IDEAS home Printed from https://ideas.repec.org/r/eee/jbrese/v123y2021icp588-603.html
   My bibliography  Save this item

Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges

Citations

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


Cited by:

  1. Mostafa Bigdeli & Mahsa Akbari, 2024. "Machine-learning-based Classification of Customers’ Behavioural Model in Instagram," Paradigm, , vol. 28(2), pages 223-240, December.
  2. 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.
  3. Brea, Edgar & Ford, Jerad A., 2023. "No silver bullet: Cognitive technology does not lead to novelty in all firms," Technovation, Elsevier, vol. 122(C).
  4. 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.
  5. Antoine Harfouche & Bernard Quinio & Mario Saba & Peter Bou Saba, 2023. "The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge," Information Systems Frontiers, Springer, vol. 25(1), pages 55-70, February.
  6. 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.
  7. 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).
  8. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.
  9. 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.
  10. 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).
  11. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.
  12. 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).
  13. 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.
  14. Potrawa, Tomasz & Tetereva, Anastasija, 2022. "How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market," Journal of Business Research, Elsevier, vol. 144(C), pages 50-65.
  15. 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).
  16. Byung-Jik Kim & Julak Lee, 2024. "The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
  17. 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.
  18. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
  19. O. C. Ferrell & Dana E. Harrison & Linda K. Ferrell & Haya Ajjan & Bryan W. Hochstein, 2024. "A theoretical framework to guide AI ethical decision making," AMS Review, Springer;Academy of Marketing Science, vol. 14(1), pages 53-67, June.
  20. 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).
  21. Sagarika Mishra & Michael T. Ewing & Holly B. Cooper, 2022. "Artificial intelligence focus and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1176-1197, November.
  22. Muhammad Kaleem Khan & Chunhui Huo & R. M. Ammar Zahid & Umer Sahil Maqsood, 2024. "The automated sustainability auditor: Does artificial intelligence curtail greenwashing behavior in Chinese firms?," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 9015-9039, December.
  23. 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.
  24. 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.
  25. 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).
  26. Amir Masoud Rahmani & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Zahid Mehmood & Amir Haider & Mehdi Hosseinzadeh & Rizwan Ali Naqvi, 2021. "Machine Learning (ML) in Medicine: Review, Applications, and Challenges," Mathematics, MDPI, vol. 9(22), pages 1-52, November.
  27. 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).
  28. 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 1-20, May.
  29. Vecchi, Alessandra & Brennan, Louis, 2022. "Two tales of internationalization – Chinese internet firms' expansion into the European market," Journal of Business Research, Elsevier, vol. 152(C), pages 106-127.
  30. 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).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.