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The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

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.

Small farmers in India are collectivized and legalized as Farmer Producer Companies (FPCs) to progress in agri-food value chains as small agribusiness enterprises. FPCs are dependent on timely information for their sustainability and profitability. Mobile apps are a cost-effective form of information and communication technology. Hence, the purpose of this study is to explore the major determinants of mobile apps adoption by FPCs.

Quantitative and qualitative data are collected by administering a semi-structured questionnaire and conducting in-depth interviews with board members of 115 FPCs, with a total membership of 30,405 farmers operating in 14 districts of the state of Kerala, India. The logit model is used for quantitative analysis, while dialog mapping is used for qualitative analysis, based on an integrated technology acceptance model and technology organization environment framework.

Logistic regression results evidence that amongst FPC characteristics, while company size and age are significantly impacting apps adoption, there is no significant association between board size, education level, multiple commodities business or export intention of companies on apps adoption. Digital literacy and technical hands-on training for FPC board members are quintessential to facilitate mobile apps adoption.

The findings are pertinent to policymakers to earmark funds for technical handholding and digital upskilling of FPCs. The need for developing comprehensive, location-centric, farmer-friendly apps by agritech companies is evidenced.

To the best of the authors’ knowledge, this is a pioneering work in the domain of mobile apps adoption from a farmers’ agribusiness enterprise perspective in an emerging market economy using a mixed-methods approach.

The objective of this paper is to develop a map of the contours of the phenomenon of retailer failure by aggregating, parsing and extracting known findings regarding business failure in marketing, business and other streams of inquiry to provide a comprehensive understanding of research on the topic. Defined as the converse of retailer performance, an understanding of retail failure is expected to yield insights for performance measurement and benchmarking studies.

The paper includes a systematic literature review, employing state-of-the-art tools such as VOSViewer.

The analysis reveals patterns in the intellectual structure of the research on retail failure, as well as patterns of influence. While the discipline of marketing has been surprisingly limited in the study of retail failure, study of retail failure has been pursued by other branches of the business discipline, and even some disciplines other than business.

This paper provides a comprehensive and systematic literature review on the topic of retail failure.

The four pillars of food security are availability, accessibility, utilization and stability. In order to facilitate food security, an attempt is made to design a fruit supply chain network (FSCN) considering multi-compartment reefer trucks (MCRT) to reduce total supply chain costs. This in turn increases affordability, decreases food loss and increases availability, which further helps in improving food security.

A mixed integer non-linear programming (MNILP) model is developed to minimize the overall cost considering MCRT and the same is solved by a heuristic approach. A real-world case study is conducted to test the robustness of the model.

There is a considerable cost saving with the new proposed model (MCRT). The number of trucks used is drastically reduced when the dedicated truck is replaced with MCRT. Overall, the design of the FSCN not only improves food security by lowering the total supply chain cost but also shows a high impact on sustainability. Since the proposed model is a mathematical formulation, the same model can be applied to other perishable commodities like vegetables.

The proposed FSCN still requires more intermediaries to be added for more practicality. The model will be suitable for emerging markets mainly because the food supply chain sector is not completely organized.

This study is one of the initial studies in the context of facility location and FSCN optimization, specifically focusing on the inclusion of capacitated DCs. This study has the potential to assist supply chain managers in achieving sustainability by optimizing location decisions, inventory levels and movement between facilities. This study provides a valuable contribution towards the sustainable development goal of zero hunger (food security) by increasing affordability for low-income people.

The countryside population has always been depended on the revenues earned from agricultural yields. These yields often suffer losses in the absence of coordination guidelines in the post-yield supply chains (PYSC). This study aims to identify, address and mitigate the post-yield supply chain impediments (PYSCIs) that lead to enormous amounts of waste and revenue losses. These are the parameters that require government and stakeholders’ attention for alleviation from losses.

Structural equation modelling (SEM) was performed to categorise the identified PYSCIs as “standard measures”. The motive for SEM results is to attract the stakeholders’ attention toward PYSCIs for business sustainability. The PYSCIs were clustered into three “standard measures” (i.e. strategic, tactical and operational measures) for revenue generation and reduced fresh produce spoilage in the countryside.

The SEM results suggest that the focus should be on revising minimum support prices and government support for initiatives, subsidy schemes and incentives at the strategic level. Tactical initiatives focus on linking markets including exports, research and development, attitude towards certification, value addition process adoption and reduced number of stages in the supply chain. The operational initiatives are attitudes towards agriculture and entrepreneurship, transportation infrastructure, supply chain coordination, information visibility, scientific design for packaging and handling and storage space availability for both long and short term at the village level.

This study was performed in India; thus, the research outcomes of this study are restricted to adaption into the developing sub-continents with sub-tropical climates.

The existing level of losses in the PYSC demands introspection and policy changes at the farm level. In the era of cold chains, the Internet of Things, and other advanced mechanisms, a few elementary parameters must be worked upon to reduce PYSC losses. These parameters were identified as impediments to PYSC, requiring public, government and stakeholders’ attention. There is an urgent need for guidelines to be issued to mitigate losses. SEM was performed to attract the public, government and stakeholders’ attention toward impediments to fresh produce spoilage, opportunity generation and business sustainability.

This study uses a novel SEM approach where the PYSCIs were identified and empirically validated in an Indian context. The SEM approach will help in effective decision-making. Similar studies to manage the PYSCIs to reduce fresh produce spoilage with standard measures have not been reported in the literature.

Information and communication technology (ICT) implementation has demonstrated usefulness in supply chain coordination and efficiency optimization in various industries and sectors. This study investigates the extent of ICT deployment in fruits and vegetable supply chains (FVSC) from “farm-to-fork” to ensure food security.

This paper employs a systematic literature review (SLR) methodology and identified a total of 99 journal articles ranging from 2001 to April 2023 for analysis. The reviewed articles have been classified based on the framework proposed from the perspective of food security. Bibliometric and content analysis is carried out with the final list of articles to extract useful insights.

The findings reveal that ICT implementation in FVSC is a relatively new research area; researchers have started investigating several aspects of ICT in FVSC through varied research methodologies. Experimental research aimed at addressing food safety and condition monitoring of fruits and vegetables (FV) has started to gain traction while theory building is yet to gain traction in the literature reviewed. Findings indicate further research is required on technologies like blockchain (BCT), artificial intelligence (AI) and machine learning (ML), especially on key objectives such as food security, and the triple-bottom-line approach of sustainability. It also indicates that implementing relevant ICTs in FVSC can help delay, if not avert, the food crisis predicted by Malthusian theory.

This study used only well-established databases to ensure quality of the studies examined. There is a possibility of missing out on articles from other sources not considered. As a result, future SLR studies may employ additional databases, such as Springer Link, Taylor and Francis, Emerald Insight and Google Scholar. Other methodologies such as expert interviews and extra empirical methodologies may also be employed to give a more balanced picture and insights into ICTs implementation in FVSC.

This study offers a summative detail of the status of ICT implementation in FVSC and can serve as a reference guide for stakeholders in developing strategies for efficient FVSC management. This research work highlights the impact of ICT implementation in FVSC on the four pillars of food security which include improved availability, accessibility, utilization and stability.

This study focuses on ICT implementation for food security in FVSC. The SLR highlights the gaps and proffers potential solutions that enhance global efforts on food security through ICT-enabled reduction in food waste and food loss in FVSC.

Food fraud as intentional deception for economic gain relies on a low-tech food value chain, that applies a ‘paper-and-pencil approach’, unable to provide reliable and trusted data about food products, accompanied processes/activities and actors involved. Such approach has created the information asymmetry that leads to erosion of stakeholders and consumers trust, which in turn discourages cooperation within the food chain by damaging its ability to decrease uncertainty and capability to provide authentic, nutritional, accessible and affordable food for all. Lack of holistic approach, focus on stand-alone measures, lack of proactive measures and undermined role of customers have been major factors behind weaknesses of current anti-fraud measures system. Thus, the process of strong and fast digitalisation enabled by the new emerging technology called Industry 4.0 is a way to provide a shift from food fraud detection to efficient prevention. Therefore, the objective of this chapter is to shed light on current challenges and opportunities associated with Industry 4.0 technology enablers' guardian role in food fraud prevention with the hope to inform future researchers, experts and decision-makers about opportunities opened up by transforming to new cyber-physical-social ecosystem, or better to say ‘self-thinking’ food value chain whose foundations are already under development. The systematic literature network analysis is applied to fulfil the stated objective. Digitalisation and Industry 4.0 can be used to develop a system that is cost effective and ensures data integrity and prevents tampering and single point failure through offering fault tolerance, immutability, trust, transparency and full traceability of the stored transaction records to all agri-food value chain partners. In addition, such approach lays a foundation for adopting new business models, strengthening food chain resilience, sustainability and innovation capacity.

The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.

This paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.

AI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.

There is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.

This paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.

This paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.