A Practical Digital-Transformation Maturity and Delivery-Readiness Framework for U.S. SMEs and Small Government Vendors: Development, Pre-Pilot Psychometric Validation and Operational Benchmark Calibration

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@article{1722514,
      author = {Nkosana Mkandla, Marlon Munjoma, Melody Sydney, Flora Phiri, Munashe Naphtali Mupa},
      title = {A Practical Digital-Transformation Maturity and Delivery-Readiness Framework for U.S. SMEs and Small Government Vendors: Development, Pre-Pilot Psychometric Validation and Operational Benchmark Calibration},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {3097-3126},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722514.pdf},
      abstract = {Digital transformation in small and medium-sized enterprises (SMEs) is frequently measured by technology adoption rather than by the operating capability created after adoption. This study develops a practical Digital-Transformation Maturity and Delivery-Readiness Framework for U.S. SMEs and small government vendors. The framework integrates seven domains: process integration, ERP readiness, data quality, internal controls, cyber hygiene, user adoption and delivery capability. A 28-item instrument was constructed through design-science synthesis of digital transformation, information-systems success, ERP, data-governance, control, cybersecurity and maturity-model research. Pre-pilot validation used a fully disclosed synthetic panel of 720 hypothetical firms and an external operational benchmark derived from 70,000 DataCo supply-chain records covering 44,026 unique orders and 229 market-department-segment-period units. The synthetic panel was designed to test factor recovery, reliability, discriminant validity, scoring sensitivity and predictive behavior before human-subject deployment. Kaiser-Meyer-Olkin adequacy was 0.936; Bartlett's test was significant (chi-square=9398.6, df=378, p<0.001); and the intended seven-factor structure explained 67.4% of item variance. Construct alpha coefficients ranged from 0.817 to 0.852, composite reliability from 0.880 to 0.901, and average variance extracted from 0.647 to 0.694. The maximum heterotrait-monotrait ratio was 0.639. Five-fold cross-validated logistic regression predicted implementation readiness with AUC=0.799, while linear regression predicted delivery performance with R-squared=0.784. Index rankings remained highly stable across equal, outcome-derived and risk-balanced weights (Spearman rho at least 0.995). Maturity stages showed monotonic increases in readiness and reliable delivery. The contribution is an instrument, scoring architecture, risk heat-map method, benchmark dashboard and staged roadmap that can be tested prospectively. The paper explicitly limits inference: the findings establish pre-pilot measurement plausibility, not population prevalence or causal impact. A multi-site U.S. validation protocol is specified for the next phase.},
      keywords = {digital transformation; maturity model; ERP readiness; SME; government vendor; data quality; internal controls; cyber hygiene; user adoption; delivery readiness; psychometric validation},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722514}
  }