This paper presents the results of a systematic review of agent-based modelling and simulation (ABMS) applications in the higher education (HE) domain. Agent-based modelling is a 'bottom-up' modelling paradigm in which system-level behaviour (macro) is modelled through the behaviour of individual local-level agent interactions (micro). This approach of considering the behaviour of systems of interacting 'agents' has been applied to a wide variety of domains. Of particular interest, are the ways that ABMS applications have been used to further understand the dynamics of the HE domain. We conduct a systematic review of literature to analyse publications by year, role of the simulator, development stage of the models, and any associated validation. We also identify areas for future work, which includes an emphasis on validating existing and future models, detailed description of simulations to allow replication and further development, and the use of agent-based models in other contexts within the increasingly complex HE domain

Purpose: Collective reputation cognition is an enterprise's perception of the general rules of reputation evaluation, jointly formed by a network's collective members. It affects the choice of enterprises' innovation behavior and guides enterprises to occupy a dominant position in the innovation network, thus achieving high innovation performance. In this process, it is inseparable from the enterprise's good network competence. This study attempts to bring collective reputation cognition, network competence and innovation performance into the same framework and aims to explore the relationship among them and determine the influential roles of collective reputation perception and network capability on innovation performance.Design/methodology/approach: This study uses 227 Chinese enterprises in the innovation network as samples and applies partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to explore the questions mentioned above.Findings: The results verify the relationship among collective reputation cognition, network competence and innovation performance. Furthermore, the results yield five paths that lead to high innovation performance, such as “putting ability first” and “both fame and competence”, which are different combinations of collective reputation cognition and network competence.Originality/value: Based on institutional theory, this study considers the network context and identifies “collective reputation cognition” as a key variable. Meanwhile, it opens the “black box” of the mechanism of reputation's influence on innovation performance and finds that the combined paths of collective reputation cognition and network competence achieve high performance in terms of innovation.