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How geographical neighboring competitors influence the strategic price behaviors of universities is still unclear because previous studies assume spatial independence between universities. Using data from the National Center for Education Statistics college navigator dataset, this study shows that the price of one university is spatially autocorrel...
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... Rights reserved. from different perspectives, focusing on how rankings trigger global competition , global competitiveness and internationalisation (Gu, 2015), and the emergence of competition in higher education (Kettunen et al., 2022). This theme focuses on how higher education institutions shape their global positions and strategies. ...
This research aimed to reveal the growth trend of competition in higher education literature, the contribution to the topic, the collaborative structure of the topic, its historical development, the intellectual structure of the knowledge base, and the research fronts. The data comprise 398 articles from the Web of Science database covering the period 1960–2023. The methodology of this research is based on bibliometric and content analysis, which integrates quantitative and qualitative methods, allowing a deeper examination of the topic. According to the results of this study, the literature on competition in higher education has shown significant growth over the past two decades. The literature has been shaped with the contribution of many disciplines. The USA has the highest productivity and collaboration, and Italy has the highest publication impact. The results of the historical citation analysis revealed seven streams that played a role in the development of the topic. The intellectual structure of the knowledge base on which the topic is based is categorised into seven clusters. The research fronts of the topic consist of eight themes: (1) sustainable competitive advantage, (2) global competition, (3) competition and management policies, (4) global mobility, knowledge economy, and innovation, (5) struggle with a competitive environment, (6) transformation, diversity, and drivers of competition, (7) excellence and competition in higher education, (8) entrepreneurship and competition in higher education. In addition, this study revealed various effects of competition in higher education. This research suggests directions for future research on the topic and provides a broad view of competition research in higher education.
... In addition, market characteristics 4 Among the differences between presential and distance higher education courses, we can mention: the geographic scope; expansion cost, which is generally higher for face-to-face courses; monthly fee (higher in face-to-face courses); student income, as students enrolled in on-site courses tend to have higher incomes than others; age of students (according to the 2016 Higher Education Census, the average age of students in on-site courses was 24 years, and in distance courses was 34 years); maintenance costs (in general, on-site courses incur higher expenses with installation, travel and faculty). tend to be spatially correlated (Gu, 2015). This justifies the choice of the relevant face-to-face higher education market as a local. ...
... HEIs compete to attract more students, and price competition between them becomes quite complex (Gu, 2015;Leslie and Brinkman, 1987;Weiler, 1984). And it is also to be expected that there will be some difference in the elasticity of demand between students who pay the full monthly fees, and those who receive discounts, because as the monthly fee increases, the elasticity coefficient should also increase. ...
... Although measuring teaching quality can be quite controversial, a proxy widely used in the literature to signal quality is teaching and research performance (Costa et al., 2015;Peracchi, 2006;Sanders, 2002). In fact, Musselin (2018) and Gu (2015) point out that the activity of HEIs is often measured and transformed into numbers, classifications and grades that can be used as reputation and quality variables. Therefore, quality measures through the General Course Index (IGC) 15 and the CPC made available by INEP, which evaluates higher education institutions, will be included as variables of interest. ...
... This suggests that universities are not independent of each other in terms of the commercialization of academic patents. This is consistent with strategic interaction theory between universities (Gu, 2012(Gu, , 2013. A negative ρ shows that less innovation distance results in more intense competition for the commercialization of academic patents. ...
The low commercialization rate of academic patents is a common challenge for developing countries. This study evaluates the impact of provincial policies on the commercialization of academic patents using spatial regression models and propensity score matching methods. It also provides a reference for optimizing university patent management. Considering provincial patent policies enacted in China in 2016 as the treatment, the results show no significant effect of provincial patent policies on rights definition and implementation economics on the commercialization of academic patents. However, promotion standard policies have a significant positive effect on the commercialization of academic patents. A significant negative spatial spillover effect is observed on the commercialization of academic patents among universities with similar innovation capabilities. Furthermore, non-geographic distances, such as innovation distance, positively affect competition among universities and their commercialization of academic patents. Therefore, local governments should consider the needs of academic inventors for title promotion when designing and introducing patent incentive policies for universities.
... As a result, the spatial knowledge spillover mechanism from universities to neighboring universities has been ignored. In fact, neighboring universities are not independent of each other, and there are strategic interactions [30,31]. Therefore, it is natural to consider this so-called strategic interaction to reveal the specific mechanism of academic patent commercialization. ...
... The commercialization of university patents not only has spillover effects on neighboring companies but also on neighboring universities. In other words, there are the so-called strategic interactions between neighboring universities, and universities are dependent of each other [30,31]. A university promoted its patents commercialization by expanding the scale of lower-ranked academic researchers, increasing R&D achievement transformation service staff, and increasing investment in basic research. ...
... It can be seen that in the process of promoting university patents commercialization, neighboring universities are not independent of each other, but are interconnected. In other words, neighboring universities have strategic interactions in promoting university patents commercialization [30,31]. This also shows that the solid path and the dashed path shown in Fig. 1 are both true. ...
The commercialization of academic patents is a basic means for universities to promote economic growth and upgrade the industrial innovation of enterprises. However, among developing countries, the commercialization rate of university patents is generally low. This study utilizes data from 65 universities which are directly under the Ministry of Education of China to analyze the influencing factors and mechanisms of academic patent commercialization. The findings show that the proportion of associate professors, the size of service staff transforming research and development achievement, and the proportion of basic research funding in universities are positively correlated with the commercialization rate of university patents. In addition, these factors indirectly affect the commercialization of university patents by affecting neighboring universities; that is, there are spatial spillover effects in the commercialization of university patents between neighboring universities. These empirical results indicate that universities can promote the commercialization of university patents by optimizing the structure of faculty, developing the R&D achievement transformation service staff team, and strengthening investment in basic research.
... As a result, the spatial knowledge spillover mechanism from universities to neighboring universities has been ignored. In fact, neighboring universities are not independent of each other, and there are strategic interactions [30,31]. Therefore, it is natural to consider this so-called strategic interaction to reveal the specific mechanism of academic patent commercialization. ...
... The commercialization of university patents not only has spillover effects on neighboring companies but also on neighboring universities. In other words, there are the so-called strategic interactions between neighboring universities, and universities are dependent of each other [30,31]. A university promoted its patents commercialization by expanding the scale of lower-ranked academic researchers, increasing R&D achievement transformation service staff, and increasing investment in basic research. ...
... It can be seen that in the process of promoting university patents commercialization, neighboring universities are not independent of each other, but are interconnected. In other words, neighboring universities have strategic interactions in promoting university patents commercialization [30,31]. This also shows that the solid path and the dashed path shown in Fig. 1 are both true. ...
The commercialization of academic patents is a basic means for universities to promote economic growth and upgrade the industrial innovation of enterprises. However, among developing countries, the commercialization rate of university patents is generally low. This study utilizes data from 65 universities which are directly under the Ministry of Education of China to analyze the influencing factors and mechanisms of academic patent commercialization. The findings show that the proportion of associate professors, the size of service staff transforming research and development achievement, and the proportion of basic research funding in universities are positively correlated with the commercialization rate of university patents. In addition, these factors indirectly affect the commercialization of university patents by affecting neighboring universities; that is, there are spatial spillover effects in the commercialization of university patents between neighboring universities. These empirical results indicate that universities can promote the commercialization of university patents by optimizing the structure of faculty, developing the R&D achievement transformation service staff team, and strengthening investment in basic research.
... Competition among players may not result in the best outcome for all players, yet if all players pursue a strategy of cooperation, then it is possible that the outcome could be mutually beneficial. Gu (2015) treated institutions of higher education (IHE) as players in a game whose objective was to maximize tuition. When the IHEs colluded on price, an optimum price result (for the IHEs) was achieved. ...
Using a cross sectional survey design, learner perceptions of their peer assessmentexperiences at institutions of higher education (IHEs) are studied. Guided by gametheory, this study examines if either the IHE’s prestige, the competitiveness, or itsextent of grade inflation has a statistical effect on these peer assessment perceptions.A Likert scale was used to measure learner perceptions of their peer assessmentexperiences and the constructs. An exploratory factor analysis was performed on thethree constructs to confirm their validity. The study found a statistically significantcorrelation between institutional prestige and peer assessment perceptions
... Therefore, in recent years, scholars have paid growing attention to the effects of crises, including on higher education (HE); such research has, for example, shed light on some of the crucial effects of a crisis on tuition fees dynamic. Following the global financial crisis, studies from the USA (Gu, 2015;Serna, 2017), the UK (Wakeling and Jefferies, 2013;Wilkins et al., 2013) and several continental European systems (e.g. Moulin et al., 2016 for France;Teixeira et al., 2014 for Portugal;Pigini and Staffolani, 2016 for Italy), have focused, in particular, on the effect of the financial crisis through cuts in public funding. ...
... The capability to attract students is strongly influenced by the presence and activities of neighbouring universities (Gu, 2012). Past studies have highlighted the role of spatial dynamics among competitors-especially neighbouring ones-in affecting university fees (Gu, 2015;McMillen et al., 2007). McMillen et al. (2007) showed that private US universities increased their tuition fees when other private universities within a 400 mile radius did the same. ...
... McMillen et al. (2007) showed that private US universities increased their tuition fees when other private universities within a 400 mile radius did the same. Gu (2015) further enriched this research by demonstrating the actual and robust importance of spatial dimension in universities' price model, by relying on the top 100 ranked US universities. He argued that the relationship between prices and the geographical distance of universities is an inverse U-shape, instead of an inverse linear relation. ...
Modern societies regularly face crises that have major disruptive effects. Learning from past crises can inform better choices and policies when facing a new one. Following the 2008 global financial crisis, higher education scholars explored its effects on students’ tuition fees through cuts in public funding. This article instead investigates how universities’ decisions on tuition fees have been affected by other factors, beyond the decrease in public funds. As such, it explores the role of competition and reputation in affecting universities’ decisions on tuition fees when facing a crisis. Using data from 59 public Italian universities in the period between 2003 and 2014, we found that universities increased tuition fee by an average of 27% per student in response to the crisis. At the same time, high competition mitigated the increase of tuition fees, except for the case of highly reputed universities, which charged even higher tuition. These findings highlight the importance of monitoring fees in times of crises, as well as the complex role of competition and reputation in containing or inflating university tuition fees.
... Under school choice regimes, K-12 schools are supposed to compete with one another to attract students (Zimmer and Buddin 2009). Hospitals, nursing homes, and institutions of higher education also compete for clients in many local markets (e.g., Gu 2015;Johansen and Zhu 2013). If residents choose between organizations based on relative expected benefit or the best relative "fit," an organization's ability to attract clients depends not only on what they offer but also on the offerings of other competing organizations in their local market. ...
Interdependence in the decision-making or behaviors of various organizations and administrators is often neglected in the study of public administration. Failing to account for such interdependence risks an incomplete understanding of the choices made by these actors and agencies. As such, we show how researchers analyzing cross-sectional or time-series-cross-sectional (TSCS) data can utilize spatial econometric methods to improve inference on existing questions and, more interestingly, engage a new set of theoretical questions. Specifically, we articulate several general mechanisms for spatial dependence that are likely to appear in research on public administration (isomorphism, competition, benchmarking, and common exposure). We then demonstrate how these mechanisms can be tested using spatial econometric models in two applications: first, a cross-sectional study of district-level bilingual education spending and, second, a TSCS analysis on state-level healthcare administration. In our presentation, we also briefly discuss many of the practical challenges confronted in estimating spatial models (e.g., weights specification, model selection, effects calculation) and offer some guidance on each.
... Recently we have seen the advent of neighbor-induced diffusion theory of policy diffusion as a way of looking afresh at the patterns and causes of policy diffusion at neighborhood level (Gu 2012b(Gu , 2013Rincke 2007;Brinks and Coppedge 2006). Traditionally, those problems have been a concern of regional-induced diffusion study at regional level as practised by researchers with traditional methods including correlation analysis, comparative analysis and classic regression models that assume that the individual observations on regions are independent of one another (Mooney 2001). ...
... We are keeping the quintiles fixed, meaning the data are pooled over space and time and the quintiles calculated for the pooled data. We estimate conditioned transition probability 3 The relationship between convergence speed and spatial neighborhood structure has been verified by previous studies (Gu 2012a(Gu , 2013. 4 It is emphasized that the regional effect is positive by some policy scholars such as Berry and Berry (1990), Mooney and Lee (1995) and Ghosh(2010). However, the negative effect is also detected (De Groot 2010; Easterly and Levine 1998;Ades and Chua 1997). ...
... As a result, the more the proportion of rural residents are, the more 7 The question of what is the optimal neighborhood size is largely dependent on the relative strength of neighbors and is particularly important when one seeks to examine the dynamic equilibrium of a spatial game. As Gu (2013)'s research shows that the choice of the optimal neighborhood size typically is influenced not solely by the quality of the various competing strategies, but by the effect of the frequency with which those various competing strategies are found in the population. As a result, the optimal neighborhood size is not invariable when the competition pattern changes. ...
There has been a vast amount of discussion about the positive and negative regional effect on policy diffusion. During this debate, the role of neighborhood structure is ignored and the linear assumption is still prevailing in this field. By analyzing the spatial convergence of local vocational education development with data of 31 provinces from 1995 to 2008 in China, we explore the effects of neighborhood interactions on policy diffusion, paying specific attention to the dynamical role of neighborhood structures in policy diffusion. The empirical results clearly indicate that the development of local vocational education systems in China is spatially autocorrelated to the neighboring provinces. Local vocational education systems converge more slowly if a spatially lagged dependent variable is introduced, while they converge faster if a spatially error variable is introduced. The policy transition between neighbors considering their local spatial context is analyzed with Spatial Markov Chain and a fundamental nonlinear connection between neighborhood structure and policy transition is unveiled. Using spatial econometric models, we also find that the socio-spatial diffusion patterns with the social factors such as consumption, urban/rural distribution and occupation serve as barriers to and amplifiers of policy diffusion. These results not only resonate with conventional linear wisdom on policy diffusion but also offer a new nonlinear perspective on socio-spatial patterns of policy diffusion that are clearly embedded within the local neighborhood structures.
... [45,46,47]). Modelling competition in higher education is related to price formation [48]. ...
This paper explores the correlation between the degree of competition between higher education institutions
(HEIs) and the efficiency of regional higher education systems using evidence from the Russian
Federation. The choice of the regional system of higher education as a unit of analysis is explained by the
features of the Russian system of higher education, especially by “closeness” in the borders of regions. We
propose a special approach for the evaluation of the regional higher education system efficiency from the
public administration perspective. Using data envelopment analysis (DEA), we investigate the efficiency of
higher education systems in the regions and compare the results with the extent of higher education competition
within them. The results indicate that higher efficiency scores and higher competition between HEIs
in Russian regions are positively correlated. Moreover, by introducing socio-economic context status as a
grouping parameter, we are able to specify the conditions of this relationship. The study explores that correlation
between efficiency and competition is stronger in developing and low-performing regions. At the same
time, higher education systems in developed regions consist of different HEIs, which create a competitive environment,
although their efficiency level varies considerably. Taking into account all limitations of the study,
these results contain several important issues for policy-making and higher education research discussions.
They challenge the universalistic assumptions for the direction of higher education development.
