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Cold supply chain technology is critical for extending the shelf life of perishable leafy green vegetables. This study aims to investigate the concept of managing leafy green products using cold supply chain technology and visualise the findings.

Using expert interviews and data visualisation approaches, this study examines how organisations deal with the complexity of cold supply chain processes and networks. Thematic data analysis was conducted. Two types of software were used to accomplish the research objectives. The first software used AntConc version 3.5.8 with word frequency (N-gram) analysis, whereas the second software, VOSViewer offered co-occurrence network visualisation and cluster analysis.

The findings show that the appropriate design of cold chain technology is critical in ensuring the freshness and quality of leafy green vegetables. The primary goal of managing the complexity of the cold supply chain is to achieve product freshness and energy efficiency. Regardless of the importance of energy efficiency, cold supply chains require warehouse management solutions for transportation and storage.

This study found that proper design and selection of appropriate technology in the cold supply chain have driven the companies to improve the firms’ competitive advantage while delivering the best quality of perishable leafy green food products. In addition, the freshness, quality, safety, and health of leafy green vegetables will be determined by the company’s capacity to handle long-distance transportation and select the appropriate distribution channels and storage. Warehouse management system technology was found to be secondary compared to cold chain technology, although distribution and warehousing practices are critical for supply chain performance.

This study has established the conceptual indicators based on best practices and outcomes for the cold supply chain. This study argued that cold supply chain management and performance should be monitored independently. Furthermore, the theory of technological adoption can be expanded to include product nature as a driver. Finally, this study has established cold chain best practices based on a perishable supply chain perspective. The findings of this study can promote healthy foods to solve zero hunger and achieve sustainable development goals. Although this study demonstrates that technology improves supply chain practises, cold storage and logistics benefit the most from technological advancements. In contrast, non-cold supply chains benefit from technology-driven improvements in performance.

Circular supply chain management (CSCM) is considered a promising solution to attain sustainability in the current industrial system. Despite the exigency of this approach, its application in the food industry is a challenge because of the nature of the industry and CSCM being a novel approach. The purpose of this study is to develop an industry-based systematic analysis of CSCM by examining the challenges for its application, exploring the effects of recognised challenges on various food supply chain (FSC) stages and investigating the business processes as drivers.

Stakeholder theory guided the need to consider stakeholders’ views in this research and key stakeholders directly from the food circular supply chain were identified and interviewed (n = 36) following qualitative methods.

Overall, the study reveals that knowledge, perception towards environmental initiatives and economic viability are the major barriers to circular supply chain transition in the UK FSC.

This research provides a holistic perspective analysing the loopholes in different stages of the supply chain and investigating the way a particular circular supply chain stage is affected by recognised challenges through stakeholder theory, which will be a contribution to designing management-level strategies. Reconceptualising this practice would be beneficial in bringing three-tier (economic, environmental and social) benefits and will be supportive to engage stakeholders in the sustainability agenda.

Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

Natural disasters cause serious operational risks and disruptions, which further impact the food supply in and around the disaster-impacted area. Resilient functions in the supply chain are required to absorb the impact of resultant disruptions in perishable food supply chains (FSC). The present study identifies specific resilient functions to overcome the problems created by natural disasters in the FSC context.

The quality function deployment (QFD) method is utilized for identifying these relations. Further, fuzzy term sets and the analytical hierarchy process (AHP) are used to prioritize the identified problems. The results obtained are employed to construct a QFD matrix with the solutions, followed by the technique for order of preference by similarity to the ideal solution (TOPSIS) on the house of quality (HOQ) matrix between the identified problems and functions.

The results from the study reflect that the shortage of employees in affected areas is the major problem caused by a natural disaster, followed by the food movement problem. The results from the analysis matrix conclude that information sharing should be kept at the highest priority by policymakers to build and increase resilient functions and sustainable crisis management in a perishable FSC network.

The study suggests practical implications for managing a FSC crisis during a natural disaster. The unique contribution of this research lies in finding the correlation and importance ranking among different resilience functions, which is crucial for managing a FSC crisis during a natural disaster.

The purpose of the study was to produce a healthier, convenient and traditional ready-to-eat (RTE) snack option with increased nutritional value, using spent hen meat, dietary fibre (DF) and simple technological methods. The product was designed to be stable without refrigeration and be easily adoptable by local self-help groups, rural women and youth and entrepreneurs in urban and semi-urban areas.

Conventional binder used for making snacks, i.e. rice flour was partially replaced by different sources of antioxidant DFs, i.e. oat flour (T₁ – 10%), finger millet flour (T₂ – 5%) and amaranth flour (T₃ –15%) to prepare spent hen snack sticks (SHSS). The snacks were then packaged in low density polyethylene (LDPE) pouches and evaluated for their storage stability at ambient temperature for a period of 35 days. Their physico-chemical, sensory and microbiological quality was evaluated at a regular interval of 7 days. The proximate composition of developed SHSS was compared to commercially available snack products (chakli/murukku – snacks without meat).

The fibre-enriched SHSS showed significant improvement in nutritive value, as they contained more fibre (p = 0.001) and protein (p = 0.029) than control SHSS. When compared to commercially available snack product SHSS showed three-fold significant increase in protein (p = 0.000) and ash content (p = 0.001) and only 11%–12% total fat as compared to 31% fat in the market-available product. The most acceptable treatment in terms of overall sensory quality and nutritional aspects was T₃; however, T₂ was more shelf-stable during the storage period. The study showed that fibre-enriched snacks can be stored at ambient temperature for up to 35 days without substantial loss in physico-chemical, sensory and microbial quality. Hence, substituting rice flour with DFs can lead to the development of products with better sensory attributes and improved functionality.

The simplicity of the product in terms of composition, machinery and low production costs makes it an easily adoptable one by small-scale entrepreneurs, especially those belonging to semi-urban areas.

Incorporation of spent hen meat, a relatively cheap but abundant source of protein, in RTE products can serve as an effective way to alleviate protein malnutrition, whereas addition of fibre further improves the functionality of the product. The methodology can be easily taken up by small-scale entrepreneurs and create a market for snack-based functional meat products.

The global food industry is faced with the dilemma of finding a balance between food wastage and food shortage. Approximately one-third of food produced globally goes to waste, while about 800 million people suffer from undernourishment. Given this context, the purpose of this study is to investigate the unresolved challenges related to enhancing transparency associated with products of high perishability and low shelf life.

The authors conducted 25 interviews with global agri-food supply chains (AFSCs) experts to ask what impedes the progress of the current technologies, such as blockchain, to enable transparency and traceability (T&T) in AFSCs.

The findings indicate barriers at the individual, firm and supply chain levels. Based on these barriers, the authors propose an interconnected framework to explain technologically-driven T&T and guide on barrier removal from AFSCs. The authors conclude that by applying technology (i.e. blockchain) the authors can resolve the tension of supporting T&T in AFSCs. This can enable the efficient and transparent tracking of goods, reduction of food waste and loss, as well as promotion of the use of recyclable packaging and further sustainable practices and materials, all of which are aligned with a range of UN Sustainable Development Goals (2, 8, 10 and 12). Moreover, the authors see that some factors are interrelated. Based on these factors, the authors build an interconnected framework to guide on barrier removal from AFSCs. Managers in AFSC would find the findings especially relevant.

Drawing on industrial network theory and signalling theory, the authors propose an interconnected framework for explaining barriers (challenges) and potential solutions (opportunities) to T&T in AFSCs. This framework is developed by examining the interconnections of barriers at micro, meso and macro levels and applying signalling theory to explain how solutions address these barriers. The specific contributions of this study are: the list of barriers that impede the implementation of technological solutions for T&T in AFSCs; and a three-stage framework that explains how to remove the barriers for T&T. The study is limited by the focus on blockchain, which calls for future research once the next decentralised technology becomes available.

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Just as the Covid-19 pandemic has hit all areas of life, it has also hit the working life. Towards the end of the pandemic period, the concept of quiet quitting entered people's lives. The phenomenon of quiet quitting has been introduced as a brand new concept in the mainstream media and social media in general. The primary objective of this study is to investigate and define the emerging phenomenon of quiet quitting. Furthermore, this study aims to compare this novel phenomenon with other established theoretical approaches in the field of management.

This review study specifically examines the concept of quiet quitting, aiming to gain a deeper understanding of this phenomenon.

Contrary to popular belief, the phenomenon of “quiet quitting” is not a recent or trendy occurrence. Instead, it has persisted for numerous years and is intertwined with various theories such as motivation, commitment, engagement and social exchange theory.

This study provides employers, managers, HR practitioners with recommendations on how to address the employee's quiet quitting behavior.

This study represents a pioneering work that explores an uncharted territory, the phenomenon of quiet quitting. The authors demonstrated and relate the historical processes of management theories to the phenomenon of quiet quitting, which is a concept that appears to lack roots.

The paper aims to explore the model of circular economy for promotion of principles of social inclusion, empathetic governance, and economic sustainability environmental resilience by examining the research gap on how to employ Environment, Social and Governance (ESG) framework, principles of biomimicry and reimagining an economic model of resourcefulness.

The paper is a descriptive study based on review of literature. To analyse the research gaps, paper employs bibliometric research technique, well-accepted meta-analytical research of literature to find overlapping factors of relevance by examining most-cited authors, papers, as well as co-citation patterns (Kim, 2008). The method analyses the published data texts, information like authorship, citations, keywords, and illustrating linkages between and among articles about certain research topic (Fetscherin, 2012).

Data were sourced from collection archival database of JSTOR, Web of Science and J-Gate till December 2022, by searching with following string – “Circular Economy,” “ESG (Environment, Social and Governance),” “Biomimicry,” “Circular Economy and ESG,” “Circular Economy and Biomimicry,” “Circular Economy and Resourcefulness.” The selection of said string of words was based on the literature review, overlaps and the research questions formulated. The findings reflect common factors of overlaps and its coherence in domain of policy formulation for the circular economy.

The research approach needs to be tested for practical application with stakeholders which includes individual-community for necessary behavioural change/acceptance, policy measures, innovations for scalability and the new business models so that the changes become an integral part of DNA of new economic model.

Emphasis on reimagined alliance between the environment, economy, and society to achieve the triple bottom line for a sustainable future. In doing so mitigate the impact on nature, generate livelihood opportunities and institute a green industry with an emphasis on circularity by incorporating the principles of ESG, biomimicry and resourcefulness.

The paper addresses the developing frontier of circular economy by identifying and mapping the factors of overlap with principles of ESG and biomimicry with circular economy for a future which is sustainable and resourceful. It attempts to advance the domain of knowledge with suggestion for implementable policy initiative arising from the study.