University maturity model – a bibliometric analysis

ECONOMICS AND ENVIRONMENT 1 4(91) • 2024 eISSN 2957-0395 UNIVERSITY MATURITY MODEL – A BIBLIOMETRIC ANALYSIS Joanna Szydło (ORCID: 0000-0002-2114-4770) – Bialystok University of Technology Agnieszka Sakowicz (ORCID: 0009-0007-9076-984X) – Bialystok University of Technology Filippo di Pietro (ORCID: 0000-0003-1573-8553) – University of Seville Correspondence address: Wiejska Street 45A, 15-312 Bialystok, Poland e-mail: [email protected] Joanna SZYDŁO • Agnieszka SAKOWICZ • Filippo DI PIETRO Economics and Environment • No. 4(91) 2024 • pages: 1-19 DOI: 10.34659/eis.2024.91.4.938 ABSTRACT: In today's dynamic and competitive environment, universities play a key role in generating, transmitting, and applying knowledge and innovation. The growing interest in evaluating university performance at national and international levels has led to developing and applying university maturity models as effective assessment tools. This article aims to present various approaches to modelling university maturity. A bibliometric analysis was based on publications in the Web of Science and Scopus databases. The research query included TITLE-ABS-KEY ("maturity model" and universit*) for Scopus and TS = ("maturity model" and universit*) for the Web of Sciences database. A total of 123 publication records were analysed. Materials published between 1994 and 2024 in English were examined. A total of 123 publications were selected for the final analysis. Based on the literature review, key factors that may influence university maturity across nine areas were identified. A theoretical University Maturity Model (UMM) is also presented, which should undergo expert evaluation in subsequent stages. Findings suggest that the application of maturity models can significantly enhance universities' management and operational efficiency, offering valuable insights for policymakers in formulating educational policies. KEYWORDS: maturity model, university ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 2 Introduction In today’s dynamic and competitive world, higher education plays a key role in creating, transmitting and applying knowledge and innovation. As a result, more and more attention is paid to functioning and evaluating universities at both national and international levels. Maturity models in universities are one of the tools that have become extremely useful in assessing universities. Higher Education Institutions are complex organisations. Although autonomous, they have to execute a number of functions and develop a variety of procedures to ensure the fulfilment of their duties, which inevitably raise constant challenges. The number of functions they perform and the variety of procedures developed under their autonomy to ensure the accomplishment of all their duties raise constant management and administration challenges. Difficulties in procedure systematisation and in workflow analysis, evaluation, and optimisation carry problems not only to management itself but also to information systems design (Zacarias & Martins, 2011). University maturity models are a comprehensive analytical tool that enables the assessment of various aspects of the functioning of higher education (university). These models allow the identification of strengths, areas for improvement and elaboration of development strategies by defining the level of maturity in areas such as management, teaching and learning, scientific research, technology transfer, international cooperation and social involvement. Since universities are organisations, maturity models have proven to be valuable in evaluating their process and determining by levels the path to academic excellence (Tocto-Cano et al., 2020). According to Mintzberg (1979), based on the interactions of people and the differentiation of their roles, the university is an organisation of “professional bureaucracies”. A bureaucracy, for M. Weber, is an efficient organisation that defines even in the smallest details how things should be done. Also, Weber believed that bureaucracies are the most efficient way to organise large organisations and were a result of the inevitable rationalisation and personalisation of society (Chiavenato, 2019). In response to the need to measure the progress achieved by an organisation, which is also a university, maturity models have been created. This article provides an overview of several university maturity models, highlighting their variety, applications, benefits, and drawbacks. Its goal is to showcase different methods for modeling university maturity. Literature review The definition of organisational maturity, although it may vary depending on context and source, generally refers to the degree to which an organisation is able to manage its processes resources, and achieve its strategic goals effectively and efficiently. It can be defined as the level of development of processes, structures, and technologies that allow the organisation to operate stable and predictably. Maturity commonly means reaching the final stage of development or process shaping, or the degree of intellectual, emotional or biological development of any individual organisation, person or unit (Głuszek & Martusewicz, 2015). P. Crosby, who, in 1979, in his book entitled Quality is Free, published the first maturity model, is believed to be a precursor of this term. It included a description of five levels of organisational skills in using quality management methods and tools. This model showed the development path for these skills, specifying what activities must be taken to reach the next level of maturity. According to Kalinowski (2011), process maturity is the ability of an organisation, including its processes, to systematically improve the delivered results in its operations. At a higher level of detail, the maturity of the process is viewed as the field to which processes are driven, well-defined, managed, flexible, measured and effective (Grajewski, 2012). In another approach, it is indicated that process maturity is the degree of optimal allocation of organisational resources in stable and measured processes (Grela, 2013). The maturity process is the awareness that the processes occurring horizontally within an organisation create that organisation (Brajer-Marczak, 2012a). Those processes need to be managed in an appropriate way. In addition, process maturity also indicates how the perception of processes fits into the company’s strategy. On the one hand, the level of process maturity informs about the awareness of employees in terms of participation in business processes, and on the other hand, how the managers use the knowledge about processes in organisational development decisions. ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 3 In another publication, Brajer-Marczak (2012b) defines the process maturity of an organisation as a state in which it is possible to repeatedly achieve the same result of processes (or characterised by a small, acceptable tilt) in relation to previously defined key factors. According to the same author, another definition of the above-mentioned process maturity is the ability to organise effective management processes supporting the achievement of strategic goals. Brajer-Marczak points out a twofold relationship here: firstly, the goals of the processes must be a result of the strategic goals, and secondly – the achievements of planned process goals enable the implementation of the strategy. Maturity models define the current state of the organisation that results from the way the organisation exists and its possibilities for the use of existing resources or previous experience, as well as what it is not possible to achieve in the future by applying department priorities and financial resources and methods of their implementation (Kosieradzka & Smagowicz, 2016). The maturity model is a set of diverse tools and practices that enable the assessment of the competencies of a given organisation in the field of management (OGC, 2007), as well as the improvement of key factors leading to achieving the assumed goals (Van Looy, 2014). In the literature on the subject of maturity models in organisations, you can find several dozen process maturity models. Szewczyk (2018) compared three maturity models: the Process and Enterprise Maturity Model (PEMM), the Business Process Maturity Model (BPMM) and Fisher’s model. The first model of PEMM was developed by M. Hammer, a specialist in reengineering theory, in 2000-2006. According to this model, to determine process maturity, you have to analyse two areas: process enablers and enterprise capabilities (Hammer, 2007; cf. Power, 2007). The second model is Business Process Maturity Model (BPMM) which the owner is the Object Management Group. BPMM model points out five levels of process maturity: initial, managing, standardised, predictable and innovative (OMG, 2008). The third model is written by D.M. Fisher. The author of the model clearly emphasises the nonlinearity and complexity of the process of increasing the maturity of the organisation, in which he distinguishes and describes 5 levels of change (Fisher, 2014). Those levels are strategy, control, people, technology, and processes. For each of the levels mentioned before, Fisher’s model defines five levels of maturity (silo organisation, tactically integrated organisation, process-driven, optimised organisation, and intelligent operational network). Kosieradzka and Smagowicz (2016) compared twenty maturity models from seven management areas. Those seven areas are: process management, production management, project management, software development management, administration management, quality management, risk and continuity speed of action management. Below the authors systemised those models according to division (cf. Kosieradzka, 2016). 1. In process management: 1.1. Business Process Maturity Model developed by OMG. 1.2. Business Process Maturity Model developed by Gartner. 1.3. Process and Enterprise Maturity Model developed by Hammer. 2. In production management: 2.1. Productivity Management Model developed by Kosieradzka. 3. In project management: 3.1. Project Management Maturity Model developed by Kerzner. 3.2. PRINCE 2 (P2M) Maturity Model developed by Office of Government Commerce. 3.3. OPM3 developed by Project Management Institute. 3.4. P3M3 developed by Cabinet Office. 4. In software development management: 4.1. Capability Maturity Model Integration developed by Software Engineering Institute. 4.2. Process Maturity Framework. 4.3. IT Service Management Maturity Model. 4.4. Model Control Objectives for Information and related Technology developed by ISACA and IT Governance Institute. 5. In quality management: 5.1. Quality Management Maturity Grid developed by Crosby. 5.2. ISO 9004. 5.3. EFQM developed by European Foundation for Quality Management. ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 4 6. In risk and continuity speed of action management: 6.1. Business Continuity Maturity Model developed by Virtual Corporation. 6.2. Enterprise Risk Management Maturity Model. 6.3. Risk and Insurance Management Society Maturity Model. 7. In administration management: 7.1. Planning of institutional development. 7.2. Common Assesment Framework. As it is seen there are many relevant maturity models in literature. In one study, a list of three maturity models was pointed out. In other study, twenty maturity models were found. One more study shows nine categories of selected Maturity Models connected with universities (Tocto-Cano et al., 2020). Those are the categories: 1. Maturity models oriented towards teaching. 2. Maturity models oriented towards Information and Communication Technology (ICT). 3. Maturity models oriented towards student monitoring. 4. Maturity models for intellectual capital. 5. Maturity models for E-Learning. 6. Maturity models aimed at evaluating university entrepreneurship. 7. Maturity model oriented to the employability of graduates. 8. Maturity model oriented to the strategic planning of universities. 9. Maturity model for IT governance in university institutions. In one more study (Duarte & Martins, 2013), it is shown comparison between nine educational maturity models. Most models found are based on CMM or on the staged representation of CMMI. The presented models by Duarte and Martins (2013) have the same five levels of maturity. They all suggest attributes that the organisation should possess to be positioned at each stage. However, unlike the model in which they were based, most teaching maturity models do not explicitly identify any key process areas. Only the models developed by Dounos and Bohoris (2010) and by Marshall and Mitchell (2002, 2004, 2005, 2006a, 2006b, 2008, 2009) provide these areas as well as the methodologies and evaluation techniques to assess the fulfilment of requirements, to effectively place an organisation in a certain level of maturity. Also strengths and weaknesses of the educational maturity models are shown in his article. Those maturity models which focused on Higher education institution are: 1. eMM (Marshall & Mitchell, 2002), 2. MRAIES (Petrie et al., 2009), 3. ICTMMEI-DV (Bass, 2010), 4. CMMI-ISC (White et al., 2003), 5. OCDMM (Neuhauser, 2004), 6. LPMM (Thompson, 2004), 7. ITIL-ITSMM (Wang & Zhang, 2007), 8. CEMM (Lutteroth et al., 2007), 9. CMMI – TQM (Dounos & Bohoris, 2010). Selection of models to present in this article is based on subjective assessment of the authors and is connected with university as an organisation. Research methods Researchers frequently use bibliometric analysis, particularly when exploring a specific research topic. Given the vast number of available publications, this method aids in the identification, synthesis, analysis, and critical evaluation of their content (Keathley-Herring et al., 2016; Gudanowska, 2017; Bornmann & Haunschild, 2017; Cichowicz & Rollnik-Sadowska, 2018; Glińska & Siemieniako, 2018; Siderska & Jadaa, 2018; Czerniawska & Szydło, 2020; Lenert-Gansiniec, 2021; Szpilko et al., 2023). The bibliometric analysis aims to provide knowledge about the main research directions in a field, research trends, changes in the number of publications over the years, the most productive authors, journals, countries, or research units (Niñerola et al., 2019; Szum, 2021). ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 5 The research process was conducted following a methodology comprising seven distinct phases (Szpilko et al., 2023). These phases encompassed the (1) selection of bibliographic databases, (2) the choice of keywords, and (3) the criteria to narrow down the search for publications. (4) Subsequently, data extraction and selection was performed, (5) followed by the analysis of the selected publications. The last two phases involved (6) identifying research areas and (7) defining thematic clusters (Figure 1). Figure 1. Methodology of bibliometric analysis The bibliometric analysis was based on publications available in Web of Science and Scopus databases. It covers publications containing the phrases (“maturity model” and universit*) in the title, abstract and keywords. The search was conducted for materials published between 1994 and 2024 in English. Articles, proceedings papers, conference papers, books, book chapters and reviews were considered. Other publication types (early access, editorial materials, retracted publications, notes) were rejected. The results of the first search are presented in Table 1. An initial search for the term “maturity model” and universit* across the entire set of articles in the first sample yielded 25 154 records in Scopus and 15 494 records in Web of Science. However, after initial analysis, it became apparent that many of these publications were not directly related to the study area. Only after narrowing the search criteria did the number of publications decrease. Ultimately, there were 151 records from the Scopus database and 132 from the Web of Science database. ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 6 Table 1. Search results Stage Web of Science Scopus First search Research query ALL: “maturity model” and universit* ALL: “maturity model” and universit* Number of articles before inclusion criteria 15 494 25 154 Number of articles after inclusion criteria 2 390 4 766 Second search Research query TOPIC: (“maturity model” and universit*) TITLE-ABS-KEY: (“maturity model” and universit*) Number of articles before inclusion criteria 146 258 Number of articles after inclusion criteria 132 151 Content evaluation and final selection of articles 123 Source: authors’ work based on the Scopus and Web of Science databases. In the next stage, the files were downloaded in CSV format. Subsequently, data from the two databases were merged, and duplicates were removed. Ultimately, after reviewing all the records, 123 publications were selected for assessment. From the authors’ perspective, it was important to explore the interest in the topic over the years as well as the most frequently cited articles. The next step involved using the VOSviewer program to generate a map that reflects the co-occurrence of keywords in the analysed set of publications. The next step involved using the VOSviewer program to generate a map that reflects the co-occurrence of keywords in the analysed set of publications. Results of the research Initially, the authors observed a growing interest in the subject over the years (Figure 2). It is important to highlight that the exploration of issues related to university maturity models increased notably after 2008. Moreover, a substantially higher number of publications was identified in the Scopus database compared to the Web of Science. Figure 2. Number of publications in the field of university maturity model in Scopus and Web of Science (indexed from January 1994 to September 2024) Source: authors’ work based on the Web of Science and Scopus. Figure 2. Number of publications in the field of university maturity model in Scopus and Web 0 2 4 6 8 10 12 14 16 18 20 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Scopus WoS ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 7 The next question concerned the most frequently cited publications. It can be seen that an average number of citations was recorded in both databases. Noteworthy are the articles published in the Journal of Intellectual Capital (Table 2). Table 2. The most cited articles related to the topic of uniformed services No. Authors Article title Journal Number of citations Scopus Web of Science 1. Ganzarain and Errasti (2016) Three stage maturity model in SME’s towards industry 4.0 Journal of Industrial Engineering and Management 262 202 2. Secundo et al. (2016) Managing intellectual capital through a collective intelligence approach: An integrated framework for universities Journal of Intellectual Capital 145 115 3. Secundo et al. (2010) Intangible assets in higher education and research: Mission, performance or both? Journal of Intellectual Capital 120 110 4. Secundo et al. (2015) An intellectual capital maturity model (ICMM) to improve strategic management in European universities: A dynamic approach Journal of Intellectual Capital 110 112 5. Pee and Kankanhalli (2009) A model of organisational knowledge management maturity based on people, process, and technology Journal of Information and Knowledge Management 91 54 6. Secundo et al. (2018) Intellectual capital management in the fourth stage of IC research: A critical case study in university settings Journal of Intellectual Capital 84 91 7. Secundo et al. (2017) Mobilising intellectual capital to improve European universities’ competitiveness: The technology transfer offices’ role Journal of Intellectual Capital 63 49 8. Frondizi et al. (2019) The evaluation of universities’ third mission and intellectual capital: Theoretical analysis and application to Italy Sustainability 61 45 9. Dzimińska et al. (2018) Trust-based quality culture conceptual model for higher education institutions Sustainability 52 169 10. Dayan and Evans (2006) KM your way to CMMI Journal of Knowledge Management 51 - 11. Heinemann and Uskov (2018) Smart university: Literature review and creative analysis Smart Innovation, Systems and Technologies 39 - 12. Secundo et al. (2016) Measuring university technology transfer efficiency: a maturity level approach Measuring Business Excellence 37 30 Note: N/A – not applicable. Source: authors’ work based on the Scopus and Web of Science databases. In the context of bibliometric analysis, keywords frequently associated with the topic of university maturity modelling were identified. The analytical process utilised VOSviewer software. The resulting dataset consisted of 145 words or phrases that appeared at least three times in the keywords of 123 analysed articles. The dataset also included terms unrelated to the main topic of the analysis (e.g., ‘article,’ ‘analysis,’ ‘survey,’ ‘literature review’). To systematise the keyword set, unnecessary terms (related to the topic of analysis) were intentionally excluded. Terms and abbreviations with similar meanings were standardised. The refined dataset contained 98 keywords. The most frequent terms and their interrelations are illustrated in Figure 3. The names of the individual clusters are presented in Figure 4. ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 8 Figure 3. Keyword co-occurrence map of university maturity model Source: authors’ work using VOSviewer software. Figure. 4. Thematic clusters of a university maturity model ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 9 The analysis generated nine clusters containing keywords (Table 3): • yellow cluster: knowledge and development (e.g. Harin et al., 2024; Korzeb et al., 2024; Alghail et al., 2017, 2022, 2023; Ilker Murat et al., 2023; Peck, 2023; Cardoso & Su, 2022; Su & Cardoso, 2021; Edirisinghe et al., 2021; Rico-Bautista et al., 2022; Secundo et al., 2010, 2015, 2016, 2017, 2018; Frondizi et al., 2019; Katiliute & Daunoriene, 2015; Kitagawa & Lightowler, 2013; Pee & Kankanhalli, 2009; Dayan & Evans, 2006; Teah et al., 2006), • green cluster: HRM (e.g. Behroozi & Khodadadi, 2017; Llamosa-Villalba et al., 2014; Kropsu-Vehkaperä & Kess, 2013; Pee & Kankanhalli, 2009), • red cluster: education (e.g. Naim & Malik, 2023; Santally et al., 2020; Annan-Diab & Molinari, 2017), • light blue cluster: digitalization (e.g. Kalender & Žilka, 2024; Paños-Castro et al., 2024; Acuna et al., 2024; Teichert, 2019; Jaico et al., 2019; Uhl & Gollenia, 2016; Bianchi & de Sousa, 2015), • dark blue cluster: organisational culture (e.g. Aboramadan, 2021; Moreira et al., 2021; Dzimińska et al., 2020; Marshall, 2010), • grey cluster: staregy (e.g. Szpilko & Ejdys, 2022; Kobylińska et al., 2024; Fowler, 2019; Heinemann & Uskov, 2018; Ganzarain & Errasti, 2016), • purple cluster: collaboration (e.g. Silva et al., 2021; Frondizi et al., 2019; Awasthy et al., 2018; Othman & Omar, 2012), • dark green cluster: security and investments (e.g. Moczydłowska et al., 2023; Miller et al., 2014; Sheen & Chung, 2011; Carcary, 2012), • blue cluster: quality (e.g. Anthony & Antony, 2016, 2022; Painén-Paillalef et al., 2022; Maciąg, 2019; Dzimińska et al., 2018; Llamosa-Villalba & Méndez Aceros, 2010). Table 3. Cluster names, keywords and generated factors in the university maturity modelling area No. Cluster name Selected keywords Factors that can influence the maturity of a university 1. Knowledge and development knowledge management, innovation, evaluation, engineering research, technology transfer, intellectual capital, capability maturity model, knowledge management maturity, sustainable development, innovations • Opportunities for training and development • Level of knowledge transfer • Level of achievement of sustainable development goals • Involvement in technology transfer • Measures to conduct innovative research • Level of commercialization of research results 2. Human Resource Management human resource management, societies and institutions, managers, personal software process, leadership • Leadership style • Ability to recruit, develop, and retain highly qualified academic and administrative staff • Distribution of roles and responsibilities • Transparency of regulations • Opportunities for advancement • Access to psychological support • Health protection 3. Education higher education, teaching, learning maturity model, interactive method, learning systems, agile methods, learning objects, personal training, quality of teaching, decision making, e-learning maturity model, students • Quality of educational programs • Diversity of teaching methods • Opportunities for training and development • Level of innovation in teaching • Level of matching of programs of study to labour market needs • Student engagement 4. Digitalization digital transformation, information use, information systems, e-learning, maturity levels, computer-aided instruction, information technology • Level of implementation of modern IT technologies • Level of implementation information management systems 5. Organisational culture organisational culture, behaviours, organisational change, management • Atmosphere at the university • Level of alignment of personal and organisational values • Level of acceptance of the organisational structure • Level of understanding of the mission ECONOMICS AND ENVIRONMENT 4(91) • 2024 DOI: 10.34659/eis.2024.91.4.938 16 Fisher, D. M. (2014). The Business Process Maturity Model. BP Trends, 1-7. https://bptrends.info/wp-content/ publicationfiles/10-04%20ART%20BP%20Maturity%20Model%20-%20Fisher.pdf Fowler, J. (2019). Business intelligence at the university. 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Collaborative Methods for Business Process Discovery. Spatial and Organisational Dynamics, 7, 45-55. https://www.researchgate.net/publication/233992927_Collaborative_Methods_ for_Business_Process_Discovery Joanna SZYDŁO • Agnieszka SAKOWICZ • Filippo DI PIETRO MODEL DOJRZAŁOŚCI UCZELNI – ANALIZA BIBLIOMETRYCZNA STRESZCZENIE: We współczesnym dynamicznym i konkurencyjnym środowisku uniwersytety odgrywają kluczową rolę wgenerowaniu, przekazywaniu i zastosowaniu wiedzy oraz innowacji. Rosnące zainteresowanie oceną funkcjonowania uniwersytetów na poziomie krajowym i międzynarodowym doprowadziło do rozwoju i zastosowania modeli dojrzałości uczelni jako skutecznych narzędzi oceny. Celem artykułu jest przedstawienie różnych podejść do modelowania dojrzałości uczelni. Zastosowano analizę bibliometryczną. Wykorzystano publikacje dostępne w bazach Web of Science i Scopus. Objęto nią teksty zawierające frazy („maturity model” i universit*) w temacie oraz w tytule, abstrakcie i słowach kluczowych. Zapytanie badawcze obejmowało TITLE-ABS-KEY („maturity model” and universit*) dla bazy Scopus oraz TS = („maturity model” and universit*) dla bazy Web of Sciences. Przeszukano materiały opublikowane w latach 1994-2024 w języku angielskim. Do ostatecznej analizy wybrano 123 publikacje. Na podstawie przeglądu literatury zidentyfikowano kluczowe czynniki, które mogą wpływać na dojrzałość uniwersytetów w dziewięciu obszarach. Przedstawiono również teoretyczny model dojrzałości uczelni, który w kolejnych etapach powinien zostać poddany ocenie ekspertów. Wyniki sugerują, że zastosowanie modeli dojrzałości może znacznie poprawić zarządzanie i efektywność operacyjną uniwersytetów, oferując cenne informacje dla decydentów w formułowaniu polityki edukacyjnej. SŁOWA KLUCZOWE: model dojrzałości, uczelnie