Differentiation strategy and rankings in higher education: Role of rankings in building a strategy

differentiation-strategy

Differentiation Strategy and Rankings in Higher Education: Role of Rankings in Building a Strategy, by Magdalena Iordache-Platis. In: Dima A. (eds) Doing Business in Europe (2018). Contributions to Management Science. Springer, Cham

Abstract (emphasis mine)

The contemporary higher education environment is dominated by uncertainty. Institutions do not disappear overnight in this industry, but study programmes decline even dramatically. Presently, ranking methodologies and indicators contribute to different and dynamic positioning of institutions at national or international level, based on a particular approach or a field-based one. Building a proper development strategy is a complex task for academic leadership. The chapter reveals the need of integrating the information provided by rankings into the decisions and actions in higher education institutions to achieve sustainable development. The main objectives of the chapter are to understand the dynamism of the contemporary competitive environment in higher education sector, to clarify the differentiation strategy as a solution for being stable on the educational market, to identify the role of rankings in defining an effective strategy. The topic is relevant for the students, contributing to their knowledge of differentiation strategy in general, but also on its applications in higher education, in particular; they will not only become more aware of the large possibilities of differentiation strategy implementation, but also better decision-makers about educational providers.

university-rankings
International rankings conceptual clarifications

Interesting excerpts:

Therefore, considering all the aforementioned connections between ranking dimensions and institutional missions, the steps to follow to generate the change towards the differentiation should be:

  • determine the higher education option for the ranking dimension
  • assess the current state of the ranking dimension
  • define possible institutional changes
  • predict the competitor’s changes related to the chosen dimension
  • implement the change.

A differentiation strategy is a way of competing in which institutions look for unfitness, through selecting one or several ranking dimensions. Higher education institutions become able to better perform on the market, but only in the case of student awareness or other stakeholder awareness, according to the specific objectives. If the students do not know or do not trust rankings, having a differentiation strategy and investing in it is similar to the case of no differentiation at all. In other words, a differentiation strategy is worth building and developing only if the students, as beneficiaries of it are aware and understand it properly. In this context, communication to the public is most important. Media and institutional press office contribute to the strategy building. If the communication is direct, continuous and clear, the strategy is effective. In case of a lack of communication, the differentiation does not reach the potential public and its impact becomes minor.

ranking-based-differentiation-strategy
Model of ranking-based differentiation strategy for higher education institutions (Source:
Author)

Measuring the academic reputation through citation networks via PageRank

Open access preprint Measuring the academic reputation through citation networks via PageRank, Massucci and Docampo, arXiv (2018).

Abstract:

The objective assessment of the prestige of an academic institution is a difficult and hotly debated task. In the last few years, different types of University Rankings have been proposed to quantify the excellence of different research institutions in the world. Albeit met with criticism in some cases, the relevance of university rankings is being increasingly acknowledged: indeed, rankings are having a major impact on the design of research policies, both at the institutional and governmental level.

Yet, the debate on what rankings are exactly measuring is enduring. Here, we address the issue by measuring a quantitive and reliable proxy of the academic reputation of a given institution and by evaluating its correlation with different university rankings. Specifically, we study citation patterns among universities in five different Web of Science Subject Categories and use the PageRank algorithm on the five resulting citation networks. The rationale behind our work is that scientific citations are driven by the reputation of the reference so that the PageRank algorithm is expected to yield a rank which reflects the reputation of an academic institution in a specific field.

Our results allow to quantifying the prestige of a set of institutions in a certain research field based only on hard bibliometric data. Given the volume of the data analysed, our findings are statistically robust and less prone to bias, at odds with ad–hoc surveys often employed by ranking bodies in order to attain similar results. Because our findings are found to correlate extremely well with the ARWU Subject rankings, the approach we propose in our paper may open the door to new, Academic Ranking methodologies that go beyond current methods by reconciling the qualitative evaluation of Academic Prestige with its quantitative measurements via publication impact.

cross-citations-engineering
The institutional network of cross-citations in the Telecommunication Engineering WoS category. Each node of the network is an academic institution featured both in the Telecommunications ARWU GRAS and as an affiliation in at least one publication of the Telecommunication Engineering WoS category. Edges are citations from a publication produced by an institution to those authored by another one (10% of the total edges are plotted). The node size is proportional to the number of publications.

Are university rankings useful to improve research? A systematic review

Open access Are university rankings useful to improve research? A systematic review, by Vernon, Balas, and Momani, PLOS One (2018).

Abstract (emphasis mine):

Introduction
Concerns about reproducibility and impact of research urge improvement initiatives. Current university ranking systems evaluate and compare universities on measures of academic and research performance. Although often useful for marketing purposes, the value of ranking systems when examining quality and outcomes is unclear. The purpose of this study was to evaluate usefulness of ranking systems and identify opportunities to support research quality and performance improvement.

Methods
A systematic review of university ranking systems was conducted to investigate research performance and academic quality measures. Eligibility requirements included: inclusion of at least 100 doctoral granting institutions, be currently produced on an ongoing basis and include both global and US universities, publish rank calculation methodology in English and independently calculate ranks. Ranking systems must also include some measures of research outcomes. Indicators were abstracted and contrasted with basic quality improvement requirements. Exploration of aggregation methods, validity of research and academic quality indicators, and suitability for quality improvement within ranking systems were also conducted.

Results
A total of 24 ranking systems were identified and 13 eligible ranking systems were evaluated. Six of the 13 rankings are 100% focused on research performance. For those reporting weighting, 76% of the total ranks are attributed to research indicators, with 24% attributed to academic or teaching quality. Seven systems rely on reputation surveys and/or faculty and alumni awards. Rankings influence academic choice yet research performance measures are the most weighted indicators. There are no generally accepted academic quality indicators in ranking systems.

Discussion
No single ranking system provides a comprehensive evaluation of research and academic quality. Utilizing a combined approach of the Leiden, Thomson Reuters Most Innovative Universities, and the SCImago ranking systems may provide institutions with a more effective feedback for research improvement. Rankings which extensively rely on subjective reputation and “luxury” indicators, such as award winning faculty or alumni who are high ranking executives, are not well suited for academic or research performance improvement initiatives. Future efforts should better explore measurement of the university research performance through comprehensive and standardized indicators. This paper could serve as a general literature citation when one or more of university ranking systems are used in efforts to improve academic prominence and research performance.

university-ranking-comparison
Conflicting global rankings of an illustrative research university (per most recent published results, 2016).

THE to launch new innovation ranking

From THE’s website (excerpts):

Times Higher Education is developing plans for a pioneering ranking focused on universities’ impact on society, to be launched at THE’s Innovation and Impact Summit in South Korea in April 2019.

Confirming that the 2019 Innovation and Impact Summit will be held in partnership with one of the world’s leading research and technology universities, Korea Advanced Institute of Science and Technology (KAIST), Phil Baty, THE’s editorial director for global rankings, also announced that THE has begun to collect new data on university-business interactions, with plans to consult on a range of new performance metrics for a ranking to be launched and debated at the summit.

Times Higher Education is delighted to be building on its 15 years of experience in global rankings and data analysis to develop, in full partnership with the global university community, new performance indicators in this exciting emerging area. After exploring some of our ideas at our inaugural Innovation and Impact Summit in 2017 with the Hong Kong Polytechnic University, there is no better place to scrutinise and explore the resulting datasets and analyses than at one of the world’s most high-impact institutions, KAIST, in a country that completely transformed its economy in a matter of decades through research and innovation.”

Currently, THE collects data on the research income universities attract from business and industry, which forms one of 13 performance indicators in its World University Rankings. But a new pilot data collection exercise has been initiated in parallel to data collection for the World University Rankings, covering:

  • Income from business consultancy
  • Turnover of all active spin-off activities
  • Number of active spin-off companies (active for at least three years) – broken down by those with some institutional ownership and those not owned by the institution.

The data could be combined with a range of existing datasets, for example on university-industry co-authored research publications, and patent and licensing data, to form a final ranking.

Predicting U.S. News & World Report ranking of regional universities in the South using public data

Predicting U.S. News & World Report ranking of regional universities in the South using public data, Ph.D. dissertation by Angela E. Henderson (2017)

USNWR-best-colleges
Process chart showing steps used by USNWR to calculate the 2016 Best Colleges institutional rankings.

Abstract:

Using correlational analyses and multiple regressions, this study uses U.S. News & World Report’s (USNWR) 2016 college rankings data and data from the National Center for Education Statistics’ (NCES) Integrated Postsecondary Education Data System (IPEDS) to examine variables that explain institutional peer assessment score and rank. This study focused on the 97 institutions included in USNWR’s 2016 Best Regional Universities (South) ranking list.

Analyses in this study addressed four major foci: 1) correlations between USNWR subfactor data values and selected IPEDS proxies, 2) IPEDS variables that explained variance in peer assessment score, 3) IPEDS variables that explained variance in rank, and 4) the extent to which rank could be predicted based on these results.

The results of this study indicated three main findings. First, USNWR subfactors with direct or indirect IPEDS proxies were highly correlated with the identified proxies. Second, more than 85% of variation in peer assessment score could be explained by five or fewer proxy variables, which differ dependent upon institution sector (private or public). Third, more than 85% of variation in institutional ranking could be explained by five proxy variables and without the inclusion of the peer assessment score subfactor. Collectively, findings suggest USNWR rankings are no more than a reflection of institutional outcomes and financial resources.

usnwr-predicted-ranks
Percentage of predicted ranks classified into same decile as actual ranking.

Universities’ Global Ranking Criteria Modification According to the Analysis of Their Websites

Universities’ Global Ranking Criteria Modification According to the Analysis of Their Websites, by Mohammed Al-Hagery, IJCSNS (2017) Vol. 17 No. 12 pp. 67-78

arwu_modified_indicators
A Modified Indicators and Weights for ARWU

Abstract:

Global universities are subject to the academic ranking every year. One of the common ranking types that are applied annually is called the Academic Ranking of World Universities (ARWU). It developed by a team of researchers and experts. The ARWU is composed of a set of common criteria related to academic tasks and it does not include any indication or factor relevant to the recent technology, such as the websites of universities. Actually, there is a lack to find out the relationship between universities’ global ranking and their website features. Therefore, this research aimed at updating the current ranking model by adding a new criterion reflexing the websites’ features related to its contents and structure. This research focuses on universities as two classes ranked and unranked. This process includes extract, analyze websites’ datasets, visualize the initial results, study the relationship and the significant differences between the two classes if found, and modify the ARWU by updating the criteria list & their weights. A special S/W tool applied to analyze websites and to extract the required data. This research contributes to modify and enhance the ARWU model to be more comprehensive than the current one. The involvement of universities’ websites in the ranking process will encourage universities to improve their websites to achieve a higher-ranking level amongst leading universities. Furthermore, it gives a good chance for all universities to participate in the global ranking competition, especially the universities that have excellent outcomes and perfect websites.

arwu_modified
Design of the ARWU Modified Model Components