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All Journal Ascendia: Journal of Economic and Business Advancement Scientia Causa: Journal of Contemporary Pedagogy and Learning Science
Rovena Prroni
Haxhi Sheh Shamia Madrasah
Economics, Econometrics & Finance

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Digital Transformation and Business Innovation: Examining the Role of Artificial Intelligence Capability in Enhancing Competitive Advantage and Sustainable Firm Performance Rovena Prroni; Sity Komalla; Alfa Rohmatin; Maya Ika Trisnawati
Journal of Economic and Business Advancement Vol. 2 No. 1 (2026): : September: Ascendia: Journal of Economic and Business Advancement
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/439b3812

Abstract

Research on artificial intelligence (AI) and digital transformation has examined business innovation, competitive advantage, and sustainable firm performance, but these outcomes are frequently addressed through separate analytical perspectives. This study synthesizes the relationships among AI capability, business innovation, competitive advantage, and sustainable firm performance, emphasizing organizational mechanisms and differences in the underlying evidence. A structured integrative literature review was conducted using a purposively selected corpus of 19 scholarly publications published between 2020 and 2026. Publications were classified according to technological capability, organizational mechanisms, innovation outcomes, competitive implications, and sustainability-related performance. Directed qualitative synthesis and comparative evidence matrices were employed to identify recurring relationships and distinguish reported findings from broader conceptual interpretations. The reviewed literature associates AI capability with data-driven decision-making, process reconfiguration, organizational adaptability, and business model innovation. Competitive implications emerge through operational efficiency, dynamic capabilities, strategic differentiation, and entrepreneurial opportunity recognition. Sustainability-related findings emphasize green innovation, knowledge integration, data-driven organizational culture, and sustainability-oriented business strategies. The synthesis identifies business innovation and complementary managerial capabilities as important explanatory mechanisms connecting AI resources with organizational outcomes. Its principal contribution is an integrated analytical framework distinguishing technological capabilities, organizational mechanisms, competitive outcomes, and sustainable performance without assuming a uniform causal sequence. Given the bounded literature corpus and heterogeneous research designs, the findings represent patterns of reported associations rather than pooled effects or generalizable causal estimates.    
Artificial Intelligence Supported Adaptive Learning: Examining Its Impact on Students' Critical Thinking, Self-Regulated Learning, and Academic Achievement in Higher Education Rovena Prroni; Mia Siti Nurazizah; Tedi Gandara; Sarbiland Durrani
Journal of Contemporary Pedagogy and Learning Science Vol. 1 No. 3 (2026): : August: Scientia Causa: Journal of Contemporary Pedagogy and Learning Science
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/6ahn6386

Abstract

Artificial intelligence (AI)-supported adaptive learning is increasingly used in higher education, yet its effects on critical thinking, self-regulated learning, and academic achievement remain unevenly understood. This study examined these outcomes using a quantitative comparative design based on secondary empirical data from a controlled university learning intervention involving 61 students, comprising 31 participants in an AI-guided treatment group and 30 in a control group. Critical thinking, self-regulated learning dimensions, and academic achievement were analyzed using descriptive statistics and covariance-based comparisons. The treatment group showed a larger critical-thinking gain than the control group (4.30 vs. 0.80 points), with a significant adjusted effect, F = 41.73, p < .001, partial η² = .418. Significant effects were also observed for cognitive engagement, behavioral engagement, self-efficacy, intrinsic motivation, and reduced amotivation. Academic achievement improved significantly at posttest, F = 6.10, p = .017, partial η² = .100, and delayed test, F = 8.20, p = .006, partial η² = .124. These findings indicate that structured AI guidance can strengthen cognitive, regulatory, and achievement outcomes when pedagogically integrated within carefully designed contemporary higher education learning environments  
Co-Authors Alfa Rohmatin Gandara, Tedi Maya Ika Trisnawati Mia Siti Nurazizah Sarbiland Durrani Sity Komalla