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Little attention has been paid to restructuring existing massive amounts of literature data such that evidence-based meaningful inferences and networks be drawn therefrom. This paper aims to structure extant literature data into a network and demonstrate by graph visualization and manipulation tool “Gephi” how to obtain an evidence-based literature review.

The main objective of this paper is to propose a methodology to structure existing literature data into a network. This network is examined through certain graph theory metrics to uncover evidence-based research insights arising from existing huge amounts of literature data. From the list metrics, this study considers degree centrality, closeness centrality and betweenness centrality to comprehend the information available in the literature pool.

There is a significant amount of literature on any given research problem. Approaching this massive volume of literature data to find an appropriate research problem is a complicated process. The proposed methodology and metrics enable the extraction of appropriate and relevant information from huge quantities of literature data. The methodology is validated by three different scenarios of review questions, and results are reported.

The proposed methodology comprises of more manual hours to structure literature data.

This paper enables researchers in any domain to systematically extract and visualize meaningful and evidence-based insights from existing literature.

The procedure for converting literature data into a network representation is not documented in the existing literature. The paper lays down the procedure to structure literature data into a network.

The paper aims to present a systematic literature review to analyze interrelated enablers of Industry 4.0 for implementation. Industry 4.0 is an integrated manufacturing strategy embedded with disruptive technologies. Adapting these technologies with the present industrial scenario is dependent on understanding the dynamics of various critical enablers in the existing literature. In this paper, an effort has been taken to validate and reinforce these enablers by experts in the field of Industry 4.0 for implementation.

A mixed-methodology is designed in this paper. A text mining approach with an expert’s linguistic assessment method is planned to discover the enablers from literature 2010 to 2019. The most critical enablers and their dependencies on other enablers are studied by using correlation analysis.

The research explores the power driving enablers in three groups: technology, features and requirements for implementing Industry 4.0 in the existing factory. In each group, a high degree of associated and dependent enablers is fragmented in detail.

This paper will benefit the research communities and practitioners to understand the significance of an integrated ecosystem of Industry 4.0 technologies, features and requirements for implementation.

The text mining approach integrated with expert’s linguistic assessment to explore the pairwise relationship among the enablers using word correlation is a novel approach in this paper. Moreover, to best of the authors’ knowledge, this is the first-ever attempt to conduct a structured literature review combined with text analysis and linguistic assessment to identify the enablers of Industry 4.0 for implementation.

The purpose of this paper is to study the production methods and potential of self-help groups (SHGs) for linking to micro, small and medium enterprises (MSME) in the Union Territory of Puducherry region.

The variables for the research work were identified through a literature review relating to SHGs production methods and 251 primary data were collected through the random sample using the survey method. The statistical software of IBM-SPSS was used to analyze the data using the statistical methods of descriptive statistics like frequency analysis simple mean and inferential statistics such as chi-square, correspondence analysis, correlation and ANOVA test.

The majority of SHGs consisting of 49.8% are willing to pay an amount up to Rs. 5,000 if training is provided through MSME organizations, a higher number of SHGs have indicated that they are very much interested in ancillary production activities, 35.5% of SHGs are using no machines and ANOVA test result shows that there is a significant difference between numbers of years of functioning with respect to production activity.

The authors have selected the Union Territory of Puducherry was taken as the sample region of the study due to its high rural poverty levels of 16.9%.

The research study endeavors to study the various production methods and preferences of SHGs and it will be of immense utility to the government, banks, microfinance organizations and other policymakers.

Existing literature reviews are conducted on various problems in service and manufacturing sectors, it is essential to conduct empirical research on an inclusive sector like SHG production activities and preferences in emerging economies like India.

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.