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

Author:

Alahmari Nala1ORCID,Mehmood Rashid2ORCID,Alzahrani Ahmed1ORCID,Yigitcanlar Tan3ORCID,Corchado Juan M.456ORCID

Affiliation:

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

2. High-Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia

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

4. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain

5. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain

6. 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.

Funder

King Abdulaziz University

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference312 articles.

1. The Services Powerhouse: Increasingly Vital to World Economic Growth;Buckley;Deloitte Insights,2018

2. Dynamic Effects of European Services Liberalisation: More to Be Gained;Kox;Munich Pers. RePEc Arch.,2006

3. Bryson, J.R., Daniels, P.W., and Warf, B. (2013). Service Worlds: People, Organisations and Technologies, Routledge.

4. The Challenges and Opportunities in the Digitalization of Companies in a Post-COVID-19 World;Almeida;IEEE Eng. Manag. Rev.,2020

5. (2023, November 11). Organisation for Economic Co-Operation and Development (OECD) The Service Economy. Available online: https://www.oecd.org/industry/ind/2090561.pdf.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3