郭鐘隆Guo, Jong-Long陳昱婷Chen, Yu-Ting2023-12-082027-09-072023-12-082022https://etds.lib.ntnu.edu.tw/thesis/detail/73a441b5501b650a580745b117acebc2/http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/119170研究背景：為了延緩退化，許多研究發現刺激認知的課程是有效的，其中以科技結合認知刺激課程，可增加課程多樣性與趣味性鼓勵長者參與認知課程並提升行為意圖，進而幫助長者延緩認知功能退化。研究目的：本研究設計樂智AI桌遊作為認知刺激的課程活動，以解構式計劃行為理論為架構探索影響長者行為意圖與影響因素的關係。研究方法：以北北基社區據點65歲以上長者為收案對象，以解構式計劃行為理論架構設計問卷，進行行為意圖因素相關性與解釋力模型驗證及路徑分析。研究結果與討論：本研究共回收126份問卷經統計分析與結構方程式模型驗證後，結果顯示運用解構式計畫行為理論在樂智AI桌遊的行為意圖上有足夠的適配度（SRMR=0.077），且對行為意圖有79.9%的解釋力，證實此結構模型具有顯著成效；在路徑分析結果顯示態度與知覺行為控制對行為意圖有顯著正向影響力，知覺有用性對態度與行為意圖有顯著正向影響力，表示知覺有用性是推動長者行為意圖的重要因子，因此要增加行為意圖可以從長者的態度與知覺行為控制及知覺有用性著手，讓長者認為「樂智AI桌遊」是吸引人的好方法、對他有幫助、且能夠有能力操作，就能持續推動使用長者樂智AI桌遊。Background: Various studies that explored approaches to delay cognitive deterioration have revealed cognitive stimulation courses to be effective. The incorporation of technology into these courses adds to the diversity and playfulness of the course content, which enhances older adults’ behavioral intentions to participate in these courses and thereby helps delay the deterioration of their cognitive functions.Objective: This study explored the relationship between the behavioral intentions of older adults and factors that affect these intentions by developing an artificial intelligence board game named Lezhi for cognitive stimulation course activities and by using the decomposed theory of planned behavior (DTPB) as the theoretical framework.Methods: Older adults aged 65 years or older who lived in a community located in the Taipei–Keelung metropolitan area were recruited as the research participants. A questionnaire survey designed using the DTPB was administered to the participants to determine the correlation of related factors with behavioral intentions, verify the explanatory power of the proposed model, and conduct a path analysis.Results and discussions: A total of 126 responses were returned and used in the statistical analyses and structural equation model verification. According to the results, the DTPB-based model had a satisfactory goodness-of-fit (standardized root mean square residual = 0.077) for the older adults’ behavioral intentions to play the Lezhi board game, with a 79.9% explanatory power for these intentions. The proposed structural model was thus verified to be an effective prediction tool for behavioral intentions. The path analysis result revealed that both attitude and perceived behavioral control significantly and positively affected behavioral intentions and that perceived usefulness significantly and positively affected attitude and behavioral intentions. This suggests that perceived usefulness is a critical driver for older adults’ behavioral intentions. Therefore, to enhance older adults’ behavioral intentions, one may start from changing their attitude and perceived behavioral control. For example, older adults may have a higher tendency to continue using the Lezhi board game if they consider the Lezhi board game an attractive and helpful way of training cognitive functions and is easy for them to play.解構式計劃行為理論AI桌遊人口老化認知訓練The Decomposed Theory of Planned Behavior (DTPB)modelAI-based board gamesElderlyCognitive trainingetd