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Integrated AI capabilities correlate with better performance at Kenyan banks and fintechs, yielding larger gains when applications are combined; however, sustainable benefits hinge on firms' dynamic capabilities to sense, seize and reconfigure around AI.

Artificial Intelligence as A Strategic Resource and Its Mediating Role of Dynamic Capabilities in Enhancing Organizational Performance in The Financial Services Sector.
Abraham Rono, P. Kimaku, Irene Njeri · February 23, 2026 · International journal of social science and humanity research
semantic_scholar correlational low evidence 7/10 relevance Full text usable extracted full text DOI Source PDF

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  1. Abraham Rodney Rono provider ID
  2. Patrick Mutua Kimaku provider ID
  3. Irene Njeri provider ID

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  1. Abraham Rono provider ID
  2. P. Kimaku provider ID
  3. I. Njeri provider ID
In a survey of Kenyan banks and FinTechs, AI applications across customer engagement, risk/fraud detection, process automation and strategic decision-making are positively associated with organizational performance, and this joint effect is mediated by firms' dynamic capabilities (sensing, seizing, reconfiguring, learning).

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Cumulative provider counts captured on specific dates; providers are never combined.

Artificial Intelligence (AI) has emerged as a strategic resource with the potential to enhance organizational performance in the financial services sector; however, empirical evidence on its performance outcomes remains inconclusive, particularly in emerging economies. This study examined the influence of artificial intelligence as a strategic resource on organizational performance in Kenya’s financial services sector, with specific focus on the mediating role of dynamic capabilities. The study was guided by six objectives: to assess the effects of AI-driven customer engagement, AI-powered risk management and fraud detection, AI-enabled process automation, AI-supported strategic decision-making, the joint effect of AI dimensions, and the mediating role of dynamic capabilities on organizational performance. The study was anchored on the Resource-Based View (RBV) and Dynamic Capabilities Perspective (DCP), supported by the Technology–Organization–Environment (TOE) framework and the Knowledge-Based View (KBV). A positivist research philosophy was adopted, employing a descriptive and explanatory cross-sectional survey design. Primary data were collected using structured questionnaires from 263 managers and technical staff drawn from commercial banks and FinTech firms operating in Kenya, achieving a response rate of 94.3%. Data were analyzed using SPSS through descriptive statistics, reliability and validity tests, correlation analysis, multiple regression analysis, and mediation analysis. Reliability was confirmed with Cronbach’s alpha coefficients exceeding the 0.70 threshold, while diagnostic tests confirmed compliance with regression assumptions. The findings revealed that AI-driven customer engagement, AI-powered risk management and fraud detection, AI-enabled process automation, and AI-supported strategic decision-making each had a positive and statistically significant effect on organizational performance (p < 0.05). These effects manifested through improved customer satisfaction and retention, reduced fraud losses, enhanced regulatory compliance, lower operational costs, improved efficiency, and stronger data-driven decision-making. Further analysis showed that the joint effect of AI dimensions was statistically significant and stronger than individual effects, confirming the presence of synergistic performance gains from integrated AI deployment. Mediation analysis established that dynamic capabilities, sensing, seizing, reconfiguring, and learning, significantly mediated the relationship between joint AI parameters and organizational performance. The study concludes that while AI adoption enhances organizational performance in Kenya’s financial services sector, sustainable performance gains depend on the development of strong dynamic capabilities. The findings validate the applicability of RBV and Dynamic Capabilities Theory in explaining AI-driven performance in emerging economies. The study recommends integrated AI strategies, continuous workforce reskilling, strengthened organizational learning systems, and robust AI governance frameworks. At the policy level, supportive AI regulations, investment in digital infrastructure, and regulatory sandboxes are recommended to promote responsible AI adoption.

Summary

Main Finding

  • AI adoption across four dimensions—AI-driven customer engagement, AI-powered risk management & fraud detection, AI-enabled process automation, and AI-supported strategic decision-making—has a positive and statistically significant effect on organizational performance in Kenya’s financial services sector (p < 0.05).
  • The combined (joint) effect of these AI dimensions is stronger than individual effects, indicating synergistic gains from integrated AI deployment.
  • Dynamic capabilities (sensing, seizing, reconfiguring, learning) significantly mediate the relationship between joint AI deployment and organizational performance: sustainable performance gains from AI depend on firms’ ability to develop and exercise these capabilities.

Key Points

  • Theoretical framing: Resource-Based View (RBV) and Dynamic Capabilities Perspective (DCP) are used to explain how AI functions as a strategic resource; supported by Technology–Organization–Environment (TOE), Knowledge-Based View (KBV), Absorptive Capacity, and Institutional theory.
  • Measured AI dimensions:
    • Customer engagement: chatbots, personalization, predictive analytics, retention programs.
    • Risk & fraud detection: ML-based anomaly detection, AI credit scoring, real-time monitoring.
    • Process automation: RPA, back-office automation, error reduction, cost/time savings.
    • Strategic decision-making: forecasting, scenario planning, portfolio optimization, dashboards.
  • Organizational performance captured across financial, operational, customer-centric, and competitive dimensions.
  • Empirical outcomes: improved customer satisfaction/retention, reduced fraud losses, enhanced compliance, lower operational costs, improved efficiency, and stronger data-driven decisions.
  • Policy and managerial recommendations from the study:
    • Integrated AI strategies that combine AI capabilities rather than piecemeal adoption.
    • Continuous workforce reskilling and strengthened organizational learning systems.
    • Robust AI governance, supportive AI regulations, investment in digital infrastructure, and regulatory sandboxes to encourage responsible experimentation.
  • Contribution: Validates applicability of RBV and Dynamic Capabilities Theory in an emerging-economy (Kenyan) financial-services context; highlights complementarities between AI investments and organizational capability investments.

Data & Methods

  • Philosophy & design: Positivist; descriptive and explanatory cross-sectional survey.
  • Population & sample:
    • Target institutions: 191 financial firms in Nairobi (39 commercial banks + 152 licensed digital credit providers).
    • Sampling frame for individual respondents: 764 managers/technical staff; stratified random sampling used.
    • Sample size (target): 263 respondents (Yamane’s formula); achieved responses: 248 returned questionnaires (response rate 94.3%).
  • Instrumentation & measurement:
    • Structured Likert-scale questionnaire.
    • Constructs: four AI dimensions (independent), dynamic capabilities (mediator: sensing, seizing, reconfiguring, learning), organizational performance (dependent).
    • Validity: expert review and exploratory factor analysis.
    • Reliability: Cronbach’s alpha > 0.70 for scales.
  • Analysis:
    • Software: SPSS.
    • Techniques: descriptive statistics, reliability/validity testing, correlation analysis, multiple regression, mediation analysis (bootstrapping), diagnostic checks for regression assumptions.
  • Hypotheses: Six null hypotheses tested (H01–H06). Empirical results reject the nulls: individual AI dimensions and their joint effect significantly influence performance, and dynamic capabilities significantly mediate the joint AI → performance relationship.

Implications for AI Economics

  • Complementarities and superadditivity: The stronger joint effect implies superadditive returns when multiple AI capabilities are integrated. Economic models of technology adoption should account for complementarities among AI subcomponents rather than treating AI as a single homogeneous input.
  • Role of organizational capital: Returns to AI investments are conditional on organizational (dynamic) capabilities—models of firm-level productivity should include endogenous capability formation (sensing, seizing, reconfiguring, learning) as complementary investments that unlock AI value.
  • Policy design for emerging economies: Supply-side interventions (digital infrastructure, data governance, skills development) and demand-side regulatory tools (sandboxes, proportionate AI rules) can increase social returns to AI by lowering frictions that prevent realization of synergistic AI gains.
  • Labor and human-capital considerations: The need for continuous reskilling implies that labor-market models should incorporate dynamic human-capital accumulation to capture how AI affects employment composition, productivity, and wage structures in financial services.
  • Measurement and evaluation: Cost-benefit and impact evaluations of AI should measure both direct technology effects and mediating capability development; otherwise, estimators of AI returns risk bias if capability complementarities are omitted.
  • Financial stability and systemic risk: While AI improves fraud detection and risk management, integrated adoption across firms can produce correlated exposures to similar models/feedback loops. Macroprudential and regulatory frameworks must consider systemic model risk when promoting AI diffusion.
  • Research directions: Need for longitudinal and quasi-experimental studies to estimate causal effects and persistence of AI-driven gains; heterogeneity analysis across firm size/type (banks vs FinTechs), and exploration of optimal sequencing (which capabilities to build first) to maximize returns.

Reference (paper summarized): Rono, A. R., Kimaku, P. M., & Njeri, I. (2026). Artificial Intelligence as A Strategic Resource and Its Mediating Role of Dynamic Capabilities in Enhancing Organizational Performance in The Financial Services Sector. International Journal of Social Science and Humanities Research, 4(1), 99–117. DOI: 10.61108/ijsshr.V4i1.251.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on self-reported cross-sectional survey data from a non-experimental design, which leaves results vulnerable to common-method bias, measurement error, reverse causality, and omitted-variable confounding; statistical significance and reliability tests support internal consistency but do not establish causal effects. Methods Rigormedium — Paper applies appropriate survey-scale validation (Cronbach's alpha), descriptive statistics, regression diagnostics, and mediation analysis, but lacks stronger identification strategies (e.g., longitudinal design, instruments, or experimental/quasi-experimental variation), details on sampling frame and nonresponse bias, and reliance on perceptual outcome measures limits causal inference. SamplePrimary data from a cross-sectional structured questionnaire answered by 263 managers and technical staff drawn from commercial banks and FinTech firms operating in Kenya (reported response rate 94.3%); sampling frame and recruitment approach not fully specified; measures are largely perceptual/self-reported. Themesproductivity org_design IdentificationCross-sectional survey of managers and technical staff with multivariate ordinary least squares regression and mediation analysis; identification relies on statistical controls and observed correlations (no randomization, instrumental variables, difference-in-differences, or other exogenous variation). GeneralizabilityCountry-specific: Kenya only — results may not generalize to other emerging or developed economies, Sector-specific: limited to financial services (commercial banks and FinTechs), Respondent-restricted: managers and technical staff perspectives rather than firm-level objective performance metrics, Cross-sectional/time-specific: captures associations at one point in time, not dynamic effects, Potential selection bias: likely includes firms/personnel already engaged with AI, limiting applicability to non-adopters or late adopters

Claims (11)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI-driven customer engagement had a positive and statistically significant effect on organizational performance (p < 0.05). Firm Productivity positive organizational performance
Reading fidelity high
Study strength medium
n=263
0.3
AI-powered risk management and fraud detection had a positive and statistically significant effect on organizational performance (p < 0.05). Firm Productivity positive organizational performance
Reading fidelity high
Study strength medium
n=263
0.3
AI-enabled process automation had a positive and statistically significant effect on organizational performance (p < 0.05). Firm Productivity positive organizational performance
Reading fidelity high
Study strength medium
n=263
0.3
AI-supported strategic decision-making had a positive and statistically significant effect on organizational performance (p < 0.05). Firm Productivity positive organizational performance
Reading fidelity high
Study strength medium
n=263
0.3
The joint effect of AI dimensions was statistically significant and stronger than individual effects, indicating synergistic performance gains from integrated AI deployment. Firm Productivity positive organizational performance
Reading fidelity high
Study strength medium
n=263
0.3
Dynamic capabilities (sensing, seizing, reconfiguring, learning) significantly mediated the relationship between joint AI parameters and organizational performance. Firm Productivity positive organizational performance
Reading fidelity high
Study strength medium
n=263
0.3
The AI-driven performance effects manifested through improved customer satisfaction and retention, reduced fraud losses, enhanced regulatory compliance, lower operational costs, improved efficiency, and stronger data-driven decision-making. Firm Productivity positive customer satisfaction; fraud losses; regulatory compliance; operational costs; efficiency; data-driven decision-making
Reading fidelity medium
Study strength medium
n=263
0.18
Cronbach's alpha coefficients for the study's scales exceeded the 0.70 threshold, confirming internal reliability. Other positive scale reliability (internal consistency)
Reading fidelity high
Study strength high
n=263
0.5
Diagnostic tests confirmed compliance with regression assumptions for the statistical analyses. Other null_result compliance with regression assumptions (e.g., normality, homoscedasticity, multicollinearity)
Reading fidelity high
Study strength medium
n=263
0.3
Primary data were collected using structured questionnaires from 263 managers and technical staff drawn from commercial banks and FinTech firms operating in Kenya, achieving a response rate of 94.3%. Other null_result sample composition and response rate
Reading fidelity high
Study strength high
n=263
94.3% response rate
0.5
The study's findings validate the applicability of the Resource-Based View (RBV) and Dynamic Capabilities Theory in explaining AI-driven performance in emerging economies. Governance And Regulation positive theoretical applicability (RBV and Dynamic Capabilities) to AI-driven organizational performance
Reading fidelity high
Study strength medium
n=263
0.3

Notes