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A case study about the creation of four scenarios that were used to make sense of the fast-moving pace of COVID-19 and the consequences for New Zealand tourism.

Adapting global visitor economy scenarios, a set of New Zealand tourism scenarios were constructed using a “back of house Shell” method and were supplemented with an expert panel to test the reliability and validity of the scenarios.

The four scenarios constructed were based on two critical uncertainties, namely economic recession and the moral dilemma of the consumer. Four scenarios were portrayed using film and TV titles to help participants visualise the scenarios. Crazy Rich Asians: Recovery represented many of the attributes of tourism in New Zealand prior to COVID-19 i.e. a focus on high value tourists from Asia. Contagion: Survival of the Fittest represented the reality of the COVID-19 pandemic. This Side of Paradise: ReThinking Tourism focused on rebuilding tourism based upon the principles of sustainability. The Colony: Gated Communities represented fortress destinations trying to keep COVID-19 at bay. Each scenario portrayed several features including a unique narrative, tourism, the tourist, vision, strategy and risks. The paper highlighted the trade-offs and conflicts between the scenarios as COVID-19 unfolded in different directions.

In a fluid situation, the paper reminds readers of the value of scenarios as framing devices to understand the fast-moving pace of COVID-19 when New Zealand was in unchartered waters. Thus, this study highlights how a scenario-planning process builds resilience and foresight to help stakeholders and actors make sense of crisis situations.

This study aims to assess the spread of environmental literacy graduation requirements at public universities in the USA, and to highlight factors that mediate the adoption of this curriculum innovation.

The author analyzed the undergraduate general education curriculum requirements at all 549 public BA-granting higher education institutions in the USA between 2020 and 2022.

The study found that only 27 US public universities out of 540 have an environmental literacy graduation requirement, which represents 5% of universities and is substantially lower than previous estimates.

First, this study provides a more complete, more reliable and more current assessment of the graduation requirement’s presence at US tertiary institutions, and shows the number of universities that have implemented this innovation is lower than was estimated a decade ago. Second, it draws from the scholarship on the infusion of sustainability into the university curriculum to provide a comprehensive discussion of factors that mediate the pursuit and implementation of the graduation requirement. As well, it identifies factors that played a key role in one pertinent case.

The purpose of this paper was to analyse the research trends on the Indian Open and Distance Education (ODE) system as reflected in the articles published in the prominent journals of distance education across the world.

A study was undertaken to review the research articles on ODE in India published in 11 prominent peer-reviewed journals of distance education during the period 2010–2019. Content analysis was done to find out areas of research undertaken in the ODE of India based on a validated classification of research areas; types of research studies conducted; and authorship and publication patterns.

Out of a total of 2,571 articles published in 11 selected journals, only 191 (7.42%) pertained to ODE in India. The majority of these 191 articles (68.42%) were published in Indian journals. The Indian and Asian journals together accounted for 93.55% of total articles. Globally, the share of articles on the Indian ODE system was significantly low ranging between 4.27 and a maximum of 10.77%, which was much below expectations from a country having the maximum number of ODE learners in the world. Results further revealed that 63% of the contributors to research on ODE in India were affiliated to Indira Gandhi National Open University.

The limitation of this study was only analysing the research articles published in journals of distance education and other types of articles, namely, book reviews, editorials, field notes and workshop reports were excluded from the analysis.

The study is intended to help researchers, policymakers, and open and distance education institutions to draw a roadmap for the promotion and conduct of system-based research, which would be vital for strengthening the system.

The quantum of research is not proportionate to the number of faculty members working in the ODE system of India and the large number of learners that it serves, which is a matter of concern. For any system to grow its periodic systemic review is essential. The research outcomes need to be ploughed back into the system for its betterment.

The study is original. There is no such study undertaken till date. This study will be extremely useful to researchers, as the gaps in distance education research which are yet to be addressed, have been identified by the authors.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

In view of organizational inertia, with the occurrence of a major event, though resource rigidity minimizes, however simultaneously, it increases process rigidity, which creates difficulties in motivating managers and dealing with the agency problem. Therefore, keeping in mind the high demand created by the China–Pakistan Economic Corridor and Naya Pakistan Housing Scheme in the cement sector of Pakistan, the purpose of this paper is to investigate the impact of corporate governance (CG) on the cost of equity (COE) in the cement sector, to deal with the problems surging during and after the completion of these projects and highlight further opportunities for the cement sector of Pakistan.

CG is a qualitative concept therefore, eight proxies have been used to measure it along with the two control variables. This study uses balance panel data of six years from 2012 to 2017, collected from 18 companies of the cement sector of Pakistan. Descriptive statistics have been used to describe the data, correlation matrix to see the nature of the relationship, and Pooled OLS as the estimation technique, while to analyze the data a statistical package 13 has been used. To measure the COE, the Capital Asset Pricing Model (CAPM) has been used.

Regression results suggest that block ownership, insider ownership and the board size are insignificant, while CEO tenure is negatively and significantly associated with the COE. Non-executive directors, independence and CEO duality are insignificant; however, diversity is positively and significantly associated with the COE. Moreover, the mean value of the COE is 8.22 percent for the cement sector, while the coefficient of determination of the model under study is 74 percent.

This paper is based on the data from the cement sector of Pakistan only. Therefore, this is the reason that these results cannot be generalized on the whole economy of Pakistan.

This study helps in finding out the COE value specific to the cement sector, which will help this sector to evaluate the capital budgeting decision more precisely and accurately than before. Moreover, the association of diversity as positive, while independence as negative with the COE highlights a room for improvement in the implementation of CG codes by SECP. This study also helps to mitigate the impact of inertia, the after-effects of high demand, and managing the agency problem in the cement sector.

This is the first study using CG data collected just after the revised promulgation of CG codes in 2012, along with a wide range of eight proxies measuring CG and its impact on the COE in the cement sector.