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This paper aims to explore the impact of metaverse technology on the hospitality and tourism industry. The introduction of metaverse technology has revolutionised the way the hospitality and tourism industry works. In the present study, the authors have investigated the role of social media marketing in the adoption of metaverse technology in hotel booking in India.

An extended technology acceptance model was proposed for an empirical investigation in the Indian context. Sample of 344 respondents was collected across India using a purposive sampling technique for the purpose of data analysis. The structural model analysis is used to analyse the data collected from the respondents using the SmartPLS software to check the structural and the measurement fit of the model.

The adoption intentions were largely influenced by the utility, attitude (ATT) and ease of use of the technology, and social media marketing plays a major role in influencing the perceived usefulness (PU) and ease of use (PEU). The study finds positive ATTs of the customers for using metaverse technology for booking their hotels. PU and PEU significantly influence the ATT of the consumer indicating the traveller’s perception of the usefulness and ease of metaverse technology influence their ATTs towards adoption.

Influence of metaverse technology is at a nascent stage in India specifically for hotel booking and tourism. The authors have used discriminant validity by using the criteria for both the square root of the average variance extracted and heterotrait–monotrait ratio tests, and the results suggest that the constructs in the research are distinct from other.

Drawing upon an integrative perspective from intellectual capital theory with upper echelon theory, we examined how intellectual capital affects resource integration capability and subsequent strategic decision-making under weak versus strong top management team (TMT) involvement behavior. The purpose of this study was to investigate the relationships between intellectual capital and strategic decision-making and the mediated moderating effect between intellectual capital and decision-making on small- and medium-sized enterprises (SMEs).

Using statistical empirical analysis, we tested our research hypotheses via large-scale survey data from 323 SMEs. A regression analysis was applied to intellectual capital, resource integration capability and TMT involvement behavior to estimate their influence on strategic decision-making.

Our findings suggest that the positive effect of intellectual capital on strategic decision-making via resource integration capability is conditional on TMT involvement behavior, underscoring the role of resource integration capability and TMT involvement behavior in intellectual capital. The results also indicate that intellectual capital and resource integration capability strengthen positive decision-making relationships. Furthermore, TMT involvement behavior strengthens the positive interaction effect of intellectual capital with resource integration capability.

Intellectual capital is a critical and preeminent strategic resource for strengthening strategic decision-making, especially for SMEs. Notably, trends related to intellectual capital can be used to explore the management of SMEs and the corresponding contributions to and improvements in strategic decision-making. Specifically, intellectual capital can be used by SME management teams to formulate and implement relevant strategic decisions and enhance the effectiveness of decision-making, which are critical steps for success in decision-making processes.

This research explored the relationships among intellectual capital, resource integration capability, TMT involvement behavior and strategic decision-making in a comprehensive mediated moderation model; it is the first known study to highlight that intellectual capital can enhance strategic decision-making and provide managerial implications regarding how to align resource integration capability and TMT involvement behavior while performing strategic decision-making.

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.