Dr Alex Zarifis
There has been rapid development in Cross-Border E-Commerce (CBEC) in recent years, and it is strengthening the global economy. However, compared to other trade mechanisms, the mechanism of CBEC is more complex. It appears that staff with special talents are needed to start a business trading across borders. Research has presented some solutions for the challenges of cross-border e-commerce from the perspective of the enterprise. However, not enough is known about the cross-border e-commerce talents requirements, and how to train people to develop them.

Requirements of CBEC talents
We found four core the requirements of Cross-Border E-Commerce talents (Cheng et al., 2019): (1) Firstly, having business and marketing knowledge is critical to Cross-Border E-Commerce talents. Both theoretical and practical knowledge are important components in business and marketing knowledge. (2) Secondly, technical skills for trading online is necessary. For example, dealing with all the problems in the process of online trading is important. (3) Thirdly, it’s common to handle difficult situations when business across borders, hence analytical ability is needed for them to solve these problems. (4) Last but not least, all these skills we mentioned are not independent of having practical ability in business, which is the core the requirement.
In summary, business and marketing knowledge, technical skills, analytical ability and practical ability in business were found to be the four core requirements of CBEC talents.

Figure 1. Training model for people working in cross-border e-commerce

Training model for CBEC talents
After finding the four core requirements, we analyzed several teaching methods. We then chose Problem-Based Learning (PBL), which typically involves learning based on solving real problems. We applied WeChat in the training model, so the real work environment is replicated more closely. Writing a business plan was also applied in the training model as the way of practicing practical skills. The problem oriented training model encourages discussions and learning new knowledge. By successfully applying the model in teaching, this research provides a new direction to cultivate the CBEC talents.

Cheng X., Su L. & Zarifis A. (2019) ‘Designing a talents training model for cross-border e-commerce: A mixed approach of problem-based learning with social media’, Electronic Commerce Research, vol.19, iss.4, pp.801-822. Available from: https://doi.org/10.1007/s10660-019-09341-y

Dr Alex Zarifis

Online learning is an extremely important part of education. By learning from the experience of COVID-19, we can have engaging and rewarding online learning, and avoid being disrupted by new natural disasters.

An individual’s ability to use their working memory to process information, and make decisions, is affected by the cognitive load they perceive. Cognitive load refers to the total amount of mental effort being used in the working memory.

Adopting the Cognitive Load Theory, this study mainly focuses on the antecedents of cognitive load generated by external factors. It explores the internal influence mechanism of cognitive load on user satisfaction, combined with the theory of expectation confirmation. At the same time, we also explore whether the level of cognitive ability will affect user satisfaction. Based on this research background, this study explores three research questions (Zuo et al. 2021).

(1) The first question explored is: In the context of a pandemic, what are the factors that affect cognitive load? We found that the influencing factors of cognitive load are very strongly related to the satisfaction with the platform. They can be divided firstly into typical factors that are important when using technology, and secondly specific factors that are caused by a pandemic. The typical factors that are important when using technology include social factors, perceived autonomy, content quality, and self-efficacy.

Figure 1. Online learner satisfaction model

(2) The second question explored is: How does cognitive load affect the satisfaction of online learning users, and what is its internal influence mechanism? We found that the antecedents of satisfaction which are emphasised by most of the learners interviewed, can be summed up in two constructs: One of the two is expectation confirmation which has more to do with the information system, and the other is perceived usefulness that is more related with what is being learned.

(3) The third and final question explored is: Will an individual with a different cognitive ability, perceive different levels of cognitive load, for the same online learning task? It appears that this is the case, but the cognitive load perceived by people with different educational levels may be a research gap that needs to be explored further.

The model of online learner satisfaction put forward here, can help optimize satisfaction, and help us be prepared to overcome challenges like pandemics.


Zuo Y., Cheng X., Bao Y. & Zarifis A. (2021) ‘Investigating user satisfaction of university online learning courses during the COVID-19 epidemic period’, Proceedings of the 54th Hawaii International Conference on System Sciences, pp.1139–1148. Available from: https://doi.org/10.24251/HICSS.2021.139

Dr Alex Zarifis

Universities, like many other organizations, are going through a disruptive digital transformation. The alure of AI and automation, allowing smarter, more responsive and scalable universities is clear. What is less clear is what a university will look like five years into this process. We identified four business models that can give leaders a destination for the digital transformation journey (Zarifis and Efthymiou 2022):

(1) This first education business models that is optimized for AI is to focus and disaggregate: In addition to the classroom the successful delivery of education requires a supply chain. With the changes in this supply chain caused by AI an educator can chose to focus on one part of this supply chain. They can focus on the part of the supply chain where their skills are best suited and build an ecosystem for the rest.

Figure 1. Four education business models that are optimised for AI (adapted from (Zarifis, Holland, and Milne 2019))

(2) The second model that is optimized for AI is to keep the existing education model and add AI: Despite the transformational nature of AI, some universities use AI to make the existing model more effective without changing it fundamentally. This may involve more back-office AI applications and less student facing applications.

(3) The third education model that is optimized for AI is an educator expanding beyond the current model: In this model the educator takes advantage of new opportunities emerging from AI and digital transformation. The educator keeps their existing part of the education supply chain, but they also add new processes that take advantage of AI to reach more students and more data.

(4) The fourth model that is optimized for AI is the model of a disruptor entering education: As technology plays a more decisive role in many areas, including education, tech savvy companies can use their advanced systems and existing user base and add other new services. Education can be added as a new feature to a platform in a similar way that banking and insurance services have been added.

The four models presented give a strategic direction and make it easier for the leader of the digital transformation to communicate it. The leader of digital transformation will have to make many choices along this journey, so it is important that all the decisions are compatible with the chosen education business model.


Zarifis A. & Efthymiou L. (2022) ‘The four business models for AI adoption in education: Giving leaders a destination for the digital transformation journey’, IEEE Global Engineering Education Conference (EDUCON), pp.1866-1870. Available from: https://doi.org/10.1109/EDUCON52537.2022.9766687

Zarifis A., Holland C.P. & Milne A. (2019) ‘Evaluating the impact of AI on insurance: The four emerging AI and data driven business models’, Emerald Open Research, pp.1-17. Available from (open access): https://emeraldopenresearch.com/articles/1-15/