Entrepreneurial Dynamic Capabilities and AI Readiness in Nigeria's Real Estate Sector: A Context-Sensitive PLS-SEM Analysis
Keywords:
Artificial Intelligence, Entrepreneurial Dynamic Capabilities, AI Readiness, Real Estate Marketing, Nigeria, PLS-SEM, Emerging MarketsAbstract
Artificial intelligence is reshaping marketing practice across global industries, yet its uptake among entrepreneurial firms operating in the Global South remains constrained by readiness frameworks built for institutionally mature markets. This study examines AI readiness among real estate ventures in Nigeria, treating readiness not as a single undifferentiated outcome but as a multidimensional condition spanning foundational, operational, and transformational tiers, each shaped differently by firms' technological, organisational, environmental, and entrepreneurial dynamic capability resources. Integrating the Technology Organisation Environment framework with Dynamic Capabilities Theory and reading both through an entrepreneurship lens attentive to context and resource constraints, the study analyses survey data from 250 registered real estate entities across Lagos and Abuja, Nigeria, using Partial Least Squares Structural Equation Modelling. Dynamic Capability is the strongest predictor of Transformational Readiness (β = .493, p < .001) and a significant predictor of Foundational Readiness (β = .357, p < .001) and Operational Readiness (β = .282, p = .001), while Technological Factors most strongly predict Foundational Readiness (β = .250, p < .001) and Organisational Factors most strongly predict Operational Readiness (β = .300, p = .002), exceeding the effect of Dynamic Capability on that particular outcome. Environmental Factors show no significant direct effect on any of the three readiness tiers; a post hoc power analysis indicates the sample is substantially underpowered to detect an effect of the size observed, so this null result is reported as inconclusive rather than as confirmation that environmental conditions are unimportant. Two hypothesised moderating effects of dynamic capability on the technological and organisational paths to readiness were not supported, though the relevant tests are similarly underpowered and are reported as preliminary. A composite Entrepreneurial AI Readiness Index, weighted according to the empirical magnitude of each dimension's structural effect rather than assumed in advance, places the sector at the upper end of Moderate Low Readiness (EAIRI = 0.598) and correlates strongly with all three measured readiness outcomes (r = .65 to .74). These results indicate that different capability and resource dimensions matter differently depending on which tier of AI readiness a firm is pursuing, with direct implications for how practitioners, technology providers, and policymakers sequence support for AI adoption in institutionally thin markets.
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