The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in ...The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).展开更多
共享汽车具有“低碳”特征且能为出行者提供安全独立的空间,为后疫情时代出行提供了新的选择。为挖掘疫情常态化下共享汽车使用意愿的影响因素以及作用机理,以网络问卷的形式对出行者进行调查,回收有效问卷109份,并对调查结果进行分析...共享汽车具有“低碳”特征且能为出行者提供安全独立的空间,为后疫情时代出行提供了新的选择。为挖掘疫情常态化下共享汽车使用意愿的影响因素以及作用机理,以网络问卷的形式对出行者进行调查,回收有效问卷109份,并对调查结果进行分析。融合疫情感知风险和财务风险因素,构建扩展的整合型技术接受与使用理论模型(unified theory of acceptance and use of technology,UTAUT),提出11条假设并采用结构方程模型探索各潜变量影响共享汽车接受意向的途径,分析假设检验结果和模型拟合程度,对假设中不显著的路径进行中介效应分析。为探究社会经济属性变量的影响过程,构建基于结构方程的多原因多指标模型,并检验观测变量与潜变量的相关性以及潜变量与潜变量之间的相关性。研究结果表明:模型拟合程度均表现良好,潜变量中绩效期望对接受意向的正向影响最为显著,其次是促进条件和社会影响,而财务风险、努力期望对接受意愿有显著负向影响。疫情感知风险的直接影响不显著,但社会影响、绩效期望和促进条件在疫情感知风险和行为意向之间具有部分中介作用,总间接影响效应为0.240,中介效应占总效应的74.8%,间接影响显著。年龄、实际驾龄、是否持有机动车驾驶证因素对疫情下共享汽车的使用态度存在显著影响,而使用频率则直接影响疫情下的共享汽车使用意向。基于本文研究为后疫情时代共享汽车的发展提供策略和方向指引,如优化出行体验、强化安全管理、刺激消费、提升品牌价值等。展开更多
文摘The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).
文摘共享汽车具有“低碳”特征且能为出行者提供安全独立的空间,为后疫情时代出行提供了新的选择。为挖掘疫情常态化下共享汽车使用意愿的影响因素以及作用机理,以网络问卷的形式对出行者进行调查,回收有效问卷109份,并对调查结果进行分析。融合疫情感知风险和财务风险因素,构建扩展的整合型技术接受与使用理论模型(unified theory of acceptance and use of technology,UTAUT),提出11条假设并采用结构方程模型探索各潜变量影响共享汽车接受意向的途径,分析假设检验结果和模型拟合程度,对假设中不显著的路径进行中介效应分析。为探究社会经济属性变量的影响过程,构建基于结构方程的多原因多指标模型,并检验观测变量与潜变量的相关性以及潜变量与潜变量之间的相关性。研究结果表明:模型拟合程度均表现良好,潜变量中绩效期望对接受意向的正向影响最为显著,其次是促进条件和社会影响,而财务风险、努力期望对接受意愿有显著负向影响。疫情感知风险的直接影响不显著,但社会影响、绩效期望和促进条件在疫情感知风险和行为意向之间具有部分中介作用,总间接影响效应为0.240,中介效应占总效应的74.8%,间接影响显著。年龄、实际驾龄、是否持有机动车驾驶证因素对疫情下共享汽车的使用态度存在显著影响,而使用频率则直接影响疫情下的共享汽车使用意向。基于本文研究为后疫情时代共享汽车的发展提供策略和方向指引,如优化出行体验、强化安全管理、刺激消费、提升品牌价值等。