An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms

Issa, Helmi; Jabbouri, Rachid; Palmer, Mark

doi:10.1016/j.techfore.2022.121874

Cited by 25 publications

(7 citation statements)

References 101 publications

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“…Furthermore, the third pillar of the literature synthesises AI adoption in the agri-food business. Issa et al. (2022) offer a comprehensive explanation of AI readiness and identify strategic elements for agritech companies to ameliorate their technology adoption process.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the third pillar of the literature synthesises AI adoption in the agri-food business. Issa et al (2022) offer a comprehensive explanation of AI readiness and identify strategic elements for agritech companies to ameliorate their technology adoption process. Ganeshkumar et al (2022) explore the agritech industry digital transformation, highlighting the potential benefits and challenges of AI-based products in agriculture.…”

Section: Systematic Analysismentioning

confidence: 99%

See 1 more Smart Citation

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

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PurposeThis study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.Design/methodology/approachThis study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.FindingsSix clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.Originality/valueThis study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

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Section: Resultsmentioning

confidence: 99%

Section: Systematic Analysismentioning

confidence: 99%

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

View full text Add to dashboard Cite

show abstract

“…Furthermore, algorithmic bias and external disruptions were identified by Galaz et al [95] as systemic risks, which may affect sustainability when AI is used. Others argue that there is a need to rethink the readiness of agri-tech firms for the use of AI [96]. Despite some uncertainties, it is believed that using big data and AI to manage risks at all production and supply chain stages can play a big role in preventing food recalls, which may be expensive and damaging to a company's brand [97].…”

Section: Potential Benefits Of Ai Optimisation Including Reduced Food...mentioning

confidence: 99%

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Onyeaka,

Tamasiga,

Nwauzoma

et al. 2023

Sustainability

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Food waste is a global issue with significant economic, social, and environmental impacts. Addressing this problem requires a multifaceted approach; one promising avenue is using artificial intelligence (AI) technologies. This article explores the potential for AI to tackle food waste and enhance the circular economy and discusses the current state of food waste and the circular economy, highlighting specific ways that AI can be used to monitor and optimise food production and supply chains, redistribute excess food to those in need, and support circular economy initiatives. As a result, we can maximise resource efficiency and minimise environmental impact with these applications, ultimately creating a more sustainable and equitable food system.

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“…As well as understanding the level of maturity contributes to demonstrating the ability, quality and consistency of e-learning and helps universities to know the level of maturity of using ICT for students in addition to identifying required information to enable universities for adopting an appropriate strategy that improves the e-learning process (Durek et al, 2018 ). E-readiness regarded as a capacity degree to follow the identified appropriate chances (Issa et al, 2022 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Designing an E-learning Readiness Assessment Model for Iraqi Universities Employing Fuzzy Delphi Method

Al-Rikabi

Montazer

2023

Educ Inf Technol

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The assessment process of readiness for adoption an educational system considers the lifeblood of the e-learning system in a particular educational organization and the ability to assess the organization's readiness among the main factors which contributes to the success and progress. The readiness models are instruments that assist educational organizations in measuring their capability level and identifying the gaps to develop a strategy for implementing and adopting e-learning system. Due to the sudden chaos that Iraqi educational institutions have been exposed to the Covid-19 epidemic since the beginning of 2020, the e-learning system has been adopted as a quick alternative educational system for the continuation of the educational process without taking into consideration the readiness of the basic components of the educational process, which comprises the readiness of the infrastructure, human and educational organization to adopt such systems. Despite increased attention by stakeholders and the government with the readiness assessment process recently, there is no comprehensive model for assessing e-learning readiness in Iraqi higher education institutions, the purpose of this study is to design a model of an e-learning readiness assessment for Iraqi universities based on the comparative studies and the experts’ views. It is worth to mention that the proposed model has objectively designed according to particular features and local characteristics country. The fuzzy delphi method was utilized for the validation process of the proposed model. The main dimensions and all factors of the proposed model reached the experts’ agreement except a number of measures that did not achieve the assessment requirements. The final analysis result indicates that the e-learning readiness assessment model includes 3 main dimensions and 13 factors with 86 measures. Iraqi higher educational institutions can employ the designed model to assess their readiness and identify the areas that need improvement and reduce the gaps failures in e-learning adoption.

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An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms

Cited by 25 publications

References 101 publications

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Designing an E-learning Readiness Assessment Model for Iraqi Universities Employing Fuzzy Delphi Method

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