Prof. Dr.
Tri Mikael Tran

The popularity of ChatGPT raises concerns about the impacts of artificial intelligence (AI) on higher education. While past research explored how ChatGPT, a widely recognized text-based generative AI, can improve productivity and efficiency in education, little is yet known about its impact on course design. Utilizing Bloom’s taxonomy and Biggs’ constructive alignment, this research examined the potential influence of ChatGPT on course design. We conducted 19 interviews with lecturers from seven universities located in different countries, who teach a range of business school courses with course objectives at various Bloom’s levels. Additionally, we interviewed three educational specialists with expertise in educational technology and pedagogy. Respondents had mixed opinions about ChatGPT's influence on learning and course design. Our findings suggest that the effectiveness of ChatGPT may be influenced by the complexity of learning outcomes as defined by Bloom’s taxonomy. The researchers propose a framework that teachers can use to mitigate its negative impacts.

Background: Over the past 2 years, the COVID-19 pandemic has placed enormous pressure on the health care industry. There has been an increase in demand and, at the same time, a shortage of supplies. This has shown that supply chain management in the health care industry cannot be taken for granted. Furthermore, the health care industry is also facing other major challenges, such as the current labor market shortage. In the literature, the Internet of Things (IoT) is highlighted as an effective tool to build a more resilient and efficient supply chain that can manage these challenges. Although using IoT in supply chain management has been extensively examined in other types of supply chains, its use in the health care supply chain has largely been overlooked. Given that the health care supply chain, compared to others, is more complex and is under growing pressure, a more in-depth understanding of the opportunities brought by IoT is necessary.
Objective: This study aims to address this research gap by identifying and ranking the drivers of and barriers to implementing IoT in the health care supply chain.
Methods: We conducted a 2-stage study. In the first, exploratory stage, a total of 12 semistructured interviews were conducted to identify drivers and barriers. In the second, confirmatory stage, a total of 26 health care supply chain professionals were asked in a survey to rank the drivers and barriers.
Results: The results show that there are multiple financial, operational, strategy-related, and supply chain-related drivers for implementing IoT. Similarly, there are various financial, strategy-related, supply chain-related, technology-related, and user-related barriers. The findings also show that supply chain-related drivers (eg, increased transparency, traceability, and collaboration with suppliers) are the strongest drivers, while financial barriers (eg, high implementation costs and difficulties in building a business case) are the biggest barriers to overcome.
Conclusions: The findings of this study add to the limited literature regarding IoT in the health care supply chain by empirically identifying the most important drivers and barriers to IoT implementation. The ranking of drivers and barriers provides guidance for practitioners and health care provider leaders intending to implement IoT in the health care supply chain.

Higher education (HE) is a challenging area for quality professionals because of its highly complex operational nature. While neither quality management (QM) in HE nor applications of analytics in QM is a new research discipline, few studies have examined the applications of analytics in the context of QM in HE. This study calls for more attention to bridge the gap between these two established research disciplines by conducting a systematic literature review and an in-depth single-case study. A systematic literature review illustrates the missing link of analytics in quality management in HE in the literature; however, in practice, highly ranked universities worldwide demonstrate various ways in which an HE institution can embed analytics and business intelligence into their QM practices. The first Nordic triple-crowned business school was selected as a single-case study to illustrate how analytics can be a powerful tool for QM in HE. This study found that analytics and business intelligence, enabled by a centralised information system, offer great tools for quality professionals at HE institutions to manage by fact and consequently allow them to make more fact-based, data-based operational, tactical, and strategic decisions. The centralised information system also alleviates the challenges associated with applying for multiple accreditations. As this is a highly contextual phenomenon, this study also calls for future research in other contexts, such as in other disciplines or in other regions where HE has significantly different operational characteristics.