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.

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.

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.

The augmentation of firm performance via customer concentration is particularly indispensable for organizational evolution. Both trade credit financing and financing constraints play pivotal roles in the nexus between customer concentration and performance. This research constructs a moderated mediation model to rigorously investigate the impact of customer concentration on firm performance, positing trade credit financing as the mediating variable and financing constraints as the moderating variable. The relevant hypotheses are evaluated empirically using panel data compiled from listed manufacturing firms in China over the period 2013–2020, yielding 8 firm-year observations. The empirical outcomes denote that customer concentration exerts a positive influence on firm performance, albeit having a negative impact on trade credit financing. Trade credit financing serves as a partial mediator in the relationship between customer concentration and manufacturing firm performance. Financing constraints are found to positively moderate the mediating role of trade credit financing in the relationship between customer concentration and firm performance. This research broadens the understanding of the implications of customer relationships on trade credit financing and performance, thereby enriching the knowledge base for managing a firm’s financing channels more effectively.

This study addresses the present limited understanding of the complex relationship between ethical leadership, job stress, and employee job performance in the hotel business. This study shows that job stress moderates the association between ethical leadership and employee job performance, underlining the necessity for more research in the industry. The present study fills a crucial research void in our understanding of the complex interaction between these factors. The study utilizes a sample of 292 employees in the accommodation and hotel industry. Prior to commencing data collection, the questionnaire underwent thorough validation and reliability testing to ensure that the instrument met all specified criteria and demonstrated robustness. Using hierarchical regression analysis, the study reveals substantial findings. It has been discovered that ethical leadership has a direct and positive effect on employee job performance. Notably, job stress emerges as a significant moderating variable that affects the relationship between ethical leadership and employee job performance. This highlights the crucial role that job stress plays in determining outcomes. The research indicates that reducing workplace stress and fostering ethical leadership can result in improved employee job performance. In addition, the study highlights the importance of social learning theory in enhancing employee job performance, with job stress and ethical leadership serving as significant moderating factors.

The main goal of this study is to assess the moderating role of digital leadership capabilities (DLC) in improving the overall performance of telecom companies through their organisational knowledge capabilities. The author builds a conceptual model with six hypotheses and tests them with data collected through an electronic questionnaire. The data is analysed using WarpPLS 8.0 software as an application of the structural equation modelling technique. The sample size included 528 participants. The study revealed that individual knowledge capability (IKC) does not significantly affect organisational performance (PR). Also, the results reveal that managerial knowledge capability (MKC) and organisational collaborative capability (OCC) have a positive but weak impact on the performance of telecom companies (PR). However, it was clear that individual knowledge capability (IKC) and organisational collaborative capability (OCC) do not affect organisational performance (PR) through the moderator, digital leadership capabilities (DLC). On the other hand, it was also evident that managerial knowledge capabilities (MKC) significantly negatively affect the performance of telecom companies (PR) through the moderator role of digital leadership capabilities (DLC). The author recommends that telecom companies adopt knowledge-based practices to ensure enduring high performance. He also suggests creating a knowledge management department to foster a culture of creativity and cooperation across departments, which is essential to establishing a work environment that promotes continuous learning and development. Findings may help telecom sector CEOs boost the company’s performance value. The research highlights the importance of fostering appropriate knowledge pillars and building digital leaders to shift telecom companies to a new successful stage. These findings offer tangible benefits that can be directly applied in the telecom industry, making the research highly relevant and valuable.