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探討可購式廣告對消費者衝動性購物之影響：以好奇心與錯失恐懼為干擾變數

為了解決Are you in any relat的問題，作者陳宣廷 這樣論述：

隨著電子商務與社群商務的迅速發展，現今線上通路正在尋找進一步簡化購物過程的方法，盡可能消除消費者線上觀看廣告和購買其特色商品之間的障礙，因此發展出了專門用於吸引和轉換消費者的廣告形式——可購式廣告（Shoppable Ads）。可購式廣告可以為消費者提供無縫式購物體驗，並且幫助品牌商在社群媒體上更有效率地吸引顧客進行購物。本研究基於S-O-R模型框架探討社群商務情境可購式廣告資訊科技能供性之互動性、視覺吸引力、可購性、交易能供性特徵如何透過消費者的愉悅、喚醒情感反應影響消費者衝動性購物，並且將消費者好奇心與錯失恐懼特質作為干擾變數探究其與可購式廣告資訊科技能供性和愉悅、喚醒情緒之影響。本研究

採用問卷調查法針對曾經使用或體驗過的消費者收集409份之有效樣本，並且利用偏最小平方法與結構方程模式進行分析。研究結果發現，可購式廣告之互動性、視覺吸引力和可購性特徵不僅對消費者的愉悅、喚醒情緒具有正向影響，也會間接正向影響衝動性購物；可購式廣告之交易能供性特徵則只對愉悅情緒有正向影響。消費者好奇心特質在可購式廣告之互動性特徵與消費者愉悅情緒間具有干擾效果。最後，愉悅、喚醒、好奇心和錯失恐懼皆會正向影響最終的消費者衝動性購物，是形成消費者衝動性購物的關鍵因素。

A MACHINE LEARNING MODEL FOR DYNAMIC PREDICTION OF CHRONIC KIDNEY DISEASE RISK USING LABORATORY DATA, NON‐LABORATORY DATA, AND NOVEL METABOLIC INDICES

為了解決Are you in any relat的問題，作者賈馬瑞 這樣論述：

Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predict and prevent complications of chronic kidney disease (CKD). This study aimed t

o develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and eff

ective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportion

al hazard regression analyses were performed to determine the variables with high prognostic value for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laborato

ry, laboratory, and novel metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well

using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, BMI, and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have dem

onstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The ML models are simple to use and flexible, because they work even with incomplete data, and can be applied in any clinical setting, including settings where laboratory data is difficu

lt to obtain.