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The purpose of this study is to identify global trends in disruptive technological change and map the social and policy implications, particularly as they relate to the educational ecosystem and main stakeholders across all levels of education.

The authors conducted a two-stage meta-analysis of 1,155 scholarly, peer-reviewed articles. The investigation involves a systematized literature review for data identification and collation adhering to defined selection criteria, and a network analysis to scrutinize data, consolidate information and unveil correlations and patterns from the literature review to produce a set of recommendations.

The study unveiled educational trends related to disruptive technologies and delineated four principal clusters representing how these technologies are transforming the education ecosystem. Additionally, a series of transversal aspects that reveal a societal vulnerability toward future prospects in the realms of ethics, sustainability, resilience, security, and policy were identified.

The findings spotlight an enlarging chasm between industry (and society at large) and conventional education, where many transformations triggered by disruptive technologies remain absent from teaching and learning systems. The study further offers recommendations and envisions potential scenarios, urging stakeholders to respond based on their positions concerning disruptive technologies.

Expanding from the meta-analysis of pertinent literature, this paper offers four collections of curated resources, four mini case studies and four scenarios for policymakers and local communities to consider, enabling them to plot courses for their optimal futures.

Online resources can be helpful for students and can augment the content presented in learning environments. A team consisting of four biologists, a graduate student, instructional designer and media developers collaborated on the design, development and evaluation of first-year biology online tutorials in a Canadian University. The tutorials were designed to address knowledge gaps resulting in low success rates and attrition of first-year students in biology. The decrease in the number of students in STEM has alarmed educators, prompting a call for efforts to increase STEM majors in universities. Large class sizes, such as first year biology with ∼900 registrants annually, with detail-oriented, content-heavy loads, can result in low success rates and attrition.

Active learning methods, including online formative assessments, which encourage student engagement in course material, can be effective in large introductory science classes, and thus, the authors provided engagement with tutorial online resources. The authors identified the tutorial topics by analyzing previous years' tests, student feedback and pedagogical research in undergraduate biology. The top five topics identified as common misconceptions or troublesome concepts within the course were selected. Standard instructional design processes were used to produce high-quality online tutorials. Tutorials included learning materials, videos, animations, self-assessments, reflective questions and badges to facilitate deep learning of the topics. Effectiveness of the tutorials was evaluated using quantitative methods and quasi-experimental design to compare the student learning results between the control year (without tutorials) and the year when tutorials were offered. Pre- and posttests measuring conceptual understanding were administered to assess gains in student learning. Additionally, student engagement was measured using the Classroom Survey of Student Engagement (CLASSE), and data from learning management system was collected.

Results of the study show that the tutorials were an effective means of providing supplementary assistance to students as well as fostering a gain in students' levels of engagement with the course. Data analysis indicates that there was a significant increased gain in learning of core concepts in biology. Specifically, using formative online assessments resulted in measurable learning gains in students who participated voluntarily, in comparison to students who chose not to engage in self-paced quiz testing.

As seen from the description earlier, the tutorials, and this project, provide suitable university-level complexity to address specific learning gaps in the first year course. They provide a valuable service to students in terms of representing content in an alternate format and motivating students as they engaged with videos and self-assessment most frequently. The project adds to the teaching and learning environment with respect to program design, mode of delivery and scheduling by providing self-paced tutorials that focus on specific concepts in biology. Students may review these resources whenever and as often as they feel necessary to better master the concepts. This makes the content applicable for the various preferences for approaches to learning and accommodation requirements found in students. Importantly, using formative online assessments resulted in measurable learning gains in students who participated voluntarily, in comparison to students who chose not to engage in self-paced quiz testing.

This paper aims to provide researchers and practitioners with a consumer-focused alternative for considering the integration of artificial intelligence (AI) into services.

The paper reviews and critiques the most popular frameworks for addressing AI in service. It offers an alternative approach, one grounded in social psychology and leveraging influential concepts from management and human–computer interaction.

The frameworks that dominate discourse on this topic (e.g. Huang and Rust, 2018) are fixated on assessing technology-determined feasibility rather than consumer-granted permissibility (CGP). Proposed is an alternative framework consisting of three barriers to CGP (experiential, motivational and definitional) and three responses (communicate, motivate and recreate).

The implication of this research is that consistent with most modern marketing thought, researchers and practitioners should approach service design from the perspective of customer experience, and that the exercise of classifying service occupation tasks in terms of questionably conceived AI intelligences should be avoided.

Indicative of originality, this paper offers an approach to considering AI in services that is nearly the polar opposite of that widely advocated by e.g., Huang et al., (2019); Huang and Rust (2018, 2021a, 2021b, 2022b). Indicative of value is that their highly cited paradigm is optimized for predicting the rate at which AI will take over service tasks/occupations, a niche topic compared to the mainstream challenge of integrating AI into service offerings.

This study focuses on (re-)introducing computer simulation as a part of the research paradigm. Simulation is a widely applied research method in supply chain and operations management. However, leading journals, such as the International Journal of Operations and Production Management, have often been reluctant to accept simulation studies. This study provides guidelines on how to conduct simulation research that advances theory, is relevant, and matters.

This study pooled the viewpoints of the editorial team of the International Journal of Operations and Production Management and authors of simulation studies. The authors debated their views and outlined why simulation is important and what a compelling simulation should look like.

There is an increasing importance of considering uncertainty, an increasing interest in dynamic phenomena, such as the transient response(s) to disruptions, and an increasing need to consider complementary outcomes, such as sustainability, which many researchers believe can be tackled by big data and modern analytical tools. But building, elaborating, and testing theory by purposeful experimentation is the strength of computer simulation. The authors therefore argue that simulation should play an important role in supply chain and operations management research, but for this, it also has to evolve away from simply generating and analyzing data. Four types of simulation research with much promise are outlined: empirical grounded simulation, simulation that establishes causality, simulation that supplements machine learning, artificial intelligence and analytics and simulation for sensitive environments.

This study identifies reasons why simulation is important for understanding and responding to today's business and societal challenges, it provides some guidance on how to design good simulation studies in this context and it links simulation to empirical research and theory going beyond multimethod studies.

Disinformation on social media is a serious issue. This study examines the effects of disinformation on COVID-19 vaccination decision-making to understand how social media users make healthcare decisions when disinformation is presented in their social media feeds. It examines trust in post owners as a moderator on the relationship between information types (i.e. disinformation and factual information) and vaccination decision-making.

This study conducts a scenario-based web survey experiment to collect extensive survey data from social media users.

This study reveals that information types differently affect social media users' COVID-19 vaccination decision-making and finds a moderating effect of trust in post owners on the relationship between information types and vaccination decision-making. For those who have a high degree of trust in post owners, the effect of information types on vaccination decision-making becomes large. In contrast, information types do not affect the decision-making of those who have a very low degree of trust in post owners. Besides, identification and compliance are found to affect trust in post owners.

This study contributes to the literature on online disinformation and individual healthcare decision-making by demonstrating the effect of disinformation on vaccination decision-making and providing empirical evidence on how trust in post owners impacts the effects of information types on vaccination decision-making. This study focuses on trust in post owners, unlike prior studies that focus on trust in information or social media platforms.

Supply chains must rebuild for resilience to respond to challenges posed by systemwide disruptions. Unlike past disruptions that were narrow in impact and short-term in duration, the Covid pandemic presented a systemic disruption and revealed shortcomings in responses. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management.

The study is based on extensive debates among the authors and their peers. The authors focus on three areas deemed fundamental to supply chain resilience: (1) forecasting, the starting point of supply chain planning, (2) the practices of supply chain risk management and (3) product design, the starting point of supply chain design. The authors’ debated and pooled their viewpoints to outline key changes to these areas in response to systemwide disruptions, supported by a narrative literature review of the evolving research, to identify research opportunities.

All three areas have evolved in response to the changed perspective on supply chain risk instigated by the pandemic and resulting in systemwide disruptions. Forecasting, or prediction generally, is evolving from statistical and time-series methods to human-augmented forecasting supplemented with visual analytics. Risk management has transitioned from enterprise to supply chain risk management to tackling systemic risk. Finally, product design principles have evolved from design-for-manufacturability to design-for-adaptability. All three approaches must work together.

The authors outline the evolution in research directions for forecasting, risk management and product design and present innovative research opportunities for building supply chain resilience against systemwide disruptions.

There is a scarcity of research studies on why people remain inactive when encountering and recognising misinformation online. The main aim of this paper is to provide a groundwork for future research into why users do not challenge misinformation on digital platforms by generating hypotheses through a synthesis of pertinent literature, including organisational behaviour, communication, human-computer interaction (HCI), psychology and education.

Given the lack of directly related literature, this paper synthesised findings from relevant fields where the findings might be relevant, as the tendency to withhold opinions or feedback is a well-documented practice in offline interaction.

Following the analysis of relevant literature, the potential reasons for online silence towards misinformation can be divided into six categories: self-oriented, relationship-oriented, others-oriented, content-oriented, individual characteristics and technical factors.

Although corrections coming from peers can effectively combat misinformation, several studies showed that people in cyberspace do not take such action. To the best of the authors’ knowledge, there has been scarce and virtually non-existent research investigating why people refrain from challenging others who post misinformation online. Thus, this paper attempts to address this gap and identify reasons in adjacent domains. The reasons provide a starting point for researching interventions to reduce reluctance and abstinence regarding the challenge of misinformation. The findings can be beneficial beyond the area of challenging misinformation and are extensible to other types of content and communication that people are hesitant to discuss and challenge, such as online injustice, prejudice and hate speech.