李景峰周開華2020-10-19不公開2020-10-192019http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22G0006702314%22.&%22.id.&http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/110996本研究旨在瞭解消費者使用汽車共享意圖關鍵因素之現況、差異及其關係。研究首先探討國內外相關研究，運用科技接受模式，發展環保意識、品牌形象、經濟效益、知覺有用性、知覺易用性、使用態度與使用意圖等七個關鍵因素，藉以論建構問卷內涵。 繼之，以問卷調查方法蒐集資料，發出600份問卷，有效問卷433份，有效回收率72.2%。並以次數分配、百分比、平均數、標準差、皮爾森積差相關與結構方程模型等方法進行分析。 經由統計分析結果，獲致主要結論如下： 一、測量模型的驗證性因子分析顯示，汽車共享消費者同意七個關鍵因 素的內涵。 二、環保意識因年齡、職業、年收入與居住地之不同而有所差異。 三、品牌形象因職業、年收入與居住地之不同而有所差異。 四、知覺易用性因年齡、職業、學歷與年收入之不同而有所差異。 五、使用態度及使用意圖因年收入與居住地之不同而有所差異。 六、七個關鍵因素間呈現極低度至普通正相關，並在SEM中呈現正向結構關係。This study was aimed to understand the current conditions, the effects of different background, and the relationship in key factors for consumers' intention of car sharing. First of all, this study was implemented by reviewing related literatures and researches. The research adopted the technology acceptance model, developed key factors and constructed the connotation of questionnaires. The seven key factors including environmental consciousness (EC), brand Image (BI), economic benefit (EB), perceived usefulness (PU), perceived ease of use (PE), attitude toward using (AT) and use intention (UI). Then, the research data was collected by questionnaires investigation, 600 samples were collected in Taiwan, 433 effective questionnaires were back, and the effective return rate was 72.2%. The acquired data was analyzed with statistical methods of frequency distribution, percentage, mean, standard deviation, Pearson correlation analysis, and structural equation model (SEM), etc. According to the analytical result, I was concluded that as follow: 1.Confirmatory factor analysis of measurement model indicated the consumers of car sharing had agreed with the connotation of EC, BI, EB, PU, PE, AT and UI factors. 2.The effects of different age, occupation, annual income and living area on EC were statistically significant. 3.The effects of different occupation, annual income and living area on BI were statistically significant. 4.The effects of different age, occupation, education and annual income on PE were statistically significant. 5.The effects of different annual income and living area on AT and UI were statistically significant. 6.The correlation among EC, BI, EB, PU, PE, AT and UI were between very low and normal positive, and demonstrated to be positive structural relationships in SEM.科技接受模式消費者汽車共享關鍵因素technology acceptance modelconsumerscar sharingkey factors