Fourth Industrial Revolution between Knowledge Management and Digital Humanities

Leading higher education into the fourth industrial revolution: an empirical investigation

The Fourth Industrial Revolution (4IR) rapidly advanced at the beginning of the 21st century. Leaders within organisations need to adjust their visions, plans, organisational structures, and management with regard to the demands, challenges and opportunities of this development. This is in particular the case in higher educational institutions (HEIs), which have to adjust to the rapid changes and new demands of skills of university graduates. Leaders in HEIs must therefore be aware of the related challenges and opportunities and might have to adjust the learning and teaching environment, the skills development of students, graduates, and faculty, as well as the technological requirements to create advanced skill sets. This article is based on a qualitative research study which was conducted at a university in South Africa. In-depth, semi-structured interviews were used to explore the views of higher-education leaders at the selected university. Data were analysed through thematic analysis. It was found that leaders in HEIs need to be aware of their leadership and preferences in times of transition toward a more technologised learning environment, as well as the needs, demands, challenges and opportunities of the new workplaces, and new skill sets needed in the 4IR. The researchers made some recommendations. "Education is the key that unlocks the golden door to freedom." George Washington Carver.

Smart Machine Health Prediction Based on Machine Learning in Industry Environment

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine’s disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine’s health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, SVM, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine’s health.

Towards an Accessible Platform for Multimodal Extended Reality Smart Environments

This article presents the DEMOS prototype platform for creating and exploring multimodal extended-reality smart environments. Modular distributed event-driven applications are created with the help of visual codeless design tools for configuring and linking processing nodes in an oriented dataflow graph. We tested the conceptual logical templates by building two applications that tackle driver arousal state for safety and enhanced museum experiences for cultural purposes, and later by evaluating programmer and nonprogrammer students’ ability to use the design logic. The applications involve formula-based and decision-based processing of data coming from smart sensors, web services, and libraries. Interaction patterns within the distributed event-driven applications use elements of mixed reality and the Internet of Things, creating an intelligent environment based on near-field communication-triggering points. We discuss the platform as a solution to bridging the digital divide, analyzing novel technologies that support the development of a sustainable digital ecosystem.

Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type

The development of online knowledge payment platforms in recent years has increased their respective market value by nurturing content resources and improving content ecology. Yet, the underlying factors of knowledge seekers’ payment behaviors and their information adoption mechanisms are poorly understood. Based on the information adoption model, this study develops a research model to examine the relationship between information adoption patterns and knowledge seekers’ payment behavior, and explore the moderating effect of product type on this relationship. To test the research model and hypotheses, we used a multi-analytic approach combining text and regression analysis on a sample of 4366 social Q&A data collected from Quora+ between August 2021 and August 2022. We further classified the product types into utilitarian and hedonic, and compared the differences in influence paths between product types. The results show that the completeness, vividness, and relevance of the product description have a significant positive impact on knowledge payment behavior. The reputation, experience, and integrity of the knowledge provider have a positive impact on knowledge payment behavior. Compared to utilitarian knowledge products, the payment behavior for hedonic products is more related to the reputation and experience of the knowledge provider. This study provides insights into the factors that influence online knowledge payment behavior and practical guidance for the development of online knowledge payment services and platforms.