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Multiple attribute decision making (MADM) is a conceptual agenda used for evaluation and selection of optimal nanofluid to assure best performance of heat exchanger. Most of the studies focus on nanofluids focus on individual ability at one time. Relatively, not even a single study is available for selection of nanofluid for heat exchanger using concurrent design and MADM approach. The purpose of this paper is to propose a concurrent design methodology using MADM approach to assist improved design of heat exchanger concurrently for all the x-abilities in an integrated manner.

A combined methodology of applying MADM approach using concurrent design for x-abilities is called CE-MADM approach. Implementation of nanofluid to improve thermal performance of heat exchanger entails thorough evaluation of nanofluids in various x-abilities (performance, maintenance, thermophysical properties and modelisation) to make exhaustive management decision. Sensitivity analysis is also proposed to study the behaviour of height of variation of density, heat capacity, thermal expansion and thermal conductivity with varying particle volume fraction and variation of relative closeness of available alternates from ideally best possible solution.

MADM approach considering various x-abilities concurrently provide an approach for relative ranking of available nanofluids for optimum performance. Fishbone diagrams of all x-abilities are constructed to identify all the attributes and converge large number of attributes into single numerical index that are concurrently responsible for the cause thus saving time for easy evaluation, comparison and ranking by decision makers. Sensitivity analysis to demonstration height of variation of pertinent attributes with varying particle volume fraction. A MATLAB programming is established to execute calculations involved in the procedure.

This paper comprises a predictable and effective mathematical approach to improve design of heat exchanger with nanofluid bearing in mind all the required x-abilities concurrently. This combined approach of CE-MADM is never applied before in the field of nanofluid to predict best possible results in feasible conditions considering all the x-abilities. Sensitivity analysis is also presented from the assumed mathematical equations of thermophysical properties.

This paper aims to identify and study the effect of identified eight barriers to sustainable consumption on consumers’ intention to purchase sustainable products.

Data were collected from a self-administered field survey in India, and 315 valid responses were obtained from the survey process. Partial least square structural equation modeling analysis was carried out to establish the validity of the measures used and to examine the impact of the identified barriers on sustainable purchase intentions.

The results of this study indicate that barriers such as low willingness to pay, low functional performance, low availability of sustainable products and difficulty of integration in the normal route have a statistically significant negative impact on consumers’ sustainable purchase intentions.

The findings of this study are useful for marketers and policymakers who want to increase the consumer adoption of sustainable products in emerging markets.

This study develops measures to capture the consumers’ perception of barriers to the adoption of sustainable products.

The present study aims to empirically investigate whether supply chain agility and lean management practices are antecedents of supply chain social sustainability.

Data were collected from 311 supply chain practitioners from the Indian manufacturing sector. Confirmatory factor analysis was employed to test the validity and reliability of the measures used, and a structural model was analyzed to test the hypotheses of the current study.

The results indicate that agility and lean practices are significant antecedents of social sustainability orientation as well as social sustainability performance. The results also suggest that agility has a significant indirect effect on operational performance via social sustainability orientation, basic social sustainability practices as well as agility is indirectly affecting social sustainability performance via social sustainability orientation and basic social sustainability practices.

The results of the present study have implications for managers that want to make their supply chain more socially sustainable.

The study is unique in the sense that it empirically links agility and lean practices with social sustainability orientation, social substantiality performance and operational performance in supply chains.

This study aims to empirically investigate the relationship between intellectual capital (IC) and corporate performance (CP), including financial, market and sustainability performance. The research also investigates the mediating role of earnings management practices (EM) in the IC and CP relationship.

The empirical connection between IC and CP for 795 nonfinancial listed Indian firms is examined for 17 years using industry and year-fixed effect panel regression models. The research has also used Baron and Kenny’s four-step model to examine the role of EM as a mediator between IC and CP.

IC plays a crucial part in improving the financial, market and sustainability performance of Indian firms. The empirical findings further claim that EM practices partially mediate the connection between IC and CP. However, the mediation effect of EM depends on its magnitude and direction, i.e. income-increasing (decreasing) EM practices. The study also claims that sustainability performance-oriented firms practice less EM.

Managers and policymakers can use the findings of this study to their advantage by focusing on the significance of IC in the Indian context and their efforts to improve financial, market and sustainability performance while limiting earnings management practices.

The research uncovers a novel facet of the IC–CP relationship where EM mediates between the two. To the best of our knowledge, this is the first study that analyzes the impact of IC on CP through the lens of mediation using both accrual and real earnings management.

Sustainable supply chain management (SSCM) concepts have received immense attention in the recent past in both academia and industries. Especially, manufacturing industries in developing countries realize the importance of adopting sustainability concepts in their supply chain. The SSCM adoption has not been to the same level across different manufacturing sectors and hence a single implementation framework will not have the same effect across sectors. This paper aims to compare the adoption level of 25 SSCM practices across three major manufacturing sectors, namely, automobile, electronics and textile, in an emerging economy, India.

A questionnaire-based data collection technique is used to obtain adoption levels of each of the identified SSCM practices on a five-point Likert-type scale with “1” representing not considering presently to “5” indicating successful implementation. Second, a hypothesis is framed and tested to compare the adoption levels across sectors using a one-way single-factor ANOVA followed by a post hoc test by Tukey’s test.

The results derived suggest that though the industries across different sectors are in the course of adopting SSCM practices, the level of adoption is found to be not the same. The textile sector has adopted the least, and the electronic sector edges ahead of the automobile sector in terms of successful transformation to SSCM.

This study focuses on the differences and similarities in the adoption of policies in the automobile, electronics and textile sectors using statistical data analysis tools. A total of 25 individual practices are identified from existing literature and classified into six groups, namely, management, supplier, collaboration, design, internal and society, based on their similarities. Based on a detailed questionnaire survey with industrial experts in relevant fields as respondents, the adoption levels of practices are rated individually and categorically.

Ideas regarding the basic character of humanity assume importance wherever people interact with one another — from the family to the political state, to the business enterprise. These conceptions, ranging from pessimism to optimism, from notions that evil, predatory competition on the one hand to goodness, co‐ operation and virtue on the other characterise the intrinsic disposition of people, derive from the culture to which the individual belongs, moulding his values and conditioning his behaviour. They not only affect the quality of human relations present in any collectivity, but exercise critical influence on the theories and practices of social control. The understanding of a range of social parameters is considerably enhanced when viewed from the perspective of prevailing cultural ideas about human nature itself.

Research on solar energy adoption offers a multidimensional scope and warrants exploration from multiple perspectives, including political, economic, management, behavioral, policy and innovation aspects. The aim of this paper is to comprehensively consolidate major research findings on the premise of solar energy adoption and to disclose gaps in the existing literature.

A bibliometric analysis of the vast literature is conducted on 1,009 meticulously shortlisted articles following the semi-systematic literature review methodology. A text analytics tool named BibExcel is used for synthesizing the literature, and the results are visualized using Gephi, Pajek and a spreadsheet application.

This paper reports the evolution of research in the selected domain. It is noted that research in this domain was primarily concentrated on four broad themes, namely, peer effects and spatial patterns, public perceptions, policies and economics and technological evolution. The analysis further reveals the merging of two of these themes as a result of transdisciplinary research and also projects future research trends emphasizing political interventions in technological evolution and diffusion.

Research trends and future research scope are identified and discussed in detail. The information revealed from the analysis, along with the research implications, will assist policymakers in noting the flaws in the current doctrines and practices, entrepreneurs in understanding potential enablers and barriers influencing solar energy adoption and budding scholars in comprehending the current research status and framing promising research objectives to close the existing research gaps.

This study focuses on investigating the numerical solution of second-kind nonlinear Volterra–Fredholm–Hammerstein integral equations (NVFHIEs) by discretization technique. The purpose of this paper is to develop an efficient and accurate method for solving NVFHIEs, which are crucial for modeling systems with memory and cumulative effects, integrating past and present influences with nonlinear interactions. They are widely applied in control theory, population dynamics and physics. These equations are essential for solving complex real-world problems.

Demonstrating the solution’s existence and uniqueness in the equation is accomplished by using the Picard iterative method as a key technique. Using the trapezoidal discretization method is the chosen approach for numerically approximating the solution, yielding a nonlinear system of algebraic equations. The trapezoidal method (TM) exhibits quadratic convergence to the solution, supported by the application of a discrete Grönwall inequality. A novel Grönwall inequality is introduced to demonstrate the convergence of the considered method. This approach enables a detailed analysis of the equation’s behavior and facilitates the development of a robust solution method.

The numerical results conclusively show that the proposed method is highly efficacious in solving NVFHIEs, significantly reducing computational effort. Numerical examples and comparisons underscore the method’s practicality, effectiveness and reliability, confirming its outstanding performance compared to the referenced method.

Unlike existing approaches that rely on a combination of methods to tackle different aspects of the complex problems, especially nonlinear integral equations, the current approach presents a significant single-method solution, providing a comprehensive approach to solving the entire problem. Furthermore, the present work introduces the first numerical approaches for the considered integral equation, which has not been previously explored in the existing literature. To the best of the authors’ knowledge, the work is the first to address this equation, providing a foundational contribution for future research and applications. This innovative strategy not only simplifies the computational process but also offers a more comprehensive understanding of the problem’s dynamics.

The purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.

The proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.

The findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.

The study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.

The study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.