As generative artificial intelligence (GenAI) becomes more prevalent across all types of organizations, interest in adopting artificial intelligence (AI) tools by charitable nonprofit organizations, who are given 501(c)(3) tax-exempt status by the internal revenue service (IRS), is worthy of attention, given the potential for increased efficiency and raised ethical concerns in resource-drained economies. Guided by the Technology–Organization–Environment (TOE) framework, this study surveys Florida nonprofit organizations (n = 168) to explore if certain factors, such as the external environment, innovative culture, and leadership, influence decisions to adopt GenAI tools in nonprofit organizations. We supplement our survey findings with follow-up qualitative data from 14 nonprofit executive directors. Findings offer guidance to nonprofit leaders for assessing their organizations’ environment and innovative capacities when making decisions on GenAI adoption. Furthermore, there are broad implications for examining the potential of GenAI tools for mission fulfillment and more effective management of nonprofit organizations.
AlsolbiI.AgarwalR.UnhelkarB.Al-JabriT.SamarawickramaM.TafavoghS.PrasadM. (2023). A conceptual design of an AI-enabled decision support system for analysing donor behaviour in nonprofit organisations. Information, 14(10), 578.
2.
AminiM.BakriA. (2015). Cloud computing adoption by SMEs in the Malaysia: A multi-perspective framework based on DOI theory and TOE framework. Journal of Information Technology & Information Systems Research (JITISR), 9(2), 121–135.
3.
AndroniceanuA. (2024). Generative artificial intelligence, present and perspectives in public administration. Administration & Public Management Review, 43.
4.
AzevedoL. (2021). The impact of cloud management platforms on nonprofit business models. Journal of Technology in Human Services, 39(4), 405–425.
5.
BabsekM.MurkoE.AristovnikA. (2025). Organisational AI readiness for public administration: A comprehensive review and framework for conceptual modelling. International Journal of Economics and Business Administration (IJEBA), 13(3), 24–47.
6.
BareghehA.RowleyJ.SambrookS. (2009). Towards a multidisciplinary definition of innovation. Management Decision, 47(8), 1323–1339. https://doi.org/10.1108/00251740910984578
BarzantiL.BenvenutiL.GaudenziE. (2024). The impact of artificial intelligence and information technology on philanthropic organizations: Case studies of non-profit and cultural associations. In UgazioG.MaricicM. (Eds.), The Routledge handbook of artificial intelligence and philanthropy (pp. 107–131). Routledge.
9.
BerlingerL. R.Te’eniD. (1999). Leaders’ attitudes and computer use in religious congregations. Nonprofit Management and Leadership, 9(4), 399–412.
10.
BestH.WolfC. (2015). Logistic regression. In BestH.WolfC. (Eds.), The SAGE handbook of regression analysis and causal inference (pp. 153–171). Sage.
11.
BorinsS. (2001). The challenge of innovating in government. PricewaterhouseCoopers Endowment for the Business of Government.
12.
BrownL.OsborneS. P. (2013). Risk and innovation: Towards a framework for risk governance in public services. Public Management Review, 15(2), 186–208. https://doi.org/10.1080/14719037.2012.707681
13.
BrionesR. L.KuchB.LiuB. F.JinY. (2011). Keeping up with the digital age: How the American Red Cross uses social media to build relationships. Public Relations Review, 37(1), 37–43. https://doi.org/10.1016/j.pubrev.2010.12.006
14.
BungerA. C. (2013). Administrative coordination in nonprofit human service delivery networks: The role of competition and trust. Nonprofit and Voluntary Sector Quarterly, 42(6), 1155–1175. https://doi.org/10.1177/0899764012451369
15.
CampbellD. A. (2008). Getting to yes . . . or no: Nonprofit decision making and interorganizational restructuring. Nonprofit Management & Leadership, 19(2), 221–241. https://doi.org/10.1002/nml.216
ChanF. T.ChongA. Y. L. (2013). Determinants of mobile supply chain management system diffusion: A structural equation analysis of manufacturing firms. International Journal of Production Research, 51(4), 1196–1213. https://doi.org/10.1080/00207543.2012.693961
18.
ChenC. A.BozemanB. (2012). Organizational risk aversion: Comparing the public and non-profit sectors. Public Management Review, 14(3), 377–402. https://doi.org/10.1080/14719037.2011.637406
19.
CihonP.SchuettJ.BaumS. D. (2021). Corporate governance of artificial intelligence in the public interest. Information, 12(7), 275.
20.
Cruz-JesusF.PinheiroA.OliveiraT. (2019). Understanding CRM adoption stages: Empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13.
21.
DamanpourF. (1991). Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of Management Journal, 34(3), 555–590. https://doi.org/10.5465/256406
22.
DefitriS. Y.BahariA.HandraH.FebriantoR. (2020). Determinant factors of e-government implementation and public accountability: Toe framework approach. Public Policy and Administration, 19(4), 37–51.
DoverG.LawrenceT. B. (2012). The role of power in nonprofit innovation. Nonprofit and Voluntary Sector Quarterly, 41(6), 991–1013. https://doi.org/10.1177/0899764011423304
25.
EfthymiouI. P.EgletonT. W. E.ChatzivasileiouS.Emmanouil-KalosA. (2023). Artificial intelligence and the future for charities. International Journal of Non-profit Sector Empowerment, 2(1), Article e35345.
26.
FanM.QalatiS. A.KhanM. A. S.ShahS. M. M.RamzanM.KhanR. S. (2021). Effects of entrepreneurial orientation on social media adoption and SME performance: The moderating role of innovation capabilities. PLOS ONE, 16(4), Article e0247320.
Fui-Hoon NahF.ZhengR.CaiJ.SiauK.ChenL. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304.
29.
GodefroidM. E.PlattfautR.NiehavesB. (2024). Identifying key barriers to nonprofit organizations’ adoption of technology innovations. Nonprofit Management and Leadership, 35(1), 237–259.
30.
HacklerD.SaxtonG. D. (2007). The strategic use of information technology by nonprofit organizations: Increasing capacity and untapped potential. Public Administration Review, 67(3), 474–487.
31.
HarrisonT. D.IrvinR. A. (2018). Competition and collaboration: When are they good for the nonprofit sector? In SeamanB. A.YoungD. R. (Eds.), Handbook of research on nonprofit economics and management (pp. 118–131). Edward Elgar Publishing.
32.
HaynesE. (2023). A new fundraising assistant: ChatGPT: The AI technology can take a first stab at writing grant applications, social-media posts, and thank-you notes to donors. The Chronicle of Philanthropy, 35(8), 16–19.
33.
HeadC. B.JasperP.McConnachieM.RaftreeL.HigdonG. (2023). Large language model applications for evaluation: Opportunities and ethical implications. New Directions for Evaluation, 2023(178–179), 33–46.
34.
HolzerJ. (2024). Machine learning for nonprofit organizations. Journal of Nonprofit Innovation, 4(2), 46–50.
35.
HughesA.MooreK.KatariaN. (2011). Innovation in Public Sector Organisations: A pilot survey for measuring innovation across the public sector. Nesta.
36.
IacovouC. L.BenbasatI.DexterA. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19, 465–485.
37.
IhmJ.KimE. (2021). When nonprofit organizations meet information and communication technologies: How organizational culture influences the use of traditional, digital, and sharing media. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 32(3), 678–694. doi:10.1007/s11266-021-00335-9
38.
IskandarovaS.SloanM. F. (2023, December). Exploring nonprofit and government agency AI policies and regulations: Systems leadership. In 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) (pp. 197–203). IEEE.
39.
JaisR.NgahA. H.RahiS.RashidA.AhmadS. Z.MokhlisS. (2024). Chatbots adoption intention in public sector in Malaysia from the perspective of TOE framework. The moderated and mediation model. Journal of Science and Technology Policy Management, 17(2), 303–324. https://doi.org/10.1108/JSTPM-04-2024-0151
40.
JaskyteK. (2004). Transformational leadership, organizational culture, and innovativeness in nonprofit organizations. Nonprofit Management and Leadership, 15(2), 153–168.
41.
JaskyteK. (2011). Predictors of administrative and technological innovations in nonprofit organizations. Public Administration Review, 71(1), 77–86.
42.
KabraG.SaharanA. (2025). Burden to be better in the Era of AI: Assessment of Barriers to GenAI Adoption Among NGOs. Voluntas, 36. https://doi.org/10.1007/s11266-025-00766-8
43.
KanterB.FineA. H. (2022). The smart nonprofit: Staying human-centered in an automated world. John Wiley.
44.
KilicalpS.O’NeillJ.SpiersD. (2024). Data science and AI among philanthropic foundations in Europe. In UgazioG.MaricicM. (Eds.), The Routledge handbook of artificial intelligence and philanthropy (pp. 172–191). Routledge.
45.
LazarsfeldP. F. (1955). Recent developments in latent structure analysis. Sociometry, 18(4), 391–403. https://doi.org/10.2307/2785875
46.
LeeR. L.BlouinM. C. (2019). Factors affecting web disclosure adoption in the nonprofit sector. Journal of Computer Information Systems, 59(4), 363–372.
47.
LewisJ. M.RicardL. M.KlijnE. H. (2018). How innovation drivers, networking and leadership shape public sector innovation capacity. International Review of Administrative Sciences, 84(2), 288–307.
48.
LewisL. K. (2014). Organizational change and innovation. In PutnamL. L.MumbyD. K. (Eds.), The SAGE handbook of organizational communication: Advances in theory, research, and methods (pp. 503–524). Sage.
49.
MadanR.AshokM. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774.
50.
MalikS.ChadharM.VatanasakdakulS.ChettyM. (2021). Factors affecting the organizational adoption of blockchain technology: Extending the technology–organization–environment (TOE) framework in the Australian context. Sustainability, 13(16), 9404.
McNuttJ. G. (2023). New data sources in nonprofit accounting and finance research. In TinkelmanD.ParsonsL. M. (Eds.), Research handbook on nonprofit accounting (pp. 403–421). Edward Elgar Publishing.
53.
McNuttJ. G.GuoC.GoldkindL.AnS. (2018). Technology in nonprofit organizations and voluntary action. Voluntaristics Review, 3(1), 1–63.
54.
MitchellD.MitchellS. (2024). Don’t go chasing waterfalls? How complexity demands more agile project management approaches to municipal strategy implementation. State and Local Government Review, 56(2), 137–156.
55.
Moro-ViscontiR.Cruz RambaudS.López PascualJ. (2023). Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms. Humanities and Social Sciences Communications, 10(1), 1–14.
56.
NeumannO.GuirguisK.SteinerR. (2024). Exploring artificial intelligence adoption in public organizations: A comparative case study. Public Management Review, 26(1), 114–141.
57.
O’BrienR. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6
58.
OdoomR.MensahP. (2019). Brand orientation and brand performance in SMEs: The moderating effects of social media and innovation capabilities. Management Research Review, 42(1), 155–171.
59.
OlfatM. (2024). ESM adoption for marketing purposes in organizations: The TOE framework. Journal of Promotion Management, 30(7), 1189–1219. https://doi.org/10.1080/10496491.2024.2386245
60.
RamanA. (2016). How do social media, mobility, analytics and cloud computing impact nonprofit organizations? A pluralistic study of information and communication technologies in Indian context. Information Technology for Development, 22(3), 400–421.
61.
RossH.VerclasK.LevineA. (Eds.). (2009). Managing technology to meet your mission: A strategic guide for nonprofit leaders. John Wiley.
62.
SchöbiS. (2024). Fast-tracking the use of AI in everyday philanthropy. In UgazioG.MaricicM. (Eds.), The Routledge handbook of artificial intelligence and philanthropy (pp. 61–75). Routledge.
63.
SeoH.VuH. T. (2020). Transnational nonprofits’ social media use: A survey of communications professionals and an analysis of organizational characteristics. Nonprofit and Voluntary Sector Quarterly, 49(4), 849–870.
64.
SlattenL. A. D. (2012). Something old and something new: Using the technology acceptance model to evaluate nonprofit certification. International Journal of Organization Theory and Behavior, 15(3), 423–449. https://doi.org/10.1108/IJOTB-15-03-2012-B003
65.
SmulowitzM.VogelP. (2024). The spandows: Pioneering AI in family philanthropy and sustainable business. In UgazioG.MaricicM. (Eds.), The Routledge handbook of artificial intelligence and philanthropy (pp. 275–286). Routledge.
66.
SongZ.NgC. P. (2023). From data to impact: How artificial intelligent is driving non-profit success. Ziheng Song.
67.
SuhJ.HarringtonJ.GoodmanD. (2018). Understanding the link between organizational communication and innovation: An examination of public, nonprofit, and for-profit organizations in South Korea. Public Personnel Management, 47(2), 217–244. https://doi.org/10.1177/0091026018760930
TornatzkyI. G.FleischerM. (1990). The processes of technological innovation. Leington Books.
70.
WallaceT.RutherfordA. C. (2021). The big bird gets the worm? How size influences social networking by Charitable Organizations. Nonprofit and Voluntary Sector Quarterly, 50(3), 626–646. https://doi.org/10.1177/0899764020959472
71.
WeerawardenaJ.Sullivan-MortG. (2001). Learning, innovation and competitive advantage in not-for-profit aged care marketing: A conceptual model and research propositions. Journal of Nonprofit & Public Sector Marketing, 9(3), 53–73. https://doi.org/10.1300/J054v09n03_04
WillemsJ.SchmidM. J.VanderelstD.VogelD.EbingerF. (2023). AI-driven public services and the privacy paradox: Do citizens really care about their privacy?Public Management Review, 25(11), 2116–2134. https://doi.org/10.1080/14719037.2022
74.
XueJ.ShierM. L.ChenJ.WangY.ZhengC.ChenC. (2024). A typology of social media use by human service nonprofits: Mixed methods study. Journal of Medical Internet Research, 26, Article e51698.
75.
YangC. C. (2012). Assessing the moderating effect of innovation capability on the relationship between logistics service capability and firm performance for ocean freight forwarders. International Journal of Logistics Research and Applications, 15(1), 53–69.
76.
YoonC. (2024). Factors affecting the adoption of digital marketing in non-profit organizations: An empirical study. Administrative Sciences, 14(1), 10.
77.
YoungM. M.BullockJ. B.LecyJ. D. (2019). Artificial discretion as a tool of governance: A framework for understanding the impact of artificial intelligence on public administration. Perspectives on Public Management and Governance, 2(4), 301–313.
78.
YuanQ.Gasco-HernandezM. (2021). Open innovation in the public sector: Creating public value through civic hackathons. Public Management Review, 23(4), 523–544.
79.
ZornT. E.FlanaginA. J.ShohamM. D. (2011). Institutional and noninstitutional influences on information and communication technology adoption and use among nonprofit organizations. Human Communication Research, 37(1), 1–33.
80.
ZuiderwijkA.ChenY. C.SalemF. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), 101577.
