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Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters

Author

Listed:
  • Nala Alahmari

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Rashid Mehmood

    (High-Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ahmed Alzahrani

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Tan Yigitcanlar

    (City 4.0 Lab, School of Architecture and Built Environment, Queensland University of Technology, Brisbane, QLD 4120, Australia)

  • Juan M. Corchado

    (BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
    Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
    Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan)

Abstract

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.

Suggested Citation

  • Nala Alahmari & Rashid Mehmood & Ahmed Alzahrani & Tan Yigitcanlar & Juan M. Corchado, 2023. "Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters," Sustainability, MDPI, vol. 15(22), pages 1-44, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16003-:d:1281467
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