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The present investigation aims to present the status of planetary science research in India using different scientometric indicators, as reflected in the Web of Science Core Collection database.

The researcher adopted systematic approaches to retrieve the data from the Web of Science Core Collection database for 20 years by using AAS Astronomical subject keywords. A total of 1,504 Indian publications and 55,572 World's publications were considered for analysis. The data were analyzed using the biblioshiny application of bibliometrix to investigate the most productive countries/territories, institutions, authors, research fields, journals, keywords, and h, g-index. The VOSviewer program is used to construct and visualize scientometric networks and analyze the co-occurrence of terms. “Webometric Analyst 2.0” is used to retrieve the Altmetric attention scores for the articles.

The results revealed that the publications on planetary science research has increased over time, with an annual growth rate of 9.66%. The study also revealed the prolific authors and institutions, productive journals and most frequently cited journals. The USA was the major collaborating partner of India. The results also provided valuable information on the citations made to these papers on planetary science, including a total number of citations, average citations per item, cited rate and h-index. There were 28,086 citations to 1,504 papers. The top 67 citation papers were the h-core papers on planetary science in India. Altmetric score for planetary science articles ranged from 1 to 2,418. Twitter (69%), news outlets (16%), blogs (6%), and Facebook (6%) were the most popular Altmetric data resources.

This investigation is the first attempt to employ scientometrics and visualization techniques to planetary science research in India.

The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models.

The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics.

The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes.

A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values.

This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

As the normality concept for frictional dilatant material has a serious drawback, the key feature in this numerical study is that the material here is characterized by elastic-viscoplastic constitutive relation with plastic non-normality effect for two different hardness functions. The paper aims to discuss this issue.

Quasi-static, mode I plane strain crack tip fields have been investigated for a plastically compressible isotropic hardening–softening–hardening material under small-scale yielding conditions. Finite deformation, finite element calculations are carried out in front of the crack with a blunt notch. For comparison purpose a few results of a hardening material are also provided.

The present numerical calculations show that crack tip deformation and the field quantities near the tip significantly depend on the combination of plastic compressibility and slope of the hardness function. Furthermore, the consideration of plastic non-normality flow rule makes the crack tip deformation as well as the field quantities significantly different as compared to those results when the constitutive equation exhibits plastic normality.

To the best of the authors’ knowledge, analyses, related to the constitutive relation exhibiting plastic non-normality in the context of plastic compressibility and softening (or softening hardening) on the near tip fields, are not explored in the literature.

Construction projects commonly encounter complicated delay problems. Over the past few decades, numerous delay analysis methods (DAMs) have been developed. There is no consensus on whether existing DAMs effectively resolve delays, particularly in the case of complex concurrent delays. Thus, the primary objective of this study is to undertake a comprehensive and systematic literature review on concurrent delays, aiming to answer the following research question: Do existing delay analysis techniques deal with concurrent delays well?

This study conducts a comprehensive review of concurrent delays by both bibliometric and systematic analysis of research publications published between 1982 and 2022 in the Web of Science (WoS) and Scopus databases. For quantitative analysis, a bibliometric mapping tool, the VOSviewer, was employed to analyze 68 selected publications to explore the co-occurrence of keywords, co-authorship and direct citation. Additionally, we conducted a qualitative analysis to answer the targeted research question, identify academic knowledge gaps and explore potential research directions for solving the theoretical and practical problems of concurrent delays.

Concurrent delays are a critical aspect of delay claims. Despite DAMs developed by a limited number of research teams to tackle issues like concurrence, float consumption and the critical path in concurrent delay resolution, practitioners continue to face significant challenges. This study has successfully identified knowledge gaps in defining, identifying, analyzing and allocating liability for concurrent delays while offering promising directions for further research. These findings reveal the incompleteness of available DAMs for solving concurrent delays.

The outcomes of this study are highly beneficial for practitioners and researchers. For practitioners, the discussions on the resolution process of concurrent delays in terms of identification, analysis and apportionment enable them to proactively address concurrent delays and lay the groundwork for preventing and resolving such issues in their construction projects. For researchers, five research directions, including advanced DAMs capable of solving concurrent delays, are proposed for reference.

Existing research on DAMs lacks comprehensive coverage of concurrent delays. Through a scientometric review, it is evident that current DAMs do not deal with concurrent delays well. This review identifies critical knowledge gaps and offers insights into potential directions for future research.

This paper's aim is to identify structural and relational factors influencing the upstream channel management of organized retailers in India.

In‐depth interviews were conducted with 15 organized retailers and two manufacturers in India. Data were analyzed using the thematic network analysis technique from qualitative research. The authors use the framework of institutional theory to guide the discussion.

The findings suggest that government regulations influence the supply chain structure in India. Relational bonds offset the uncertainty borne of weak institutions. Informational transparency and long‐term orientation foster trust between channel partners. This trust, in turn, leads to collaborative partnerships.

The findings have implications for multinationals planning to enter the Indian retail industry. There is a need to understand complex regulatory and social institutions in India. The uniqueness of these institutions calls for adaptive strategies toward channel management.

The paper contributes to the literature on the supply chain structure in India. Despite growing interest in the Indian retailing industry, not many studies reflect on the specific B2B exchange structure in India. The paper fills this gap and also provides several marketing implications for multinational retailers planning to enter the Indian market.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

The purpose of this paper is to provide managers of multinational companies and researchers with a framework for investigating and effectively managing organizational factors and supply‐chain networks in lieu of conditions prevalent in developing countries.

This paper draws from the international management, supply‐chain management, and organizational behavior literature to construct a framework and formulate propositions linking important organizational factors to supply‐chain effectiveness in developing countries.

The paper reviews the literature, develops a useful framework, formulates propositions, and provides important managerial insights for supply‐chain management in developing countries.

Supply‐chain effectiveness is a broad construct in this paper. Future research might want to isolate and examine the potential effects of organizational variables on more narrow supply‐chain outcomes.

The modeled framework can help managers of Multinational Corporations (MNCs) better understand and manage important organizational variables in ways that render their supply chains in developing countries more effective.

Currently, the literature does not provide managers of MNCs and researchers a framework for investigating and effectively managing key organizational factors affecting supply‐chain networks in lieu of conditions prevalent in developing countries. This paper attempts to fill this gap. Propositions developed in this paper can serve as a foundation for future empirical research needed to support the development of theory in this area.

To carry out a comprehensive survey on 3PL practices in India and to establish the impact of usage of third party logistics services on business results.

Begins by analysing the studies carried out on 3PL practices and establishing a research framework. The input variables to the research framework depict the organization‐specific characteristics, such as the extent of usage of 3PL services, the reasons for outsourcing and the impact of the usage of 3PL services. The output function of future usage of 3PL services is influenced by the three input variables. Data collected through survey questionnaire on the input and output variables are used to establish impact of usage of 3PL services on business performance through statistical analysis.

The usage of 3PL services reveals positive and significant impact on business performance though 3PL practices are still at a nascent stage in India. Additionally, results clearly indicate that significant increase in outsourcing has been planned across all activities of the logistics function in the next 2‐5 years.

Further research work should focus on managing 3PL relationships; selection and contract management of 3PL service providers for building collaborative supply chain partnerships; and identifying critical success factors for 3PL implementation and establishing performance measures for long‐term 3PL relationships.

Detailed study to motivate supply chain managers to outsource and use 3PL services to focus on the core business, drive logistics cost reduction, and improve supply chain efficiency. These indicators should also help the 3PL service providers plan the depth and scope of their service offerings to user organizations in India.

The paper contributes to the current 3PL and supply chain practices and the emerging trends in 3PL services for achieving improved business results and supply chain efficiencies.

To assess the state of supply chain management (SCM) research in India and to understand the research trends and methodologies used. The present study also aims to create a taxonomy of the subject areas researched in India.

The present study employed the systematic literature review methodology. Literature from 395 peer journal papers in 67 leading journals over a 20-year period (2000–2020 Quarter-1) was comprehensively reviewed and assessed.

SCM research in India started around the year 2000. The quantum of research was low (single digit) until 2010. There has been steady growth over the last decade, and over 50% of the total papers up until now has been published in the last four years. The present study created a three-tiered taxonomy of the subject areas and classified the papers as per it. The first tier (level-1) has seven categories (SCM strategy, network design, SCM processes and integration, IT systems, skills, performance measurement and others). A perusal of the newly created taxonomy revealed that, except for a few areas under level-1 categories (such as SCM processes and SCM strategy), the other level-1 categories have not seen much research. Similarly, there is little or no research in a large number of level-2 categories (such as outsourcing strategy, channel strategy, demand management, demand fulfillment, customer relationship management, integrated supply chain planning, new product development, returns, supply chain orientation, performance monitoring, performance improvement, SCM adoption process, SCM implementation issues and quantified benefits of SCM). Methodologically, the rigor of SCM research in India needs improvement.

A comprehensive taxonomy of SCM subject areas researched in India at three cascading levels was created for the first time in the present study. The taxonomy will help provide researchers with a clear understanding of the structure of the subject areas and help in identifying areas where research has been carried out and the subject areas where gaps exist for future research to proceed. The present study also provides an overview of the methodological rigor of SCM research in India and points out some of the limitations that researchers should avoid in future studies.

Quite a few statistical and artificial neural network (ANN) models have been developed for the mass appraisal of the real estate by the municipalities. The purpose of this paper is to report the results of a research conducted to compare the prediction accuracy of the three most used models: multiple regression model, additive nonparametric regression, and ANN.

The three models were developed using the housing database of a town with 33,342 residential houses. In this database, the cutoff point for higher priced homes was $88 per square foot of living area.

The research confirmed that using statistical and ANN models are reliable and cost‐effective methods for mass appraisal of residential housing.

It was found that any of the three models can be used, with similar accuracy, for lower and medium‐priced houses, but the ANN is considerably more accurate for higher priced houses.