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The construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties.

The dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor.

The prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R² was from 0.95 to 0.97, indicating better efficiency compared with other prediction models.

The researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices.

The research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes.

Delays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements.

This is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.

Nanotechnology is nowadays very much successful in producing specifically functionalized nano-sized particles. In this work, copper nanoparticles were prepared by reduction method which is greener and environmentally suitable, cheap and best as compared to other conventional methods, particularly in the context of COVID in globalized world. The formation and size of copper nanoparticles was evidenced by the X-ray diffraction and transmission electron microscopy. The very high surface area of 35–50 m²/gm and very small crystallite sizes of 5–15 nm of these metal nanoparticles is mainly responsible for their effective involvement in removal of carbon dioxide gas as one of major hazardous pollutants from the environment. This chapter, as its main objective, mainly focuses on utility of nano technology and its beneficiary in creating a sustainable environment in economic world. Apart from laboratory experimental procedure and characterizations for preparation of copper nanoparticles, appropriate research methods such as simple statistical, econometric tools and mathematical tools have been used for economic analysis. However, as major findings of the results, developed countries have been successful in maintaining a sustainable human development, in spite of having higher per capita income (PCI) growth as compared to the role of developing countries with lower PCI in this global world.

The purpose of this study is to investigate the determinants of behavioral intention to use smartphone diet applications within the emerging market. Specifically, it focuses on the Privacy Calculus Model constructs, encompassing perceived risk and perceived benefit, as well as the pivotal elements of trust and self-efficacy. It also explores the moderating influence of experience on the influencing factors and intention to use a diet application.

In a survey with 572 respondents, data analysis was conducted using partial least squares (PLS) structural equation modeling.

The findings reveal that perceived risk exerts a significant negative influence on behavioral intention. Conversely, perceived benefit, trust and self-efficacy exhibit a positive impact on behavioral intention. Moreover, the study delves into the moderating role of users' experience, which is found to significantly influence these relationships, suggesting that user experience plays a pivotal role in shaping the adoption dynamics of diet applications.

The limitations of this study may include the sample size and the specific focus on the emerging market of Saudi Arabia. The implications of the findings are relevant for scholars, developers, marketers, and policymakers seeking to promote the use of smartphone diet applications.

This study adds value by exploring the determinants of behavioral intention in the context of smartphone diet applications, and it is a first attempt to test the moderating role of users' experiences, providing valuable insights for various stakeholders in the field.

The purpose of this study is to (1) identify the factors that affect the adoption of virtual reality (VR) for spectator sports, (2) examine the differences in the factors among the four adopter categories (i.e. continuers, discontinuers, potentials and resistors) and (3) determine whether these factors are useful for discriminating among the adopter categories, based on the “diffusion of innovation” and “uses and gratification” theories.

In total, 216 participants were included in the analysis. Logistic regression and multiple analyses of variance were conducted to identify the factors that affect the adoption of VRS and examine the differences in the factors between adopter and non-adopter as well as between the continuers, discontinuers, potentials and resistors.

This study found that actualized innovativeness, complexity, companionship and gender significantly affect user adoption of VR for spectator sports. There were significant differences in the factors among the four adopter categories. The factors were also useful in discriminating between the four adopter categories.

This study highlights how individuals embrace emerging technologies differently based on their adopter category characteristics. From a marketing perspective, the insights gained from this study can inform the development of targeted strategies, campaigns and user experiences for VR spectator sports (VRS). This approach promises new revenue streams for the spectator sport industry and offers solutions to challenges like declining viewership and digital marginalization. It underscores the potential success of VR technology in transforming the spectator sport industry.

The coronavirus disease 2019 (COVID-19) pandemic altered business and personal activities globally especially stimulating contactless financial transactions. However, despite the similar national lockdowns in cash-based economies, the adoption of contactless transactions through the widely available mechanism, mobile wallets, remained low. This research aimed to identify the factors surrounding this peculiarity.

The study was investigated using a composite model based on the diffusion of innovation theory (DIT), technology acceptance model (TAM) and information systems success model (ISSM). Data were collected from 621 Cameroonian mobile wallet users and analyzed using partial least squares structural equation (PLS-SEM) modeling.

The key findings revealed that the usage of mobile wallets, in the current form, were not affected by the perceived ease of use and did not match the existing lifestyle of users in Cameroon (no compatibility). The branding of mobile wallets (image) which was based on global messaging did not appeal to Cameroonians; in fact, the branding gave mobile wallets a negative image.

These key findings reveal the dangers of assuming that global strategies which have been effective in dealing with the pandemic will be effective in low-income or cash-based economies. The findings suggest that considering essential contextual dispositions is critical.

The aim of this article is to demonstrate the development of environment friendly, low cost natural fibre composites by robust engineering approach. More specifically, the prime objective of the study is to optimise the composition of natural fibre reinforced polymer nanocomposites using a robust statistical approach.

In this research, the material is prepared using multi-walled carbon nanotubes (MWCNT), Cantala fibres and Epoxy Resin in accordance with the ASTM (American Society for Testing and Materials) standards. Further, the composition is prepared and optimised using the mixture-design approach for the flexural strength of the material.

The results of the study indicate that MWCNT plays a vital role in increasing the flexural strength of the composite. Moreover, it is observed that interactions between second order and third order parameters in the composition are statistically significant. This leads to proposing a special cubic model for the novel composite material with residual analysis. Moreover, the methodology assists in optimising the mixture component values to maximise the flexural strength of the novel composite material.

This article attempts to include both MWCNT and Cantala fibres to develop a novel composite material. In addition, it employs the mixture-design technique to optimise the composition and predict the model of the study in a step-by-step manner, which will act as a guideline for academicians and practitioners to optimise the material composition with specific reference to natural fibre reinforced nanocomposites.

This study aims to find a new alternate source for biodiesel conversion. The alternate source must be easily available, and it should give more oil yield than available edible, inedible sources. To meet the fuel demand in the transportation sector with edible oil-based biodiesel causes food versus fuel crisis. In addition to this, it increases NO_x and CO₂ in the environment.

The present paper reviews the comparison of algae oil yield, fatty acid composition and its biodiesel properties’ effect on diesel engine characteristics.

Algae were the only source to fulfil fuel demand because its oil and biodiesel yield is higher than other sources. Algae can grow by capturing carbon dioxide from the environment, and its fatty acid composition is more suitable to run diesel engines.

There is an improvement in engine performance–emission tradeoff with algal biodiesel.

Nowadays, it is hard to retain a knowledge monopoly since tacit knowledge has become essential for innovation and organizational effectiveness (ORP). This study analyzed the role of product innovation as a mediator in the relationship between the tacit knowledge management process (TKMP) and organizational performance. In addition, two moderating variables were examined: (1) Affective trust (AFT) between the tacit knowledge management process and product innovation relationship and (2) Task efficiency in product innovation and organizational performance (ORP) relationship.

Around 344 questionnaires were collected from various Chinese regions between February and April 2023 to conduct this study. The regression, mediation and moderation analyses on lower and higher-order data were evaluated using the SmartPLS approach.

The results validate that product innovation mediates the connection between managing tacit knowledge and the organization’s performance. Affective trust also plays a positive moderating role between tacit knowledge and product innovation. These results provide valuable theoretical and practical insights, substantiating various direct, indirect, mediate, and moderated effects hypotheses.

The scope of the study was restricted to manufacturing companies; however, further research may broaden the model’s scope to include other industries. Furthermore, future research should continue to explore the role of task efficiency in the innovation process and identify strategies for enhancing task efficiency in organizations.

The study establishes the significance of effectively managing tacit knowledge for fostering product innovation. Company managers and leaders can promote employee trust, enhancing innovation capabilities and overall organizational effectiveness.

This study, involving dual moderation, explores the connections between processes of managing tacit knowledge, product innovation and organizational performance. It addresses research gaps, enriching the understanding of managing tacit knowledge, leading to organizational innovation and performance improvements. The study also highlights how affective trust is vital in strengthening the connection between TKMP and product innovation.