Automatic analysis of the sustainability of public procurement based on Text Mining: The case of the Moroccan ICT markets

Haddadi, Tarik El; Haddadi, Oumaima El; Mourabit, Taoufik; Allaoui, Ahmad El; Ahmed, Mohamed Ben

doi:10.1016/j.clrc.2021.100037

Cited by 6 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from this, El Haddadi et al [25] discusses the importance of sustainable public procurement (SPP) in reducing the environmental impact of information and communication technologies (ICTs) and the challenges of implementing it in Morocco. The study analyzes tenders for the purchase of IT equipment in the public administration in Morocco using text mining techniques and finds a low infiltration rate of environmental issues, indicating the need for more SPP efforts.…”

Section: Related Workmentioning

confidence: 99%

Predicting the outcome of regional development projects using machine learning

Satri,

El Mokhi,

Hachimi

2024

IJ-AI

View full text Add to dashboard Cite

<p><span lang="EN-US">Morocco, in its pursuit of inclusive and sustainable territorial development, initiated the advanced regionalization experiment over six years ago. The primary challenge facing government officials today is the management of a burgeoning number of regional development projects. In this article we developed a predictive model based on artificial intelligence and Machine Learning to predict the outcomes of regional development projects, in order to identify the risks associated with their potential failure, and anticipate their impact. To accomplish this, we implemented various data mining techniques and classification algorithms. We collected and analyzed data from past and ongoing regional development projects, considering diverse factors that influence their success or failure. Through rigorous experimentation, we assessed the effectiveness of different predictive models. Our findings reveal that the Random Forest classifier stands out as an efficient algorithm for predicting the outcomes of regional development projects. This research contributes to the broader discourse on the practical implementation of artificial intelligence in public policy and regional development, showcasing its potential to optimize resource allocation, and alleviate the burden of repetitive administrative tasks for organizationsoperating with limited resources.</span></p>

show abstract

Section: Related Workmentioning

confidence: 99%

Predicting the outcome of regional development projects using machine learning

Satri,

El Mokhi,

Hachimi

2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…This article involves text mining to automatically analyze energy efficiency issues. This method was effectively used to a similar extent by Haddadi et al [7], who examined public procurement of the Moroccan ICT, and by Tavana et al [8], who applied this technique to provide a systematic review of the literature prioritizing the fields of energy and sustainability, and by Abdelaziz et al, who examined energy consumption in buildings [9].…”

Section: Motivationmentioning

confidence: 99%

Latent Dirichlet Allocation in Public Procurement Documents Analysis for Determining Energy Efficiency Issues in Construction Works at Polish Universities

Pamuła

Gontar

et al. 2023

Energies

View full text Add to dashboard Cite

This paper presents a comprehensive analysis of public procurement documents in the domain of university buildings taken from the e-procurement platform, particularly focusing on their transformation towards more efficient energy consumption. Using a corpus of the titles of the public procurement documents from 2020 to 2022, we used Latent Dirichlet Allocation (LDA) for topic modeling to understand the key thematic areas of focus. The methodology presented in this study incorporated a bifurcated approach. This two-stage procedure began with preprocessing and dictionary creation from the corpus of titles of procurement documents. Following this, the Latent Dirichlet Allocation (LDA) model was employed for topic extraction and trend analysis, thereby providing a comprehensive understanding of the thematic progression in procurement practices over time. Our analysis revealed a shift in emphasis from modernization towards the adoption of energy-saving technologies as well as a growing focus on broader sustainability initiatives. However, a less prevalent topic was adherence to cooling & heating systems, suggesting potential areas for improvement in procurement practices. These findings contribute to the growing body of knowledge on sustainable procurement in university buildings and offer valuable insight for universities to enhance their energy efficiency strategies.

show abstract

“…Zhang et al [39] collected big data from policy texts and conducted network analysis and core periphery analysis based on the keyword dataset. El Haddadi et al [40] used text mining techniques to analyse and evaluate texts. In recent years, the application of text mining in management has increased exponentially [41].…”

Section: Literature Review and Theoreticalmentioning

confidence: 99%

Quantitative Evaluation of Big Data Development Policy: Text Data Analysis Based on Coword Network and Policy Tools

Hong

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In the context of the continuous promotion of China’s big data development strategy, this paper quantitively analyses China’s existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China’s big data policies. This paper carries out a quantitative textual analysis of China’s national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies.

show abstract

Automatic analysis of the sustainability of public procurement based on Text Mining: The case of the Moroccan ICT markets

Cited by 6 publications

References 49 publications

Predicting the outcome of regional development projects using machine learning

Predicting the outcome of regional development projects using machine learning

Latent Dirichlet Allocation in Public Procurement Documents Analysis for Determining Energy Efficiency Issues in Construction Works at Polish Universities

Quantitative Evaluation of Big Data Development Policy: Text Data Analysis Based on Coword Network and Policy Tools

Contact Info

Product

Resources

About