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• 2006

Although the study of post-adoption has increased in recent years, few studies have focused on the moderating effect of uncertainty avoidance on the relationship between post-expectation and behavior. The purpose of this study is to examine the moderating effect of uncertainty avoidance in the mobile Internet domains. This study proposed a post-adoption model based on prior continuance model. This theoretical model was verified empirically by conducting web surveys and multi-group analysis. Based on the survey data, we classified users into those with high uncertainty avoidance and those with low uncertainty avoidance. The results indicate that post expectations have significant impacts on satisfaction and continuance intention. The results also show that the impacts of intrinsic motivational factors of mobile Internet services on satisfaction and continuance intention are stronger for users with high uncertainty avoidance. On the other hand, the impacts of extrinsic motivational factors on satisfaction and continuance intention are stronger for users with low uncertainty avoidance, with a few exceptions. This paper ends with theoretical and managerial implications of the study results, as well as limitations and future research directions.

• 한국컴퓨터정보학회논문지
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• 제27권10호
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• pp.1-9
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• 2022

본 논문에서는 영상 데이터와 센서 데이터를 활용한 딥러닝 기반의 반려동물 이상행동 탐지 서비스를 제안한다. 최근 반려동물 보유 가구의 증가로 인해 기존 푸드 및 의료 중심의 반려동물 시장에서 인공지능을 더한 펫테크(Pet Tech) 산업이 성장하고 있다. 본 연구에서는 인공지능을 통한 반려동물의 건강관리를 위해 영상 및 센서 데이터를 활용한 딥러닝 모델을 기반으로 반려동물의 행동을 분류하고, 이상행동을 탐지하였다. 자택의 CCTV와 직접 제작한 펫 웨어러블 디바이스를 활용하여 반려동물의 영상 데이터 및 센서 데이터를 수집하고, 모델의 입력 데이터로 활용한다. 행동의 분류를 위해 본 연구에서는 반려동물의 객체를 검출하기 위한 YOLO(You Only Look Once) 모델과 관절 좌표를 추출하기 위한 DeepLabCut을 결합하여 영상 데이터를 처리하였고, 센서 데이터를 처리하기 위해 각 센서 별 연관관계 및 특징을 파악할 수 있는 GAT(Graph Attention Network)를 활용하였다.

• 한국문헌정보학회지
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• 제54권1호
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• pp.343-364
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• 2020

Historically, mothers have been noted as active health information seekers, reflecting their roles as health mangers and caregivers for their family members. However, previous studies have focused on health-related information behavior among mothers in native populations or mothers of children with specific diagnoses. To fill this research gap, this study focused on health information behavior among mothers of healthy infants and toddlers. Using Wilson's (1997) information-seeking model, this study aimed to uncover the relationships between mothers' demographic characteristics and their health information source use. Online surveys were completed by 851 mothers: 255 U.S.-born mothers, 296 Korean-born mothers, and 300 Korean immigrant mothers living in the United States. Results indicated that there were statistically significant relationships between mothers' nine demographic characteristics (mother's age, education level, household income, employment status, the number of children, years living in the U.S. (or Korea), fluency in speaking English, size of household, housing status) and their health information source use. Based on the results, the implications for information professionals at diverse organizations are discussed when they provide health information services to this specific population.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.897-907
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• 2020

The development of information technology and the demands of society on an application in an operating system, as well as increasing the specifications and sophistication of smartphones, require banks to make changes to their mobile banking applications. The transformation of the mobile banking application into a digital banking application conducted by banks has made users re-evaluate based on their preferences. This study presents the behavior of users of digital banking applications in Yogyakarta, Indonesia. The hypothesis model is based on Technology Acceptance Model (TAM) with additional factors of the social image, perceived risk and perceived trust adopted from Muñoz-Leiva et al. (2017). The methodology in this study includes data collection through questionnaires distributed online and data analysis using the Structural Equation Model. The results of this study illustrate that the perceived trust and perceived risk have a more dominant part in influencing user attitude and user intention to use digital banking. Meanwhile, social image, perceived ease-of-use and perceived usefulness are not significant in influencing user attitude and user intention to use digital banking. The implication of this research helps to determine the right communication and strategy so that more users with more benefits can utilize this digital banking application.

• 한국의류학회지
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• 제46권5호
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• pp.868-889
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• 2022

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

• 한국정보시스템학회지:정보시스템연구
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• 제32권3호
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• pp.165-181
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• 2023

Purpose Digital platforms, together with the innovative technologies of modern society, are accelerating the digital innovation of the entire economy and society. Although social media platforms are gradually integrated into daily life, due to social media overload, users limit their use of the platform for a certain period of time or eventually choose to stop using it. In the context of social media platform, the purpose of this paper is to study the effects of information overload, social overload and system function overload on users' unintentional avoidance behavior, mediated by fatique and dissatisfaction. Design/methodology/approach This study empirically examines the influence of social media overload characteristics on users' unintentional avoidance behavior of platform utilization using the S-O-R framework. Data from 236 Chinese social media users were collected through a questionnaire survey, and the hypotheses were validated by evaluating the research model using the SmartPLS 4.0 program using Partial Least Square (PLS) method. Findings According to the empirical analysis result, based on the S-O-R model, first, it is confirmed that information overload and system feature overload have significant positive(+) effects on fatigue. Second, this study finds that information overload, social overload and fatigue have significant positive(+) effects on dissatisfaction. Thirdly, fatigue and dissatisfaction have significant positive(+) effects on unintentional avoidance. In addition, social overload has no significant effect on fatigue, while system feature overload has no significant effect on dissatisfaction.

• Asia Pacific Journal of Business Review
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• 제6권2호
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• pp.27-60
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• 2022

Previous studies stated that low carbon innovation performance could be influenced by government regulations and the green market, which is the new trend of consumer consumption in the present time, mainly focusing on external factors. Before study augured that low carbon innovation performance could be driven by internal and external factors of cooperation such as institutional pressure, stakeholder pressure, and innovation resources. However, the study of green transformational leadership and organizational environmental culture on low carbon innovation performance is rare, especially in Chinese manufacturing, as well as the effect of influencing factors of TPB model: environmental attitude, subjective norm, and perceived behavior capability on low carbon innovation performance. Previous studies mostly used the TPB model for predicting individual behavior. This study established a theoretical model combining the TPB model with green transformational leadership and organizational environmental culture of Chinese automobile manufacturing on low carbon innovation performance. This study consists of two sections of research methodology: section 1 related to questionnaire design and data collection. We established a questionnaire and distributed it online, targeting responses from the managerial level working in Chinese automobile manufacturing. Eventually, 155 valid questionnaires were used for analysis. Section 2 involved data analysis using statistical software. Reliability and data validity was examined by reliability analysis and factor analysis. Correlations and convergent validity analyses were applied, and structural equation modeling was conducted to test the proposed hypotheses. The findings indicated that green transformational leadership, organizational environmental culture, and essential factors of TPB model; environmental attitude, subjective norm and perceived behavior capability positively affect low carbon innovation performance. In addition, the indirect effect of green transformational leadership was tested and found that organizational environmental culture and TPB factors mediated the relationship between transformational leadership and low carbon innovation performance.

• 한국BIM학회 논문집
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• 제6권3호
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• pp.34-41
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• 2016

The previous studies argue that using the semantic properties of BIM objects is efficient for simulating the behaviors of autonomous, computer agents, called virtual-users, but such assumption is not proven via evidence-based research approaches. Hence, this present study aims to investigate the empirical effects of a human behavior simulation model equipped the semantics of spaces on the architecture-major students' operation and understanding of the simulation system, compared to a typical path-finding model. To achieve the aim, this study analyzed the survey and interview data, collected in the authentic design projects. The analysis indicates that (1) using a simulation model equipped the semantics of spaces helps the students' operation of the simulation, and (2) it also aids understanding the relationship between the variables of spaces and virtual-users (${\alpha}=0.74$). In addition, the qualitative data inform that the advantages of the simulation model that computes the semantics of spaces stem in the automatic behavioral changes of massive numbers of virtual-users, and efficient detection and activation on the what-if situations. The analysis also reveals that the simulation model has shortcomings in orchestrating the complex data structure between the semantics properties of spaces and virtual-users under multi-sequential scenarios. The results of this study contribute to develop a future design system combining BIM with human behavior simulation.

• 지능정보연구
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• 제24권2호
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• pp.221-241
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• 2018

최근 딥러닝 기술이 주목을 받고 있다. 대중들의 관심을 받았던 국제 이미지 인식 기술 대회(ILSVR)와 알파고(AlphaGo)에서 사용된 딥러닝 기술이 바로 합성곱 신경망(CNN; Convolution Neural Network)이다. 합성곱 신경망은 입력 이미지를 작은 구역으로 나누어 부분적인 특징을 인식하고 이것을 결합하여 전체를 인식하는 특징을 가진다. 이러한 딥러닝 기술이 우리의 생활에 있어 많은 변화를 야기할 것이라는 기대를 주고 있지만 현재까지는 이미지 인식과 자연어 처리 등에 그 성과가 국한되어 있다. 비즈니스 문제에 대한 딥러닝 활용은 아직까지 초기 연구 단계로 향후 마케팅 응답 예측이나 허위 거래 식별, 부도 예측과 같은 전통적 비즈니스 문제들에 대해 보다 깊게 활용되고 그 성능이 입증된다면 딥러닝 기술의 활용 가치가 보다 더 주목받게 될 것으로 기대된다. 이러한 때 비교적 고객 식별이 용이하고 활용 가치가 높은 빅데이터를 보유하고 있는 전자상거래 기업의 사례를 바탕으로 하여 딥러닝 기술의 비즈니스 문제 해결 가능성을 진단해보는 것은 학술적으로 매우 의미 있는 시도라 할 수 있겠다. 이에 본 연구에서는 전자상거래 기업의 고객 행태 예측력을 높이기 위한 방안으로 합성곱 신경망을 활용한 '이종 정보 결합(Heterogeneous Information Integration)의 CNN 모델'을 제시한다. 이는 정형과 비정형 정보를 결합하여 다층 퍼셉트론 구조의 합성곱 신경망에서 학습시키는 모델로서 최적의 성능을 발휘하도록 '이종 정보 결합'과 '비정형 정보의 벡터 전환', 그리고 '다층 퍼셉트론 설계'로 하는 3개의 내부 아키텍처를 정의하고 각 아키텍처 단위로 구성되는 방식에 따른 성능을 평가하여 그 결과를 바탕으로 제안 모델을 확정하고 그 성능을 평가해보고자 한다. 고객 행태 예측을 위한 목표 변수는 전자상거래 기업에서 중요하게 관리하고 있는 재구매 고객, 이탈 고객, 고빈도 구매 고객, 고빈도 반품 고객, 고단가 구매 고객, 고할인 구매 고객 등 모두 6개의 이진 분류 문제로 정의한다. 제안한 모델의 유용성을 검증하기 위해서 국내 특정 전자상거래 기업의 실제 데이터를 활용하여 실험을 수행하였다. 실험 결과 정형과 비정형 정보를 결합하여 CNN을 활용한 제안 모델이 NBC(Naïve Bayes classification)과 SVM(Support vector machine), 그리고 ANN(Artificial neural network)에 비해서 예측 정확도와 F1 Measure가 높게 평가되었다. 또 NBC, SVM, ANN에서 정형 정보만을 사용할 때 보다 정형과 비정형 정보를 결합하여 입력 변수로 함께 활용한 경우에 예측 정확도가 향상되는 것으로 나타났다. 따라서 실험 결과로부터 비정형 정보의 활용이 고객 행태 예측의 정확도 향상에 기여한다는 점과 CNN 기법의 특징 추출 알고리즘이 VOC에 사용된 단어들의 분포와 위치 정보를 해석하여 문장의 의미를 파악하는데 효과적이라는 점을 실증적으로 확인하였다는데 그 의미가 있다고 할 수 있겠다. 이를 통해서 CNN 기법이 지금까지 소개된 이미지 인식이나 자연어 처리 분야 외에 비즈니스 문제 해결에도 활용 가치가 높다는 점을 확인하였다는데 이 연구의 의의가 있다 하겠다.

• 정보화정책
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• 제21권4호
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• pp.81-97
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• 2014

본 연구는 정보통신 기기 및 관련 서비스에 대한 소비자들의 구매행태를 분석하고, 다양한 정보통신 기기 및 서비스 간의 상관관계를 파악하고자 한다. 본 연구는 다양한 제품과 서비스의 다중선택 상황을 동시에 고려함으로써, 각 제품 및 서비스 그룹 내의 상관관계뿐만 아니라 그룹 간의 상관관계도 추가적으로 살펴보고자 한다. 분석자료는 소비자 설문조사를 통해 수집하였으며, 인구통계학 변수를 고려한 다변량 프로빗 모형(Multivariate Probit Model)과 분산-공분산 행렬(Variance-covariance Matrix)을 분석하기 위한 대안상수 모형(Alternative Specific Constant Model)을 각각 추정하였다. 또한 다차원척도 분석(Multi-dimensional Scaling Method)을 이용하여 제품 및 서비스 간의 관계도를 도식화하였으며, 다양한 정보통신 기기 및 서비스 간의 대체 또는 보완 관계를 도출하였다. 본 연구는 소비자들의 구매행태를 이해하고 예측함으로써 신제품과 서비스의 개발에 유용한 정보를 제공할 것으로 기대된다.