Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top

2021 | Book

Operations Research and Management Science in Higher Education: An Overview Read chapter Read first chapter

Handbook of Operations Research and Management Science in Higher Education

Editor: Zilla Sinuany-Stern

Publisher: Springer International Publishing

Book Series : International Series in Operations Research & Management Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

Login to get access
insite
SEARCH

About this book

This handbook covers various areas of Higher Education (HE) in which operations research/management science (OR/MS) techniques are used. Key examples include: international comparisons, university rankings, and rating academic efficiency with Data Envelopment Analysis (DEA); formulating academic strategy with balanced scorecard; budgeting and planning with linear and quadratic models; student forecasting; E-learning evaluation; faculty evaluation with questionnaires and multivariate statistics; marketing for HE; analytic and educational simulation; academic information systems; technology transfer with systems analysis; and examination timetabling. Overviews, case studies and findings on advanced OR/MS applications in various functional areas of HE are included.

Table of Contents

Frontmatter

Reviews/Overviews

Frontmatter
Chapter 1. Operations Research and Management Science in Higher Education: An Overview
Abstract
Operations Research (OR) is an interdisciplinary discipline that evolved during WWII to solve complex operational military problems such as scarce resource allocations and logistics. Thereafter, it has been used successfully in the private and public sectors—including in higher education (HE). The purpose of this chapter is to give an overview of the use of OR methodologies in HE and the functional areas in which they are used. First, a search in 40 OR journals reveals that HE and OR methodology appeared in the title of 0.3% of their articles during 1950–2019. Moreover, in 17 OR journals, HE did not appear in any article. Second, looking at the web of science during 2000–2019, we identified 58 main OR methods relevant to HE. Leading these methods (in terms of the number of articles) is simulation. Third, we identified 30 functional areas relevant to HE. Leading these areas is strategy. For specific methods by area, the leading combination is data envelopment analysis by efficiency. In summary, there is great potential for academics in OR to utilize OR models to improve their own HE system, especially during and after crises such as a pandemic.
Zilla Sinuany-Stern
Chapter 2. A Systematic and Updated Review of the Literature on Higher Education Marketing 2005–2019
Abstract
The purpose of this review is to identify key research themes in the field of higher education (HE) supply-side marketing through a systematic search of journal article databases of papers published between 2005 and 2019, to report on current issues and themes, and ascertain research gaps in the literature for exploitation in future research. Based on an analysis of 105 papers from the field of HE marketing, five major themes characterizing the research on HE marketing are presented in the paper: the marketization of HE; marketing communications; branding, image, and reputation; marketing strategy; and recruitment, alumni and gift-giving. Some thoughts about the nature of the knowledgebase in this field and recommended topics for research conclude the paper. Note, 46 papers were based on quantitative methodologies (constitutes 43.8% of the reviewed papers).
Izhar Oplatka, Jane Hemsley-Brown
Chapter 3. Overview of Simulation in Higher Education: Methods and Applications
Abstract
Simulation is one of the most widely used methodologies in many areas such as industry, army, leisure activities, and education. This chapter is devoted to simulation in higher education (HE) and its use for administrative and educational purposes. Many types of simulation models have been used in HE for various purposes. Literature reviews have been published on specific types of simulations in HE but, to the best of our knowledge, there has not been a comprehensive overview of all types of simulation models in HE. We classify simulation models in HE into two types: (a) analytic simulations models such as discrete event simulation and system dynamics, and (b) educational simulation models such as, virtual reality, games, and simulators for training. This overview presents examples for each method, which gives some idea of the specific areas of HE they were used for. Moreover, taxonomy tables summarize the number of articles published over the last 20 years for each simulation method in HE in the Web of Science, along with the area of applications they are used for in HE. The number of articles on educational simulations is larger by far than the number of articles on analytic simulations. This overview is useful for developers of HE simulations and for administrators in HE institutions.
Efrat Tiram, Zilla Sinuany-Stern
Chapter 4. Managing Minds: The Challenges of Current Research Information Systems for Improving University Performance
Abstract
This chapter describes the failure of Current Research Information Systems (CRIS) to modernize routine managerial tasks involved in academic administration. It argues that most universities maintain traditional decision-making procedures in regards to hiring, promotions, preparation of annual reports, and submission of portfolios for accreditation and assessment. I argue that developers of CRIS programs failed to appreciate the tasks of academic management. At the same time, management teams seem reluctant to change traditional academic practices. I discuss reasons for this disconnect and suggest ways to close the gap so as to modernize academic decision-making in universities.
Gad Yair
Chapter 5. Forecasting Methods in Higher Education: An Overview
Abstract
Forecasting is the first, crucial stage of planning in any organization, and in higher education (HE) in particular. Student enrollment projections are particularly important, since they affect institutions’ income, the number of faculty needed, facility requirements, budgets, etc. There are overviews of forecasting and classifications in general and for particular methods and applications. However, to the best of our knowledge, the last overview of forecasting in HE was published in 1997. Since then, two major approaches sipped from business to HE and became dominant in HE forecasting: data mining and questionnaires for marketing. The purpose of this chapter is to provide an updated overview of forecasting methods used in HE and their main areas of application. We cover a large array of forecasting methods and areas of HE application, we classify them, and point at examples from the literature, rather than providing an exhaustive annotated review, since there are too many publications in the literature on forecasting in HE. Counting the number of articles published in the Web of Science during the last 20 years, we find that, out of six main forecasting methods identified and classified, four methods are used most often in HE: regression, simulation, data mining (including its sub-methods), and questionnaires. Furthermore, four areas of application for forecasting are used most often in HE: enrollment, marketing, teaching, and performance. The two relatively new forecasting methods used in HE, during the last 20 years, are data mining and questionnaires. These two, relatively new forecasting methods, educational data mining and questionnaires (for marketing), are classified in this chapter as active forecasting methods in HE, as they provide the administrator with control over the forecast by pointing (directly or indirectly) at actions which can achieve a better-targeted forecast. While the old methods, time series, and ratio methods, are classified as passive methods with no control. Though regression and simulation forecasting methods are often active, they can sometimes be passive.
Zilla Sinuany-Stern
Chapter 6. Survey of Methods for Ranking and Benchmarking Higher Education Institutions
Abstract
The objective of this chapter is to provide managers and leaders of Higher Education Institutions (HEI) with the knowledge that allows them to evaluate the possibilities and limitations of rankings and benchmarking techniques, and how these techniques can be applied correctly in the different levels of higher education systems.
The chapter includes a presentation of six of the most known international university rankings, a selection of multicriteria decision-making techniques necessary to select the variables to be used, normalize their values, determine their weights, choose the aggregation method and measure how the position of each institution in the ranking depends on the methodological options adopted. For each technique, examples of application to HEI are shown.
The HEI benchmarking interactive tool (HEIBIT), a tool developed to facilitate learning these techniques in international postgraduate programs in the management of HEI in Colombia, Cuba, Dominican Republic, Ecuador, and Spain, is also presented.
José-Luis Pino-Mejías, Pedro-Luis Luque-Calvo
Chapter 7. Balanced Scorecard in Strategic Planning of Higher Education: Review
Abstract
This chapter provides a review of the Balanced Scorecard (BSC) applied to higher education institutions (HEIs). BSC was developed in the 1990s as a comprehensive method to measure and manage organizational performance. BSC translates an organization’s mission statement and strategy into specific, measurable goals and monitors the organization’s performance in regard to achieving these goals. We review publications reporting application of BSC to higher education institutions over 25 years reported by the organizational level, country of application, and use of other methodologies combined with BSC. The large number of applications in many different ways suggests continued future potential for additional applications of BSC in higher education, such as quality assurance.
Zilla Sinuany-Stern, H. David Sherman
Chapter 8. System Analysis of Technology Transfer Policies and Models in Higher Education
Abstract
The current chapter presents a critical evaluation of the modes of knowledge and technology transfer from academia, based on the evaluation of data which spans over the last three decades and a case study of technology transfer in the fields of artificial intelligence, data science, and smart robotics. A need emerges for reevaluating and revisiting university policies with regards to its third mission. Such policies should be set to guide the activities of the Technology Transfer Offices of universities, to balance between technology commercialization, which is more linear in nature, and technology transfer with industry, which is more holistic, interactive, and entrepreneurial in nature. Greater emphasis on technology transfer and more intimate cooperation with industry may result in an increase in research funding as well as in improved level and significance of research. More than that, such policies are more likely to be met by support of the academic faculty, and they should be an inherent part of the development of entrepreneurial activities in universities, which include education that is suitable to the needs of the industry and society, as well as more significant and effective research. It is recommended that the achievements of the universities regarding the third mission should be quantified and ranked on the national and international levels. They should be based on multiple indices to reflect various modes of achieving such goals, in view of the range of mechanisms of university–industry interactions.
Arnon Bentur, Daphne Getz, Oshrat Katz Shacham
Chapter 9. Models for Planning and Budgeting in Higher Education
Abstract
The budget of an organization reflects its plan in monetary terms over a given period—usually for a year. This chapter is devoted to planning and budgeting in higher education (HE). We present various budgeting procedures, such as incremental budgeting, along with costing methods in HE institutions (HEIs). A variety of optimization models with budget constraints and bounds on the allocations to organizational units are given, where the bounds are a result of the planning process and past budgets. We present linear and nonlinear objective functions (quadratic), with and without constraints, intertwined with a simulation scheme in an HEI. The optimization models are presented in a single and multilevel hierarchy, and over time. Moreover, several aggregative long-run financial models are presented. We conclude that the quadratic model fits HE better than the linear model since it provides allocations around the midpoints of the upper and lower bounds of the allocations rather than the extreme linear model’s allocation. We determine that the quadratic model procedure is preferred, as its optimal solution is intuitive and does not require mathematical formulation and skills. Moreover, the relationship between the mathematical models and known budgeting procedures are analyzed, and we conclude that the optimization/simulation scheme described here results in a combination of several budgeting procedures—as actually happens in practice.
Zilla Sinuany-Stern

New Methodologies

Frontmatter
Chapter 10. Funding Research in Higher Education Institutions: The Game Theory Approach
Abstract
This chapter presents a unique model, based on game theory, that can help decision-makers in higher education (HE) institutions determine an optimal research budget. The model can then help them decide how to allocate that budget among academic units such as researchers, institutions, and departments. The model considers the management of the institution as a contest organizer and the academic units as contestants that compete with each other to win the contest. The prize of this contest is a desired research budget. The proposed model includes a form of two contestants with different abilities, as well as a form with unlimited (N) contestants with the same abilities. The model enables decision-makers to determine the size of the optimal research budget (the prize), and the optimal distribution mechanism (a contest or a budget division) of that prize among the contestants. To the best of our knowledge, determining the size of the Tullock contest prize according to the contestants’ abilities with comparison to a bargaining model has not previously been studied. This is an application that is new to the HE budget allocation process. The study includes a numerical example that demonstrates the model and its applicability.
Baruch Keren, Yossi Hadad, Yizhaq Minchuk
Chapter 11. A Fast Threshold Acceptance Algorithm for the Examination Timetabling Problem
Abstract
In this chapter, an accelerated variant of the threshold acceptance (TA) metaheuristic, named FastTA, is proposed for solving the examination timetabling problem. FastTA executes a lower number of evaluations compared to TA while not worsening the solution cost in a significant way. Each exam selected for scheduling is only moved if that exam had any accepted moves in the immediately preceding threshold bin; otherwise, the exam is fixed and is not evaluated anymore. If an exam had zero accepted movements in the preceding threshold bin, it is likely to have few or zero accepted movements in the future, as it is becoming crystallised. The FastTA and TA were tested on the Toronto and Second International Timetabling Competition benchmark (ITC 2007) sets. Compared to TA, the FastTA uses 38% and 22% less evaluations, on average, on the Toronto and ITC 2007 sets, respectively. On the ITC 2007 set, the FastTA is competitive with TA attaining the best average solution cost value in four out of twelve instances while requiring less time to execute. Compared with the state-of-the-art approaches, the FastTA is able to achieve competitive results. The main contribution/value of this chapter is the proposal of a new acceptance criterion for the TA metaheuristic, which leads to a significantly faster variant (FastTA), and its application to solve public examination timetabling benchmark sets.
Nuno Leite, Fernando Melício, Agostinho C. Rosa
Chapter 12. Robust Efficiency via Average Correlation: The Case of Academic Departments
Abstract
Many types of efficiency methods for decision-making units (DMUs) have been suggested in the literature. Often in the same application, several efficiency methods are given, making it hard for decision makers to choose which efficiency method to use. This chapter provides a simple method to choose a robust efficiency, by calculating the average correlations of each method with all other methods. This robust method is applied to the case of 21 academic departments within a university, taken from the literature, with two inputs and three outputs. A variety of data envelopment analysis (DEA) methods for measuring DMUs’ efficiencies are considered here: constant return to scale (CRS), variable return to scale (VRS), super efficiency (SE), and cross-efficiency (CE). A few multivariate statistical efficiency methods in the DEA context are also used: discriminant analysis, canonical correlation, and regression analysis. For this case study, the robust continuous efficiency scale method turned out to be the CE-CRS method. For validating the results, we also used rankings of the efficiencies, and applied nonparametric statistical tests. Since the two inputs used in the case study had monetary values, we also created a one-input model using the sum of the two inputs. Thus, we were able to use, in addition to the above-mentioned efficiency methods, a version of stochastic frontier analysis via multiple linear regression. Even in this case, CE-CRS turned out to be the robust efficiency method, including for the ranks of efficiencies.
Zilla Sinuany-Stern, Lea Friedman

Applications

Frontmatter
Chapter 13. Course Evaluation Using Preference Disaggregation Analysis: The Case of an Information Communication Technology Course
Abstract
The purpose of this chapter is to evaluate students’ satisfaction attending an Information Communication Technology (ICT) course. An adapted version of the Course Experience Questionnaire (CEQ) was used in order to evaluate students’ satisfaction, based on their experience. The 36-item scale includes measures for Good teaching, Clear Goals, Appropriate Workload, Appropriate Assessment, and Independence in Learning and development of Generic Skills. The data were analyzed using preference disaggregation analysis, a method of multiple criteria analysis, based on the principles of ordinal regression. The findings of this chapter provide many practical implications for educators because they can identify the strengths and weaknesses of the course. Therefore, they can improve teaching weaknesses based on students’ satisfaction rates. Additionally, the methodology that was used will provide curriculum designers with a tool in order to evaluate their current courses and will support them to incorporate improved educational approaches in the future. Multi-criteria decision analysis offers many benefits for education practitioners, such as the identification of problems as well as the opportunity to prioritize the necessary interventions for improving their teaching. Last but not least, this method supports educators to organize the course more flexibly and facilitate students communicate with teachers and participate more actively in the educational process.
Ioannis Sitaridis, Fotis Kitsios, Stavros Stefanakakis, Maria Kamariotou
Chapter 14. The Application of a Balanced Scorecard in Higher Education Institutions: A Case Study of Wuls
Abstract
The purpose of this chapter is to share the experience of a Balanced Scorecard (BSC) development and application by a public university. This case study is used to describe the process of developing a strategy and translating it into a customized Balanced Scorecard. The project presented in this chapter did not lead to amazing success, but it did not completely fail. This project was adapted to the specific conditions of a particular time and place and spanned 10 years, even though changes in the model were made over time due to new laws at the national level. Some of the lessons learned are discussed in the last section of this chapter. The administrators of Higher Education Institutions can directly use or adapt the BSC framework presented in this chapter. Moreover, they can find some useful tips and tricks regarding the basic steps in the process of strategy and Balanced Scorecard development. Moreover, interested parties could deliberate upon some problems and challenges pinpointed in this case study and consider such issues in advance. This study will be helpful in designing customized strategic management systems adapted to the specific circumstances of a time and place, instead of simply imitating and replicating solutions developed for business firms.
Michał Pietrzak
Chapter 15. E-Learning in Times of Crisis: An Incidental or Facilitative Event?
Abstract
As a result of the COVID-19 epidemic that erupted in 2020 the various higher education institutions in Israel, as elsewhere, were compelled to embrace E-Learning at short notice. This was a revolution that appeared with no preparation and that put on the agenda the efficacy of E-Learning from pedagogical aspects and the implications of the lecturer’s functions and the act of teaching for the quality of students’ learning as well as for the meaning of the learning expanse (campus—home) in teaching and learning processes. The current study examined the opinions of students and lecturers regarding the advantages and disadvantages of E-Learning from various aspects in a systemic, multi-institutional perspective. The study included 2015 students studying at various academic institutions: universities, academic teachers’ colleges, academic colleges, and private colleges. The study also included 223 lecturers.
The research findings show that the respondents did not display a high preference for E-Learning: less than half the students and about one-third of the lecturers expressed a preference for E-Learning. Both groups noted the lack of personal, social, and emotional interaction with both students and lecturers as one of the main shortcomings of E-Learning. Most of the students and lecturers did not grasp E-Learning as providing them with better quality teaching and learning. The study illuminates the role of the lecturer in the digital era as a teacher, and particularly—the role of the professional elements in charge of teaching and learning at academic institutions, particularly in the pedagogical aspects. According to student evaluations, the use of technological platforms and tools does not improve teaching, as they are used by the faculty only technically with no matching pedagogy. In order to succeed, E-Learning requires other pedagogical educational approaches aside from copying frontal teaching patterns using the Zoom platform, as well as others: Weber, MS Teams, etc.
In addition, the study indicates the need for perceptual changes, both by the students, who must take responsibility for their learning, and by the lecturers, who must reexamine the teaching and learning processes and adapt their role and areas of responsibility to the new opportunities afforded by the technological tools. The research findings also indicate that effective teaching is teaching that arouses in student’s inquisitiveness, motivation, and learning experiences, and note that learning products must be adapted to include essential skills in addition to knowledge. Further, the study illuminates the thorough discussion that must be held by leaders of higher education and of the academic institutions concerning the new effective designation of the campus after the COVID-19 crisis, including distinguishing between the virtual and the realistic in academic teaching and challenges and ways of dealing with the new circumstances.
Nitza Davidovitch, Rivka Wadmany
Chapter 16. The Relative Efficiencies of Higher Education in OECD Countries
Abstract
Studies of productivity of systems of Higher Education (HE) on the national level are of interest for two main reasons: education is an important factor for productivity growth for the macro-economy, and the efficiency of spending public resources on HE is of key interest in the context of accountability specifically relative efficiency compared with other developed countries. The objective of this study is to evaluate the relative efficiency of HE in OECD countries from the public viewpoint; how well OECD countries utilize their public resources to achieve their outputs relative to each other. For this study, two inputs are chosen reflecting the public investment in HE. Six outputs are chosen reflecting the main outcomes of HE in terms of: accessibility of tertiary education, employment level, earnings level relative to secondary education, net financial returns from HE, internal rate of return, and research articles level. The data is taken, mostly, from the OECD report on education in 2019. Out of 37 OECD countries 29 are considered in this study. Due to missing data 8 countries are not included. The stress on efficiency from the public viewpoint is a strength of this study in relation to previous OECD efficiency studies. The original Data Envelopment Analyses (DEA) basic models are, which provide dichotomy of the countries into two groups: efficient and inefficient. Moreover, several efficiency rank-scaling methods based on DEA, and several multivariate statistic methods are utilized here. The use of a variety of efficiency rank-scaling methods, while choosing the robust one, is another strength of this research. The results indicate that the robust method is cross efficiency, as it is significantly correlated with each of the other efficiency methods, and it has the highest average correlation with other efficient methods. From the 29 studied OECD countries, the USA is found to be the most efficient in HE. However, when we use only the first input versus the six above outputs, Italy became the most efficient country. The USA is ranked third in this case, while Italy is ranked fourth in the original case.
Zilla Sinuany-Stern, Arthur Hirsh
Backmatter
Metadata
Title
Handbook of Operations Research and Management Science in Higher Education
Editor
Zilla Sinuany-Stern
Copyright Year
2021
Electronic ISBN
978-3-030-74051-1
Print ISBN
978-3-030-74049-8
DOI
https://doi.org/10.1007/978-3-030-74051-1