「慢」步雲端：Nike+ Running使用者行為意圖分析

Abstract

研究以科技接受模式為理論基礎，旨在建構Nike+ Running使用者行為意圖模式，瞭解使用者對系統之接受情形，並提供系統開發者改善之建議。依據研究目的，本研究於2014年3月間，以曾使用過Nike+ Running的420位使用者為研究對象，透過「Nike+ Running使用者行為意圖模式問卷」進行調查，並以描述性統計、獨立樣本t檢定、單因子變異數分析，以及結構方程進行資料分析。研究結果如下：一、Nike+ Running使用者以25歲以下大專校院學生為主要使用者，且多數具有規律運動之習慣；二、Nike+ Running使用者於品牌知識、知覺有用性、知覺易用性、使用態度與行為意圖等各構面皆有中等以上之評分；三、最常購買運動鞋之品牌在品牌知識、知覺有用性、知覺易用性、使用態度與行為意圖皆有顯著差異；四、Nike+ Running使用者行為意圖模式符合各項適配度指標之檢驗，其中使用態度正向影響使用者之行為意圖。基於上述，建議系統開發者掌握慢跑Nike+ Running使用者特徵，並多與社群媒體結合，且縮短系統的啟動時間，降低選擇模式的繁瑣操作步驟，提升使用者專業數據。針對未來研究，建議擴大研究方向，建立運動App使用者行為意圖之論述，並加入其他構面因子探討，提高科技接受模式預測力，且延長研究時程，長期縱貫性的對運動App使用者進行觀察。The study constructed the behavioral intention model of user in the Nike+ Running based on the theory of technology acceptance model. The purpose of this study was to understand the user acceptance of the system and provide advice to system developers. The subjects were 420 users who had used Nike+ Running App. A self-developed questionnaire “Questionnaire of User’s Behavioral Intention Model in Nike+ Running” was used as the instrument. Descriptive statistics, independent t-test, one-way ANOVA and Structural Equation Modeling (SEM) were used for data analysis. The results were as followed: 1. The demographics of Nike+ Running user were predominantly aged 25 and below, college students and had regular exercise habit. 2. Nike+ Running users in brand knowledge, perceived usefulness, perceived ease to use, attitude toward using and behavioral intention to use had above average evaluation. 3. Often buy sports brand in brand knowledge, perceived usefulness, perceived ease to use, attitude toward using and behavioral intention to use was significant differences. 4. The user’s behavioral intention model in Nike+ Running was the goodness of fit and attitude toward using was positively related to behavioral intention to use. According to the results, the researcher suggested the system developer should get over the characteristics of Nike+ Running users and establish the market segmentation. By improving system operation interface and reducing the tedious steps to upgrade the function of the system. The future research can establish sports App user behavioral intention topic, and add other dimensions to explore that can improve the prediction of TAM. In addition, longitudinal method is also suggested for future research.