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Celebrity endorsers are usually considered to bring positive effects to associated nodes, such as brands and corporations. However, limited evidence suggests that brands and corporations are equally responsible for affecting celebrities and their credibility. Drawing on associative network theory, this study explores the effects of brand credibility and corporate credibility on celebrity credibility, both directly and through the mediating and moderating effects of advertising credibility. The research addresses three main issues: (1) whether brand credibility, corporate credibility and advertising credibility have significant effects on celebrity credibility; (2) whether advertising credibility has a significant mediating effect on the effects of brand credibility and corporate credibility on celebrity credibility and (3) whether advertising credibility has a significant moderating effect on the effects of brand credibility and corporate credibility on celebrity credibility.

The study used a quantitative approach involving structural equation modelling. Data were collected from 675 participants from London and focussed on four leading international brands, corporations and celebrity endorsers.

The findings show that brand credibility and advertising credibility have positive direct effects on celebrity credibility; and that advertising credibility mediates the effects of both credibility constructs on celebrity credibility. Furthermore, moderating effects of advertising credibility are also found.

This study will help managers to understand the reverse effects, i.e. the effects of brand credibility and corporate credibility on celebrity credibility. They will be able to understand that a credible brand and corporation like a credible celebrity can also bring significant effects on the associated elements. This will help them to recruit celebrity endorsers who have historically earned their credibility from previous endorsements of credible brands and corporations. Further, these findings will help managers to understand that credibility of the brand and corporation can also affect the credibility of the associated advertising, resulting in having a significant effect on the credibility of the celebrity. This on the consumers’ side will enhance their preferences, attitudes and behaviours, while for the corporation, it will enhance their economic and commercial performance.

This is the first study in the literature, where a conceptual model based on the reverse effects of both credibility constructs on celebrity credibility is examined, directly and based on the moderating and mediating effects of advertising credibility. Hence, the contributions to the literature are threefold: first, the study examines the reverse effect of celebrity endorsement, whereby the credibility of a brand or corporation is transferred to a celebrity endorser; second, the study examines the mediating and moderating effects of advertising credibility on this reverse effect and finally, associative network theory is used to examine the importance of the model.

The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand.

It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites.

The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect.

This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

This study aims to investigate the heat and mass transfer characteristics of a temperature-sensitive ternary nanofluid in a porous medium with magnetic field and the Soret–Dufour effect through a tapered asymmetric channel. The ternary nanofluid consists of Boron Nitride Nanotubes (BNNT), silver (Ag) and copper (Cu) nanoparticles, with a focus on understanding the thermal behaviour and performance across mono, hybrid and tri-hybrid nanofluids. This paper also examines the thermal behaviour of MHD oscillatory nanofluid flow and carries out an uncertainty analysis of the model using the Taguchi method.

The governing equations for this system are transformed into coupled linear partial differential equations using non-similarity transformations and solved numerically with the Crank–Nicolson scheme. The impact of temperature sensitivity at three distinct temperatures (5°C, 20°C and 60°C) is incorporated to analyse variations in viscosity and Prandtl number. The study also examines the combined effects of Soret–Dufour numbers and thermal radiation on heat and mass transfer within the nanofluid.

The results demonstrate that the inclusion of BNNT, Ag and Cu nanoparticles significantly enhances heat and mass transfer rate, with copper nanoparticles showing superior performance in terms of skin friction and heat transfer rates. The Soret and Dufour effects play critical roles in modulating heat and mass diffusion within tri-hybrid nanofluids. The study reveals that temperature sensitivity alters heat and mass transfer characteristics depending on the temperature range, with pronounced variations at elevated temperatures. The influence of thermal radiation and the Peclet number is found to significantly impact temperature distribution and overall heat transfer performance within the asymmetric channel.

To the best of the authors’ knowledge, this study is the first to analyse the heat and mass diffusion in a ternary nanofluid composed of BNNT, Ag and Cu nanoparticles, considering porous media, oscillatory flow and thermal radiation within a tapered asymmetric channel. The research extends to a novel examination of temperature sensitivity in mono, hybrid and tri-hybrid nanofluids at varying temperature gradients. Furthermore, a comparative analysis of skin friction and heat transfer rates between copper, alumina and ferro composites is presented for optimising the nanofluid performance.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Despite the global shift toward smart building features and technologies, the level of awareness among stakeholders in Nigeria’s construction sector remains unclear, limiting engagement with these innovations. This study examines the awareness of smart building features and technologies, providing insights to address knowledge gaps and improve understanding within the sector.

This study adopted the quantitative research approach, using a questionnaire survey to obtain data from construction stakeholders that were purposively selected in Lagos State and Abuja, Nigeria. The collected data were analyzed using various statistical tools such as frequencies, percentiles, mean item scores, standard deviation and the Mann–Whitney U test.

From the result of the analysis, the study concluded that the most cognizant smart building features and technology were security doors, escalators and lifts, solar panels and energy-saving equipment, fire alarms, heating, ventilation, air and conditioning.

This study provides insights into the awareness of smart building features and technologies among Nigerian construction stakeholders, bridging theory and practice. It informs policy development, enhances professional knowledge and promotes educational initiatives. Its findings support sustainable construction efforts, potentially improving societal attitudes and quality of life.

This study uniquely explores the level of awareness of smart building features and technologies among clients and professionals in Nigeria’s construction sector. Identifying existing knowledge gaps provides critical insights that can guide efforts to enhance understanding and foster deeper engagement with these innovations.

The purpose of this study is to investigate the simultaneous effects of normal wall transpiration, stretching strength parameter, velocity slip and nanoparticles on the flow of a ternary hybrid nanofluid through an elastic surface. The goal is to understand the behavior of the flow field, temperature distribution, skin friction and temperature gradient under these conditions, and to explore the existence and nature of solutions under varying parameter values.

The analysis involves expressing the flow field, power-law temperature field, skin friction and temperature gradient in closed-form formulas. The study examines both stretching and shrinking surfaces, distinguishing between unique and dual solutions. The methodology includes deriving exact solutions for exponential and algebraic temperature and temperature rate formulas analytically by deriving the system of governing equations into ordinary differential equations.

The study reveals that for a stretching sheet, the solution is unique, whereas dual solutions are observed for a shrinking surface. Special solutions are provided for various parametric values, showing the behavior of the exponential and algebraic temperature and temperature rate, with a focus on identifying turning points that demarcate the existence and non-existence of single or multiple solutions. The solutions are represented through graphs and tables to facilitate a comprehensive qualitative analysis. The research identifies turning points that determine the presence or absence of single or multiple solutions, uncovering multiple solutions for different parameter sets. These findings are displayed graphically and in tabular form, highlighting the complex interplay between the parameters and the resulting flow behavior.

This analysis contributes to the field by providing new insights into the multiple solution phenomena in ternary hybrid nanofluid flows, particularly under the combined effects of normal wall transpiration, stretching strength, velocity slip and nanoparticle presence. The identification of turning points and the exact solutions for various temperature profiles are of significant value, offering a deeper understanding of the factors influencing the flow and thermal characteristics in such systems. The study’s findings have potential applications in optimizing fluid flow in engineering systems where such conditions are prevalent.

On the basis of demand survey feedback from individuals with disabilities and caregivers, this study designed two sets of functional garments for long-term bedridden patients, with the primary objective of increasing convenience and reducing the physical workload of caregivers.

Wear trials were conducted by employing 24 subjects to perform 11 different tasks to compare the performance of the two newly developed garments with that of conventional hospital patient apparel. Task operation time, heart rate (HR), electromyography (EMG) signals, and subjective perceptions were evaluated.

The new functional garments reduced the time required to perform tasks by 29–79%, maintained the average HR of caregivers at approximately the resting threshold and resulted in a 37–74% reduction in the root mean square (RMS) of the EMG at the arm muscles in the private and thigh nursing tasks. All the subjective and objective evaluation results of the caregivers demonstrated varying degrees of correlation.

This study has practical implications for the design of functional clothing for long-term bedridden patients and provides guidance for evaluating the ergonomics of garments that can be utilized only with caregiver support.

In contrast to previous studies that focused primarily on individuals with disabilities while overlooking the indispensable role of caregivers in the nursing process, this study shifted its emphasis to long-term bedridden patients who relied exclusively on caregivers for daily activities. Additionally, this study attempted to analyze the correlations between the evaluation parameters to explore the relationships between the evaluation methods.

This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal.

The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making.

This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption.

This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector.

This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.