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This paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices.

The conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework.

The findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity.

This study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC.

This study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.

With its presence, which can create inefficiencies, uncertainties and risks, information asymmetry poses a significant challenge to successfully managing the agri-food supply chain (AFSC). Understanding the variables that influence information asymmetry is crucial for devising more effective strategies to mitigate it. This study, therefore, explores the variables that influence information asymmetry in the AFSC.

A qualitative analysis was conducted, relying on semi-structured interviews with 17 experts representing different actors in the AFSC (e.g. seed producers, retailers, etc.) in Germany. The collected data was analysed using the GABEK® method.

The study confirms that the influencing variables derived from the existing theory, such as price performance, digitalisation, environmental, process and quality measures, contribute to information asymmetry. It further reveals new variables that associate with information asymmetry, including documentation requirements, increasing regulation, consumer behaviour, incorrect data within the company as well as crises, political conflicts and supplier–buyer conflicts. Furthermore, the study shows that supply chain actors counteract asymmetry by focusing on social behaviour and monitoring suppliers through key performance indicators, employees and social aspects.

The study was limited to the universal influence of the variables on information asymmetry in the AFSC, making the magnitude of the influence and its context-specific nature unexplained.

This study is one of the very few that examines information asymmetry across the AFSC from the perspective of different actors, providing a more overarching and deeper understanding of information asymmetry.

Generative artificial intelligence (GenAI) is one of the most diffused AI technologies, capable of generating manifold forms of content, including music, text, images and synthetic data. The purpose of this study is to analyze the determinants that affect GenAI acceptance and its outcomes on both the explorative and exploitative forms of innovation.

The study employs a conceptual framework based on the technology-organization-environment (TOE) paradigm. Through Smart-PLS analysis, it examines empirical data retrieved from an online survey where 302 manufacturing companies took part.

It is found that GenAI has the potential to facilitate both exploratory and exploitative innovation, particularly via the moderating effect of environmental dynamism. Hence the adoption of GenAI has potential to improve organizational performance.

The study is the first empirical project to investigate factors that influence manufacturing firms' adoption of GenAI. As the first project to have integrated the TOE paradigm when examining the impact of environmental dynamism on exploratory and exploitative innovation, the study emphasizes the double innovation potential of GenAI in organizational performance improvement.

This study examines and estimates the relationship among identified factors or determinants for the adoption of water ionizers by customers.

To this end, the questionnaire is prepared to have the preferences of customers based on the factors or enablers identified for the analysis of customers' perceptions toward the adoption of water ionizers. Recipients are identified to collect their preferences for the factors responsible for the adoption. The convenience sampling method is used in administering the questionnaire for the study. The structural equation modeling (SEM) technique is used to construct the model for defining the relationship between measured and latent variables using the lavaan, haven, psych and semPlot libraries in R software.

The study reveals that product features, which include pH value, oxidation-reduction potential (ORP) and micro-clustering (MC), are influenced by environmental sustainability (ES), and as a result, product features and product outcomes (brand value (BV), customer recommendation (CR) and perceived usefulness (PU)) together influence the adoption of water ionizers by customers.

Water ionizers have appeared as consumer electronic appliances designed to alter the pH and ORP of water through an electrolysis process. There are proven benefits to improved water quality across different pHs for its health benefits (HB) and other commercial and household uses. The study recommends that potential HB perceived through water ionizers and product features, which are supported by ES, and helps customers decide on the adoption of water ionizers.

The study supports comprehending the relationships between consumer behavior, sustainable practices and innovative technologies like water ionizers as society places a greater emphasis on environmentally conscious living and sustainable living. This study aims to clarify the elements affecting the adoption and perception of water ionizers from a sustainability perspective through an extensive assessment of the literature and empirical analysis.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

This study aims to investigate the effect of push, pull, balanced, and emotional factors on the motivational level of female entrepreneurship in Haryana state, India.

A quantitative study was conducted in Haryana's five districts among 384 women entrepreneurs using a convenience sampling method with a structured questionnaire. This study used structural equation modeling (SEM) to test the hypotheses.

The study found that Push, Emotional Factors, and Challenges have a significant impact on the motivational level of women’s entrepreneurship and Challenges only mediate with the Push and Pull factors.

Generalization of results based on data from a few districts of Haryana state in India. A cross-sectional study was performed as a Longitudinal study helps in analyzing the changing character of women entrepreneurs and the prime reasons behind their success. Non-probability sampling technique was used in the study whereas probability sampling technique promotes reliability and reduces systematic error and sampling bias.

Pull factors can be promoted through mentoring, counseling, and entrepreneurship training programs. Creating a conducive external environment with job security and financial support from external agencies is essential. The Government of India should take the necessary steps to reduce the hindrances faced by women entrepreneurship while raising funds through banks and financial institutions.

The study employed a mediating variable, challenges, to investigate the impact of factors on the motivational levels of women entrepreneurs. To the best of our knowledge, this approach has not been utilized in previous research, making the current study a novel contribution to the field.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).

Integrating digital technologies such as artificial intelligence and blockchain make the agri-food supply chain (ASCM) transparent, resilient and flexible. However, its adoption is quite complex due to various anticipated barriers. So, the presented research purposes to explore and investigate the barriers.

This study employs hybrid approach including Best-Worst Method (BWM) and Graph Theoretic Approach (GTA). Data were collected from industry experts employed in the agri-food sector and analyzed by means of standard operating procedures.

GTA results show that Technological barriers have the highest barrier intensity. Moreover, BWM results show that “Increased operational complexity” is the topmost barrier to adopting blockchain in ASCM. “Lack of interoperability” ranks second among the identified barriers.

The results benefit the managers, practitioners and researchers to understand the anticipated barriers so that necessary strategies can be developed, and organizations can become more resilient, agile, transparent and traceable.

The presented work is the first to develop a mathematical model and assess the industry’s eagerness to adopt blockchain in ASCM. The proposed framework will greatly benefit the stakeholders working in agri-food sector.

The short/fast-food and perishable food supply chains (PFSC) have similar characteristics of lower lifespan and variable demand, leading to significant waste. However, the global population surge and increased health awareness make it impossible to continue wasting food because it is responsible for the loss of economy, resources, and biodiversity. A sustainable transition in short and PFSC is necessary; thus, addressing challenges is critical to explore the best strategy for redesigning PFSC.

An extensive literature review helped to identify 40 challenges, while a Delphi study highlighted 21 critical challenges. The fuzzy decision-making trial and evaluation laboratory method establishes a causal relationship between sustainable development (SD) challenges to help redesign PFSC.

From a strategic development perspective, frequent transportation disruption is the main critical challenge. Lack of supplier reliability is the most substantial cause of independence, with a causal value of 2.878. Overhead costs and lack of green maintenance strategies are part of the performance-oriented challenges. As it belongs to the driving zone, the second quadrant requires control while transforming PFSC for better sustainable development.

The study has several implications, such as lack of supplier reliability and frequent transportation disruption, which have the most robust causal value used as short-term strategy development. For short- and fast-food supply chains, it is necessary to study market and consumer behavior patterns to optimize inventory and customer service. Combating transportation disruption and supplier reliability challenges is vital in both PFSC and short and fast-food supply chains to reduce waste and promote sustainability.

The study’s findings are unique and put value toward the sustainable transition of PFSC by revealing critical challenges and their impact.