Ghana AI Innovation Readiness Dataset
Description
Title: Ghana AI Innovation Readiness Dataset: A Harmonised Secondary Dataset for SME Digital Transformation Research Description: This dataset is a confirmatory analytical dataset of 412 small and medium enterprise (SME) observations from Ghana, constructed to support the empirical investigation of AI-driven innovation readiness and its effects on knowledge creation and firm performance. The dataset was developed through a systematic secondary data integration and harmonisation strategy drawing on four authoritative institutional sources: the World Bank Enterprise Survey 2023, the Ghana Statistical Service SME Competitiveness Report 2024, the UNDP Ghana Digital Readiness Assessment 2025, and the Bank of Ghana FinTech Sector Report Q1 2025. The dataset contains 56 variables organised into three categories. The first category comprises firm-level demographic and classification variables, including unique firm identifier (ID), sector (Trade and Commerce; Manufacturing; Financial and Fintech Services; Agriculture and Agribusiness; ICT and Digital Services), firm size (Micro: 5-9 employees; Small: 10-49 employees; Medium: 50-249 employees), regional location (Greater Accra; Ashanti; Other Regions), digital adoption level (Low, Moderate, High), years in operation (range: 1-35 years; mean: 18.3 years), and employee count (range: 5-249; mean: 59.9 employees). The second category contains 39 reflective indicator variables operationalising ten theoretical constructs drawn from the integrated TAM-TOE-KBV framework. These constructs are: Digital Infrastructure Quality (DIQ1-DIQ4; 4 items), Perceived Usefulness of AI (PU1-PU4; 4 items), Top Management Commitment (TMC1-TMC5; 5 items), Absorptive Capacity (AC1-AC4; 4 items), Financial Resource Availability (FRA1-FRA3; 3 items), Regulatory Environment (RE1-RE3; 3 items), Competitive Pressure (CP1-CP3; 3 items), AI Innovation Readiness (AIIR1-AIIR5; 5 items), Knowledge Creation (KC1-KC4; 4 items), and Firm Performance (FP1-FP4; 4 items). All indicator variables are measured on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). The third category contains ten computed composite score variables (DIQ_score, PU_score, TMC_score, AC_score, FRA_score, RE_score, CP_score, AIIR_score, KC_score, FP_score), representing the mean score of all indicators within each respective construct, pre-computed to facilitate structural equation modelling analysis using SmartPLS 4.0.
Files
Institutions
- University of Cape CoastCentral, Cape Coast