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Drawing from stakeholder (ST) and social exchange theory (SET), the purpose of this study is to examine the relationship between customer perceived ethicality (CPE), electronic word of mouth (eWOM), customer trust (CT) and customer loyalty (CL). Furthermore, this study aimed to understand the dual role of CPE and eWOM in obtaining CT and achieving CL.

Using a quantitative, cross-sectional research design, data were collected from face-to-face surveys, yielding 358 responses. The partial least square algorithm was used to test the proposed hypothesis.

The analysis revealed that CPE and eWOM positively affect CT and CL, and CT has a mediating effect on the association between CPE–CL and eWOM–CL. CT was also found to positively affect CL.

Hotel managers can prioritize ethical practices and leverage the power of eWOM to build trust and achieve loyalty. This integrated approach not only enhances customer satisfaction and retention but also creates a competitive advantage.

The novelty of this study lies in the investigation of the dual role played by CPE and eWOM as antecedents of CT and CL within the hotel industry. Finally, this study explains the drivers of CT and CL, thereby making a novel contribution to the literature.

The purpose of this research was to develop and evaluate a machine learning (ML) algorithm to accurately predict bamboo compressive strength (BCS). Using a dataset of 150 bamboo samples with features such as cross-sectional area, dry weight, density, outer diameter, culm thickness and load, various ML algorithms including artificial neural network (ANN), extreme learning machine (ELM) and support vector regression (SVR) were tested. The ELM algorithm outperformed others, showing superior accuracy based on metrics like R2, MSE, RMSE, MAE and MAPE. The study highlights the efficacy of ELM in enhancing the precision and reliability of BCS predictions, establishing it as a valuable tool for assessing bamboo strength.

This study experimentally created a dataset of 150 bamboo samples to predict BCS using ML algorithms. Key predictive features included cross-sectional area, dry weight, density, outer diameter, culm thickness and load. The performance of various ML algorithms, including ANN, ELM and SVR, was evaluated. ELM demonstrated superior performance based on metrics such as coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), establishing its robustness in predicting BCS accurately.

The study found that the ELM algorithm outperformed other ML algorithms, including ANN and SVR, in predicting BCS. ELM achieved the highest accuracy based on key metrics such as R2, MSE, RMSE, MAE and MAPE. These results indicate that ELM is a highly effective and reliable tool for predicting the compressive strength of bamboo, thereby enhancing the precision and dependability of BCS evaluations.

This study is original in its application of the ELM algorithm to predict BCS using experimentally derived data. By comparing ELM with other ML algorithms like ANN and SVR, the research establishes ELM’s superior performance and reliability. The findings demonstrate the significant potential of ELM in material strength prediction, offering a novel and robust approach to evaluating bamboo’s compressive properties. This contributes valuable insights into the field of material science and engineering, particularly in the context of sustainable construction materials.