Examining the synergies between industry 4.0 and sustainability dimensions using text mining, sentiment analysis, and association rules

The transformation to Industry 4.0 has significantly revolutionized manufacturing and production processes, raising important questions about their impact on sustainability. This study aims to explore the interplay between Industry 4.0 and the economic, social, and environmental dimensions of sustainability. The methodological approach includes advanced text-mining, sentiment analysis, and association rule-mining techniques to examine 6,759 abstracts from the Scopus database. The text mining highlighted frequent keywords related to Industry 4.0 and the three sustainability dimensions, characterized by “economic growth,” “circular economy,” “social responsibility,” “education 4.0,” “energy efficiency,” and “waste management.” Sentiment analysis revealed a predominantly positive perspective, with 2,608 positive sentiments out of 2,761 in the economic dimension, 1,604 out of 1,728 in the social dimension, and 1,352 out of 1,527 in the environmental dimension. The association rule mining uncovered the associations between Industry 4.0 and each sustainability dimension. The highest support was observed between Industry 4.0 and economic sustainability, with a support value of 0.444, confidence of 0.855, and a lift of 1.060. These findings highlight the role of Industry 4.0 in promoting resource efficiency and reducing waste through circular economy principles and advanced manufacturing technologies. For the social dimension, the analysis revealed a strong association with Industry 4.0 (support: 0.430, confidence: 0.831, lift: 1.030), emphasizing its role in enhancing worker safety and job satisfaction by automating hazardous tasks and creating new high-tech job opportunities. In the environmental dimension, a significant association was found (support: 0.380, confidence: 0.827, lift: 1.024), showing Industry 4.0′s contribution to sustainability through optimized energy consumption and emissions reduction as the integration of big data and IoT enables real-time monitoring of environmental impacts. The rule combining economic and social aspects with Industry 4.0 (support: 0.219, confidence: 0.87, lift: 1.078) highlights the interconnected nature of these dimensions, suggesting many studies consider economic and social dimensions together in the Industry 4.0 context.

Authors

Institutions

Publication Details

Journal
Sustainable Futures
Published
2024-12-19
DOI
https://doi.org/10.1016/j.sftr.2024.100423
Citations
11
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
3.84
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Examining the synergies between industry 4.0 and sustainability dimensions using text mining, sentiment analysis, and association rules

Mutaz AlShafeey, Mohamad Ali Saleh Saleh
11 citations
Sustainable Futures
Digital Transformation in Industry
3.84
article

Examining the synergies between industry 4.0 and sustainability dimensions using text mining, sentiment analysis, and association rules

Mutaz AlShafeey, Mohamad Ali Saleh Saleh
article en
11 citations

Abstract

The transformation to Industry 4.0 has significantly revolutionized manufacturing and production processes, raising important questions about their impact on sustainability. This study aims to explore the interplay between Industry 4.0 and the economic, social, and environmental dimensions of sustainability. The methodological approach includes advanced text-mining, sentiment analysis, and association rule-mining techniques to examine 6,759 abstracts from the Scopus database. The text mining highlighted frequent keywords related to Industry 4.0 and the three sustainability dimensions, characterized by “economic growth,” “circular economy,” “social responsibility,” “education 4.0,” “energy efficiency,” and “waste management.” Sentiment analysis revealed a predominantly positive perspective, with 2,608 positive sentiments out of 2,761 in the economic dimension, 1,604 out of 1,728 in the social dimension, and 1,352 out of 1,527 in the environmental dimension. The association rule mining uncovered the associations between Industry 4.0 and each sustainability dimension. The highest support was observed between Industry 4.0 and economic sustainability, with a support value of 0.444, confidence of 0.855, and a lift of 1.060. These findings highlight the role of Industry 4.0 in promoting resource efficiency and reducing waste through circular economy principles and advanced manufacturing technologies. For the social dimension, the analysis revealed a strong association with Industry 4.0 (support: 0.430, confidence: 0.831, lift: 1.030), emphasizing its role in enhancing worker safety and job satisfaction by automating hazardous tasks and creating new high-tech job opportunities. In the environmental dimension, a significant association was found (support: 0.380, confidence: 0.827, lift: 1.024), showing Industry 4.0′s contribution to sustainability through optimized energy consumption and emissions reduction as the integration of big data and IoT enables real-time monitoring of environmental impacts. The rule combining economic and social aspects with Industry 4.0 (support: 0.219, confidence: 0.87, lift: 1.078) highlights the interconnected nature of these dimensions, suggesting many studies consider economic and social dimensions together in the Industry 4.0 context.

Sustainable FuturesVol. 9
Corvinus University of Budapest (HU), University of Dunaújváros (HU)
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Digital Transformation in Industry
3.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.