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Systematic Review

Unveiling Challenges to Management Control Systems in Higher Education: A Systematic Literature Review

by
Maya Lambovska
1,* and
Antoaneta Angelova-Stanimirova
2
1
Department of Management, University of National and World Economy, 1700 Sofia, Bulgaria
2
Department of Financial Control, University of National and World Economy, 1700 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
World 2025, 6(3), 100; https://doi.org/10.3390/world6030100
Submission received: 17 May 2025 / Revised: 25 June 2025 / Accepted: 9 July 2025 / Published: 15 July 2025
(This article belongs to the Special Issue Data-Driven Strategic Approaches to Public Management)
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Abstract

In light of constrained resources and the rise of digitalisation in higher education, management control systems (MCSs) have emerged as essential tools for university management because of their integrity, flexibility, and effectiveness. This paper aims to elucidate the current challenges in the implementation and functioning of MCSs in higher education. To this end, a systematic literature review was undertaken in the Scopus and Web of Science databases, following the PRISMA guidelines. The review yielded 15 relevant sources published between 2020 and June 2025. Induction, deduction, content analysis, and K-means clustering were employed to analyse them. Forty-eight challenges to MCSs in higher education were identified and systematised into four groups (Growth Threats, Limitations, Malpractices, and Stakeholder Issues), covering twelve subgroups. These subgroups were ranked according to their frequency of mention. The top-ranked subgroups were HR problems (first), organisational constraints and management engagement (second), and technological integration and lack of technology training (third). All challenges were classified into clusters based on the countries analysed in the reviewed sources. This review primarily contributes to the existing knowledge by identifying and categorising the challenges to MCSs in higher education. Practically, it lays the groundwork for improving these MCSs, thus contributing to enhanced university management.

1. Introduction

In recent years, the world has transformed almost beyond recognition [1]. Innovation, technology, and dynamism are now part of daily life worldwide [2]. Fast-paced lifestyles, uncertainty, and constant change are prevalent [3]. Competition is continuously increasing. The successful development and operation of organisations depend on their ability to react swiftly to shifting external circumstances and to succeed in being differentiated and competitive. The ability to generate optimal solutions in a specific circumstance is essential to achieve this. All organisations, including universities, confront the problem of needing to increase their competitiveness [4,5]. To accomplish this, adopting a data-driven approach stands out as a highly effective and contemporary strategy [6,7]. Applying this strategy allows for well-informed decisions to be taken [8]. A key feature of the data-driven approach is its comprehensiveness [9].
In light of the growing uncertainty and increasing demand for digitalisation, the higher education sector has experienced substantial transformations over the past twenty years [10,11]. Central to these transformations has been the COVID-19 pandemic of 2020–2021. In this period, universities encountered a significant obstacle that compelled them to alter their working methods. The unprecedented circumstances arising from the COVID pandemic [12] placed enormous pressure on administrative teams and hastened their transition to digitalisation [13]. Online learning emerged as the predominant form in higher education [14,15,16], requiring significant modifications to various pedagogical approaches [17]. The application and processing of big data for learning, teaching, and higher education management have significantly increased [18,19,20,21,22,23]. Consequently, universities have encountered the swift integration of digitalisation, fundamentally transforming the educational landscape as they embrace technology in an unprecedented manner. Another reason for this transformation is that, over the past two decades, governments have reduced financial support for higher education institutions (HEIs) [24,25]. To secure public funding, attract students, and provide an appealing option for researchers, HEIs are forced to enhance their competitiveness, which necessitates the development of new and advanced governance models, as well as increasing HEIs’ efficiency and effectiveness [26,27]. The next reason for the transformations in the higher education sector relates to the specifics of HEIs and their governance, particularly concerning their role in achieving sustainable development. According to Vakkuri and Johanson [28], the higher education sector is evolving into hybrid organisations that perpetually integrate components of distinct, competing institutional logics. Leal Filho et al. [29] highlight the unique nature of universities as socially engaged entities (referred to as the “third mission”) that facilitate the transfer of knowledge and showcase the dichotomy between teaching and research, which leads to their complex governance. Barnett [30] clarifies the characteristics of various types of universities in relation to their functions and purpose. Theisens and Enders [31] emphasise the national specificity of universities, despite their shared features, which are typical of hybrid organisations that operate between state and market (or quasi-market) regulation on one side and institutional and academic self-regulation on the other. Despite the varying perceptions regarding the specifics of HEIs, most authors agree that higher education is an area of national importance for the economic, social, and environmental sectors [32], which are collectively referred to as the “triple bottom line” of sustainable development [29,33].
In the context of the features of universities and their highly uncertain settings, the issues of effective and efficient management, along with the challenges faced by universities, come to the fore. Addressing these issues necessarily requires integrating the management toolkit with contemporary data-driven technologies. To function effectively, higher education institutions are compelled to utilise data analysis tools, including learning analytics, business intelligence, data mining, and artificial intelligence (AI) [34,35]. These tools, commonly designated as data-driven technologies, have become vital for managing and decision-making processes within higher education institutions [36]. Their effectiveness and efficiency, which are fundamental prerequisites for competitiveness and sustainable development, are ensured by the management control function, executed through management control systems (MCSs) [37].
MCSs have always been data-driven tools for collecting and processing organisational data, with the purpose of allowing for well-grounded management decisions to be made. There is no single accepted definition of MCSs. Abernethy and Chua [38] characterise MCSs as an amalgamation of several control mechanisms and the oversight of employee behaviour to attain established objectives. Identifying control indicators, which form the basis of control mechanisms, is a central part of the risk assessment procedure and is incorporated in the “Select and determine the control norm” stage of the control process [39]. It can thus be logically concluded that MCSs are relevant to this stage of the control process. Chenhall [40] defines MCSs as a synthesis of budgeting and product costs. Garrison et al. [41] interpret MCSs as measures implemented in organisations by management to enhance the likelihood of attaining established objectives during the planning phase. Merchant and Otley [42] assert that MCSs serve as instruments for strategic development and financial management. Merchant and Van der Stede [43] define MCSs as all actions undertaken by managers to ensure the successful execution of goals, strategies, and plans. In essence, it can be concluded that MCSs are structured, routine-oriented frameworks that support the maintenance or modification of organisational processes [43]. They enable an organisation to accomplish its objectives effectively and efficiently by optimally utilising its resources [44]. Alastal et al. [45] describe MCSs as the systems employed by top managers to optimise the utilisation of internal resources in order to attain their objectives [45]. These system types encompass tools that furnish managers with relevant information to facilitate decision-making, planning, and control processes [45]. The authors of this review define MCSs as management systems related to the management function of control. In more detail, MCSs are organisation’s internal systems, which provide managers with the necessary tools, methods, and information to control (monitor, supervise, and regulate) personnel and operations in order to achieve their organisational goals. In today’s world, MCSs are predominantly technological systems that can enhance the efficiency and effectiveness of management. Regarding the challenges in the implementation and functioning of MCSs in higher education, this literature review found a limited number of contemporary sources on this topic (refer to Section 3, Table 1). This finding indicates that the existing scientific literature on this topic is still underdeveloped.
In this context, the current review aims to elucidate the challenges faced by MCSs in the realm of higher education during the implementation and functioning of these systems in contemporary times. To achieve this aim, we pose the following research question: What challenges do MCSs in higher education face, according to the literature indexed in Scopus and the Web of Science (WoS) since the commencement of 2020?
Methodologically, this study is based on a systematic literature review, follows PRISMA guidelines, and employs K-means clustering, content analysis, deduction, and induction methods.
In our opinion, this review expands the knowledge base in management control by identifying, categorising, and mapping the challenges facing MCSs in higher education. In practical terms, this study lays a foundation for enhancing the implementation, functioning, and effectiveness of MCSs in HEIs, thereby contributing to improved research management in HEIs and national academic governance.

2. Methods and Data

For the purpose of data collection, a systematic literature review was conducted, which strictly adhered to the PRISMA guidelines. The methods of deduction and induction were utilised in order to analyse the data. To further examine the results, K-means clustering was also utilised. The data was presented graphically in Microsoft Excel.
The research was conducted in three phases. In the first phase, we conducted a thorough systematic literature review following the PRISMA guidelines to collect relevant data from literature indexed in the Scopus and WoS (Core Collection) databases. In the second phase, we developed a ranking procedure to assess the collected data. In the final phase, we conducted a K-means clustering analysis to discern the relationships within the collected data, enabling us to group the data and draw conclusions based on these groups.
The first phase of the systematic literature review was divided into five stages related to the PRISMA guidelines [46] and Lambovska and Raitskaya’s [47] approach: Identification, Screening, Eligibility, Inclusion, and Synthesis. A PRISMA flowchart illustrating the first four stages in this review is shown in Figure 1. All results from the systematic literature review, including the sample, are described in detail in the results section.
In the Identification stage, structured initial searches of the Scopus database and databases within the Core Collection of the WoS of academic documents were conducted on 4 November 2024. Figure 2 presents the PRISMA protocol. The search was developed by combining the keywords “management control” and “management accounting systems” with the main synonyms of the terms “higher education” and “challenges”. Figure 2 presents the exact search term used. The search strategy encompassed the following fields: “Topic” for the WoS, and “Article Title, Abstract, and Keywords” for Scopus. Additionally, the search strategy covered the period 2020–2025. The authors of this review made decisions regarding the inclusion criteria, included languages, and included document types. The exclusion criteria required papers to be relevant to the research topic and aim, and to have been published between 2020 and 2025.
The second stage in the PRISMA flow entailed screening the gathered literature. Initially, we verified the pool for duplicate records and removed them using automation in Clarivate EndNote. Furthermore, both authors independently checked and removed records that were incompatible with the search terms. Finally, the documents were assessed by each of the authors based on the inclusion and exclusion criteria.
A comprehensive evaluation of the gathered literature was performed using the content analysis method in the eligibility stage. Titles and abstracts were screened, and full papers were reviewed. We eliminated the documents that were inconsistent with the research topic and the research aim. We also confirmed the accessibility of the full text among the remaining records. Documents that were unavailable as open access and could not be located in alternative databases or obtained through author contact were excluded. Additionally, we excluded the documents that failed to satisfy the language inclusion criterion.
In the fourth stage, the study sample was finalised. This is presented in the results section. Before finalising this process, each author performed a second comprehensive review of the chosen records. The sample is illustrated in Section 3, alongside a timeline showing their year of publication.
During Synthesis, we identified the challenges found in the sample. All sources selected for inclusion were read by the authors of this review, who independently extracted information regarding the research sample. The induction method was employed to systematise the challenges into groups, and subsequently into subgroups, according to their nature. The last update of the sample was on 20 June 2025.
In the second phase of this review, the total number of mentions for each challenge within the subgroups was computed. This analysis allowed us to identify the most prevalent challenges. The findings are presented in the results section, where the highest-ranked items are arranged.
The third phase of our research included the application of cluster analysis. This type of analysis [48] classifies the data into distinct groups. Each cluster was formed according to the characteristics of the data: their similarities and the differences. According to Herman [49], this method allows researchers to understand complex information by identifying patterns and interactions in the data. One of the most widely used techniques to divide up clusters is K-means clustering [50]. In this technique, the distance [51] is used to evaluate the similarities among the unsorted data. In our study, the clustering of the sampled sources was based on the country under study and the group of challenges identified. Cluster analysis variables included the identified groups of challenges to MCSs in higher education. We opted for four clusters. The optimal number of clusters [52] was determined through a trial-and-error method. We focused on balancing the results of these two criteria: country distribution by clusters and their sum of squared errors (SSE). We depicted the clusters using MS Excel.

3. Results

During the first stage of the review, we obtained a total of 64 documents (WoS: 21 and Scopus: 43). In the second stage, we removed 13 duplicates found in the two databases. In the third stage, 36 documents were eliminated: 22 were off-topic, 6 were missing full texts, 3 were in different languages, and 5 were irrelevant to the research aim. The 22 excluded documents, deemed irrelevant to the research topic, do not provide information on the application of MCSs in higher education. The five removed documents, identified as irrelevant to the research aim, do not address studies mentioning challenges to MCSs in higher education. Fifteen documents were ultimately included in the sample (Table 1 and Figure 3).
We organised the final sample in Table 1, which has four columns. The first column contains the source number. The second column presents the authors’ names. Table 1 is arranged alphabetically according to the authors’ surnames. The third column shows the year of publication. The last column presents the country under research.
Table 1. Research Sample.
Table 1. Research Sample.
SourceAuthorsYearCountry
Under
Research
[53]Anyim, Dr. W.O.2020Nigeria
[54]Aryawati, N.P.A.; Triyuwono, I.; Roekhudin, R.; Mardiati, E.2024Undefined
[55]Chalmeta, R.; Estevez, M.F.2023Spain
[56]De Villiers, C.; Dimes, R.; Molinari, M.2024UK
[57]Dudycz, H.; Hernes, M.; Kes, Z.; Mercier-Laurent, E.; Nita, B.; Nowosielski, K.; Oleksyk, P.; Owoc, M.L.; Palak, R.; Pondel, M.; Wojtkiewicz, K.2021Poland
[58]Frei, J.; Greiling, D.; Schmidthuber, J.2023Austria
[59]Khalaf, M.H.R.; Azim, Z.M.A.; Elkhateeb, W.H.A.H.; Shahin, O.R.; Taloba, A.I.2022Undefined
[60]Khudhair, A.H.; Daud, Z.M.; Mustafa, H.A.R.; Al-Zubaidi, A.N.J.2025Iraq
[61]Liying, H.; Mengying, Z.2024China
[62]Ma, Y.; Dai, B.; Ding, B.2022China
[63]Rigby, J.; Kobussen, G.; Kalagnanam, S.; Cannon, R.2021Canada
[64]Rosalina, K.; Jusoh, R.2024Indonesia
[65]Rosalina, K.; Jusoh, R.2025Indonesia
[66]Susilawati, W.; Alamanda, D.T.; Ramdani, R.M.; Adi Prabowo, F.S.; Ramdhani, A.2020Indonesia
[67]Vale, J.; Amaral, J.; Abrantes, L.; Leal, C.; Silva, R.2022Undefined
We present a timeline of the documents in the sample in Figure 3. The documents were published between 1 January 2020 and 20 June 2025. In most years, two documents were published, with the exceptions of 2022 and 2024. In 2022, there were three publications. The highest number of papers were published in 2024, in which four of the documents in the sample were published.
The synthesised results from the first stage of the study are presented in Table 2. The table contains 48 challenges derived from the sample. The vertical part of the table presents the challenges, grouped into groups and subgroups, arranged in alphabetical order. The header presents the sources. In this matrix, the sources of the presented challenges are visualised with the help of a bullet. The last column presents the total mentions per subgroup. The summary results for each group are displayed in rows beneath the group. The last row of the table shows the total number of challenges relative to the source for all groups.
Two consecutive syntheses of the challenges were made when organising the table. First, the challenges were synthesised into the following subgroups: Global Threats, Lack of Technology Training, Threats Regarding Control Criteria, Technological Integration, Data Gathering, Funding, HR Problems, Organisational Constraints, HR Resistance, Management Engagement, Stakeholder Behaviour Issues, and Other Stakeholder Issues. Second, a synthesis of the groups was implemented, thus reducing the challenges to four overarching groups: Growth Threats, Limitations, Malpractices, and Stakeholder Issues. This sequential grouping was used for subsequent analysis.
The results of the subgroup ranking are presented in Figure 4. They are based on the most frequently mentioned items systematised in the column ST (subgroup total) of Table 2. The ranked challenges to MCSs in higher education show that the subgroups have the following sequence of mentions, in descending order: first, HR Problem, with 13 mentions; second, Organisational Constraints and Management Engagement, cited 9 times; third, Technological Integration and Lack of Technology Training, mentioned 7 times; fourth, Funding, with 5 mentions; fifth, Other Stakeholder Issues, HR Resistance, and Data Gathering, with 4 mentions; sixth, Threats Regarding Control Criteria, cited 2 times; and finally, Global Threats and Threats Regarding Control Criteria, mentioned just once. Each of the subgroups and the included challenges can be examined in detail in Table 2.
The highest-ranked subgroup, HR Problems, includes the following challenges, arranged in alphabetical order: counterproductive work behaviour, dissatisfaction with psychological needs, human errors, increased work stress/scepticism, lack of human ingenuity, potential/unintended human errors, staff incompetence, and work overload. The second-ranked subgroup, Management Engagement, includes abuse of authority, abuse of responsibilities, diminishing employees’ autonomy, fraud, lack of coordination, lack of management support, the need to introduce a MCS evaluation team, and overriding established controls. The subgroup of Organisational Constraints, which is also ranked second, encompasses the following challenges: alterations in the ERP, competing priorities, ineffective communication system, lack of capacity, poor remuneration system, poor working conditions, short analysis periods, and staff competencies. The third-ranked subgroup, Technological Integration, covers the challenges of automated system errors, inadequate infrastructure, inappropriate timing for implementation, lack of necessary equipment, and shortage of experienced IT personnel. The subgroup of Lack of Technology Training, also ranked third, includes the following challenges: absence of implementation methodologies, inadequate staff training, lack of an ICS culture, and the need for a training team.
Regarding cluster analysis, we focused on achieving a balance between two key criteria—the distribution of countries across clusters and the SSE of each cluster—and we selected the results using four clusters. We generated four clusters, illustrated in Figure 5. All challenges were taken into consideration in the generation of the results. To highlight the results, the groups representing the most frequently mentioned challenges were presented along the abscissa and coordinates in Figure 5. Figure 5a is a colour-coded matrix, where each colour represents a cluster. Figure 5b shows the combination of each colour element in a single colour, along with the distance or similarity measures between each cluster and its cluster centroid. This visualisation assists in discerning the data predominantly associated with a specific cluster.
Cluster 1 accounts for 30% of the sample cases. This cluster comprises three countries: Austria, China, and Spain. The centroid coordinates were (1.00; 2.67). Cluster 2 is the smallest, consisting of one country, Nigeria, with centroid coordinates of (8.00; 5.00). Cluster 3 included two countries: Indonesia and an undefined country. The centroid coordinates for Cluster 3 were (6.00; 1.50). Cluster 4 is the largest, encompassing four countries, Canada, Iraq, Poland, and the UK, with centroid coordinates of (2.00; 0.25). The SSE values for the cluster analysis are as follows: Cluster 1–11.3, Cluster 2–0.0, Cluster 3–9.0, and Cluster 4–4.3.

4. Discussion

Through the systematic literature review conducted here, we identified 48 challenges to MCSs in the domain of higher education. These challenges are listed in Table 2, providing a clear response to the research question articulated in the introductory section. Additionally, we classified these challenges into four main groups and twelve subgroups. The largest group is Limitations, which encompasses 31 mentions and 4 subgroups. That group was mentioned in 12 documents in the sample. This result appears logical given the nature of the research topic. In addition, this group covers the most diverse challenges faced by MCSs in higher education. The second largest group is Growth Threats, with 17 mentions and 4 subgroups. This was explored by seven authors from the sample. The third group, Malpractices, has 13 mentions; it contains two subgroups and was explored by six authors from the sample. The last group, Stakeholder Issues, contains five mentions; it contains two subgroups and was mentioned by five authors from the sample.
From the results obtained over the years, the highest number of studies can be observed as being published in the year 2024, followed by 2022, and subsequently 2020, 2021, and 2023, with an equal number of studies being represented in each of these years. An analysis of the diversity of challenges to MCSs in higher education revealed a peak in 2022, where the highest diversity was observed in the Limitations group, followed by the Growth Threats and Malpractice groups. In 2020, no research was conducted on challenges from the Stakeholder Issues group. Regarding the diversity of challenges, the studies from 2024 showed an increase in diversity in the Limitations group. The second greatest increase pertained to challenges from the Growth Threats group, then Malpractices, and lastly, Stakeholder Issues. The studies from 2022 ranked third, with research identifying challenges from each of the four groups throughout that year. The fourth position is held by 2025, specifically the period preceding 20 June, where the primary focus was on challenges from the Limitations group, followed by the Growth Threats group and then the Malpractices group. In 2025, no research was conducted on challenges from the Stakeholder Issues group. Research in 2023 primarily focused on the challenges from the Growth Threats group, with no studies conducted regarding the Limitations group. Lastly, the studies from 2021 concentrated on the Limitations group, followed by the Growth Threats group; in 2021, no studies addressed challenges from the Malpractices and Stakeholder Issues groups. In other words, it can be concluded that the number of publications and the different challenges covered over the years represent the most relevant group of challenges for the respective period found in the scientific literature. Interestingly, as of June 2025, there are still no publications regarding challenges from the Limitations group.
The analysis of ranked subgroups of challenges to MCSs in higher education shows that the top three ranked subgroups are HR Problems (13 mentions), Management Engagement (9 mentions), and Organisational Constraints (9 mentions). The first and third subgroups are categorised under the Limitations group, while the second is classified under the Malpractices group. These results indicate that central subgroups of challenges to MCSs in higher education are related to internal organisational problems, emphasising the need for focused strategies to address these issues. The results also underscore the pivotal role of managers in navigating the complexities associated with change. To address challenges, it is imperative for managers to proactively guide the change management process by systematically developing and implementing comprehensive, strategic plans, along with detailed tactical and operational plans. Ranking third, just behind the top two, are the subgroups of Technological Integration and Lack of Technology Training. This high ranking suggests that management in higher education faces difficulties in adopting new digital technologies.
The interpretations of the resulting clusters from the cluster analysis can be summarised as follows: Cluster 4 is the largest, comprising 40% of the sample. Cluster 1 ranks second in size, accounting for 30% of the studies under review. Next is Cluster 3, which represents 20% of the sample. Finally, Cluster 2 is the smallest, covering only 10% of the sample. The coordinates of clusters 1, 3, and 4 appear relatively close in the context of the overall cluster map. Cluster 2 stands out the most, being visually represented as the furthest from the other three clusters. The results of the obtained SSE indicate that Cluster 1 has the highest score of 11.3, followed by Cluster 3, with a score of 9.0. Cluster 4 has an SSE of 4.3, whereas Cluster 2 has the lowest SSE, at 0.0. Except for Cluster 1, all clusters demonstrate low SSEs. Based on the clustering tests conducted for up to five clusters, this outcome represents the optimal result with the lowest total SSE (24.3), yielding a relatively balanced distribution of countries across the clusters. Consequently, our results concerning clustering can be considered dependable. The results of the cluster analysis can serve as an initial point or a broad guideline for developing strategies to address the challenges faced by MCSs in higher education within the countries of a respective cluster. The primary reason for using cluster analysis is that higher education MCSs within countries in the same cluster encountered similar challenges during the period under review (2020 to June 2025), considering the limitations of this study, as outlined below. In this context, higher education management and governance within countries of the same cluster can share insights, learn from each other’s experiences, and adopt various suitable approaches and tools. However, we would like to emphasise that universal approaches and strategies do not exist. In our view, developing strategies to tackle the challenges faced by MCSs in higher education is a highly complex process. Furthermore, such strategies should be based on a multi-layered model that considers the influence of national culture, multicultural communication in education and research, local and organisational culture, modern digital technologies, and national education policy. As far as the available information suggests, there is a lack of both theoretical and practical studies that simultaneously account for the impact of these factors on MCSs in higher education. Nonetheless, we would like to highlight certain studies that managers and governance in higher education can utilise when developing models as a foundation for selecting an appropriate strategy in this context. Initially, Patra [68] introduces a tri-polar framework of “local–national–global” to advance the current educational system. Subsequently, Mitter et al. [69] analyse how different aspects of national culture influence MCSs. In their article, de Waal and de Boer [70] investigate the interaction between national culture and the multinational environment, focusing on their impact on management control. Next, Lee [71] examines the challenges of integrating national cultures into a unified organisational culture. Furthermore, Hardman and Sandi [72] review the literature on the application of educational policy in local and specialised contexts. Finally, and equally importantly, Kumar et al. [73] explore how digital educational technologies and various stakeholders influence educational policy.
The main limitation of this research pertains to the review protocol utilised (Figure 2), specifically regarding its search sources, search phrases, and inclusion and exclusion criteria. The pool would expand if elements of this protocol were altered. This is particularly relevant for the search sources and synonyms included in the search term. It is important to note that we do not claim representativeness beyond the review protocol. Additionally, this research does not account for the field of study of the sampled sources. Moreover, the correlations among the challenges are outside the scope of this study. A further limitation is that this review does not cover future management plans, activities, strategies, or policies, as they are beyond the scope of the research. The identified limitations indicate that the data should be interpreted with caution.
For future research, we recommend the following studies: First, integrate studies addressing other challenges by expanding the search terms. Second, explore strategies for overcoming these challenges. Third, review the data by incorporating supplementary sources like books, papers indexed in secondary databases, and grey literature. Fourth, establish metrics to evaluate the effectiveness of management control systems. Fifth, conduct empirical research to examine specific universities and countries. Sixth, explore the integration and application of management control systems in higher education. Seventh, conduct an empirical study on attitudes and satisfaction regarding the use of management control systems in higher education.

5. Conclusions

This review provides an examination of international research indexed in the Scopus and Web of Science (Core Collection) databases from 2020 onwards on the challenges faced by management control systems (MCSs) in contemporary higher education. These challenges were meticulously identified, organised, categorised, and mapped. The methodology employed comprises a systematic literature review, content analysis, deductive and inductive reasoning, K-means clustering methods, and the PRISMA guidelines, which were used to establish the methodological framework for this research.
Forty-eight challenges to MCSs in higher education were identified and organised into four groups (Growth Threats, Limitations, Malpractices, and Stakeholder Issues), encompassing twelve subgroups. The challenge subgroups were ranked based on the frequency with which they were mentioned in the literature. The top-ranked subgroups were HR issues (ranked first), management engagement (ranked second), and organisational constraints (also ranked second). The first subgroup, HR Problems, encompasses the subsequent challenges, listed in alphabetical order: counterproductive work behaviour, dissatisfaction with psychological needs, human errors, increased work stress/scepticism, lack of human ingenuity, potential/unintended human errors, staff incompetence, and work overload. The second subgroup, Management Engagement, encompasses abuse of authority, abuse of responsibilities, diminishing employees’ autonomy, fraud, lack of coordination, lack of management support, need to introduce a MCS evaluation team, and overriding established controls. The third subgroup, with the same number of mentions, Organisational Constraints, covers alterations in the ERP, competing priorities, ineffective communication system, lack of capacity, poor remuneration system, poor working conditions, short analysis periods, and staff competencies. The challenge subgroups of Technological Integration and Lack of Technology Training are also highly ranked (third ranking). For the former, the primary challenge contributing to this result is inadequate infrastructure, while for the latter, the primary contributors are inadequate staff training and the need for a training team. Additionally, all challenges were classified into four clusters according to the countries studied in the reviewed sources.
Theoretically, this study enriches and complements the body of knowledge on the field of management control, particularly regarding MCSs. Specifically, its theoretical contributions encompass the identification, summarisation, classification, and mapping of the challenges to MCSs in higher education found in the scientific literature from 2020 to June 2025. The results of the current study can be utilised by researchers, university lecturers in the fields of management, control, and education, and government authorities in higher education.
Practically, recognising and systematising the challenges to MCSs establishes a foundation for improving the implementation, operation, and effectiveness of those MCSs, which ultimately leads to enhanced research management within these organisations. This result is particularly anticipated regarding the management of HEIs in the countries examined in the literature sample, as their managers can take advantage of the clustering results obtained in this study. This also applies to the management of HEIs in countries with educational features comparable to those in the sample. Furthermore, identifying and addressing the challenges faced by MCSs, primarily relating to internal organisational and technological issues, would improve academic governance nationally and encourage further advancements, including in technology, within higher education, science, and society.

Author Contributions

Conceptualization, A.A.-S.; methodology, M.L.; software, A.A.-S.; validation, A.A.-S. and M.L.; formal analysis, A.A.-S.; investigation, A.A.-S.; resources, A.A.-S.; data curation, A.A.-S. and M.L.; writing—original draft preparation, A.A.-S. and M.L.; writing—review and editing, A.A.-S. and M.L.; visualisation, A.A.-S. and M.L.; supervision, M.L.; project administration, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Dataset available on request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MCSsManagement control systems
WoSWeb of Science
HEIsHigher education institutions
AIArtificial intelligence
STSubgroup total
ICSInformation and communication system
ERPEnterprise resource planning

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World 06 00100 g001
Figure 1. The flow diagram of PRISMA.
Figure 1. The flow diagram of PRISMA.
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Figure 2. The PRISMA protocol.
Figure 2. The PRISMA protocol.
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Figure 3. The sample timeline.
Figure 3. The sample timeline.
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Figure 4. Ranked challenges to management control systems in higher education.
Figure 4. Ranked challenges to management control systems in higher education.
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Figure 5. Clusters regarding the challenges to management control systems in higher education: (a) map of the sample; (b) centroids.
Figure 5. Clusters regarding the challenges to management control systems in higher education: (a) map of the sample; (b) centroids.
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Table 2. Challenges to management control systems in higher education.
Table 2. Challenges to management control systems in higher education.
Sources *
Challenges535455565758596061626364656667ST **
GROWTH THREATS
Global Threats 1
Strategy uncertainty •
Lack of Technology Training 7
Absence of implementation methodologies •
Inadequate staff training• •
Lack of an ICS *** culture•
Need for a training team • • •
Threats Regarding Control Criteria 2
Need for standardisation • •
Technological Integration 7
Automated system errors•
Inadequate infrastructure• • •
Inappropriate timing for implementation •
Lack of necessary equipment•
Shortage of experienced IT personnel •
GROUP TOTAL503001100410200
LIMITATIONS (regarding)
Data Gathering 4
Data collection problems • •
Need for data availability • •
Funding 5
Financial pressure on the budget •
High costs •
Inadequate funding • ••
HR Problems 13
Counterproductive work behaviour • ••
Dissatisfaction with psychological needs • •
Human errors•
Increased work stress/scepticism ••
Lack of human ingenuity•
Potential/unintended human errors•
Staff incompetence•
Work overload ••
Organisational Constraints 9
Alteration in the ERP ****•
Competing priorities •
Ineffective communication system•
Lack of capacity • •
Poor remuneration system•
Poor working conditions•
Short analysis periods •
Staff competencies •
GROUP TOTAL820310 22 24310
MALPRACTICES
HR Resistance 4
Eroding performance quality •
Resistance to reforms •
Staff collusion•
Undesirable employee behaviours •
Management Engagement 9
Abuse of authority•
Abuse of responsibilities•
Diminishing employees’ autonomy ••
Fraud•
Lack of coordination •
Lack of management support •
Need to introduce a MCS evaluation team •
Overriding established controls•
GROUP TOTAL501100300002100
STAKEHOLDER ISSUES
Stakeholder Issues Behaviour 1
Issues with implementing controls due to stakeholders’ perceptions •
Other Stakeholder Issues 4
Challenges in balancing multiple stakeholders’ information • •
Need for a system to identify stakeholders’ interests •
Political influence •
GROUP TOTAL001101001000001
TOTAL FOR ALL GROUPS1825512623536611
* Sources match those in Table 1; ** ST = subgroup total; *** ICS = information and communication system; **** ERP = enterprise resource planning.
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Lambovska, M.; Angelova-Stanimirova, A. Unveiling Challenges to Management Control Systems in Higher Education: A Systematic Literature Review. World 2025, 6, 100. https://doi.org/10.3390/world6030100

AMA Style

Lambovska M, Angelova-Stanimirova A. Unveiling Challenges to Management Control Systems in Higher Education: A Systematic Literature Review. World. 2025; 6(3):100. https://doi.org/10.3390/world6030100

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Lambovska, Maya, and Antoaneta Angelova-Stanimirova. 2025. "Unveiling Challenges to Management Control Systems in Higher Education: A Systematic Literature Review" World 6, no. 3: 100. https://doi.org/10.3390/world6030100

APA Style

Lambovska, M., & Angelova-Stanimirova, A. (2025). Unveiling Challenges to Management Control Systems in Higher Education: A Systematic Literature Review. World, 6(3), 100. https://doi.org/10.3390/world6030100

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