Critical factors that influence the effectiveness of facility maintenance management practice in public university buildings in Ethiopia: an exploratory factor analysis

Abstract

Facility maintenance management (FMM) is essential for ensuring long-term values and to sustain project goals throughout the life cycle delivery process. However, in underdeveloped nations such as Ethiopia, facility maintenance management is an immature and underutilised process that requires a holistic intervention for practical improvement. The main aim of this study was to identify and prioritise critical factors that affect the effectiveness of FMM, with a focus on public universities in Ethiopia. Initially, a total of thirty-three (33) crucial variables were identified with a systematic literature review and desk study. To collect primary data, a survey research design approach was utilised using questionnaires and informant interviews. A total of seventy-five (75) data sets were obtained from 180 online surveys for conducting exploratory factor analysis (EFA). The outcome of the study revealed thirteen (13) critical attributes grouped into four factors that affect the effectiveness of facility maintenance management practises. The final four-factor model includes F1, internal processes and organisation; F2, community culture, learning, and growth; F3, impacts of design and construction quality; and F4, facility maintenance approach and management. This study indicated that facility maintenance management practises in public universities in Ethiopia are immature and require extensive enhancement. The identified influencing factors highlight the need for a comprehensive intervention to promote improved facility maintenance management practises and applications in Ethiopia. Further research is needed to analyse a wider range of attributes and data using confirmatory factor analysis.

Keywords:

• Facility maintenance management (FMM)
• critical factors
• underdeveloped nations
• public universities
• exploratory factor analysis (EFA)

REVIEWING EDITOR:

• Sanjay Kumar Shukhla, Associate Professor and Program Leader, School of Engineering, Edith Cowan University, Australia

SUBJECTS:

• Engineering Management
• Civil, Environmental and Geotechnical Engineering
• Building and Construction
• Building Project Management
• Building Regulations
• Building Services Engineering

1. Introduction

Recent technological developments and trends in the industry 4.0 era, including the application of artificial intelligence (AI), digital twin (DT), Internet of Things (IoT), building information modelling (BIM), and mobile technologies, have improved the performance of the early phases of the asset/facility lifecycle. The shift from the conventional design and construction approach towards energy efficiency and environmental friendliness is also leading the state of the art in the domain of Architecture, Engineering, Construction and Operation (AECO). This initiative also leads to a shift in maintenance from traditional reactive to proactive and forward-looking strategies.

As buildings share considerable natural resources and energies, the strategies also consider the concerns of a sustainable environment (Asmone et al., 2019; Ismail, 2021; Okoro, 2023). Nevertheless, this technological advancement and concern for environmental sustainability has not been well implemented in developing countries (Dzulkifli et al., 2021; Okoro, 2023). Until the initiative is equally recognised and applied to the entire life cycle of buildings, global concern would not be achieved without the full commitment and capacity building of developing countries. Okoro (2023) stated that corruption, inadequate funding, lack of maintenance culture, improper handling and misuse of facilities, and lack of information on maintenance are some of the many challenges in these countries.

According to a report from the United Nations (UN, 2022), rapid urbanisation and population growth in developing countries are increasing and accounting over 80% of the global population and building footprint. Small interventions in the most populus regions to implement the initiative can make significant changes. The responsibility of buildings to consume natural resources is approximately 40% (Ashworth et al., 2019; Okoro, 2023). Strategic and effective decision-making approaches, such as sustainable building maintenance, adaptation, and deconstruction, have economic, social, and environmental consequences. However, developing countries still lag behind industrialized countries in terms of awareness and a holistic view of the triple bottom line especially in FM phase.

Public universities are an integral part of a nation’s capacity building scheme in transferring up-to-date and international knowledge and fostering an active and thinking citizenry (Odediran et al., 2015). These universities can achieve their objectives by creating a suitable and conducive environment and operational facility management practises. According to Price (2003) and Anker Jenson (Anker Jensen, 2011), the efficiency of a university’s core competencies can be determined by the performance of its FM. This is because deplorable facilities create a barrier to achieving university objectives. In recent years, Ethiopia has made significant investments in establishing new universities and upgrading existing colleges. This initiative began in 2005 with the aim of creating more higher education opportunities in regional cities across the country and producing highly qualified graduates. However, despite the considerable investment and construction of new facilities at public universities in Ethiopia, the post-construction phase (operations and maintenance) is often neglected in the asset delivery process. Because of its significant impact on student and faculty productivity, the importance of FMM is undeniable (Hassanain et al., 2019; Lavy, 2008; Lavy & Bilbo, 2009; Wong et al., 2006). This is because once an asset or facility is handed over to the user or client, facility management (FM) plays a significant role in the overall performance, productivity, and liveability of facilities (Alsayyari et al., 2019; Eric Teicholz, 2004; Sampaio et al., 2022). However, the FM of public universities in Ethiopia is not well organised and needs considerable improvement. Nevertheless, the management of public universities in Ethiopia has focussed on establishing new facilities to meet the growing demand for higher education across the country. While increasing the enrolment capacity of students is beneficial for a developing nation, it is critical to manage existing facilities efficiently and ensure adequate maintenance and create a conducive working environment and productive teaching and learning conditions while meeting sustainability concerns. To ensure effective management of facilities in public universities, comprehensive intervention is urgently required. To find sustainable solutions to the challenges, it is necessary to realise the factors that can be attributed to effective practises and to act accordingly. Failure to address these challenges can result in universities being unable to deliver their intended services, causing inconvenience to students, faculty, administrators, and staff, and erode public confidence in universities. Subsequently, to ensure the sustainable support of the core competencies of universities (teaching, learning, research, and community services), there is a need for improved maintenance management practises of facilities in Ethiopia. Creating an efficient and effective framework for facility maintenance management requires consideration of several factors. It is important to identify the critical factors that influence the effectiveness of facility maintenance to improve organisational capabilities, promote appropriate FMM practises and deliver quality services. Effective facility maintenance management helps maintain the project benefits achieved throughout the life cycle of the facility. This study identified the critical factors that influence the effectiveness of facility maintenance management practises. It is intended to raise awareness among stakeholders and support efforts that lead to improved practises.

The remainder of this article is divided into four sections. Section 2 contains a literature review, and Section 3 explains the research design and methodology. Section 4 analyses and discusses the results, followed by a conclusion in Section 5.

2. Literature review

2.1. Facility management

Facility management (FM) integrates several disciplines to influence the efficiency and productivity of the economy of societies, communities, and organisations as well as the way individuals interact with the built environment. FM affects the health, well-being, and quality of life of many of the world’s societies and population through the services it manages and delivers (ISO 41001, 2018). While FM has such a broad impact and recognition of its principles and practises at a global level, it has been deficient and overlooked in the case of developing countries, such as Ethiopia.

Once organisations occupy any facility for implementing its core business services and processes, facilities management is then required to support the core business and ensure continuity (Alsayyari et al., 2019; Eric Teicholz, 2004; Sampaio et al., 2022). For instance, the core business or competency of an educational institutes is to deliver education within its scope and defined level of quality. Because there exist different facilities to support the primary business goal in the premise, the need for facility management is undisputable. The main purpose of integrating the 3Ps (people, place, and process) within the built environment is to improve the quality of life and productivity of the core business (IFMA, 2022). On the other hand, facility management is a term closely associated with building management. More broadly, facility management should not only be understood as general building management connected with everyday building operation but also include long-term planning and focus on its users (Potkany et al., 2015). Given its long-term benefits and energy efficiency over its lifespan, sustainability is also closely connected to performance-based facility management in the built environment.

Despite the initial focus on construction costs, project stakeholders should also consider the subsequent operation and maintenance costs during the facility management of a building over its life cycle. These ongoing costs can exceed the initial investment, which includes the costs of planning, design, and construction (Becerik-Gerber et al., 2012; Flanagan & Jewell, 2008). Furthermore, Giglio et al. (2018) stated that lifecycle management of infrastructure and facilities is essential for all public sector assets. Public sector assets include but are not limited to transport, water, buildings, education, and commercial and health facilities. Emphasis should be placed on ensuring that public capital assets are safeguarded and maintained to achieve a lifecycle of physical assets that provides the required level of service for present and future owners and users in the most cost-effective way.

2.2. FM principles and theories

As explained by Eric Teicholz (2004), the roles and responsibilities of facility managers are described in different ways. During the 1970s, Herman Muller Inc. established a facility management institute (FMI) and developed a three-element model of people, place, and process. These three interlocking circles, People, Process, and Place, form the foundation of facility management. They are interconnected and rely on each other to create a holistic approach to effectively managing facilities optimising resources and enhancing the overall workplace experience for occupants. By integrating these elements, Facility Managers can achieve operational excellence, increase productivity, and contribute to the organization’s success (IFMA, 2023). Technology in the age of information has been recognised as an important enabler of facility management functions (Sedhom et al., 2023). This addition emphasises that technology plays a significant role alongside the basic three P - people, process, and place principle towards improved performance of the core business.

Due to the increasing importance of sustainability, the 3 P concept is integrated with the triple bottom line principle to achieve the goals of sustainable development. This integration aims to harmonise economic success with social well-being and environmental protection, as illustrated in Figure 1.

Figure 1. Integration of 3 P FM theory to TBL in sustainability towards sustainable Facility Management (SFM).

2.3. Facility management (FM)

Facility management is an integrated approach for organisations to operate, maintain, improve, and adapt their buildings and infrastructure (Brooks & Atkin, 2015). FM encompasses various areas, but FM management constitutes a significant portion of the total costs incurred by FM activities. FMM accounts for more than 85% of these costs (Flanagan & Jewell, 2008; Jawadekar et al., 2014). Additionally, Zakaria et al. (2018) defined FM as a combination of non-core organisation services primarily focussed on building maintenance. The operation and maintenance management phases are crucial, requiring substantial capital investment and often lasting longer than other stages in the Architecture, Engineering, Construction, and Operation (AECO) life cycle (Heaton et al., 2019; Zhao et al., 2022). However, there is generally a lack of focus on post-construction stage management in the built environment (Ashworth et al., 2019; Shaw et al., 2022), particularly in developing countries. This oversight has significantly impacted building performance by leading to various defects and even premature failure before reaching their intended lifespan.

2.4. Definition of maintenance

Building maintenance prevents or minimise the decay and deterioration of a building’s quality, ensuring that it remains functional for its intended lifespan. The primary focus is on preserving the components of the building and does not involve making improvements beyond its initial design specifications. When a building reaches the end of its service life, replacement or extensive renovations are necessary (Stanford, 2010). On the other hand, Hon Yin Lee and Scott (2009) and Puķīte and Geipele (2017) defined maintenance as ‘work undertaken in order to keep, restore or improve every part of a building, its services and surrounds, to a currently acceptable standard, and to sustain the utility and value of the building’. According to this concept, the scope of maintenance goes beyond initial design requirements to the acceptable standard to meet the increased expectations. In this context, the improvement and adaptation of buildings can be regarded as maintenance.

Another extended definition of maintenance by Barrett (1995) goes beyond keeping the original design stage of the building in which FM is an ‘Integrated approach to maintain, improving, adapting the buildings of an organisation to create an environment that strongly supports the primary objective of the organisation’.

Based on the concepts discussed above, building maintenance is defined as a range of activities that focus on sustaining the value and optimising the benefits realized from earlier phases of an asset lifecycle. This includes tasks such as preservation, adaptation, and deconstruction in line with sustainable practises

2.5. Nature of maintenance

According to Booty (2009), building maintenance accounts for 70% of the operation and maintenance costs. Flanagan and Jewell (2008) also stated that facility operation and maintenance contribute to 65% of the entire life cycle cost. Olanrewaju and Abdul-Aziz (2015) further emphasise that 90% of a building’s useful life necessitates regular maintenance, making it impossible to have a building that does not require maintenance. Therefore, maintenance is an integral part of an asset in the built environment.

2.6. Why maintenance is required

Maintenance is necessary to ensure that buildings, infrastructure, and equipment are maintained in optimal condition for normal use. Neglecting maintenance can lead to a decline in the performance and functionality of buildings, which can result in safety hazards and non-compliance with regulations (Soh et al., 2019). Organizations need to recognise that maintaining a safe environment is not just a legal obligation but also a moral responsibility. In addition, there has been a change in thinking about maintenance. It is now seen as a valuable activity and is no longer just a ‘necessary evil’ (Lateef, 2009; Olanrewaju & Abdul-Aziz, 2015). The emphasis on improving building performance while managing limited finances and minimising environmental impact has underscored the significance of maintenance in the built environment (Asmone et al., 2019; Che-Ghani et al., 2023; Lateef, 2010; Mong et al., 2019).

2.7. Maintenance management

As stated by Brooks and Atkin (2015), the maintenance of a facility in general and building service engineering installations in particular are long-standing interests within facility management. While BS EN 13306 (BSI, 2010) defines ‘all activities of the management that determine the maintenance objectives, strategies and responsibilities and implementation by such means as maintenance planning, maintenance control and the improvement of maintenance activities and economics’, Maintenance management should support the realisation of corporate goals by a proper set of policies and resources (Asmone et al., 2019). As per the BS EN 13306 (BSI 2010) definition, the main goals and objectives of maintenance include cost reduction, product quality, environmental sustainability, safety, and asset value preservation.

2.8. Maintenance policy

Maintenance Policy is a description of the interrelationship between the maintenance levels, arrangement levels, and set of maintenance actions for the maintenance of an item BS (2010). As stated by Brooks and Atkin (2015), a policy should be developed to support the preparation of operational plans in line with the maintenance strategy. Moreover, the policy must outline the scope and actions to be taken to meet business objectives and how these relate to the goals defined in the facility management strategy with respect to maintenance.

2.9. Maintenance strategy or approach

Maintenance strategy or approach shall be prepared in a way to meet current and future needs by considering the facility’s capacity to deliver the service demanded of it (Hon Yin Lee and Scott, 2009). Corrective, preventive, condition-based, and predictive maintenance are the basic elements for facility or building maintenance management strategy/approach.

A corrective maintenance strategy, referred to as maintenance 1.0, can be implemented following the occurrence of defects in a building/facility or their components (Hauashdh et al., 2022). This reactive approach involves rectifying a specific fault condition and can lead to higher maintenance costs and potential risks as the root cause of the problem is not addressed (Cheng et al., 2020). In contrast, preventive maintenance is a more proactive approach that reduces the likelihood of failure and avoids sudden breakdowns by performing planned maintenance tasks according to a set schedule. Preventive maintenance can help minimise downtime, reduce maintenance costs, and improve the health and safety of users compared with corrective strategies. It also optimises the functionality and service life of building/facility components, leading to cost reduction, improved user satisfaction, and minimised downtime (Sahli et al., 2021). Predictive maintenance strategy on the other hand is an approach aimed at forecasting potential malfunctions or defects to promptly intervene and prolong the operational lifespan of various building components. This approach relies on the collection of operational data through sensors and involves the integration of various sources of information, including monitoring data, maintenance records, and work orders (Cheng et al., 2020). In contrast to preventive maintenance, predictive maintenance recognises the need for maintenance based on the current asset condition rather than following a predetermined schedule (Sahli et al., 2021). An effectively structured predictive maintenance program has the potential to yield considerable cost savings and enhance machine performance (Sahli et al., 2021). The implementation of BIM-based facility maintenance management can further optimise the efficiency of predictive maintenance operations by integrating BIM with other cutting-edge technologies such as geographic information systems (GIS) and augmented reality (AR), virtual reality (VR), Digital twin (DT) and sensors (Cheng et al., 2020; Volk et al., 2014; Zou et al., 2018). Since the end of the Second World War, maintenance strategies and approaches have evolved over the years. Just as the industry has evolved from Industry 1.0 to Industry 5.0, so has the maintenance industry. Figure 2 shows the evolutionary process of maintenance, which has responded to increasing complexity and operational efficiency requirements over the years (Arunraj & Maiti, 2007; Jasiulewicz-Kaczmarek et al., 2023; Psarommatis et al., 2023; Werbińska-Wojciechowska & Winiarska, 2023).

Figure 2. Evolution of maintenance strategies.

2.10. Green/sustainable maintenance principles

A key element of incorporating environmentally friendly practises into building maintenance is to prioritise repairs, improve energy efficiency, reduce waste, and guarantee the safety and comfort of the society throughout the lifespan of the building. These practises aim to tackle the root causes of maintenance problems in buildings, including design deficiencies, difficulties related to maintenance procedures, financial considerations, and external factors impacting maintenance, as stated in the works of (Asmone et al., 2019; Ismail, 2021; Lok et al., 2023; Mewomo et al., 2022; Okoro, 2023). Green maintenance planning includes consideration of the condition and behaviour of construction materials, sustainability design, human comfort, component durability and cost efficiency (Ismail, 2020; Lok et al., 2023; Okoro, 2023). As stated in the studies of Lok et al. (2023) and Okoro (2023), various strategies are available to improve green maintenance planning. One effective approach to enhance the implementation of green maintenance planning is the use of building information modelling (BIM). BIM effectively manages practises that prioritise safety and energy efficiency while promoting sustainability in building maintenance (Dzulkifli et al., 2021; Okoro, 2023).

To improve building maintenance in developing nations, it is important to implement management solutions that align with sustainable construction and maintenance principles (Asmone et al., 2019; Ismail, 2020; Lok et al., 2023; Toyin & Mewomo, 2023). These solutions should focus on reducing impact optimising functionality and safety, improving energy efficiency, and ensuring financial performance. However, a lack of knowledge and expertise in maintenance practises often hinders effective maintenance.

2.11. Why sustainable facility management should be a concern

As cities continue to grow and more people move in demand, the demand for buildings and infrastructure has significantly increased worldwide (Okoro, 2023; Toyin & Mewomo, 2023). This has increased the need for housing, commercial spaces, and public structures to accommodate the expanding population (Dzulkifli et al., 2021; UN, 2023). However, this rapid construction boom also presents challenges in managing and maintaining facilities while ensuring the use of resources (Ismail, 2020; Lok et al., 2023; Okoro, 2023). In this context, the importance of a sustainable facility management strategy is now becoming an area of research for the green initiatives of the future. In this regard, maintenance management in the field of renewable energy has demonstrated considerable achievement by integrating environmental, social, and economic dimensions. Although sustainability has been a global issue, it lacks sufficient attention for maintenance and facility management in the built environment (Okoro, 2023). The implementation of an effective maintenance management strategy has improved the performance and longevity of renewable energy systems (Li et al., 2021, 2022, 2023). Strategies such as data-driven advanced condition monitoring fault prediction technologies have facilitated the decision-making process in the maintenance management of offshore wind farms (Li et al., 2023). The enactment of a closed-loop approach has shown potential reduction of revenue loss and improved profitability in renewable energy systems (Li et al., 2020). Additionally, the use of predictive maintenance based on the remaining useful life (RUL) prediction of components can optimize maintenance actions and minimize downtime (Li et al., 2021; Márquez, 2022; Mirsaeedi et al., 2023). The optimization of maintenance activities, considering maintenance opportunities and component condition, has gained attention in recent years, leading to improved maintenance strategies for renewable energy systems (Li et al., 2022) and growing rapidly in FM (Okoro, 2023). Effective maintenance management is essential for successful operation and cost-effective maintenance systems.

2.12. The need for further research in the field of sustainable FM

This research performed a systematic literature review using Scopus and Emerald Insight databases to attest the level of concern given to sustainability in the FM domain. The search included the string: [(TITLE-ABS-KEY (‘green maintenance*’ OR ‘Sustainable Maintenance*’) AND TITLE-ABS KEY ((building*OR facility* OR facilities* OR ‘Built environment*’)) AND TITLE-ABS-KEY ((plant* OR manufacturing*))) AND (LIMIT-TO (DOCTYPE, ‘ar’) OR LIMIT-TO (DOCTYPE, ‘cp’)) ] during the period from (2014–2023).

The search result with the keywords yielded 46 articles from Scopus and 58 articles from Emerald insight, which shows that there are still few studies in the field of sustainable/green maintenance/facilities management strategies in the built environment. From a Scopus search, for instance, if the search is further screened for specific keywords within the specific field from 2015 to 2023, the number of published works decreased to 49, as shown in Figure 3.

Figure 3. Number of publications based on search string (2014–2023).

By merging the bibliographic information of the search results from the two databases, the landscape of key works is visualised in Figure 4 to demonstrate the areas of focus that indicate further research is required within the knowledge domain.

Figure 4. Landscape of keywords in green maintenance for facility management.

Buildings can only remain sustainable if they are properly maintained, operated, and responsibly managed throughout their life cycle. This is essential regardless of how sustainably the buildings are initially designed and constructed (Ismail, 2021; Khalid et al., 2019; Okoro, 2023). Consequently, the integration of the triple bottom lines in the maintenance of buildings and facilities is no longer a question of necessity but of survival. Identifying the critical factors that influence the practise of effective facility maintenance management has the advantage of transforming current trends into sustainability concerns.

2.13. Maintenance in the fourth industrial revolution

The fourth industrial revolution (Industry 4.0) has introduced various digital technologies such as artificial intelligence (AI), big data (BD), digital twin (DT), building information modelling (BIM), the Internet of things (IoT), and advanced analytical tools into industrial practises The construction industry has proven performance improvement through this digitization process (Ding et al., 2019; Heaton & Parlikad, 2020; Liu et al., 2021; Sampaio et al., 2022). The incorporation of emerging technologies for maintenance activities to improve performance through data-driven, predictive, and automated approaches is referred to as Maintenance 4.0. (Jasiulewicz-Kaczmarek et al., 2023; Volk et al., 2014; Werbińska-Wojciechowska & Winiarska, 2023; Zou et al., 2018). Applying these technologies to maintenance activities enables features such as predictive maintenance, remote monitoring, real-time data collection, and advanced analytics to anticipate and address failures or defects in buildings or components and equipment before they occur. Nevertheless, the lack of accurate and reliable information poses a practical challenge to the integration of BIM-FM into the built environment (Daniotti et al., 2020; Munir et al., 2020; Tsay et al., 2023). However, BIM-FM integration is growing rapidly because of its potential to improve performance and facilitate easy access to data.

2.14. Critical factors influencing the practise of facility maintenance management

This section presents a systematic review of various studies to describe the attributes that influence the practise of facility maintenance with specific attention to developing countries. Furthermore, attributes identified from informant interviews with six experts in the study context are also included in the list of attributes/variables. Following the systematic review and conducting interviews, attributes that appear more than five times are considered the most important.

The search strings used in this section are [factors* OR challenges OR barriers* OR influence* OR issues*] AND [building* OR facility* OR facilities*] AND [maintenance* OR ‘Maintenance management*’] AND [practice* OR practises ‘Developing countries*’ OR ‘developing economies*’ OR ‘Emerging market economies*’]. The search yielded 168 articles from three databases (Scopus, Emerald insight and google search). Accordingly, the preferred reported items for systematic reviews and meta-analysis (PRISMA) process are followed to include articles for the identification of critical factors diagram, as shown in Figure 5.

Figure 5. PRISMA process followed to identify the most important attributes.

The VOSviewer software allows researchers to analyse and visualise bibliometric information from multiple databases. This software can help identify focus areas based on keyword co-occurrence patterns in research articles, as shown in Figure 6.

Figure 6. Landscape of keywords related to attributes.

VOSviewer software was used to determine the most frequently occurring keywords from the identified literature. The circles in the network represent the topics that were analysed during the search period (Leong et al., 2021). In the bibliometric analysis, a network diagram of common keyword occurrences reflects the most important research areas (Fan et al., 2020). Keywords were analysed with all author keywords. With a minimum total link strength (TLS) of 5, the most important keywords were analysed These included terms such as factors, barriers, challenges, influences, university buildings, maintenance cost management, building maintenance, university buildings, hospital buildings, and public buildings from Malaysia, Nigeria, and South Africa. Other keywords that appeared most frequently in the general search were excluded. Of the total 3132 keywords, 142 met the criterion of 5 occurrences. However, only 85 items were eligible for inclusion, taking 60% based on relevance for further study. From the remaining 85 items, further screening was performed to select some unrelated items. Finally, 19 items in four clusters were used to generate the final network map, as shown in Figure 6. This criterion is also used to check the eligibility of the papers from which the factors are selected.

Table 1 summarises the critical attributes or variables affecting maintenance management practises in developing countries identified in the systematic review.

Table 1. Most important attributes that influence the effectiveness of facility maintenance management practises.

Notation	Variables/attributes	(Palis & Misnan, 2018)	(Dzulkifli et al., 2021)	(Mong et al., 2019))	(Z. A. Ismail, 2017)	(Hauashdh et al., 2020)	(Khalid et al., 2019)	(Asmone et al., 2019)	((Zolkafli et al., 2019)	(Jandali & Sweis, 2018)	(Islam et al., 2019)	(Odediran et al., 2015)	(Mewomo et al., 2022)	(Hassanain et al., 2019)	Informant interview (2021) with six experts	Total
CF1	Maintenance standards, policies, and strategies		1			1	1	1	1				1	1	4	11
CF2	Method of awarding maintenance contracts					1		1		1				1	2	6
CF3	Availability of specialist maintenance contractors		1			1		1			1			1		5
CF4	Maintenance productivity standards and specifications	1						1			1			1	3	7
CF5	Errors in design/design fault		1			1	1						1	1	3	8
CF6	Construction materials specification and selection	1					1	1			1			1		5
CF7	Design phase consideration of the life cycle cost analysis					1	1	1			1		1	1		6
CF8	Involvement of FM professionals during the design phase	1					1							1	3	6
CF9	Design Efficiency					1		1			1		1	1	3	8
CF10	Design Complexity	1		1	1			1					1			5
CF11	Experience of Design Professionals						1	1				1		1		4
CF12	Construction Quality	1					1	1			1		1	1	3	9
CF13	Communication and Collaboration Skills	1			1			1					1	1		5
CF14	Workmanship quality during construction	1						1		1			1	1		5
CF15	Replacement building spare parts and components	1			1	1			1	1				1	2	8
CF16	Coordination between construction & maintenance team	1				1				1		1		1		5
CF17	Accuracy of as-built documentation				1	1					1		1	1	4	9
CF18	Preventive maintenance strategy					1	1			1				1	4	8
CF19	Maintenance plan and program/schedule			1		1			1	1				1		5
CF20	Training maintenance personnel					1			1	1	1		1	1		6
CF21	Skill of the appointed maintenance staff		1						1	1	1			1	5	10
CF22	Maintenance budget	1		1		1	1	1	1		1		1	1		9
CF23	Failure to forecast accurate maintenance expenditures					1		1						1	3	6
CF24	Procurement rules and regulations		1		1			1						1	2	6
CF25	Concerns about future maintenance/design maintainability		1	1				1					1	1		5
CF26	Maintenance of work quality by internal staff		1	1	1	1				1	1			1		7
CF27	Quality of defect rectification in buildings			1	1	1			1				1	1		6
CF28	Building Maintenance Manual				1	1			1					1	5	9
CF29	Database for recording and storing maintenance data				1	1				1			1	1	5	10
CF30	Effective maintenance culture			1	1	1					1		1	1		6
CF31	Attitude/awareness of users/occupants/misuse of facilities	1		1					1		1		1	1	3	9
CF32	Post completion delay in using the building				1			1		1	1				2	6
CF33	Underestimating the impact of FM	1	1	1			1	1		1					3	9

3. Research design and methodology

The research design for this study was a quantitative method. The quantitative approach was used to collect primary data from respondents. An online survey was conducted based on attributes/variables identified from the systematic literature review. A questionnaire was designed and primarily reviewed by three (3) experts as a pilot study before distribution. University Directors, Managers, Maintenance Managers, and academic and non-academic staff that closely work on facility management and university projects from forty-six (46) public universities participated in this study. Six (6) experts deliberately selected from three case universities participated in the key informant interviews to reflect on their experiences with the semi-structured interview questions related to the research questions. The interview data were transcribed, coded, and synthesised to substantiate the attributes in the context of the study.

Thus, the research adopted a snowballing and purposive sampling technique by which viable data can be obtained from a specific group of people, as stated by Wong et al. (2006). Purposive sampling methods have been criticised for their bias and lack of randomisation in data collection. However, this error can be eliminated by choosing appropriate data sources from the most trusted experts, as stated by Neuman (2014) and BSI (2010). Accordingly, data for the present study were collected from respondents who are deeply involved in the management of university facilities, to benefit from the expected high-quality, reliable information.

A total of one hundred eighty (180) online survey links were sent to respondents. The survey remained online from June 5, 2021, to September 30, 2021. The mailed survey method was preferred because of its high response rate, as described in Creswell (2014). According to Bougie and Sekaran (2016), the acceptable sample size range for this type of study is between 30 and 500, and the present sample size falls within this range.

The research design process is shown in Figure 7, which illustrates the various phases of the study. This includes identifying attributes from the literature, collecting data from survey participants, ensuring the reliability and validity of the data, and presenting the results and conclusions.

Figure 7. Overall flow of the research.

The online questionnaire contained three sections. The first section collected general information about the respondents, such as their role in the university, academic qualifications, and experience. The second section focussed on facility maintenance management and included relevant questions. Finally, the third section asked participants to rate thirty-three important attributes that affect facility maintenance management practise

To evaluate the influence of each attribute, a five-point Likert scale was employed. Previous studies conducted by Islam et al. (2019) and Tan et al. (2014) used similar approaches to address related research questions in construction and facility management. The numerical rating assigned to each critical factor was converted into a relative importance index (RII) using equation (1)(1) $$\mathit{\text{RII}}=\frac{\sum _{i=1}^{n}W}{N\times W_H}$$(1) to determine its ranking. (1) $$\mathit{\text{RII}}=\frac{\sum _{i=1}^{n}W}{N\times W_H}$$(1) where 0 ≤ RII ≤ 1; W_i = score of each factor as rated by the respondents ranging from 1 to 5 (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree; N = total number of respondents; and W_H = highest score (i.e. 5) adopted in the survey. Principal component analysis (PCA) was used to reduce and group individual attributes/variables based on exploratory factor analysis, resulting in a manageable size. PCA simplifies the analysis process by identifying common underlying themes among the variables.

In the exploratory factor analysis using PCA, values of average variance extracted along with composite reliability were computed using Equations (2)(2) $$\mathit{\text{AVE}}=\frac{\sum _{i=1}^n{\lambda }^2}{n}$$(2) and (3)(3) $$\mathit{\text{CR}}=\frac{{\left(\sum \lambda \right)}^2}{{\left(\sum \lambda \right)}^2+\sum (\epsilon )}$$(3) , respectively, as explained by Hair et al. (2018). (2) $$\mathit{\text{AVE}}=\frac{\sum _{i=1}^n{\lambda }^2}{n}$$(2) where $\lambda$ represents the completely standardised factor loading for the ith measured variable and n is the number of item indicators for a construct. (3) $$\mathit{\text{CR}}=\frac{{\left(\sum \lambda \right)}^2}{{\left(\sum \lambda \right)}^2+\sum (\epsilon )}$$(3) (4) $$\epsilon =1-{\lambda }^2$$(4)

The variance inflated factor (VIF) was computed using Equation (5)(5) $$\mathit{\text{VIF}}=\frac{1}{1-R^2}$$(5) . (5) $$\mathit{\text{VIF}}=\frac{1}{1-R^2}$$(5)

A measure of sampling adequacy must be checked before further analysis of the factor model. According to Kaiser (1974), the values of the measure sampling adequacy can be calculated using Equation (6)(6) $${\mathit{\text{MSA}}}_j=\frac{\sum _{k\ne j}^m{r}_{\mathit{\text{jk}}}^2}{\sum _{}^m{r}_{\mathit{\text{jk}}}^2+\sum _{}^m{p}_{\mathit{\text{jk}}}^2}$$(6) . (6) $${\mathit{\text{MSA}}}_j=\frac{\sum _{k\ne j}^m{r}_{\mathit{\text{jk}}}^2}{\sum _{}^m{r}_{\mathit{\text{jk}}}^2+\sum _{}^m{p}_{\mathit{\text{jk}}}^2}$$(6)

4. Analysis and results

Effective presentation of research findings is critical to accurately communicating key findings and avoiding potential inaccuracies (Shukla, 2022). Results are presented using tables and figures, followed by a discussion.

4.1. Sample characteristics

At the end of the survey period, a response rate of 42.20% was achieved with 76 responses received. Of these, 75 responses met all requirements and were considered valid (Tan et al., 2014). Detailed demographic information about the respondents can be found in Table 2.

Table 2. Demographic information of the respondents.

Characteristics	Category			Response percentage	Response count (N = 75)
Role in the university	University Lecturers			34	45.3
	University Vice President			13	17.3
	Construction Project Coordinator			8	10.7
	Facility/Maintenance managers			7	9.3
	Students			6	8.0
Academic qualification	Professor			3	4.0
	Associate Professor			7	9.3
	Assistant Professor			27	36.0
	Lecturers			33	44.0
	Others			5	6.7
Profession	Engineering (Civil, Electrical and Mechanical)			26	34.7
	Others			49	65.3
Years of experience in managing or using a university facility	<5			6	8
	5–10			15	20
	10–15			42	56
	15–20			9	12
	>20			3	4
Interviewee information		Experience (yrs.)	Case university information
Qualification	Position		No blocks	Sample no.	Founded in
PhD (Civil Engineering)	VP for Institutional Development	>15	>60	1	2011
M.Sc. (Industrial Management)	FM & Operation Directorate Director	12
B.Sc. (Civil Engineering)	Senior Engineer	>5	>100	2	1950
M.Sc. (Civil Engineering)	Facility Management Director	>12
M.Sc. (Civil Engineering)	Project office Director	16	>34	3	1993
B.Sc. (Electrical Engineering)	Facility Management Director	12

In Ethiopian public universities, presidents, vice presidents, and academic staff play key roles in the planning, management, and operation of university facilities. Lecturers accounted for approximately 45% of the respondents, followed by vice presidents with a share of 17%. Construction project managers accounted for 10.7%, while FM or maintenance managers accounted for 9.3% of respondents. Approximately 34.70% have an engineering background. In terms of academic achievements and experience, approximately 13.3% of respondents hold a position as a full or associate professor, approximately 36% have a PhD, and approximately 44% have a master’s degree. To assess the reliability of the data, the respondents’ professional experience at the university should also be considered. Approximately 56% of the respondents stated that they had more than 10 years of experience in managing or coordinating university-related tasks.

4.2. Measures of research quality

Validity: to ensure the validity of study measures, we referred to previous studies in similar areas. We also sought input from three experienced experts in the construction industry, who provided valuable feedback on the questionnaire and helped us make the necessary revisions.

Reliability Test: The reliability of the questionnaire was assessed using Cronbach’s α coefficient. Previous studies have indicated that an alpha value between 0.71 and 0.99 is acceptable for internal consistency (Islam et al., 2019; Taber, 2018). According to Taber (2018), an alpha value > 0.9 indicates outstanding internal consistency, while a value > 0.7 implied adequate internal consistency. In this study, the calculated α value for the most important attributes influencing facility maintenance practises was 0.894, indicating a strong internal consistency of the scale used and reliable data obtained.

4.3. Results

4.3.1. Facility maintenance practises

Integrating facility and built asset management into the strategic plan can improve facility management practises. This requires the top-level management to understand and implement this integration throughout all levels of the management functions, ensuring that public asset owners prioritise the management of their built assets in alignment with corporate objectives.

4.3.2. Strategic considerations for facilities maintenance management

The lack of strategic integration between facility management and the core organisation management can lead to contradictory objectives and goals, as mentioned in Brooks and Atkin (2015), Ikediashi et al. (2014), and Lok et al. (2021). Respondents were asked to evaluate the level of agreement on integrating FM into the main strategic plan of public universities. The results showed that 70.6% of respondents agreed that FM was addressed in the strategic plan. Additionally, 86.6% of respondents acknowledged the importance of integrating FM with universities’ core business objectives. However, there appeared to be a discrepancy between the university’s strategic plan and FM’s performance, as emphasised in the expert interviews. This discrepancy is primarily attributed to the university administration prioritising core activities such as teaching, research, community service, and new building projects.

4.3.3. Maintenance policy at the university

According to the study, only 36% of the respondents agreed with the existence of a maintenance policy in the university, while the rest (64%) stated that there is no maintenance policy. In line with this result, all six interviewees also verified the absence of a policy as a determinant for effective facility maintenance management practises. The lack of an appropriate policy accompanies facility management at public universities in Ethiopia, as this clear reference shows.

4.3.4. The organisational position of facility management in public universities

The role of facilities management in defining policies and integrated plans aligned with the university’s core strategies is crucial. Nevertheless, the management of public universities at various levels pays little attention to this aspect.

Figure 8 shows the typical organisational structure of public universities in Ethiopia. The structure in Figure 8 shows the underrepresentation and lack of strategic leadership and responsibility in FM within the core business organisation. Adequate representation of FM in organisations plays a crucial role in driving change towards improvement (Mewomo et al., 2022).

Figure 8. Typical high level organizational structure of public universities. Note: VP: Vice President. Source: Author based on (Proc.351, 2003).

4.3.5. Maintenance strategy used in public universities

Studies recommend prioritising preventive maintenance over corrective maintenance for effective facility management (Brooks & Atkin, 2015; Hauashdh et al., 2020; Hon Yin Lee and Scott, 2009). However, in Ethiopian public universities, most respondents (74.65%) reported the implementation of a corrective maintenance strategy. This reactive approach can result in unexpected expenses on emergency repairs and indicates a lag behind current facility management trends. To align with industry advancements such as digitisation, data-driven predictive and performance-based approaches should be embraced for proactive maintenance, addressing needs efficiently (Dzulkifli et al., 2021; Khalid et al., 2019; Li et al., 2023).

4.3.6. Maintenance procurement/sourcing strategy

Most public universities in the study context use a combination of in-house and outsourced procurement strategies. Approximately 27% is done in-house, whereas 45.97% follows a mixed approach. Core competency theory supports outsourcing non-core functions to prioritise core objectives (Ikediashi et al., 2014; Jensen, 2017). Many respondents reported difficulties in accessing FM-related information for facility operations. In particular, 78.35% of respondents expressed difficulty obtaining drawings, data models, and project details required for effective maintenance operations. which significantly impaired the maintenance efficiency (Ashworth et al., 2022; Shaw et al., 2022; Zhao et al., 2022).

4.3.7. Attributes influencing the effectiveness of FMM practise

Factors influencing facility maintenance management practises in public universities in Ethiopia were analysed. The results are summarised in Table 3, which includes the mean and standard deviation for each factor and their ranking based on the relative importance index values. In cases where multiple attributes have the same RII value, their SD values were compared to determine the ranking. If both RII and SD are the same, they are assigned the same rank, as mentioned in previous studies (Islam et al., 2019; Tan et al., 2014).

Table 3. Rank of attributes level of importance.

Notation	Attribute/Variable	Mean	SD	RII	Rank
CF1	Maintenance standards, policies, and strategies	4.133	0.977	0.827	9
CF2	Method of awarding maintenance contracts	3.493	0.978	0.699	32
CF3	Availability of specialised maintenance contractors	3.880	1.052	0.776	23
CF4	Maintenance productivity standards and specifications	4.093	0.947	0.819	14
CF5	Errors in the design/design fault	3.733	0.991	0.747	26
CF6	Construction materials specification and selection	4.120	0.972	0.824	13
CF7	Design phase consideration of the life cycle cost analysis	4.147	0.881	0.829	8
CF8	Involvement of FM professionals during the design phase	3.676	1.160	0.746	27
CF9	Design efficiency	3.667	0.920	0.733	28
CF10	Design complexity	2.667	0.991	0.533	33
CF11	Experience of design professionals	3.520	0.991	0.704	30
CF12	Construction quality	4.240	0.819	0.848	5
CF13	Communication and collaboration skills	3.987	0.862	0.797	19
CF14	Workmanship quality during construction	4.067	0.875	0.813	17
CF15	Replacement building spare parts and components	3.905	0.996	0.792	20
CF16	Coordination between construction & maintenance team	4.173	0.860	0.835	6
CF17	Accuracy of the as-built documentation	3.773	0.994	0.755	25
CF18	Preventive maintenance strategy	4.467	0.684	0.893	1
CF19	Maintenance plan and program/schedule	4.400	0.658	0.880	2
CF20	Training maintenance personnel	4.160	0.823	0.832	7
CF21	Skill of the appointed maintenance staff	3.867	1.057	0.773	24
CF22	Maintenance budget	3.667	1.166	0.733	29
CF23	Accurate forecast of maintenance expenditure	4.133	0.963	0.827	10
CF24	Procurement rules and regulations	3.947	1.025	0.789	21
CF25	Concerns about future maintenance/design maintainability	4.014	0.697	0.825	11
CF26	Maintenance of work quality by internal staff	4.068	0.782	0.824	12
CF27	Quality of defect rectification in buildings	4.067	0.777	0.813	18
CF28	Lack of building maintenance manual	4.093	0.933	0.819	15
CF29	Database for recording and storing FMM tasks	4.307	0.838	0.861	3
CF30	Effective maintenance culture	4.280	0.863	0.856	4
CF31	Attitude/awareness of users/misuse of facilities	4.093	0.857	0.819	16
CF32	Post completion delay in using the building	3.507	1.083	0.701	31
CF33	Underestimating the impact of FM	3.893	1.110	0.779	22

Bold values indicate attributes with a mean value ≥ 4.00.

As indicated in Table 3, the respondents ranked lack of a preventive maintenance strategy (mean = 4.47) as the most critical attribute that affects facility maintenance practises frequently caused by the absence of an appropriate maintenance strategy or approach. The findings from the interview also revealed that university maintenance operations are still not being accompanied by a sound strategy. This attribute was also found to be the most critical in Talib et al. (2014) and Jandali and Sweis (2018) in Malaysia and Jordan, respectively.

Since maintenance personnel are heavily involved in unplanned day-to-day (corrective) operations, a lack of preventive maintenance/predictive approach has a negative impact on the overall process and practise of maintenance operations. As shown in Table 3, respondents ranked lack of maintenance plans and schedules as the second most influential attribute affecting facility maintenance management (mean 4.40, RII = 0.88). The result also indicated that proper maintenance planning and scheduling was identified as non-existent in public universities in Ethiopia, which is in congruence with the findings of prior studies by Islam et al. (2019), whereby lack of a maintenance management plan and schedule was attributed to high maintenance budgets, which ultimately resulted in high maintenance operations costs.

The third top-ranked critical factor is lack of as-built documentation and unavailability of database for maintenance activities (mean = 4.47, RII value 0.861), Table 3. In the age of digitisation outdated traditional file documentation systems and lack of database for maintenance activities are critical challenges in public institutes in a similar context (Hassanain et al., 2019, 2023; Hauashdh et al., 2020; Ismail, 2017; Mewomo et al., 2022). Lack of an effective maintenance culture has been affecting maintenance practises in developing countries such as Saudi Arabia and Malaysia (Islam et al., 2019; Ismail, 2017; Mong et al., 2019). The community culture, learning, and growth that arise from the community’s learning and cultural background of facility users impede the proper practise of FMM in public universities (Ismail, 2018; Mong et al., 2019; Zolkafli et al., 2019).

The results of the survey show that poor construction quality (construction stage) is another influencing factor (5th), with an RII of 0.848 (Table 4). This value also indicates that construction stage supervision, as stated in Khalid et al. (2019), Mewomo et al. (2022), and Palis and Misnan (2018), poor quality components, and poor workmanship (Asmone et al., 2019; Jandali & Sweis, 2018; Mewomo et al., 2022; Palis & Misnan, 2018) are highly associated with quality management system used during construction stage affect maintenance operations. Moreover, design stage attributes such as failure to consider life cycle cost analysis and non-involvement of FM professionals during design and construction stages can be attributed to poor construction quality (Asmone et al., 2019; Islam et al., 2019; Jandali & Sweis, 2018; Khalid et al., 2019; Palis & Misnan, 2018).

Table 4. Eigenvalues and variance explained (PCA, rotated and unrotated with Kaiser Normalization).

	Unrotated			Rotated
Factor	Eigen value	Variance explained	Cumulative variance	Eigen value	Variance explained	Cumulative variance
F1	4.54	34.92%	34.92%	2.58	19.82%	19.82%
F2	1.56	12.01%	46.93%	2.16	16.59%	36.42%
F3	1.47	11.35%	58.27%	2.14	16.48%	52.89%
F4	1.27	9.81%	68.08%	1.97	15.19%	68.08%

Lack of effective coordination and communication between construction and maintenance groups is ranked 6th (mean = 4.17 and RII = 0.835) and is also considered as another influencing attribute in FMM practises. This result agrees with the findings of (Hassanain et al., 2013; Hauashdh et al., 2020; Jandali & Sweis, 2018; Palis & Misnan, 2018). Efficient use of recent developments in asset information management, such as building information modelling (BIM), has the potential to improve the coordination and communication gap among involved stakeholders in construction and maintenance stages (Hassanain et al., 2023; Moreno et al., 2019). After the rank analysis, Exploratory factor analysis was performed on the top 18 attributes to further cluster and reduce into a manageable size.

4.3.8. Exploratory factor analysis (EFA)

An explanatory factor analysis using principal component analysis was conducted to reduce dimensionality and group the attributes into new framework factors based on PCA.

4.3.8.1. Preliminary factor model

In the preliminary analysis of the EFA, the top eighteen attributes were explained in five factors (with eigenvalues greater than one). In total, the data explained 68.8% of the variance. The factor loading results and a series of iterations on the number of factors were performed, and thirteen (13) attributes in the four factors were retained and qualified for further analysis.

Parallel analysis based on Horn (1965) was conducted using random eigenvalues generated through Monte Carlo simulation (O’Connor, 2000). This technique compares the eigenvalues from factor analysis with randomly generated correlation matrices to determine if any eigenvalue is less than the corresponding random values. In this study, no eigenvalue was found to be lower than its random counterpart (Syntax parameters: number of variables = 1, sample size = 75, analysis type = PCA, number of random correlation matrices to generate = 2500).

The analysis yielded four factors, which accounted for 68.08% of the variance. After applying Varimax orthogonal rotation with Kaiser’s normalisation, these factors explained 19.82%–15.19% of the variance, as shown in Table 4. Generally, a total explained variance of at least 50% is recommended; therefore, this level of explanation is satisfactory.

Test for adequacy of PCA. The Kaiser–Meyer– Olkin (KMO) test and Bartlett’s test of sphericity were used in this study for factor analysis. A KMO measure above 0.50 is considered appropriate for PCA on a correlation matrix, with values higher than 0.60 being recommended as good by Fidell (2014). According to Kaiser (1974), KMO values greater than 0.7 are considered ‘middling’ and adequate for exploratory factor analysis. In the present study, the overall KMO value was found to be 0.797, as shown in Table 5, indicating that conducting PCA on the data was appropriate, which aligns with similar studies conducted by (Jandali & Sweis, 2018; Mewomo et al., 2022; Yu & Richardson, 2015).

Table 5. KMO and Bartlett’s test.

Kaiser–Meyer–Olkin measure of sampling adequacy		0.797
Bartlett’s Test of Sphericity	Approx. Chi-square	342.603
	df	78
	Sig.	.000

The results shown in Table 5 reveal that the sampling adequacy was considered strong, as indicated by the KMO sampling adequacy measure with KMO values greater than 0.70. Because all Bartlett’s test values were 0.05, the correlations between all items were sufficiently large for EFA.

Exploratory factor analysis was repeated after each such activity using the same approach. In total, 25 iterations were performed. Because of this stepwise elimination and considering other PCA quality measures, the final factor structure consists of four factors and 13 variables/attributes.

Check for common method bias. After conducting Harman’s single-factor test and Podsakoff et al. (2003), it was found that a single factor is not sufficient to explain the variance of the sixteen attributes remaining in the EFA. The largest unrotated factor only accounted for 34.92% of the variance (Table 6), which is below the recommended threshold of 50%. This implies that the results of the EFA analysis were not influenced by common method bias.

Table 6. Eigenvalues and variance explained (PCA, rotated and unrotated with Kaiser Normalization).

	Unrotated			Rotated
Factor	Eigen value	Variance explained	Cumulative variance	Eigen value	Variance explained	Cumulative variance
F1	4.54	34.92%	34.92%	2.58	19.82%	19.82%
F2	1.56	12.01%	46.93%	2.16	16.59%	36.42%
F3	1.47	11.35%	58.27%	2.14	16.48%	52.89%
F4	1.27	9.81%	68.08%	1.97	15.19%	68.08%

Factor extraction. Harman’s approach showed that all factors are adequate to indicate all variance. Therefore, various criteria were used to determine the appropriate number of factors in the final PCA. According to Kaiser’s criterion, all factors with eigenvalues greater than one are adequate for measuring variance (Streiner, 1994). From 13 attributes left in the analysis, we recommend retaining four factors. Their eigenvalues are presented in Table 5. The scree plot shown in Figure 9 was used to determine the number of factors. Curve bending indicates four factors to be incorporated in the PCA, which is consistent with Kaiser’s criterion (Zaleski & Michalski, 2021).

Figure 9. Scree plot for PCA-based EFA, Varimax rotation with Kaiser normalisation for 13 attributes.

Various criteria were used to determine the appropriate number of factors in the final PCA. According to the criterion of Kaiser (1974) and Streiner (1994), factors with eigenvalues greater than one are sufficient to measure variance. Of the 13 attributes analysed, four factors were determined on the basis of their eigenvalues. The scree plot in Figure 9 supports the use of the four factors in PCA and is consistent with the Kaiser criterion (Table 7).

Table 7. Basic statistics of the final exploratory analysis with four factors (PCA, Varimax rotation with Kaiser Normalization).

Factor	Variable	Mean	Communalities	^aSD	^bMSA	^cVIF
F1	CF27	4.07	0.763	0.777	0.814	2.005
	CF29	4.31	0.655	0.838	0.761	2.621
	CF26	4.07	0.696	0.782	0.816	4.051
	CF28	4.09	0.751	0.932	0.743	2.587
F2	CF30	4.28	0.755	0.863	0.800	2.991
	CF23	4.13	0.578	0.963	0.749	2.964
	CF31	4.09	0.600	0.857	0.853	2.233
F3	CF12	4.24	0.706	0.819	0.786	2.891
	CF6	4.12	0.772	0.972	0.708	2.279
	CF16	4.17	0.697	0.860	0.786	3.368
F4	CF18	4.47	0.681	0.684	0.783	2.377
	CF19	4.40	0.650	0.658	0.809	2.776
	CF25	4.01	0.547	0.688	0.839	3.681

^aStandard deviation, ^bmeasure of sampling adequacy, and ^cvariance inflation factors.

The measures of sampling adequacy for all variables from the four-factor analysis results are presented in Table 8. The results demonstrated that the values for these parameters (min = 0.71, max = 0.85) confirm the accuracy of the results. As shown in Table 7, the values of variance inflation factors (VIF) are calculated. High values of this parameter indicate extreme multi-collinearity between variables. VIF values under 10 are measured acceptable as by Hair et al. (2018), which was also applied by Zaleski & Michalski, (2021). In this study, no variable exceeded the limits. VIF ranged between 4.05 and 2.01, with a mean of 2.83.

Table 8. Final exploratory factor analysis with reliability and validity measures (PCA as the extraction method, Varimax rotation with Kaiser Normalization, converged in eight iterations).

	Factors and measurement variables	FL*
F1. Internal Process and organisation (α = 0.771, CR = 0.789 and AVE = 0.416)
CF27	Quality of defect rectification in buildings	0.796
CF29	Database for recording and storing maintenance data	0.752
CF26	Maintenance of work quality by internal staff	0.713
CF28	Building Maintenance Manual	0.669
F2. Community Culture, Learning, and growth (α = 0.78, CR = 0.823 and AVE = 0.719)
CF30	Effective maintenance culture	0.872
CF23	Failure to forecast accurate maintenance expenditures	0.797
CF31	Attitude/awareness of users/occupants/misuse of facilities	0.665
F3. Impacts of design and construction quality (α = 0.720, CR = 0.789 and AVE = 0.555)
CF12	Construction Quality	0.765
CF6	Construction materials specification and selection	0.737
CF16	Coordination between construction and maintenance team	0.731
F4. Maintenance Approach and Management (α = 0.674, CR = 0.765 and AVE = 0.525)
CF18	Preventive maintenance strategy	0.817
CF19	Maintenance plan and program/schedule	0.754
CF25	Concerns about future maintenance/design for maintainability	0.581

* FL = factor loading, α Cronbach’s, CR = composite reliability, and AVE = average variance extracted.

The analysis confirmed the accuracy with parameter values between 0.71 and 0.85 for all variables in the four-factor analysis, as shown in Table 8. To assess the multicollinearity between the variables, the VIF values were calculated and are presented in Table 7. According to Hair et al. (2018), VIF values below 10 are considered acceptable, which was also applied in the study by Zaleski & Michalski (2021). In this study, none of the variables exceeded these limits, with VIF values ranging from 2.01 to 4.05 and a mean value of 2.83.

Final factor loading. The factor loading of the final exploratory analysis is presented in Table 8. The result involves four factors that include 13 variables or attributes. The factors are labelled and named based on the extent to which they explain the attribute/variable. The first factor is named internal process and organisation (F1) and includes four variables/attributes: CF27, CF29, CF26, and CF28. Factor two (F2) is labelled as community culture, learning, and growth towards maintenance and includes three variables: CF30, CF23, and CF31. The third factor (F3) is associated with the impacts of design and construction quality, which includes three variables/attributes (CF12, CF6 & CF16), while the fourth factor (F4) focuses on maintenance approach and management, including three variables (CF18, CF19, and CF25). A complete description of the variables used in data collection is provided in Table 8. In this study, principal component analysis was used as the factor extraction method followed by Varimax orthogonal rotation with Kaiser normalisation The model converged after seven iterations.

Verification of the reliability and validity of the EFA. The average variance extracted values provided in the above section range from 0.416 to 0.719, with a mean value of 0.553. According to Hair et al.(2018), an AVE should ideally be larger than 0.5, indicating that the measurement error-related variance is smaller than the explained factor variance, as recommended by Fornell & Larcker ((Fornell & Larcker, 1981; Hair et al., 2018).

From the EFA results, it can be concluded that most factor values meet the recommended limit. Despite having an AVE smaller than 0.5, CR exceeds 0.6 (CR = 0.789), which confirms convergent validity based on the above criteria. The analysis also shows no cross-loadings between factors, and each extracted factor has at least three variables loading on it, indicating acceptable EFA validity for this study.

The reliability of the variables was assessed using Cronbach’s alpha and composite reliability measures. The values range from 0.674 to 0.771, with an average of 0.737, which according to various authors indicates acceptable consistency. In addition, the values for composite reliability range from 0.765 to 0.823, which also confirms satisfactory reliability for this analysis.

4.3.8.2. Factor labelling and explanation

Once the analysis produced an acceptable factor solution, the naming of the factor patterns was based on the factor loading values for each variable/attribute. In accordance with the recommendation of Hair et al. (2018), a variable with a higher value of must be emphasised when naming the factors. Accordingly, the four factors are named on the basis of their respective factor loadings (FL) values.

F1: Internal process and organisation. The first factor relates to the internal process and organisation of maintenance activities. The quality of the maintenance work carried out on buildings by the internal maintenance team is highly related to the skills of the staff and the maturity of the internal process to ensure the quality of the work. The absence of as-built documentation and a database for maintenance and manuals for activities is also an indication of an immature internal process that affects the performance of the maintenance team (Hauashdh et al., 2020; Ismail, 2017; Mewomo et al., 2022).

F2: Community culture, learning, and growth. The second factor is the failure to promote a culture of effective maintenance within the community and among professionals. Failure to accurately forecast maintenance costs is related to the mindset of staff involved in budgeting maintenance activities. In addition, inappropriate use of public facilities reflects the overall cultural attitude within the community and highlights the need for education and growth in the care and appropriate use of facilities at public universities.

F3: Impacts of design and construction quality. The third factor involves the design, construction, and material quality specifications in the design phase. It is also essential to have effective coordination between construction and maintenance teams for successful maintenance operations. Several studies highlight the significance of involving facility management professionals early in the design process to ensure that maintainability factors are considered (Asmone et al., 2019; Mewomo et al., 2022; Mong et al., 2019). This also has the potential to address the issue of sustainability throughout the entire life cycle delivery process.

F4: Maintenance approach and management. The fourth factor includes the lack of a preventive maintenance approach, which significantly affects the achievement of maintenance management. The importance of planning and scheduling for maintenance task execution is essential for proactive decision making. Proper planning and scheduling are vital to support effective preventive maintenance approaches and management. Stakeholders involved in the FM delivery process should take care of maintainability and adopt an approach that facilitates planning towards sustainability.

4.4. Discussion

Based on a series of exploratory factor and qualitative analyses, the authors proposed four critical factors that affect the effectiveness of FMM practises, defined by 13 specific variables.

A comparison of the findings of the present study with the outcomes of similar research conducted in developing countries by factor analysis shows differences in the final results and the variables clustered under four main factors. For instance, Jandali and Sweis (2018) grouped the factor into seven categories by performing an EFA. Hassanain et al. (2013), on the other hand, clustered the factors that affect the cost performance of private and public hospitals in Saudi Arabia and ranked the 33 attributes in their order of impact using RII values.

The study uses four clearly identified factors with 13 attributes distributed almost evenly with the smallest number of variables equal to three and the largest to four variables or attributes. Furthermore, the model’s formal and statistical quality appears to be among the highest obtained in the field of FMM and its performance. Furthermore, the proposed factor loading structure has sound validity and reliability, which is unusual in an exploratory study of this type. When scrutinizing the outcomes of the factors from a substantive perspective in comparison to findings from other studies, numerous parallels have been identified. For instance, community culture, learning, and growth factors in the present study include lack of effective maintenance culture and misuse of facilities by occupants due to their attitude, which is in agreement with the findings of Jandali and Sweis (2018). Furthermore, Mewomo et al. (2022) included attributes such as attitude of users and knowledge of occupants about FM under end users’ factor. Factor F2 in this study included failure to forecast accurate maintenance expenditures as a new attribute.

Similarly, maintenance work quality by the internal staff, under the internal process and organisation factor (F1) from the present study corresponds to the same variable under operations conducted by the maintenance group factors identified in Jandali and Sweis (2018) and Mewomo et al. (2022) in their EFA. Lack of maintenance manual (CF28) clustered under this factor is in agreement with Hassanain et al. (2013, 2019) finding. Accuracy/non-existent of as-built documentation and database for recording maintenance management tasks also agrees with previous studies (Hassanain et al., 2013; Islam et al., 2019; Jandali and Sweis, 2018) but is included in a new factor of the present study in contrast with Jandali and Sweis (2018) and received great emphasis from the interview. The lack of information about the building/as-built documentation in the study by Mewomo et al. (2022) and Hassanain et al. (2019) included organisational factors similar to the present study, but the latter included maintenance management factors. The challenges related to information and as-built documentation can be resolved using cutting-edge technologies. In this context, the use of new digital technologies such as BIM, CMMS, DT, VR, and AR is proposed as a potential solution to improve the performance of FMM’s internal processes and organisation.

The third factor (F3), design and construction quality, of the present study merged two independent factors of design phase and construction phase, as stated in Jandali and Sweis (2018), Mewomo et al. (2022), and Hassanain et al. (2013, 2019). However, all the variables included in the main factors of this study are also included in a factor in the previous studies.

The fourth factor (F3), maintenance approach and management involves lack of preventive maintenance strategy, lack of maintenance plans and schedules, and low concern for future maintenance/maintainability are also included in factors pertaining to the operations conducted by the maintenance team in studies (Hassanain et al., 2013, 2019; Jandali and Sweis, 2018; Mewomo et al., 2022). Furthermore, the low concern to future maintenance activities (Talib et al., 2014).

The final factor model is illustrated in Figure 10, which shows how the attributes are related to the respective factors, including the factor loading values.

Figure 10. The final four factors explained by their attributes/variables.

5. Conclusion

This study is among a few that deals specifically with FMM practise and its impact on core business objectives (teaching, learning, research, and community services) in public universities in developing countries.

• The study concluded that moving from traditional corrective (reactive) maintenance to a proactive, predictive, and data-driven strategy improves building management performance and enhances sustainability efforts. It also emphasised the commonalities of facility management practises in developing countries, which are influenced by the economic situation.

• The study also found that maintenance plays a crucial role in both aesthetics and functionality and contributes to the value and sustainability of buildings. It also highlights the maintenance of sustainable facility maintenance because it relies not only on how well the building is designed or constructed but also on responsible operation and sustainable maintenance practises

• The exploratory factor analysis in this study established a four-factor model that revealed a new set of dimensions representing the attributes. The culture, learning and growth factor has introduced a dimension for developing countries.

• The importance of considering maintainability and life cycle cost analysis during design and construction to support sustainability initiatives has been emphasised thus promoting awareness in society.

• On a broader level, F-1 is linked to the perspective of the organisational process, whereas F-2 can be directly linked to the social dimension. F-3 and F-4, on the other hand, are linked to the technical aspect and the application of facility management methods, principles, and technologies.

6. Recommendation

Further research is recommended to incorporate climatic condition attributes to reveal how they affect maintenance practises by expanding the sample size and involving regional public universities as part of the case study. Structural equation modelling (SEM) using confirmatory analysis (CFA) is also recommended for future research.

7. Limitation

The study may be limited in its generalizability because of reliance on expert opinions and case studies in the capital city. This approach could introduce subjectivity and potential bias because different respondents may have varying views on the same topic. Using a larger sample of subject matter experts for factor analysis could have improved the quality of the factor model.

8. Contribution

This study examines the factors that influence the effectiveness of FMM, using public universities as a case study. It also explores the potential of digitalisation in addressing FMM challenges and its benefits for core business performance. Furthermore, it raises awareness of maintenance-friendly and sustainable design concepts among professionals, stakeholders, and users. This study contributes to improving FMM practises and promoting sustainability among rapid population growth and infrastructure needs in developing countries.

Authors’ contribution

Muluken Tilahun Desbalo: substantially contributions and designed the study, collected, and analysed data, and wrote the initial draft of the manuscript. Asregidew Kassa Woldesenbet: Contributed to critically review the manuscript for intellectual content and enhanced the final draft of the manuscript. Mitiku Damtie Yehualaw: Provided expertise in statistical analysis, assisted in data interpretation, and critically reviewed and edited the manuscript and final approval of the manuscript to be published. Professor Dr. Ing. Hans-Joachim Bargstädt: critically reviewed and edited the manuscript and final approval of the manuscript to be published.

Acknowledgments

Chair of construction Engineering and Management, Faculty of Civil Engineering Bauhaus Universität-Weimar and the Ethiopian Institute of Architecture, Building Construction, and City Development (EiABC) were honoured for their financial support during the study.

Disclosure statement

There are no potential financial or non-financial conflicts of interest related to this research, its funding, or the publication of this article.

Data availability statement

The data supporting the results of this study are available and can be viewed upon request from the corresponding author.

Additional information

Notes on contributors

Muluken Tilahun Desbalo

Muluken Tilahun is currently a lecturer at the Ethiopian Institute of Architecture Building Construction and City Development in Ethiopia. He is a Ph.D. candidate with keen interest in post-construction maintenance and building information modelling. His dissertation focuses on the development of mechanisms to evaluate building maintenance management performance under budget constraints. Muluken Tilahun has been a dedicated research fellow at Bauhaus University in Weimar, Germany, since 2019. He obtained a Master and bachelor’s degree in civil engineering with specialisation in Construction Management from Adama University, Ethiopia in 2010 and 2006 respectively. His research interests include project management, building information modelling, building maintenance and multi-criteria decision making.