知覺關鍵多數對於採用物聯網技術輔助學習意向之影響－以QR code使用經驗為干擾變項

The Perceived Critical Mass and the Adoption of Using QR code for Assisting Learning: Moderating Effect of Usage Experience

徐明珠(Ming-Ju Hsu)

知覺關鍵多數 ； 科技接受模式 ； 物聯網 ； QR code ； 結構方程模式 ； 干擾變項 ； Perceived Critical Mass ； Technology Acceptance Model ； IoT Technology ； QR code ； Structural Equation Modeling (SEM) ； Moderator Variable

中華印刷科技年報

2021（2021 / 05 / 01）

182 - 195

繁體中文

本研究建構了一個以「知覺關鍵多數」為外部變項，結合「科技接受模式」內「知覺易用性」和「知覺有用性」兩個變項，以探討使用物聯網技術QR code輔助學習意向。本研究採用便利抽樣透過問卷調查蒐集資料，共計獲得361份有效問卷資料，並將所獲資料進行「結構方程模式（Structural Equation Modeling, SEM）」之統計與分析。本研究以驗證性因素分析，進行收歛效度、區別效度分析，達到良好配適水準，於模型評估之各項配適度指標方面，亦有不錯的配適度。整體模式中所有路徑係數均顯著，包括知覺關鍵多數至知覺易用性、知覺有用性、行為意向，知覺易用性至知覺有用性，知覺易用性、知覺有用性至行為意向均達到顯著性影響；並以使用QR code經驗為干擾變項，結果發現，QR code使用經驗在知覺關鍵多數至知覺易用性，知覺有用性至行為意向的關係中扮演干擾的角色。由本研究結果顯示，物聯網技術的採用，應於初期打開新科技的知名度，以此創造樂隊車效果，並考慮使用者經驗之不同，進行差異化的推廣，使用經驗不足者，加強易用性；使用經驗充足者，強化有用性。本研究結果將可提供給科技推廣、教育推廣和學術界研究相關單位參考。

Internet of Things-based applied in education are increasingly important to personal and organizational learning, QR code is one of its important applications. The study uses perceived critical mass as an external factor, combined with perceived ease-of-use (PEOU), perceived usefulness (PU) which are two important variables in the technology acceptance model (TAM) to propose a model for the adoption of Internet of Things (IoT) Technology to assist learning. The model is empirically examined using survey data collected from 361 valid questionnaires in Taiwan that are analyzed to empirically test the hypotheses in the research using Structural Equation Modeling with AMOS statistical software. All the hypothesized determinants have a significant direct effect on intention to use the QR code for assisting learning. The results also indicate that perceived critical mass influences behavioral intentions (BI) directly and through PEOU, PU. Other relationships postulated in the model are also found to be significant. Usage Experience has a significant moderating effect on the relationship between PCM and PEOU, PU and BI through multi-group analysis. The implications of the study for future research and applied in education are discussed.

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