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The purpose of this research paper is to understand the major factors considered before choosing the mode of transportation for the freight movement in India by different stakeholders and look into the future prospects on each of these sectors, i.e. railways, roadways and inland waterways.

This paper collected the primary data from the various stakeholders in the transportation sector and the secondary data through websites and various ministries of each of the sectors. The various factors are then determined by thoroughly analysing the responses by performing factor analysis in SPSS.

Earlier railways were the preferred medium of transportation, but the dynamics shifted during the 90’s to roadways, and now, it is responsible for nearly 60% of the freight traffic with waterways slowing increasing its share of the pie. Also, there are a lot of factors which stakeholders consider, but the major factors that came out are cost, sustainability, timing, government initiatives, visibility and performance.

The result of this study implies that sectors should create a robust network for easy reach of the customers and try working in conjunction to create an efficient, affordable and highly connected network. This study will also help in taking vital decisions regarding the future planning of transportation sector.

The findings help in improving the transportation network and help in better decision-making by various stakeholders while choosing the mode of transportation.

This paper aims to develop a new high accuracy numerical method based on off-step non-polynomial spline in tension approximations for the solution of Burgers-Fisher and coupled nonlinear Burgers’ equations on a graded mesh. The spline method reported here is third order accurate in space and second order accurate in time. The proposed spline method involves only two off-step points and a central point on a graded mesh. The method is two-level implicit in nature and directly derived from the continuity condition of the first order space derivative of the non-polynomial tension spline function. The linear stability analysis of the proposed method has been examined and it is shown that the proposed two-level method is unconditionally stable for a linear model problem. The method is directly applicable to problems in polar systems. To demonstrate the strength and utility of the proposed method, the authors have solved the generalized Burgers-Huxley equation, generalized Burgers-Fisher equation, coupled Burgers-equations and parabolic equation in polar coordinates. The authors show that the proposed method enables us to obtain the high accurate solution for high Reynolds number.

In this method, the authors use only two-level in time-direction, and at each time-level, the authors use three grid points for the unknown function u(x,t) and two off-step points for the known variable x in spatial direction. The methodology followed in this paper is the construction of a non-polynomial spline function and using its continuity properties to obtain consistency condition, which is third order accurate on a graded mesh and fourth order accurate on a uniform mesh. From this consistency condition, the authors derive the proposed numerical method. The proposed method, when applied to a linear equation is shown to be unconditionally stable. To assess the validity and accuracy, the method is applied to solve several benchmark problems, and numerical results are provided to demonstrate the usefulness of the proposed method.

The paper provides a third order numerical scheme on a graded mesh and fourth order spline method on a uniform mesh obtained directly from the consistency condition. In earlier methods, consistency conditions were only second order accurate. This brings an edge over other past methods. Also, the method is directly applicable to physical problems involving singular coefficients. So no modification in the method is required at singular points. This saves CPU time and computational costs.

There are no limitations. Obtaining a high accuracy spline method directly from the consistency condition is a new work. Also being an implicit method, this method is unconditionally stable.

Physical problems with singular and non-singular coefficients are directly solved by this method.

The paper develops a new method based on non-polynomial spline approximations of order two in time and three (four) in space, which is original and has lot of value because many benchmark problems of physical significance are solved in this method.

The purpose of this paper is to investigate the impact of total productive maintenance (TPM) and total quality management (TQM) practices on operational performance and their inter-relationship.

The present study includes three main constructs, namely, TPM, TQM and operational performance of pharmaceutical industry. Under TPM, four constructs, namely, disciplined maintenance, information tracking, housekeeping and operator involvement has been considered with the help of literature. In TQM, four constructs, namely, quality data and reporting, product innovation, research and development (R&D) management and technology management has been considered. Out of 410 Indian pharmaceutical plants contacted for survey, 254 responses have been used in the study for analysis. The factor analysis, path model and structural equation modeling has been used to analyze the proposed framework. The results for alternate models has been studied, interpreted and reported. Finally the direct and indirect effect of TPM and TQM on operational performance has been tested and checked for proving and disproving the hypotheses.

TPM practices have a significant impact on plant-level operational performance. When TPM and TQM practices are coming together to achieve operational performance, then TPM is having strong influence on operational performance. TQM is having significant support from TPM to achieve operational performance. TPM impact TQM and TQM in turn helps to achieve operational performance. TPM practices impact significantly R&D, product innovation and technology management, whereas quality data and reporting is the least contributor toward TQM. This may help industry to understand implications of implementation of TPM and TQM to achieve plant-level operational performance. TPM will help to reduce the cost of quality in terms of reduced scrap and less defective products.

The present study provides the useful insights to practicing managers. In literature it has been mentioned that TQM helps in TPM implementation. In practice TPM plays a great role to achieve quality in processes and therefore in products. In turn quality products, with reduced work in process inventory, less defective products and reduced scrap helps to achieve the operational performance at plant level. TPM practices will help the organization to improve the pace of product innovation and improvement in productivity, which is critical to pharmaceutical industry. The continuous monitoring of TPM practices can help organizations to run day to day operations and maintenance requirement of each machine over a specified period of time.

The present study diagnoses the inter-dimensional linkage between TPM, TQM and operational performance. The pharmaceutical industry is complex system of advance equipment’s and processes. After human resources, the health of machines/equipment’s describe the strength of an organization. The machines require the regular maintenance to produce the products with desired specifications. The specifications in medicines and very tight, which can be achieved only if machines/testing equipment’s are updated and maintained regularly. The TPM practices will helps the plants to achieve the operational performance by having quality in processes.

Internet of Things (IoT) is an up-and-coming conception that intends to link multiple devices with each other. The aim of this study is to provide a significant analysis of Green IoT. The IoT devices sense, gather and send out significant data from their ambiance. This exchange of huge data among billions of devices demands enormous energy. Green IoT visualizes the concept of minimizing the energy consumption of IoT devices and keeping the environment safe.

This paper attempts to analyze diverse techniques associated with energy-efficient protocols in green IoT pertaining to machine-to-machine (M2M) communication. Here, it reviews 73 research papers and states a significant analysis. Initially, the analysis focuses on different contributions related to green energy constraints, especially energy efficiency, and different hierarchical routing protocols. Moreover, the contributions of different optimization algorithms in different state-of-the-art works are also observed and reviewed. Later the performance measures computed in entire contributions along with the energy constraints are also checked to validate the effectiveness of entire contributions. As the number of contributions to energy-efficient protocols in IoT is low, the research gap will focus on the development of intelligent energy-efficient protocols to build up green IoT.

The analysis was mainly focused on the green energy constraints and the different robust protocols and also gives information on a few powerful optimization algorithms. The parameters considered by the previous research works for improving the performance were also analyzed in this paper to get an idea for future works. Finally, the paper gives some brief description of the research gaps and challenges for future consideration that helps during the development of an energy-efficient green IoT pertaining to M2M communication.

To the best of the authors’ knowledge, this is the first work that reviews 65 research papers and states the significant analysis of green IoT.

This paper presents the concerns in agri-food supply chain. Further the research investigates the role of information and communication technology (ICT) in agri-food supply chain and determines the impact of supply chain management (SCM) practices on firm performance.

The theoretical framework was proposed for the study on the basis of existing literature. Data for the study was collected with the help of structured questionnaire from 121 executives and officers of the public food distribution agency. Partial least square (PLS)–structured equation modeling was employed to test the framework and hypotheses.

The results indicate that ICT and SCM practices (logistics integration and supplier relationships) have a significant relationship. Furthermore, SCM practices (information sharing, supplier relationship and logistics integration) have a significant and positive impact on performance of the organization.

Further research could be carried out to test the moderation effect of SCM practices between ICT and organizational performance (OP). Extending the research study to the companies operating in other sectors can enhance the external validity of the study and improve the accuracy of parameters examined.

This study can be of interest to the agri-food industry as well as other industry practitioners interested in improving the performance of the organization from the view of supply chain.

The outcomes of this study have important implications that translate into a series of recommendations for the management of public food distribution as well as other agri-food-based supply chains.

The coronavirus disease 2019 (COVID-19) pandemic created heavy pressure on firms, by increasing the challenges and disruptions that they have to deal with on being sustainable. For this purpose, it is aimed to reveal the role of the smart circular supply chain (SCSC) and its enablers towards achieving Sustainable Development Goals (SDGs) for post-pandemic preparedness.

Total interpretive structural modelling and Matrice d'Impacts Croises Multipication Applique' a un Classement (MICMAC) have been applied to analyse the SCSC enablers which are supported by the natural-based resource view in Turkey's food industry. In this context, industry experts working in the food supply chain (meat sector) and academics came together to interpret the result and discuss the enablers that the supply chain experienced during the pandemic for creating a realistic framework for post-pandemic preparedness.

The results of this study show that “governmental support” and “top management involvement” are the enablers that have the most driving power on other enablers, however, none of them depend on any other enablers.

The identification of the impact and role of enablers in achieving SDGs by combining smart and circular capabilities in the supply chain for the post-pandemic.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

Agriculture value chains (AVCs) have experienced unprecedented disruption during the COVID-19 pandemic, with lockdowns and stringent social distancing restrictions making buying and selling behaviours complex and uncertain. This study aims provide a theoretical framework describing the stakeholder behaviours that arise in severely disrupted value chains, which give rise to inter-organisational initiatives that impact industry sustainability.

A mixed-methods approach is adopted, in which uncertainty theory and relational governance theory and structured interviews with 15 AVC stakeholders underpin the initial conceptual model. The framework is empirically validated via partial least squares structural equation modelling using data from an online survey of 185 AVC stakeholders based in India.

The findings reveal that buyer and supplier uncertainty created by the COVID-19 lockdowns gives rise to behaviours that encourage stakeholders to engage in relational governance initiatives. Progressive farmers and other AVC stakeholders welcome this improved information sharing, which encourages self-reliance that positively impacts agricultural productivity and sustainability.

The new framework offers farmers and other stakeholders in developing nations possibilities to sustain their AVCs even in dire circumstances. In India, this also requires an enabling ecosystem to enhance smallholders' marketing power and help them take advantage of recent agricultural reforms.

Research is scarce into the impact of buyer and seller behaviour during extreme supply chain disruptions. This study applies relational governance and uncertainty theories, leading to a proposed risk aversion theory.