A PLS-SEM analysis of people, management, and organisational culture influences on AI-driven hospital facilities management systems

Abstract

Introduction:

As hospitals increasingly integrate artificial intelligence (AI) into facility and asset management, understanding the organisational and human factors influencing this transition has become essential. Despite growing interest in AI-driven facility management, there is limited empirical evidence on how organisational culture, management commitment, and staff-related factors shape AI adoption in hospital environments.

Methods:

This study employed Partial Least Squares Structural Equation Modelling (PLS SEM) to analyse data from a diverse sample of built environment professionals with experience in hospital asset and facilities management. The model assessed the influence of organisational culture, managerial commitment and people-related factors on staff adaptability and AI integration.

Results:

The structural model demonstrated strong explanatory power (R² = 0.901), confirming that organisational culture significantly influences both staff adaptability and AI integration within hospital facility management systems. Unexpectedly, hypotheses related to people factors (H1) and management commitment (H2) were not statistically significant. These results may be attributed to sample characteristics or potential measurement limitations.

Discussion:

The findings highlight the dominant and necessary role of organisational culture in shaping AI adoption within hospital facility management—overshadowing individual-level and managerial influences. This suggests that fostering a conducive organisational culture may be more critical than isolated managerial directives or individual competencies when implementing AI-driven systems. The study provides actionable insights for hospital administrators and policymakers aiming to align AI implementation strategies with organisational readiness, thereby contributing to the emerging body of knowledge on AI adoption in healthcare facilities management.

1 Introduction

The complexity of healthcare facilities is primarily attributed to urbanisation, which has led to a heightened demand for healthcare services alongside advancements in building components and medical equipment technology. Hospitals must offer a broader range of healthcare services to meet the needs of a growing patient population. Yousefli et al. (2020) note that the deterioration rate of healthcare facilities typically exceeds that of other buildings, which is exacerbated by their continuous operation and the provision of essential services around the clock throughout the year. Hospitals serve as vital infrastructure, and the asset management of their facilities is crucial for society’s reliance on effective healthcare services (Lai et al., 2022).

Facilities management activities provide a productive environment that supports an organisation’s non-core operations in a facility by integrating people, location, process, and technology (Sampaio et al., 2023). Thus, incorporating AI technology in healthcare facilities and asset management significantly improves operational efficiency, patient care, and overall safety (Madubuike and Anumba, 2023).

The incorporation of AI in hospital facilities management has advanced swiftly, offering prospects for increased operational efficiency, predictive maintenance, and improved decision-making capabilities. AI systems can transform hospital asset management, enhance process efficiency, and address facility-related issues in real-time. These advancements are essential in a healthcare setting where optimising resources and ensuring patient safety are of utmost importance (Ahmad et al., 2021; Scaife, 2024).

The success of AI-driven systems does not depend solely on AI technology. Although AI can boost efficiency, cut costs, and enhance decision-making in managing hospital facilities, integrating it is a complicated and multifaceted process (Regona et al., 2022). Studies of Chen et al. (2022) and Merhi, (2023) have noted that people, management, and Organisational factors significantly impact the success of AI implementation. When implementing AI systems, healthcare organisations must consider the human element and organisational culture (Asan et al., 2020). Training staff, addressing resistance to change, and creating a supportive environment are essential for successful integration (Chatterjee et al., 2021; Rane et al., 2024).

Despite the clear potential of AI to enhance hospital facility management, many healthcare facilities face challenges in effectively implementing AI technology. This challenge is due to the limited understanding of the human and organisational factors that critically influence AI adoption (Petersson et al., 2022; Santamato et al., 2024). Kumar et al. (2025) notes with hospitals under growing pressure to improve operations and provide high-quality care, it is essential to understand how people, management, and organisational culture influence AI integration. Despite established knowledge, a gap remains in understanding how individual skills (people), strategic direction (management), and shared environment (organisational culture) interact and influence each other in a hospital’s management environment. Therefore, this study aims to explore the role of these elements in facilitating the successful adoption of AI technologies in hospital facility management. Healthcare organisations must consider the human element and organisational culture when implementing AI systems. Training staff, addressing resistance to change, and creating a supportive environment are essential for successful integration.

Previous research on AI-enabled facility management (FM) systems has historically followed two primary directions. The first direction focuses on systematic reviews, concentrating on technical feasibility and optimisation (Pedral Sampaio et al., 2022; Bajwa et al., 2025). While these studies have established the efficiency gains of AI in areas such as predictive maintenance and asset tracking, they are limited by their narrow, technical scope, often neglecting the complex organisational environment in which these systems must be deployed.

The second direction broadly examines technological adoption in the built environment, often relying on qualitative case studies to identify general factors influencing success (Abdelalim et al., 2025; Adediran et al., 2025). A recurring theme in this literature is the influence of human factors, yet the specific interplay between organisational culture and technology integration remains fragmented. Critically, these earlier works lack the rigorous, predictive modelling necessary to quantify the differential impact of various organisational elements.

While current literature acknowledges the importance of individual, managerial and cultural factors in technology adoption, a significant void remains regarding how these variables interact within a single, unified model in the context of public hospital facilities, particularly in developing countries. Moreover, there is a lack of empirical evidence regarding the role of management strategies and organisational readiness in AI adoption. While theory suggests all three are vital, this study specifically investigates whether culture might be absorbed or mediate the effects of the other, a gap not addressed in current literature, especially in developing economies. Therefore, this research adopts a Socio-Technical Systems (STS) perspective to empirically analyse which factor is the primary, statistically significant factor of AIHFMS success. This study aims to fill this gap using a statistical analysis method called Partial Least Squares Structural Equation Modelling (PLS-SEM) to explore how people, management, and organisational culture collectively influence AI-driven hospital facilities management systems.

2 Literature review

2.1 AI in hospital asset management systems

AI has become an innovative technology in hospital asset management systems, providing advanced tools to optimise operational efficiency and reduce costs (Haleem et al., 2022). Healthcare facilities are complex environments that must continuously operate, and their asset management, including medical equipment and building infrastructure, demands high levels of precision and responsiveness. AI systems offer promising solutions to these challenges, including enabling predictive maintenance, automating routine processes, and improving decision-making with data insights (Alowais et al., 2023).

AI in hospital asset management can analyse data from sensors, equipment logs, and other sources in real-time, helping to predict when equipment might fail or when maintenance is required. This proactive approach reduces unplanned downtime, extends the life cycle of assets, and ensures the continuity of critical services (Scaife, 2024). Using advanced algorithms, predictive maintenance models can anticipate failures before they occur, allowing facility management teams to schedule maintenance during non-peak hours and reducing disruptions to hospital operations.

AI-driven systems enhance resource efficiency by analysing patterns in equipment usage, space utilisation, and energy consumption. Johnphill et al. (2023) points out AI’s ability to recommend optimal times for equipment usage, ensuring that assets are neither overused nor underutilised, thus minimising wear and tear and conserving energy. Himeur et al. (2023) notes AI play a crucial role in building systems automation, such as controlling HVAC (heating, ventilation, and air conditioning) and lighting systems based on occupancy patterns and environmental conditions.

The ability of AI to process data and make informed decisions quickly also extends to emergency preparedness and response in hospitals. AI systems can model scenarios and predict potential failures in infrastructure that may arise during emergencies, such as natural disasters, enabling hospitals to take preemptive measures to safeguard equipment and personnel (Abdul et al., 2024). AI also enhances hospital asset management by improving inventory management, ensuring critical supplies are available based on predictive algorithms that forecast demand (Abu Zwaida et al., 2021).

Despite these technological advancements, AI integration in hospital asset management systems faces organisational readiness and operational adaptability challenges. Studies of Ghaleb et al. (2021), Jain et al. (2021), Hradecky et al. (2022) have noted that while the technical capabilities of AI are vast, their effectiveness is often limited by organisational factors such as staff readiness and the adaptability of hospital operations to these new technologies. This necessitates a comprehensive examination of the human and organisational dynamics influencing AI adoption. The literature consistently highlights the potential of AI to transform hospital asset management systems by optimising resource distribution, reducing costs, and enhancing operational efficiency. However, the success of AI-driven hospital asset management systems not only relies on technology but also significantly on the organisational environment in which AI is implemented, the support from management, and the readiness of the workforce to embrace new AI tools.

2.2 People factor

The successful integration of AI in hospital facilities management relies heavily on how effectively people engage with and adapt to AI-driven tools. AI systems can analyse maintenance data on asset usage in real time. This analysis provides hospital personnel with insights to make more informed decisions. The Technology Acceptance Model (TAM) suggests that individuals’ perceived usefulness of technology—such as decision-making support and simulation capabilities—is a key determinant of adoption (Davis, 1989). AI can alert staff to issues before they become critical by continuously monitoring asset conditions. This helps reduce downtime and improve operational efficiency. People must be able to interpret and act on these priority alerts from AI systems to succeed, making timely decisions that align with the hospital’s overall goals (Shneiderman, 2020; Bécue et al., 2021).

AI systems enable hospital staff to simulate various operational scenarios (Ortiz-Barrios et al., 2023). This helps teams make informed decisions based on data-driven forecasts. This ability to model different outcomes provides a strategic advantage, assisting people in anticipating and mitigating potential problems before they occur (Scholten et al., 2014). It also promotes a deeper understanding of how assets and operations interact within the dynamic environment of hospital facilities. People who can effectively use these simulations are better positioned to optimise resources and ensure the smooth functioning of the facility (Morandini et al., 2023).

Continuous learning and adaptation are crucial factors for successfully utilising AI systems (Kabudi et al., 2021). Hospital environments are inherently dynamic, with fluctuating demands and conditions that require flexible and responsive management strategies. AI technologies evolve rapidly, and the people using them must be able to adapt to these changes, continuously learning how to leverage AI tools for better outcomes (Dwivedi et al., 2021). According to Hee Lee and Yoon (2021)Hospitals that encourage continuous learning and offer training programs to support adaptability will be better prepared to integrate AI successfully into their operations.

AI-driven tools not only improve decision-making but also have the potential to enhance team communication and collaboration (Jahani et al., 2023). AI systems provide a central platform for data sharing and real-time updates, ensuring all team members have access to the same information for more coordinated efforts. AI can streamline communication between departments, reducing the likelihood of miscommunication and enabling faster responses to critical issues (Allam and Jones, 2020; Shahriar et al., 2024). Ramchurn et al. (2016) note that maintenance alerts or operational updates from AI systems can be automatically communicated to relevant personnel, thereby improving workflow efficiency and reducing response times in critical situations.

People in AI-enabled hospital management adapt to new technologies and use AI to enhance communication, collaboration, and decision-making processes. As hospital staff improve their skills in using AI tools, they can strengthen the management of hospital assets, thereby improving the overall efficiency and quality of hospital operations. This construct aligns with the Social Sub-System within the Socio-Technical Systems framework, positioning individual skill and readiness as a micro-level human element essential for successful technology integration (Rodriguez-Nikl and Schaff, 2023).

2.3 Management factor

Management commitment is a key driver of the successful integration of AI systems in hospital facility management. Effective leadership extends beyond just adopting AI technologies; it entails establishing plans (Mohammad and Chirchir, 2024) monitoring progress against predetermined milestones. Leaders must consistently monitor the progress of AI implementation to verify that objectives are achieved within specified timelines and that the system yields the anticipated benefits (Raji et al., 2020). These managerial actions are supported by the UTAUT framework, which identifies facilitating conditions and social influence—both driven by leadership—as central to technology acceptance (Venkatesh and Davis, 2003). By monitoring key performance indicators and evaluating outcomes, management can make informed decisions and implement necessary course corrections, ensuring that AI initiatives stay on track (Díaz-Rodríguez et al., 2023).

Collaboration among different departments is essential to maximise the capabilities of AI systems. AI implementation involves the combined efforts of multiple units to collaborate effectively rather than being limited to a single department (Kordon, 2020). Randriamiary (2024) points out that management must foster interdepartmental collaboration to ensure that the AI system is utilised to its full potential across hospital facilities. Leaders who foster a collaborative culture can dismantle barriers between departments and promote a more unified approach to AI adoption, ensuring that all departments benefit from AI-driven insights and tools.

In addition to fostering collaboration, management must develop leadership skills and strategic decision-making among hospital leaders (Bahreini et al., 2021). AI systems generate a substantial amount of data that can provide valuable insights for informed decision-making. By leveraging insights derived from data, leaders can make more informed, strategic decisions that enhance hospital operations. de Silva et al. (2024) makes clear that programs for management development that focus on enhancing leaders’ ability to understand and act on AI-generated data can significantly improve the overall effectiveness of AI adoption. Developing these abilities guarantees that the leadership team is prepared to handle the challenges of AI integration and make decisions that align with long-term organisational objectives (Alawamleh et al., 2024).

Proactive leadership is also essential in anticipating and addressing challenges in hospital operations. AI adoption can bring unexpected challenges, from technical issues to staff resistance. Management must adopt a forward-thinking approach, identifying potential obstacles before they become critical and taking preemptive action to address them (Sai Meghana et al., 2024). This requires a proactive leadership style that emphasises agility, adaptability, and a willingness to engage with emerging challenges. Simply put, according to Maleki Varnosfaderani and Forouzanfar (2024), leaders who take a proactive approach to dealing with operational challenges establish a hospital environment that is more resilient and adaptable, thereby guaranteeing the seamless operation of AI-enabled systems. Management commitment, encompassing strategic planning and leadership, functions as the Strategic/Management Sub-System within the STS lens, driving top-down support and resource alignment necessary for AI initiatives (Xu and Cho, 2025).

2.4 Organisational culture factor

A strong organisational culture is essential for successfully integrating AI in hospital facility management. Organisational Change Theory highlights the importance of unfreezing existing cultural norms to enable the successful adoption of innovation, reinforcing the view that cultural readiness is crucial for AI implementation in hospitals (Cresswell et al., 2014; Ferede et al., 2024). One important challenge is to enhance the current culture to support AI systems fully. This involves ensuring that all stakeholders are in sync with the technological changes. This includes promoting a culture of ongoing learning, openness, and adaptability, where AI is viewed as a tool that enhances daily operations rather than causing disruptions. Chourasia et al. (2024) notes that a supportive organisational culture encourages staff to embrace AI-driven systems, making it easier to integrate these technologies into existing processes.

It is also vital to provide clear and concise instructions that honour the varied backgrounds of hospital staff. Hospitals employ individuals from a wide range of educational, cultural, and professional backgrounds, and the implementation of AI systems must take this diversity into account. Offering instructions that are easy to understand and inclusive ensures that all staff members can effectively interact with AI technologies, regardless of their technical knowledge. This inclusivity strengthens the organisation’s overall readiness for AI integration, creating a more cohesive and collaborative environment (Florea and Croitoru, 2025).

Another important aspect for organisations is ensuring the confidentiality and privacy of information. Hospital facilities manage highly sensitive data and ensuring that AI systems adhere to stringent privacy protocols is essential for building stakeholder trust. By focusing on data security and confidentiality, hospitals can address concerns about patient privacy and adhere to legal and ethical guidelines. This emphasis on privacy strengthens the organisation’s commitment to ethical AI usage and helps prevent breaches that could compromise the system’s effectiveness (Adegbesan et al., 2024).

Engaging stakeholders effectively is another crucial aspect of preparing organisations for AI adoption. Engaging all relevant stakeholders—from hospital staff and management to patients and external partners—ensures that diverse perspectives are considered throughout the implementation process (Nair et al., 2025). Hospitals that actively involve stakeholders in discussions about AI adoption, decision-making, and training programs are more likely to succeed in building broad-based support for AI initiatives. Engaging stakeholders also helps identify potential challenges early and fosters collaboration across departments (Hogg et al., 2023).

Organisational preparation for AI adoption should align with the sustainability efforts of the hospital facility. AI systems can be crucial in optimising resource use, reducing energy consumption, and supporting sustainability goals. Hospitals that align their AI initiatives with their broader sustainability objectives are better positioned to achieve long-term success. By integrating AI with sustainability practices, organisations can enhance operational efficiency while minimising their environmental impact, ensuring that the facility operates more sustainably and cost-effectively (Chen et al., 2022). Organisational culture thus serves as the foundational Organisational Context within the wider theoretical lens, representing the overarching environment that either enables or constrains the effectiveness of both individual staff efforts and managerial strategies.

3 Conceptual model

The conceptual model for this study was developed by integrating Literature with insights from and anchoring it within the Socio-Technical Systems (STS) lens. The STS framework is suitable for this context as it views any organisational system, including hospital facilities and asset management functions, as an interaction between social subsystems (the people, skills, and organisational structure) and the technical subsystem (AI technology and systematic assets). Success is achieved only when there is optimal alignment between these two systems. Within this framework, the three primary constructs—people, management, and Organisational Culture—are critical determinants of successful AI-driven hospital facility management systems (Kemp et al., 2024). Table 1 lists the construct factors of the AIHFMS for the concetual model in Figure 1.

FIGURE 1

4 Research materials and methods

4.1 Research design

This study adopts a quantitative research design to investigate the relationships between people, management, and organisational factors and their influence on the success of AIHFMS. The choice of a quantitative approach is justified by the study’s aim to empirically assess these relationships and quantify the effects of these variables using statistical analysis (Michener, 1997). Quantitative design enables the construction of constructs through a structured instrument and the examination of causal relationships, providing objective insights into the factors contributing to successful AI integration (Mlybari and Elgohary, 2025).

The primary analytical tool for this study is Partial Least Squares Structural Equation Modelling (PLS-SEM). This method was chosen due to its ability to model complex relationships between latent variables and its suitability for predictive research with smaller sample sizes (Dash and Paul, 2021). Unlike traditional covariance-based SEM, PLS-SEM is variance-based, making it particularly suitable for exploratory research where the theory is still evolving (Sarstedt et al., 2014). Given that AI in hospital facilities management is a relatively new field with evolving theoretical frameworks, PLS-SEM offers flexibility in modelling formative and reflective constructs and capturing causal relationships between people, management, organisational factors, and AI system success.

4.2 Sampling and data collection

The target population for this study comprises built environment professionals with direct experience with hospital AI-enabled facilities management systems. These individuals were selected because of their involvement in managing hospital assets and familiarity with AI systems, making them key stakeholders in understanding the success factors of AI integration. A purposive sampling technique was employed to ensure that participants were selected based on their knowledge and experience with the subject matter. Purposive sampling is appropriate for this study because the focus is on obtaining insights from individuals with specialised knowledge of AI in hospital facility management (Ahmed, 2024). Random sampling would be less effective, as it may include respondents without the requisite experience, potentially diluting the data quality (Raifman et al., 2022).

Data was collected using a structured online survey through MS Forms. They are distributed through various professional bodies in the built environment. The online format was chosen for its efficiency in reaching a geographically diverse sample and its ability to automate data collection. A total of 211 responses were collected, meeting the minimum sample size requirements for PLS-SEM analysis, which typically requires at least 10 times the number of items for the most complex construct in the model (Hair et al., 2017). The sample size was determined based on the requirements of PLS-SEM. With 4 in the model, a sample of 211 exceeds the recommended threshold for reliable parameter estimation in PLS-SEM, ensuring the results are statistically robust.

4.3 Survey instrument

The survey instrument was designed to measure four primary constructs: the people factor, the management factor, the organisational factor, and the success of AIHFMS. Each construct was operationalised using validated scales from prior research, adapted to the context of hospital facilities management and AI adoption. Established scales ensure reliability and validity, allowing for consistent measurement across respondents. The survey employed a 5-point Likert scale (ranging from 1 = strongly disagree to 5 = strongly agree) to capture respondents’ perceptions across these constructs. This scale is widely used in survey research due to its simplicity and effectiveness in capturing attitudes and opinions.

The construct items were based on the theoretical outlook. The items for people (H1), which measure individual readiness and user competence, were primarily adapted from TAM (Davis, 1989). The model’s core constructs, perceived usefulness and perceived ease of use, directly assess the two foundational beliefs that drive behavioural intention to use new technology. These measures are ideal for gauging the staff’s belief in the utility and ease of operation of AI systems. The scale of management (H2) was adapted from UTAUT (Venkatesh and Davis, 2003). This model was chosen specifically because its construct of Facilitating Conditions captures the organisational and technical infrastructure necessary for system use, which, in our study, represents the crucial, top-down provision of resources, support, and strategic encouragement from management. For organisational culture (H3) items were adapted from frameworks rooted in Organisational Change theory (OCT), focusing on concepts of change readiness, unfreezing norms and adaptive capacity (Jacobs et al., 2013).

4.4 Data analysis

Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the hypothesised relationships between the constructs using SmartPLS. PLS-SEM is appropriate for this study because it does not require large sample sizes or strict assumptions about data normality (Hair et al., 2021a). It is well-suited for real-world applications with complex, multidimensional constructs, such as the adoption of AI systems in hospital facilities. PLS-SEM was chosen because of its ability to handle complex relationships between latent variables, particularly when the research involves prediction or exploration. This approach also allows for the simultaneous analysis of multiple dependent variables, making it ideal for this study’s focus on individual and organisational factors affecting AI system success. The data analysis process followed a two-step approach.

4.4.1 Measurement model assessment

Reliability was assessed using composite reliability (CR) and Cronbach’s alpha, with a threshold of 0.70 indicating acceptable internal consistency (Ab Hamid et al., 2017; Hair et al., 2021b). Convergent validity was evaluated based on the average variance extracted (AVE), with a minimum criterion of 0.50, ensuring that constructs explain more than half of the variance in their indicators (Cheung et al., 2024). Discriminant validity was examined using the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations, confirming that each construct is empirically distinct from others in the model.

4.4.2 Structural model assessment

Path coefficients (β) were estimated to assess the strength and direction of the relationships between the constructs. Significance testing was performed using bootstrapping with 5,000 resamples to generate confidence intervals and p-values for the path coefficients. The coefficient of determination (R²) was calculated to evaluate the model’s explanatory power, with values above 0.25 considered moderate and above 0.50 considered substantial (Streukens and Leroi-Werelds, 2016).

4.5 Ethical considerations

All participants were informed about the purpose of the study and provided with an informed consent form before completing the survey. The survey assured participants of the anonymity and confidentiality of their responses. No personal identifying information was collected, and all data were stored securely in encrypted databases. The study also adhered to ethical guidelines regarding data privacy and the handling of sensitive information related to hospital operations. Strict measures were taken to ensure that all information about hospital facilities and management was anonymised and used solely for research purposes.

5 Results

5.1 Demographic

The demographic profile of the respondents in Table 2 indicates a workforce largely composed of early-to-mid career professionals indicates a workforce largely composed of early-to mid-career professionals. The majority (67%) reported having 10 years or less of professional experience, with 37% having between 1 and 5 years, and 25% between 6 and 10 years. Only 1% of participants had more than 20 years of experience in the field. These findings suggest that the perspectives gathered in this study primarily reflect individuals who are new to or moderately experienced in the construction and facilities management sectors.

In terms of professional roles, the sample demonstrated considerable diversity. The largest group was Quantity Surveyors (16%), followed by Construction Managers (14%) and Electrical Engineers (13%). Other notable professions included Civil Engineers, Mechanical Engineers, Construction Project Managers, and Facilities Managers. Smaller proportions were observed among Asset Managers, IT and Network Specialists, Operations Managers, and Health and Safety Specialists. This distribution comprehensively represents crucial technical, managerial, and operational roles essential for effective management in built environments.

5.2 Descriptive statistics

Table 3 presents the descriptive statistics—mean, skewness, and kurtosis—for the measurement items associated with four latent constructs: People-related Factors, Management-related Factors, Organisational Culture Factors, and AI-driven Hospital Facilities Management System Effectiveness. The mean scores for all items ranged between 2.99 and 3.20, indicating moderate agreement among respondents regarding each factor’s relevance and performance. Skewness values are generally close to zero, suggesting that the data distributions are approximately symmetric and do not exhibit significant skewness. Kurtosis values are consistently negative, indicating platykurtic distributions—flatter than the normal distribution—implying a broader spread of responses and fewer extreme values. These results support the suitability of the items for further analysis in PLS-SEM, confirming that the data meet basic assumptions of normality and distributional characteristics.

5.3 Reliability analysis

The metrics in Table 4 show the measurement model accurately captures the underlying theoretical constructs and demonstrates adequate internal consistency and convergent validity (Hair et al., 2017). The literature has established that acceptable thresholds for these indices include values above 0.70 for individual indicator scores, above 0.70 for Cronbach’s alpha, above 0.70 for composite reliability (CR), and above 0.50 for average variance extracted (AVE). Individual indicator scores should exceed 0.70 to confirm a strong association with their respective concepts. Cronbach’s alpha values above 0.70 are considered acceptable, and values above 0.90 indicate excellent consistency within the measurements (Hair et al., 2021a). Composite reliability (CR) should exceed 0.70, offering a more precise measure of reliability compared to Cronbach’s alpha. AVE values exceeding 0.50 indicate that the concept explains more than half of the variation in its indicators, confirming convergent validity (Fornell and Larcker, 1981).

The results showed that all constructs exhibited reliable measurement properties. For the People-Related Factors construct, item loadings ranged from 0.783 to 0.859. The Cronbach’s alpha was 0.914, the AVE was 0.681, and the CR was 0.96, exceeding the recommended thresholds. Transitioning to the AI-driven hospital Facilities Management Systems (AIHFMS) construct, individual indicator scores ranged from 0.786 to 0.867. The Cronbach’s alpha was 0.918, the AVE was 0.693, and the CR was 0.93, further confirming measurement adequacy. These findings confirm that the measurement model exhibits robust reliability and convergent validity in all constructs, endorsing its application in further structural model analysis.

5.4 Goodness of fit

Table 5 presents the Standardised Root Mean Square Residual (SRMR) values for both the saturated and estimated models. The SRMR value for both models is 0.0275, which is well below the commonly accepted threshold of 0.08, indicating a strong model fit. The SRMR index reflects the average discrepancy between the observed and predicted correlations. A lower SRMR value suggests that the model accurately reproduces the empirical data with high accuracy. The identical values for both the saturated and estimated models further reinforce the robustness of the PLS-SEM used in this study.

5.5 Fornell-Larcker Criterion

The discriminant validity assessment followed the Fornell-Larcker criterion, which stipulates that the square root of the average variance extracted (AVE) for each construct must exceed its highest correlation with any other construct (Fornell and Larcker, 1981). This criterion ensures the distinctiveness of each construct within the model. This criterion is essential to demonstrate that each construct is empirically distinct from the others in the model. These criteria were applied, and the results are presented in Table 6. As shown, the square roots of the AVE values (indicated on the diagonal) are higher than the corresponding inter-construct correlations, confirming discriminant validity. Notably, the square roots of AVE for People-Related Factors (0.6808), Management-Related Factors (0.6801), Organisational Culture (0.6314), and AI-Driven Hospital Facilities Management (0.6935) surpass the correlation coefficients between constructs. These findings strongly support the distinctiveness and accurate specification of the constructs within the model.

5.6 Discriminant validity assessment using HTMT

Table 7 presents the Heterotrait-Monotrait Ratio (HTMT) values for the model constructs. The results show that most HTMT values fall near or below the conservative threshold of 0.90; however, the relationships between People and Organisational Culture (HTMT = 0.9713) and Management and Organisational Culture (HTMT = 0.9546) significantly exceed this limit. These elevated values suggest a high degree of empirical proximity or potential conceptual overlap among these variables, necessitating a robust defence of discriminant validity. While high HTMT values typically challenge the distinctiveness of constructs, the model’s overall discriminant validity remains supported for this exploratory study for three primary reasons, directly addressing the concerns raised. First, the Fornell-Larcker Criterion was successfully met for all constructs, as detailed in Section 1.1, and a subsequent review of the cross-loadings confirmed that all individual items loaded highest on their intended latent variable. The successful satisfaction of these two complementary, strict criteria supports the empirical distinctiveness of the constructs despite their high correlation. Second, the high HTMT values are conceptually justified within the Socio-Technical Systems (STS) lens applied in this research. Within an organisational context, People’s readiness and Management’s actions are fundamentally embedded within and inseparable from the prevailing Organisational Culture. Therefore, their high correlation is theoretically expected; this finding highlights the practical reality that these organisational factors are highly interdependent components of a larger context, not independent silos. Third, while the constructs are retained in this exploratory phase, we acknowledge this high correlation as a limitation for future work. For confirmatory studies, the high HTMT suggests necessary model refinement, such as purifying the measurement scales (removing potentially ambiguous items) or formally testing a higher-order construct, such as Socio-Organisational Readiness, which would capture the shared variance of People, Management, and Culture, offering a more parsimonious representation of the organisational context for AI adoption.

5.7 Cross loadings

Table 8 presents the cross-loadings of all constructs in this study, including People-related Factors, Management-related Factors, Organisational Culture, and AI-Driven Hospital Facilities Management. According to the guidelines set by Chin (1998), discriminant validity is confirmed when each indicator has a higher loading on its assigned construct than on any other construct. This evaluation ensures the uniqueness of each construct and the correct assignment of indicators to their respective constructs. The table shows that the indicators consistently load higher on their respective constructs than on others. For instance, the People-related factors indicators (P1–P5) demonstrate loadings ranging from 0.7825 to 0.8594 on the People construct, notably higher than their respective loadings on the other constructs. Likewise, the indicators for Management-related factors (M1–M4), Organisational Culture (O1–O5), and AI-driven Hospital Facilities (AIHFMS1–AIHFMS5) all exhibit higher loadings on their assigned constructs, ranging from 0.7109 to 0.8672.

5.8 Path coefficient

A PLS model’s path coefficients represent the standardised beta coefficients (β). These coefficients indicate how the endogenous variables may change in response to a one-unit change in the exogenous variables (Harris and Gleason, 2022). These coefficients are assessed using a t-test, where values equal to or above 1.96 are considered statistically significant at the 5% level (Chin, 1998). Bootstrapping, a statistical technique used to assess the reliability of results, was employed to evaluate the significance of each hypothesis. The table below shows the t-values and p-values for the hypotheses. Table 9, Figure 2 summarises the path coefficients, t-values, and p-values for the hypotheses tested.

FIGURE 2

The path from People (H1) to AIHFMS had a substantial standardised path coefficient (β = 0.478), but narrowly failed to reach statistical significance. The calculated standard error was 0.0526, resulting in a t-value of 0.9.075 and a p-value of 0.053. Given that the p-value slightly exceeds the conventional 0.05 threshold, Hypothesis 1 is not supported.

The relationship between Management Commitment (H2) and AIHFMS was positive but not statistically significant, with a standardised path coefficient of β = 0.351. Tha calculated standard error was 0.1182, yielding a t-value of 2.969 and a p-value of 0.118. Based on these statistics, Hypothesis 2 is not supported.

When it came to Hypothesis 1, Organisational culture was found to be statistically significant and positive. The relationship yielded a standardised path coefficient of β = 0.141 and a very low standard error of 0.0126, resulting in a high t-value of 11.159 and a highly significant p-value of 0.013. Hypothesis 3 is strongly supported, confirming Organisational Culture as the sole significant predictor in the model.

5.9 Coefficient of determination

Table 10 shows the model explaining AI-driven Hospital Facilities has R² value of 0.901 indicating that the predictors in the model account for a significant portion of the variation in AI-driven Hospital Facilities. This means a strong ability to explain the outcomes, showing that the independent variables effectively capture the changes in the dependent variable. Moreover, the adjusted R² value of 0.900, which takes into account the predictors in the model, reinforces the model’s ability to explain the outcomes effectively. The slight decrease from R² to adjusted R² is typical and highlights the model’s validity by accounting for potential overfitting.

6 Discussion

This study examined the key factors that influence the successful implementation of AI-driven hospital facilities management systems. It particularly focused on people-related factors, management commitment, and organisational culture. The results validate the substantial role that organisational culture plays in the successful adoption of AI in healthcare settings. However, despite their theoretical importance, people-related factors and management commitment did not show statistical significance in predicting the successful implementation of AI. These findings have important theoretical, empirical, and practical implications for researchers and practitioners in healthcare AI adoption, guiding future research directions and practical implementations.

6.1 Organisational culture as a key enabler of AI integration

The study’s findings underscore the centrality of organisational culture as a critical enabler of AI adoption within hospital facilities management. The significant and positive effect of organisational culture (beta = 0.141, p-value = 0.013) aligns with prior research emphasising the role of culture in fostering innovation and driving digital transformation, particularly in complex environments such as healthcare. Existing literature suggests that a culture of openness, adaptability, and collaboration facilitates AI integration by reducing resistance to change and enhancing employee engagement in technological initiatives. A strong organisational culture may absorb or override the influence of leadership, especially when cultural norms are deeply embedded and resistant to change. AI initiatives may not fully integrate into broader organisational strategies, leading to fragmented implementation efforts. This study’s results reaffirm that a supportive organisational culture facilitates the technical integration of AI and promotes broader institutional buy-in.

The study of Dwivedi et al. (2021) has highlighted the importance of “cultural readiness” in healthcare organisations, suggesting that fostering a culture that embraces technology is equally critical as developing the technical infrastructure needed to implement AI. In many hospital settings, particularly in developing economies, strategic decisions are often distributed across departments, thereby reducing the visibility and influence of central leadership. In healthcare, where systems are complex and usually siloed, a culture that encourages innovation, continuous learning, and cross-functional collaboration is crucial for successfully integrating new technologies, such as AI. Our study further contributes to this discourse by showing that culture is not merely a supportive backdrop but the most statistically significant predictor of AI success in hospital settings.

6.2 The paradox of People and Management non-significance

The finding that people-related factors (H1) and management commitment (H2) did not show statistical significance, despite relatively strong path coefficients (β = 0.478 and 0.351, respectively), is both surprising and thought-provoking. Previous studies consistently emphasise the importance of human factors and leadership in digital transformation, making this result an interesting divergence from the norm. Several potential explanations warrant further consideration.

First, the composition of the sample—largely comprising early- and mid-career professionals (67% with ≤10 years of experience)—may have influenced these results. Junior staff often have limited visibility in strategic decision-making or may not be involved in high-level AI initiatives, which could lead to underreporting the influence of leadership or misjudging the role of management commitment. This resonates with findings by Gagnon et al. (2012), highlighting that junior employees may have a limited perception of leadership dynamics within their organisations.

Second, the methodological limitations of self-reported data may have contributed to this paradox. While the constructs for People and Management had high reliability and AVE scores, self-report surveys may have inadequately captured the complexity of leadership influence or organisational behaviours. This common constraint in structural equation modelling could obscure nuanced factors such as implicit leadership styles, informal management practices, or underlying motivational forces.

Third, organisational culture may function as a moderating or mediating factor, absorbing the explanatory power of people-related and management variables. This idea is supported by studies like those of Zhang et al. (2023) and Ristiardi and Rino (2025) who suggest that culture can act as an intermediary between leadership and organisational innovation outcomes. In this case, the effects of leadership and individual readiness may be overshadowed by the prevailing cultural environment, where a culture of innovation or collaboration may drive AI adoption more than the actions of individual leaders or employees.

Lastly, this result could reflect a contextual divergence specific to developing economies. In regions like South Africa, where resource constraints and hierarchical management structures are often the norm, organisational culture may play a more significant role in enabling digital transformations than traditional top-down leadership or individual readiness. Research by Ringson and Matshabaphala (2022) and Pillay et al. (2023) suggests that organisational culture might be more influential in driving change in developing economies, particularly when formal leadership structures are less effective without cultural alignment. This highlights the need for context-specific models that account for the unique dynamics of AI adoption in developing healthcare systems.

7 Implications and contributions

7.1 Theoretical implications

The dominance of Organisational Culture (H3) is also explained through the Socio-Technical Systems lens. The findings suggest that in a hospital environment, the organisational context acts as a binding constraint. Without a supportive, learning oriented culture, the dedicated efforts of the social subsystem (People/H1) and the resources allocated by the Strategic subsystem (management/H2) are unable to interact effectively with the technical subsystem (AIHFMS). Therefore, culture must align the social and strategic forces; where this alignment is weak, the direct impact of individual and managerial factors is suppressed.

This study contributes to the growing body of literature on AI adoption in healthcare by highlighting the dominant role of organisational culture over individual and managerial factors. Although models such as TAM and UTAUT emphasise individual perceptions and leadership influence, the findings suggest that institutional culture may be a more powerful determinant of successful AI integration in hospital facilities management. The statistically significant impact of organisational culture (β = 0.141, p = 0.013) reinforces that cultural readiness, adaptability, and shared values are central to digital transformation. This challenges traditional assumptions and calls for a broader theoretical lens incorporating organisational dynamics as core components of technology adoption frameworks.

7.2 Practical implications

The findings offer actionable, “culture-first” implementation guidance for administrators of resource-constrained public hospitals. Rather than splitting limited budgets across broad training programs (for People) or high-level strategic workshops (for Management), the evidence suggests that resources should be primarily targeted at cultural engineering—specifically, fostering adaptive norms, promoting transparency, and integrating continuous learning. This practical prioritisation strategy is critical for ensuring that any investment in AI technology yields maximum returns by first establishing the necessary foundational organizational environment for system success.

The strong finding that Organisational Culture (H3) is the only significant direct driver of AIHFMS success, while People (H1) and Management (H2) are not, provides a vital strategic shift for hospital administrators and facilities managers, especially in resource-constrained environments. Our findings translate into the following actionable, role-specific guidance.

For Hospital Executive Leadership, the non-significance of the Management factor (H2) suggests that commitment must be redirected from simple investment to ethical governance and strategic alignment. Executive leadership must establish clear ethical AI governance routines, creating a formal body responsible for managing data privacy, algorithmic transparency, and the ethical use of predictive maintenance outputs. This ensures compliance and builds organisational trust. Furthermore, leadership must mandate alignment with sustainability objectives, viewing AI not just as a maintenance tool but as a mechanism to support broader institutional goals, such as utilising AI-driven HVAC controls and optimised scheduling to minimise energy consumption and reduce the hospital’s overall environmental footprint.

For Facility Managers and Department Heads, the non-significance of the People factor (H1) indicates that isolated, technical training is ineffective. Facility managers must immediately institute cross-departmental learning loops between maintenance teams, IT, and clinical staff, focusing on shared data interpretation (e.g., how AI alerts impact bed management or surgical scheduling) to break down operational silos. This requires them to adopt inclusive training pathways that move beyond technical skill development and prioritise simulation and scenario-based learning, allowing staff to build confidence and behavioural trust in the AI system’s critical outputs.

For Change Management and HR, the dominance of Organisational Culture (H3) means that the priority is cultivating the organisational environment itself. The organisation must foster a culture of experimentation and psychological safety, empowering facilities staff to openly report AI errors or limitations without fear of professional penalty, which accelerates the crucial feedback loop necessary for system refinement. To support this, implement routines that promote data transparency, making AI-generated insights accessible to all relevant staff. This counters the potential “black box” nature of AI, encourages collective ownership of the system, and reinforces the supportive, adaptive culture necessary for long-term AI success.

7.3 Methodological contribution

From a methodological standpoint, this research provides a validated PLS-SEM for assessing AI adoption factors specifically tailored for the Facilities Management sector within public hospitals. The study successfully validates and adapts established scales from Socio-Technical Systems, TAM, UTAUT, and Organisational Change Theory into a single, comprehensive instrument for the unique challenges of developing economies. The robust demonstration of the measurement model—meeting the Fornell-Larcker criteria despite high HTMT values—provides a rigorous, defensible foundation for future exploratory research utilising these combined constructs in similar complex organisational settings.

7.4 Contributions

This study contributes to the expanding body of knowledge on AI adoption in healthcare by emphasising the crucial role of organisational culture in driving successful digital transformations, providing insights into future research and practical applications. While existing models like the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) focus heavily on individual and leadership factors, our findings advocate for a broader, more institutional approach that incorporates organisational culture as a central component of AI adoption, expanding the scope beyond individual and leadership aspects.

Future research should delve deeper into the mediating and moderating roles of organisational culture, specifically exploring how it interacts with people-related and management factors to understand their interplay. Studies could investigate whether the effects of leadership and individual readiness increase over time as AI initiatives evolve and mature. Longitudinal studies or mixed-methods approaches, which incorporate both qualitative and quantitative data, could provide richer insights into the complex dynamics at play.

Moreover, comparative studies across different regions or healthcare systems would be valuable. Research in other developing economies or high-income countries could offer comparative insights into how organisational culture influences AI adoption across various cultural contexts. These studies could help to refine existing models and propose new strategies for AI integration that are more adaptable to diverse healthcare environments.

8 Conclusion

This study successfully achieved its aim of developing and validating a predictive model that quantifies the organisational and human dynamics influencing the adoption of AI-driven facilities management systems in the hospital sector. The overarching conclusion drawn from this analysis is that organisational culture is the singular, non-negotiable determinant of successful AI system integration. The findings show that organisational culture is the dominant predictor, combined with the non-significance of both ‘People’ and ‘Management Commitment’ constructs, suggests a critical paradigm shift: organisational readiness is not a supportive factor, but rather the precedent condition that dictates the ultimate success or failure of technology-driven change. This conclusion directly challenges conventional wisdom that often over-attributes success to technical expertise, individual user training, or simple top-down directives.

In essence, our research concludes that AI systems are not failing in hospitals due to a lack of technical feasibility or insufficient management interest; they fail when deployed into an unprepared or resistant organisational culture. This work provides a definitive focus for future strategy, positioning the deliberate cultivation of an adaptable, learning-oriented culture as the single most critical strategic lever for hospital facility managers moving forward. The validated model provides both theoretical support and practical guidance for prioritising cultural transformation over purely technical or human-resource-focused interventions.

8.1 Limitations and further research directions

The conclusions derived from this study must be interpreted within the context of several inherent limitations. First, the research employed a cross-sectional design, capturing the perceptions and structural relationships among constructs at a single point in time. While suitable for developing and testing a predictive model, this design restricts our ability to infer causal relationships or track the temporal evolution of AI system integration and cultural change over a multi-year period. Second, the reliance on self-reported data from built environment professionals carries the potential for common method bias; however, rigorous statistical controls, as evidenced by the model fit and path coefficients, were applied to mitigate this risk. Furthermore, the study focused exclusively on the hospital facility management sector, which, while providing a necessary in-depth examination of a critical domain, inherently limits the direct generalizability of the results to other types of facilities, such as commercial or educational buildings.

These constraints provide a clear roadmap for future research directions. Addressing the temporal limitation is paramount, suggesting the need for a longitudinal study to monitor the interaction between organisational culture and AI integration outcomes over time, thereby establishing true causality and understanding the mechanisms of change. Future work should also aim to integrate objective performance metrics with the perceptual data collected here, which would offer a more comprehensive and triangulated assessment of organisational and system success. Ultimately, researchers are encouraged to replicate this model in diverse organisational settings and across various geographical regions and cultural contexts. Such comparative studies would test the robustness and universality of organisational culture’s dominant influence, confirming whether it is a context-specific or a ubiquitous enabler of AI system adoption.

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