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The purpose of this paper is to review the agriculture supply chain (ASC) literature along many dimensions which include but are not restricted to scope, objective, wastages, driver, obstacle, outcome, etc.

In total, 203 relevant and scholarly articles of various researchers and practitioners during 2000-2016 were reviewed. The information related to definition, research methodology, global research spread, supply chain strategy, various types of produce, author profile and year of publication of ASC were collected and analysed.

The information related to empirical research and viewpoint of various ASC drivers were captured, studied and analysed in detail. Although inventory policy, demand forecasting and ASC integration were found to be important areas of ASC, they were less focused, studied and researched.

Mainly post-harvest ASC of different agricultural produces were considered whereas products such as dairy, fishery and meat supply chains were not included in the study.

The paper provides an insight into various aspects of ASC in general and one can get a deeper and richer knowledge on it which will help in formulating effective strategies to design of an effective and efficient ASC. It uncovers the research gaps for the new future research paths. This systemic review is strongly felt to fill the gap in the ASC literature.

This paper aims to address the conceptual and practical challenges in integrating triple bottom line (TBL) sustainability in the agriculture supply chain (ASC). It identifies the key enablers for each of the three dimensions of TBL sustainability, analyses their causal relationships as well as cross-dimensional interactions under each TBL dimension. Further, it develops a decision support framework (DSF) for the assessment of TBL sustainability practices and policies in ASC and validates it through a case study.

An interpretive structure modelling (ISM) methodology is deployed to establish the interrelationships among all TBL enablers and to identify the enablers with high driving power on sustainable ASC. Brainstorming by a group of experts was used to identify the relevant enables. Finally, a DSF was developed as a resultant of ISM.

The paper provides a set of enablers with high driving power that can significantly influence the sustainability practices and policies in ASC. The social enablers directly help to enhance the effect of economic enablers and collectively these enhance the effect of environmental enablers. If agriculture firms and supply chains design innovative policies and develop practices based on these enablers, they can achieve sustainable ASC. Consequently, the living standards of the people directly or indirectly associated with the agriculture firm or supply chain can be improved without compromising on economic performance.

The paper consolidates the fragmented knowledge of sustainable supply chain management in the agriculture sector and suggests a DSF to policymakers, managers and practitioners for assessing TBL sustainability practices and policies. The DSF has wide applicability in other sectors of production and operations management as these sectors also face the challenge of achieving TBL sustainability across their supply chain.

The DSF, developed in the paper, is a useful tool for practitioners to frame and analyse sustainability initiatives and policies for ASC. A firm or supply chain may achieve TBL sustainability if it succeeds in uplifting the social status of its stakeholders.

It is a first step towards addressing the practical challenge of integrating sustainability in the agriculture sector of emerging economies and provides a path to improve the livelihood of people in the agriculture sector. Stakeholder engagement with a focus on collaboration and awareness may lead to the desired social and environmental consequences. Potential adverse social effects also need to be considered.

This paper focusses on the so far rather neglected but essential aspect of integrating TBL sustainability in the agriculture sector of emerging economies. The hierarchal representation and classification of the TBL sustainability enablers of sustainability is a unique effort in the field of ASC. Development of DSF is one of the first attempts to create a mapping between various enablers of TBL sustainability. The novelty of the study lies in the sector-specific, holistic evaluation of TBL sustainability policy measures that may lead to improvements in practice.

Performance measurement (PM) of any supply chain is prerequisite for improving its competitiveness and sustainability. This paper develops a framework for supply chain performance measurement (SCPM) for agriculture supply chain (ASC) based on internet of things (IoT). Moreover, this article explains the role of IoT in data collection and communication (SC visibility) based on the supply chain operation reference (SCOR) model.

This research identifies various key performance indicators (KPIs) and also their role in SCPM for improving its sustainability by using SCOR. Further, Shannon entropy is utilized for weighing the basic processes of SCPM and by using weights, fuzzy TOPSIS is applied for ranking of identified KPIs at metrics level 2 (deeper level).

“Flexibility” and “Responsiveness” have been reported as two most important KPIs in IoT based SCPM framework for ASC towards achieving sustainability.

In this research, metrics are explained only at SCOR level 2. But, this research will guide the managers and practitioners of various organizations to set their benchmark for comparing their performance at different levels of business processes. Further, this paper has managerial implications to develop an effective system for PM of IoT based data-driven ASC.

By using IoT based data driven system, this article fills the gap between SCPM by measuring different SC strategies in their performance measurable form of reliable, responsive and asset management etc.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

The prime aim of this paper is the identification and prioritization of performance indicators, which motivate the development of an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC). Also, this research aims for checking the robustness of obtained results.

Ten performance indicators have been identified based on the five “criteria in the IoT-based traceable system”. Further, based on five criteria, performance indicators were ranked by using grey-based “Additive Ratio Assessment”.

Sustainable practices obtained first rank, and certification of agri-products obtained worst ranking. Further, based on sensitivity analysis, tracking of agri-products and stakeholders' behavior have found high sensitivity. Also, information sharing and global distribution networks have found the least sensitive performance indicators.

This research has some limitations of taking only a few criteria and alternatives. This study may also contribute as a practical insight to the practitioners and managers in decision-making in the adoption of an IoT-based traceable system within the ASC.

This research may motivate the implementation of an IoT-based efficient traceability mechanism that improved the sustainability and consumer's trust in the ASC during different types of hazardous activities and other outbreaks (COVID-19). Also, this research has provided a theoretical insight based on the dynamic capability theory (DCT).

The purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes.

The authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture.

Four factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business.

The proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance.

Practitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production.

This paper brings new developed methods and practices in agriculture for betterment of society.

This paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448

This paper identifies, evaluates and structures research that focuses on “collaboration” (C), “trust” (T) and “performance” (P) in the agri-food supply chain (AFSC) and reveals its intellectual foundation. It aims to synthesize research published over a period of 18 years (from 2003 to the beginning of 2020) and provide a platform for practitioners and researchers in their efforts to identify the existing state of work, gaps in current research and future directions in the area of collaboration–trust–performance (CTP) in the AFSC.

Prior to carrying out a bibliometric analysis (BA), literature search was performed, identifying 69 related papers focused on CTP in the AFSC. The content of the papers was further analysed in a systematic literature review (SLR) with regard to the subject area, theoretical lenses, research methodology, supply chain (SC) category and other relevant categories.

CTP in the AFSC are based on a relationship marketing and operations management fundament but show specific particularities. AFSCM is a multi-dimensional design task, and collaboration is considered a necessity, whereas trust significantly affects the AFSC effectiveness. The paper also develops a conceptual CTP model, which shows the interrelations between all identified construct variables, where the authors were able to see also bi-directional relations. Furthermore, the paper presents viable future research opportunities, e.g. focus on organic food chains or multi-actor analysis.

Results of the conducted BA refer to the CTP discussion within a preselected number of peer-reviewed academic articles, which are provided by the WoS CC (Web of Science Core Collection) database.

CTP measurements within the AFSC context are a relevant subject with increasing academic interest in the area of agricultural economics as well as operations and supply chain management (SCM). Therefore, further studies are necessary to develop the related theory and ascertain the practical implications of collaboration, trust and performance among members in the consistently complex AFSC.

CTP have been recognized as important factors for designing a sustainable SCM strategy, particularly in the case of the AFSC. However, although previous studies have addressed the AFSC, there is insufficient knowledge regarding all three pillars (CTP) and how they enable successful AFSCM. The originality of this paper lies in systematically mapping the intellectual base of CTP research and providing path forward for research in AFSCM.

The purpose of this paper is to analyze the value chain activities of shrimp firms in Bangladesh, and mapping the Porter’s (1985) value chain framework to see if it works or not. The present study identifies the gap, synthesizes and analyzes those gaps which lead the firms to create more values from firms to consumers.

Interviews were conducted with the shrimp industry managers in the southern region of Bangladesh. Exploratory qualitative research method was used and the questionnaire was semi-structured. Data were gathered from 43 firm managers. After sending multiple phone calls and face to face meeting, the response rate was 35.83 percentages.

Poor transportation, communication gap between the stakeholders, shortage of raw shrimps and lack of quality standard were the areas where shrimp industries were suffering. It was found that some of the primary and secondary activities of shrimp industries did not map with Porter’s framework. Based on Porter’s framework, the study suggested that analyzing and synthesizing those gaps can lead the firm more value and competitive advantages.

Limitations include a lack of knowledge on value chain and shortages of raw materials for the processing plants. Moreover, the sample size was small for this exploratory study.

Shrimp industries will learn standard value chain activities, and identify the gaps based on the mapping of Porter’s value chain.

Using Porter’s value chain this is the first empirical study in the shrimp firms in Bangladesh. The primary research contribution is the revised theoretical framework which can be used for further research on shrimp industries in Bangladesh.

Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA).

A systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research.

The combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation.

The study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully.

Due to consumption changes in the post-pandemic era, the production safety of agricultural products is affecting global consumers. This paper constructs an evaluation index of the agricultural Internet of things (IOT) traceability system and evaluates it using the dynamic hesitant-fuzzy linguistic term sets (HFLTS)-based DEMATEL method to improve agricultural supply-chain links and improve production quality.

The agricultural IOT traceability index system is constructed using the literature and expert interviews; it comprises 6 first-level indices and 20 second-level indices. The agricultural IOT traceability system is evaluated using the dynamic HFLTS-DEMATEL method.

Producers' awareness of agricultural-production safety (A11) has the most significant impact on production and processing links, while warehouse location and storage capacity (A31) have the largest impact on the circulation link. Inspection authenticity and transparency and quarantine information (A41) have the largest impact on the detection-consumption link. The extent to which the traceability-platform construction is complete (A62) has the largest impact on technical support.

The present paper may be limited to the era of post-pandemic, and it is hard to consider all the indices. Further research can broaden the research context and establish a more comprehensive index system.

The index system constructed in this study will surely help relevant regulatory authorities in China to promote the construction of agricultural IOT traceability system and establish a unified standard, so as to provide a basis for future developers to enter the field. Accordingly, it also can help every subject to identify the key indices of each process in the agricultural-product supply chain and guide relevant departments to conduct targeted information tracking and management. The consumers could also understand the standards of traceable agricultural products and effectively protect their own rights and interests.

The existing literature does not provide an objective, unified standard for measuring a decentralized traceability system or identifying key processes. This study therefore proposes a new evaluation index system and uses a dynamic evaluation method to determine the importance of key indices. This study identifies the most important indices in each process, making it possible to discover, improve, and enhance the quality of agricultural products at a practical level.