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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.

During the COVID-19 pandemic, public sector organisations produced thousands of social media messages weekly answering citizens questions and informing the public on safety related matters. The purpose of this study was to investigate how the pandemic shaped social media listening in Finland's public sector organisations and how these organisations aligned their listening and strategic communication to address emerging questions, news (real and fake) and rumours during the pandemic.

Building on a theoretical background from strategic communication, organisational listening, digital marketing and public sector communication, qualitative interview data included communicators (N = 14) from all central Finnish public sector organisations in charge of COVID-19 communication. Findings were themed and analysed qualitatively to understand the level of alignment of strategic communication on social media.

The findings revealed that the pandemic had strained public sector organisations' communication capabilities, forcing them to align their processes and resources reactively to enable useful content and limit false/misleading content. The results confirmed that organisational listening remained somewhat unaligned. A dual role of public sector communication as speakers but increasingly as listeners was highlighted.

The study’s findings point to organisational listening on social media being a central requisite for public sector organisations overcoming a crisis.

The huge demand for fresh goods has stimulated lots of research on the perishable food supply chain. The characteristics of perishable food and the cross-regional transportation have brought many challenges to the operation models of perishable food supply chain. The purpose of this paper is to address these challenges based on the real-time data acquired by the Internet of Things (IoT) devices.

IoT and the modeling of the Supply Hub in Industrial Parks were adopted in the perishable food supply chain.

A conceptual model was established for the IoT-enabled perishable food supply chain with two-echelon supply hubs. The performance of supply chain has improved when implementing the proposed model, as is demonstrated by a case study.

By our model, the supply hubs which act as the dominators of the supply chain can respond to the real-time information captured from the operation processes of an IoT-enabled supply chain, thus to provide public warehousing and logistic services.

The association between social media and jealousy is an aspect of the dark side of social media that has garnered significant attention in the past decade. However, the understanding of this association is fragmented and needs to be assimilated to provide scholars with an overview of the current boundaries of knowledge in this area. This systematic literature review (SLR) aims to fulfill this need.

The authors undertake an SLR to assimilate the current knowledge regarding the association between social media and jealousy, and they examine the phenomenon of social media-induced jealousy (SoMJ). Forty-five empirical studies are curated and analyzed using stringent protocols to elucidate the existing research profile and thematic research areas.

The research themes emerging from the SLR are (1) the need for a theoretical and methodological grounding of the concept, (2) the sociodemographic differences in SoMJ experiences, (3) the antecedents of SoMJ (individual, partner, rival and platform affordances) and (4) the positive and negative consequences of SoMJ. Conceptual and methodological improvements are needed to undertake a temporal and cross-cultural investigation of factors that may affect SoMJ and acceptable thresholds for social media behavior across different user cohorts. This study also identifies the need to expand current research boundaries by developing new methodologies and focusing on under-investigated variables.

The study may assist in the development of practical measures to raise awareness about the adverse consequences of SoMJ, such as intimate partner violence and cyberstalking.

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.

The article investigates different types of strategies for managing user-generated content (UGC) and provides some insights into their implications.

A unique sample of Italian hotels with current and prospective customers in the digital environment is investigated. A taxonomy of user-provider interactions mediated by UGC is developed. A mixed approach was designed to meet the study aims. Firstly, an exploratory factor analysis was performed in order to illuminate different strategies of UGC and electronic word-of-mouth (E-WOM) management. Secondly, a cluster analysis was implemented in order to explain hoteliers' behavior toward users' contents.

The study results suggested the existence of three clusters, which reflected three different types of interactions between hotels and customers in the digital domain. Interestingly, most of Italian hotels were found to adopt a reductionist approach to UGC and E-WOM management, turning out to be ineffective to exploit them for the purpose of quality improvement and hospitality service excellence.

Hotels were found to be largely unaware of the importance of UGC and web-based communication with customers to improve their digital business strategy. Tailored management approaches are needed to realize the full potential of hotels' online content responsiveness for the purpose of value co-creation and service co-production.

This is one of the first studies investigating the strategic and management perspectives embraced by hotels to handle their interactions with customers in the digital arena.

The nature and extent of adults' engagement in diverse manifestations of technology-facilitated aggression is not yet well understood. Most research has focused on victimization. When explored, engagement in online aggression and abuse has centered on children and young people, particularly in school and higher education settings. Drawing on nationally representative data from New Zealand adults aged 18 and over, this chapter explores the overall prevalence of online aggression with a focus on gender and age. Our findings support the need to also understand adult aggressors' behaviors to better address the distress and harm caused to targets through digital communications. The chapter discusses the implications of the results for policy and practice and proposes some directions for future research.

User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations.

The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data.

After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation.

For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN.

This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.

The paper documents the roles played by the institutional quality assurance cells (IQACs) to implement the University Grants Commission (UGC) directive to shift to online education during the COVID-19 pandemic period in Bangladesh. It highlights the government's efforts to ensure quality education, particularly in utilizing IQACs in the various public and private universities. The paper starts with the pandemic situation in the country, followed by the overall responses of the government, including its directives regarding the conduct of higher education.

The paper utilizes as framework the Sloan Consortium's Five Pillars of Quality Online Learning – learning effectiveness, student satisfaction, teacher satisfaction, scale and access. The paper then analyzes the oral reports of 26 IQACs, which were presented to government officials in two meetings.

From the analysis, it is apparent that IQACs have contributed toward the shift to full online education during the pandemic period. Universities responded to the crisis mainly in the areas of governance, teaching and learning and student support services. To guide policy formulation, the IQACs (with the help of other university units) conducted surveys among their faculty and students regarding their access to Internet, financial difficulties and mental health situation. From here, they drafted academic policies (attendance, student assessment and online teaching), conducted capacity building activities, monitored faculty performance, formulated guidelines on student online behavior and encouraged stakeholders to trust the online system.

The study is original since (like many countries) the pandemic has forced education activities to go online. It looks at both the macro level (the concerns of the University Grants Commission or UGC concerning quality education in an online setting) and the micro level (what universities have undertaken to address the concerns of the UGC).

The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.