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Naim Ahmad

Researcher at King Khalid University

Publications -  30
Citations -  484

Naim Ahmad is an academic researcher from King Khalid University. The author has contributed to research in topics: Cloud computing & Critical success factor. The author has an hindex of 10, co-authored 27 publications receiving 291 citations.

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Journal ArticleDOI

Enterprise systems: are we ready for future sustainable cities

TL;DR: In this article, the adoption reasons of enterprise systems (ES) and supply chain management systems (SCMS) and the new dimensions of sustainability required to be added in the whole process of adoption of these systems are explored.
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E-learning services to achieve sustainable learning and academic performance: An empirical study

TL;DR: A holistic E- learning service framework is proposed to ensure effective delivery and use of E-Learning Services that contributes to sustainable learning and academic performance and will help achieve the sustainable and successful adoption of E -Learning services.
Proceedings ArticleDOI

Internet of medical things: Architectural model, motivational factors and impediments

TL;DR: Using Carley's eight step content analysis technique, motivational factors for the adoption of IoMT have been identified and the paper sheds light on the major impediment such as security and privacy issues.
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Relationship Modeling of Critical Success Factors for Enhancing Sustainability and Performance in E-Learning

TL;DR: In this article, critical success factors (CSFs) have been used to identify the sustainable E-learning implementation model and 15 CSFs have been modeled for interdependence using interpretive structural modeling and Matriced-Impact Croise Multiplication Appliquee a UN Classement (MICMAC) analysis.
Proceedings ArticleDOI

The 51 V's Of Big Data: Survey, Technologies, Characteristics, Opportunities, Issues and Challenges

TL;DR: A comprehensive overview of Big Data, its characteristics, opportunities, issues, and challenges have been explored and described with the help of 51 V's to help in understanding the Big Data in a systematic way.