Increasingly, universities across the globe are involved in collaborations at both national and international levels. In a nutshell, the collaborations are intended to expose students and academia to different environments, to facilitate enhanced teaching and learning, and research activities. However, many of the collaborative initiatives have not been successful, particularly at international level. This can be attributed to many factors, which are either unknown or too complex to address by the drivers of collaborations in many universities and countries. As a result, various approaches such as physical exchange of materials and humans (students, professors, and other university staff) have been adopted over the years, yet the success rate has not improved. The objective of this research was to develop a model, which can be used to guide an understanding of how to employ global virtual teams for university’s collaborations. The qualitative methods from the perspectives of the interpretive stance and inductive approach were employed. Data were gathered from literature, and an existing collaboration, which involves universities from three different continents, Africa, Europe and North America. The data were analysed following the hermeneutics technique, from the interpretivist perspective. From the analysis, we found three sets of factors: human sphere, collaborative activity and technology artefacts as the main facilitators and influence on universities collaborations. Based on the findings, a global virtual team model was developed, which can be useful in guiding and advancing the way in which team members interact and enable activities of collaborations between universities, and company as well.

全球各地的大学越来越多地参与国家和国际层面的合作。简而言之，这些合作旨在使学生和学术界处于不同的环境中，以促进增强的教与学以及研究活动。但是，许多合作倡议并未取得成功，特别是在国际一级。这可以归因于许多因素，这些因素要么未知，要么太复杂，以致于许多大学和国家/地区的合作推动力无法解决。结果，多年来已经采用了各种方法，例如物质和人（学生，教授和其他大学工作人员）的物理交换，但是成功率并未提高。这项研究的目的是开发一个模型，可以用来指导对如何使用全球虚拟团队进行大学合作的理解。从解释立场和归纳方法的角度出发，采用了定性方法。数据是从文献中收集的，并且是一项现有的合作，涉及来自非洲，欧洲和北美三大洲的大学。从解释论者的角度出发，采用诠释学方法对数据进行了分析。通过分析，我们发现了三组因素：人为因素，协作活动和技术伪像是主要推动者，并且对大学协作产生了影响。基于这些发现，开发了一个全球虚拟团队模型，