Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry

Alghamdi, Mohammed I.

doi:10.13164/mendel.2020.2.039

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…As far as vendor partnership concerns, it can be explained as a task or activity that has been assigned out to a service provider based on a legal contract when the organization does not have an in-house technical skill (Alghamdi, 2020). Thus, the main purpose of technology and innovation vendor partnership is lowering the costs of managing and maintaining technical assets, while increasing the quality of the developments.…”

Section: Adopting Factors Of Ai In Hrm Scenariosmentioning

confidence: 99%

Adoption Factors of Artificial intelligence in Human Resources Management

Tuffaha¹,

Perello-Marin²

2022

Future Bus. Adm.

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The phenomenon of artificial intelligence has been widely studied in several areas. In opposite, in terms of AI in HRM, the literature shows limited research on the adoption factors of artificial intelligence (AI) in HRM. AI has been enrolled in several HRM’s areas starting from staffing till management performance or compensation. A set of suggestions on how to adopt AI in HRM has been raised. This piece of research aims to identify the adoption factors of six scenarios of AI in HRM. These scenarios are turnover prediction with artificial neural networks, candidate search with knowledge-based search engines, staff rostering with genetic algorithms, HR sentiment analysis with text mining, résumé data acquisition with information extraction and employee self-service with interactive voice response. As a result, compatibility, relative advantage, complexity, managerial support, government involvement, and vendor partnership are determinants affected factors of AI adoption in HRM. This paper tries to address new insights for practitioners and academics by minimizing the risks associated with AI adoption in some areas of HRM through exploring determinant factors of adoption.

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Section: Adopting Factors Of Ai In Hrm Scenariosmentioning

confidence: 99%

Adoption Factors of Artificial intelligence in Human Resources Management

Tuffaha¹,

Perello-Marin²

2022

Future Bus. Adm.

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Artificial intelligence in retail – a systematic literature review

Heins

2022

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Purpose The purpose of this study is to present a systematic literature review of academic peer-reviewed articles in English published between 2005 and 2021. The articles were reviewed based on the following features: research topic, conceptual and theoretical characterization, artificial intelligence (AI) methods and techniques. Design/methodology/approach This study examines the extent to which AI features within academic research in retail industry and aims to consolidate existing knowledge, analyse the development on this topic, clarify key trends and highlight gaps in the scientific literature concerning the role of AI in retail. Findings The findings of this study indicate an increase in AI literature within the field of retailing in the past five years. However, this research field is fairly fragmented in scope and limited in methodologies, and it has several gaps. On the basis of a structured topic allocation, a total of eight priority topics were identified and highlighted that (1) optimizing the retail value chain and (2) improving customer expectations with the help of AI are key topics in published research in this field. Research limitations/implications This study is based on academic peer-reviewed articles published before July 2021; hence, scientific outputs published after the moment of writing have not been included. Originality/value This study contributes to the in-depth and systematic exploration of the extent to which retail scholars are aware of and working on AI. To the best of the author’s knowledge, this study is the first systematic literature review within retailing research dealing with AI technology.

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AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education during the Post-COVID-19 Era

Mehrabi,

Morphew,

Araabi

et al. 2024

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The onset of the COVID-19 pandemic has compelled a swift transformation in higher-education methodologies, particularly in the domain of course modality. This study highlights the potential for artificial intelligence and machine learning to improve decision-making in advanced engineering education. We focus on the potential for large existing datasets to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and e-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees, support vector machines, and random forest—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID-19 educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education.

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Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry

Cited by 4 publications

References 16 publications

Adoption Factors of Artificial intelligence in Human Resources Management

Adoption Factors of Artificial intelligence in Human Resources Management

Artificial intelligence in retail – a systematic literature review

AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education during the Post-COVID-19 Era

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