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arxiv: 2606.17887 · v1 · pith:6HYN55HRnew · submitted 2026-06-16 · 💻 cs.HC · cs.AI

AI Adoption Across a Multinational Workforce: Sociotechnical Conditions for GenAI Acceptance in Human Resources

Dalia Ali , Maria Jos\'e Rodr\'iguez Vel\'azquez , Manoel Horta Ribeiro , Vera Liao , Orestis Papakyriakopoulos This is my paper

Pith reviewed 2026-06-26 22:58 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords generative AIworkplace adoptionhuman resourcessociotechnical systemstrust in AIorganizational changeAI deploymentsearch systems
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The pith

GenAI adoption in HR succeeds when the system's design assumptions align with employees' roles, spoken languages, and tenure.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies a multinational tech company replacing its legacy HR search system with a GenAI-supported one. Analysis of logs, a 25-person survey, and ten interviews shows uneven adoption driven by how well the tool's built-in assumptions match workers' specific positions. Trust formed when employees could check sources, compare outputs across systems, and consult colleagues or HR. The authors argue these sociotechnical conditions must be addressed for inclusive rollout in high-stakes settings. Their evidence points to treating company knowledge resources as part of the AI infrastructure itself.

Core claim

Adoption depended on the fit between the GenAI system's design assumptions and employees' work positionalities (role, spoken language, tenure). Employees built trust in GenAI answers through source-checking, comparison among systems, and seeking input from colleagues or HR when in doubt. The study supplies empirical evidence from a live organizational transition and converts the patterns into design considerations for inclusive deployment.

What carries the argument

The fit between GenAI design assumptions and employees' work positionalities (role, spoken language, tenure)

If this is right

  • Organizations must design GenAI systems to account for role- and context-sensitive benefits across different employee groups.
  • Treating organizational knowledge infrastructure as AI infrastructure improves accountability and usability of GenAI systems.
  • Search literacy, trust calibration, content quality, training, and guidance shape adoption alongside positional fit.
  • Inclusive deployment requires attention to situational fit rather than uniform rollout.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same positional fit issues could appear in other knowledge-work settings where information quality affects decisions.
  • Targeted design adjustments for language or tenure groups might raise overall adoption without changing the underlying model.
  • Longitudinal tracking of the same employees after initial rollout could reveal whether trust-building habits persist or change.

Load-bearing premise

Observations from a single tech company's HR transition and a small sample of participants can inform broader GenAI deployment in other high-stakes environments.

What would settle it

A study in another organization or with a larger workforce where GenAI adoption rates show no link to differences in role, language, or tenure.

Figures

Figures reproduced from arXiv: 2606.17887 by Dalia Ali, Manoel Horta Ribeiro, Maria Jos\'e Rodr\'iguez Vel\'azquez, Orestis Papakyriakopoulos, Vera Liao.

Figure 1
Figure 1. Figure 1: Sequential mixed-methods study design. Each phase informed the next, with search log patterns shaping the survey [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Adoption unfolded through selective use across parallel systems, varied by situational fit, required learning a new [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average query length in STEVE and PEOPLE TOOL. Steve People Tool 0 20 40 60 80 100 83.4 83.3 Queries (%) [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: illustrates the same trend from a different per￾spective. The proportion of one- or two-token searches re￾mained virtually unchanged for both systems. It is note￾worthy that while GenAI-based searches might demand more precise phrasing, a significant number of people still searched with short, keyword-type searches. Steve People Tool 0 1 2 1.87 2.11 Avg. tokens [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: distinguishes between three types of interaction phases in PEOPLE TOOL, which include whether there was a response from the search query, whether one of the re￾sponses was clicked on, and whether there was a response generated by GenAI. The reason for this distinction is that the use of PEOPLE TOOL does not automatically imply the use of GenAI. Results Click GenAI 0 20 40 60 80 100 90.9 58.2 28 Queries (%)… view at source ↗
Figure 6
Figure 6. Figure 6: Click behavior by GenAI answer exposure in P [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: Survey ratings for ease, helpfulness, and trust. Ease [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 7
Figure 7. Figure 7: Respondents’ weekly or daily use of each system. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: System preference reported in the survey. [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 11
Figure 11. Figure 11: Frequency of clicking sources displayed in [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Reported responses when GenAI did not provide [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
read the original abstract

Generative AI (GenAI) deployment in the workplace is accelerating rapidly. Nevertheless, questions of who adopts, who benefits, and who is left behind and why are still understudied. In this paper, we investigate these dynamics in the context of a multinational tech company transitioning from a legacy Human Resources (HR) search system to a GenAI-supported system, analyzing search log data, survey data (n=25), and ten semi-structured interviews. Our findings show that adoption depended on the fit between the GenAI system's design assumptions and employees' work positionalities (role, spoken language, tenure). Further, we find that employees' trust in GenAI answers was built through source-checking, comparison among systems, and seeking input from colleagues or HR when in doubt. Our contribution is twofold. First, we provide empirical evidence of workplace GenAI adoption during a live organizational transition, showing that adoption is influenced by factors such as situational fit, search literacy, and trust calibration. It is also further shaped by knowledge conditions such as the system's content quality, employee training, and guidance. Second, we translate these findings into design considerations for inclusive deployment and adoption in high-stakes environments such as HR. We argue that organizations should design systems considering the role and context-sensitive benefits they yield to different social groups. They also need to treat the organizational knowledge infrastructure as AI infrastructure to improve the accountability and usability of GenAI systems

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports a mixed-methods case study of GenAI adoption during a live transition from a legacy HR search system to a GenAI-supported system at one multinational tech company. Drawing on search logs, a survey (n=25), and 10 semi-structured interviews, it claims that adoption is shaped by the fit between the system's design assumptions and employees' positionalities (role, spoken language, tenure), and that trust is constructed via source-checking, cross-system comparison, and consultation with colleagues or HR. The stated contributions are empirical evidence on sociotechnical adoption factors (situational fit, search literacy, trust calibration, knowledge infrastructure) plus design considerations for inclusive GenAI deployment in high-stakes HR settings.

Significance. If the patterns hold, the work supplies timely, real-world evidence on how positionalities and trust-calibration practices affect workplace GenAI uptake, which could usefully inform HCI and CSCW research on sociotechnical systems. The live-transition setting and mixed-methods design are strengths. However, the single-site, small-sample evidence base (explicitly noted as n=25 survey + 10 interviews) substantially constrains the strength of any broader claims about adoption dynamics or design recommendations.

major comments (2)
  1. [Methods / Findings] Methods and Findings sections: The abstract and introduction state that search log data were analyzed alongside the survey and interviews, yet no quantitative results, metrics, or triangulation from the logs are reported to corroborate the positionalities (role/language/tenure) claims; the central findings therefore rest entirely on the small qualitative sample.
  2. [Discussion] Discussion section: The leap from the single-company case to 'design considerations for inclusive deployment' in high-stakes HR environments (final paragraph of abstract and Discussion) is presented without an explicit limitations subsection addressing external validity, cross-site replication, or tests of whether the same positionalities predict adoption elsewhere.
minor comments (1)
  1. [Abstract / Introduction] The term 'work positionalities' is used repeatedly from the abstract onward but receives no explicit operational definition or citation to prior literature on the first use, which reduces clarity for readers outside the immediate subfield.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address the two major comments point by point below, indicating the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Methods / Findings] Methods and Findings sections: The abstract and introduction state that search log data were analyzed alongside the survey and interviews, yet no quantitative results, metrics, or triangulation from the logs are reported to corroborate the positionalities (role/language/tenure) claims; the central findings therefore rest entirely on the small qualitative sample.

    Authors: We acknowledge the referee's observation. The search log data were used to guide participant recruitment and to provide background context on overall usage patterns during the transition, but no quantitative metrics or direct triangulation from the logs were reported to support the positionalities findings. The core claims rest on the survey and interview data. We will revise the Methods section to clarify the specific role of the log data, adjust the abstract and introduction to avoid overstating its contribution to the reported findings, and add any non-sensitive descriptive statistics from the logs if they can be included without privacy issues. revision: yes

  2. Referee: [Discussion] Discussion section: The leap from the single-company case to 'design considerations for inclusive deployment' in high-stakes HR environments (final paragraph of abstract and Discussion) is presented without an explicit limitations subsection addressing external validity, cross-site replication, or tests of whether the same positionalities predict adoption elsewhere.

    Authors: We agree that an explicit limitations discussion is warranted. We will add a dedicated Limitations subsection that directly addresses the single-site case-study design, the modest sample (n=25 survey + 10 interviews), constraints on external validity, and the absence of cross-site replication or predictive testing. The design considerations will be reframed as case-derived insights intended to inform future work rather than as broadly generalizable prescriptions. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical qualitative study with data-grounded observations

full rationale

The paper reports findings from search logs, a small survey (n=25), and 10 interviews in a single-organization case study. No equations, fitted parameters, predictions, or derivations appear. Central claims about adoption depending on fit with positionalities and trust-building behaviors are presented as direct observations from the collected data rather than reductions to prior self-citations or constructed quantities. The contribution section frames results as empirical evidence translated into design considerations without any load-bearing self-citation chains or ansatzes. This is a standard honest non-finding for an empirical HCI paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard HCI assumptions that qualitative data from interviews and surveys reliably reveal adoption factors and that positionalities shape technology use; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Qualitative interviews and surveys can reliably capture adoption factors and trust mechanisms in workplace technology use
    Invoked implicitly when interpreting survey and interview data as evidence for the fit and trust findings.

pith-pipeline@v0.9.1-grok · 5814 in / 1205 out tokens · 38829 ms · 2026-06-26T22:58:50.203592+00:00 · methodology

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Reference graph

Works this paper leans on

100 extracted references · 10 canonical work pages · 2 internal anchors

  1. [2]

    Journal of Technology in Behavioral Science , year=

    Employees' Perceptions of the Implementation of Robotics, Artificial Intelligence, and Automation on Job Satisfaction, Job Security, and Employability , author=. Journal of Technology in Behavioral Science , year=

  2. [3]

    Generative

    Brynjolfsson, Erik and Li, Danielle and Raymond, Lindsey , journal=. Generative. 2025 , doi=

    2025

  3. [4]

    Technovation , volume=

    When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy , author=. Technovation , volume=. 2022 , doi=

    2022

  4. [5]

    2025 , institution=

    The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise , author=. 2025 , institution=

    2025

  5. [6]

    R.; and Verstyuk, S

    Douglas, Michael R. and Verstyuk, Sergiy , title =. arXiv preprint arXiv:2501.17894 , year =. doi:10.48550/arXiv.2501.17894 , eprint =

    work page doi:10.48550/arxiv.2501.17894

  6. [7]

    arXiv preprint arXiv:2503.14499 , year=

    Kwa, Thomas and West, Ben and Becker, Joel and Deng, Amy and Garcia, Katharyn and Hasin, Max and Jawhar, Sami and Kinniment, Megan and Rush, Nate and Von Arx, Sydney and Bloom, Ryan and Broadley, Thomas and Du, Haoxing and Goodrich, Brian and Jurkovic, Nikola and Miles, Luke Harold and Nix, Seraphina and Lin, Tao and Parikh, Neev and Rein, David and Sato,...

    work page doi:10.48550/arxiv.2503.14499

  7. [8]

    Information Systems Frontiers , year =

    Enholm, Ida Merete and Papagiannidis, Emmanouil and Mikalef, Patrick and Krogstie, John , title =. Information Systems Frontiers , year =

  8. [9]

    Annals of Operations Research , year =

    Venkatesh, Viswanath , title =. Annals of Operations Research , year =

  9. [10]

    Proceedings of the National Academy of Sciences , year =

    Humlum, Anders and Vestergaard, Emilie , title =. Proceedings of the National Academy of Sciences , year =

  10. [11]

    2026 , month = jan, url =

    Deloitte AI Institute , title =. 2026 , month = jan, url =

    2026

  11. [12]

    2025 , month = jul, url =

    The GenAI Divide: State of AI in Business 2025 , author =. 2025 , month = jul, url =

    2025

  12. [13]

    2025 , month = dec, type =

    The Dividend Age: How AI Is Turning Promise into Payoff , author =. 2025 , month = dec, type =

    2025

  13. [14]

    MIS Quarterly , volume=

    Perceived usefulness, perceived ease of use, and user acceptance of information technology , author=. MIS Quarterly , volume=. 1989 , doi=

    1989

  14. [15]

    MIS Quarterly , volume=

    User acceptance of information technology: Toward a unified view , author=. MIS Quarterly , volume=. 2003 , doi=

    2003

  15. [16]

    Journal of Biomedical Informatics , volume =

    Shachak, Aviv and Kuziemsky, Craig and Petersen, Carolyn , title =. Journal of Biomedical Informatics , volume =. 2019 , doi =

    2019

  16. [17]

    , title =

    Mogaji, Emmanuel and Viglia, Giampaolo and Srivastava, Pallavi and Dwivedi, Yogesh K. , title =. International Journal of Contemporary Hospitality Management , volume =. 2024 , doi =

    2024

  17. [18]

    Kanesaraj and Subbarao, Anusuyah , title =

    Lee, Ann Thong and Ramasamy, R. Kanesaraj and Subbarao, Anusuyah , title =. Healthcare , volume =. 2025 , doi =

    2025

  18. [19]

    How to raise technology acceptance: user experience characteristics as technology-inherent determinants , journal =

    Mlekus, Lisa and Bentler, Dominik and Paruzel, Agnieszka and Kato-Beiderwieden, Anna-Lena and Maier, G. How to raise technology acceptance: user experience characteristics as technology-inherent determinants , journal =. 2020 , volume =

    2020

  19. [20]

    Proceedings of the CHI Conference on Human Factors in Computing Systems , series =

    How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study , author =. Proceedings of the CHI Conference on Human Factors in Computing Systems , series =. 2024 , publisher =

    2024

  20. [21]

    Proceedings of the CHI Conference on Human Factors in Computing Systems , series =

    The Metacognitive Demands and Opportunities of Generative AI , author =. Proceedings of the CHI Conference on Human Factors in Computing Systems , series =. 2024 , publisher =

    2024

  21. [22]

    Proceedings of the 2022 ACM conference on fairness, accountability, and transparency , year =

    Designing for Responsible Trust in AI Systems: A Communication Perspective , author =. Proceedings of the 2022 ACM conference on fairness, accountability, and transparency , year =

    2022

  22. [24]

    Human Resource Management Journal , year =

    Human Resource Management in the Age of Generative Artificial Intelligence: Perspectives and Research Directions on ChatGPT , author =. Human Resource Management Journal , year =

  23. [26]

    1990 , publisher=

    The Processes of Technological Innovation , author=. 1990 , publisher=

    1990

  24. [27]

    Information Systems Theory: Explaining and Predicting Our Digital Society, Vol

    The technology--organization--environment framework , author=. Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1 , pages=. 2011 , publisher=

    2011

  25. [28]

    Journal of Enterprise Information Management , volume=

    Integrated technology-organization-environment (TOE) taxonomies for technology adoption , author=. Journal of Enterprise Information Management , volume=. 2017 , publisher=

    2017

  26. [29]

    2011 , journal=

    Literature review of information technology adoption models at firm level , author=. 2011 , journal=

    2011

  27. [30]

    Revisiting

    Wolfe, Diana and Price, Matt and Choe, Alice and Kidd, Fergus and Wagner, Hannah , journal=. Revisiting. 2025 , doi=

    2025

  28. [31]

    Proceedings of the

    Woodruff, Allison and Shelby, Renee and Kelley, Patrick Gage and Rousso-Schindler, Steven and Smith-Loud, Jamila and Wilcox, Lauren , title =. Proceedings of the. 2024 , doi =

    2024

  29. [32]

    Proceedings of the

    Kawakami, Anna and Taylor, Jordan and Fox, Sarah and Zhu, Haiyi and Holstein, Kenneth , title =. Proceedings of the. 2024 , doi =

    2024

  30. [33]

    Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT) , pages =

    Raghavan, Manish and Barocas, Solon and Kleinberg, Jon and Levy, Karen , title =. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT) , pages =. 2020 , doi =

    2020

  31. [34]

    Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT) , pages =

    Sanchez-Monedero, Javier and Dencik, Lina and Edwards, Lilian , title =. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT) , pages =. 2020 , doi =

    2020

  32. [35]

    Proceedings of the

    Ingber, Alexis Shore and Andalibi, Nazanin , title =. Proceedings of the. 2025 , doi =

    2025

  33. [36]

    and Woo, Sang Eun , title =

    Bankins, Sarah and Ocampo, Anna Carmella and Marrone, Mauricio and Restubog, Simon Lloyd D. and Woo, Sang Eun , title =. Journal of Organizational Behavior , volume =. 2024 , doi =

    2024

  34. [37]

    Journal of the Association for Information Systems , year =

    A Knowledge Management Perspective of Generative Artificial Intelligence , author =. Journal of the Association for Information Systems , year =

  35. [38]

    MIS Quarterly , year =

    Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues , author =. MIS Quarterly , year =

  36. [39]

    Proceedings of the

    Bansal, Gagan and Wu, Tongshuang and Zhou, Joyce and Fok, Raymond and Nushi, Besmira and Kamar, Ece and Ribeiro, Marco Tulio and Weld, Daniel , title =. Proceedings of the. 2021 , doi =

    2021

  37. [40]

    Proceedings of the 2023 CHI conference on human factors in computing systems , pages=

    Why Johnny can't prompt: how non-AI experts try (and fail) to design LLM prompts , author=. Proceedings of the 2023 CHI conference on human factors in computing systems , pages=. 2023 , doi=

    2023

  38. [41]

    and Cox, Anna L

    Xia, Qing Nancy and Constantinides, Marios and Sarkar, Advait and Brumby, Duncan P. and Cox, Anna L. , journal=. ``. 2026 , doi=

    2026

  39. [43]

    and boyd, danah and Friedler, Sorelle A

    Selbst, Andrew D. and boyd, danah and Friedler, Sorelle A. and Venkatasubramanian, Suresh and Vertesi, Janet , title =. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT) , pages =. 2019 , doi =

    2019

  40. [44]

    2020 , publisher =

    Design Justice: Community-Led Practices to Build the Worlds We Need , author =. 2020 , publisher =

    2020

  41. [45]

    Proceedings of the ACM on Human-Computer Interaction , volume =

    Cheon, EunJeong and Erickson, Ingrid , title =. Proceedings of the ACM on Human-Computer Interaction , volume =. 2025 , doi =

    2025

  42. [46]

    2003 , publisher =

    Technology and Social Inclusion: Rethinking the Digital Divide , author =. 2003 , publisher =

    2003

  43. [47]

    2005 , publisher =

    The Deepening Divide: Inequality in the Information Society , author =. 2005 , publisher =

    2005

  44. [48]

    2021 , publisher =

    The Digital Disconnect: The Social Causes and Consequences of Digital Inequalities , author =. 2021 , publisher =

    2021

  45. [49]

    and See, Katrina A

    Lee, John D. and See, Katrina A. , title =. Human Factors , volume =. 2004 , doi =

    2004

  46. [50]

    , title =

    Knowles, Bran and Richards, John T. , title =. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency , pages =. 2021 , doi =

    2021

  47. [51]

    2025 , month = jul, note =

    Large Language Models in Human Resource Management: A Systematic Literature Review of Applications, Open Issues and Future Research Directions , author =. 2025 , month = jul, note =

    2025

  48. [52]

    Qualitative Research in Psychology , volume =

    Braun, Virginia and Clarke, Victoria , title =. Qualitative Research in Psychology , volume =. 2006 , doi =

    2006

  49. [53]

    Alavi, M.; and Leidner, D. E. 2001. Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1): 107--136

    2001

  50. [54]

    E.; and Mousavi, R

    Alavi, M.; Leidner, D. E.; and Mousavi, R. 2024. A Knowledge Management Perspective of Generative Artificial Intelligence. Journal of the Association for Information Systems, 25(1): 1--12

    2024

  51. [55]

    Ali, D.; Ahmed, M.; Wang, H.; Khan, A.; Jordan, N. P. A.; Kim, S. S.; Muchhala, M. D.; Merkle, A. K.; and Papakyriakopoulos, O. 2025. AI Adoption Across Mission-Driven Organizations. arXiv preprint arXiv:2510.03868

    work page arXiv 2025

  52. [56]

    O.; Ojiabo, O

    Awa, H. O.; Ojiabo, O. U.; and Orokor, L. E. 2017. Integrated technology-organization-environment (TOE) taxonomies for technology adoption. Journal of Enterprise Information Management, 30(6): 893--921

    2017

  53. [57]

    Baker, J. 2011. The technology--organization--environment framework, 231--245. Springer

    2011

  54. [58]

    C.; Marrone, M.; Restubog, S

    Bankins, S.; Ocampo, A. C.; Marrone, M.; Restubog, S. L. D.; and Woo, S. E. 2024. A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2): 159--182

    2024

  55. [59]

    T.; and Weld, D

    Bansal, G.; Wu, T.; Zhou, J.; Fok, R.; Nushi, B.; Kamar, E.; Ribeiro, M. T.; and Weld, D. 2021. Does the Whole Exceed Its Parts? T he Effect of AI Explanations on Complementary Team Performance. In Proceedings of the CHI Conference on Human Factors in Computing Systems

    2021

  56. [60]

    Bhargava, A.; Bester, M.; and Bolton, L. 2021. Employees' Perceptions of the Implementation of Robotics, Artificial Intelligence, and Automation on Job Satisfaction, Job Security, and Employability. Journal of Technology in Behavioral Science, 6: 106--113

    2021

  57. [61]

    Braun, V.; and Clarke, V. 2006. Using Thematic Analysis in Psychology. Qualitative Research in Psychology, 3(2): 77--101

    2006

  58. [62]

    Brynjolfsson, E.; Li, D.; and Raymond, L. 2025. Generative AI at Work. The Quarterly Journal of Economics, 140(2): 889--942

    2025

  59. [63]

    J.; Beltran, J

    Budhwar, P.; Chowdhury, S.; Wood, G.; Aguinis, H.; Bamber, G. J.; Beltran, J. R.; Boselie, P.; Cooke, F. L.; Decker, S.; DeNisi, A.; Dey, P. K.; Guest, D.; Knoblich, A. J.; Malik, A.; Paauwe, J.; Papagiannidis, S.; Patel, C.; Pereira, V.; Ren, S.; Rogelberg, S.; Saunders, M. N. K.; Tung, R. L.; and Varma, A. 2023. Human Resource Management in the Age of G...

    2023

  60. [64]

    Cheon, E.; and Erickson, I. 2025. Fulfillment of the Work Games: Warehouse Workers' Experiences with Algorithmic Management. Proceedings of the ACM on Human-Computer Interaction, 9

    2025

  61. [65]

    Costanza-Chock, S. 2020. Design Justice: Community-Led Practices to Build the Worlds We Need. Cambridge, MA: The MIT Press

    2020

  62. [66]

    K.; Giannopoulos, P

    Dasaklis, T. K.; Giannopoulos, P. G.; Koutras, D.; Malamas, V.; and Chountalas, P. T. 2025. Large Language Models in Human Resource Management: A Systematic Literature Review of Applications, Open Issues and Future Research Directions. Preprint submitted to Elsevier

    2025

  63. [67]

    Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3): 319--340

    1989

  64. [68]

    Dell'Acqua, F.; Ayoubi, C.; Lifshitz, H.; Sadun, R.; Mollick, E.; Mollick, L.; Han, Y.; Goldman, J.; Nair, H.; Taub, S.; and Lakhani, K. R. 2025. The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise. Technical Report w33641, NBER

    2025

  65. [69]

    R.; and Verstyuk, S

    Douglas, M. R.; and Verstyuk, S. 2025. Progress in Artificial Intelligence and its Determinants. arXiv preprint arXiv:2501.17894

    work page arXiv 2025

  66. [70]

    M.; Papagiannidis, E.; Mikalef, P.; and Krogstie, J

    Enholm, I. M.; Papagiannidis, E.; Mikalef, P.; and Krogstie, J. 2022. Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24: 1709--1734

    2022

  67. [71]

    Ernst & Young LLP . 2025. The Dividend Age: How AI Is Turning Promise into Payoff. Us ai pulse survey: Wave 4, EY. Survey of 500 US senior business leaders

    2025

  68. [72]

    Gu, K.; Grunde-McLaughlin, M.; McNutt, A.; Heer, J.; and Althoff, T. 2024. How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study. In Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI '24. ACM

    2024

  69. [73]

    Helsper, E. J. 2021. The Digital Disconnect: The Social Causes and Consequences of Digital Inequalities. London: SAGE Publications

    2021

  70. [74]

    Humlum, A.; and Vestergaard, E. 2025. The unequal adoption of ChatGPT exacerbates existing inequalities among workers. Proceedings of the National Academy of Sciences, 122(1): e2414972121

    2025

  71. [75]

    S.; and Andalibi, N

    Ingber, A. S.; and Andalibi, N. 2025. Emotion AI in Job Interviews: Injustice, Emotional Labor, Identity, and Privacy. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT)

    2025

  72. [76]

    Institute, D. A. 2026. State of AI in the Enterprise: The Untapped Edge. Technical report, Deloitte AI Institute

    2026

  73. [77]

    Kawakami, A.; Taylor, J.; Fox, S.; Zhu, H.; and Holstein, K. 2024. AI Failure Loops in Feminized Labor: Understanding the Interplay of Workplace AI and Occupational Devaluation. In Proceedings of the AAAI/ACM Conference on AI , Ethics, and Society (AIES)

    2024

  74. [78]

    Knowles, B.; and Richards, J. T. 2021. The Sanction of Authority: Promoting Public Trust in AI. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 262--271

    2021

  75. [79]

    arXiv preprint arXiv:2503.14499 , year=

    Kwa, T.; West, B.; Becker, J.; Deng, A.; Garcia, K.; Hasin, M.; Jawhar, S.; Kinniment, M.; Rush, N.; Von Arx, S.; Bloom, R.; Broadley, T.; Du, H.; Goodrich, B.; Jurkovic, N.; Miles, L. H.; Nix, S.; Lin, T.; Parikh, N.; Rein, D.; Sato, L. J. K.; Wijk, H.; Ziegler, D. M.; Barnes, E.; and Chan, L. 2025. Measuring AI Ability to Complete Long Tasks. arXiv prep...

    work page arXiv 2025

  76. [80]

    T.; Ramasamy, R

    Lee, A. T.; Ramasamy, R. K.; and Subbarao, A. 2025. Understanding Psychosocial Barriers to Healthcare Technology Adoption: A Review of TAM and UTAUT Frameworks. Healthcare, 13(3): 250

    2025

  77. [81]

    D.; and See, K

    Lee, J. D.; and See, K. A. 2004. Trust in Automation: Designing for Appropriate Reliance. Human Factors, 46(1): 50--80

    2004

  78. [82]

    S.; Kim, T.; Choi, S.; and Kim, W

    Lee, Y. S.; Kim, T.; Choi, S.; and Kim, W. 2022. When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy. Technovation, 118: 102590

    2022

  79. [83]

    V.; and Sundar, S

    Liao, Q. V.; and Sundar, S. S. 2022. Designing for Responsible Trust in AI Systems: A Communication Perspective. In Proceedings of the 2022 ACM conference on fairness, accountability, and transparency. ACM

    2022

  80. [84]

    MIT Project NANDA . 2025. The GenAI Divide: State of AI in Business 2025. Technical report, Massachusetts Institute of Technology. Preliminary findings

    2025

Showing first 80 references.