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The purpose of the study is to identify the factors affecting the entrepreneur's choice of the dairy value chain and evaluate the impact of the value chain on farm performance (profit).

Primary data were collected from dairy entrepreneurs in India, covering nine states. A multinomial treatment effect model (controlling for selection bias and endogeneity) was used to evaluate the impact of the choice of the value chain on entrepreneurs' profit.

Dairy entrepreneurs operating in any recognized value chain other than the value chain driven by the consumer household realize a comparatively lesser profit. Dairy farmers have established direct linkages with customers in urban areas – who could pay premium prices for safe and quality milk. Food safety compliance is positively associated with profit and entrepreneurs (who have undergone formal training in dairying) preferred partnerships with a formal value chain. The prospects of starting a dairy enterprise are slightly higher in villages compared to urban areas.

Dairy entrepreneurs can make a shift in accordance with the study's findings and boost their profitability. It aids in comprehending how trainees (who obtained advice and training for raising dairy animals from R&D organizations) and non-trainee dairy farmers make value chain selections, which ultimately affect profitability. However, purposive sampling and a small sample size limit the universal implications of the study.

Developing entrepreneurial behavior and startup culture is at the center of policymaking in India. The findings imply that the emerging value chain not only enhances the profit of dairy farmers by resolving consumer concerns about food safety and the quality of milk and milk products but also builds consumer trust.

This paper offers insight into how the benefits of dairy entrepreneurs vary with their participation in the different value chains. The impact of skill development/training programs on value chain selection and farm profitability has not yet been fully understood. Here is an attempt to fill this gap. This paper through light on how trained and educated dairy entrepreneurs are able to establish a territorial market by approaching premium customers – this is an addition to the existing literature.

Blockchain technology (BCT), since its emergence touted to be disruptive, is gaining momentum, especially in the agri-food system owing to its multiple benefits.

The authors attempted to conduct a systematic bibliometric visualization analysis of the BCT in the agri-food system. The analysis investigated the list of countries and institutions that conducted research on BCT in agriculture, growth trend analysis in research publications, bibliographic coupling of journals using the VOSviewer tool, and the countries and institutions researching BCT.

The authors discovered that China, the USA and India were the highly active countries in BCT research and publication. However, India has only limited research collaboration with other countries as compared to China and the USA. The keyword analysis indicates the role of BCT in order to maintain the transparency of the supply chain by means of protecting the privacy of the personal data of the stakeholders.

More research related to the implementation of BCT in livestock, fishery and agro-forestry sector is recommended.

The case examined is of particular interest as it is concerned with efficient supply chain management.

This study adds value and evidence to the scope and benefits of BCT by providing a comprehensive literature review, with a special focus on the opportunities and challenges concerned with implementation of BCT in the Indian agri-food system.

1. Blockchain technology (BCT) – a promising tool to resolve issues in agriculture supply chain.

2. BCT ensures transparency and protection of information along the supply chain transactions.

3. China, the USA and India are the highly active countries in BCT research and publication.

4. Multiple potential benefits to stakeholders are attributed to the BCT in the agri-food system.

5. The key challenge is to bridge the digital gap between developed and developing nations.

6. Future research on BCT should aim at easing and undistorted competition among stakeholders.

Blockchain technology (BCT) – a promising tool to resolve issues in agriculture supply chain.

BCT ensures transparency and protection of information along the supply chain transactions.

China, the USA and India are the highly active countries in BCT research and publication.

Multiple potential benefits to stakeholders are attributed to the BCT in the agri-food system.

The key challenge is to bridge the digital gap between developed and developing nations.

Future research on BCT should aim at easing and undistorted competition among stakeholders.

The purpose of the paper is to contribute to the understanding of regional entrepreneurial development by constructing an institutional environment index in India, where high heterogeneity is found in the economic development and entrepreneurial activities across its states. It tries to fill the gap of research which hampers the effectiveness of policy efforts to promote job growth through entrepreneurship development in India and contributes in understanding the phenomenon why the relative contribution of entrepreneurship varies across states.

The paper uses a composite index of institutional quality based on the Penalty for Bottleneck (PFB) methodology to capture the institutional environment differences across the states of India. The relationship between the institutional environment and the measures of entrepreneurship is established through various statistical and econometric techniques such as correlation and regression.

The paper finds the regional and contextual institutional environment differences in India starker and more varied than is generally viewed. The empirical evidence suggests that the differences in institutional quality scores can play a significant role in explaining the variations in the extent of entrepreneurial activities across the Indian states. The findings of this paper demonstrate a differential influence of the local institutional environment on the entrepreneurial activities at a regional level in a developing economy like India.

This study is limited by the data sources and index design; therefore, it cannot completely specify all institutional factors and their combined influences on entrepreneurial activities at the regional level. However, it makes a significant contribution to expanding the current body literature of institutional environment reform and entrepreneurship development in developing countries.

The policy implication of the paper highlights the need of policymakers to think outside the individual policy silos and consider the institutional environment as a whole. Priority attention of the policymakers should be on the institutional reform for any type of entrepreneurship development. A corollary implication of the O-ring theory of development and the PFB methodology is that the policy effort is allocated most effectively when it seeks to alleviate bottlenecks.

The main implication of this paper for the policymaking is that it is necessary to focus more on the weak institutional factors (bottlenecks) and on the general environment improvement for any entrepreneurship development.

The study contributes to study the problem of entrepreneurship discrepancy in India through the lens of institutions and institutional environment. This study is an improvement over the previous studies by testing the statistical significance of the institutional environment on the entrepreneurship.

The operational activities of manufacturing organizations are continuously degrading the environment. Circular economy adoption can help industries optimize their resources along with minimal waste generation. The purpose of this study is to identify and analyze the barriers that hinder the adoption of circular economy (CE) in the manufacturing sector.

The barriers are extracted from a critical review of the literature and listed into three categories as social, economic and environmental. The barriers in each category are then analyzed using the fuzzy-based analytic hierarchy process method. The approach is capable to consider the fuzziness in the preference of barriers and determine their priority.

The pairwise comparison and weight of all the main and sub-criteria are computed, which helps in deciding the ranking of barriers. The results show that social criteria are having the highest importance followed by economic and environmental criteria. Among all the sub-criteria, low demand and acceptance of remanufactured products is at the highest level followed by lack of government support and legislation barrier.

The outcome of this study would be helpful for the decision makers and business managers in the manufacturing sector to focus on the barriers in each category and accordingly formulate strategies for CE adoption.

The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.

The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.

Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.

Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.