Abstract
Purpose
Cyber-physical systems (CPS) offer improved delivery of facilities management (FM) mandates through their advanced computational capabilities. Using second-order multivariate analysis, this study explores the drivers of the espousal of this digital technology for FM.
Design/methodology/approach
The study employed a deductive approach underpinned by a post-positivist philosophical stance using a quantitative technique aided by a well-structured questionnaire. Data retrieved from the study’s respondents were analysed with descriptive statistics, Kruskal–Wallis h-test, exploratory factor analysis and confirmatory factor analysis.
Findings
The result of the analysis conducted portrayed evidence of convergence and good measures while the estimated model parameters all attained prescribed fit indexes. Also, it was revealed that the most influential drivers for the uptake of CPS for FM mandates are resource allocation for system procurement, top management willingness, system stability and compatibility with the previous system.
Practical implications
The study’s findings unravel the necessitated parameters that would instigate the adoption of CPS for the delivery of FM activities by organisations while also propelling the digital transformation of construction project delivery at the post-occupancy phase.
Originality/value
This is the first study to empirically assess the propelling measures for incorporating CPS for FM using second-order multivariate analysis. Consequently, the study's outcome helps close this knowledge gap.
Keywords
Citation
Ikuabe, M.O., Aigbavboa, C., Anumba, C. and Oke, A.E. (2024), "Structural determinants of the uptake of cyber-physical systems for facilities management – a confirmatory factor analysis approach", Smart and Sustainable Built Environment, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SASBE-07-2023-0176
Publisher
:Emerald Publishing Limited
Copyright © 2024, Matthew Osivue Ikuabe, Clinton Aigbavboa, Chimay Anumba and Ayodeji Emmanuel Oke
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
According to Nutt (2004), facilities management (FM) is “the management of infrastructure resources and services to support and sustain the operational strategy of an organisation over time”. Also, it is portrayed as a system that integrates the functions of an organisation for the development and maintenance of stipulated services that help and enhance the core duties of an organisation (BIFM, 2019). Furthermore, Kamarazaly et al. (2013) stated that FM is a procedure that allows the attainment of a sustainable initiative within the framework of the management of the organisation’s lifecycle targeted at improving productivity and business sustenance. Also, IFMA (2023) describes FM as an organizational function that integrates people, places and processes within the built environment to improve people's quality of life and the productivity of the core business. Hence, FM is a fundamental and strategic constituent of any business concern in accomplishing the organisation's outlined objectives. This can also be viewed from its role in any organisation's enterprise that projects a strategic value in conjunction with evaluating facility use and delivery systems. Consequently, FM covers various service mandates spanning space management, maintenance, safety, asset management, real estate, hospitality and productivity (Kok et al., 2011; Li et al., 2017).
According to Hoxha et al. (2021) and Ikuabe et al. (2022), due to outdated and obsolete systems and methods, the delivery of FM functions is characterised by inefficiencies and ineffectiveness, hence hindering performance outcomes. These pitfalls characterising the processes of FM influence the attainment of organisations’ objectives negatively (Ikuabe et al., 2023a). These setbacks include but are not limited to poor quality control, ineffective acquisition of facility data, poor maintenance schedule, elongation of processing time, delays in response to dysfunctional components of facilities, non-adherence to stipulated standards and undocumented history of facility maintenance (Njuangang et al., 2018; Ikuabe et al., 2023b; Aldowayan et al., 2020; Nidhi and Ali, 2020). These challenges are exacerbated and have been quite predominant, resulting from the delay or reluctance to the digital transformation of FM functions (Aghimien et al., 2022; Ikuabe et al., 2020a). Considering the enormous problems associated with the execution of FM functions, it is essential that the integration of digital technologies into the FM framework is propagated. To attain longevity in facility functions and operations, the building industry and by extension, FM, requires incorporating systems instigated by digital technologies (Islam et al., 2019; Aghimien et al., 2021). This would proffer solutions to some of the inherent challenges associated with FM functions in the form of expedited delivery of tasks, optimisation of operations, quality assurance and effective cost management. Cyber-Physical Systems (CPS) is an innovative technology with the aforementioned attributes.
Cyber-Physical Systems are computing systems characterised by advanced computational abilities whose functionality is built on the dual integration of the physical processes and the computational structure (Yuan et al., 2016; Ikuabe et al., 2020b; Akanmu et al., 2013). Moreover, the system’s operability is multidisciplinary, complex and physically alert, with an improved engineering setup premised on amalgamating the physical process and the computing technology for transformational use. The innovative system outlines a setup for resolving problems achieved in real-time through providing an inter-working framework of various technologies, including dispersed systems, sensor networks, control systems and real-time systems (Lee, 2015; Fitzgerald et al., 2015). For its application in FM functions, CPS offers a framework that assists the monitoring and coordination of the constituents of a facility propagated by functions delivered in real-time resulting from the integrated working capabilities of the virtual and physical components (Terreno et al., 2020; Akanmu and Anumba, 2015). Since FM is transdisciplinary and multidisciplinary, its output is considerably influenced by human expertise, which is susceptible to managerial challenges. Hence, the utilisation of CPS for FM task delivery would aid in the abatement of the inherent shortcomings associated with the traditional mode of FM.
Resulting from the aforementioned challenges associated with the delivery of FM and the potential benefits of using an advanced computational system such as CPS, this study is motivated to evaluate the propelling measures for the uptake of the system for FM. The study’s outcome will contribute immensely to the body of knowledge by shoring up the knowledge gap, as no study has focused on showcasing the drivers of using CPS for FM using confirmatory factor analysis (CFA), a second-order multivariate statistics technique. A previous study used a Delphi approach to present the driving influences of CPS for FM (Ikuabe et al., 2023b). Furthermore, the study’s outcome would be a sound theoretical base for further studies on digitalising construction projects in the post-construction phase. While attempting to showcase the drivers of the use of CPS for FM, the study’s outcome would present a framework for the proponents of post-construction digital transformation to relevant stakeholders saddled with the responsibility of FM. The other sections comprising the paper’s structure are a detailed review of the literature on the enablers of the use of CPS for FM, the research methodology, the result of the data analysis, a discussion of the findings and conclusion and recommendations.
Literature review
The stipulated mandates of the delivery of FM tasks are attributed to a spectrum of bottlenecks, which include delay in the detection of a dysfunctional building component, increase in energy cost, dissatisfied occupants of facilities, improper maintenance records and irregular space management (Ikuabe et al., 2023a; Han et al., 2012). Pre-programmed inactive standards inherently possess quite a number of FM approaches. However, these should be set up to accommodate flexible, altered and complicated situations expansively. The drive to seek answers to these daunting challenges has presented the window of offering systems that would efficiently and effectively capture and evaluate the collective FM functions for the organisation’s benefit. One such route is the espousal of innovative technologies for FM duties. The utilisation of innovative systems in the delivery of FM tasks through coordination and monitoring seeks to enhance the attainment of the projected mandate of the organisation (Terreno et al., 2020). Akanmu and Anumba (2015) noted that a CPS approach for FM functions would advance the coordination and monitoring of the facility’s components through the utilisation of real-time functionalities emanating from the bi-directional coordination of the physical and virtual worlds propagated by networks and advanced computational abilities. Using sensor technology to acquire data, relevant information pertaining to the breakdown of the facility’s elements, safety surveillance, detection of potential system failure, energy utilisation monitoring and effective space management can easily be obtained. Furthermore, a CPS-drive FM propagates the components of facilities for engaging in cognitive duties, therefore aiding the acknowledgement of the physical world through an advanced level of intelligence (Wu et al., 2014).
The choice of resource allocation constantly influences organisations due to the substantial implications these have on the survival and growth within the business landscape. As a result of the growing competition within the business environment, organisations make committed efforts to ensure that decisions on resource allocation are in tandem with the organisation’s upward trajectory, which ultimately expands their market capacity (Erdoğmuş and Esen, 2011). Also, the drive to improve a system’s efficiency and the rise in the level of consumer demands would instigate competitiveness propelled by the creation of innovative methods and approaches (Abiri Jahromi and Kundur, 2020). While the ease of accessing cost-effective technological infrastructure is a significant propeller for the espousal of emerging digital technologies (Lee et al., 2005). Moreover, the persistent call for improving the methods and approaches employed by organisations in delivering FM tasks is a driving factor for the espousal of emerging technologies such as CPS for FM. This is further reinforced by the high level of connection that is enabled through the unhindered human-machine collaboration (Parasuraman and Colby, 2014). Also, the ability of personnel to acquire the necessary skills and knowledge on the use of a given technology aids in the drive for the uptake of the technology (Serdyukov, 2017). This becomes important due to the human dimension in the use of any innovative system which falls within the precept of management and other dimensions.
The idea of utilising CPS is driven by several factors, including the transformation encountered in wireless communication, increased use of low-cost sensors capacitated with high-end deliverables and expanded application of Internet bandwidth (Hakansson et al., 2015). These features encompass the requisite technological infrastructure needed to implement emerging innovative technologies. Moreover, Howard et al. (2017) affirmed that the uptake of digital technologies is furthered by the provision of essential infrastructure for setting up and implementing the system. Furthermore, an important feature of technology adoption for organisations is the willingness of the top management to incorporate the realities of technological use for the propagation of service delivery. Agbim (2013) stated that an organisation’s structure comprises functions and responsibilities that are outlined to facilitate the attainment of its objectives; therefore, the top management of organisations responsible for making critical decisions plays a vital function in determining the espousal of innovative technologies for service delivery. Also, the intellectual resource for the application of innovative systems plays a significant role in the pursuit of employing technological applications for service delivery (Talukder and Quazi, 2011). At the same time, the system’s security forms a vital base for decision-making on embracing innovative systems. The enactment of cryptographic algorithms, provision of trust and protection against attacks by secure formations aid in the push for the implementation of emerging technologies for service output (Tomlinson et al., 2022).
The function of market demands in pushing for the uptake of emerging innovative systems such as CPS is vital. Organisations seek requisite knowledge in projecting business models for driving the espousal of innovative systems; a framework to outline the latent derivatives from the innovation would aid in propagating the significance of adopting the technology by the organisation (Hastings and Sethumadhavan, 2020). This is in tandem with implementing CPS for FM, as most organisations would require outlining the potential performance expectancies from using the system. Likewise, Potter (2020, p. 4) noted that “key stakeholders are placing increased pressure on companies to demonstrate with evidence how they invest in and use security technology to protect digital assets”. Moreover, the training given to the relevant personnel to boost their technical competence for delivering the functionalities of the technology can aid in accelerating the drive for its implementation. Providing the necessary knowledge to the relevant stakeholders mandated for the management and utilisation of the system aids in driving the implementation of these advanced technological systems (Odumeru, 2013). Normally, the system application would not receive wide acceptance when the right knowledge for efficient use is not adequately disseminated.
Palem (2013) noted that since a major function of FM is the constant review of the state of the facilities’ elements, this presents continuous pressure on the service and operational delivery of the system employed. Utilising CPS for the delivery of FM tasks would assist in optimising predictive strategies, thereby abating some of the bottlenecks associated with the conventional system. When an upscale is guaranteed by using a new system, which presents a comparative advantage, it helps push for the implementation of the new system (Odumeru, 2013). The use of CPS for FM outlines enhanced deliverables due to the high computational capabilities, thereby averting the failures associated with the elements of the facilities. In complex facilities, an element-level evaluation must be performed in place of the overall facility evaluation (Shen et al., 2016). Therefore, the amalgamation of data at a cumulative scale of facility evaluation is important in steering the implementation of digital technologies such as CPS. Table 1 summarises the identified drivers for the uptake of CPS for FM.
Research methodology
The study aims to evaluate the drivers of the uptake of CPS for FM. This was actualised using a deductive approach underpinned by a post-positivist philosophical stance employing a quantitative technique. Quantitative data was retrieved from built environment professionals using a questionnaire survey. According to Tan (2011), the questionnaire is a useful tool for gathering data from a large group of respondents and allows for the study's quantifiability and objectivity. The study area for the research was Gauteng province of South Africa. This was chosen due to its attribute as a base for a large pool of built environment professionals while also boasting of a vast number of built-up facilities. The sampling method employed for the study was snowball sampling due to the difficulty faced in getting built environment professionals with foreknowledge of digital technologies for FM. It is noted that this sampling technique has the tendency to increase the sample size (Atkinson and Flint, 2001). Therefore, the number of respondents that partook in the survey resulted in 218. The research instrument was categorised into two sections. The first elicited responses based on the background information of the survey participants, while the second enquired about the drivers of using CPS for FM delivery. The survey was self-conducted using electronic means, which aided the seamless collection of the responses from the study respondents. For the data received on the respondents' background information, the data analysis method employed was percentage. The dataset on the drivers of the use of CPS for FM was analysed using mean item score (MIS), Kruskal–Wallis h-test, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) as shown in Figure 1. The MIS was used in ranking the identified drivers based on their significance, while the Kruskal–Wallis h-test was employed to ascertain if there is any significant difference in the viewpoints of respondents based on their professional designation. This is a non-parametric test conducted to ascertain if there is a difference in the opinion given by more than two groups of respondents (Pallant, 2020). When the given p-value >0.05, it is judged that there is no significant difference in the views of the categories of respondents. Similarly, when the given p-value <0.05, it is judged that there is a significant difference in the opinions of the categories of respondents. Also, EFA is used in assessing the unidimensionality and factor-analysability of the identified drivers (Ikuabe et al., 2022; Aghimien et al., 2021). This was conducted using the statistical package for social science (SPSS) version 27. The EFA was executed using principal component analysis (PCA), which employed varimax rotation as the technique for extraction. Varimax rotation was employed due to uncorrelated attribute of the variables (Osborne, 2015). Furthermore, the study used CFA to evaluate the measurement equivalency of the derived constructs from the EFA. This was achieved with the aid of EQation software (EQS) version 6.4. The study adopted a multi-dimensional approach in assessing the model by using these indexes: Satorra-Bentler scaled chi-square (S - Bχ2), root mean square error of approximation (RMSEA), standardised root mean square residual (SRMR) and root mean square error of approximation with 95% or 90% confidence interval (RMSEA @ 95% or 90% CI), goodness-of-fit index (GFI) and Bentler comparative fit index (CFI).
Result and discussion
Demographic information of respondents
From the total number of respondents that participated in the survey, quantity surveyors comprised 26.1%, while facility managers, construction managers and construction project managers accounted for 21.6%, 17.9% and 11%, respectively. Engineers, architects and computer scientists/programmers comprised 10.6%, 7.8% and 5% of the respondents, respectively. With respect to the years of professional experience of the survey participants, it is revealed that the respondents having 6–10 years make up 30.7% of the overall participants. This is followed by participants with 1–5 years, accounting for 24.8% of the total respondents. Also, 19.3 and 10.6% of the total respondents represent professionals with 11–5 years and 16–20 years of professional experience, respectively. At the same time, 4.6% of the respondents account for those with more than 20 years of professional experience.
Descriptive statistics and Kruskal–Wallis h-test
Table 2 indicates the mean ranking and K-W test results for the enabling measures for using CPS for FM. The overall most rated variables are technology infrastructure (MIS = 4.42), compatibility with work procedures (MIS = 4.34), speed and reliability of systems (4.34), financial resources (MIS = 4.30) and learning capability of personnel (MIS = 4.29). In comparison, the least ranked variables are intellectual resource (MIS = 4.19) and compatibility with the previous system (MIS = 4.06). For the individual groups of working organisations, respondents affiliated with government establishments mostly rated compatibility with work procedures and learning capability of personnel, while respondents working for contracting organisations mostly rated speed and reliability of systems and system stability. Respondents working for consulting firms mostly rated technology infrastructure and compatibility with work procedures. Moreover, the overall group mean was 4.25, while respondents working for government establishments, contracting organisations and consulting firms were 4.22, 4.21 and 4.33, respectively. Generally, the MIS of the measurement variables is well above 3.0, thus indicating a high significance of the variables for adopting cyber-physical systems for facilities management. Results from the K-W test show that from the 16 variables tested, only 1 variable had a p-value < 0.05, thus indicating a significant statistical difference in the group of respondents based on their working organisation. The variable is system stability with a p-value of 0.034. In essence, this translates to the fact that the opinions of the different respondents based on the grouping of their working organisations differ concerning the variable. Also, the Cronbach alpha value for the measuring variables was indicated to be 0.784, thus indicating the high reliability of the research instrument and the internal consistency of the measurement variables making up the latent construct.
Exploratory factor analysis
Exploratory Factor Analysis (EFA) was engaged to provide structured patterns of the identified drivers of the use of CPS for FM. Table 3 presents the result of the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO), which gave a value of 0.915. The value is > 0.6, set as the threshold set by the study as recommended by Chetty (2015). Furthermore, the outcome of the Bartlett test of sphericity gave a value of 1019.916 and an accompanying p-value of 0.000, indicating that it is significant. Moreover, the correlation output given for the study indicates that values are ≥0.3, while a Cronbach alpha value of 0.784 was presented. These results give credence to using the study’s dataset for EFA.
Table 4 shows the communalities of the identified drivers. The outcome shows that all the variables have values > 0.5, which serves as the cut-off for the study. Moreover, the cumulative variance explained for the variables indicates a value of 72.1%, which is > the cut-off of 50% set for the study. Therefore, indicating all the variables do explain much of the variance. Also, with the aid of PCA, adopting the varimax rotation, two components with an eigenvalue >1 are extracted.
Table 5 shows the result of the rotated component matrix of the measurement variables for enabling measures. As recommended by Osborne (2015), the result outlines that the factor loading for the variables is above 0.4, which is the threshold set for the study. This helps in extraction of correlated variables with similar unidimensionality. These results, in conjunction with the values of the extracted communalities, showcase that all the variables within a given component attain a good relationship with each other. The result of the rotated component matrix indicates that two components are extracted. The first component has factor loadings ranging from 0.684 to 0.599, while the second component has factor loadings ranging from 0.885 to 0.509.
Confirmatory factor analysis
The construct was initially made up of sixteen measurement variables, however, before the confirmatory factor analysis (CFA), nine variables were deemed to possess acceptable values, which served as determinants for inclusion in further analysis. The variables are ENM1, ENM2, ENM4, ENM5, ENM10, ENM11, ENM13, ENM14 and ENM15. According to Byrne (2006), it is recommended that the residual covariance distribution should be centred around zero and symmetrical. The latent construct with these features is adjudged to be well fit, and its addition for a detailed CFA is reasonable (Boomsma, 2000). Also, Gao et al. (2008) noted that these characteristics proffer answers to the hurdle of multicollinearity and excessive correlations that affect the model’s output. The outcome of the residual covariance attributed to the model comprising nine measurement variables indicates good measures of the residual matrix, therefore outlining evidence of convergence. Bentler (2005) noted that the characteristic of the residual matrix with evidence of convergence and good measures is the possession of residual values ranging from −1.00 to +1.00, whose value is closer to zero. Hence, considered a good fit. The outcome of the analysis indicates that the frequency distribution of the residual values aligns with the desired range of −1.00 to +1.00. While the outcome of the unstandardised average off-diagonal residual is 0.0394. Residual values > 2.58 are adjudged to be too large. The given results indicate that the measurement model is well fit, although the result of the goodness-of-fit analysis would further strengthen the fit indices and appropriateness of the measurement model.
The estimated model parameters determine the choice of acceptance or rejection of a conceptualised model (Lei and Wu, 2008). The fitness of a model for a dataset is evaluated by assessing the various parameter estimates, including validity, reliability and statistical significance (Lei and Wu, 2008; Hair et al., 2013). Furthermore, Hair et al. (2013) recommend the evaluation of the model fit to be conducted using a wide range of criteria which includes incremental and absolute fit indices in conjunction with the chi-square test. Table 6 outlines the findings of the fit indexes and the accompanying derivate output estimates. It is revealed that the GFI and CFI have values of 0.971 and 0.992, respectively. Iacobucci (2010) affirms that to attain a good fit for GFI and CFI, their value must be ≥ 0.95, while values ≥ 0.90 are deemed to be acceptable fit. Therefore, the values of GFI and CFI obtained from the analysis are considered a good fit, respectively. Also, the result shows that the values given for SRMR and RMSEA are 0.035 and 0.022, respectively. Values of SRMR and RMSEA that are ≤0.05 are adjudged to be good fit, while values ≤ 0.08 are deemed to be an acceptable fit. Consequently, the results affirm that the study’s output for both SRMR and RMSEA met the good fit criterion. Furthermore, the sample data for the model gave S - Bχ2 of 25.054 with 5° freedom, accompanied by a p-value of 0.000. Considering the high sensitivity associated with chi-square concerning sample size and associated data normality, it is conventionally tagged as less reliable and largely inadequate (Zhong and Yuan, 2011). Hence, a normed chi-square is recommended (Kline, 2005). This is achieved by dividing the chi-square by the accompanying degree of freedom. Therefore, the resulting normed chi-square from the analysis is 4.811. Byrne (2006) recommends a normed chi-square value on a range of 3.00–5.00 to be a good fit. This affirms the adequacy of the model.
Table 7 presents the coefficient of correlation, standard error and test statistics of the analysis. The coefficient of determination (R2) and z-values provide insightful context for interpreting the significance and effects of the parameters in a model (Bentler, 2005). It is shown that the correlation coefficients are less than 1.00 while the z-statistics given are above 1.96, therefore indicating the suitability of the measurement variables. The z-statistics outlines the significance level of the path coefficients of the inner model. According to Hair et al. (2014), a significant outcome of the z-statistics is one whose value is greater than 1.96 at a two-tailed z-test at a 5% confidence level. Also, the predictive accuracy of the model is outlined by the resulting R2. A resulting value closer to 1.00 indicates a high predictive accuracy (Kline, 2010). As shown in the analysis result, the given R2 of the measurement variables are above 0.5. Therefore portraying that the enabling measures explain a large proportion of the variance of the indicator variables. Consequently, the measurement variables significantly predict and define the model.
The internal reliability and consistency of the propelling measures were subject to further testing. The tests conducted entailed parameters such as factor loadings, Cronbach alpha and Rho coefficient. These parameters provide sufficient information to assess the consistencies, validity and reliability of the given dataset (Hair et al., 2014). The coefficient alluding to the reliability ought to fall within the range of 0–1.00 (Kline, 2005). Whereas a value closer to 1.00 is preferred. Table 8 presents the results of the reliability and validity tests and the construct. It is noted that the Cronbach alpha and the Rho coefficient gave values of 0.833 and 0.849, respectively. This gives credibility to the consistency and reliability of the indicator variables. Moreover, the resulting factor loadings of the measurement variables were used to ascertain the construct validity's magnitude and reasonableness. The coefficients indicate that the measurement variables are attributed with a potent relationship with the formed construct, therefore, convergence at a shared point. Resulting coefficients greater than 0.5 indicate a close relationship between the measurement variable and the construct. The result outlined in Table 8 portrays that the coefficient of all the indicator variables except ENM1, ENM2 and ENM14 are beyond 0.70, which is the recommended coefficient. Kline (2005) recommended 0.7 as the factor loading coefficient for convergent validity, while the average variance extracted for each construct should be above 0.5. Hence, affirming the good convergent validity of the measurement variables.
Discussion and implication of findings
The study evaluated the propelling measures for the uptake of CPS for the delivery of FM mandates. The descriptive analysis conducted in the study revealed that the most influential drivers for the uptake of CPS for FM mandates are technology infrastructure, compatibility with work procedures, speed and reliability of system and financial resource. At the same time, the variables majorly defined by the CFA are resource allocation for system procurement, top management willingness, system stability and compatibility with the previous system. According to Venkatesh and Bala (2008), enabling measures can be given as the extent to which the belief of an individual or organisation in technical or organisational infrastructure exists to serve as an enabler to the functioning of a system. The ability of organisations to meet the financial requirements for inculcating innovations and technological systems in their delivery processes is an important yardstick in the drive towards digital transformation. The outcome of this study fully supports this stance, as it is noted that the financial capacity of any organisation is a significant prerequisite for technology adoption since the entire chain procurement, utilisation and maintenance is hugely dependent on the financial outlay of the given organisation (Erdoğmuş and Esen, 2011). Also, the study’s outcome presents a new worldview with respect to its alignment with the notion that an organisation’s willingness to commit to an entirely delivery system aided by the utilisation of technological innovations such as CPS is a prominent determinant of the system’s uptake. Hence, the commitment shown by the top hierarchy of the organisation towards driving the digitalisation process is very vital. Therefore, the top management of organisations must be acquainted with the accompanying benefits and the comparative advantage that these digital innovations provide for optimum service delivery (Oliveira et al., 2014). The top hierarchy of organisations is saddled with the responsibility of taking strategic decisions which aim to place the organisation in a vantage position in the business environment by acquiring a competitive advantage over competing organisations. Likewise, without top management's support and clear conviction, the push for digital transformation can be significantly hindered. The findings of the study place emphasis on the system’s stability as a significant influencing drive for the espousal of CPS for FM. This is affirmed by Hooks et al. (2022), who noted that the capacity attributed to the innovative system to showcase its stability while delivering optimised functions is a major drive for adopting such a system. Therefore, the system’s delivery characteristics must match the stability requirements during service delivery. Moreover, organisations seek requisite knowledge in projecting business models for driving the espousal of innovative systems, a framework to outline the latent derivatives from the innovation would aid in propagating the significance of adopting the technology by the organisation (Hastings and Sethumadhavan, 2020). This is in tandem with implementing CPS for FM, as most organisations would require outlining the potential performance expectancies from using the system. Also, the ability of personnel to acquire the necessary skills and knowledge on the use of a given technology aids in the drive for the uptake of the technology (Serdyukov, 2017). This becomes important due to the human dimension in the use of any innovative system which falls within the precept of management and other dimensions.
Conclusion
The study empirically assessed the propelling measures for the drivers of the uptake of cyber-physical systems for facilities management. A review of extant literature was conducted, which unveiled sixteen variables. Using a quantitative approach, these variables were presented to the study respondents for rating based on their influence. The retrieved data was subjected to a five-stage data analysis, which included validity and reliability of the instrument, descriptive statistics, Kruskal–Wallis h-test, exploratory factor analysis and confirmatory factor analysis. The results of the descriptive statistics showed that the top influencing drivers of the espousal of CPS for FM are technology infrastructure, compatibility with work procedures, speed and reliability of the system and financial resource. Also, it was reflected that there was a convergence of opinion among the classification of the respondents based on their working affiliation for all the drivers except for one, which is system stability. Also, the CFA affirmed the significant influence of nine of the drivers, which are compatibility with work procedures, compatibility with the previous system, comparison with the previous system, training and support, resource allocation for procurement of the system, user satisfaction, system stability, top management willingness and financial resource.
The study’s findings significantly contribute to the body of knowledge by unveiling the significant propelling measures for the uptake of innovative technology in the form of CPS for the delivery of FM functions. An innovative platform such as CPS is attributed to advanced computational capabilities with the potential to provide solutions to the challenges faced in the delivery of FM. Although scholarly research is yet to be done that unravels the drivers of the adoption. This study fills the knowledge gap by presenting empirical evidence which culminates in articulating the required parameters for the uptake of the digital system. Furthermore, the study’s outcome presents practical perceptions to organisations saddled with the responsibility of FM on the influential elements necessitating the use of CPS. The outline of these findings would assist organisations in formulating informed decisions and policies that align with the revolutionary pursuit of digital transformation. Moreover, the study’s findings serve as an underpinning theoretical base for future studies that strive to contribute to the deliberations on the digitalisation process of construction project delivery, especially in the post-occupancy phase. Also, it is important to state that the study was conducted in the Gauteng province of South Africa; hence, future studies can be conducted in other developing countries to compare the outcome of the current study. Also, the study was limited to the use of a quantitative approach; other studies can explore qualitative approaches such as interviews. Furthermore, future studies can explore the views of other stakeholders, such as end users, facility owners, etc.
Figures
Figure 1
Research framework
Enabling Measures of the uptake of CPS for FM
| Drivers | Authors |
|---|---|
| Compatibility with work procedures | Jaafar et al. (2007) |
| Compatibility with previous system | Jaafar et al. (2007) |
| Speed and reliability of system | Gupta et al. (2008) |
| Comparison with previous system | Odumeru (2013) |
| Training and support | Odumeru (2013), Serdyukov (2017) |
| Learning capability of personnel | Froese and Gallagher (2010), Serdyukov (2017) |
| Convenience of the system’s use | Merschbrock and Nordahl-Rolfsen (2016) |
| Technology infrastructure | Howard et al. (2017) |
| Resource allocation | Erdoğmuş and Esen (2011) |
| User satisfaction | Parasuraman and Colby (2014) |
| System security | Watson (2018), Parn and Edwards (2019) |
| System stability | Serdyukov (2017) |
| Top management willingness | Lee (2015), Oliveira et al. (2014) |
| Financial resource | Erdoğmuş and Esen (2011) |
| Intellectual resource | Talukder and Quazi (2011) |
Source(s): Authors’ Compilation
Enabling Measures (Mean Ranking and Kruskal–Wallis h-test)
| Drivers | Govt | Contra | Consult | Total | K-W | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| M | R | M | R | M | R | M | R | X2 | Sig | |
| Technology infrastructure | 4.13 | 13 | 4.26 | 4 | 4.81 | 1 | 4.42 | 1 | 1.511 | 0.470 |
| Compatibility with work procedures | 4.45 | 1 | 4.08 | 15 | 4.36 | 4 | 4.34 | 2 | 1.533 | 0.465 |
| Speed and reliability of system | 4.21 | 8 | 4.34 | 1 | 4.41 | 2 | 4.34 | 2 | 1.963 | 0.375 |
| Financial resource | 4.32 | 3 | 4.26 | 4 | 4.33 | 6 | 4.30 | 4 | 0.198 | 0.906 |
| Learning capability of personnel | 4.40 | 2 | 4.16 | 13 | 4.39 | 3 | 4.29 | 5 | 5.181 | 0.075 |
| System stability | 4.06 | 15 | 4.34 | 1 | 4.33 | 6 | 4.28 | 6 | 4.840 | 0.034** |
| User satisfaction | 4.32 | 3 | 4.21 | 8 | 4.32 | 8 | 4.27 | 7 | 0.829 | 0.661 |
| System’s learning flexibility | 4.26 | 6 | 4.23 | 7 | 4.28 | 10 | 4.25 | 8 | 0.071 | 0.965 |
| Comparison with previous system | 4.21 | 8 | 4.24 | 6 | 4.29 | 9 | 4.25 | 8 | 0.114 | 0.945 |
| Resource allocation for procurement of the system | 4.26 | 6 | 4.21 | 8 | 4.27 | 12 | 4.24 | 10 | 0.319 | 0.852 |
| Top management willingness | 4.19 | 10 | 4.10 | 14 | 4.36 | 4 | 4.21 | 11 | 3.981 | 0.137 |
| Training and support | 4.17 | 11 | 4.18 | 10 | 4.28 | 10 | 4.21 | 11 | 0.575 | 0.750 |
| Convenience of the system’s use | 4.13 | 13 | 4.17 | 11 | 4.27 | 12 | 4.19 | 13 | 0.971 | 0.615 |
| Intellectual resource | 4.15 | 12 | 4.17 | 11 | 4.25 | 14 | 4.19 | 14 | 0.502 | 0.778 |
| System security | 4.28 | 5 | 4.08 | 15 | 4.19 | 15 | 4.16 | 15 | 1.792 | 0.408 |
| Compatibility with previous system | 3.98 | 16 | 4.27 | 3 | 4.07 | 16 | 4.06 | 16 | 0.624 | 0.732 |
| Group mean | 4.22 | 4.21 | 4.33 | 4.25 | ||||||
| Cronbach alpha | 0.784 | |||||||||
Note(s): NB: M = Mean Item Score, R=Rank, Govt. = Government, Contra. = Contracting organisation, Consult. Consulting firm, K-W=Kruskal–Wallis H-test, X2 = Chi-square, ** = significant (p < 0.05)
Source(s): Authors’ own work
KMO and Bartlett’s test
| Kaiser-Meyer-Olkin measure of sampling adequacy | 0.915 | |
| Bartlett's test of Sphericity | Approx. Chi-Square | 1019.916 |
| df | 120 | |
| Sig | 0.000 | |
Source(s): Authors’ own work
Communalities
| Label | Enabling measures | Initial | Extraction |
|---|---|---|---|
| ENM1 | Compatibility with work procedures | 1.000 | 0.587 |
| ENM2 | Compatibility with the previous system | 1.000 | 0.680 |
| ENM3 | Speed and reliability of system | 1.000 | 0.791 |
| ENM4 | Comparison with previous system | 1.000 | 0.798 |
| ENM5 | Training and support | 1.000 | 0.792 |
| ENM6 | System’s learning flexibility | 1.000 | 0.683 |
| ENM7 | Learning capability of personnel | 1.000 | 0.619 |
| ENM8 | Convenience of the system’s use | 1.000 | 0.720 |
| ENM9 | Technology infrastructure | 1.000 | 0.523 |
| ENM10 | Resource allocation for procurement of the system | 1.000 | 0.666 |
| ENM11 | User satisfaction | 1.000 | 0.669 |
| ENM12 | System security | 1.000 | 0.793 |
| ENM13 | System stability | 1.000 | 0.504 |
| ENM14 | Top management willingness | 1.000 | 0.724 |
| ENM15 | Financial resource | 1.000 | 0.865 |
| ENM16 | Intellectual resource | 1.000 | 0.696 |
Source(s): Authors’ own work
Rotated component matrix
| Drivers | Component | |
|---|---|---|
| 1 | 2 | |
| Intellectual resource | 0.684 | |
| Financial resource | 0.682 | |
| User satisfaction | 0.680 | |
| Top management willingness | 0.642 | |
| Comparison with previous system | 0.635 | |
| System’s learning flexibility | 0.618 | |
| System stability | 0.616 | |
| Training and support | 0.610 | |
| Compatibility with work procedures | 0.607 | |
| Resource allocation for procurement of the system | 0.599 | |
| Compatibility with previous system | 0.885 | |
| Learning capability of personnel | 0.696 | |
| Technology infrastructure | 0.161 | |
| Convenience of the system’s use | 0.565 | |
| System security | 0.511 | |
| Speed and reliability of system | 0.509 | |
Note(s): Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation
Source(s): Authors’ own work
Robust fit indexes for enabling measures construct
| Fit index | Cut-off value | Estimate | Indication |
|---|---|---|---|
| S - Bχ2 | 24.054 | ||
| df | x>0.00 | 5 | Good fit |
| CFI | x ≥ 0.90 (acceptable) | 0.992 | Good fit |
| x ≥ 0.95 (good fit) | |||
| GFI | x ≥ 0.90 (acceptable) | 0.971 | Good fit |
| x ≥ 0.95 (good fit) | |||
| RMSEA | x ≤ 0.08 (acceptable) | 0.022 | Good fit |
| x ≤ 0.05 (good fit) | |||
| SRMR | x ≤ 0.08 (acceptable) | 0.035 | Good fit |
| x ≤ 0.05 (good fit) | |||
| NFI | x ≥ 0.90 (acceptable) | 0.940 | Acceptable fit |
| x ≥ 0.95 (good fit) | |||
| NNFI | x ≥ 0.90 (acceptable) | 0.989 | Good fit |
| x ≥ 0.95 (good fit) | |||
| RMSEA 90% CI | 0.000:0.061 | Acceptable range | |
| p-value | x>0.05 | 0.00 | Acceptable range |
Note(s): NB: (S - Bχ2) - Satorra-Bentler scaled chi-square; CFI - Bentler comparative fit index; GFI - goodness-of-fit index; RMSEA - Root mean square error of approximation; SRMR - Standardised root mean square residual; NFI - Normed fit index; NNFI – Non-normed fit index
Source(s): Authors’ own work
Factor loading and Z-statistics
| Indicator variable | Unstandardised coefficient (λ) | Standardised coefficient (λ) | Z-Statistics | R2 | Significant at 5% level? |
|---|---|---|---|---|---|
| ENM1 | 0.610 | 0.793 | 6.778 | 0.793 | Yes |
| ENM2 | 0.568 | 0.823 | 7.469 | 0.823 | Yes |
| ENM4 | 0.646 | 0.763 | 7.153 | 0.763 | Yes |
| ENM5 | 0.609 | 0.793 | 6.382 | 0.793 | Yes |
| ENM10 | 0.526 | 0.850 | 7.455 | 0.850 | Yes |
| ENM11 | 0.644 | 0.765 | 6.749 | 0.763 | Yes |
| ENM13 | 0.565 | 0.825 | 6.690 | 0.825 | Yes |
| ENM14 | 0.558 | 0.830 | 7.541 | 0.830 | Yes |
| ENM15 | 0.655 | 0.756 | 7.122 | 0.756 | Yes |
Source(s): Authors’ own work
Reliability and construct validity of the enabling measures construct
| Indicator variable | Factor loading | Cronbach's alpha | Rho coefficient |
|---|---|---|---|
| ENM1 | 0.6414 | 0.833 | 0.849 |
| ENM2 | 0.5841 | ||
| ENM4 | 0.7222 | ||
| ENM5 | 0.7995 | ||
| ENM10 | 0.7331 | ||
| ENM11 | 0.8822 | ||
| ENM13 | 0.7824 | ||
| ENM14 | 0.6711 | ||
| ENM15 | 0.8849 |
Source(s): Authors’ own work
References
Abiri Jahromi, A. and Kundur, D. (2020), “Fundamentals of cyber-physical systems”, in Anumba, C.J. and Roofigari-Esfahan, N. (Eds), Cyber-Physical Systems in the Built Environment, Springer, doi: 10.1007/978-3-030-41560-0_1.
Agbim, K.C. (2013), “The impact of organizational structure and leadership styles on innovation”, IOSR Journal of Business and Management, Vol. 6 No. 6, pp. 56-63, doi: 10.9790/487x-0665663.
Aghimien, D., Aigbavboa, C., Meno, T. and Ikuabe, M. (2021), “Unravelling the risks of construction digitalisation in developing countries”, Construction Innovation, Vol. 21 No. 3, pp. 456-475, doi: 10.1108/CI-02-2020-0026.
Aghimien, D., Ikuabe, M., Aghimien, L., Aigbavboa, C., Ngcobo, N. and Yankah, J. (2022), “PLS-SEM assessment of the impediments of robotics and automation deployment for effective construction health and safety”, Journal of Facilities Management, Vol. 22 No. 3, pp. 458-478, doi: 10.1108/JFM-04-2022-0037.
Akanmu, A. and Anumba, C. (2015), “Cyber-physical systems integration of building information models and the physical construction”, Engineering, Construction and Architectural Management, Vol. 22 No. 5, pp. 516-535, doi: 10.1108/ECAM-07-2014-0097.
Akanmu, A., Anumba, C. and Messner, J. (2013), “Active monitoring and control of light fixtures during building construction and operation: cyber-physical systems approach”, Journal of Architectural Engineering, Vol. 20 No. 2, pp. 16-24, doi: 10.1061/(ASCE)AE.1943-5568.0000140.
Aldowayan, A., Dweiri, F. and Venkatachalam, S. (2020), “A review on current status of facility management practices in building industry and prospective BIM intervention to manage the facilities effectively during its service life”, Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, Detriot, Michigan, USA, August 10-14, 2020, pp. 831-846, available at: http://www.ieomsociety.org/detroit2020/papers/196.pdf (accessed 23 August 2023).
Atkinson, R. and Flint, J. (2001), “Accessing hidden and hard-to-reach populations: snowball research strategies”, Social Research Update, University of Surrey, Vol. 33, pp. 1-4, available at: https://sru.soc.surrey.ac.uk/SRU33.html (accessed 16 March 2022).
Bentler, P.M. (2005), EQS 6 Structural Equation Program Manual, Multivariate Software, Encino, CA, available at: http://www.econ.upf.edu/∼satorra/CourseSEMVienna2010/EQSManual.pdf (accessed 3 February 2023).
Boomsma, A. (2000), “Reporting analyses of covariance structures”, Structural Equation Modeling: A Multidisciplinary Journal, Vol. 7 No. 3, pp. 461-483, doi: 10.1207/S15328007SEM0703_6.
British Institute of Facilities Management (BIFM) (2019), “Facilities management viewed from”, available at: http://www.bifm.org.uk/bifm/about/facilities (accessed 30 October 2021).
Byrne, B.M. (2006), Structural Equation Modelling with EQS- Basic Concepts, Applications and Programming, 2nd ed., Routledge, Lawrence Erlbaum Associates, Mahwah, New York. doi: 10.4324/9780203726532.
Chetty, P. (2015), “Interpretation of factor analysis using SPSS”, Project Guru, available at: https://www.projectguru.in/interpretation-of-factor-analysis-using-spss/
Erdoğmuş, N. and Esen, M. (2011), “An investigation of the effects of technology readiness on technology acceptance in e-hrm”, Procedia - Social and Behavioral Sciences, Vol. 24, pp. 487-495, doi: 10.1016/j.sbspro.2011.09.131.
Fitzgerald, J., Gamble, C., Larsen, P., Pierce, K. and Woodcock, J. (2015), “Cyber-physical systems design: formal foundations, methods and integrated tool chains”, in 2015 IEEE/ACM 3rd FME Workshop on Formal Methods in Software Engineering, Florence, pp. 40-46, doi: 10.1109/FormaliSE.2015.14.
Froese, T. and Gallagher, S. (2010), “Phenomenology and artificial life: toward a technological supplementation of phenomenological methodology”, Husserl Studies, Vol. 26, pp. 83-106.
Gao, S., Mokhtarian, P.L. and Johnston, R.A. (2008), “Non-normality of data in structural equation”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2082 No. 1, pp. 116-124, doi: 10.3141/2082-14, available at: https://escholarship.org/uc/item/7nf8f0v7
Gupta, B., Dasgupta, S. and Gupta, A. (2008), “Adoption of ICT in a government organization in a developing country: an empirical study”, The Journal of Strategic Information Systems, 17(2), 140-154.
Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2013), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage, Thousand Oaks, 978-1-4522-1744-4.
Hair, J.F., Sarstedt, M., Hopkins, L. and Kuppelwieser, V.J. (2014), “Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research”, European Business Review, Vol. 26 No. 2, pp. 106-121, doi: 10.1108/EBR-10-2013-0128.
Hakansson, A., Hartung, R. and Moradian, E. (2015), “Reasoning strategies in smart cyber-physical systems”, Procedia Computer Science, Vol. 60, pp. 1575-1584, doi: 10.1016/j.procs.2015.08.267.
Han, Z., Gao, R.X. and Fan, Z. (2012), “Occupancy and indoor environment quality sensing for smart buildings”, 2012 IEEE international instrumentation and measurement technology conference proceedings, IEEE, pp. 882-887, doi: 10.1109/I2MTC.2012.6229557.
Hastings, A. and Sethumadhavan, S. (2020), “A new doctrine for hardware security”, Proceedings of the Fourth ACM Workshop on Attacks and Solutions in Hardware Security–ASHES 2020, New York, NY, Association for Computing Machinery, pp. 127-136, available at: https://www.cs.columbia.edu/∼hastings/doctrine.pdf (accessed 10 June 2023).
Hooks, D., Davis, Z., Agrawal, V. and Li, Z. (2022), “Exploring factors influencing technology adoption rate at the macro level: a predictive model”, Technology in Society, Vol. 68, 101826, doi: 10.1016/j.techsoc.2021.101826.
Howard, R., Restrepo, L. and Chang, C. (2017), “Addressing individual perceptions: an application of the unified theory of acceptance and use of technology to building information modelling”, International Journal of Project Management, Vol. 35 No. 2, pp. 107-120, doi: 10.1016/j.ijproman.2016.10.012.
Hoxha, V., Hoxha, D. and Hoxha, J. (2021), “Current situation, challenges and future development directions of facilities management in Kosovo”, Property Management, Vol. 40 No. 3, pp. 343-369, doi: 10.1108/PM-05-2021-0034.
Iacobucci, D. (2010), “Structural equations modeling: fit indices, sample size, and advanced topics”, Journal of Consumer Psychology, Vol. 20 No. 1, pp. 90-98, doi: 10.1016/j.jcps.2009.09.003.
Ikuabe, M.O., Aghimien, D.O., Aigbavboa, C.O. and Oke, A.E. (2020a), “Exploring the adoption of digital technology at the different phases of construction projects in South Africa”, Proceedings of the International Conference on Industrial Engineering and Operations Management, Dubai, UAE, March, 10-12, pp. 1553-1561, available at: http://www.ieomsociety.org/ieom2020/papers/56.pdf (accessed 22 November 2022).
Ikuabe, M.O., Aigbavboa, C. and Oke, A. (2020b), “Cyber-Physical Systems: matching up its application in the construction industry and other selected industries”, Proceedings of the International Conference on Industrial Engineering and Operations Management, Dubai, UAE, March 10-12, pp. 1543-1552, available at: http://www.ieomsociety.org/ieom2020/papers/18.pdf (accessed 22 November 2022).
Ikuabe, M.O., Aigbavboa, C.O., Anumba, C., Oke, A.E. and Aghimien, L. (2022), “Confirmatory factor analysis of performance measurement factors determining the uptake of CPS for facilities management”, Buildings, Vol. 12 No. 4, p. 466, doi: 10.3390/buildings12040466.
Ikuabe, M.O., Aigbavboa, C., Anumba, C., Oke, A. and Adekunle, S. (2023a), “Business environment as a determinant in the uptake of cyber-physical systems for facilities management”, Facilities, Vol. 41 Nos 3/4, pp. 211-228, doi: 10.1108/F-02-2022-0022.
Ikuabe, M.O., Aigbavboa, C., Anumba, C. and Oke, A. (2023b), “Cyber-physical systems for facilities management: a Delphi study on the propelling measures”, Construction Innovation, Vol. 24 No. 7, pp. 257-272, doi: 10.1108/CI-04-2023-0063.
International Facilities Management Association (2023), “What is facilities management?”, available at: https://www.ifma.org/about/what-is-fm/ (accessed 17 November 2023).
Islam, R., Hassan, T., Sarajul, N. and Mohamed, F. (2019), “Factors influencing facilities management cost performance in building projects”, Journal of Performance of Construction Facilities, Vol. 33 No. 3, 04019036, doi: 10.1061/(ASCE)CF.1943-5509.0001284.
Jaafar, M., Ramayah, T., Abdul‐Aziz, A.R. and Saad, B. (2007), “Technology readiness among managers of Malaysian construction firms”, Engineering, Construction and Architectural Management, Vol. 14 No. 2, pp. 180-191, doi: 10.1108/09699980710731290.
Kamarazaly, M.A., Mbachu, J.I. and Phipps, R. (2013), “Challenges faced by facilities managers in the Australian universities”, Journal of Facilities Management, Vol. 11 No. 2, pp. 136-151, doi: 10.1108/14725961311319755.
Kline, R.B. (2005), Principles and Practice of Structural Equation Modelling, 2nd ed., Guilford Press, New York, 1593850751.
Kline, R.B. (2010), Principles and Practice of Structural Equation Modelling, 3rd ed., Guilford Press, New York, 1606238760.
Kok, H.B., Mobach, M.P. and Omta, O.S. (2011), “The added value of facility management in the educational environment”, Journal of Facilities Management, Vol. 9 No. 4, pp. 249-265, doi: 10.1108/14725961111170662.
Lee, E.A. (2015), “The past, present and future of cyber-physical systems: a focus on models”, Sensors, Vol. 15 No. 3, pp. 4837-4869, doi: 10.3390/s150304837.
Lee, H., Lee, Y. and Kwon, D. (2005), “The intention to use computerized reservation systems: the moderating effects of organisational support and supplier incentive”, Journal of Business Research, Vol. 58 No. 11, pp. 1552-1561, doi: 10.1016/j.jbusres.2004.07.008.
Lei, P. and Wu, Q. (2008), “Introduction to structural equation modeling: issues and practical considerations”, An NCME Instructional Module, Vol. 26 No. 3, pp. 33-43, doi: 10.1111/j.1745-3992.2007.00099.x.
Li, L., Yuan, J., Ning, Y., Shao, Q. and Zhang, J. (2017), “Exploring space management goals in institutional care facilities in China”, Journal of Healthcare Engineering, Vol. 2017, pp. 1-16, doi: 10.1155/2017/6307976.
Merschbrock, C. and Nordahl-Rolfsen, C. (2016), “BIM technology acceptance among reinforcement workers - the case of Oslo airport’s terminal 2”, Journal of Information Technology in Construction, Vol. 21, pp. 1-12.
Nidhi, M. and Ali, S. (2020), “The challenges and benefits of facility management”, International Journal of All Research Education and Scientific Methods, Vol. 8 No. 9, pp. 1-6, available at: http://www.ijaresm.com/search?x=0&y=0&keyword2=The+challenges+and+benefits+of+facility+management (accessed 23 December 2022).
Njuangang, S., Liyanage, C. and Akintoye, A. (2018), “The history of healthcare facilities management services: a UK perspective in infection control”, Facilities, Vol. 36 Nos 7/8, pp. 369-385, doi: 10.1108/F-07-2016-0078.
Nutt, B. (2004), “New alignments in FM”, Facilities, Vol. 22 Nos 13/14, pp. 330-334, doi: 10.1108/02632770410563022.
Odumeru, J.A. (2013), “Going cashless : adoption of mobile banking in Nigeria”, Arabian Journal of Business and Management Review, Vol. 1 No. 2, pp. 9-17, doi: 10.12816/0003615, available at: https://www.arabianjbmr.com/pdfs/NG_VOL_1_2/2.pdf (accessed 10 November 2022).
Oliveira, T., Thomas, M. and Espadanal, M. (2014), “Assessing the determinants of cloud computing adoption: an analysis of the manufacturing and services sectors”, Information and Management, Vol. 51 No. 5, pp. 497-510, doi: 10.1016/j.im.2014.03.006.
Osborne, J. (2015), “What is rotating in exploratory factor analysis?”, Practical Assessment, Research and Evaluation, Vol. 20 No. 2, pp. 1-7, available at: http://pareonline.net/getvn.asp?v=20&n=2
Palem, G. (2013), “Condition-based maintenance using sensor arrays and telematics”, International Journal of Mobile Communications and Telematics, Vol. 3 No. 3, pp. 19-28, doi: 10.48550/arXiv.1309.1921.
Pallant, J. (2020), SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS, 7th ed., Routledge Publishing, London, ISBN: 9781003117452, doi: 10.4324/9781003117452.
Parasuraman, A. and Colby, C. (2014), “An updated and streamlined technology readiness index: tri 2.0”, Journal of Service Research, Vol. 2 No. 4, pp. 1-6, doi: 10.1177/1094670514539730.
Parn, E.A. and Edwards, D. (2019), “Cyber threats confronting the digital built environment: Common data environment vulnerabilities and block chain deterrence”, Engineering, Construction and Architectural Management, Vol. 26 No. 2, pp. 245-266, doi: 10.1108/ECAM-03-2018-0101.
Potter, R. (2020), “CFOs can improve the value of cybersecurity investments: here’s how”, Forbes, available at: https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2020/11/04/cfos-can-prove-the-value-of-cybersecurity-investments-heres-how/?sh=24551b701927 (assessed 2 October 2022).
Serdyukov, P. (2017), “Innovation in education: what works, what doesn't, and what to do about it?”, Journal of Research in Innovative Teaching and Learning, Vol. 10 No. 1, pp. 4-33, doi: 10.1108/JRIT-10-2016-0007.
Shen, L., Edirisinghe, R. and Yang, M.G. (2016), “An investigation of BIM readiness of owners and facility managers in Singapore: institutional case study”, Proceedings of the CIB World Building Congress, Volume IV: Understanding Impacts and Functioning of Different Solutions, Tampere, 30 May-3 June, available at: https://researchportal.tuni.fi/en/publications/proceedings-of-the-cib-world-building-congress-2016-volume-iv-und
Talukder, M. and Quazi, A. (2011), “The impact of social influence on individuals' adoption of innovation”, Journal of Organisational Computing and Electronic Commerce, Vol. 21 No. 2, pp. 111-135, doi: 10.1080/10919392.2011.564483.
Tan, W.C.K. (2011), Practical Research Methods, 4th ed., Pearson Custom, Singapore, 9810689020.
Terreno, S., Akanmu, A., Anumba, C. and Olayiwola, J. (2020), “Cyber-physical social systems for facilities management”, in Anumba, C.J. and Roofiari-Esfahan, N. (Eds), Cyber-Physical Systems in the Built Environment, pp. 297-308, doi: 10.1007/978-3-030-41560-0_16.
Tomlinson, A., Parkin, S. and Shaikh, S. (2022), “Drivers and barriers for secure hardware adoption across ecosystem stakeholders”, Journal of Cybersecurity, Vol. 8 No. 1, tyac009, doi: 10.1093/cybsec/tyac009.
Venkatesh, V. and Bala, H. (2008), “Technology acceptance model 3 and a research agenda on interventions”, Decision Sciences, Vol. 39 No. 2, pp. 273-315, doi: 10.1111/j.1540-5915.2008.00192.x.
Watson, S. (2018), “Cyber-security: what will it take for construction to act?”, available at: https://www.constructionnews.co.uk/tech/cyber-security-whatwill-it-take-for-construction-to-act-22-01-2018/ (accessed 3 June 2022).
Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J. and Long, K. (2014), “Cognitive internet of things: a new paradigm beyond connection”, IEEE Internet of Things Journal, Vol. 1 No. 2, pp. 129-143, doi: 10.1109/JIOT.2014.2311513.
Yuan, X., Anumba, C.J. and Parfitt, M.K. (2016), “Cyber-physical systems for temporary structure monitoring”, Automation in Construction, Vol. 66, pp. 1-14, doi: 10.1016/j.autcon.2016.02.005.
Zhong, X. and Yuan, K.H. (2011), “Bias and efficiency in structural equation modeling: maximum likelihood versus robust methods”, Multivariate Behavioral Research, Vol. 46 No. 2, pp. 229-265, doi: 10.1080/00273171.2011.558736.
Acknowledgements
The authors appreciate the financial support received from the University of Johannesburg Commonwealth Scholarship for funding the research.