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To be green creative is to come up with fresh, original and practical ideas for green products, green services, green processes or green activities. The purpose of this study is to provide a comprehensive overview of green creativity (GC) research by tracing the development of important theories, contexts, characteristics and methodologies (TCCM), and to illustrate how they relate to one another based on the systematic review and analysis of the existing literature relevant to GC from 2013 to 2023.

The research takes a methodical, structured approach to its literature evaluation, identifying prior contributions and offering frameworks for future study.

This research aims to highlight the challenges associated with planning, developing and implementing GC to realize the firm’s strategic and operational goals. Comprehensive networks, important countries, notable authors, key TCCM are provided by a TCCM and bibliographic analysis of the current GC literature.

The research addresses the concerns of managers across all types of entities and fills in the gaps, such as the skewed focus on GC’s applicability in large businesses and developing countries, as well as the limitations of a single-level analysis.

The research as a whole provides the taxonomy, utilization and mapping of logical concepts that strengthen GC. The study also highlights areas where more research is needed and where gaps and unresolved tensions remain. By delving into the nature of knowledge, the authors can better understand the factors that will ultimately shape the scope of future studies.

Given the growth of virtual reality (VR)-based tourism experiences in the past five years, this study aims to investigate the impact of VR-based interactions (ergonomics and embodiment) on memorable experiences and revisit intention mediated by cognitive and emotional responses.

This study has used an exploratory sequential mixed methodology research design to operationalise this research. Study 1 uses qualitative in-depth interviews to explore the proposed research questions, and Study 2 uses a 3 × 3 factorial experimental research design to test the proposed hypothetical model with 355 samples.

The results indicate that embodiment plays a more crucial role than VR ergonomics. Also, the cognitive response in the virtual tour indirectly generates a more memorable experience than the emotional response.

This research uses the theory of technological mediation as an overarching framework to conceptualise the research. Also, the research has applied the tenets of cognitive embodiment theory, metacognitive theory and other related theories to develop the arguments. Thus, the results of this research will extend the holistic understanding of these theories.

This research will guide VR tourism developers in understanding the requirements and expectations of tourists. It also serves as a manual to understand how tourists process the VR tour psychologically.

Very minimal focus was given to understanding the tourists’ interaction with technology in VR tours. The concept of ergonomics and embodiment investigated as an experimental variable is a novel approach in technology-based tourism research.

This study aims to investigate the use of 20 commonly applied regression methods to predict concrete corrosion. These models are assessed for accuracy and interpretability using SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analysis to provide structural health monitoring prognostic tools.

This study evaluated model performance using standard measures including root mean square error (RMSE), mean square error (MSE), R-squared (R²) and mean absolute error (MAE). Interpretability was evaluated using SHAP and LIME. The X and Y distances, concrete age, relative humidity and temperature were input parameters, whereas half-cell potential (HCP) values were considered output. The experimental data set consisted of observations taken for 270 days.

Gaussian process regression (GPR) models with rational quadratic, square exponential and matern 5/2 kernels outperformed others, with RMSE values around 16.35, MSE of roughly 267.50 and R² values near 0.964. Bagged and boosted ensemble models performed well, with RMSE around 17.20 and R² values over 0.95. Linear approaches, such as efficient linear least squares and linear SVM, resulted in much higher RMSE values (approximately 40.17 and 40.02) and lower R² values (approximately 0.79), indicating decreased prediction accuracy.

The findings highlight the effectiveness of GPR models in forecasting corrosion in concrete buildings. The use of both SHAP and LIME for model interpretability improves the transparency of predictive maintenance models, making them more reliable for practical applications.

Safe infrastructure is crucial to public health. Predicting corrosion and other structural problems improves the safety of buildings, bridges and other community-dependent structures. Public safety, infrastructure durability and transportation and utility interruptions are improved by reducing catastrophic breakdowns.

This study reduces the gap between model accuracy and interpretability in predicting concrete corrosion by proposing a data-driven method for structural health monitoring. The combination of GPR models and ensemble approaches provides a solid foundation for future research and practical applications in predictive maintenance. This comprehensive approach underscores the potential of data-driven methods for predictive maintenance in concrete structures, with implications for broader applications in various industries.

This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search.

A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed.

The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency.

This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA.

Using VOSviewer software, this research delves into the various implications of ethical artificial intelligence (AI) within the retail industry. We explored the latest research trends using bibliometric analysis unveiling the journals, organisations, sources, articles, and documents that topped the chart. To shed light on the critical areas, we leveraged a citation analysis approach to explore the numerous trending research areas that were associated with fostering trust and transparency in AI-based retail applications. The research recognised the most influential areas by investigating the highly cited works. This research insight works as a guiding roadmap to navigate the complexities related to the ethical use of AI and direct towards fostering trust.

Sustainable practices are the modern-day necessities for organisations as the world is becoming highly dynamic. The purpose of this study is to examine the influence of sustainable training and creativity practices (STP and SCP) on organisational citizenship behaviour (OCB-individual and OCB-organisation) via the mediating role of psychological contract fulfilment (PCF).

A sample of 326 white-collar Indian service industry employees was collected. The data are analysed using structural equation modelling and random forest regression supervised learning (RFRSL).

The findings indicate that sustainable training practices (STP) had an indirect impact on organisational citizenship behaviour (OCB-I, OCB-O) via the mediating effect of transactional (T-PCF) and relational psychological contract fulfilment (R-PCF). In terms of sustainable creative practices (SCP), the impact on OCB-I was indirect due to T-PCF. In addition, R-PCF acts as a mediator between SCP and OCB-O. In the latter portion of the analysis, the RFRSL approach created a prediction model for T-PCF, R-PCF, OCB-I and OCB-O, with demographic characteristics such as industry experience, gender, age, etc. playing a constructive role.

The study conducts a combination of both traditional and newer technology (machine learning), resulting in highlighting the uniqueness of the relationship between variables and the role of demographic variables.