科技强国品牌原产地形象对品牌再购意愿的影响：基于科技创新与建构思维的分析

基于人们对科技强国具有刻板印象，探究来自非科技强国的品牌如何从科技创新和消费者思维的角度突破“来源国偏见”。采用实证研究法，通过实验证明相比于来源于非科技强国的品牌，来自科技强国的品牌将会让消费者产生更高的再购买意向，而实现科技创新中的技术创新能有效扭转非科技强国对消费者购买行为的负面影响，消费者建构思维也将调节消费者固有的来源国效应。最后根据实证研究结果，从国家层面、行业层面以及消费者层面提出具体的突破路径建议。     

Video demonstrations can predict the intention to use digital learning technologies

The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to use. Technology acceptance is typically measured after the technology has been used for at least three months. This study aims to identify whether a minimal amount of exposure to the technology using video demonstrations is sufficient to predict the intention to use it three months later. In two studies—one using TAM and one using GETAMEL—we showed students of different cohorts (94 and 111 participants, respectively) video demonstrations of four digital technologies (classroom response system, classroom chat, e-lectures, mobile virtual reality). We then measured technology acceptance immediately after the demonstration and after three months of technology use. Using partial least squares modelling, we found that perceived usefulness significantly predicted the intention to use three months later. In GETAMEL, perceived usefulness significantly predicted the intention to use for three of the four learning technologies, while subjective norm only predicted the intention to use for mobile virtual reality. We conclude that video demonstrations can provide valuable insight for decision-makers and educators on whether students will use a technology before investing in it.

Practitioner notes

What is already known about this topic

• The technology acceptance model helps decision-makers to determine whether students and teachers will adopt a new technology.
• Technology acceptance is typically measured after users have used the technology for three to twelve months.
• Perceived usefulness is a strong predictor of intention to use the technology.
• The predictive power of perceived ease of use for the intention to use varies from insignificant to strong.

What this paper adds

• For the four digital learning technologies (classroom chat, classroom response system, e-lectures and mobile virtual reality), we measure technology acceptance after a video demonstration and again after three months of usage.
• Using structural equation modelling, we are able to predict intention to use after three months, with perceived usefulness measured after the video demonstration.
• We replicate these findings with a second study using the general extended technology acceptance model.

Implications for practice and/or policy

• Short video demonstrations can provide information for educators to predict whether students will use a technology.
• Early impressions of perceived usefulness are very important and valuable to predict whether students will use a technology.