IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i10p4309-d1398119.html
   My bibliography  Save this article

The Link between Environment and Organizational Architecture for Decision-Making in Educational Institutions: A Systemic Approach

Author

Listed:
  • Fernanda Neves Tavares Serra

    (Industrial and Systems Engineering Program, Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil)

  • Marcelo Carneiro Gonçalves

    (Mechatronic Systems Graduate Program, University of Brasilia, Brasilia 70910-900, Brazil)

  • Sandro César Bortoluzzi

    (Industrial and Systems Engineering Program, Federal Technological University of Paraná, Pato Branco 85503-390, Brazil)

  • Sergio Eduardo Gouvêa Costa

    (Industrial and Systems Engineering Program, Federal Technological University of Paraná, Pato Branco 85503-390, Brazil)

  • Izamara Cristina Palheta Dias

    (Industrial and Systems Engineering Program, Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil)

  • Guilherme Brittes Benitez

    (Industrial and Systems Engineering Program, Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil)

  • Lisianne Brittes Benitez

    (Environmental Technology Graduate Program, University of Santa Cruz Do Sul-Unisc, Santa Cruz Do Sul 96815-900, Brazil)

  • Elpidio Oscar Benitez Nara

    (Industrial and Systems Engineering Program, Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil)

Abstract

Numerous organizations employ decision-making processes to support operational activities; however, decisions and mistakes can significantly impact Market Performance (MP) due to the oversight of organizational architecture and the environment. This becomes particularly critical in the realm of strategic management, where improper practices and a lack of management understanding can lead to substantial losses. Hence, a systemic investigation was undertaken to explore the repercussions of not adopting such an approach concerning organizational architecture and the environment. Employing a quantitative analysis via hierarchical regression involving Confirmatory Factor Analysis and Ordinary Least Squares, using data gathered from a survey encompassing 134 collaborators from Brazilian Federal Universities. The findings show that the organizational environment positively impacts decision-making, leading to better MP. Additionally, organizational architecture partially mediates the link between the organizational environment and decision-making. Remarkably, national literature lacked research combining Student Assistance Program (PNAES) actions with MP improvement to assess Brazilian Federal Universities’ effectiveness.

Suggested Citation

  • Fernanda Neves Tavares Serra & Marcelo Carneiro Gonçalves & Sandro César Bortoluzzi & Sergio Eduardo Gouvêa Costa & Izamara Cristina Palheta Dias & Guilherme Brittes Benitez & Lisianne Brittes Benitez, 2024. "The Link between Environment and Organizational Architecture for Decision-Making in Educational Institutions: A Systemic Approach," Sustainability, MDPI, vol. 16(10), pages 1-21, May.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4309-:d:1398119
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/10/4309/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/10/4309/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamann, P. Maik & Schiemann, Frank, 2021. "Organizational performance as a set of four dimensions: An empirical analysis," Journal of Business Research, Elsevier, vol. 127(C), pages 45-65.
    2. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    3. Zhemchugova, Oksana & Levshina, Violetta, 2020. "The risk-based approach in organization quality management systems," Revista Galega de Economía, University of Santiago de Compostela. Faculty of Economics and Business., vol. 29(3), pages 1-13.
    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. de Jong, Jeroen P.J. & Ben-Menahem, Shiko M. & Franke, Nikolaus & Füller, Johann & von Krogh, Georg, 2021. "Treading new ground in household sector innovation research: Scope, emergence, business implications, and diffusion," Research Policy, Elsevier, vol. 50(8).
    2. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    3. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).
    4. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.
    5. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    6. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    7. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    8. Lin, Shunzhi & Lin, Jiabao, 2023. "How organizations leverage digital technology to develop customization and enhance customer relationship performance: An empirical investigation," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    9. Liu, Qian & Gao, Jian & Li, Shijie, 2024. "The innovation model and upgrade path of digitalization driven tourism industry: Longitudinal case study of OCT," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    10. Markus Binder & Bernd Heinrich & Marcus Hopf & Alexander Schiller, 2022. "Global reconstruction of language models with linguistic rules – Explainable AI for online consumer reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2123-2138, December.
    11. Mahmoud Abdulhadi Alabdali & Sami A. Khan & Muhammad Zafar Yaqub & Mohammed Awad Alshahrani, 2024. "Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis," Sustainability, MDPI, vol. 16(11), pages 1-30, June.
    12. Clement Nangpiire & Francis Oheneba Gyebi & Theophile Nasse, 2024. "Sustainable Procurement Practices and Organisational Performance of Small and Medium Enterprises in Ghana," International Journal of Economics and Financial Issues, Econjournals, vol. 14(1), pages 95-106, January.
    13. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    14. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
    15. Oliveira, Fabio & Kakabadse, Nada & Khan, Nadeem, 2022. "Board engagement with digital technologies: A resource dependence framework," Journal of Business Research, Elsevier, vol. 139(C), pages 804-818.
    16. Feifei Huang & Mingxia Lin & Shoukat Iqbal Khattak, 2024. "Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development," Sustainability, MDPI, vol. 16(14), pages 1-24, July.
    17. Tatiana Karkoszka, 2023. "Operational Control Model Based on Integrated Failure Analysis and Risk Assessment in Sustainable Technological Processes," Sustainability, MDPI, vol. 15(24), pages 1-24, December.
    18. Panagiota Xanthopoulou & Alexandros Sahinidis & Zorzeta Bakaki, 2022. "The Impact of Strong Cultures on Organisational Performance in Public Organisations: The Case of the Greek Public Administration," Social Sciences, MDPI, vol. 11(10), pages 1-15, October.
    19. François-Xavier de Vaujany & Stefan Haefliger & Paula Ungureanu, 2022. "From Collaborative Spaces to New Modes of Organizing: Society, Democracy and Commons on the Way to Novelty [Des espaces collaboratifs aux nouvelles formes d'organisation : société, démocratie et co," Post-Print hal-03827462, HAL.
    20. Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).

    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:2024:i:10:p:4309-:d:1398119. 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.