Adoption intentions of cloud-based business intelligence technologies among SMEs in Palestine: A PLS-SEM, ANN, and moderation analysis

In Palestine, where SMEs constitute the backbone of the economy but face several barriers, including financial, infrastructure, and institutional ones, the present study examines the drivers of the intention to adopt cloud-based business intelligence technologies. The analysis was conducted using PLS-SEM and ANN, based on data collected from 397 SME leaders across 11 governorates, following the TOE paradigm, with Environmental Turbulence (ET) as a moderator variable. The hybrid approach combines PLS-SEM and ANN to provide complementary explanatory and predictive perspectives on cloud-based BI adoption intention. While PLS-SEM identifies statistically significant relationships among the study constructs, ANN complements these findings by assessing the relative predictive importance of the significant determinants. The structural model indicated that all eight antecedents significantly predicted the intention to adopt, with standardized effects ranging from | β | = 0.244 to 0.302, all 95% confidence intervals excluding zero, and effect sizes ranging from f 2 = 0.22 to 0.32. Perceived risk was negatively associated with intention to adopt, whereas the remaining antecedents had positive effects. In both the PLS-SEM and ANN models, competitive pressure is the primary determinant, whereas employee participation and regulatory compliance are among the strongest predictors. On the other hand, compatibility, perceived risk, perceived ease of use, and management commitment are of intermediate importance. Digital maturity plays a strong role but is mostly supportive. ANN analysis supports cross-method convergence by confirming the salience of competitive pressure, employee involvement, and regulatory compliance while also showing minor rank shifts among mid-tier predictors. ET functions as a selective, limited boundary condition rather than a broad moderator. Only two small but statistically significant interactions are found: ET strengthens the effect of management commitment on adoption intention, while it weakens the effect of competitive pressure. The integrated PLS-SEM–ANN approach confirms the robustness of the principal predictors while revealing modest differences in their relative predictive importance across the two analytical methods. Predictive validity is supported by Q 2 predict values greater than zero and by lower PLS-Predict RMSE (root mean square error) values than the linear model benchmark for all intention indicators. The results refine the TOE framework within turbulent socio-economic settings, in that the role of external pressures and organizational preparedness is far greater in shaping intentions than ET, which plays only a limited moderating role. The findings help policymakers and SME owners prioritize risk mitigation while strengthening competitive pressure, regulatory compliance, managerial commitment, and employee involvement to support cloud-BI adoption.