Artificial Intelligence Adoption and Corporate Financial Decision-Making: The Roles of Financial Analytics Capability, Organizational Agility, and Corporate Governance
Artificial intelligence (AI) is reshaping how firms sense, interpret, and act upon financial information, yet the mechanisms through which AI adoption translates into superior corporate financial decision-making remain theoretically under-specified and empirically contested. Drawing on the resource-based view, dynamic capability theory, and organizational information processing theory, this study advances and tests an integrated model in which financial analytics capability and organizational agility jointly mediate the effect of AI adoption on financial decision-making outcomes, while corporate governance moderates the strength of that effect. Using survey data from 412 senior finance and technology executives across listed and large private firms, and applying partial least squares structural equation modeling (PLS-SEM) with complementary robustness procedures, the study finds that AI adoption exerts a modest direct effect on financial decision-making outcomes but a substantially larger indirect effect operating through financial analytics capability and organizational agility, which together account for roughly two-thirds of the total effect. Corporate governance positively moderates the AI-outcome relationship, indicating that governance quality functions as a complementary asset that amplifies rather than merely constrains AI-enabled value creation. The findings reconcile competing accounts of the AI-performance relationship by identifying the capability-based pathways through which value is realized, extend information processing theory to the AI context, and reframe governance as an enabling condition. The study offers actionable guidance for executives, boards, regulators, and technology providers seeking to convert AI investment into disciplined financial decisions.
Cite this paper
Prince, I. C. and Song, Y. (2026). Artificial Intelligence Adoption and Corporate Financial Decision-Making: The Roles of Financial Analytics Capability, Organizational Agility, and Corporate Governance. Open Access Library Journal, 13, e15724. doi: http://dx.doi.org/10.4236/oalib.1115724.
Iansiti, M. and Lakhani, K.R. (2020) Competing in the Age of AI. <i>Harvard Business Review</i>, 98, 60-67. <br>https://hbr.org/2020/01/competing-in-the-age-of-ai
Krakowski, S., Luger, J. and Raisch, S. (2023) Artificial Intelligence and the Changing Sources of Competitive Advantage. <i>Strategic Management Journal</i>, 44, 1425-1452. <br>https://doi.org/10.1002/smj.3387
Goldstein, I., Spatt, C.S. and Ye, M. (2021) Big Data in Finance. <i>The Review of Financial Studies</i>, 34, 3213-3225. <br>https://doi.org/10.1093/rfs/hhab038
Bughin, J., Seong, J., Manyika, J., Chui, M. and Joshi, R. (2018) Notes from the AI Frontier: Modeling the Impact of AI on the World Economy (Discussion Paper). McKinsey Global Institute. <br>https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy
Organisation for Economic Co-Operation and Development (2021) Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges and Implications for Policy Makers. OECD Publishing. <br>https://doi.org/10.1787/98e761e7-en
Duan, Y., Edwards, J.S. and Dwivedi, Y.K. (2019) Artificial Intelligence for Decision Making in the Era of Big Data—Evolution, Challenges and Research Agenda. <i>International Journal of Information Management</i>, 48, 63-71. <br>https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Rana, N.P., Chatterjee, S., Dwivedi, Y.K. and Akter, S. (2022) Understanding Dark Side of Artificial Intelligence (AI) Integrated Business Analytics: Assessing Firm’s Operational Inefficiency and Competitiveness. <i>European Journal of Information Systems</i>, 31, 364-387. <br>https://doi.org/10.1080/0960085x.2021.1955628
Shrestha, Y.R., Ben-Menahem, S.M. and von Krogh, G. (2019) Organizational Decision-Making Structures in the Age of Artificial Intelligence. <i>California Management Review</i>, 61, 66-83. <br>https://doi.org/10.1177/0008125619862257
Mikalef, P. and Gupta, M. (2021) Artificial Intelligence Capability: Conceptualization, Measurement Calibration, and Empirical Study on Its Impact on Organizational Creativity and Firm Performance. <i>Information & Management</i>, 58, Article ID: 103434. <br>https://doi.org/10.1016/j.im.2021.103434
Wamba-Taguimdje, S., Fosso Wamba, S., Kala Kamdjoug, J.R. and Tchatchouang Wanko, C.E. (2020) Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-Based Transformation Projects. <i>Business Process Management Journal</i>, 26, 1893-1924. <br>https://doi.org/10.1108/bpmj-10-2019-0411
Ghasemaghaei, M. and Calic, G. (2020) Assessing the Impact of Big Data on Firm Innovation Performance: Big Data Is Not Always Better Data. <i>Journal of Business Research</i>, 108, 147-162. <br>https://doi.org/10.1016/j.jbusres.2019.09.062
Enholm, I.M., Papagiannidis, E., Mikalef, P. and Krogstie, J. (2022) Artificial Intelligence and Business Value: A Literature Review. <i>Information Systems Frontiers</i>, 24, 1709-1734. <br>https://doi.org/10.1007/s10796-021-10186-w
Barney, J. (1991) Firm Resources and Sustained Competitive Advantage. <i>Journal of Management</i>, 17, 99-120. <br>https://doi.org/10.1177/014920639101700108
Collins, C., Dennehy, D., Conboy, K. and Mikalef, P. (2021) Artificial Intelligence in Information Systems Research: A Systematic Literature Review and Research Agenda. <i>International Journal of Information Management</i>, 60, Article ID: 102383. <br>https://doi.org/10.1016/j.ijinfomgt.2021.102383
Gupta, M. and George, J.F. (2016) Toward the Development of a Big Data Analytics Capability. <i>Information & Management</i>, 53, 1049-1064. <br>https://doi.org/10.1016/j.im.2016.07.004
Mikalef, P., Boura, M., Lekakos, G. and Krogstie, J. (2019) Big Data Analytics and Firm Performance: Findings from a Mixed-Method Approach. <i>Journal of Business Research</i>, 98, 261-276. <br>https://doi.org/10.1016/j.jbusres.2019.01.044
Sambamurthy, V., Bharadwaj, A. and Grover, V. (2003) Shaping Agility through Digital Options: Reconceptualizing the Role of Information Technology in Contemporary Firms1. <i>MIS Quarterly</i>, 27, 237-263. <br>https://doi.org/10.2307/30036530
Lu, Y. and Ramamurthy, K. (2011) Understanding the Link between Information Technology Capability and Organizational Agility: An Empirical Examination. <i>MIS Quarterly</i>, 35, 931-954. <br>https://doi.org/10.2307/41409967
Jensen, M.C. and Meckling, W.H. (1976) Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. <i>Journal of Financial Economics</i>, 3, 305-360. <br>https://doi.org/10.1016/0304-405x(76)90026-x
Li, J., Li, M., Wang, X. and Thatcher, J.B. (2021) Strategic Directions for AI: The Role of Cios and Boards of Directors. <i>MIS Quarterly</i>, 45, 1603-1644. <br>https://doi.org/10.25300/misq/2021/16523
Cao, G., Duan, Y. and Cadden, T. (2019) The Link between Information Processing Capability and Competitive Advantage Mediated through Decision-Making Effectiveness. <i>International Journal of Information Management</i>, 44, 121-131. <br>https://doi.org/10.1016/j.ijinfomgt.2018.10.003
Chatterjee, S., Rana, N.P., Dwivedi, Y.K. and Baabdullah, A.M. (2021) Understanding AI Adoption in Manufacturing and Production Firms Using an Integrated TAM-TOE Model. <i>Technological Forecasting and Social Change</i>, 170, Article ID: 120880. <br>https://doi.org/10.1016/j.techfore.2021.120880
Nam, D., Lee, J. and Lee, H. (2019) Business Analytics Adoption Process: An Innovation Diffusion Perspective. <i>International Journal of Information Management</i>, 49, 411-423. <br>https://doi.org/10.1016/j.ijinfomgt.2019.07.017
Grover, P., Kar, A.K. and Dwivedi, Y.K. (2022) Understanding Artificial Intelligence Adoption in Operations Management: Insights from the Review of Academic Literature and Social Media Discussions. <i>Annals of Operations Research</i>, 308, 177-213. <br>https://doi.org/10.1007/s10479-020-03683-9
Jöhnk, J., Weißert, M. and Wyrtki, K. (2021) Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors. <i>Business & Information Systems Engineering</i>, 63, 5-20. <br>https://doi.org/10.1007/s12599-020-00676-7
Raisch, S. and Krakowski, S. (2021) Artificial Intelligence and Management: The Automation-Augmentation Paradox. <i>Academy of Management Review</i>, 46, 192-210. <br>https://doi.org/10.5465/amr.2018.0072
Davenport, T.H. and Ronanki, R. (2018) Artificial Intelligence for the Real World. <i>Harvard Business Review</i>, 96, 108-116. <br>https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
Ransbotham, S., Kiron, D., Gerbert, P. and Reeves, M. (2017) Reshaping Business with Artificial Intelligence: Closing the Gap between Ambition and Action. <i>MIT Sloan Management Review</i>, 59, 1-17.<br>https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/
Coombs, C., Hislop, D., Taneva, S.K. and Barnard, S. (2020) The Strategic Impacts of Intelligent Automation for Knowledge and Service Work: An Interdisciplinary Review. <i>The Journal of Strategic Information Systems</i>, 29, Article ID: 101600. <br>https://doi.org/10.1016/j.jsis.2020.101600
Bartram, S.M., Branke, J. And Motahari, M. (2020) Artificial Intelligence in Asset Management (CFA Institute Research Foundation Literature Review). CFA Institute Research Foundation.
Kou, G., Chao, X., Peng, Y., Alsaadi, F.E. and Herrera-Viedma, E. (2019) Machine Learning Methods for Systemic Risk Analysis in Financial Sectors. <i>Technological and Economic Development of Economy</i>, 25, 716-742. <br>https://doi.org/10.3846/tede.2019.8740
Brynjolfsson, E. and McElheran, K. (2016) The Rapid Adoption of Data-Driven Decision-Making. <i>American Economic Review</i>, 106, 133-139. <br>https://doi.org/10.1257/aer.p20161016
Mikalef, P., Pappas, I.O., Krogstie, J. and Giannakos, M. (2018) Big Data Analytics Capabilities: A Systematic Literature Review and Research Agenda. <i>Information Systems and e-Business Management</i>, 16, 547-578. <br>https://doi.org/10.1007/s10257-017-0362-y
Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R. and Childe, S.J. (2017) Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities. <i>Journal of Business Research</i>, 70, 356-365. <br>https://doi.org/10.1016/j.jbusres.2016.08.009
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J. (2016) How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment? <i>International Journal of Production Economics</i>, 182, 113-131. <br>https://doi.org/10.1016/j.ijpe.2016.08.018
Ashrafi, A., Zare Ravasan, A., Trkman, P. and Afshari, S. (2019) The Role of Business Analytics Capabilities in Bolstering Firms’ Agility and Performance. <i>International Journal of Information Management</i>, 47, 1-15. <br>https://doi.org/10.1016/j.ijinfomgt.2018.12.005
Overby, E., Bharadwaj, A. and Sambamurthy, V. (2006) Enterprise Agility and the Enabling Role of Information Technology. <i>European Journal of Information Systems</i>, 15, 120-131. <br>https://doi.org/10.1057/palgrave.ejis.3000600
Lee, O., Sambamurthy, V., Lim, K.H. and Wei, K.K. (2015) How Does IT Ambidexterity Impact Organizational Agility? <i>Information Systems Research</i>, 26, 398-417. <br>https://doi.org/10.1287/isre.2015.0577
Ravichandran, T. (2018) Exploring the Relationships between IT Competence, Innovation Capacity and Organizational Agility. <i>The Journal of Strategic Information Systems</i>, 27, 22-42. <br>https://doi.org/10.1016/j.jsis.2017.07.002
Tallon, P.P. and Pinsonneault, A. (2011) Competing Perspectives on the Link between Strategic Information Technology Alignment and Organizational Agility: Insights from a Mediation Model. <i>MIS Quarterly</i>, 35, 463-486. <br>https://doi.org/10.2307/23044052
Shleifer, A. and Vishny, R.W. (1997) A Survey of Corporate Governance. <i>The Journal of Finance</i>, 52, 737-783. <br>https://doi.org/10.1111/j.1540-6261.1997.tb04820.x
Aguilera, R.V. and Jackson, G. (2003) The Cross-National Diversity of Corporate Governance: Dimensions and Determinants. <i>Academy of Management Review</i>, 28, 447-465. <br>https://doi.org/10.5465/amr.2003.10196772
Fama, E.F. and Jensen, M.C. (1983) Separation of Ownership and Control. <i>The Journal of Law and Economics</i>, 26, 301-325. <br>https://doi.org/10.1086/467037
Hambrick, D.C. and Mason, P.A. (1984) Upper Echelons: The Organization as a Reflection of Its Top Managers. <i>Academy of Management Review</i>, 9, 193-206.
Teece, D.J., Pisano, G. and Shuen, A. (1997) Dynamic Capabilities and Strategic Management. <i>Strategic Management Journal</i>, 18, 509-533. <br>https://doi.org/10.1002/(sici)1097-0266(199708)18:7<509::aid-smj882>3.0.co;2-z
Eisenhardt, K.M. and Martin, J.A. (2000) Dynamic Capabilities: What Are They? <i>Strategic Management Journal</i>, 21, 1105-1121. <br>https://doi.org/10.1002/1097-0266(200010/11)21:10/11<1105::aid-smj133>3.0.co;2-e
Rialti, R., Marzi, G., Ciappei, C. and Busso, D. (2019) Big Data and Dynamic Capabilities: A Bibliometric Analysis and Systematic Literature Review. <i>Management Decision</i>, 57, 2052-2068. <br>https://doi.org/10.1108/md-07-2018-0821
Warner, K.S.R. and Wäger, M. (2019) Building Dynamic Capabilities for Digital Transformation: An Ongoing Process of Strategic Renewal. <i>Long Range Planning</i>, 52, 326-349. <br>https://doi.org/10.1016/j.lrp.2018.12.001
Zhu, K. and Kraemer, K.L. (2005) Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry. <i>Information Systems Research</i>, 16, 61-84. <br>https://doi.org/10.1287/isre.1050.0045
DiMaggio, P.J. and Powell, W.W. (1983) The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. <i>American Sociological Review</i>, 48, 147-160. <br>https://doi.org/10.2307/2095101
Vial, G. (2019) Understanding Digital Transformation: A Review and a Research Agenda. <i>The Journal of Strategic Information Systems</i>, 28, 118-144. <br>https://doi.org/10.1016/j.jsis.2019.01.003
Verhoef, P.C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., <i>et al</i>. (2021) Digital Transformation: A Multidisciplinary Reflection and Research Agenda. <i>Journal of Business Research</i>, 122, 889-901. <br>https://doi.org/10.1016/j.jbusres.2019.09.022
Hanelt, A., Bohnsack, R., Marz, D. and Antunes Marante, C. (2021) A Systematic Review of the Literature on Digital Transformation: Insights and Implications for Strategy and Organizational Change. <i>Journal of Management Studies</i>, 58, 1159-1197. <br>https://doi.org/10.1111/joms.12639
Ferraris, A., Mazzoleni, A., Devalle, A. and Couturier, J. (2019) Big Data Analytics Capabilities and Knowledge Management: Impact on Firm Performance. <i>Management Decision</i>, 57, 1923-1936. <br>https://doi.org/10.1108/md-07-2018-0825
Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M. (2019) When to Use and How to Report the Results of PLS-SEM. <i>European Business Review</i>, 31, 2-24. <br>https://doi.org/10.1108/ebr-11-2018-0203
Becker, J., Cheah, J., Gholamzade, R., Ringle, C.M. and Sarstedt, M. (2023) PLS-SEM’s Most Wanted Guidance. <i>International Journal of Contemporary Hospitality Management</i>, 35, 321-346. <br>https://doi.org/10.1108/ijchm-04-2022-0474
Armstrong, J.S. and Overton, T.S. (1977) Estimating Nonresponse Bias in Mail Surveys. <i>Journal of Marketing Research</i>, 14, 396-402. <br>https://doi.org/10.1177/002224377701400320
Hair, J.F., Howard, M.C. and Nitzl, C. (2020) Assessing Measurement Model Quality in PLS-SEM Using Confirmatory Composite Analysis. <i>Journal of Business Research</i>, 109, 101-110. <br>https://doi.org/10.1016/j.jbusres.2019.11.069
Sarstedt, M., Hair, J.F., Nitzl, C., Ringle, C.M. and Howard, M.C. (2020) Beyond a Tandem Analysis of SEM and PROCESS: Use of PLS-SEM for Mediation Analyses! <i>International Journal of Market Research</i>, 62, 288-299. <br>https://doi.org/10.1177/1470785320915686
Preacher, K.J. and Hayes, A.F. (2008) Asymptotic and Resampling Strategies for Assessing and Comparing Indirect Effects in Multiple Mediator Models. <i>Behavior Research Methods</i>, 40, 879-891. <br>https://doi.org/10.3758/brm.40.3.879
Nitzl, C., Roldan, J.L. and Cepeda, G. (2016) Mediation Analysis in Partial Least Squares Path Modeling: Helping Researchers Discuss More Sophisticated Models. <i>Industrial Management & Data Systems</i>, 116, 1849-1864. <br>https://doi.org/10.1108/imds-07-2015-0302
Sarstedt, M., Ringle, C.M., Cheah, J., Ting, H., Moisescu, O.I. and Radomir, L. (2019) Structural Model Robustness Checks in PLS-SEM. <i>Tourism Economics</i>, 26, 531-554. <br>https://doi.org/10.1177/1354816618823921
Podsakoff, P.M., MacKenzie, S.B., Lee, J. and Podsakoff, N.P. (2003) Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. <i>Journal of Applied Psychology</i>, 88, 879-903. <br>https://doi.org/10.1037/0021-9010.88.5.879
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P. (2012) Sources of Method Bias in Social Science Research and Recommendations on How to Control It. <i>Annual Review of Psychology</i>, 63, 539-569. <br>https://doi.org/10.1146/annurev-psych-120710-100452
Kock, N. (2015) Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. <i>International Journal of e-Collaboration</i>, 11, 1-10. <br>https://doi.org/10.4018/ijec.2015100101
Fornell, C. and Larcker, D.F. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. <i>Journal of Marketing Research</i>, 18, 39-50. <br>https://doi.org/10.1177/002224378101800104
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015) A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. <i>Journal of the Academy of Marketing Science</i>, 43, 115-135. <br>https://doi.org/10.1007/s11747-014-0403-8
Wright, S.A. and Schultz, A.E. (2018) The Rising Tide of Artificial Intelligence and Business Automation: Developing an Ethical Framework. <i>Business Horizons</i>, 61, 823-832. <br>https://doi.org/10.1016/j.bushor.2018.07.001
Trocin, C., Mikalef, P., Papamitsiou, Z. and Conboy, K. (2023) Responsible AI for Digital Health: A Synthesis and a Research Agenda. <i>Information Systems Frontiers</i>, 25, 2139-2157. <br>https://doi.org/10.1007/s10796-021-10146-4