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This paper describes an exploratory, international research collaboration that seeks to gain a deeper understanding of the development and experiences of peer leaders in higher education across different international contexts, namely the USA, Canada (CAN), Australasia (Australia and New Zealand) (ANZ), the United Kingdom (UK) and South Africa (SA).

Data are summarized and compared across each of the participating countries, providing a more global context and depth of perspective on peer leadership (PL) in higher education than is currently available in the literature.

The findings highlight some apparent differences between countries in relation to student engagement in peer leader roles and the ways in which PL is supported by higher education institutions, as well as some similarities across the different international contexts, particularly in the way peer leaders view the benefits of their involvement in PL.

These insights provide a valuable addition to the literature on PL and practical information to higher education institutions for supporting student leadership development and involvement.

This paper aims to advance recent literature on the performativity of accounting by examining how project accounting affects a project organization’s ability to deliver a relevant project outcome, such as a product or a building, for a receiving client organization.

The paper draws on a longitudinal case study of a 41.4-billion-kroner (5.5-billion-euro) Danish project of constructing 16 new public hospitals. Its objective was to reduce the average unit costs and improve the quality of patient care. Each hospital construction was managed by a separate project organization and handed over to a separate receiving hospital organization. The project organizations applied a common approach to project accounting. The paper relies on Michel Callon’s concepts of performativity and sociotechnical agencement – approaching project accounting as an arrangement of devices.

The paper shows that the project-accounting agencement simultaneously supported and undermined the project organizations’ ability to deliver hospitals relevant to the receiving hospital organizations. The agencement performed hospital designs, disciplined project actors and guided decision-making, thus supporting the overall work of the project organizations. It also, however, compelled the project organizations to compromise on hospital designs when unexpected events occurred. These compromises led to the delivery of hospitals, which largely prevented the receiving hospital organizations from achieving the project’s objective.

This paper advances our limited understanding of the dynamic and complex relationship between project accounting and the relevance of project outcomes. It introduces the concept of a “contronymity device” to capture the way project accounting simultaneously produces two opposing consequences, both supporting and undermining the enactment of a particular reality. The paper lastly enriches our understanding of how project-accounting devices impact hospital organizations’ operating cost structures and challenge patient care capabilities.

This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.

The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.

The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R², yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.

This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.