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To improve the academic integrity of online examinations, digital proctoring systems have recently been implemented in higher education institutions (HEIs). The paper aims to understand how digital proctoring has been practised in higher education (HE) and proposes future research directions for studying digital proctoring in HE.

A systematic literature review was conducted. The PRISMA procedure was adapted for the literature search. The topics were identified by topic modelling techniques from 154 relevant publications in seven databases.

Seven widely discussed topics in literature were identified, including solutions for detecting cheating and student authentication, challenges/issues of uptakes and students' performance in different proctoring environments.

This paper provides insights for academics, policymakers, practitioners and students to understand the implementation of digital proctoring in academia, its adoption by HEIs, impacts on students' and educators' performance and the rapid increase in its use for digital exams in HEIs, with particular emphasis on the impacts of the systems on digitalising examinations in HE.

This review paper has systematically and critically described the state-of-the-art literature on digital proctoring in HE and provides useful insights and implications for future research on digital proctoring, and how academic integrity in online examinations can be enhanced, along with digitalising HE.

Fake news stories have become a central element of crises that corporate public relations practitioners have confronted. Whether such stories are rumors, outright lies or deliberate attempts to discredit corporations, they have the same impact and require specific strategies for public relations practitioners to effectively respond. The purpose of this study is to uncover strategies to manage crises that arise from fake news and if and how these strategies differ for other corporate crises.

In this multi-method study of 21 in-depth interviews and a 8-person focus group with senior-level corporate public relations practitioners, authors explored decision-making strategies for responding to fake news crises. Transcripts of interviews and the focus group were thematically analyzed.

Results reveal insights regarding how public relations practitioners determine if and when to respond to fake news crises in corporations; what response strategies public relations practitioners have the autonomy to employ for fake news crises in corporations, and how public relations practitioners control media narratives during fake news crises in corporations.

The findings guide public relations practitioners to craft an autonomous decision-making process and effective online listening strategies—establishing a watchful waiting approach—and determine if the fake news issue is a passing moment or movement swirling into a crisis.

Few studies have examined the perspectives of crisis communication experts about minimizing and managing fake news crises. The study identifies opportunities for future research focused on crises originating from fake news and disinformation.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Effective safety supervision plays a crucial role in ensuring safe production within coal mines. Conventional coal mine safety supervision (CMSS) in China has suffered from the problems of power-seeking, excessive resource consumption and poor timeliness. This paper aims to explore the Internet+ CMSS mode being emerged in China.

The evolution of CMSS systems underwent comprehensive scrutiny through a blend of qualitative and quantitative approaches. First, evolutionary game theory was used to analyze the necessity of incorporating Internet+ technology. Second, a system dynamics model of Internet+ CMSS was crafted, encompassing a system flow diagram and equations for various variables. The model was subsequently simulated by taking the W coal mine in Shanxi Province as a representative case study.

It was revealed that the expected safety profit from the Internet+ mode is 296.03% more than that from the conventional mode. The precise dissemination of law enforcement information was identified as a pivotal approach through which the Internet+ platform served as a conduit to foster synergistic collaboration among diverse elements within the system.

The outcomes of this study not only raise awareness about the potential of Internet+ technology in safety supervision but also establish a vital theoretical foundation for enhancing the efficacy of the Internet+ CMSS mode. The significance of these findings extends to fostering the wholesome and sustainable progress of the coal mining industry.

This research stands out as one of the limited studies that delve into the influence of Internet+ technology on CMSS. Building upon the pivotal approach identified, to the best of authors’ knowledge, a novel “multi-blind” working mechanism for Internet+ CMSS is introduced for the first time.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

The COVID-19 pandemic has impacted the construction industry, yet still, it is unclear from existing studies about the critical challenges imposed on quality assurance (QA), particularly Cross-border Construction Logistics and Supply Chain (Cb-CLSC). Thus, this study aims to identify and examine the critical challenges of QA of Cb-CLSC during the COVID-19 pandemic.

The aim is achieved via an embedded mixed-method approach pragmatically involving a desk literature review and engaging 150 experts across the globe using expert surveys, and results confirmed by semi-structured interviews. The approach is based on Interpretive Structural Modelling (ISM) as its foundation.

The study revealed ten critical challenges of QA, with the top four including “the shortage of raw construction material (C7)”, “design changes (C6)”, “collaboration and communication difficulties (C1)” and “changes in work practices (C10)”. However, examining the interrelationships among the critical challenges using ISM confirmed C7 and C10 as the most critical challenges. The study again revealed that the critical challenges are sensitive and capable of affecting themselves due to the nature of their interrelationship based on MICMAC analysis. Hence, being consistent with why all the challenges were considered critical amid the pandemic. Sentiment analysis revealed that the critical challenges have not been entirely negative but also positive by creating three areas of opportunities for improvement: technology adoption, worker management, and work process management. However, four areas of challenges in the QA include cost, raw material, time, and work process, including inspection, testing, auditing, communication, etc.

The finding provides a convenient point of reference to researchers, policymakers, practitioners, and decision-makers on formulating policies to enhance the effectiveness of construction QA during the pandemic through to the post-pandemic era.

The study enriches the extant literature on QA, Cb-CLSC, and the COVID-19 pandemic in the construction industry by identifying the critical challenges and examining the interrelationships among them. This provides a better understanding of how the construction QA has been affected by the pandemic and the opportunities created.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

The purpose of this scoping rapid review was to identify and analyse existing qualitative methodologies that have been used to investigate K-12 teachers' lived experiences of adult cyber abuse as a result of student content “going viral” to propose a novel methodological stance incorporating the Australian Online Safety Act 2021.

A search of Google Scholar was conducted using keywords and phrases related to cyber trauma, teachers, qualitative methods and the Online Safety Act. Inclusion criteria for the review were: (1) published in English, (2) focused on teachers' experiences of online abuse and cyberbullying associated with viral posts and (3) employed a qualitative inquiry methodology. Full-text articles were obtained for those that met the inclusion criteria. Data were extracted and analysed using a PRISMA flowchart and inductive thematic analysis.

This methodology is considered to be justified, as the eSafety Commissioner's Safety-by-Design principles do not have any legal or regulatory enforceability, whereas the Online Safety Act 2021 provides the Australian eSafety Commissioner an avenue to drive greater algorithmic transparency and accountability.

The findings of this review informed the development of a novel methodological stance for investigating Australian teachers' lived experiences of adult cyber abuse associated with viral posts. It provides a methodological positioning to support trauma informed qualitative research into adult cyber abuse, informed by the work of the eSafety Commissioner and the Online Safety Act.

Cybertrauma is described as “any trauma that is a result of self- or, other-directed interaction with, mediated through, or from any electronic Internet/cyberspace ready device or machine learning algorithm, that results in impact now or the future” (Knibbs, 2021). It may result from the tracking of movement through various mobile phone features and applications such as location sharing, non-consensual monitoring of social media, and humiliation or punishment through the sharing of intimate images online, through to direct messages of abuse or threats of violence or humiliation. These actions are further perpetuated through automated searches, insights and recommendations on social media (i.e. engagement metrics promote memes, Facebook posts, Tweets, Tiktoks, Youtubes and so on). This is a novel methodology, as it not only considers direct cybertrauma but also automated forms of cybertrauma.

Faced with the existential threat of COVID-19, the College of Business Administration (CBA) at Princess Nourah Bint Abdulrahman University (PNU) decided to work toward the dual goals to achieve accreditation and an effective social and educational response to the pandemic. CBA’s quality improvement process was implemented synergistically with Seligman’s (2011) PERMA well-being model, namely positive emotions, engagement, relationship, meaning, and accomplishment/achievement. The case study methodology, which was based on direct observations, faculty and students’ interviews, surveys, feedback, documents, and archival data records, allowed to capture the context and lived reality of all the participants in QAA processes. Results showed that quality improvement was facilitated through three domains: (1) governance and academic leadership, (2) teaching and learning, and (3) digitalization and technology. Within each domain, specific programs, activities, human resources, and networks were enabled through a framework based on the PERMA well-being model. As a result of this hybrid QAA well-being implementation process, CBA achieved NCAAA accreditation for its offered programs, while experiencing high levels of well-being and commitment to teaching and learning by the staff members and students. The description of CBA’s accreditation journey might be insightful for other higher education institutions (HEIs).