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Internet of Things (IoT) interconnects many heterogeneous devices to each other, collecting and processing large volumes of data for decision making without human intervention. However, the information security concern it brings has attracted quite a lot of attention, and, at this stage, the smart step would be to analyze the security issues of IoT platform and get to the state of readiness before embarking upon this attractive technology. The purpose of this paper is to address these issues.

IoT risk assessment through the application of the analytical hierarchy process (AHP), a favorite multi-criteria decision making technique, is proposed. The IoT risks are prioritized and ranked at different layers, before which a well-defined IoT risk taxonomy is defined comprising of 25 risks across six layers of the IoT model for developing control and mitigation plans for information security of IoT.

People and processes layer, network layer and applications layer are the top three critical layers with risks like the lack of awareness, malware injection, malicious code injection, denial of service and inefficient policies for IoT practice get the highest priority and rank. Pareto analysis of the overall risk factors revealed that the top ten factors contribute to 80 percent of the risks perceived by information security experts.

The study focuses only on certain predefined constructs or layers of the IoT model traced from legacy studies. It is essential to re-look these constructs on a timely basis to prolong the results’ validity. The study’s empirical scope is confined only to the risk perception of select IoT experts and does not encompass a broader segment of the IoT ecosystem. Therefore, the risks assessment may not be sweeping to a bigger audience.

The study implications are two-fold: one it consolidates the earlier siloed works to intensify the need for risk assessment in the IoT domain, and second the study brings yet another contextual avenue of extending the application AHP and Pareto principle combination. The paper also draws specific critical organizational interventions about IoT risks. A comprehensive approach to prioritizing and ranking IoT risks are present in this research paper.

The contribution of this study to the benchmarking of IoT risk assessment is two-fold. One, a comprehensive risk assessment taxonomy is proposed, and two, the risks are prioritized and ranked to give a convincing reference for the organizations while making information security plans for IoT technology.

Consumers in a Metaverse have an immersive engagement with products and services. This study aims to explore the multidimensional features of the Metaverse that influence consumers’ usage and gratifications, leading to their purchase behavior.

This study uses uses and gratifications theory to examine how hedonic, utilitarian and social gratifications influence Metaverse consumers’ retail behavior. Empirical analysis (N = 291) using structural equation modeling via SmartPLS and PLSpredict validated the model’s robustness and predictive accuracy.

Metaverse retail authenticity and virtuality influenced hedonic gratifications; autonomy and virtuality affected social gratification. Hedonic and social gratifications drove purchases, while utilitarian gratification had no significant effect.

This study applies uses and gratification theory to identify Metaverse-specific motivation factors, contributing to literature and aiding the practical design and development of Metaverse retail environments.

Applying critical analysis as the methodological framework for assessing the literature, the review seeks to present a summary and evaluation of the existing body of knowledge. This approach helps to establish the basis for developing forthcoming recommendations.

The articles were selected through a Systematic Literature Review following the PRISMA guidelines, and utilising Scopus, Web of Science, Science Direct, and the Education Resources Information Center database. Field taxonomy is presented based on the outcomes.

Through a critical review, we offer narrative arguments that document the shortcomings in the existing literature by scrutinising study designs and highlighting suboptimal approaches. Finally, we issue a call to action for future research, envisioning its potential to reorient and reconstruct the field while enhancing the quality of future studies. This proactive stance aims to foster the development of more competent and insightful perspectives, theories, and policy recommendations within design thinking in management education and training.

The research in this field holds significant potential for providing valuable practical and policy insights, contingent upon the rigorous and thorough execution of studies.

This article presents a robust critical review of 57 state-of-the-art articles investigating design thinking in the context of management education and training.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.

Research questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school.

The email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features.

Those faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school.

Faculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration.

Business schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school.

Although past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition.

The primary objective of this study was to aggregate and synthesize existing research findings in the realm of educational financing considering the adoption of educational technology (EdTech).

The study involves the application of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Model, considering the journal articles from the last 10 years (2013 through 2023) obtained from the EBSCO and Emerald Insight databases to perform a systematic literature review.

EdTech has given institutions new sources of income. The demand for online courses, credentials and other digital learning tools is rising, opening up new revenue potential for schools. Take-now-pay-post-placement is a new business model for the upskilling sector that enables learners to enroll in pertinent upskilling courses and pay back once they are placed (at a higher salary).

The development of technology has significantly lowered the overall educational cost, democratized access to education and empowered people to take charge of their education regardless of their location or socioeconomic status. With technology acting as a potent instrument to facilitate learning and close access barriers to education, this trend is expected to continue.

The purpose of this study is to analyze the existing literature on the application of the Internet of Things (IoT) and to give an overview of how libraries are adopting this emerging technology to make users feel more engaging with the library resources and service provision.

The basic architecture of IoT with its functionality has been enumerated and its different layers have been depicted.

A conceptual library IoT model, working on Message Queuing Telemetry Transport protocol, based on the fog-cloud computing layer of architecture has been proposed. This study also highlights the sensor-based current library services.

This study focuses on the existing practices of adoption of library IoT and discusses the possibilities of its future expansion in different areas.

This study intends to find the industries that have leveraged Robotic Process Automation (RPA) technology and elucidate the extent of the adoption of RPA in various industry domains with benefits. The identification of tasks eligible for RPA itself is a challenge. Therefore, the study further brings out the challenges faced in various industry verticals and postulates the future direction of research and applications in RPA.

The study focuses on articles from popular databases such as SCOPUS, Web of Science and Google scholar. PRISMA methodology is used for systematic literature review and 113 papers are shortlisted for study. Three questions are framed to carry out the review and set the research direction.

It is evident from this study that RPA has been widely used in banking and related areas with moderate use in healthcare and manufacturing leading to operational efficiency and productivity. However, there are a lot more opportunities in other domains that need to be taped by leveraging technology advancements and a research agenda has been devised by postulating future directions.

The study brings out a new comprehensive perspective as regards RPA implementation across domains. There is no promising study found that gathers three-dimensional aspects of the meta-themes applications, benefits and challenges. The study summarizes the research agenda and projects the industry domains that have not yet explored, the benefits of RPA. This will be a good reference article for those who develop RPA techniques and organizations that have plans to go for RPA.