Purpose: 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.Design/methodology/approach: 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.Findings: 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.Research limitations/implications: 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.Originality/value: 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.

Studying consumer behaviour towards food and nutrition labelling (F&NL) is increasingly becoming important across the world. Bibliometric analysis is a way to identify influential research, track trends, and understand methodologies in any field. In this paper, we used bibliometric analysis and a review of theories too to critically evaluate theories and concepts used in studying consumer behaviour towards F&NL. Using the Scopus database, we obtained 1017 articles, which were further filtered to perform various analyses like Co-occurrence and co-authorship network analysis. We found limited East and Southeast Asia-based research. Personal beliefs, attitudes, and past experiences were identified as influencers of consumer behaviour regarding food labelling. Trending topics were food waste, purchase intention, and sustainability labels.

Chatbots are increasingly becoming a fundamental part of social life. They allow us to communicate in more sophisticated ways using the internet. It is crucial to study chatbots to gain comprehensive insights into their developments, trends, impact, and advancement in the field. This paper presents a bibliometric analysis of chatbot research conducted using the R bibliometrix tool and SPAR-4 protocol. The analysis includes 805 scholarly articles published between 2005 and 2021, collected from the Web of Science and Scopus databases. A conceptual, intellectual, and social structure analysis was conducted to analyse impactful authors, journals, and documents. The study reveals that adoption of chatbots in business accelerated during the pandemic period. The findings also highlight a plethora of opportunities for research in areas like chatbot design cues, user experience, chatbots in collaboration with human agents, and chatbot ethics and privacy. This study's findings provide valuable insights for practitioners, policymakers, developers and researchers to understand the current state of the chatbot research landscape and to identify promising future research directions.

Purpose: Several businesses have begun to use memes as part of social media marketing. Although memes have been independently explored through various theories, their use in social media marketing has not yet been explored. This study analyzes theories used to study memes and suggests popular marketing theories to do the same for memes in social media marketing.Design/methodology/approach: This study employed a mixed methodology. We used the Scopus database and the SPAR-4 protocol to gather, organize, and evaluate the literature. A bibliometric analysis was performed to understand the themes explored in the literature. Manual content analysis was performed to identify the theories used to elucidate study memes.Findings: We profiled meme research through a bibliometric analysis. Relevance theory, Rhetoric theory, Theories of humor, Evolutionary theory, and the theory of conceptual blending are the most frequently used theories in meme literature. Furthermore, we found that the marketing theories used to study memes are limited.Practical implications: The findings of this study will benefit academia, marketers, and social media managers by offering a comprehensive picture of theories used in meme research. It also suggests new avenues for conducting future research on memes based on identified theories.Originality/value: This is one of the first known studies to employ both bibliometric and content analyses to review theories in meme literature. Furthermore, we suggest marketing theories and research questions to explain meme marketing.