A Comparison of Competencies in the Self-Management and Task Management Domain for Marketing Course Undergraduates

The shift from knowledge-based curriculum to a competence-based curriculum for Marketing course undergraduates is crucial in producing work-ready talents. The study focuses on the comparison of Self-Management and Task Management domain attained by final-year marketing students in 5 different higher learning institutions in Malaysia. A survey questionnaire consisting 25 items was distributed to compare the competencies in the Self-Management and Task Management domains among 289 undergraduates. The data was analysed using one-way ANOVA on SPSS program version 26.0. The results indicated a significant difference among the undergraduates’ competency in Self-Management domain between the different groups of HEIs. However, there was no significant difference in the Task Management domain. The Public university and Distance Learning university displayed a high Self-Management competencies with a mean score of 4.04 and 4.02 respectively. The competencies attainment for Task Management domains were moderate. All the universities in this study recorded a high score for the knowledge and skills competencies in the Self-Management domain. This comparative study indicates the emphasis of knowledge and skills in their Marketing courses compared to other competencies. This study is significant to identify instructional improvement to enhance competency based learning to produce work-ready marketing undergraduates. Keywords: Competency, Higher Education, Marketing, Self-Management, Task-Management

Using AI to Learn Industry Specific Big Data for Business Operation and Crisis Management

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.

Dealing Crisis Management using AI

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.

Dealing Crisis Management using AI

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.

A Blockchain-Based IoT Cross-Domain Delegation Access Control Method

The collaborative demand in the Internet of Things (IoT) is becoming stronger. One of the collaborative challenges is the security of interoperability between different management domains. Although cross-domain access control mechanisms exist in IoT, the majority of them are based on a trusted third party. In addition, the heterogeneity of multidomain policies makes it difficult for authority delegation to satisfy the principle of least authority. In this paper, we propose a blockchain-based IoT cross-domain delegation access control method (CDDAC). The delegation-trajectory-on-blockchain strategy proposed enhances the scalability of the cross-domain delegation system. The presented multidomain delegation trajectory aggregation scheme supports the forensic analysis of the cross-domain delegation system. The performance of CDDAC is evaluated in the Ropsten, which is the Ethereum’s official public blockchain test network. The experimental results show that CDDAC has faster delegation verification speed and higher decision-making efficiency than existing work, demonstrating the lightweight and scalability of the method.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

Intent-driven Closed Loops for Autonomous Networks

Closed loops are key enablers for automation that have been successfully used in many industries for long, and more recently for computing and networking applications. The Zero-touch network and service management (ZSM) framework introduced standardized components that allow the creation, execution, and governance of multiple closed loops, enabling zero-touch management of end-to-end services across different management domains. However, the coordinated and optimal instantiation and operation of multiple closed loops is an open question that is left for implementation by the ZSM specifications. In this paper, we propose a methodology that uses intents as a way of communicating requirements to be considered by autonomous management domains to coordinate hierarchies of closed loops. The intent-driven methodology facilitates hierarchical and peer interactions for delegation and escalation of intents. Furthermore, it extends the existing management capabilities of the ZSM framework and facilitates conflict-free integration of closed loops by setting optimal (and non-conflicting) goals that each closed loop in the hierarchy needs to account for. We show an example of the application of the proposed methodology in a network slicing assurance use case. The new capabilities introduced in this paper can be considered as an extension of the ZSM framework to be used in scenarios where multiple intent-driven closed loops exist.