Logo PTI Logo FedCSIS

Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

From Agents to Copilots: A Systematic Review of Digital Assistant Technology Adoption in Proprietary Productivity Software

,

DOI: http://dx.doi.org/10.15439/2025F3271

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 565576 ()

Full text

Abstract. This study provides a systematic review of how the impact and adaptation of digital assistant technologies (DATs) are defined, operationalized, and studied, synthesizing key domains where DATs generate or are expected to generate value. Based on an analysis of 61 articles published since 2013, it identifies five main areas of impact: productivity and efficiency, business development, resource optimization, quality enhancement, and the promotion of learning and creativity. The review highlights DAT adoption across various disciplines and industries, while revealing limited longitudinal research on benefits and adaptation. Key gaps remain in understanding strategic use and sustained impact. Future research should explore longitudinal comparisons of recently introduced generative AI-driven DATs and their organizational implications. This review contributes to information systems research by structuring current knowledge on DAT adoption and outcomes, and by proposing a research agenda to support deeper exploration of their value and long-term integration.

References

  1. N. Preiß and M. Westner, ‘Unraveling jingle-jangle fallacies in digital assistant technologies: A comprehensive systematic review and research agenda’, in ICICT 2025: 10th International Congress on Information and Communication Technology, London, 2025.
  2. P. Amiri and E. Karahanna, ‘Chatbot use cases in the Covid-19 public health response’, Journal of the American Medical Informatics Association, vol. 29, no. 5, pp. 1000–1010, 2022, https://dx.doi.org/10.1093/jamia/ocac014.
  3. M. McKillop, B. R. South, A. Preininger, M. Mason, and G. P. Jackson, ‘Leveraging conversational technology to answer common COVID-19 questions’, Journal of the American Medical Informatics Association, vol. 28, no. 4, pp. 850–855, 2021, https://dx.doi.org/10.1093/jamia/ocaa316.
  4. H. Al Naqbi, Z. Bahroun, and V. Ahmed, ‘Enhancing work productivity through generative artificial intelligence: A comprehensive literature review’, Sustainability, vol. 16, no. 3, Art. no. 3, Jan. 2024, https://dx.doi.org/10.3390/su16031166.
  5. D. Czarnitzki, G. P. Fernández, and C. Rammer, ‘Artificial intelligence and firm-level productivity’, Journal of Economic Behavior & Organization, vol. 211, pp. 188–205, 2023, https://dx.doi.org/10.1016/j.jebo.2023.05.008.
  6. Google, ‘Introducing Gemini: our largest and most capable AI model’, Google. Accessed: Aug. 21, 2024. [Online]. Available: https://blog.google/technology/ai/google-gemini-ai/
  7. Microsoft Corporation, ‘Introducing Microsoft 365 Copilot: your copilot for work’. Accessed: Sep. 07, 2024. [Online]. Available: https://blogs.microsoft.com/blog/2023/03/16/introducing-microsoft-365-copilot-your-copilot-for-work
  8. L. S. Vailshery, ‘Office productivity software global market share 2025’, Statista. Accessed: Apr. 24, 2025. [Online]. Available: https://www.statista.com/statistics/983299/worldwide-market-share-of-office-productivity-software/
  9. J. D’Souza, ‘Microsoft 365 Statistics By Downloads, Subscribers, Revenue and Facts’, Electro IQ. Accessed: Apr. 24, 2025. [Online]. Available: https://electroiq.com/stats/microsoft-365-statistics/
  10. M. McDade, ‘Microsoft 365 Usage and Security Statistics for 2025’, Expert Insights. Accessed: Apr. 24, 2025. [Online]. Available: https://expertinsights.com/email-security/microsoft-365-usage-and-security-statistics-for-2024
  11. D. Darwish, ‘Chatbots vs. AI chatbots vs. virtual assistants’:, in Advances in Computational Intelligence and Robotics, D. Darwish, Ed., IGI Global, 2024, pp. 26–50. https://dx.doi.org/10.4018/979-8-3693-1830-0.ch002.
  12. P. Preiß and M. Westner, ‘Towards a taxonomy for digital assistant technologies: Addressing the jingle-jangle fallacies’, in Proceedings of the 38th Bled eConference, 2025.
  13. J. Uszkoreit, ‘Transformer: A novel neural network architecture for language understanding’, 2017, [Online]. Available: https://blog.research.google/2017/08/transformer-novel-neural-network.html
  14. E. Han, D. Yin, and H. Zhang, ‘Bots with feelings: Should AI agents express positive emotion in customer service?’, Information Systems Research, vol. 34, no. 3, pp. 1296–1311, 2023, https://dx.doi.org/10.1287/ISRE.2022.1179.
  15. T. Li, X. Zhang, Y. Wang, Q. Zhou, Y. Wang, and F. Dong, ‘Machine learning for requirements engineering (ML4RE): A systematic literature review complemented by practitioners’ voices from Stack Overflow’, Information and Software Technology, vol. 172, 2024, https://dx.doi.org/10.1016/j.infsof.2024.107477.
  16. E. Brynjolfsson, D. Li, and L. Raymond, ‘Generative AI at work’, The Quarterly Journal of Economics, no. 140, pp. 889–942, 2023, https://dx.doi.org/10.3386/w31161.
  17. S. Hazmoune and F. Bougamouza, ‘Using transformers for multimodal emotion recognition: Taxonomies and state of the art review’, Engineering Applications of Artificial Intelligence, vol. 133, no. 6, pp. 107–113, Jul. 2024, https://dx.doi.org/10.1016/j.engappai.2024.108339.
  18. OpenAI, ‘Introducing ChatGPT’. Accessed: Aug. 07, 2024. [Online]. Available: https://openai.com/index/chatgpt/
  19. T. Vaikunta Pai, P. S. Nethravathi, R. Birau, V. Popescu, B. H. Karthik Pai, and P. V. Naik, ‘Multimodal ChatGPT: Extending ChatGPT to enable rich multimodal conversations using deep neural network’, Journal of Intelligent & Fuzzy Systems, vol. Preprint, no. Preprint, pp. 1–17, 2024, https://dx.doi.org/10.3233/JIFS-239465.
  20. L. Gkinko and A. Elbanna, ‘Chatbots at work: A taxonomy of the use of chatbots in the workplace’, in Responsible AI and Analytics for an Ethical and Inclusive Digitized Society, D. Dennehy, A. Griva, N. Pouloudi, Y. K. Dwivedi, I. Pappas, and M. Mäntymäki, Eds., Cham: Springer International Publishing, 2021, pp. 29–39. https://dx.doi.org/10.1007/978-3-030-85447-8_3.
  21. Y. Gao, H. Rui, and S. Sun, ‘The power of identity cues in text-based customer service: Evidence from Twitter’, MIS Quarterly, vol. 47, no. 3, pp. 983–1014, 2023, https://dx.doi.org/10.25300/MISQ/2022/17366.
  22. L. R. Anderson and S. Conyers, ‘Freeware versus commercial office productivity software’, Acquisition Research Program, Technical Report, Dec. 2016. Accessed: Jan. 11, 2025. [Online]. Available: https://dair.nps.edu/handle/123456789/2191
  23. M. Keith, R. Santanam, and R. Sinha, ‘Switching costs, satisfaction, loyalty, and willingness to pay for office productivity software’, in 2010 43rd Hawaii International Conference on System Sciences, Jan. 2010, pp. 1–9. https://dx.doi.org/10.1109/HICSS.2010.359.
  24. N. Gandal, S. Markovich, and M. H. Riordan, ‘Ain’t it “suite”? Bundling in the PC office software market’, Strategic Management Journal, vol. 39, no. 8, pp. 2120–2151, Aug. 2018, https://dx.doi.org/10.1002/smj.2797.
  25. D. Tapscott, ‘Office efficiency, effectiveness, and productivity’, in Office Automation: A User-Driven Method, D. Tapscott, Ed., Boston, MA: Springer US, 1982, pp. 71–79. https://dx.doi.org/10.1007/978-1-4613-2489-8_5.
  26. K. Koenigsbauer, ‘Microsoft 365 Copilot Chat’, techcommunity.microsoft.com. Accessed: Mar. 15, 2025. [Online]. Available: https://techcommunity.microsoft.com/blog/microsoft365copilotblog/microsoft-365-copilot-chat-%e2%80%93-copilot-for-all-your-employees/4392002
  27. H. Snyder, ‘Literature review as a research methodology: An overview and guidelines’, Journal of Business Research, vol. 104, pp. 333–339, Nov. 2019, https://dx.doi.org/10.1016/j.jbusres.2019.07.039.
  28. D. Tranfield, D. Denyer, and P. Smart, ‘Towards a methodology for developing evidence-informed management knowledge by means of systematic review’, British Journal of Management, vol. 14, no. 3, pp. 207–222, 2003, https://dx.doi.org/10.1111/1467-8551.00375.
  29. R. F. Baumeister and M. R. Leary, ‘Writing narrative literature reviews’, Review of General Psychology, vol. 3, no. 1, pp. 311–320, 1997, https://dx.doi.org/10.1037/1089-2680.1.3.311.
  30. B. Kitchenham and S. Charters, ‘Guidelines for performing systematic literature reviews in software engineering’, vol. 2, Jan. 2007, https://doi.org/10.53832/edtechhub.1003.
  31. D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, ‘Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement’, International Journal of Surgery, vol. 8, no. 5, pp. 336–341, Jan. 2010, https://dx.doi.org/10.1016/j.ijsu.2010.02.007.
  32. B. M. Shrestha, ‘Systematic reviews and meta-analysis: Principles and practice’, JNMA J Nepal Med Assoc, vol. 57, no. 215, pp. 1–2, Feb. 2019, https://dx.doi.org/10.31729/jnma.3986.
  33. F. Tingelhoff, M. Brugger, and J. M. Leimeister, ‘A guide for structured literature reviews in business research: The state-of-the-art and how to integrate generative artificial intelligence’, Journal of Information Technology, vol. 40, no. 1, pp. 77–99, Mar. 2025, https://dx.doi.org/10.1177/02683962241304105.
  34. J. Wolfswinkel, E. Furtmueller, and C. Wilderom, ‘Using grounded theory as a method for rigorously reviewing literature’, European Journal of Information Systems, vol. 22, pp. 1–11, Nov. 2013, https://dx.doi.org/10.1057/ejis.2011.51.
  35. C. Hart, Doing a Literature Review: Releasing the Research Imagination, 2nd ed. Sage, 2018.
  36. O. Al-Tabbaa, S. Ankrah, and N. Zahoor, Systematic literature review in management and business studies: A case study on university-industry collaboration. 2019. https://dx.doi.org/10.4135/9781526467263.
  37. J. Webster and R. T. Watson, ‘Analyzing the past to prepare for the future: Writing a literature review’, MIS Quarterly, vol. 26, no. 2, pp. 8–13, 2002.
  38. B. González-Albo and M. Bordons, ‘Articles vs. proceedings papers: Do they differ in research relevance and impact? A case study in the Library and Information Science field’, Journal of Informetrics, vol. 5, no. 3, pp. 369–381, Jul. 2011, https://dx.doi.org/10.1016/j.joi.2011.01.011.
  39. S. Boell and B. Wang, ‘www.litbaskets.io, an it artifact supporting exploratory literature searches for information systems research’, presented at the ACIS 2019 Proceedings. 71, Dec. 2019. [Online]. Available: https://aisel.aisnet.org/acis2019/71
  40. Scopus, ‘Scopus | The largest database of peer-reviewed literature | Elsevier’. Accessed: Aug. 05, 2024. [Online]. Available: https://elsevier.international/en-in/solutions/scopus.html
  41. R. D. Jong and D. Bus, ‘VOSviewer: putting research into context’, Research Software Community Leiden, Mar. 2023, https://dx.doi.org/10.21428/a1847950.acdc99d6.
  42. R. Kumar, Research methodology : A step-by-step guide for beginners, 5th ed., vol. 5. in 5, vol. 5. Los Angeles : Sage, 2014.
  43. D. Rooein, D. Bianchini, F. Leotta, M. Mecella, P. Paolini, and B. Pernici, ‘aCHAT-WF: Generating conversational agents for teaching business process models’, Software and Systems Modeling, vol. 21, no. 3, pp. 891–914, 2022, https://dx.doi.org/10.1007/s10270-021-00925-7.
  44. D. Rooein, D. Bianchini, F. Leotta, M. Mecella, P. Paolini, and B. Pernici, ‘Chatting about processes in digital factories: A model-based approach’, presented at the Lecture Notes in Business Information Processing, Nurcan S., Reinhartz-Berger I., Soffer P., and Zdravkovic J., Eds., Springer, 2020, pp. 70–84. https://dx.doi.org/10.1007/978-3-030-49418-6_5.
  45. F. De Luzi, M. Macrì, M. Mecella, and T. Mencattini, ‘Cicero: An AI-Based Writing Assistant for Legal Users’, presented at the Lecture Notes in Business Information Processing, Cabanillas C. and Pérez F., Eds., Springer Science and Business Media Deutschland GmbH, 2023, pp. 103–111. https://dx.doi.org/10.1007/978-3-031-34674-3_13.
  46. H. W. Marsh et al., ‘The murky distinction between self-concept and self-efficacy: Beware of lurking jingle-jangle fallacies.’, Journal of Educational Psychology, vol. 111, no. 2, pp. 331–353, Feb. 2019, https://dx.doi.org/10.1037/edu0000281.
  47. J. Creswell, Research design: Qualitative, quantitative, and mixed methods approaches, vol. 4th. SAGE Publications, 2014. Accessed: Aug. 20, 2024. [Online]. Available: http://archive.org/details/methodology-alobatnic-libraries-creswell
  48. A. Rindfleisch, A. J. Malter, S. Ganesan, and C. Moorman, ‘Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines’, Journal of Marketing Research, vol. 45, no. 3, pp. 261–279, Jun. 2008, https://dx.doi.org/10.1509/jmkr.45.3.261.
  49. M. E. Koltko-Rivera, ‘The Psychology of Worldviews’, Review of General Psychology, vol. 8, no. 1, pp. 3–58, 2004, https://dx.doi.org/10.1037/1089-2680.8.1.3.
  50. P. Johnson and J. Duberley, Understanding management research: An introduction to epistemology, 1st ed., vol. 1. Sage Publications Ltd, 2000. https://dx.doi.org/10.4135/9780857020185.
  51. C. A. Saliya, ‘Research philosophy: paradigms, world views, perspectives, and theories’, 2023, pp. 35–51. https://dx.doi.org/10.4018/978-1-6684-6859-3.ch004.
  52. A. VanScoy, H. Julien, A. Buckley, and J. Goodell, ‘Theory usage in empirical research in ISIC conference papers (1996-2020)’, Sep. 2022, https://dx.doi.org/10.18452/25229.
  53. O. Boubker, ‘From chatting to self-educating: Can AI tools boost student learning outcomes?’, Expert Systems with Applications, vol. 238, 2024, https://dx.doi.org/10.1016/j.eswa.2023.121820.
  54. F. Brachten, T. Kissmer, and S. Stieglitz, ‘The acceptance of chatbots in an enterprise context – A survey study’, International Journal of Information Management, vol. 60, 2021, https://dx.doi.org/10.1016/j.ijinfomgt.2021.102375.
  55. J. Zhang, X. Lu, W. Zheng, and X. Wang, ‘It’s better than nothing: The influence of service failures on user reusage intention in AI chatbot’, Electronic Commerce Research and Applications, vol. 67, 2024, https://dx.doi.org/10.1016/j.elerap.2024.101421.
  56. D. Kudryavtsev, ‘AI-driven digital business design assistant: A prototype demo’, presented at the CEUR Workshop Proceedings, CEUR-WS, 2024.
  57. F. Rowe, ‘What literature review is not: diversity, boundaries and recommendations’, Eur J Inf Syst, vol. 23, no. 3, pp. 241–255, May 2014, https://dx.doi.org/10.1057/ejis.2014.7.
  58. S. Haag and A. Eckhardt, ‘Organizational cloud service adoption: a scientometric and content-based literature analysis’, J Bus Econ, vol. 84, no. 3, pp. 407–440, Apr. 2014, https://dx.doi.org/10.1007/s11573-014-0716-6.
  59. W. L. Neuman, Social research methods: Qualitative and quantitative approaches, 7th ed. Pearson Education, 2002.
  60. D. Dennehy, ‘The use of open, axial and selective coding echniques: A literature analysis of IS research’, in Academy for Information Systems Conference Proceedings 2023, 2023. [Online]. Available: https://aisel.aisnet.org/ukais2023/20
  61. T. I. Wilhelm, J. Roos, and R. Kaczmarczyk, ‘Large language models for therapy recommendations across 3 clinical specialties: Comparative study’, Journal of Medical Internet Research, vol. 25, 2023, https://dx.doi.org/10.2196/49324.
  62. F. M. Calisto, C. Santiago, N. Nunes, and J. C. Nascimento, ‘Bots with feelings: Should AI agents express positive emotion in customer service’, International Journal of Human Computer Studies, vol. 150, pp. 1–24, 2021, https://dx.doi.org/10.1016/j.ijhcs.2021.102607.
  63. N. Belhaj, A. Hamdane, N. El Houda Chaoui, H. Chaoui, and M. El Bekkali, ‘Engaging students to fill surveys using chatbots: University case study’, Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 1, pp. 473–483, 2021, https://dx.doi.org/10.11591/ijeecs.v24.i1.pp473-483.
  64. T. Lewandowski, E. Kučević, S. Leible, M. Poser, and T. Böhmann, ‘Enhancing conversational agents for successful operation: A multi-perspective evaluation approach for continuous improvement’, Electronic Markets, vol. 33, no. 1, pp. 1–20, 2023, https://dx.doi.org/10.1007/s12525-023-00662-3.
  65. F. Almeida, ‘Implementation of a chatbot in a unified communication channel’, Journal of Systems and Information Technology, vol. 27, no. 1, pp. 94–115, 2025, https://dx.doi.org/10.1108/JSIT-08-2023-0160.
  66. S. Jackson and N. Panteli, ‘AI-based digital assistants in the workplace: An idiomatic analysis’, Communications of the Association for Information Systems, vol. 55, pp. 627–653, 2024.
  67. E. Brynjolfsson and A. McAfee, ‘Winning the race with ever-smarter machines’, MIT SMR, no. 53, pp. 53–60, Dec. 2011.
  68. B. Zhong, W. He, Z. Huang, P. E. D. Love, J. Tang, and H. Luo, ‘A building regulation question answering system: A deep learning methodology’, Advanced Engineering Informatics, vol. 46, pp. 1–11, 2020, https://dx.doi.org/10.1016/j.aei.2020.101195.
  69. M. Nadeem, L. Javed, S. S. Sohail, E. Cambria, and A. Hussain, ‘Are foundation models the next-generation social media content moderators?’, IEEE Intelligent Systems, vol. 39, no. 6, pp. 70–80, 2024, https://dx.doi.org/10.1109/MIS.2024.3477109.
  70. J. Choi and D. L. Nazareth, ‘Repairing trust in an e-commerce and security context: An agent-based modeling approach’, Information Management and Computer Security, vol. 22, no. 5, pp. 490–512, 2014, https://dx.doi.org/10.1108/IMCS-09-2013-0069.
  71. G. Graham, T. M. Nisar, G. Prabhakar, R. Meriton, and S. Malik, ‘Chatbots in customer service within banking and finance: Do chatbots herald the start of an AI revolution in the corporate world?’, Computers in Human Behavior, vol. 165, pp. 1–15, 2025, https://dx.doi.org/10.1016/j.chb.2025.108570.
  72. J. J. Bird and A. Lotfi, ‘Customer service chatbot enhancement with attention-based transfer learning’, Knowledge-Based Systems, vol. 301, pp. 1–12, 2024, https://dx.doi.org/10.1016/j.knosys.2024.112293.
  73. Y. Zhang, C. Liang, and X. Li, ‘Understanding virtual agents’ service quality in the context of customer service: A fit-viability perspective’, Electronic Commerce Research and Applications, vol. 65, pp. 1–12, 2024, https://dx.doi.org/10.1016/j.elerap.2024.101380.
  74. N. Köster, C. Mundt, and H. Lödding, ‘Planning and control of maritime commissioning: planning concept’, presented at the IFIP Advances in Information and Communication Technology, Alfnes E., Romsdal A., Strandhagen J.O., von Cieminski G., and Romero D., Eds., 2023, pp. 735–749. https://dx.doi.org/10.1007/978-3-031-43670-3_51.
  75. Q. Chen, Y. Lu, Y. Gong, and J. Xiong, ‘Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty’, Internet Research, vol. 33, no. 6, pp. 2205–2243, 2023, https://dx.doi.org/10.1108/INTR-09-2021-0686.
  76. S. W. Chae, Y. W. Seo, and K. C. Lee, ‘Task difficulty and team diversity on team creativity: Multi-agent simulation approach’, Computers in Human Behavior, vol. 42, pp. 83–92, 2015, https://dx.doi.org/10.1016/j.chb.2014.03.032.
  77. P. Mikalef and M. Gupta, ‘Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance’, Inf. Manage., vol. 58, no. 3, pp. 1–20, Apr. 2021, https://dx.doi.org/10.1016/j.im.2021.103434.
  78. R. Abdelghani, P.-Y. Oudeyer, E. Law, C. de Vulpillières, and H. Sauzéon, ‘Conversational agents for fostering curiosity-driven learning in children’, International Journal of Human Computer Studies, vol. 167, pp. 1–21, 2022, https://dx.doi.org/10.1016/j.ijhcs.2022.102887.
  79. X. Lin, X. Wang, B. Shao, and J. Taylor, ‘How chatbots augment human intelligence in customer services: A mixed-methods study’, Journal of Management Information Systems, vol. 41, no. 4, pp. 1016–1041, 2024, https://dx.doi.org/10.1080/07421222.2024.2415773.
  80. S.-L. Cheng et al., ‘Comparisons of quality, correctness, and similarity between ChatGPT-generated and human-written abstracts for basic research: Cross-sectional study’, Journal of Medical Internet Research, vol. 25, no. 1, pp. 1–9, 2023, https://dx.doi.org/10.2196/51229.
  81. L. Willcocks, M. Lacity, and A. Craig, ‘Robotic process automation: Strategic transformation lever for global business services?’, Journal of Information Technology Teaching Cases, vol. 7, no. 1, pp. 17–28, May 2017, https://dx.doi.org/10.1057/s41266-016-0016-9.
  82. R. Figliè, T. Turchi, G. Baldi, and D. Mazzei, ‘Towards an LLM-based intelligent assistant for industry 5.0’, presented at the CEUR Workshop Proceedings, Dix A., Roach M., Turchi T., Malizia A., and Wilson B., Eds., CEUR-WS, 2024.
  83. Z. Eberhart, A. Bansal, and C. McMillan, ‘A Wizard of Oz Study Simulating API Usage Dialogues With a Virtual Assistant’, IEEE Transactions on Software Engineering, vol. 48, no. 6, pp. 1883–1904, 2022, https://dx.doi.org/10.1109/TSE.2020.3040935.
  84. N. Ben-Shabat et al., ‘Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study’, International Journal of Medical Informatics, vol. 168, 2022, https://dx.doi.org/10.1016/j.ijmedinf.2022.104897.
  85. A. Siddig and A. Hines, ‘A psychologist chatbot developing experience’, presented at the CEUR Workshop Proceedings, Curry E., Keane M., Ojo A., and Salwala D., Eds., CEUR-WS, 2019, pp. 200–211.
  86. C. Wang et al., ‘Application of large language models in medical training evaluation—Using ChatGPT as a standardized patient: multimetric assessment’, Journal of Medical Internet Research, vol. 27, pp. 1–15, 2025, https://dx.doi.org/10.2196/59435.
  87. S. Gregor, ‘The nature of theory in information systems’, MIS Quarterly, vol. 30, no. 3, pp. 611–642, 2006, https://dx.doi.org/10.2307/25148742.
  88. K. Larsen, N. Saini, and R. Mueller, ‘Theories used in IS research wiki’, Information Systems Theories. Accessed: Mar. 31, 2025. [Online]. Available: https://is.theorizeit.org/wiki/Main_Page
  89. J. Reis, M. Amorim, N. Melão, Y. Cohen, and M. Rodrigues, ‘Digitalization: A literature review and research agenda’, in Proceedings on 25th International Joint Conference on Industrial Engineering and Operations Management - IJCIEOM 2019, Z. Anisic, B. Lalic, and D. Gracanin, Eds., Cham: Springer International Publishing, 2020, pp. 443–456. https://doi.org/10.1007/978-3-030-43616-2_47.