AI IN PREDICTIVE BUSINESS ANALYTICS: EVIDENCE AND IMPLICATIONS FOR PRIVATE HIGHER EDUCATION IN THAILAND

Chanidapha Nunualvuttiwong; Nurhaeni Sikki; Yuyun Yuniarsih; Adi Permana Sidik; Reza Saeful Rachman

doi:10.61397/mfc.v4i1.573

Authors

Chanidapha Nunualvuttiwong Krirk University
Nurhaeni Sikki Universitas Sangga Buana YPKP
Yuyun Yuniarsih Universitas Sangga Buana YPKP
Adi Permana Sidik Universitas Sangga Buana YPKP
Reza Saeful Rachman Universitas Sangga Buana YPKP

DOI:

https://doi.org/10.61397/mfc.v4i1.573

Keywords:

data-driven decision making, higher education, learning analytics, key performance indicators, Balanced Scorecard, artificial intelligence, Thailand

Abstract

Digital transformation has driven higher education institutions to adopt data-driven approaches to support strategic decision-making, performance measurement, and improvements in the quality of academic services. This study aims to synthesize developments in data-driven business analysis in higher education, focusing on the use of Key Performance Indicators (KPIs), the Balanced Scorecard (BSC), learning analytics, artificial intelligence (AI), internationalization, and data governance, particularly at private universities in Thailand and the ASEAN region. The study employed a narrative scoping review of relevant academic literature, drawing on sources from various databases and scholarly publications in the fields of management, education, and analytics. The synthesis results reveal five main themes: aligning KPIs with institutional strategies; utilizing learning analytics to enhance student success; the dynamics of internationalization and competition in the higher education market; the need to strengthen data governance and ethics; and the use of AI to support business analysis and decision-making. Integrating the BSC with learning analytics enables universities to link strategic objectives with operational indicators more systematically. However, the effectiveness of this approach depends on data quality, analytical capacity, data-driven leadership, digital infrastructure, and responsible AI governance. This study underscores the importance of an integrated, data-driven decision-making ecosystem to enhance the effectiveness, competitiveness, accountability, and sustainability of higher education institutions.

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