Skip to main content

AI and Human Relationship in the Workplace: A Literature Review and Future Research Agenda

  • Conference paper
  • First Online:
Transfer, Diffusion and Adoption of Next-Generation Digital Technologies (TDIT 2023)

Abstract

The relationship between human and artificial intelligence has attracted debates and polarized views. A key area of this debate that received research attention is human and AI capability to augment each other to achieve better outcomes. While there is a growing research interest in the topic, research is currently dispersed and spread across the management disciplines making it hard for researchers to benefit from an accumulated knowledge in this domain. This study synthesises the literature and describes the current research findings in order to provide foundation for future research in this area. Based on a systematic review, we identify and discuss three emerging themes in the literature and highlight different possible challenges related to integrating AI in organisations. A future research agenda is also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. McCarthy, J., et al.: Artificial Intelligence (AI) Coined at Dartmouth. Retrieved October, 1956 28 (2021)

    Google Scholar 

  2. McCarthy, J., et al.: A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. wwwformal. p. 11 (1955). http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

  3. Allen, G.: Understanding AI technology. Joint Artificial Intelligence Center (JAIC) The Pentagon United States (2020)

    Google Scholar 

  4. Gkinko, L., Elbanna, A.: Hope, tolerance and empathy: employees’ emotions when using an AI-enabled chatbot in a digitalised workplace. Information Technology & People (2022) (ahead-of-print)

    Google Scholar 

  5. Dwivedi, Y.K., et al.: Artificial Intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage. 57, 101994 (2021)

    Article  Google Scholar 

  6. Park, H., et al.: Human-AI interaction in human resource management: understanding why employees resist algorithmic evaluation at workplaces and how to mitigate burdens. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (2021)

    Google Scholar 

  7. Haenlein, M., Kaplan, A.: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif. Manage. Rev. 61(4), 5–14 (2019)

    Article  Google Scholar 

  8. Wamba-Taguimdje, S.L., et al.: Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Bus. Process. Manag. J. 26(7), 1893–1924 (2020)

    Article  Google Scholar 

  9. Basri, W.: Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. International Journal of Computational Intelligence Systems 13(1), 142 (2020)

    Article  Google Scholar 

  10. Buntak, K., Kovačić, M., Mutavdžija, M.: Application of artificial intelligence in the business. International Journal for Quality Research 15(2), 403 (2021)

    Article  Google Scholar 

  11. Needham, M.: Worldwide Spending on AI-Centric Systems Forecast to Reach $154 Billion in 2023, According to IDC (2023)

    Google Scholar 

  12. Schneider, J., et al.: Artificial intelligence governance for businesses. Inf. Syst. Manag. 40(3), 229–249 (2023)

    Article  Google Scholar 

  13. Michael Chui, M.I., Roberts, R., Yee, L.: Technology Trends Outlook 2023. Digital McKinsey (2023)

    Google Scholar 

  14. Van Veldhoven, Z., Vanthienen, J.: Digital transformation as an interaction-driven perspective between business, society, and technology. Electron. Mark. 32(2), 629–644 (2022)

    Article  Google Scholar 

  15. Brynjolfsson, E., McAfee, A.: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. WW Norton & Company (2014)

    Google Scholar 

  16. Wilson, H.J., Daugherty, P.R.: Collaborative intelligence: humans and AI are joining forces. Harv. Bus. Rev. 96(4), 114–123 (2018)

    Google Scholar 

  17. Davenport, T.H., Kirby, J.: Only Humans Need Apply: Winners and Losers in the Age Of Smart Machines. Harper Business New York (2016)

    Google Scholar 

  18. Johnson, P.C., et al.: Digital innovation and the effects of artificial intelligence on firms’ research and development–Automation or augmentation, exploration or exploitation? Technol. Forecast. Soc. Chang. 179, 121636 (2022)

    Article  Google Scholar 

  19. Xue, M., et al.: Is college education less necessary with AI? evidence from firm- level labor structure changes. J. Manag. Inf. Syst. 39(3), 865–905 (2022)

    Article  Google Scholar 

  20. Enholm, I.M., et al.: Artificial intelligence and business value: a literature review. Inf. Syst. Front. 24(5), 1709–1734 (2022)

    Article  Google Scholar 

  21. Raisch, S., Krakowski, S.: Artificial intelligence and management: the automation– augmentation paradox. Acad. Manag. Rev. 46(1), 192–210 (2021)

    Article  Google Scholar 

  22. Amershi, S., et al.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35(4), 105–120 (2014)

    Google Scholar 

  23. Rahwan, I., et al.: Machine behaviour. Nature 568(7753), 477–486 (2019)

    Article  Google Scholar 

  24. Grewal, D., et al.: Frontline cyborgs at your service: how human enhancement technologies affect customer experiences in retail, sales, and service settings. J. Interact. Mark. 51, 9–25 (2020)

    Article  Google Scholar 

  25. Metcalf, L., Askay, D.A., Rosenberg, L.B.: Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making. Calif. Manage. Rev. 61(4), 84–109 (2019)

    Article  Google Scholar 

  26. Gronsund, T., Aanestad, M.: Augmenting the algorithm: emerging human-in-the- loop work configurations. J. Strat. Inf. Syst. 29(2), 16 (2020)

    Article  Google Scholar 

  27. Siemon, D.: Elaborating team roles for artificial intelligence-based teammates in human-AI collaboration. Group Decis. Negot. 31(5), 871–912 (2022)

    Article  MathSciNet  Google Scholar 

  28. Coombs, C., et al.: The strategic impacts of intelligent automation for knowledge and service work: an interdisciplinary review. J. Strat. Inf. Syst. 29(4), 30 (2020)

    Article  Google Scholar 

  29. Cao, L.: AI in finance: challenges, techniques, and opportunities. ACM Computing Surveys (CSUR) 55(3), 1–38 (2022)

    Article  MathSciNet  Google Scholar 

  30. Chintalapati, S., Pandey, S.K.: Artificial intelligence in marketing: a systematic literature review. Int. J. Mark. Res. 64(1), 38–68 (2022)

    Article  Google Scholar 

  31. Zhang, K., Aslan, A.B.: AI technologies for education: recent research & future directions. Computers and Education: Artificial Intelligence 2, 100025 (2021)

    Google Scholar 

  32. Budhwar, P., et al.: Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management 33(6), 1065–1097 (2022)

    Article  Google Scholar 

  33. Rajpurkar, P., et al.: AI in health and medicine. Nat. Med. 28(1), 31–38 (2022)

    Article  Google Scholar 

  34. Fink, A.: Conducting Research Literature Reviews: From the Internet to Paper. Sage publications (2019)

    Google Scholar 

  35. Nilsson, N.J.: Problem-Solving Methods in Artificial Intelligence. McGraw-Hill (1971)

    Google Scholar 

  36. Stone, P., et al.: Artificial Intelligence and Life in 2030: The One Hundred Year Study On Artificial Intelligence. arXiv preprint arXiv:2211.06318 (2022)

  37. Rai, A., Constantinides, P., Sarker, S.: Next generation digital platforms: toward human-ai hybrids. MIS Q. 43(1), iii–ix (2019)

    Google Scholar 

  38. Androutsopoulou, A., et al.: Transforming the communication between citizens and government through AI-guided chatbots. Gov. Inf. Q. 36(2), 358–367 (2019)

    Article  Google Scholar 

  39. Newell, A., Simon, H.: The logic theory machine–a complex information processing system. IRE Transactions on Information Theory 2(3), 61–79 (1956)

    Article  Google Scholar 

  40. Newell, A., Shaw, J.C., Simon, H.A.: Report on a General Problem Solving Program. in IFIP Congress. Pittsburgh, PA (1959)

    Google Scholar 

  41. Lindebaum, D., Vesa, M., Den Hond, F.: Insights from “the machine stops” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Acad. Manag. Rev. 45(1), 247–263 (2020)

    Article  Google Scholar 

  42. Amabile, T.: GUIDEPOST: Creativity. Artificial Intelligence, and a World of Surprises Guidepost Letter for Academy of Management Discoveries. Academy of Management Discoveries (2019)

    Google Scholar 

  43. Tegmark, M.: Life 3.0: Being Human in the Age of Artificial Intelligence. Vintage (2018)

    Google Scholar 

  44. Maragno, G., et al.: AI as an Organizational Agent to Nurture: Effectively Introducing Chatbots in Public Entities. Public Management Review, pp. 1–31 (2022)

    Google Scholar 

  45. Schanke, S., Burtch, G., Ray, G.: Estimating the impact of “humanizing” customer service chatbots. Inf. Syst. Res. 32(3), 736–751 (2021)

    Article  Google Scholar 

  46. Huang, M.-H., Rust, R.T.: A framework for collaborative artificial intelligence in marketing. J. Retail. 98(2), 209–223 (2022)

    Article  Google Scholar 

  47. Arslan, A., et al.: Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower (2021)

    Google Scholar 

  48. Loske, D., Klumpp, M.: Intelligent and efficient? An empirical analysis of human– AI collaboration for truck drivers in retail logistics. The International Journal of Logistics Management (2021)

    Google Scholar 

  49. Vassilakopoulou, P., et al.: Developing human/AI interactions for chat-based customer services: lessons learned from the Norwegian government. Eur. J. Inf. Syst. 32(1), 10–22 (2023)

    Article  Google Scholar 

  50. Brachten, F., Kissmer, T., Stieglitz, S.: The acceptance of chatbots in an enterprise context–a survey study. Int. J. Inf. Manage. 60, 102375 (2021)

    Article  Google Scholar 

  51. Marr, B.: The Amazing Ways how Unilever uses Artificial Intelligence to Recruit & Train thousands of Employees. Forbes (2018)

    Google Scholar 

  52. Fuegener, A., et al.: Cognitive challenges in human–artificial intelligence collaboration: investigating the path toward productive delegation. Inf. Syst. Res. 33(2), 678–696 (2022)

    Article  Google Scholar 

  53. Makarius, E.E., et al.: Rising with the machines: a sociotechnical framework for bringing artificial intelligence into the organization. J. Bus. Res. 120, 262–273 (2020)

    Article  Google Scholar 

  54. Vassilakopoulou, P., et al.: Developing human/AI interactions for chat-based customer services: lessons learned from the Norwegian government. European Journal of Information Systems, pp. 1–13 (2022)

    Google Scholar 

  55. Teodorescu, M.H., et al.: Failures of fairness in automation require a deeper understanding of human-ML augmentation. MIS Quarterly 45(3) (2021)

    Google Scholar 

  56. Barile, S., et al.: Empowering value co-creation in the digital age. Journal of Business & Industrial Marketing (2021)

    Google Scholar 

  57. Dellermann, D., et al.: Hybrid intelligence. Bus. Inf. Syst. Eng. 61(5), 637–643 (2019)

    Article  Google Scholar 

  58. Larson, D.A.: Artificial intelligence: robots, avatars, and the demise of the human mediator. Ohio St. J. on Disp. Resol. 25, 105 (2010)

    Google Scholar 

  59. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, pp. 13-23 (2002)

    Google Scholar 

  60. Liñán, F., Fayolle, A.: A systematic literature review on entrepreneurial intentions: citation, thematic analyses, and research agenda. International Entrepreneurship and Management Journal 11(4), 907–933 (2015)

    Article  Google Scholar 

  61. Guest, G., MacQueen, K.M., Namey, E.E.: Applied Thematic Analysis. sage publications (2011)

    Google Scholar 

  62. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)

    Article  Google Scholar 

  63. Benbya, H., Pachidi, S., Jarvenpaa, S.: Special issue editorial: artificial intelligence in organizations: implications for information systems research. J. Assoc. Inf. Syst. 22(2), 10 (2021)

    Google Scholar 

  64. Lebovitz, S., Lifshitz-Assaf, H., Levina, N.: To engage or not to engage with Al for critical judgments: how professionals deal with opacity when using AI for medical diagnosis. Organ. Sci. 33(1), 126–148 (2022)

    Article  Google Scholar 

  65. Huang, M.H., Rust, R.T.: Artificial intelligence in service. J. Serv. Res. 21(2), 155–172 (2018)

    Article  Google Scholar 

  66. Jarrahi, M.H.: Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus. Horiz. 61(4), 577–586 (2018)

    Article  Google Scholar 

  67. Cooper, A.F., Abrams, E.: Emergent Unfairness: Normative Assumptions and Contradictions in Algorithmic Fairness-Accuracy Trade-Off Research (2021)

    Google Scholar 

  68. Weber, M., et al.: Organizational capabilities for ai implementation—coping with inscrutability and data dependency in ai. Information Systems Frontiers, pp. 121 (2022)

    Google Scholar 

  69. Chowdhury, S., et al.: AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework. J. Bus. Res. 144, 31–49 (2022)

    Article  Google Scholar 

  70. Chowdhury, S., et al.: Unlocking the value of artificial intelligence in human resource management through AI capability framework. Hum. Resour. Manag. Rev. 33(1), 100899 (2023)

    Google Scholar 

  71. Raftopoulos, M., Hamari, J.: Human-Ai Collaboration In Organisations: A Literature Review On Enabling Value Creation (2023)

    Google Scholar 

  72. Chatterjee, S., et al.: Assessing the implementation of AI integrated CRM system for B2C relationship management: Integrating contingency theory and dynamic capability view theory. Information systems frontiers pp. 1–19 (2022)

    Google Scholar 

  73. Vössing, M., et al.: Designing transparency for effective human-AI collaboration. Inf. Syst. Front. 24(3), 877–895 (2022)

    Article  Google Scholar 

  74. Chuang, S.: Indispensable skills for human employees in the age of robots and AI. European Journal of Training and Development (2022) (ahead-of-print)

    Google Scholar 

  75. Russell Stuart, J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (2009)

    Google Scholar 

  76. Di Vaio, A., Hassan, R., Alavoine, C.: Data intelligence and analytics: a bibliometric analysis of human–artificial intelligence in public sector decision-making effectiveness. Technol. Forecast. Soc. Chang. 174, 121201 (2022)

    Article  Google Scholar 

  77. Puranam, P.: Human–AI collaborative decision-making as an organization design problem. J. Organization Design 10(2), 75-80 (2021)

    Google Scholar 

  78. Christensen, C.M.: The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business Review Press (2013)

    Google Scholar 

  79. Bazerman, M.H., et al.: Book review: blind spots: why we fail to do what’s right and what to do about it. Public Integrity 14(4), 413–422 (2012)

    Article  Google Scholar 

  80. Frank, D.-A., et al.: Human decision-making biases in the moral dilemmas of autonomous vehicles. Sci. Rep. 9(1), 1–19 (2019)

    Article  Google Scholar 

  81. Faraj, S., Pachidi, S., Sayegh, K.: Working and organizing in the age of the learning algorithm. Inf. Organ. 28(1), 62–70 (2018)

    Article  Google Scholar 

  82. Einola, K., Khoreva, V.: Best friend or broken tool? exploring the co-existence of humans and artificial intelligence in the workplace ecosystem. Hum. Resour. Manage. 62(1), 117–135 (2023)

    Article  Google Scholar 

  83. Huang, M.-H., Rust, R., Maksimovic, V.: The feeling economy: managing in the next generation of artificial intelligence (AI). Calif. Manage. Rev. 61(4), 43–65 (2019)

    Article  Google Scholar 

  84. Spring, M., Faulconbridge, J., Sarwar, A.: How information technology automates and augments processes: insights from artificial-Intelligence-based systems in professional service operations. J. Oper. Manag. 68(6–7), 592–618 (2022)

    Article  Google Scholar 

  85. Jussupow, E., et al.: Augmenting medical diagnosis decisions? an investigation into physicians’ decision-making process with artificial intelligence. Inf. Syst. Res. 32(3), 713–735 (2021)

    Article  Google Scholar 

  86. Huang, M.H., Rust, R.T.: Engaged to a robot? the role of AI in service. J. Serv. Res. 24(1), 30–41 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nguyen Trinh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Trinh, N., Elbanna, A. (2024). AI and Human Relationship in the Workplace: A Literature Review and Future Research Agenda. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Lal, B., Elbanna, A. (eds) Transfer, Diffusion and Adoption of Next-Generation Digital Technologies. TDIT 2023. IFIP Advances in Information and Communication Technology, vol 698. Springer, Cham. https://doi.org/10.1007/978-3-031-50192-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50192-0_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50191-3

  • Online ISBN: 978-3-031-50192-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics