1. Introduction Accordingly, this study pursues four primary objectives: (1) to empirically examine the direct effects of four dimensions of digital leadership capabilities, strategic, delivery-related, interpersonal, and personal attributes on AI adoption intention among Nigerian SMEs; (2) to investigate the mediating role of the organisational innovation climate in the relationship between digital leadership capabilities and AI adoption intention; (3) to evaluate the moderating effect of firm size on the associations between the dimensions of digital leadership capability and AI adoption intention; and (4) to generate evidence-based recommendations for policymakers and SME support agencies seeking to accelerate AI adoption within Nigeria’s SME sector. Situating the study within this framework embeds it in a broader research agenda: AI increasingly functions not only as a driver of operational efficiency but also as a catalyst for the green and sustainable transformation of firms operating under resource constraints, with digital leadership shaping the initial conditions and trajectory of this transformation [ 18]. This study offers three principal contributions. Theoretically, it extends Diffusion of Innovation Theory and the Tigre–Henriques-Curado digital leadership model to the context of small and medium-sized enterprises (SMEs) in developing economies. It conceptualises the organisational innovation climate as the transmission mechanism that links individual leader competencies to collective adoption intentions, thereby elucidating why leadership capabilities require an organisational conduit to influence firm-level decision-making. Empirically, it delivers one of the first capability-disaggregated PLS-SEM examinations of digital leadership and artificial intelligence (AI) adoption among African SMEs. By estimating mediation and moderation effects within a unified structural model, it simultaneously identifies both the underlying causal pathway and the firm size-contingent boundary of the effect, rather than analysing these dimensions in isolation. Practically, it translates these results into segment-specific and capability-specific recommendations for owner-managers, as well as for the design of differentiated SME support and training policies. 2. Literature Review and Hypothesis Development 2.1. AI Adoption Intention: Conceptual Foundations Systematic reviews further reveal heterogeneous evidence regarding the specific leadership competencies that influence AI adoption; operational leadership capabilities appear more strongly associated with post-adoption implementation and routinisation than with the initial decision to adopt [ 29]. Moreover, the extant literature is heavily skewed towards European and North American SMEs, with comparatively limited empirical attention to African contexts [ 30]. This geographical imbalance highlights a critical need for context-sensitive, region-specific investigations of AI adoption dynamics. 2.2. Digital Leadership and Digital Leadership Capabilities Synthesising and extending these prior conceptualisations, Tigre et al. [ 31] propose a four-dimensional framework of digital leadership capabilities comprising: strategic capabilities (vision, change management, innovation, agility, and calculated risk-taking); delivery-related capabilities (analytical thinking, technological proficiency, team performance, and results orientation); interpersonal capabilities (relationship building, communication, coaching, and fostering psychological safety); and personal attributes (adaptability, lifelong learning, ethical behaviour, and empathy). Empirical evidence further indicates that leaders’ digital capabilities constitute a central antecedent of internal AI implementation, as executives’ digital awareness has a direct and significant influence on organisational AI adoption [ 8, 9]. 2.3. Strategic Capabilities and AI Adoption Intention These findings are strongly underpinned by Diffusion of Innovation (DOI) theory. In particular, Rogers’ concept of “relative advantage” provides a theoretical rationale for why visionary leaders not only recognise the potential of AI but also actively champion and communicate its anticipated benefits throughout the organisation [ 43]. Thus: Hypothesis 1 ( H 1 ) .Strategic capabilities have a significant positive influence on the intention to adopt AI among SMEs in Nigeria. 2.4. Delivery-Related Capabilities and AI Adoption Intention Delivery-related capabilities comprise the practical competencies required to convert digital strategies into realised outcomes, including analytical reasoning, technological proficiency, team performance, collaborative capacity, and outcome orientation [ 31]. Their relationship with adoption intention is theoretically ambiguous. On the one hand, Sony et al. [ 44] contended that operational competence facilitates the implementation of Industry 4.0 technologies. On the other hand, “strategy-before-execution” perspectives posit that firm-level performance improvements are primarily driven by top-level strategic design; in the absence of adequate strategic cognition, enhancements in execution alone generate limited benefits [ 45]. Given that adoption intention is a cognitive, pre-implementation rather than operational competence, the needed skills may be more consequential in the post-adoption phase [ 46]. Consistent with this view, Arroyabe et al. [ 47], using data from 12,108 EU SMEs, reported that digital maturity and innovation capability, rather than operational competence, constituted the principal enablers of AI adoption. To formally examine this proposition, we hypothesise: Hypothesis 2 ( H 2 ) .Delivery-related capabilities have a significant positive influence on the intention to adopt AI among SMEs in Nigeria. 2.5. Interpersonal Capabilities and AI Adoption Intention Hypothesis 3 ( H 3 ) .Interpersonal capabilities have a significant positive influence on the intention of adopting AI among SMEs in Nigeria. 2.6. Personal Attributes and AI Adoption Intention Hypothesis 4 ( H 4 ) .Personal attributes have a significant positive influence on the intention to adopt AI among SMEs in Nigeria. 2.7. The Mediating Role of Organisational Innovation Climate The organisational innovation climate denotes the collectively shared perception that the organisation systematically encourages, supports, and rewards innovative behaviour [ 15, 16]. Digital leadership capabilities constitute a key antecedent of this climate: leaders who exhibit a clear strategic vision, foster collaboration, and demonstrate adaptive thinking are more likely to establish environments that are conducive to innovation. A stronger innovation climate, in turn, attenuates perceived risks and enhances employees’ receptivity to artificial intelligence (AI)–based initiatives. Taken together, these findings suggest that the innovation climate serves as a central mediating mechanism by which digital leadership translates into AI adoption intentions. Individual-level digital competence among leaders requires an organisational channel to translate into collective, firm-level strategic decisions [ 14]. Within this process, innovation climate represents the most immediate collective manifestation of leadership behaviours that encourage experimentation, tolerate ambiguity, and reward novelty. Accordingly: Hypothesis 5 ( H 5 ) .The organisational innovation climate mediates the relationship between the ability to lead digitally and the intention to adopt AI among SMEs in Nigeria. 2.8. The Moderating Role of Firm Size Hypothesis 6 ( H 6 ) .Firm size moderates the relationship between digital leadership capabilities and the intention to adopt AI, so that the relationship is stronger for medium-sized enterprises than for micro and small enterprises. 2.9. Theoretical Framework This study integrates two complementary perspectives. First, Diffusion of Innovation (DOI) Theory [ 43] explains how innovations spread through social systems through four elements: innovation, communication channels, time, and the social system. It identifies five perceived attributes influencing adoption (relative advantage, compatibility, complexity, trialability, observability), and categorises adopters by innovation. DOI provides a foundation for understanding how individual characteristics (personal attributes), social influence (interpersonal capabilities), and perceived innovation benefits (strategic capabilities) shape the intention of adoption. Second, the Tigre, Henriques, and Curado Model of Digital Leadership Capabilities [ 31] operationalises the four dimensions examined. The integration works as follows: the Tigre–Henriques–Curado model provides the antecedent structure (the four capability dimensions), while the DOI provides the explanatory logic for why each dimension affects the intention of adoption. Strategic capabilities map to relative advantage, interpersonal capabilities to social influence, personal attributes to individual innovation, and delivery-related capabilities to the management of perceived complexity at implementation. The organisational innovation climate is positioned as the mediator: the collective, climate-level realisation of leader behaviours that turns individual capability into firm-level decisions. Firm size enters as a contextual moderator. The conceptual model is shown in Figure 1 and summarised in Table 1. 4. Results 4.1. Demographic Profile of Respondents Table 4 presents the demographic characteristics of the 306 respondents. The majority (66%) were male; 52.3% were 18–28 years old, indicating a predominantly young respondent profile. Most (58.8%) had at least a bachelor’s degree, and more than half (53.9%) had operated their business for five years or less. Regarding firm size, 47.1% of the respondents were micro-enterprises (19 employees), 38.6% were small companies (10–49 employees), and 14.3% were medium companies (50–199 employees). Figure 2 provides a visual summary of the demographic distribution. 4.2. Measurement Model Assessment 4.2.1. Internal Consistency Reliability 4.2.2. Convergent and Discriminant Validity Convergent validity was confirmed as all AVE values exceeded 0.50 ( Table 6), indicating that each construct explains more than half of the variance in its indicators [ 65]. Discriminant validity was assessed using the Fornell–Larcker criterion ( Table 7), confirming that the AVE of each construct exceeds its correlations with all other constructs [ 65]. The HTMT analysis ( Table 8) further confirmed discriminant validity, with all values below 0.90 [ 66]. 4.3. Structural Model Assessment 4.3.1. Direct Effects: Path Coefficients and Hypothesis Testing Table 9 presents the analysis of the path coefficients, and Figure 3 illustrates the results of the structural model. Beyond statistical significance, the relative importance of predictors is informative. The ranking of the standardised coefficients and effect sizes places strategic capabilities first ( β = 0.298, f 2 = 0.171, medium), interpersonal capabilities second ( β = 0.245, f 2 = 0.134, small-to-medium), and personal attributes third ( β = 0.129, f 2 = 0.062, small), with delivery-related capabilities not reaching significance. The four dimensions of leadership and the innovation climate path jointly explain a substantial share of variance in adoption intention (R 2 = 0.418), which in practical terms means that the abilities of the owner-manager and the climate factors that are developed through training account for a large part of why some firms intend to adopt AI and others do not. The gap between the strategic and delivery coefficients (0.298 versus 0.090) is itself practically consequential: it implies that an SME-support programme that raises strategic-cognitive capability is likely to move adoption intention several times more than one of equal intensity targeting operational execution. To probe the non-significant delivery-related effect, two robustness checks were performed. First, the model was re-estimated with delivery-related capabilities entered alone (without the competing strategic and interpersonal paths); the coefficient rose modestly but remained non-significant ( β = 0.121, p = 0.071), indicating the null result is not merely an indication of multicollinearity among capability dimensions (full-collinearity VIFs were all below 3.3). Second, splitting the sample by adoption stage showed that the delivery effect was uniformly weak between firms with lower and higher readiness. The most plausible interpretation is therefore substantive rather than methodological: at the pre-adoption intention stage, execution capacity is latent rather than activated, so its influence is muted until firms move into implementation. 4.3.2. Mediation Analysis The mediating role of the organisational innovation climate ( H 5 ) was tested using bootstrapped indirect effects and the Sobel test. Table 10 presents the results. The results indicate that the organisational innovation climate partially mediates the relationship between strategic capabilities and AI adoption intention (indirect β = 0.076, p = 0.003) and between interpersonal capabilities and AI adoption intention (indirect β = 0.048, p = 0.024). The total effect of strategic capabilities on the intention to adopt AI is thus 0.374 (direct 0.298 + indirect 0.076), reinforcing its position as the strongest predictor. Hypothesis H 5 is partially supported. 4.3.3. Moderation Analysis The moderating effect of firm size ( H 6 ) was tested by analysing the interaction term ( Table 11). The company size was coded as a categorical variable (1 = micro, 2 = small, 3 = medium) and introduced as a moderator for each digital leadership capability path. The size of the company significantly moderates the relationship between interpersonal capabilities and the intention to adopt AI ( β interaction = 0.109, p = 0.029), with the effect of interpersonal capabilities being stronger in medium-sized enterprises than in micro-sized enterprises. Hypothesis H 6 is partially supported. 5. Discussion This study developed and tested a comprehensive model that examined how four dimensions of digital leadership capabilities, strategic, delivery-related, interpersonal, and personal attributes, influence the intention to adopt AI among Nigerian SMEs, with the organisational innovation climate as a mediator and the size of the firm as a contextual moderator. The findings advance both theoretical understanding and practical knowledge regarding the leadership antecedents of AI adoption in SMEs in developing economies. 5.1. Direct Effects of Digital Leadership Capabilities The strongest predictor of AI adoption intention is strategic capabilities ( β = 0.298, p 0.36 0.505 Accepted SPR Sympson’s Paradox Ratio ≥0.70 0.857 Accepted RSCR R-Sq. Contribution Ratio ≥0.90 0.981 Accepted SSR Statistical Suppression Ratio ≥0.70 1.000 Accepted GoF thresholds: small > 0.10; medium > 0.25; large > 0.36 [ 62]. Table 4. Demographic characteristics of respondents ( n = 306). Table 4. Demographic characteristics of respondents ( n = 306). Variable Frequency Percentage (%) Gender Male 202 66.0 Female 104 34.0 Age Less than 18 years 12 3.9 18–28 years 160 52.3 29–39 years 85 27.8 40–50 years 42 13.7 51–60 years 5 1.6 61 years and above 2 0.7 Educational Qualification PhD 7 2.3 MSc 29 9.5 BSc/BA 180 58.8 HND 53 17.3 OND 37 12.1 Years of Business Operation 0–5 years 165 53.9 6–10 years 89 29.1 11–15 years 34 11.1 16–20 years 6 2.0 21+ years 12 3.9 Firm Size Micro (1–9 employees) 144 47.1 Small (10–49 employees) 118 38.6 Medium (50–199 employees) 44 14.3 Table 5. Internal consistency reliability results. Table 5. Internal consistency reliability results. Construct Indicator Loading α CR AI Adoption Intention (AIAI) AIAI1 0.815 0.76 0.85 AIAI2 0.749 AIAI6 0.757 AIAI7 0.719 Personal Attributes (PA) PA7 0.761 0.69 0.83 PA8 0.758 PA9 0.842 Strategic Capabilities (SC) SC2 0.819 0.76 0.86 SC3 0.841 SC5 0.800 Interpersonal Capabilities (IC) IC11 0.778 0.80 0.87 IC12 0.780 IC13 0.836 IC14 0.762 Delivery-Related Capabilities (DRC) DRC15 0.752 0.84 0.88 DRC16 0.725 DRC17 0.774 DRC18 0.771 DRC19 0.734 DRC20 0.720 Org. Innovation Climate (OIC) OIC1 0.804 0.78 0.86 OIC2 0.791 OIC3 0.768 OIC4 0.742 α = Cronbach’s alpha; CR = composite reliability. All loadings exceed 0.70. Table 6. Convergent validity results (average variance extracted). Table 6. Convergent validity results (average variance extracted). Construct AVE AI Adoption Intention 0.58 Personal Attributes 0.62 Strategic Capabilities 0.67 Interpersonal Capabilities 0.62 Delivery-Related Capabilities 0.56 Org. Innovation Climate 0.60 Table 7. Fornell–Larcker criterion results. Table 7. Fornell–Larcker criterion results. AIAI PA SC IC DRC OIC AIAI (0.761) PA 0.476 (0.788) SC 0.573 0.615 (0.820) IC 0.548 0.500 0.569 (0.789) DRC 0.542 0.625 0.661 0.753 (0.746) OIC 0.564 0.512 0.621 0.583 0.549 (0.775) Diagonal values in parentheses = AVE ; off-diagonal = inter-construct correlations. Table 8. Heterotrait–monotrait (HTMT) ratio results. Table 8. Heterotrait–monotrait (HTMT) ratio results. AIAI PA SC IC DRC OIC AIAI — PA 0.661 — SC 0.756 0.850 — IC 0.703 0.675 0.733 — DRC 0.677 0.822 0.828 0.875 — OIC 0.718 0.694 0.789 0.741 0.683 — All HTMT values fall below the 0.90 threshold. Table 9. Direct effects: path coefficients, t-statistics, effect sizes, and hypothesis testing. Table 9. Direct effects: path coefficients, t-statistics, effect sizes, and hypothesis testing. Path β t p f 2 Decision H 1 : SC → AIAI 0.298 5.459 <0.001 0.171 Supported H 2 : DRC → AIAI 0.090 1.589 0.057 0.049 Not Supported H 3 : IC → AIAI 0.245 4.453 <0.001 0.134 Supported H 4 : PA → AIAI 0.129 2.304 0.011 0.062 Supported SC → OIC 0.383 7.143 <0.001 0.238 Significant OIC → AIAI 0.198 3.612 <0.001 0.112 Significant f 2 effect sizes: 0.02 = small; 0.15 = medium; 0.35 = large [ 64]. R 2 (AIAI) = 0.418; R 2 (OIC) = 0.385; Q 2 (AIAI) = 0.412; Q 2 (OIC) = 0.379. Table 10. Mediation analysis results: indirect effects through organisational innovation climate. Table 10. Mediation analysis results: indirect effects through organisational innovation climate. Indirect Path Indirect β 95% CI p Mediation Type SC → OIC → AIAI 0.076 [0.031, 0.128] 0.003 Partial IC → OIC → AIAI 0.048 [0.009, 0.096] 0.024 Partial PA → OIC → AIAI 0.032 [−0.008, 0.074] 0.112 None DRC → OIC → AIAI 0.021 [−0.015, 0.059] 0.234 None CI = confidence interval (bias-corrected bootstrapping, 5000 resamples). Partial mediation: both direct and indirect effects are significant. Table 11. Moderation analysis: firm size as moderator. Table 11. Moderation analysis: firm size as moderator. Interaction Path β interaction p Decision SC × Firm Size → AIAI 0.067 0.124 Not Significant DRC × Firm Size → AIAI 0.038 0.268 Not Significant IC × Firm Size → AIAI 0.109 0.029 Significant PA × Firm Size → AIAI 0.054 0.178 Not Significant Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Idowu, A.; Babalola, Y.T. Leading in the Digital Age: Digital Leadership Capabilities, Organisational Innovation Climate, and AI Adoption Intention Among SMEs in Nigeria. Systems 2026, 14, 657. https://doi.org/10.3390/systems14060657 Idowu A, Babalola YT. Leading in the Digital Age: Digital Leadership Capabilities, Organisational Innovation Climate, and AI Adoption Intention Among SMEs in Nigeria. Systems. 2026; 14(6):657. https://doi.org/10.3390/systems14060657 Idowu, Ayodeji, and Yemisi Tomilola Babalola. 2026. "Leading in the Digital Age: Digital Leadership Capabilities, Organisational Innovation Climate, and AI Adoption Intention Among SMEs in Nigeria" Systems 14, no. 6: 657. https://doi.org/10.3390/systems14060657 Idowu, A., & Babalola, Y. T. (2026). Leading in the Digital Age: Digital Leadership Capabilities, Organisational Innovation Climate, and AI Adoption Intention Among SMEs in Nigeria. Systems, 14(6), 657. https://doi.org/10.3390/systems14060657
