Digital Business Strategy and Artificial Intelligence as Determinants of Corporate Competitiveness in Indonesia

Anung Haryanto; Muhamad Ammar Muhtadi

doi:10.31004/riggs.v4i4.3823

Authors

Anung Haryanto Akademi Sekretaris dan Manajemen Kencana Bandung
Muhamad Ammar Muhtadi Universitas Nusa Putra

DOI:

https://doi.org/10.31004/riggs.v4i4.3823

Keywords:

Digital Business Strategy, Artificial Intelligence, Competitiveness, Digital Transformation, Indonesia

Abstract

This study investigates the influence of Digital Business Strategy (DBS) and Artificial Intelligence (AI) adoption on corporate competitiveness among companies in Indonesia. Using a quantitative, cross-sectional design, data were collected from 85 managerial respondents representing various industries through a structured questionnaire using a 5-point Likert scale. The data were processed with SPSS 25, including descriptive statistics, tests of validity and reliability, classical assumption diagnostics, and multiple linear regression. The empirical results show that both DBS and AI adoption have positive and statistically significant effects on corporate competitiveness, whether examined separately or jointly. The regression model indicates that the two predictors together explain 50.6% of the variance in competitiveness, suggesting that digital transformation constitutes an important driver of strategic performance. DBS emerges as the dominant predictor, underscoring the need to align digital initiatives with organizational goals, resources, and market positioning. AI adoption further strengthens competitiveness by supporting data-driven decision making, improving operational efficiency, and enhancing customer value creation. The findings highlight the growing importance of digital capabilities for firms seeking to sustain advantage in Indonesia’s dynamic, technology-intensive business environment and provide practical insights for managers and policymakers aiming to accelerate digital transformation in emerging markets. Future studies may extend this work by using larger samples, additional performance indicators, and comparative settings across countries or sectors.

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