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One of the most contested and anticipated research issues is the acceptability of using recycled aggregates instead of fresh aggregates. This study aims to look at the possibility of replacing fresh aggregates with 15%, 30%, 60% and 100% recycled aggregates.

The research is divided into two stages. The compressive, split tensile, flexural and bond strength of the various mixes were examined in the first phase using untreated recycled concrete aggregates (RCA). The second phase entails chemically treating RCA with a 10% 0.1 M sodium metasilicate solution to evaluate differences in strength, indicating the success of the treatment performed. Microstructural experiments such as scanning electron microscopy and X-ray diffraction were also conducted to evaluate the formation of interfacial transition zone (ITZ) in treated and untreated RCA specimens.

The observed findings reveal a decrease in concrete strength with increasing RCA concentration; however, when treated RCA was used, the strengths increased significantly when compared to untreated samples. The findings also include curves indicating the correlation between compressive strength and other mechanical strength parameters for an optimum mix of concrete prepared with 30% RCA replacement.

The study through its novel approach, demonstrates the effect of pretreatment of RCA in the absence of any standardized chemical treatment methodology and presents significant potential in minimizing reliance on fresh aggregates used in concrete, lowering building costs and promoting the use of waste materials in construction.

The purpose of this paper is to explore the impact of five dimensions of green supply chain management (GSCM) practices (i.e. internal environmental management, green purchasing, cooperation with customers, eco-design, and investment recovery) on three dimensions of organizational performance (i.e. environmental, economic and operational performance).

Data were collected based on a cross-sectional survey of managers of 160 manufacturing firms in India. Structural equation modelling was used to test the influence of GSCM practices on each of the organizational performance outcomes.

Except for internal environmental management and green purchasing, all other GSCM dimensions are found to significantly impact at least one of the performance dimensions, either directly or indirectly. The results highlight that investment recovery practice is a key predictor of environmental performance, whereas eco-design is a key predictor of operational performance. The structural equation modeling result also suggests that GSCM do not directly affect economic performance, but can improve it indirectly.

Since data was collected from managers of manufacturing firms on the basis of their subjective evaluations, future research studies should employ objective performance indicators for analysis. Also, the study did not consider some dimensions of GSCM practices, which can be included in future studies.

Manufacturing firms should implement GSCM practices not just because of the pressure from regulatory bodies but also to elevate their environmental, operational and economic performance. The proposed model in this paper suggests practitioners which GSCM factors are driving these performance changes and supports the building of a roadmap for GSCM implementation in their organization.

Manufacturing firms from four different operating sectors, namely automotive, electrical and electronics, process and machinery sectors are the respondents. The originality of the paper lies in testing the influence of GSCM practices on organizational performance in a novel context, where most GSCM initiatives fail for one reason or another. Furthermore, the assessment of the interaction between five constructs of GSCM practices and three constructs of organizational performance in the Indian manufacturing context offers unique conceptual contribution to the researches in the GSCM field.

Sustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.

Validity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.

The work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.

This study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.

The main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.

Manufacturing organization has adopted the concept of sustainability to improve the performance of product and process as well as to focus on environmental issues. Despite technological advancements and awareness, there exist several performance obstacles for the implementation of sustainable manufacturing in an organization. The objective of the current study is to identify the performance obstacles, propose a structural model and validate the proposed model.

Twelve performance obstacles are identified through critical literature review and discussion with field experts. Primary and secondary factor analysis, that is, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), are used for the creation of the structural model, and further, structural equation modeling is used as a validating tool. EFA deals with the categorization of all performance obstacles in four major criteria, and CFA works for proposing a model for a relationship among all obstacles.

A validated structural model is provided through hypothesis acceptance for structural equation modeling. The outcome of this study can be helpful for decision-makers to incorporate sustainable practices in the manufacturing organization.

This study has extracted and identified performance obstacles for the implementation of sustainable manufacturing based on critical literature review and discussion with field experts. The study proposes as well as validates the structural equation model.

Sustainable customer relationship management (SCRM) is a combination of business strategy, customer-oriented business processes and computer systems that seeks to integrate sustainability into customer relationship management. The purpose of this paper is to contribute to the body of knowledge of marketing, business management and computer systems research domains by classifying in research categories the current state of knowledge on SCRM, by analysing the major research streams and by identifying a future research agenda in each research category.

To identify, select, collect, synthesise, analyse and evaluate all research published on SCRM, providing a complete insight in this research area, the PRISMA methodology, content analysis and bibliometric tools are used.

In total, 139 papers were analysed to assess the trend of the number of papers published and the number of citations of these papers; to identify the top contributing countries, authors, institutions and sources; to reveal the findings of the major research streams; to develop a classification framework composed by seven research categories (CRM as a key factor for enterprise sustainability, SCRM frameworks, SCRM computer tools and methods, case studies, SCRM and sustainable supply chain management, sustainable marketing and knowledge management) in which academics could expand SCRM research; and to establish future research challenges.

This paper have an important positive social and environmental impact for society because it will lead to an increase in the number of green and socially conscious customers with an ethical behavior, while also transforming business processes, products and services, making them more sustainable.

Customer relationship management in the age of sustainable development is an increasing research area. Nevertheless, to the authors' knowledge, there are no systematic literature reviews that identify the major research streams, develop a classification framework, analyse the evolution in this research field and propose a future research agenda.

Despite numerous positive aspects of digital contact tracing, the implied nature of contact tracing is still viewed with skepticism. Those in favor of contact tracing often undermine various risks involved with it, while those against it often undermine its positive benefits. However, unless the government and the app makers can convince a significant section of the population to use digital contact apps, desired results cannot be achieved. This study aims to focus on analyzing the perception of citizens belonging to developing countries about digital contact tracing.

For this study, data were collected from Twitter. Tweets containing hashtag and the word “contact tracing” were crawled using Python library Tweepy. Tweets across the top five developing countries (India, Brazil, South Africa, Argentina and Columbia) with high COVID-19 cases were collected for this study. After eliminating tweets of other languages, we selected 50,000 unique English tweets for this study. Using the machine learning algorithm, we have detected the sentiment of all the tweets belonging to each country. Structural topic modeling was performed for the tweets to understand the concerns shared by citizens of the developing countries about digital contact tracing.

The study was conducted in two parts. Study 1 results show that Indians and Brazilians citizens record more negative sentiments toward “digital contact tracing” than other major developing countries. Surprisingly, the citizens of India and Brazil also records more positive sentiments about contact tracing. This shows the polarized nature of the population of both countries while dealing with digital contact tracing. Overall, only 33.3% of total tweets were positively related to contact tracing, while 53.7% of the total tweets were neutral. Study 2 results show that factors such as the reliability of the contact tracing apps, contact tracing may lead to unnecessary panic, invasion of privacy and data misuse as the prominent reasons why the citizens of the five countries feel pessimistic about contact tracing.

After the COVID-19 strikes, numerous studies were conducted to analyze and suggest the best possible way of implementing digital contact tracing to curb COVID. However, only a handful of studies were conducted examining how the general public perceives the concept of digital contact tracing, especially pertaining to developing countries. This study fills that gap.

This study is to examine the impact of viscous dissipation, thermal radiation and Ohmic heating on the magnetohydrodynamic (MHD) flow with thermal and mass transport over a horizontally stretching surface. Cattaneo–Christov heat flux model on a non-Newtonian viscous fluid along with two viscosity models and convective boundary condition has been employed. Tri-hybrid nanofluid has been used to increase thermal performance.

Governing mathematical model has been transposed into a dimensionless system of ordinary differential equations (ODEs) by applying suitable similarity transformation. Numerical solution has been found by applying the bvp4c shooting method in MATLAB software.

Velocity and thermal profiles of Model-I dominate the profiles of Model-II whereas opposite behavior is noticed for concentration profiles. It is concluded that there is an increase in temperature due to thermal radiation, viscous dissipation and convective boundary condition.

The novelty of presented work is to examine the impact of Ohmic heating, viscous dissipation, thermal radiation, chemical reaction and two models of viscosity on Cattaneo–Christov heat flux model of tri-hybrid non-Newtonian nanofluid with convective boundary constraint. The accuracy and effectiveness of presented model have been compared with already published research.