Consolidating Human-AI Collaboration Research in Organizations: A Literature Review
DOI:
https://doi.org/10.71222/7dehvd30Keywords:
human-AI collaboration, generative artificial intelligence, systematic review, organizational change, industry 5.0Abstract
The purpose of this study is to depict the value added by human-AI collaboration in organizations to collaboration system design by virtue of the studies reached by the literature review on different databases are examined. Web of Science content and covering the title of “human-AI collaboration” has been selected in this study. Research using bibliometric analysis has been conducted and it has been determined that the terms “human-AI collaboration” and “generative artificial intelligence” should be searched for simultaneously in each and every article published in the journal between the years 1975 and 2024. Our study used a combination of bibliometric analysis and literature review, bibliometric analysis tools include HistCite and CiteSpace. The citation map shows three phases: human-machine collaboration into practice (before 2020), the intelligent and automated segment of AI (2020-2021), and the generative AI phase represented by ChatGPT (2022 to present). This article conducted a systematic overview study on human-AI collaboration in organizations, established a conceptual framework for the conceptual framework of human-machine collaboration guided by generative AI integration, and provided certain theoretical insights to guide the corresponding practical activities. Bibliometric analysis is a method that can be used to evaluate the performance of a research topic. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single theme. This circumstance is elaborately described as a limitation of this study. This article builds on data from WoS Core Collection, and some new but important articles may not be analyzed, since bibliometrics consider high citation as an indicator to select influential articles. while previous research focused on researching modes of collaboration between humans and cobats, such as virtual assistants, this study extends the literature on different types of AI. Our research addresses the emerging field of collaboration with software that is not just a mere tool designed for performing knowledge work but becomes a collaborative partner.
References
1. Y. R. Shrestha, S. M. Ben-Menahem, and G. V. Krogh, "Organizational decision-making structures in the age of artificial intel-ligence," Calif. Manag. Rev., vol. 61, no. 4, pp. 66–83, 2019, doi: 10.1177/0008125619862257.
2. J. Schmidtler, V. Knott, C. Hölzel, and K. Bengler, "Human centered assistance applications for the working environment of the future," Occup. Ergonom., vol. 12, no. 3, pp. 83–95, 2015, doi: 10.3233/OER-150226.
3. F. Csaszar and T. Steinberger, "Organizations as artificial intelligences: The use of artificial intelligence analogies in organiza-tion theory," Acad. Manag. Ann., vol. 16, pp. 1–37, 2021, doi: 10.5465/annals.2020.0192
4. A. Murray, J. Rhymer, and D. G. Sirmon, "Humans and technology: Forms of conjoined agency in organizations," Acad. Manag. Rev., 2020, doi: 10.5465/amr.2019.0186
5. E. H. Manser Payne, A. J. Dahl, and J. Peltier, "Digital servitization value co-creation framework for AI services: A research agenda for digital transformation in financial service ecosystems," J. Res. Interact. Mark., vol. 15, no. 2, pp. 200–222, 2021, doi: 10.1108/JRIM-12-2020-0252.
6. K. Christensen, S. Nørskov, L. Frederiksen, and J. Scholderer, "In search of new product ideas: Identifying ideas in online communities by machine learning and text mining," Creat. Innov. Manag., vol. 26, no. 1, pp. 17–30, 2016, doi: 10.1111/caim.12202.
7. K. Sowa, A. Przegalinska, and L. Ciechanowski, "Cobots in knowledge work," J. Bus. Res., vol. 125, pp. 135–142, 2021, doi: 10.1016/j.jbusres.2020.11.038.
8. S. Chowdhury, P. Budhwar, P. K. Dey, S. Joel-Edgar, and A. Abadie, "AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems, and organisational socialisation framework," J. Bus. Res., vol. 144, pp. 31–49, 2022, doi: https://doi.org/10.1016/j.jbusres.2022.01.069.
9. F. Kruse, V. Dmitriyev, and J. M. Gómez, "Building a connection between decision maker and data-driven decision process," Eur. Conf. Data Anal., vol. 4, no. 1, 2017, doi: 10.5445/KSP/1000085951/03.
10. G. M., M. K. Gourisaria, M. Pandey, and S. S. Rautaray, "A comprehensive survey and analysis of generative models in machine learning," Comput. Sci. Rev., vol. 38, p. 100285, 2020, doi: 10.1016/j.cosrev.2020.100285.
11. C. Fisch and J. H. Block, "Six tips for your (systematic) literature review in business and management research," Manag. Rev. Q., vol. 68, pp. 103–106, 2018, doi: 10.1007/s11301-018-0142-x.
12. S. Bankins, A. C. Ocampo, M. Marrone, S. L. D. Restubog, and S. E. Woo, "A multilevel review of artificial intelligence in or-ganizations: Implications for organizational behavior research and practice," J. Organ. Behav., vol. 45, no. 2, pp. 159–182, 2023, doi: 10.1002/job.2735.
13. J. Barney, "Firm resources and sustained competitive advantage," J. Manag., vol. 17, no. 1, pp. 99–120, 1991, doi: 10.1177/014920639101700108.
14. H. J. Klein and B. Polin, "Are organizations on board with best practices onboarding?" in The Oxford Handbook of Organizational Socialization, C. R. Wanberg, Ed. Oxford: Oxford University Press, 2012, doi: 10.1093/oxfordhb/9780199763672.013.0014.
15. C. Reis, P. Ruivo, T. Oliveira, and P. Faroleiro, "Assessing the drivers of machine learning business value," J. Bus. Res., vol. 117, pp. 232–243, 2020, doi: 10.1016/j.jbusres.2020.05.053.
16. S. L. Wamba-Taguimdje, S. F. Wamba, J. R. K. Kamdjoug, and C. E. T. Wanko, "Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects," Bus. Process Manag. J., vol. 26, no. 7, pp. 1893–1924, 2020, doi: 10.1108/BPMJ-10-2019-0411.
17. Y. Chen and S. Jin, "Artificial intelligence and carbon emissions in manufacturing firms: The moderating role of green innova-tion," Processes, vol. 11, no. 9, p. 2705, 2023, doi: 10.3390/pr11092705.
18. J.-M. Li, T.-J. Wu, Y. J. Wu, and M. Goh, "Systematic literature review of human–machine collaboration in organizations using bibliometric analysis," Manag. Decis., vol. 61, no. 10, pp. 2920–2944, 2023, doi: 10.1108/MD-09-2022-1183.
19. G. S. Kearns and R. Sabherwal, "Strategic alignment between business and information technology: A knowledge-based view of behaviors, outcomes, and consequences," J. Manag. Inf. Syst., vol. 23, no. 3, pp. 129–162, 2006, doi: 10.2753/MIS0742-1222230306.
20. S. Kim, "Working with robots: Human resource development considerations in human–robot interaction," Hum. Resour. Dev. Rev., vol. 21, no. 1, pp. 48–74, 2022, doi: 10.1177/15344843211068810.
21. L. Martin and N. Wilson, "Opportunity, discovery and creativity: A critical realist perspective," Int. Small Bus. J., vol. 34, no. 3, pp. 261–275, 2016, doi: 10.1177/0266242614551185.
22. S. Bahoo, M. Cucculelli, and D. Qamar, "Artificial intelligence and corporate innovation: A review and research agenda," Technol. Forecast. Soc. Change, vol. 188, p. 122264, 2023, doi: 10.1016/j.techfore.2022.122264.
23. R. W. Palmatier, M. B. Houston, and J. Hulland, "Review articles: Purpose, process, and structure," J. Acad. Mark. Sci., vol. 46, pp. 1–5, 2018, doi: 10.1007/s11747-017-0563-4.
24. F. Li, "The information content of forward‐looking statements in corporate filings—A naïve Bayesian machine learning ap-proach," J. Account. Res., vol. 48, no. 5, pp. 1049–1102, 2010, doi: 10.1111/j.1475-679X.2010.00382.x.
25. S. Syam and A. Sharma, "Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice," Ind. Mark. Manag., vol. 69, pp. 135–146, 2018, doi: 10.1016/j.indmarman.2017.12.019.
26. T. Haesevoets, D. D. Cremer, K. Dierckx, and A. V. Hiel, "Human-machine collaboration in managerial decision making," Comput. Human Behav., vol. 119, p. 106730, 2021, doi: 10.1016/j.chb.2021.106730.
27. D. M. Steininger, P. Mikalef, A. Pateli, and A. Ortiz-de-Guinea, "Dynamic capabilities in information systems research: A critical review, synthesis of current knowledge, and recommendations for future research," J. Assoc. Inf. Syst., vol. 23, no. 2, pp. 447–490, 2022, doi: 10.17705/1jais.00736.
28. M. Hoornaert, M. Ballings, E. C. Malthouse, and D. Van den Poel, "Identifying new product ideas: Waiting for the wisdom of the crowd or screening ideas in real time," J. Prod. Innov. Manag., vol. 34, no. 5, pp. 580–597, 2017, doi: 10.1111/jpim.12396.
29. J. Wirtz, P. G. Patterson, W. H. Kunz, T. Gruber, V. N. Lu, S. Paluch, and A. Martins, "Brave new world: Service robots in the frontline," J. Serv. Manag., vol. 29, no. 5, pp. 907–931, 2018, doi: 10.1108/JOSM-04-2018-0119.
30. M. Tang, J. Koopman, S. T. McClean, J. H. Zhang, C. H. Li, D. D. Cremer, Y. Lu, and C. T. S. Ng, "When conscientious employees meet intelligent machines: An integrative approach inspired by complementarity theory and role theory," Acad. Manag. J., vol. 65, no. 3, pp. 1019–1054, 2022, doi: 10.5465/amj.2020.1516.
31. E. A. Inigo and L. Albareda, "Sustainability oriented innovation dynamics: Levels of dynamic capabilities and their path-dependent and self-reinforcing logics," Technol. Forecast. Soc. Chang., vol. 139, pp. 334–351, 2019, doi: 10.1016/j.techfore.2018.11.023.
32. A. D. Vaio, R. Palladino, R. Hassan, and O. Escobar, "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," J. Bus. Res., vol. 121, pp. 283–314, 2020, doi: 10.1016/j.jbusres.2020.08.019.
33. T. Nguyen, Q. H. Duong, T. V. Nguyen, Y. Zhu, and L. Zhou, "Knowledge mapping of digital twin and physical internet in supply chain management: A systematic literature review," Int. J. Prod. Econ., vol. 244, p. 108381, 2022, doi: 10.1016/j.ijpe.2021.108381.
34. S. M. Noble, M. Mende, D. Grewal, and A. Parasuraman, "The Fifth Industrial Revolution: How harmonious human–machine collaboration is triggering a retail and service revolution," J. Retailing, vol. 98, no. 2, pp. 199–208, 2022, doi: 10.1016/j.jretai.2022.04.003.
35. W. Fruehwirt and P. Duckworth, "Towards better healthcare: What could and should be automated?" Technol. Forecast. Soc. Chang., vol. 172, p. 120967, 2021, doi: 10.1016/j.techfore.2021.120967.
36. S. Raisch and S. Krakowski, "Artificial intelligence and management: The automation-augmentation paradox," Acad. Manag. Rev., vol. 46, no. 1, pp. 192–210, 2021, doi:10.5465/amr.2018.0072.
37. J. Samuel, R. Kashyap, Y. Samuel, and A. Pelaez, "Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations," Int. J. Inf. Manag., vol. 65, p. 102505, 2022, doi: 10.1016/j.ijinfomgt.2022.102505.
38. L. Shen, W. Sun, and V. Parida, "Consolidating digital servitization research: A systematic review, integrative framework, and future research directions," Technol. Forecast. Soc. Chang., vol. 191, p. 122478, 2023, doi: 10.1016/j.techfore.2023.122478.
39. D. K. Kanbach, L. Heiduk, G. Blueher, M. Schreiter, and A. Lahmann, "The GenAI is out of the bottle: Generative artificial in-telligence from a business model innovation perspective," Rev. Manag. Sci., 2023, doi: 10.1007/s11846-023-00696-z.
40. M.-H. Huang, R. Rust, and V. Maksimovic, "The feeling economy: Managing in the next generation of artificial intelligence (AI)," California Manag. Rev., vol. 61, no. 4, pp. 43–65, 2019, doi: 10.1177/0008125619863436.
41. L. D. Hollebeek, C. Menidjel, M. Sarstedt, J. Jansson, and S. Urbonavicius, "Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research," Psychol. Mark., 2024, doi: 10.1002/mar.21957.
42. G. Liadeli, F. Sotgiu, and P. W. J. Verlegh, "A meta-analysis of the effects of brand owned social media on social media en-gagement and sales," J. Mark., 2022, doi: 10.1177/00222429221123250.
43. H. Terho, J. Mero, L. Siutla, and E. Jaakkola, "Digital content marketing in business markets: activities, consequences, and contingencies along the customer journey," Ind. Mark. Manag., vol. 105, pp. 294–310, 2022, doi: 10.1016/j.indmarman.2022.06.006.
44. R. Wahid, J. Mero, and P. Ritala, "Editorial: Written by ChatGPT, illustrated by Midjourney: Generative AI for content mar-keting," Asia Pacific J. Mark. Logist., vol. 35, no. 8, pp. 1813–1822, 2023, doi: 10.1108/APJML-10-2023-994.
45. A. Iskender, "Holy or Unholy? Interview with Open AI’s ChatGPT," Eur. J. Tourism Res., vol. 34, p. 3414, 2023, doi: 10.54055/ejtr.v34i.3169.
46. B. D. Lund and T. Wang, "Chatting about ChatGPT: How may AI and GPT impact academia and libraries?" Library Hi Tech News, 2023, doi: 10.2139/ssrn.4333415.
47. C. Fisch and J. Block, "Six tips for your (systematic) literature review in business and management research," Manag. Rev. Q., vol. 68, pp. 103–106, 2018, doi: 10.1007/s11301-018-0142-x.
48. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative ad-versarial nets," in Proc. 27th Int. Conf. Neural Inf. Process. Syst. (NIPS’14), vol. 2, MIT Press, 2014, pp. 2672–2680. ISBN: 9781510800410.
49. F. Wang, J. Yang, X. Wang, J. Li, and Q. Han, "Chat with ChatGPT on Industry 5.0: Learning and decision-making for intelligent industries," IEEE/CAA J. Automatica Sinica, vol. 10, no. 4, pp. 831–834, 2023, doi: 10.1109/JAS.2023.123552.
50. D. Sjödin, V. Parida, M. Jovanovic, and I. Visnjic, "Value Creation and Value Capture Alignment in Business Model Innovation: A Process View on Outcome-Based Business Models," J. Prod. Innov. Manag., vol. 37, no. 2, pp. 158–183, 2020, doi: 10.1111/jpim.12516.
51. A. Sklyar, C. Kowalkowski, B. Tronvoll, and D. Sörhammar, "Organizing for digital servitization: A service ecosystem per-spective," J. Bus. Res., vol. 104, pp. 450–460, 2019, doi: 10.1016/j.jbusres.2019.02.012.
52. X. Zhang, Z. Liu, Y. Cheng, X. Wang and Z. Wang, "The Impact of AIGC on Organizational Knowledge Creation : From the Perspective of Adaptive Structuration Theory," 2023 IEEE International Conference on Data Mining Workshops (ICDMW), Shanghai, China, 2023, pp. 1469-1476, doi: 10.1109/ICDMW60847.2023.00187.
53. M. Roux, S. Chowdhury, P. Kumar Dey, et al., "Small and medium-sized enterprises as technology innovation intermediaries in sustainable business ecosystem: interplay between AI adoption, low carbon management and resilience," Ann. Oper. Res., 2023, doi: 10.1007/s10479-023-05760-1.
54. S. G. Winter, "Understanding dynamic capabilities," Strateg. Manag. J., vol. 24, no. 10, pp. 991–995, 2003, doi: 10.1002/smj.318.
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