IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p41-d1303581.html
   My bibliography  Save this article

Sustainability-Driven Green Innovation: Revolutionising Aerospace Decision-Making with an Intelligent Decision Support System

Author

Listed:
  • Galimkair Mutanov

    (Institute of Information and Computational Technologies, P.O. Box 050010 Almaty, Kazakhstan)

  • Zhanar Omirbekova

    (Institute of Information and Computational Technologies, P.O. Box 050010 Almaty, Kazakhstan
    Department of Computer Science, Al-Farabi Kazakh National University, P.O. Box 050040 Almaty, Kazakhstan)

  • Aijaz A. Shaikh

    (Department of Marketing, Jyväskylä University School of Business and Economics, University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland)

  • Zhansaya Issayeva

    (Faculty of Oriental Studies, Al-Farabi Kazakh National University, P.O. Box 050040 Almaty, Kazakhstan)

Abstract

Green innovation refers to developing and implementing new technologies, practices, products, and processes that promote sustainability and reduce environmental impacts. This article postulates the conceptualisation and implementation of an intelligent decision support system (IDSS) tailored to the aerospace technology sector. The data were collected from open sources such as social media and analyzed using the natural language processing tool. The envisaged IDSS is a comprehensive and seamlessly integrated platform designed to undergird decision-making, problem-solving, and research initiatives within the aerospace industry. Catering to the sector’s engineers, technicians, and managerial cadres, it aims to unravel complex datasets, proffer incisive analyses, and furnish prudent advice and recommendations. Its multifaceted capabilities range from data search and optimisation to modelling and forecasting. With an emphasis on harmonious integration with extant aerospace systems, it strives to provide engineers and technicians with enriched data insights. Moreover, its design ethos is centred on user-friendliness, underscored by an intuitive graphical interface that expedites seamless access and utilisation. Ultimately, the envisioned IDSS will augment the aerospace industry’s analytical prowess and will serve as a potent instrument for effective decision-making.

Suggested Citation

  • Galimkair Mutanov & Zhanar Omirbekova & Aijaz A. Shaikh & Zhansaya Issayeva, 2023. "Sustainability-Driven Green Innovation: Revolutionising Aerospace Decision-Making with an Intelligent Decision Support System," Sustainability, MDPI, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:41-:d:1303581
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/41/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/41/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mais Haj Qasem & Mohammad Aljaidi & Ghassan Samara & Raed Alazaidah & Ayoub Alsarhan & Mohammed Alshammari, 2023. "An Intelligent Decision Support System Based on Multi Agent Systems for Business Classification Problem," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    2. William B Bonvillian, 2018. "DARPA and its ARPA-E and IARPA clones: a unique innovation organization model," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(5), pages 897-914.
    3. Yoshiyuki Takeda & Yuya Kajikawa, 2009. "Optics: a bibliometric approach to detect emerging research domains and intellectual bases," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(3), pages 543-558, March.
    4. Du, Shuili & Xie, Chunyan, 2021. "Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities," Journal of Business Research, Elsevier, vol. 129(C), pages 961-974.
    5. Woo Hyoung Lee, 2008. "How to identify emerging research fields using scientometrics: An example in the field of Information Security," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 503-525, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    2. Ivan Jarić & Jelena Knežević-Jarić & Mirjana Lenhardt, 2014. "Relative age of references as a tool to identify emerging research fields with an application to the field of ecology and environmental sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 519-529, August.
    3. Hanning Guo & Scott Weingart & Katy Börner, 2011. "Mixed-indicators model for identifying emerging research areas," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 421-435, October.
    4. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    5. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    6. Cloarec, Julien, 2022. "Privacy controls as an information source to reduce data poisoning in artificial intelligence-powered personalization," Journal of Business Research, Elsevier, vol. 152(C), pages 144-153.
    7. Patrick S. Roberts & Jon Schmid, 2022. "Government‐led innovation acceleration: Case studies of US federal government innovation and technology acceleration organizations," Review of Policy Research, Policy Studies Organization, vol. 39(3), pages 353-378, May.
    8. Chang-Ping Hu & Ji-Ming Hu & Sheng-Li Deng & Yong Liu, 2013. "A co-word analysis of library and information science in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 369-382, November.
    9. Kim, Hyoungshick & Yoon, Ji Won & Crowcroft, Jon, 2012. "Network analysis of temporal trends in scholarly research productivity," Journal of Informetrics, Elsevier, vol. 6(1), pages 97-110.
    10. Hsia-Ching Chang, 2016. "The Synergy of Scientometric Analysis and Knowledge Mapping with Topic Models: Modelling the Development Trajectories of Information Security and Cyber-Security Research," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-33, December.
    11. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    12. Liu, Yunmei & Yang, Liu & Chen, Min, 2021. "A new citation concept: Triangular citation in the literature," Journal of Informetrics, Elsevier, vol. 15(2).
    13. Shanwu Tian & Xiurui Xu & Ping Li, 2021. "Acknowledgement network and citation count: the moderating role of collaboration network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7837-7857, September.
    14. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    15. Sofia Patsali, 2021. "University Procurement-led Innovation," GREDEG Working Papers 2021-13, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    16. Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    17. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    18. Stock-Homburg, Ruth & Kirchhoff, Jérôme & Heinisch, Judith S. & Ebert, Andreas & Busch, Philip & Rawal, Niyati & David, Klaus & Wendt, Janine & Spiecker gen. Döhmann, Indra & Stryk, Oskar von & Hannig, 2022. "Responsible Human-Robot Interaction with Anthropomorphic Service Robots: State of the Art of an Interdisciplinary Research Challenge," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130084, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    19. Seung-Pyo Jun, 2012. "An empirical study of users’ hype cycle based on search traffic: the case study on hybrid cars," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 81-99, April.
    20. Rodney Duffett & Rodica Milena Zaharia & Tudor Edu & Raluca Constantinescu & Costel Negricea, 2024. "Exploring the Antecedents of Artificial Intelligence Products’ Usage. The Case of Business Students," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 106-106, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:41-:d:1303581. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.