- 期刊
組織氛圍與風險下的AIGC：基於整合型科技接受模式的實證分析
Organizational Climate and Risk in AIGC: An Empirical Analysis Based on the UTAUT
摘要
面對全球化競爭與科技進步的雙重壓力，台灣中小企業急需透過數位轉型提升其競爭力。人工智慧生成內容（AIGC）在促進數位轉型中具有重要的潛力。透過AIGC，企業可加速轉型速度並降低成本，因此如何提高使用者對AIGC的正面態度及使用意願，成為了重要的議題。本研究運用整合型科技接受模式（UTAUT）做為基礎，並納入其他變數，以了解影響使用者對AIGC的態度及使用意向。透過立意抽樣法進行問卷調查，並使用Amos28進行數據分析。研究結果顯示，因AIGC獲得的高成效、容易學習、週遭的鼓勵、充份的軟硬體設備，以及組織的正面氛圍皆會提高使用者對AIGC的正面態度，進而提高使用意向。即使在使用AIGC之際可能面臨財務風險、績效風險及隱私風險，使用者仍有正面的使用意向。此結果有助於企業規畫與擬定數位轉型策略執行面的參考，藉著透過提高使用著的使用意向，進而促進轉型成效。此外，本研究結果亦可擴展UTAUT以及AIGC相關領域的研究。
並列摘要
Due to the competing forces of globalization and technological innovation, Taiwan's small and medium-sized firms must immediately improve their competitiveness by undergoing digital transformation. Artificial Intelligence Generated Content (AIGC) has great promise in helping with this digital revolution. Small and medium-sized firms can save costs and quicken the speed of change with AIGC. Thus, it becomes crucial to comprehend how to increase users' favorable attitudes and intentions to use AIGC. This study is grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). It incorporates several variables to comprehend the factors influencing users' attitudes and intentions towards AIGC. A questionnaire survey was conducted using purposive sampling, and data analysis was performed using Amos28. The research findings suggest that various factors, such as the high efficiency achieved through AIGC, the ease of learning, the user support, the abundance of hardware and software resources, and a positive organizational climate, all contribute to enhancing users' positive attitudes towards AIGC. Consequently, this has a positive effect on their tendency to make use of it. Despite the possible risks of financial, performance, and privacy risks, users are still strongly inclined to use AIGC. This finding aids in firms planning and developing strategies for carrying out projects related to digital transformation. It increases users' intention to utilize, further boosting the digital transformation's efficacy. Furthermore, this study's conclusions can further research in the domains of AIGC and UTAUT.