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Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters
被引:4
|作者:
Alahmari, Nala
[1
]
Mehmood, Rashid
[2
]
Alzahrani, Ahmed
[1
]
Yigitcanlar, Tan
[3
]
Corchado, Juan M.
[4
,5
,6
]
机构:
[1] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Comp Sci, Jeddah 21589, Saudi Arabia
[2] King Abdulaziz Univ, High Performance Comp Ctr, Jeddah 21589, Saudi Arabia
[3] Queensland Univ Technol, Sch Architecture & Built Environm, City 4 0 Lab, Brisbane, Qld 4120, Australia
[4] Univ Salamanca, BISITE Res Grp, Salamanca 37007, Spain
[5] Air Inst, IoT Digital Innovat Hub, Salamanca 37188, Spain
[6] Osaka Inst Technol, Fac Engn, Dept Elect Informat & Commun, Osaka 5358585, Japan
关键词:
service economy;
smart society;
autonomous services;
smart services;
sustainable services;
deep learning;
big data analytics;
Natural language Processing (NLP);
internet of things (IoT);
PRIVACY PROTECTION SCHEME;
SOCIAL MEDIA ANALYTICS;
BIG DATA;
SCIENTOMETRIC ANALYSIS;
SYSTEM;
MANAGEMENT;
INFORMATION;
TECHNOLOGY;
INNOVATION;
INTERNET;
D O I:
10.3390/su152216003
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
The rise in the service economy has been fueled by breakthroughs in technology, globalization, and evolving consumer patterns. However, this sector faces various challenges, such as issues related to service quality, innovation, efficiency, and sustainability, as well as macro-level challenges such as globalization, geopolitical risks, failures of financial institutions, technological disruptions, climate change, demographic shifts, and regulatory changes. The impacts of these challenges on society and the economy can be both significant and unpredictable, potentially endangering sustainability. Therefore, it is crucial to comprehensively study services and service economies at both holistic and local levels. To this end, the objective of this study is to develop and validate an artificial-intelligence-based methodology to gain a comprehensive understanding of the service sector by identifying key parameters from the academic literature and public opinion. This methodology aims to provide in-depth insights into the creation of smarter, more sustainable services and economies, ultimately contributing to the development of sustainable future societies. A software tool is developed that employs a data-driven approach involving the use of word embeddings, dimensionality reduction, clustering, and word importance. A large dataset comprising 175 K research articles was created from the Scopus database, and after analysis, 29 distinct parameters related to the service sector were identified and grouped into 6 macro-parameters: smart society and infrastructure, digital transformation, service lifecycle management, and others. The analysis of over 112 K tweets collected from Saudi Arabia identified 11 parameters categorized into 2 macro-parameters: private sector services and government services. The software tool was used to generate a knowledge structure, taxonomy, and framework for the service sector, in addition to a detailed literature review based on over 300 research articles. The conclusions highlight the significant theoretical and practical implications of the presented study for autonomous capabilities in systems, which can contribute to the development of sustainable, responsible, and smarter economies and societies.
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