Enhancing Stakeholder Value: Managerial Activities in the Value Creation Process for Suppliers and Buyer—Evidence from Slovak Enterprises
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
- Dana Kušnírová & Mária Ďurišová & Eva Malichová, 2023. "Indicators of Value Creation and Their Perception by Suppliers in Slovakia," Administrative Sciences, MDPI, vol. 13(8), pages 1-20, July.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Boršoš Patrik & Koman Gabriel, 2025. "Overview of Current Research on Artificial Intelligence in Logistics," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 16(1), pages 13-24.
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.- Philippe Jardin, 2025. "Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 149-209, January.
- Stefan Feuerriegel & Mateusz Dolata & Gerhard Schwabe, 2020. "Fair AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(4), pages 379-384, August.
- Gahm, Christian & Uzunoglu, Aykut & Wahl, Stefan & Ganschinietz, Chantal & Tuma, Axel, 2022. "Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning," European Journal of Operational Research, Elsevier, vol. 296(3), pages 819-836.
- Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
- repec:iim:iimawp:14638 is not listed on IDEAS
- Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
- Justyna Łapińska & Iwona Escher & Joanna Górka & Agata Sudolska & Paweł Brzustewicz, 2021. "Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland," Energies, MDPI, vol. 14(7), pages 1-20, April.
- Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
- Md. Iftekharul Alam Efat & Petr Hajek & Mohammad Zoynul Abedin & Rahat Uddin Azad & Md. Al Jaber & Shuvra Aditya & Mohammad Kabir Hassan, 2024. "Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales," Annals of Operations Research, Springer, vol. 339(1), pages 297-328, August.
- Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
- Sobrie, Léon & Verschelde, Marijn & Hennebel, Veerle & Roets, Bart, 2023. "Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1201-1217.
- Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Jula, Payman & Pirayesh, Amir & Ahmadi, Hadi, 2020. "A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty," European Journal of Operational Research, Elsevier, vol. 285(2), pages 513-537.
- Shao-Bo Lin & Shaojie Tang & Yao Wang & Di Wang, 2022. "Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3096-3116, November.
- Ameling, Justus & Gust, Gunther, 2024. "Automated feeder routing for underground electricity distribution networks based on aerial images," European Journal of Operational Research, Elsevier, vol. 318(2), pages 629-641.
- Punit Anand & Anand Mohan Sharan, 2025. "Bitcoin Return Dynamics Volatility and Time Series Forecasting," IJFS, MDPI, vol. 13(2), pages 1-16, June.
- Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
- Imad El Harraki & Mohammad Zoynul Abedin & Amine Belhadi & Sachin Kamble & Karim Zkik & Mustapha Oudani, 2024. "Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 9141-9160, December.
- Janssens, Bram & Schetgen, Lisa & Bogaert, Matthias & Meire, Matthijs & Van den Poel, Dirk, 2024. "360 Degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, Elsevier, vol. 317(2), pages 366-381.
- Maarouf, Abdurahman & Feuerriegel, Stefan & Pröllochs, Nicolas, 2025. "A fused large language model for predicting startup success," European Journal of Operational Research, Elsevier, vol. 322(1), pages 198-214.
- Julius Peter Landwehr & Niklas Kühl & Jannis Walk & Mario Gnädig, 2022. "Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 707-728, December.
- Sheshadri Chatterjee & Ranjan Chaudhuri & Demetris Vrontis & Thanos Papadopoulos, 2024. "Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support," Annals of Operations Research, Springer, vol. 339(1), pages 163-183, August.
More about this item
Keywords
managerial activities; value creation process; sustainable relationships; stakeholders; techniques and procedures; map of managerial activities; diversification of suppliers and buyers; value identification; value creation variants; perception and feedback;All these keywords.
Statistics
Access and download statisticsCorrections
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:jadmsc:v:14:y:2024:i:8:p:186-:d:1459775. 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.