مدل DEA شبکه و مدل تولید با کیفیت: یک برنامه کاربردی برای خدمات تحقیقاتی دانشگاه استرالیا  A network DEA quantity and quality-orientated production model: An application to Australian university  research services

1. Introduction The motivation for this study is the Australian Commonwealth government’s Excellence in Research for Australia (ERA) program, recently developed to assess the quality of research in Australian universities. Implemented in 2008, the ERA initiative was a key element in the previous Labor government’s agenda for the reform of Australia’s higher education system as a means of providing assurances to government, industry, business, and others stakeholders of the quality of research conducted in Australian universities. Moreover, the ERA was aimed not only at improving the overall level of research quality in Australia, but also ‘…sits within a broader movement internationally, with the emphasis on excellence and quality being found in the performance-based funding schemes of a number of other countries’ [1]. There is then clearly a need for an appropriate measure of the productive efficiency of research. This is not least because of the need to account for the billions of dollars of research income (both public and private) the sector attracts, the significant contribution this research makes to the Australian economy, and for the purpose of enhancing the domestic and international reputations of both individual institutions and the sector as a whole. In doing so, it should create greater transparency and accountability of publicly funded institutions and appropriately recognize the efforts of those universities producing research and therefore deserving of funding. As Herbst [2] points out, ‘The rationale of performance funding is that funds should flow to institutions where performance is manifest: ‘performing’ institutions should receive more income than lesser performing institutions, which would provide performers with a competitive edge and would stimulate less performing institutions to perform. Output should be rewarded, not input’. To meet this need, we set two objectives for this analysis. First, the development of a suitable production model that best represents university research activity while considering the appropriate inputs that generate outputs. One of the most contentious issues in existing higher education efficiency studies is that the number of research publications produced and the amount of grants received are both considered outputs, yet may also comprise inputs [3–9]. Without exception, these studies employ standard data envelopment analysis (DEA) to measure technical efficiency based on the production assumption of some set of inputs used to generate some set of outputs. However, the rigidity of standard DEA treats the production process as a ‘black box’ in that it simply transforms inputs into outputs and neglects any possible intervening processes, including dissimilar series or parallel functions.