Organisational Response to Explainable Analytics in Smaller Enterprises

Abstract

Smaller enterprises may adopt explainable analytics without knowing whether their staff find the explanations useful or credible. This gap has consequences for interpreting performance outside carefully curated evaluation settings. This study aims to examine how explanation format changes perceived usefulness and accountability for consequential business decisions. The evaluation would follow a survey and vignette-based measurement study with role-specific scenarios. The choice of evaluation units and exclusion criteria is informed by the broader methodological record [3–7]. The analysis will contrast feature-attribution views, plain-language rationales and no-explanation baselines. The design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. Two storage-systems references are included only for the technology-governance background [1] and resilience context [2], not as evidence of human responses. Assessment will focus on construct validity, decision confidence, error detection and willingness to challenge an automated recommendation. The supporting evidence base motivates the chosen comparator conditions and robustness criteria [8–12]. Critical sensitivity checks address voluntary response bias, digital literacy differences and separation of trust from justified reliance. Further systems and modelling literature supplies context for the proposed sensitivity analyses [13–17]. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. The expected output is a reusable evaluation framework with transparent uncertainty and reporting rules, informed by wider reproducibility literature [18–23].