Evaluating the Intention for the Adoption of Artificial Intelligence-Based Robots in the University to Educate the Students

Roy, Rita; Babakerkhell, Mohammad Dawood; Mukherjee, Subhodeep; Pal, Debajyoti; Funilkul, Suree

doi:10.1109/access.2022.3225555

Cited by 37 publications

(27 citation statements)

References 88 publications

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“…In addition, Roy et al (2022) showed a positive thought of both teachers and students in adopting AI in universities hence, providing an affirmative answer to their research question, "Do teacher and student attitudes impact the intention to adopt AI-based robots in the education sector?''. The use of AI was found to be helpful and accessible.…”

Section: Behavioural Intention and Adoption Of Behaviourmentioning

confidence: 90%

Navigating the Adoption of Artificial Intelligence in Higher Education

Mohsin,

Md Isa,

Ishak

et al. 2024

IJBT

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With the emergence of Education 4.0, Artificial Intelligence (AI) is increasingly being used and integrated in higher education institutions in recent years. It is hardly surprising as we are living in the era of digital technologies and a transformational shift in the educational system. This conceptual article proposes a study on adoption of artificial intelligence (AI) in higher education among undergraduate students. Drawing from the Theoretical model of The Unified Theory of Acceptance and Use of Technology (UTAUT), this study aims to examine the influence between the key variables in the UTAUT model such as performance expectation, effort expectation, social influence and facilitating conditions on attitudes and behavioral intention towards AI adoption in higher education institutions. This study will utilize quantitative research design using Structural Equation Modeling - Partial Least Squares (SEM-PLS) to analyze the data. It becomes central to investigate such AI adoption tendency among students as this will aid the institutions to tap into the potential problems and opportunities that may arise with its adoptions and usage. This study also attempts to clarify the potential linkages by engaging in a discussion prior to conducting empirical testing.

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Section: Behavioural Intention and Adoption Of Behaviourmentioning

confidence: 90%

Navigating the Adoption of Artificial Intelligence in Higher Education

Mohsin,

Md Isa,

Ishak

et al. 2024

IJBT

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“…A pilot survey of 57 responses was conducted to test the questionnaire’s reliability. Cronbach’s alpha ( α ) values for all the items were valid and above 0.70 (Hair et al , 2014; Roy et al , 2022a).…”

Section: Methodsmentioning

confidence: 99%

Linking supply chain resilience with knowledge management for achieving supply chain performance

Mukherjee,

Nagariya,

Mathiyazhagan

et al. 2023

JKM

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Purpose Supply chain (SC) and knowledge management (KM) have been studied; still, there is a need to understand how KM can be used for SC resilience and improving the firm’s performance. The purpose of the paper is to study and analyze SC resilience strategies based on KM processes to enhance SC performance considering six SC strategies: SC reengineering, collaboration, SC innovation, SC integration, SC agility and SC risk management. Design/methodology/approach By adopting the dynamic capability theory, the empirical research is conducted on a sample of 312 Indian micro, small to medium enterprises. To evaluate 312 samples, the structural equation modeling approach is adopted. Findings The study found a is a positive relationship between SC reengineering, SC collaboration, SC integration, SC agility, SC risk management and KM. Nevertheless, the relationship between SC innovation and KM is not significant. This study also found the mediating effect of KM on SC performance, and the results shows that SC reengineering, SC collaboration, SC agility and SC risk management are having complementary mediation, while SC innovation and SC integration did not show any mediation. Originality/value This is the only research that integrates resilience strategies and KM for improving SC performance. Using KM, SC reengineering will improve SC performance by enhancing readiness and recovery strategies to avoid SC disruption. KM will improve SC collaboration. It will enhance the SC process’ overall visibility, transparency and so on. Agility leads to increased speed, visibility and flexibility, which aids in dealing with uncertainty in the environment. SCRM entails investments and additional resources (such as equipment and labor) to navigate uncertainty and risks in the SC and improve SC performance.

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“…Perceived benefits (PB): Researchers and analysts believe that BT-engaged SC gains benefits, including progressing information sharing, cost decrease, adequacy, straightforwardness, tracking, tracing, and improving operational excellence (Roy, Babakerkhell, et al, 2022). BT-engaged SC aims to grow the association's effectiveness and reality in the market (Hassani et al, 2018;van Hoek, 2019).…”

Section: Technological Factors (Tf)mentioning

confidence: 99%

Investigating the factors of blockchain technology influencing food retail supply chain management: a study using TOE framework

Baral,

Chittipaka,

Pal

et al. 2023

SiTns

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In history, the food sector has remained the most vulnerable and is accountable for many crises and food scandals, so to avoid this in the near future, it is essential to have better control over the flow of products and the usage of blockchain technology can achieve this control in the supply chain, which can add trust, transparency, and traceability in the entire system. This paper investigates the factors affecting blockchain adoption in the food retail supply chain and creates awareness among retail managers for its adoption in their operations. A structured literature review is performed to identify the factors and a TOE framework is adopted for the research. Factors of technology, organization and environment are taken as independent variables; the intention to adopt the technology is taken as a mediating variable and blockchain adoption is considered a dependent variable. The findings contributed that TOE factors contribute to blockchain adoption by keeping the intention to adopt the technology as a mediating variable.

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Evaluating the Intention for the Adoption of Artificial Intelligence-Based Robots in the University to Educate the Students

Cited by 37 publications

References 88 publications

Navigating the Adoption of Artificial Intelligence in Higher Education

Navigating the Adoption of Artificial Intelligence in Higher Education

Linking supply chain resilience with knowledge management for achieving supply chain performance

Investigating the factors of blockchain technology influencing food retail supply chain management: a study using TOE framework

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