IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11252-d909820.html
   My bibliography  Save this article

From Public E-Procurement 3.0 to E-Procurement 4.0; A Critical Literature Review

Author

Listed:
  • Aristotelis Mavidis

    (Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece)

  • Dimitris Folinas

    (Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece)

Abstract

Public procurement is an important part of public finances; therefore, its management is challenging for the quality of the citizen’s relationship with the public authorities. Existing electronic public procurement optimization tools are systematically attempting to standardize procedures by improving access to information and transparency in management. Nevertheless, the next day requires the definition of the transition to modern tools and technologies of the fourth industrial revolution. This study attempts to identify common and additional critical success factors from implementing e-procurement in the 3.0 and 4.0 eras. Identifying the key challenges will be the basis for the roadmap plan suitable for maximizing the achievement of new public management in Industry 4.0.

Suggested Citation

  • Aristotelis Mavidis & Dimitris Folinas, 2022. "From Public E-Procurement 3.0 to E-Procurement 4.0; A Critical Literature Review," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11252-:d:909820
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11252/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/18/11252/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erica Bosio & Simeon Djankov & Edward Glaeser & Andrei Shleifer, 2022. "Public Procurement in Law and Practice," American Economic Review, American Economic Association, vol. 112(4), pages 1091-1117, April.
    2. Asma I. Magaireah* & HidayahSulaiman & Nor’ashikin Ali, 2019. "Identifying the Most Critical Factors to Business Intelligence Implementation Success in the Public Sector Organizations," The Journal of Social Sciences Research, Academic Research Publishing Group, vol. 5(2), pages 450-462, 02-2019.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Kishor Vaidya & John Campbell, 2016. "Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency," Information Systems Frontiers, Springer, vol. 18(2), pages 333-348, April.
    5. Panayiotou, Nikolaos A. & Gayialis, Sotiris P. & Tatsiopoulos, I.P.Ilias P., 2004. "An e-procurement system for governmental purchasing," International Journal of Production Economics, Elsevier, vol. 90(1), pages 79-102, July.
    6. Kevin Zhu & Kenneth L. Kraemer & Sean Xu, 2006. "The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business," Management Science, INFORMS, vol. 52(10), pages 1557-1576, October.
    7. Popa, Mircea, 2019. "Uncovering the structure of public procurement transactions," Business and Politics, Cambridge University Press, vol. 21(3), pages 351-384, September.
    8. Sean Lewis-Faupel & Yusuf Neggers & Benjamin A. Olken & Rohini Pande, 2016. "Can Electronic Procurement Improve Infrastructure Provision? Evidence from Public Works in India and Indonesia," American Economic Journal: Economic Policy, American Economic Association, vol. 8(3), pages 258-283, August.
    9. Berman, Barry, 2012. "3-D printing: The new industrial revolution," Business Horizons, Elsevier, vol. 55(2), pages 155-162.
    10. Youngseok Choi & Habin Lee & Zahir Irani, 2018. "Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector," Annals of Operations Research, Springer, vol. 270(1), pages 75-104, November.
    11. Veale, Michael & Brass, Irina, 2019. "Administration by Algorithm? Public Management meets Public Sector Machine Learning," SocArXiv mwhnb, Center for Open Science.
    12. Hart O. Awa & Ojiabo Ukoha & Bartholomew C. Emecheta, 2016. "Using T-O-E theoretical framework to study the adoption of ERP solution," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1196571-119, December.
    13. Andrzej Sobczak & Leszek Ziora, 2021. "The Use of Robotic Process Automation (RPA) as an Element of Smart City Implementation: A Case Study of Electricity Billing Document Management at Bydgoszcz City Hall," Energies, MDPI, vol. 14(16), pages 1-22, August.
    14. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    15. Jani Saastamoinen & Timo Tammi & Helen Reijonen, 2018. "E-procurement and SME involvement in public procurement of innovations: an exploratory study," International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 11(4), pages 420-442.
    16. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    17. Lucica MATEI & Corina-Georgiana LAZĂR, 2011. "Quality Management and the Reform of Public Administration in Several States in South-Eastern Europe. Comparative Analysis," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(557)), pages 65-98, April.
    18. J. F. Rockart & M. S. Scott Morton, 1984. "Implications of Changes in Information Technology for Corporate Strategy," Interfaces, INFORMS, vol. 14(1), pages 84-95, February.
    19. de Alcantara, Douglas Pedro & Martens, Mauro Luiz, 2019. "Technology Roadmapping (TRM): a systematic review of the literature focusing on models," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 127-138.
    20. Walsh, Clara & O’Reilly, Philip & Gleasure, Rob & McAvoy, John & O’Leary, Kevin, 2021. "Understanding manager resistance to blockchain systems," European Management Journal, Elsevier, vol. 39(3), pages 353-365.
    21. Fazekas, Mihály & Kocsis, Gábor, 2020. "Uncovering High-Level Corruption: Cross-National Objective Corruption Risk Indicators Using Public Procurement Data," British Journal of Political Science, Cambridge University Press, vol. 50(1), pages 155-164, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aristotelis Mavidis & Dimitris Folinas & Dimitrios Skiadas & Alexandros Xanthopoulos, 2024. "Emerging Technologies Revolutionising Public Procurement: Insights from Comprehensive Bibliometric Analysis," Administrative Sciences, MDPI, vol. 14(2), pages 1-29, January.

    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. Volkan Ezcan & Jack Steven Goulding, 2022. "Offsite Sustainability—Disentangling the Rhetoric through Informed Mindset Change," Sustainability, MDPI, vol. 14(8), pages 1-27, April.
    2. Jozé Braz de Araújo & Silvia Novaes Zilber, 2016. "What Factors Lead Companies to Adopt Social Media in their processes: Proposal and Test of a Measurement Model," Brazilian Business Review, Fucape Business School, vol. 13(6), pages 260-290, November.
    3. repec:dau:papers:123456789/8129 is not listed on IDEAS
    4. Wu, Ing-Long & Chen, Jian-Liang, 2014. "A stage-based diffusion of IT innovation and the BSC performance impact: A moderator of technology–organization–environment," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 76-90.
    5. Debora Bettiga & Lucio Lamberti & Emanuele Lettieri, 2020. "Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach," Health Care Management Science, Springer, vol. 23(2), pages 203-214, June.
    6. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    7. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    8. Peter Mantello & Manh-Tung Ho & Minh-Hoang Nguyen & Quan-Hoang Vuong, 2023. "Machines that feel: behavioral determinants of attitude towards affect recognition technology—upgrading technology acceptance theory with the mindsponge model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    9. Fox, Stephen & Groesser, Stefan N., 2016. "Reframing the relevance of research to practice," European Management Journal, Elsevier, vol. 34(5), pages 457-465.
    10. Mäntymäki, Matti & Salo, Jari, 2013. "Purchasing behavior in social virtual worlds: An examination of Habbo Hotel," International Journal of Information Management, Elsevier, vol. 33(2), pages 282-290.
    11. Fatima Zahra Barrane & Gahima Egide Karuranga & Diane Poulin, 2018. "Technology Adoption and Diffusion: A New Application of the UTAUT Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-19, December.
    12. Fazekas,Mihály & Blum,Jurgen Rene, 2021. "Improving Public Procurement Outcomes : Review of Tools and the State of the Evidence Base," Policy Research Working Paper Series 9690, The World Bank.
    13. Joan Torrent-Sellens & Cristian Salazar-Concha & Pilar Ficapal-Cusí & Francesc Saigí-Rubió, 2021. "Using Digital Platforms to Promote Blood Donation: Motivational and Preliminary Evidence from Latin America and Spain," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    14. Garima Malik & A. Sajeevan Rao, 2019. "Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy," Information Technology & Tourism, Springer, vol. 21(4), pages 461-482, December.
    15. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    17. Scott, Stephanie & Hughes, Paul & Hodgkinson, Ian & Kraus, Sascha, 2019. "Technology adoption factors in the digitization of popular culture: Analyzing the online gambling market," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    18. Pan Gong & Ningshuang Zeng & Kunhui Ye & Markus König, 2019. "An Empirical Study on the Acceptance of 4D BIM in EPC Projects in China," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    19. Cabrera-Sánchez, Juan-Pedro & Villarejo-Ramos, à ngel F., 2020. "Acceptance and use of big data techniques in services companies," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    20. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    21. Cowan, Kelly R. & Daim, Tugrul U., 2011. "Review of technology acquisition and adoption research in the energy sector," Technology in Society, Elsevier, vol. 33(3), pages 183-199.

    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:14:y:2022:i:18:p:11252-:d:909820. 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.