This paper aims to investigate the factors of competitive success and examine the relationship between Strategic Management Accounting, management accounting information systems and sustainability performance. This research was conducted with a quantitative approach. The survey involved 125 managers of 4- and 5-star hotels in Bali, Indonesia, a research sample of 154 managers was determined to be given a research questionnaire. Analysis of research data was carried out through Structural Equation Modeling. The results showed that strategic management accounting and Information Management Accounting System had a positive effect on sustainability performance. MAS information also mediates the relationship between strategic management accounting and sustainability performance which can involve a variety of factors. This study only focuses on competitive strategy and MAS information variables, so further research is needed on the sustainability performance of the hospitality industry. The positive relationship between strategic management accounting and social and environmental performance supports the argument that hotels use socio-environmental aspects for their strategic management accounting. Hotel managers also need government regulations and information related to social and environmental issues such as the Indonesian occupational health and safety management system and the Indonesia Green Hotel Award.

Bali is the most famous tourist destination in the world, and this popularity has led to a significant rise in the island’s economy. The rise in income has also driven an increase in demand for infrastructure. Moreover, the Bali regional competitiveness index, in the infrastructure pillar, shows a lower figure compared to the national level. So that the Bali Provincial Government focuses on building an infrastructure strategy. This research uses the Input-Output Table (IOT) model, namely the 2016 Bali Province IOT which will be released in 2021. This analysis was chosen because IOT assumes that one sector can be an input for other sectors, in terms of this this is the construction sector. With investment in strategic and monumental infrastructure marking the New Era of Bali, it will result in additional Gross Regional Domestic Product (GRDP) of IDR 18.7 trillion, or in other words Bali’s GRDP will increase by 9.71% from the condition of no investment. This shows that infrastructure development is able to boost Bali’s economy. Further research is needed to be able to qualitatively analyze development infrastructure strategies in Bali. Remembering that a qualitative approach is also important to be able to analyze in depth.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

The connection between the gendered division of housework and intimate partner violence (IPV) is a complex reality and context-dependent. In this article, I explore the perceptions of gender norms among African men and how these perceptions intersect with their experiences of housework and IPV. Employing a qualitative approach, the article examines the viewpoints of 25 African men who have encountered IPV in Johannesburg, South Africa. The findings reveal a spectrum of attitudes towards gender norms among these men, ranging from more traditional patriarchal views to less patriarchal and egalitarian perspectives. The analysis indicates that men who adhere to both more and less patriarchal expressions of gender norms tend to view being forced to perform housework as a form of abuse within the context of controlling behaviour in intimate partner relationships. Conversely, men who lean towards egalitarianism perceive the expectation of women to solely manage housework as a form of abuse. However, many of the men express resistance towards gender equality discourses in South Africa, perceiving them as disruptors of traditional gender roles and enablers of women’s refusal to solely perform domestic housework. These findings deepen our understanding of the complexities and tensions within intimate relationships amidst evolving gender norms in South Africa.

Smallholder paprika farmers in Zimbabwe contribute to local economies and food security but face supply chain challenges like limited market access and poor infrastructure which lead to post harvest losses and unpredictable prices. To survive, these farmers must adopt sustainable value networks to reduce operational costs and improve performance. This study sought to establish the effect of sustainable value networks on the operational performance of smallholder paprika farming in Zimbabwe. This study, using a positivist research philosophy and a quantitative approach, surveyed 288 smallholder paprika farmers in Zimbabwe. Exploratory factor analysis and partial least squares structural equation modelling were used to validate the constructs and test the hypothesised relationships. Results demonstrate a moderate level of implementation of value networks in smallholder paprika farming characterised by successes and challenges. The findings illustrated resource sharing among smallholder farmers, facilitated by initiatives, such as recycled seed exchanges and financial support through village savings and loan associations. However, results show that challenges persist, particularly with market access and financial support. Results indicate that there is a significant awareness and implementation of green supply chain management practices among smallholder paprika farmers even though they do not have access to resources and live in rural areas. The findings demonstrate that value networks significantly influence the adoption of green supply chain management practices, which in turn positively impact operational performance, environmental performance, and social performance. Green supply chain management practices were found to mediate the relationship between value networks and environmental performance, social performance, and operational performance, underlining the critical role of sustainable practices in enhancing performance outcomes. While environmental performance showed a positive effect on operational performance, the direct influence of social performance on operational performance was found to be statistically insignificant, suggesting the need for further exploration of the factors linking social benefits to operational efficiency. The research contributes to both theory and practice by presenting a sustainable value network model for smallholder paprika farmers, integrating value network, green supply chain management practices and environmental performance to enhance operational performance. Practical implications include policy recommendations to strengthen collaboration between smallholder farmers and other stakeholdersand address power imbalances with intermediaries. Future research should extend the study to other agricultural sectors and incorporate more diverse stakeholder perspectives to validate and generalise the proposed sustainable value network model.