Efficiency, technology and productivity change in Australian universities: a two-decade analysis
Amir Moradi-Motlagh
What the paper says
Purpose This study aims to analyse productivity change and its determinants in Australian public universities from 2001 to 2022. Design/methodology/approach The study uses bootstrap data envelopment analysis (DEA) and the Malmquist Productivity Index to examine productivity trends. Findings The main findings reveal that technological improvement has been the primary driver of productivity growth in universities. The technological frontier generally drives productivity, while the relative distance of universities from this frontier has changed only marginally, except during the COVID-19 period. During the pandemic, technological advancements slowed, but individual universities improved their relative positions. However, this trend reversed in 2021–2022, marking the worst period for productivity in the last two decades. Practical implications This study highlights the advantages of using multi-inputs and multi-outputs models over traditional ratio measures in accounting literature. The findings are expected to be of interest to universities, higher education policymakers and researchers. Social implications The productivity changes in Australian public universities have significant social implications. Enhanced productivity, driven by technological advancements, can improve educational quality and accessibility, thereby making public higher education more sustainable. This can also lead to better educational outcomes and greater social equity aligned with Sustainable Development Goals. Originality/value This study provides a comprehensive analysis of productivity trends over two decades, contributing to the existing literature on higher education productivity in Australia.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.