Orientasi Pemasaran dan Kesiapan Digital: Mengungkap Pengaruh Adopsi Kecerdasan Buatan terhadap Kinerja Bisnis UMKM

Hanifa Sri Nuryani

doi:10.55681/sentri.v5i9.7225

Authors

Hanifa Sri Nuryani Universitas Teknologi Sumbawa

DOI:

https://doi.org/10.55681/sentri.v5i9.7225

Keywords:

Marketing Orientation, Digital Readiness, Artificial Intelligence Adoption, Business Performance, MSMEs, PLS-SEM

Abstract

This study examines the structural associations of Marketing Orientation and Digital Readiness with the Business Performance of MSMEs, with Artificial Intelligence (AI) Adoption as an intervening variable, in Sumbawa Regency, West Nusa Tenggara. An explanatory quantitative, cross-sectional survey involved 100 owners or principal decision makers purposively selected from a frame of 387 MSMEs with basic digital activity. The AI Adoption construct captured operational uses, including generative-AI assistance for promotional copy and content, automated instant-message responses, simple customer and trend analysis, and support for demand or inventory planning. Five-point Likert data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4. AVE ranged from 0.773 to 0.779 and Composite Reliability from 0.968 to 0.974, although the latter values warrant scrutiny for possible indicator redundancy. The model explained 71.2% of the variance in AI Adoption and 83.1% in Business Performance within the sample. Bootstrapping with 5,000 subsamples supported seven hypothesized structural associations. Marketing Orientation was positively associated with Business Performance (β = 0.287; p = 0.004) and AI Adoption (β = 0.438; p < 0.001). Digital Readiness was also positively associated with Business Performance (β = 0.263; p = 0.008) and AI Adoption (β = 0.392; p < 0.001), while AI Adoption was positively associated with Business Performance (β = 0.298; p = 0.005) and statistically mediated both relationships in part. Because all observations were collected at one point in time through a single subjective instrument, these results indicate within-sample structural associations rather than temporal causality or verified out-of-sample prediction. The findings suggest that AI use should be accompanied by stronger market-sensing, digital literacy, and data-management capabilities.

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