The study sets out to develop a model which examines the interrelationship between infrastructure elements of smart campuses as a tool for smart cities in developing countries using a system thinking approach. The novelty of this study lies in the research analytical method of systems thinking archetypes that can be used to micro-transfer smart infrastructure from smart campuses to cities in developing countries.

The remaining part of this article is presented as follows: Literature Review presents the technology and IT infrastructure for the efficient smart campus and smart learning and teaching analytics. Methods and Materials reveals the research approach for carrying out the study; Analysis and Results presents the results and discussion of findings. The last part of the article is the implications and conclusion for the study.

The concept of a smart city is based on the principle of smart infrastructure, where there is an interconnection of digital components to generate and use data intelligently to improve the performance of systems through the efficient use of resources (UN, 2016). The core infrastructural elements needed for smart city solutions include but are not limited to a sustainable environment, comprehensive IT digitalisation, efficient waste management, energy management, effective urban mobility, e-governance, telemedicine/education and constant electricity supply as identified by Omotayo et al. (2021) as presented the framework of Figure 1. Some of the smart city infrastructures within a city allow smart campus development within the same city. The smart city infrastructural systems become the platform for establishing mini-cities in the form of co-existence between the university and its communities.

Smart campus initiatives are based on the concept of IoT and its relative interaction with educational buildings through smart network grids (Omotayo et al., 2021), utilising and controlling a large volume of data remotely in the process. The initiative is premised on the construction of smart buildings or the repurposing of old campus buildings to create a sustainable environment essential for academic activities (Chen et al., 2017). The study of Omotayo et al. (2021) revealed that IoT-smart building interaction necessary for the smart campus is driven by 5G and 6G flagship, smart grid, artificial intelligence, fog computing, green computing, repurposing, energy management, smart teaching and learning. Using the technology of IoT and smart classrooms, social benefits arising from the management of resources within the university ecosystem as well as the efficient and effective citizen and e-governance services to the society emerge (Zhang L. et al., 2020).

Moura et al. (2021) argued that the use of large educational buildings and other facilities encourages high energy consumption costs within campuses, prompting the need for efficient energy management systems (EMS) through smart campus solutions. However, the integration of IoT provides monitoring and controlling platforms to evaluate the generation and demand of electricity supply, achieve energy use sustainability, and adopt smart devices to control loads through data. Cloud computing coupled with machine learning analysers is an essential component for the effective running of EMS within a smart campus network grid (Omotayo et al., 2021). The significant drivers are building information modelling to building energy model, renewable energy, photovoltaic, laser scanner and net-zero energy. Conversely, the most important driver in establishing an EMS for refurbishing an existing education building towards a smart campus is the application of BIM in BEM (Mazza, 2015; Michailidis et al., 2018).

The conceptual framework of the infrastructural element of smart campus developed by Omotayo et al. (2021) highlighted the components of technology and IT infrastructure for a smart campus to include smart campus construction, micro-grid, robotic course, and cognitive course load, critical thinking, microservice, integrated service and load characterisation. According to Alrashed (2020), smart technologies are employed by educational institutions to address the problems related to the privacy of users, technology uptake and climate change through the development of smart network grids. Several authors including Chenoweth (2020), reckoned that the use of micro-grid in university campuses is on the rise as a recent development in the quest to achieving smart campus solutions.

The smart, inclusive technologies technology covers all students, especially students with disabilities, supports equality among students and the efficient management of teaching and learning within the academic environment. According to Omotayo et al. (2021), smart buildings in smart campuses reinforce big data principles, encourage infotainment learning and ubiquitous game-based learning for disabled students, and most notably, fragmented learning participatory involvement of students with diverse backgrounds. The concept of smart campus applications is made more effective with mobile apps by major stakeholders, academic and non-academic staff with and including students. Omotayo et al. (2021) hinted that IT continuance, IT adoption, social networks, and IT use are the significant drivers for smart campus applications. Other notable variables related to smart campus applications are higher education, teaching and learning, information systems and applications.

The sub-elements of deep learning architecture in data mining processes in the smart campus are virtual reality, attendance systems, augmented reality, android, mobile edge computing, accuracy metrics, sliding window filters and face recognition (Omotayo et al., 2021). One of the major features of deep learning architecture is face recognition, which ensures campus security and safety. In the same vein, virtual and augmented reality in teaching and learning has many benefits, especially to ensure interaction and arouse study interest in different subject matters.

Data mining involves discovering valuable information, knowledge and pattern behind a large volume of data using machine learning to make decisions and measure performance (Qin and Chi, 2020; Vivi et al., 2015; Jiang et al., 2017). Most university campuses use data mining to assess student’s grades, evaluate student’s involvement in class activities, library usage, and campus applications. Similarly, smart campus data analytics are used to measure the presence of the staff activities and researches online. The drivers of data mining in smart campuses are web service, data analysis, statistics, random, ranking analytics, bibliometric and cite score. Auto-analysers are relevant to the effectiveness of smart buildings within a smart campus network infrastructure. Machine learning, experiment, testbed, interference, air quality monitoring and packet error rate are auto-analyser variables used to assess the continuous and active performance of the buildings within the territory (Omotayo et al., 2021). The concept of auto-analysers as an important component in smart campus studies is new, although advances in research in the area is on the increase.

Performance measurement and forecasting is an essential element of smart campus infrastructure as it involves understanding the smart grid network and other smart applications (Bilau et al., 2015). The large volume of data at the disposal of the university is processed and used for establishing the level of performance in academic-related activities. The key variables associated with performance measurement and forecasting in the smart campus include path loss, auto-scaling, loss models, forecasting, horizontal scaling, quality of service, radio propagation and vertical scalability (Omotayo et al., 2021). The state of the smart campus infrastructure can be measured with the quality of service, use of path loss and loss model. While performance measurement is suitable for assessing energy consumption in university buildings, forecasting seeks to evaluate energy future usage in those campus buildings and assess the efficiency of smart technologies in ensuring quality services and learning activities of the students. Putman et al. (2019) opined that horizontal scalability ensures more resources are added to existing smart campus network infrastructure. Vertical scalability is responsible for resizing servers and sensors to add more features for the efficiency of the smart network infrastructure.

Campus information portal (CIP) is an integrated platform in campus life that is set out to collect and release information to ensure efficiency, personalisation, security and other important campus resources (Zhicheng and Feng, 2018). Omotayo et al. (2021) enumerated that energy consumption and efficiency, blended learning, education data mining, smart classroom, collaborative learning, sustainable education, and learning analytics are key ingredients of CIP. One of the many features of CIP is the ability to oversee efficiency in energy consumption, considering the huge cost of energy worldwide. The data of the amount of energy used within campus should be available and ways to achieve energy efficiency. In addition, CIP should provide the platform for blended and hybrid learning for the teaching of students, which the current times allow (Zhang L. et al., 2020; Zhang Y. et al., 2020).

Learning management system (LMS) is a recent technology adopted in university education to create course content, its associated delivery and management (Mershad and Wakim, 2018). The centralised technology that incorporates instructional features involves student administration, evaluating attendances, and assessing student submissions. The major components of LMS in a smart campus are real-time monitoring, wireless fidelity (Wi-Fi), camera, automation, autonomous vehicle, sensor, wireless sensor network, and it has become an integral part of the smart campus. The technology of LMS assists in the monitoring of student’s attendance, hinged on good Wi-Fi systems and sensor networks to aid effective learning scenarios (Sun et al., 2018). Transportation for students in a big campus environment could be a problem that can be resolved by providing autonomous vehicles within the university premises. In addition, the provision of an effective Wi-Fi system is a bedrock for efficient IoT for any campus environment. The use of a close circuit camera will help ensure safety on smart campuses, which aid the security of students and staff.

Campus equipment management services (CEMS) include components in the smart campus such as mobile robotics, optimal deployment, application framework, radio frequency identification, smart devices, Petri net modelling, localisation and hazard area management service (Omotayo et al., 2020a). Zhou et al. (2020) reckon that RFIS is an important ingredient for establishing smart campus solutions. The hazard area management service and campus visitor management systems ensure on-site and online campus security (Luo, 2018; Liang, 2020).

The last element of smart learning and teaching analytics considered significant in smart campus infrastructure is the education management system (EMS). As reported in Omotayo et al. (2021), the related components of EMS are mobile computing, e-learning, mobile learning, and artificial intelligence. The adoption of mobile computing and learning is further enhanced with smart mobile gadgets and devices by lecturers and students (Li, 2020). The management team of smart campus are to provide enabling framework and environment powered by mobile computing and artificial intelligence to enhance smart learning by students.

Recent studies by Min-Allah and Alrashed (2020); Omotayo et al. (2021); Zhicheng and Feng, (2018); Zhou et al. (2020); and Moura et al. (2021) reviewed and developed frameworks for smart campuses. The aforementioned studies did not consider the typologies of transferring smart cities to cities. The relationship between the attributes of smart campuses have only been identified and not studied. This study therefore intends to develop transferrable attributes of smart campuses to cities in developing countries with a focus on South African Universities. The causal relationships between smart campus features will be studied for the purpose of understanding what can be transferred to a larger environment.

All seven interviewees in this study are based in South African Universities as shown in Table 1. The respondents are in administrative positions in the university. One of the interviewees, SUA5, is the university’s deputy vice-chancellor and has been involved in strategy formulation. SUA6 is the director of estates and facilities management. All respondents have BSc and MSc degrees with over 2 years in the role they occupy in their various Universities.

Two (2) of the interviewees classified their university as a smart campus. However, the remaining five (5) find it difficult to categorically classify their campus as a smart campus even though they have some available technologies. Notwithstanding, the questions asked the interviewees were about:

• The applicability of smart technologies on campus.

The interview outcomes, which were partly conducted through computer-mediated interviews using MS Teams, were analysed using NVIVO and presented in the next section.

The seven (7) themes in this section were drawn from the outcomes of the thematic analysis. Internet-of-things (IoT); Building management systems; Campus Energy management systems; Security systems with facial recognition technology; Performance measurement; Class instruction and engagement platforms; and Smart renewable energy grid were identified as the applicable technologies for the smart campus.

All seven interviewees noted that contractors are required to supply and install smart campus infrastructure. The themes drawn from the procurement contracting and construction were analysed to produce the themes of competitive procurement, design and management, and contractual arrangement.

This final category of themes aimed to identify the challenges of smart campus knowledge transfer between Universities in South Africa and the government for the creation of smart cities. The themes which emerged from the interview analysis are ignorance, resistance to change, the university’s consultancy role and upskilling, source of capital for smart technologies, integration with existing technology, and the development of prototypes and stakeholder engagement and focus on transportation.

The initial causal loop diagram is designed from the constituents of Figure 3. The process of creating the causal loop diagram coloured smart campus infrastructure in red, smart campus procurement, contracting and construction in purple colour, and opportunities and challenges for micro-transferring smart campus infrastructures and knowledge to cities in blue colour. The new variables where were not identified in the analysis were indicated in black colour.

These new variables began with data mining from the left-hand side of Figure 4, and it has a positive impact on performance measurement and deep learning architecture. Cloud computing also influenced performance measurement. Smart building construction is central to the construction and energy management system as a positive variable. Education management systems and inclusive technology positively influence classroom instruction and engagement. Notably, in the top right corner, world bank and international grants, grant and loan applications positively influence the government’s desire to have a smart campus.

Community engagement meetings balance the negativity of ignorance and resistance to change. Tender analysis and specialist contractor selection and award of the contract positively influence contract administration and procurement of smart buildings. The initial casual loop diagram of Figure 4 must be validated before relying on the system’s findings.

The purpose of the validation process is to investigate the applicability, practicality and ease of using the causal loop diagram in Figure 4, not only in developing countries, but around the world. The validation process was also to verify, investigate and identify any deficiencies in the variables included in the causal loop diagram. The causal loop diagram in Figure 4 was sent out by email to selected experts using the same purposive sampling approach adopted for the qualitative analysis. Five (5) experts in three continents of Europe, Asia and Africa provided dynamic viewpoints of the causal loop diagram for improvement and practical application as presented in Table 2. In all, two (2) experts from South Africa SA-EXP1 and SA-EXP2 with 27 and 10 years’ experience individually provided their African perspective on the model. An IT expert, PA-EX, from Pakistan with a year of experience, provided expert opinion on the model. Experts from the United Kingdom, UK-EXP1 and UK-EXP2, having 21 and 7 years of experience, respectively, responded to the validation process.

Table 3 summarises the expert opinions’ comments and the nature of changes effected in the model to produce a validated causal loop diagram. The validation questions were about the nature of variables, connectivity of the variables, and practicality. All five experts agreed that the model could be used in any country to transfer knowledge from smart campuses to cities. For improvement purposes, new variables were suggested by SA-EXP1, UK-EXP1, UK-EXP2, and SA-EXP2.

The changes effected as presented in Table 3 enhanced the quality of the causal loop model illustrated in Figure 5. The significant improvement included variables such as University community, building information management system, smart resource management, city information management system, facilities management, digital twins, big data and 5G technologies. The validation of the initial causal loop diagram was achieved effectively by enhancing the constituents of the model in Figure 5. The validated causal loop diagram can be used to generate archetypes of explaining the findings and further discussions.

The purpose of including system archetypes in this model is to clearly express the interconnectedness of important variables, reinforcing and balancing loops. Wolstenholme (2004) and Clancy (2018) explained that system archetypes express the commonalities and interactions in a causal loop diagram. System archetypes are extracted from their larger environment to show that they can stand alone in another environment. Systems archetypes are the important aspects of a causal loop diagram that can be used for practical implementation. Figures 5–10 consist of systems archetypes taken out from the causal loop diagram of Figure 5.

The practical application of the systems archetypes 1 to 3 for smart campus development considers the essence of smart technology, the internet of things with data management, and procurement. Min-Allah and Alrashed (2020) and Omotayo et al. (2021) sketch the infrastructure required for a smart campus. However, Min-Allah and Alrashed (2020) and Omotayo et al. (2021) did not include the living lab and smart resource management in their frameworks. Negreiros et al. (2020) noted that a smart campus could become a living lab for simulating how a smart city should look like.

The outcome of performance measures derived for continuous improvement of smart campuses can be shared with the government to benefit the city. Likewise, smart resource management provides a virtual library or database of all knowledge and data generated from smart technology and infrastructure (He et al., 2016). The living lab and smart resource management concept may not be viewed in many emerging smart campuses as opportunities for a city-wide transference of knowledge. The system archetypes 1 to 3 provides a practical approach for developing and enhancing existing smart campuses in developing countries as supported by Min-Allah and Alrashed (2020); Omotayo et al. (2021); Zhicheng and Feng, (2018); Zhou et al., 2020; and Moura et al. (2021). Procurement and tendering practices in smart campus construction are salient attributes of that can be shared with local governments in developing countries for smart city development. Information management systems of smart campuses infrastructure is another important aspect that can be facilitated with building and city information modelling. The purpose of understanding the nature of smart campus development in this study portends the nature of smart cities in developing countries. It is important to note that the systems thinking archetypes may also be valid for developed countries with existing smart campuses.

Unlike other frameworks designed by AbuAlnaaj et al. (2020); Imbar et al. (2020) for smart campuses and cities where IoTs and cloud computing formed the main attributes, this study identified more with the end-users and management of smart infrastructure. The systems archetypes 4 and 5 in Figures 9, 10 support smart campus technology’s transference to cities. The funding sources for smart campuses, procurement, contractual arrangements, and construction approaches can be shared with the government. The concept of a smart campus has been embraced in different parts of the world with its many solutions to different aspects of an academic environment, primarily as a micro-scale city for smart cities. Lehman (2020) highlighted that Arizona state university, the University of Texas, the University of Michigan and the University of Wisconsin-Madison are a few US Universities that have adopted smart campus solutions. Some of the solutions include but are not limited to sensors and powerful cameras to ensure operational efficiency and improved entertainment scenes in a football stadium in Arizona State University. At the same time, the University of Texas Austin provides all of the electricity needed through the largest microgrids for 150 buildings within its premises. The both University of Michigan and the University of Wisconsin-Madison designed an autonomous vehicle trend that increases safety and reduces energy emissions. The aforementioned smart technologies are present in the city’s housing the universities. Developing smart campuses as a living lab for smart cities can be adopted in developing countries. In Brazil, Negreiros et al. (2020) campus smart grid and energy management are expected to be expanded to the large urban environment. Cloud computing and digital asset management provides a foundation for developing smart transportation systems in cities (Abuarqoub et al., 2017; Negreiros et al., 2020).

Smart campus development is premised on the concept of cloud computing as well as other recent technologies (Benltoufa et al., 2018; Flammini et al., 2018). Isolated systems such as office management, finance management, education management and other related systems are integrated into one system through cloud computing (Nie, 2013). Ikrissi and Mazri (2020) study listed the components relating to cloud computing in smart campus solutions, which has proven important for ease of operation within the campus ecosystem. They comprise embedded computing, biometrics, smart community and other smart applications. The technology adopts numerous devices and sensors connected to carry out simple tasks such as crowd detection, adaptive learning and local binary patterns. There are no differences in smart technologies on campuses and the concepts for commercial offices, residential accommodation, factory, transport, health and energy management systems in cities (Bates et al., 2015; Agarwal et al., 2020). Consequently, smart campuses have also been postulated as living labs for smart cities (Atif et al., 2015; Nina et al., 2016; Flammini et al., 2018). The concept of living labs in smart campuses can be used to understudy end-user interaction with smart technologies, identify what works well and propose continuous improvement through the transference of smart technologies.

Irrespective of the resistant force of transitioning smart campuses to cities, as indicated in Figure 11, developing countries can leverage any existing smart campus as a living lab to gradually include smartness in their cities’ infrastructures. The key driving forces of transitioning smart campuses to cities are the capabilities of IoTs and data management and the university’s knowledge of smart campus construction. With the abovesaid driving forces, the challenge of integration with existing technology and community engagement issues can be resolved.