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This study aims to explore auto mechanics awareness of repairs and maintenance related to the car’s cybersecurity and provide insights into challenges based on current practice.

This study is based on an empirical study consisting of semistructured interviews with representatives from both branded and independent auto workshops. The data was analyzed using thematic analysis. A version of the capability maturity model was introduced to the respondents as a self-evaluation of their cybersecurity awareness.

Cybersecurity was not found to be part of the current auto workshop work culture, and that there is a gap between independent workshops and branded workshops. Specifically, in how they function, approach problems and the tools and support available to them to resolve (particularly regarding previously unknown) issues.

Only auto workshop managers in Sweden were interviewed for this study. This role was picked because it is the most likely to have come in contact with cybersecurity-related issues. They may also have discussed the topic with mechanics, manufacturers or other auto workshops – thus providing a broader view of potential issues or challenges.

The challenges identified in this study offers actionable advice to car manufacturers, branded workshops and independent workshops. The goal is to further cooperation, improve knowledge sharing and avoid unnecessary safety or security issues.

As cars become smarter, they also become potential targets for cyberattacks, which in turn poses potential threats to human safety. However, research on auto workshops, which has previously ensured that cars are road safe, has received little research attention with regards to the role cybersecurity can play in repairs and maintenance. Insights from auto workshops can therefore shed light upon the unique challenges and issues tied to the cybersecurity of cars, and how they are kept up-to-date and road safe in the digital era.

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

This study aims to investigate the intricate relationships between a community energy system, water resources and biodiversity conservation, with a specific focus on augmenting community energy resilience in Bondo. The primary objective is to gain an in-depth understanding of how community members perceive and experience the challenges related to balancing the often-conflicting demands of energy, water and biodiversity conservation within this context.

The research uses a qualitative approach to unravel the multifaceted dynamics of community energy systems, water resources and biodiversity conservation in Bondo. Data were collected through focus groups and direct observations, enabling a nuanced exploration of community perspectives and lived experiences. The subsequent analysis of this qualitative data follows established thematic analysis procedures.

The study's findings shed light on the formidable barriers that impede rural communities in Malawi from accessing electricity effectively. Even in communities fortunate enough to have electricity connections, the lack of knowledge regarding productive electricity use results in community energy systems operating at significantly reduced load factors. Furthermore, the intricate challenge of managing a biodiversity hotspot persists, exacerbated by the densely populated peripheral communities' continued reliance on forest, land and water resources. These activities, in turn, contribute to ecosystem degradation.

In a context where government-led management of forest reserves and game reserves has not yielded the expected results due to a multitude of factors, there arises a compelling need for innovative approaches. One such innovation involves fostering partnerships between the government and experienced trusts as lead organisations, providing a fresh perspective on addressing the complex interplay between community energy systems, water resources and biodiversity conservation. This novel approach opens doors to explore alternative pathways for achieving the delicate balance between human energy needs and the preservation of vital ecosystems.

A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on self-monitoring activities and physical health related problems. However, very little is known about the impact of time window models on discovering self-quantified patterns that can yield new self-knowledge insights. This paper aims to discover the self-quantified patterns using multi-time window models.

This paper proposes a multi-time window analytical workflow developed to support the streaming k-means clustering algorithm, based on an online/offline approach that combines both sliding and damped time window models. An intervention experiment with 15 participants is used to gather Fitbit data logs and implement the proposed analytical workflow.

The clustering results reveal the impact of a time window model has on exploring the evolution of micro-clusters and the labelling of macro-clusters to accurately explain regular and irregular individual physical behaviour.

The preliminary results demonstrate the impact they have on finding meaningful patterns.

This paper examines the short- and long-run effects of foreign direct investment (FDI) on tax revenue in Ghana.

The paper adopts the autoregressive distributed lag approach to estimate FDI’s long-run and short-run effects on tax revenue. The study uses time-series data from 1983 to 2019 for Ghana, mainly obtained from The Bank of Ghana, the World Bank and the IMF.

The results show that, in the short-run, FDI has no significant effect on direct tax revenue and total tax revenue but significantly hurts indirect tax revenue. In the long run, however, the results show that FDI has significant positive effects on indirect tax revenue and total tax revenue but no significant effect on direct tax revenue.

Empirical studies often fail to analyse the short-run and long-run effects of FDI on tax revenue. This study contributes to the mixed literature by analysing the short-run and long-run effects of FDI on tax revenue in an emerging market context. Additionally, this study employs three tax revenue measures in analysing the nexus.

The study aims to assess the effectiveness of engaging tutors in designing and using ICT integrated lesson activities in strengthening their pedagogical use of ICT competences.

Survey data from an intervention group of 70 tutors from two teachers colleges (TCs) were used to compare their level of ICT competences and domains of professional practice before and after the intervention. Document analysis, lesson observations and feedback from the learning management system (LMS) were used to describe tutors’ experiences from the intervention.

There was a statistically significant increase in tutors’ level of pedagogical use of ICT competences and domains of professional practice associated with hands-on practice in designing and implementing the intervention.

The intervention focus on hands-on practice, actual teaching and learning needs, and the use of active learning strategies like flipped classroom and the LMS, were useful means for tutors to make sense of pedagogical use of ICT competences.

The results offer useful insights to teacher education institutions and policymakers on how to prepare professional learning and supportive policies to enhance teaching and learning with ICT for addressing the learning needs of the subject matter.

Creating 16 ICT integrated lesson activities helped tutors to learn pedagogical use of ICT competences by doing. Use of such intervention could be a useful strategy in teacher education institutions to reposition ICT competence development from reproducing technological competences toward developing knowledge creators who could innovate their pedagogical practice with support from mentors, digital learning resources and networks.

This study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.

In this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.

The results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.

The findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.

This viewpoint paper aims to explore the past, present and future of travel visas granting permission to travel. Visa restrictions are used by governments as an efficient method of restricting access in advance of travel. This paper explores how this may change in the future resulting in a shift of power from tourist to destination.

The Futures Triangle method was used to create a scenario incorporating the three dimensions of the triangle, i.e. the pulls of the future, the pushes of the present and the weights of the past. An artefact of the future was created to help visualise this future.

This analysis suggests that the role of visas may change in the future such that visa regimes may become part of a destination strategy. A future scenario is postulated in which destinations demand proof of fit with the destination strategy before granting a visa.

This viewpoint paper develops an artefact of the future based on the changing role of travel visas. It suggests that tourists might need to market themselves to the destination, proving that they are a good destination fit, before they are granted a visa to travel.

Most empirical papers on threshold effects between debt and growth focus on developed countries or a mix of developing and developed economies, often using public debt. Evidence for developing economies is inconclusive, as is the analysis of other threshold effects such as those probably caused by the level of relative development or the repayment capacity. The objective of this study was to examine threshold effects for developing economies, including external and total debt, and identify them in the debt-growth relation considering three determinants: debt itself, initial real Gross Domestic Product (GDP) per capita and debt to exports ratio.

We used a panel threshold regression model (PTRM) and a dynamic panel threshold model (DPTM) for a sample of 47 developing countries from 1970 to 2019.

We found (1) no evidence of threshold effects applying total debt as a threshold variable; (2) one critical value for external debt of 42.32% (using PTRM) and 67.11% (using DPTM), above which this factor is detrimental to growth; (3) two turning points for initial GDP as a threshold variable, where total and external debt positively affects growth at a very low initial GDP, it becomes nonsignificant between critical values, and it negatively influences growth above the second threshold; (4) one critical value for external debt to exports using PTRM and DPTM, below which external debt positively affects growth and negatively above it.

The outcome suggests that only poorer economies can leverage credits. The level of the threshold for the debt to exports ratio is higher than that found in previous literature, implying that the external restriction could be less relevant in recent periods. However, the threshold for the external debt-to-GDP ratio is lower compared to previous evidence.

This study aims to examine the relationship between medication for opioid use disorder (MOUD) among pregnant individuals, referral source, mental health, political affiliation and substance use policies in all 50 states in the USA.

This study describes MOUD receipt among pregnant people with an opioid use disorder (OUD) in 2018. The authors explored sociodemographic differences in MOUD receipt, referrals and co-occurring mental health disorders. The authors included a comparison of MOUD receipt among states that have varying substance use policies and examined the impact of these policies and the political affiliation on MOUD. The authors used multilevel binary logistic regression to examine effects of individual and state-level characteristics on MOUD.

Among 8,790 pregnant admissions with OUD, the majority who received MOUD occurred in the Northeast region (71.52%), and 14.99% were referred by the criminal justice system (n = 1,318). Of those who were self-referred, 66.39% received MOUD, while only 30.8% of referrals from the criminal justice system received MOUD. Those referred from the criminal justice system or who had a co-occurring mental health disorder were least likely to receive MOUD. The multilevel model showed that while policies were not a significant predictor, a state’s political affiliation was a significant predictor of MOUD.

The study has some methodological limitations; a state-level analysis, even when considering the individual factors, may not provide sufficient description of community-level or other social factors that may influence MOUD receipt. This study adds to the growing literature on the ineffectiveness of prenatal substance use policies designed specifically to increase the use of MOUD. If such policies are consistently assessed as not contributing to substantial increase in MOUD among pregnant women over time, it is imperative to investigate potential mechanisms in these policies that may not facilitate MOUD access the way they are intended to.

Findings from this study aid in understanding the impact that a political affiliation may have on treatment access; states that leaned more Democratic were more likely to have higher rates of MOUD, and this finding can lead to research that focuses on how and why this contributes to greater treatment utilization. This study provides estimates of underutilization at a state level and the mechanisms that act as barriers, which is a stronger assessment of how state-specific policies and practices are performing in addressing prenatal substance use and a necessary step in implementing changes that can improve the links between pregnant women and MOUD.

To the best of the authors’ knowledge, this is the first study to explore individual-level factors that include mental health and referral sources to treatment that lead to MOUD use in the context of state-level policy and political environments. Most studies estimate national-level rates of treatment use only, which can be useful, but what is necessary is to understand what mechanisms are at work that vary by state. This study also found that while substance use policies were designed to increase MOUD for pregnant women, this was not as prominent a predictor as other factors, like mental health, being referred from the criminal justice system, and living in a state with more Democratic-leaning affiliations.