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This research aimed to assess the leadership role of principals in the implementation of peace education in selected secondary schools in the Western Cape, South Africa.

This study employed qualitative research approach to assess the leadership role of principals in the implementation of peace education in selected secondary schools in the Western Cape, South Africa. Data were gathered from a small sample of six principals from six selected secondary schools which were engaged in the implementation of a peace education programme, and data were analysed using thematic content analyses.

Findings of the study suggest that principals possess a low level of understanding or awareness of their leadership role in the implementation of peace education. The study pointed out the constraints such as time constraints and learners' negative attitudes and social influences hinder the effective implementation of peace education in selected secondary schools.

First, the data were self-reported and therefore subject to social desirability bias; participants may have provided socially desirable responses rather than their true belief or experiences. Thus, participants may have overstated their role in and commitment to the peace education programme.

Studies that aim to explore alternative approaches to combat violence, such as peace education, are still limited in South Africa. Hence, this paper served to close that gap by contributing to the growing body of research on the leadership role of the principal in the implementation of peace education in the school and exploring barriers hampering its effective implementation.

The purpose of this study was to explore school principals’ job crafting profiles during the prolonged COVID-19 crisis in 2021, and investigate profile differences regarding principals’ own perceived servant leadership, stress and work meaningfulness.

Using latent profile analysis (LPA), two job crafting profiles were identified: (1) active crafters (55%) and (2) average crafters (45%). By auxiliary measurement-error-weighted-method (BCH), we examined whether and how job crafting profiles differed in terms of servant leadership, stress and work meaningfulness.

Active crafters reported higher than the overall mean level of approach-oriented job crafting (increasing job resources and demands), whereas average crafters reported an overall mean level of approach-oriented job crafting. Avoidance-oriented job crafting by decreasing hindering job demands did not differentiate the two profiles. Active crafters reported significantly higher servant leadership behavior, stress and work meaningfulness than average crafters.

Study findings provide new knowledge and reflect the implications that the unprecedented pandemic had for education. This study contributes to the existing literature within the scholarship of job crafting through empirical research during the prolonged COVID-19 pandemic. For practitioners, these study findings reflect contextual constraints, organizational processes and culture, and leadership in workplaces.

This paper comprehensively addresses the influence of chopped strand mat glass fiber-reinforced polymer (GFRP) patch configurations such as geometry, dimensions, position and the number of layers of patches, whether a single or double patch is used and how well debonding the area under the patch improves the strength of the cracked aluminum plates with different crack lengths.

Single-edge cracked aluminum specimens of 150 mm in length and 50 mm in width were tested using the tensile test. The cracked aluminum specimens were then repaired using GFRP patches with various configurations. A three-dimensional (3D) finite element method (FEM) was adopted to simulate the repaired cracked aluminum plates using composite patches to obtain the stress intensity factor (SIF). The numerical modeling and validation of ABAQUS software and the contour integral method for SIF calculations provide a valuable tool for further investigation and design optimization.

The width of the GFRP patches affected the efficiency of the rehabilitated cracked aluminum plate. Increasing patch width WP from 5 mm to 15 mm increases the peak load by 9.7 and 17.5%, respectively, if compared with the specimen without the patch. The efficiency of the GFRP patch in reducing the SIF increased as the number of layers increased, i.e. the maximum load was enhanced by 5%.

This study assessed repairing metallic structures using the chopped strand mat GFRP. Furthermore, it demonstrated the superiority of rectangular patches over semicircular ones, along with the benefit of using double patches for out-of-plane bending prevention and it emphasizes the detrimental effect of defects in the bonding area between the patch and the cracked component. This underlines the importance of proper surface preparation and bonding techniques for successful repair.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

This study aims to examine the role of mindfulness in promoting customers’ purchase intentions, especially with the mediation effects of perceived ease of use and perceived usefulness toward online ride-hailing services. As such, several recommendations for ride-hailing service providers and researchers are suggested for better implementation in a practical and theoretical context.

The data collection process is carried out online during the period of February 2022 to March 2022, with a sample of 237 respondents being analyzed by the covariance-based structural equation modeling approach.

First of all, mindfulness had a direct influence on perceived ease of use and perceived usefulness, as well as an indirect influence on purchase intention through the mediation role of perceived usefulness. Besides, perceived usefulness is found to have a positive influence on the customer’s purchase intention, while perceived ease of use positively affects both perceived usefulness and intention.

This study just ends with customers’ intentions to adopt ride-hailing services; nevertheless, the actual behavior has not been examined. Further research might consider actual usage as a dependent factor when investigating the topic of ride-hailing services.

This study is notably different from the existing literature by filling the literature gap on the role of mindfulness in promoting customers’ purchase intentions toward ride-hailing services.

Este estudio tiene como objetivo examinar el papel del mindfulness en la promoción de las intenciones de compra de los clientes, especialmente con los efectos de mediación de la facilidad de uso percibida y la utilidad percibida hacia los servicios de transporte en línea. De este modo, se sugieren varias recomendaciones para los proveedores de servicios de transporte en coche y los investigadores para una mejor aplicación en un contexto práctico y teórico.

El proceso de recopilación de datos se lleva a cabo en línea durante el período de febrero de 2022 a marzo de 2022, con una muestra de 237 encuestados que se analizan mediante el enfoque CB-SEM.

En primer lugar, mindfulness tuvo una influencia directa sobre la facilidad de uso percibida y la utilidad percibida, así como una influencia indirecta sobre la intención de compra a través del papel de mediación de la utilidad percibida. Además, se observa que la utilidad percibida influye positivamente en la intención de compra del cliente, mientras que la facilidad de uso percibida afecta positivamente tanto a la utilidad percibida como a la intención.

Este estudio se limita a analizar la intención de los clientes de adoptar los servicios de transporte rápido; sin embargo, no se ha examinado el comportamiento real. En futuras investigaciones se podría considerar el uso real como un factor dependiente a la hora de investigar el tema de los servicios de transporte rápido.

Este estudio es notablemente diferente de la literatura existente al llenar el vacío bibliográfico sobre el papel de mindfulness en la promoción de las intenciones de compra de los clientes hacia los servicios de ride-hailing.

本研究旨在探讨 “正念 “在促进顾客购买意向方面的作用, 尤其是在感知易用性和感知有用性对在线叫车服务的中介效应方面。因此, 本研究为叫车服务提供商和研究人员提出了若干建议, 以便在实践和理论背景下更好地实施这些建议。

数据收集过程于 2022 年 2 月至 2022 年 3 月期间在线进行, 采用 CB-SEM 方法对 237 个受访者样本进行分析。

首先, 正念对感知易用性和感知有用性有直接影响, 并通过感知有用性的中介作用对购买意向产生间接影响。此外, 还发现感知有用性对顾客的购买意向有积极影响, 而感知易用性对感知有用性和购买意向都有积极影响。

本研究仅对顾客采用打车服务的意向进行了分析, 但并未对实际行为进行研究。在研究叫车服务时, 进一步的研究可能会将实际使用情况作为一个因果因素。

独创性 本研究填补了关于正念在促进顾客乘车服务购买意向方面作用的文献空白, 与现有文献有明显不同。

In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized.

An empirical analysis is conducted with an illustrative sample of 130 economies over the period 1991–2019 and classified into four subsamples: Organisation for Economic Co-operation and Development (OECD), developing, least developed and net food importing developing countries. Forecast error variance decompositions and panel vector auto-regressive estimations are computed, with insightful findings.

Higher levels of output stimulate the economic development in the agricultural sector, mainly via the productivity channel and, in the most developed economies, also through access to credit. Differently, in developing and least developed economies, the role of access to credit is marginal. The findings have practical implications for stakeholders involved in the planning of long-run investments. In less developed economies, priorities should be given to investments in technology and innovation, whereas financial markets are more suited to boost the development of the agricultural sector of developed economies.

The authors conclude on the credit–output–productivity nexus and contribute to the literature in (at least) three ways. First, they assess how credit access, agricultural output and agricultural productivity are jointly determined. Second, they use a novel approach, which departs from most of the case studies based on single-country data. Third, they conclude on potential causality links to conclude on policy implications.