Determinants of strategic management accounting implementation in Higher Education Institutions (HEIs) in Indonesia

Abstract

Research aims: This study aims to examine determinants of strategic management accounting implementation, including market orientation, top management characteristics, strategy, and information technology.Design/Methodology/Approach: This research was conducted in higher education institutions (HEIs) in some areas, covering Sumatra, Java, Bali, Nusa Tenggara, Kalimantan, Sulawesi, and Papua. The research respondents were 368 HEIs leaders. Data were obtained by distributing questionnaires, and the hypotheses were tested using the partial least squares method.Research findings: The results revealed that market orientation, top management characteristics, HEIs strategy, and information technology positively affected strategic management accounting implementation.Theoretical contribution/ Originality: This research contributes to determining contingency variables in implementing strategic management accounting in HEIs.

Factors Influencing the Effectiveness of Information System Governance in Higher Education Institutions (HEIs) through a Partial Least Squares Structural Equation Modeling (PLS-SEM) Approach

“…This research adopts a cross-sectional study design to collect data from various stakeholders within multiple HEIs. The cross-sectional approach allows for the collection of data at a single point in time, providing a snapshot of the current state of ISG effectiveness across different institutions [13], [14], [15].…”

Ensemble learning with imbalanced data handling in the early detection of capital markets

“…A study (Bluwstein et al, 2021) using macro-financial data from 17 countries from 1870 to 2016 approached the machine learning model in the stock market crisis study, where it applied one of the ensemble models, Extremely Randomized Trees (Extra Trees) and produced the most accurate results. Tölö (2020), in predicting systemic financial crises one to five years ahead, which includes the crisis dates and annual macroeconomic series of 17 countries over the period 1870-2016, found that machine learning models can be significantly improved by using Long-Short Term Memory (RNN-LSTM) and Gated Recurrent Unit (RNN-GRU) neural nets (Marlina et al, 2023). In another study, Coffinet and Kien (2019) focused on detecting rare events in banking crisis cases, using data collected from 32 European and non-European countries from 2010 to 2017 (Pratama & Wijaya, 2023).…”