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중국 배달 앱의 시스템, 정보, 서비스 품질이 고객만족에 미치는 영향에 관한 연구: 이용자 저항성의 조절효과
최격기,진창현 국제e-비즈니스학회 2019 e-비즈니스 연구 Vol.20 No.4
본 연구는 중국 배달 앱의 서비스 품질이 고객 만족과 지속적인 사용의도에 미치는 영향관계를 규명하고자한다. 중국 배달 앱의 서비스 품질의 구성요소를 3가지 요소로 세분화하였다(시스템 품질, 정보 품질, 서비스품질). 또한 고객 만족을 감성과 이성적 만족으로 양분하여 인과관계를 파악하여 보았다. 이용자 저항성을조절 변수로 설정하여 조절효과를 파악하고자 하였다. 중국 배달 앱을 사용한 경험이 있는 중국 소비자849명을 대상으로 설문조사를 실시하였다. 연구가설을 검증하기 위하여 구조방정식 통계 패키지를 사용하여분석하였다. 연구 결과를 보듯이, 시스템, 정보, 서비스 품질은 고객만족에 긍정적인 영향을 미치는 것으로드러났다. 그러나 정보 품질은 감성적 만족에 무의미한 영향을 미치는 것으로 드러났다. 감성과 이성적 만족은배달 앱의 지속적 사용의도에 정의 영향관계가 있는 것으로 드러났다. 이용자 저항성의 조절 효과 분석을보면, 배달 앱의 시스템, 정보 그리고 서비스 품질이 고객 만족에 영향을 미칠 때 이용자 저항성을 조절역할을 하는 것으로 드러났다. 따라서, 중국의 배달 앱 개발은 이용자들의 욕구에 맞게 디자인, 지능, 다양한정보를 바탕으로 하는 것이 효과적인 것으로 판단된다. This study examines theories and models related to APP experience, mobile marketing, and consumer purchase intention, and has read many documents and analyzed the current situation of Chinese food industry. The results were combined and hypothesized. This study applies three factors that apply system quality, service quality, and information quality to consumers' delivery apps. In addition, three factors influencing customer satisfaction were used to model the effect of the customer's ongoing use intensity. In this paper, we analyze the questionnaire data using SPSS as an empirical analysis. The three factors of the app experience are affecting consumers' perception of the delivery app. The satisfaction of consumers' delivery apps has affected the continued use of the delivery apps. Thus, consumers' attitudes to supply are positively related to their persistent purchase intentions. After analyzing the results of the actual analysis and the current situation of development of the app industry, the delivery companies have taken advantage of the design of their apps, Attracting more consumers and attracting consumers. you should experience using consumer apps in each direction and strengthen the relationship between consumers and delivery apps. And promote attitudes that promote consumers' continued use intentions.
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AttDRP: 주의집중 메커니즘 기반의 항암제 약물 반응성 예측 모델
최종환,서상민,박상현 한국정보과학회 2021 정보과학회논문지 Vol.48 No.6
Resistance to anti-cancer drugs makes chemotherapy ineffective for cancer patients. Drug resistance is caused by genetic alterations in cancer cells. Many studies have investigated drug responses of diverse cancer cell lines to various anti-cancer drugs to understand drug response mechanisms. Existing studies have proposed machine learning models for drug response prediction to find effective anti-cancer drugs. However, currently there are no models to learn the relationship between anticancer drugs and genes to improve the prediction accuracy. In this paper, we proposed a predictive model AttDRP that could identify important genes associated with anti-cancer drugs and predict drug responses based on identified genes. AttDRP exhibited better predictive accuracy than existing models and we found that the attention scores of AttDRP could be effective tools to analyze molecular structures of anticancer drugs. We hope that our proposed method would contribute to the development of precision medicine for effective chemotherapy. Resistance to anti-cancer drugs makes chemotherapy ineffective for cancer patients. Drug resistance is caused by genetic alterations in cancer cells. Many studies have investigated drug responses of diverse cancer cell lines to various anti-cancer drugs to understand drug response mechanisms. Existing studies have proposed machine learning models for drug response prediction to find effective anti-cancer drugs. However, currently there are no models to learn the relationship between anticancer drugs and genes to improve the prediction accuracy. In this paper, we proposed a predictive model AttDRP that could identify important genes associated with anti-cancer drugs and predict drug responses based on identified genes. AttDRP exhibited better predictive accuracy than existing models and we found that the attention scores of AttDRP could be effective tools to analyze molecular structures of anticancer drugs. 암환자 중 일부는 항암제에 대한 약물 저항성을 보여 약물을 이용한 항암치료를 어렵게 만든다. 약물 저항성은 암세포의 유전체 이상에 기인하는 것으로 밝혀져, 암세포주 및 항암제에 대한 약물 반응성 데이터를 분석하는 연구가 활발히 이루어지고 있다. 기존 연구들은 기계학습을 이용하여 약물 민감성 또는 저항성을 예측하는 모델을 여럿 제안하였으나, 항암제와 유전자의 관계를 학습하는 모델의 부재로 인하여 예측 정확도 향상을 위한 여지가 남아있었다. 본 논문에서는 주의집중 메커니즘을 활용하여 항암제 관련 유전자들을 식별하고, 그러한 유전자들 정보에 기반하여 항암제 반응성을 예측하는 AttDRP를 제안한다. 제안하는 모델은 CCLE 데이터에서 기존 모델들보다 높은 예측 정확도를 보여주었고, AttDRP이 학습한 주의집중 스코어가 항암제의 분자구조 분석에 효과적으로 활용될 수 있음을 확인하였다.
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박효찬,장철,박창제,이동운 한국농약과학회 2023 농약과학회지 Vol.27 No.4
Pesticide sellers are responsible for not only selling pesticides needed by farmers, but also delivering and communicating information about the selection and use of pesticides to farmers. In order to use it as basic data for establishing a pesticide resistance management system, this study investigated the level of awareness of pesticide resistance among pesticide sellers and their awareness of the role of pesticide sellers in managing pesticide resistance. A survey was conducted on 83 dealers who privately sell pesticides nationwide and 39 prescribers in charge of prescribing pesticides at Nonghyup. More than 97% of pesticide sellers had above average knowledge about resistance, and more than 60% of respondents considered the problem of pesticide resistance in the region to be more serious than average. The majority of respondents (54.1%) answered that the cause of the pesticide resistance problem was the repeated use of the same pesticide. Pesticide sellers gave priority to efficacy when recommending pesticides, and accordingly tended to actively consider information on resistance. The majority of opinions were that pesticide sellers were the most suitable agents for managing pesticide resistance. It is believed that it is necessary to establish a system in which information on pesticide resistance is quickly shared by region, and based on this information, it is reflected when prescribing pesticides to farmers and sales of pesticides are made.
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