The Mediating Role of Organizational Culture in Resource Repurposing and the Transition from Industry 4.0 to 5.0: Evidence from the Architectural, Engineering, and Construction Industry

Abstract

In a time of extraordinary global volatility, the survival and competitiveness of the Architectural, Engineering, and Construction (AEC) industry rely less on technological supremacy and more on cultural agility to repurpose resources effectively. Although Industry 4.0’s digital transformation offered tools for operational efficiency, the new Industry 5.0 paradigm emphasizes a human-centric approach, with organizational culture serving as a crucial link between advanced technology and organizational resilience. This study explores how organizational culture mediates resource repurposing and the shift from Industry 4.0 to Industry 5.0 in the AEC sector. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a sample of 120 AEC professionals, it examines how cultural traits—viewed as strategic leadership influence, employee adaptability, and innovation— mediate operational results. The results indicate that employee technology use and innovation are key drivers of resource reconfiguration, directly improving productivity and lowering project costs. Importantly, the findings show that organizational culture is not merely a background factor but a strategic enabler that partly mediates the link between Industry 4.0 adoption and cost savings. Thus, this research offers a theoretical framework for AEC firms to harness cultural flexibility as a strategic resource, advancing beyond simple digital adoption to embed innovation for sustainable long-term growth.

1. Introduction

In an era of unprecedented global volatility, the survival and competitiveness of the Architectural, Engineering, and Construction (AEC) industry depend less on technological dominance and more on the cultural agility to repurpose resources effectively. While the transformative potential of Industry 4.0 and Industry 5.0 is widely recognized, little is known about how firms navigate crises (such as pandemics, economic recessions, or disruptive technological change) by adapting their innovation approaches [1]. Despite heavy investment in digital tools, productivity in the construction sector remains stagnant, often due to a failure to integrate the “human factor” into technology adoption.

The primary gap in the literature is the lack of empirical evidence on how organizational culture specifically enables innovation repurposing, the ability to use existing tools for new challenges during a crisis. Most existing studies isolate technological advancement from human-centric strategies, failing to examine how culture mediates the balance between the two in high-uncertainty environments. This study is motivated by the urgent need for AEC firms to move beyond mere digital adoption and instead institutionalize crisis-driven innovation as a sustainable competitive advantage.

The purpose of this research is to investigate the mediating role of organizational culture in the transition from Industry 4.0 to Industry 5.0 within the AEC sector. Specifically, this study aims to develop an interrelation model to analyze variables such as leadership, employee adaptability, and innovation reconfiguration. To achieve this, the study employs a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, analyzing data from 120 AEC professionals to evaluate 25 distinct cultural and technological indicators. This study contributes a novel theoretical framework that reframes organizational culture not as a passive backdrop but as a strategic enabler of institutional resilience, offering actionable insights for AEC firms to optimize productivity and reduce project costs through cultural flexibility.

The structure of this article is as follows: Section 2 presents the literature review. Section 3 outlines the theoretical framework supporting the proposed model and validates the Industry 4.0 and Industry 5.0 variables that will be used in the subsequent analysis. Section 4 outlines the research methodology, detailing the development of the survey instrument, the data collection process, and the PLS-SEM-grounded research design. Section 5 presents and analyzes the results of the measurement and structural model analyses, focusing on the interrelationships among Industry 4.0, Industry 5.0, and Organizational Culture variables. Section 6 presents the discussion and, finally, Section 7 provides the conclusions, along with recommendations for future research directions.

2. Literature Review

Recent research highlights that the convergence of Industry 4.0 and Industry 5.0 presents the AEC sector with a unique opportunity to enhance its competitiveness. Industry 4.0 focuses on digital transformation through automation, 3D printing, and the Internet of Things (IoT), while Industry 5.0 prioritizes human-centered dimensions such as leadership and ethics. Current literature identifies a critical need for cultural shifts to enable flexible practices such as bricolage and exaptation.

Table 1. Summary of existing studies of the current state of research.

Author(s) & Year	Key Findings	Contribution to Literature
Azeem et al. [1]	Identified barriers to construction performance.	Establishes the baseline for industry competitiveness challenges.
Cillo et al. [2]	Culture must be rethought through knowledge management.	Links Industry 5.0 transition to organizational culture.
Leng et al. [3]	Industry 5.0 builds on 4.0 by adding human–machine collaboration.	Defines the technological vs. human evolution path.
Yang et al. [4]	Leadership is a core competency for Construction 4.0.	Identifies specific leadership traits needed for digital shifts.

summarizes existing studies to highlight the current state of research and the specific areas addressed by this paper.

2.1. Contextualizing the Knowledge Gap

Current literature falls short in addressing how organizational culture functions as a critical enabler of innovation repurposing in times of high uncertainty, particularly within the AEC sector [5]. While research has examined the role of technology adoption and human-centric approaches separately, there is limited understanding of how organizational culture shapes the conditions under which firms successfully adapt, repurpose, or reconfigure their strategies during crises such as pandemics, economic downturns, or technological disruptions [6]. The values, norms, and practices embedded in organizational culture are crucial for determining whether organizations can effectively mobilize resources, foster creativity, and sustain innovation when operating under volatile conditions [2].

Most existing studies isolate technological advancement (Industry 4.0) from human-centric approaches (Industry 5.0), without sufficiently exploring how organizational culture mediates the balance between efficiency-driven innovation and values-oriented strategies under crisis scenarios. In the AEC sector, which is often resistant to rapid change due to its traditional structures, this gap is especially pronounced [7]. A cultural shift is required not only to embrace digital transformation but also to enable flexible, human-centered practices such as bricolage, exaptation, and repurposing. Addressing this research gap is critical for understanding how crisis-driven innovation can be institutionalized and leveraged for long-term transformation [8]. To fully realize the potential of Industry 5.0 in high-uncertainty contexts, the construction sector must deepen its comprehension of how organizational culture drives the interplay between technological resilience and human values, ensuring that repurposing strategies create sustainable pathways forward [9].

2.2. The Evolution of Industry 4.0 and Industry 5.0

The Fourth Industrial Revolution, or Industry 4.0, is marked by the incorporation of cloud computing, artificial intelligence (AI), cyber-physical systems, and the IoT into industrial and manufacturing processes [10]. IoT sensors facilitate real-time data collection and analysis, improve resource distribution, and support predictive maintenance. AI and machine learning algorithms also improve decision-making, enabling manufacturing systems to be more flexible and self-optimizing [11].

Industry 5.0 is a concept that builds upon the advancements of Industry 4.0, which focuses on integrating automation, data exchange, and AI in manufacturing processes, and the human factor [3]. Industry 5.0 represents the next stage of industrial evolution, emphasizing collaboration between humans and machines to achieve higher levels of productivity and innovation, which significantly affects a project’s execution costs. Figure 1 shows the industry’s stages up to Industry 5.0.

The convergence of Industry 4.0 and Industry 5.0 provides the AEC sector a unique opportunity to enhance resilience and competitiveness during volatile times [13]. Industry 4.0 emphasizes digital transformation and operational efficiency through technologies such as automation, 3D printing, augmented reality (AR), and the IoT. In contrast, Industry 5.0 prioritizes human-centered dimensions, focusing on leadership, creativity, ethics, and collaboration [14]. Understanding how organizational culture enables firms to balance these paradigms while simultaneously repurposing resources and strategies in moments of crisis is critical [15]. A deeper examination of this intersection will shed light on the conditions under which crisis-driven innovation, through mechanisms such as bricolage, exaptation, and repurposing, can translate into sustainable organizational transformation in the AEC industry.

Industry 4.0 research has primarily focused on technological innovation in industries such as construction and manufacturing, with an emphasis on automation, data-driven decision-making, and increased efficiency [16]. IoT enables innovative, interconnected systems, while 3D printing is transforming the manufacture of materials, and augmented reality (AR) allows real-time visualization of construction sites [17]. On the other hand, recent studies highlight that, beyond industrial automation, using AI to strategically manage urban services is vital for developing sustainable smart cities; however, this approach also presents complex managerial and legal challenges for the AEC sector [18]. Despite the importance of these aspects, they are sometimes viewed through a technology lens that overlooks the human elements essential to an organization’s long-term success. A different viewpoint, presented by Industry 5.0, emphasizes the importance of human innovation, individualized experiences, and machine collaboration [19]. The workforce will need to play meaningful, creative, and socially conscious roles, in addition to adapting to new technologies, as a result of this industrial revolution [20]. Under these circumstances, technology complements human qualities rather than takes their place.

2.3. Theoretical Underpinnings of Research Goals

To close the identified gap, this research examines the interaction between Industry 4.0’s technological innovations and Industry 5.0’s human-centered approach in the construction sector, highlighting the crucial role that organizational culture plays in this context. Thus, the principal aspects to address in the research are:

• The role of organizational culture in comprehending and illustrating the connection between Industry 4.0 and Industry 5.0.

Employing organizational culture as the mediating framework, the study seeks to understand and empirically demonstrate the link between Industry 4.0’s technological elements and the human-centric features of Industry 5.0. To enhance operational efficiency and streamline processes, Industry 4.0 focuses on integrating advanced technologies such as AR, 3D printing, and IoT. Industry 5.0, on the other hand, reintroduces the human element, fostering creativity, ethical decision-making, and collaboration between humans and machines [21].

2.

The development of an interrelation model to analyze the influential variables in adopting Industry 4.0 and 5.0 concepts.

Creating a comprehensive interrelation model to identify and examine the key factors influencing the AEC’s adoption of Industry 4.0 and Industry 5.0 concepts is a primary objective of the study. This model will map the following technology and human elements:

• Technological Innovation: How Industry 4.0 technologies, such as automation, 3D printing, AR, and IoT, are affecting construction operations.
• Human–Machine Collaboration: Industry 5.0 emphasizes the importance of hu-man participation in decision-making, personalization, and creativity.
• Organizational Culture: How these new paradigms are integrated and impacted by values, teamwork, leadership, and flexibility.

3.

Reflecting on the role of the human factor in adopting technology in the AEC industry.

Examining the role of people in adopting technology in the construction sector is a key objective of the study. Industry 5.0 shifts the focus from Industry 4.0’s emphasis on cutting-edge technologies to human talent and values, including creativity, problem-solving, and individualized machine collaboration.

3. Theoretical Positioning and the Conceptual Model

This section presents the theoretical framework supporting the research, addressing organizational culture, Industry 4.0 and 5.0 in the AEC sector, and the integration of the Resource-Based View (RBV) and the Technology Acceptance Model (TAM). Together, these elements provide a comprehensive framework for explaining both the organizational and technological dimensions of Industry 4.0 and 5.0.

3.1. Organizational Culture

The typical values and beliefs ingrained in an organization constitute its organizational culture. These values and beliefs, therefore, shape individuals’ attitudes and behavior within the organization. People are woven into communities and given collective mental programming that sets them apart from one another; this is how cultures are created as a set of shared values and beliefs [22]. While beliefs convey knowledge of how things operate, these values show what is vital to the company. The interactions among individuals, control systems, organizational structures, and shared values and beliefs shape behavioral norms within an organization.

It is critical to remember that organizational cultures are dynamic and subject to change. Several things, including external pressures, employee relationships, organizational procedures, and leadership, impact it. Understanding and shaping a company’s culture is crucial for fostering unity, boosting productivity, and creating a harmonious and efficient work environment [23]. Organizational culture is first established by clearly and successfully conveying key values that align with the company’s strategic objectives. Second, by setting a consistent example and modeling desired behaviors, leaders have a significant impact on the culture they inhabit. Third, the culture’s durability is reinforced by the recruitment and development of people who share its values. The final step in creating a dynamic and resilient organizational culture is recognizing that culture evolves over time and periodically evaluating it to ensure it aligns with strategic objectives [2,22].

By integrating culture into the company’s plan, it can leverage its cultural strengths to gain a competitive advantage. This approach involves leveraging the distinctive qualities of corporate culture to promote innovation and flexibility, integrating culture into decision-making processes, and aligning culture with a broader strategic objective [24,25]. Effective management and incorporation of corporate culture into business plans can help businesses increase productivity, competitiveness, and adaptability to market changes [26].

3.2. Main Factors of the Industry 4.0 and 5.0 in the AEC

A manager or director must concentrate resources on integrating and improving aspects of Industry 4.0 and Industry 5.0. The primary variables of Industry 4.0 and Industry 5.0 are taken from the literature. These variables will be used in the interrelation model to determine the most important in the construction sector.

A well-aligned organizational culture that values human creativity, encourages collaboration, and supports a cooperative work environment can create fertile ground for Industry 4.0 and 5.0 to flourish [27]. In addition, to successfully implement these industries, organizations should assess their existing culture and consider aligning it with the principles and requirements of this new industrial era. According to Hwang et al. [28], this may involve fostering a culture of innovation, promoting a growth mindset, encouraging collaboration and knowledge sharing, and developing an organizational structure that supports agility and flexibility. By aligning the organizational culture with the needs of Industry 4.0 and 5.0, organizations can create an environment that facilitates technology adoption, change management, and overall success in this new era of industrialization [3].

The literature analysis has emphasized certain organizational culture factors that can influence the implementation of Industry 4.0 and 5.0. The conceptual model for this research is built by hypothesizing relationships among these factors to enhance Industry 4.0 and 5.0 implementation (as shown later in Figure 2). The organizational culture factors critical to Industry 4.0 and 5.0 implementation include [12,29,30]: Employee use of technology; Productivity; Innovation; Adaptability; Values; Teamwork; Cost; Leadership; Mentality; Employee satisfaction; Reward, and Commitment. Based on the definitions of the study’s significant variables, the following hypotheses are proposed, with the variables and their correlations supported by the existing literature.

H1: 

The implementation of technology in the company significantly affects employees’ use of technology [31].

H2: 

The employee’s use of technology significantly impacts their teamwork [32].

H3: 

The employee’s productivity significantly affects the cost of project execution [11].

H4: 

The employee’s innovation significantly affects the employee’s productivity [33].

H5: 

The Adaptability of the employee significantly affects the employee’s use of technology [3].

H6: 

The values of the company significantly affect employees’ mentality and employee satisfaction [2].

H7: 

The employee’s teamwork significantly affects the employee’s productivity and innovation [34].

H8: 

The employee’s mentality significantly affects the teamwork [35].

H9: 

Good leadership significantly affects employees’ values, mentality, rewards, and the Technology in the company [4].

H10: 

The employee’s mentality significantly affects their adaptability [36].

H11: 

The employee’s values significantly affect the employee’s satisfaction [37].

H12: 

The rewards significantly affect the employee’s satisfaction [38].

H13: 

The employee’s commitment significantly affects the employee’s productivity [39].

H14: 

The employee’s teamwork significantly affects the employee’s satisfaction [23].

H15: 

The employee’s use of technology significantly affects the Innovation [40].

H16: 

The employee’s satisfaction has a significant impact on commitment and productivity [41].

In this context, the conceptual model of the hypotheses is depicted in Figure 2. It is predicated on the idea that Industry 5.0 variables, such as leadership, values, mentality, adaptability, reward and promotion systems, commitment, teamwork, and their impact on innovation, productivity, and ultimately on project execution costs, can be successfully implemented through technological change. To address conceptual overlap, variables are reclassified using the Competing Values Framework (CVF). Organizational culture is treated as the overarching environment, while Industry 4.0 components represent technological ‘assets’ and Industry 5.0 components represent ‘human-centric capabilities’ such as creativity and cognitive adaptability.

3.3. Integration of the Resource-Based View (RBV) and the Technology Acceptance Model (TAM)

The theoretical foundation of the proposed model, presented in Figure 2, is grounded in the integration of the Resource-Based View (RBV) and the Technology Acceptance Model (TAM), which together provide a comprehensive framework for explaining both the organizational and technological dimensions of Industry 5.0 and Industry 4.0. This dual-theoretical approach complements the literature review by offering a robust conceptual justification for the relationships shown in the model.

From the Resource-Based View (RBV) perspective, organizations achieve sustainable competitive advantage by effectively managing and developing valuable, rare, inimitable, and non-substitutable resources. In this context, intangible assets such as organizational culture, leadership influence, and employee creativity are critical drivers of firm performance, particularly in uncertain and disruptive environments [42]. The transition toward Industry 5.0 emphasizes human-centricity, resilience, and sustainability, which are inherently linked to these intangible capabilities. Therefore, the incorporation of organizational culture into the model is theoretically supported by RBV, as it enables firms to leverage internal human and cultural resources to enhance adaptability, innovation capacity, and long-term resilience. This perspective justifies including constructs related to organizational culture as mediating and enabling factors that influence how firms respond to external challenges and achieve improved outcomes [43].

Complementarily, the Technology Acceptance Model (TAM) provides the theoretical basis for understanding the Industry 4.0 dimension of the model. TAM explains how users come to accept and use new technologies, highlighting the role of perceived usefulness and perceived ease of use as key determinants of adoption [44]. In the context of Industry 4.0, external technological investments, such as AR, IoT, and AI, are critical inputs that shape employee interactions with digital systems. These technologies do not generate value on their own; rather, their impact depends on how effectively they are adopted and used within the organization. Thus, TAM supports the inclusion of technological variables as antecedents that influence internal adoption processes, which in turn affect operational outcomes such as productivity improvements and cost efficiency [45].

The integration of RBV and TAM provides a holistic understanding of how organizations navigate the convergence of Industry 4.0 and Industry 5.0 paradigms [46]. While TAM explains the mechanisms by which technological innovations are adopted and translated into operational benefits, RBV highlights the strategic importance of internal organizational capabilities that enable firms to fully capitalize on these technologies. Together, these theories provide a coherent justification for the proposed model’s structure, in which technological factors and human-centered organizational elements interact to drive performance outcomes. This theoretical alignment not only strengthens the study’s conceptual rigor but also reinforces the model’s relevance for addressing contemporary challenges in the construction industry and similar sectors undergoing digital and organizational transformation.

4. Research Method

4.1. Overall Approach

The research employed the method shown in Figure 3 to achieve the previously mentioned goals. First, a thorough literature search was conducted to identify the variables related to organizational culture in the AEC that fall under Industry 4.0 and Industry 5.0. These variables were identified and explained in the previous section. Based on these variables, an online questionnaire survey was conducted using a 5-point Likert scale, where 1 indicated “strongly disagree,” and 5 indicated “strongly agree,” and was sent to construction companies.

The measurement scales were adapted from validated instruments (e.g., Denison’s Culture Survey) and refined through a pilot study with 5 AEC experts [47]. For example, to ensure indicator validity, the ‘Leadership’ construct was revised from ‘Acceptance’ to ‘Strategic Leadership Influence,’ focusing on the leader’s ability to drive technological change. Once the data were collected and the survey instrument’s reliability and validity were demonstrated, a PLS-SEM model was developed to identify the underlying factors (latent variables) of organizational culture. A regression analysis of these underlying factors was then developed to evaluate the influence of organizational culture on the Industry 4.0 and 5.0 variables.

The literature review was conducted to identify key variables associated with Industry 4.0, Industry 5.0, and organizational culture, which form the basis for the conceptual model proposed in this study. A systematic approach was employed, adhering to established guidelines for conducting a rigorous literature review. Studies that examined the relationship between human-centered elements (e.g., ethics, creativity, and cooperation) and technological breakthroughs (e.g., automation, IoT, AI) in Industry 4.0 and 5.0 contexts were the focus of the inclusion criteria. Furthermore, priority was given to papers that examined how organizational culture influences the adoption of new technologies in the construction sector. As long as they had a solid theoretical or empirical basis that applied to the AEC, papers from other industries were also considered for cross-industry insights.

For this study, ‘employees’ refers to subordinate staff, while ‘colleagues’ refers to peer-level collaborative interactions. Similarly, ‘goals’ are defined as broad organizational milestones, whereas ‘objectives’ are specific, measurable project targets.

4.2. Validation and Critical Analysis

A two-tier method was employed to critically analyze the literature, ensuring the variables were complete and applicable. To ensure the sources complied with academic norms, the methodological rigor and theoretical foundation of each study were evaluated first. Secondly, the applicability of the variables identified in the AEC context was assessed. Special focus was placed on how these variables interact with organizational culture to affect the uptake of Industry 4.0 and Industry 5.0 technologies.

The conceptual model, which links the crucial factors influencing the integration of Industry 4.0 and Industry 5.0 in the AEC, was therefore made possible by the rigorous literature review.

4.3. Questionnaire Survey Definition

A questionnaire was created to gather empirical data from AEC companies. Before conducting the study, the questionnaire was validated using expert review and a pilot survey [48]. The applied instrument was designed to collect information on the current status of Industry 4.0, Industry 5.0, and organizational culture variables, thereby validating the conceptual model. The questionnaire consisted of three primary sections. The study’s goals and objectives were outlined in the first section of the questionnaire, which also defined and clarified the concepts of Industry 4.0, Industry 5.0, and organizational culture. The objective of the second section was to collect demographic information about the participants (years of experience, role in the company, and the number of employees in the organization). Finally, in the third section, the constructs of the conceptual model were formulated through various measures and shown in

Table 2. The 25 indicators in the survey questionnaire.

Variable	Measures
Leadership	X1: Acceptance of leadership by employees and colleagues. X2: Impact of leadership on project goals and objectives.
Rewards	X1: An evaluation of the degree of employee incentives and rewards. X2: Assessment of incentive programs concerning employee dedication and performance. X3: Assessment of the impact of the employee’s incentives and rewards on productivity.
Commitment	X1: Level of employee commitment in the company.
Mentality	X1: Perception of the average mentality of the worker in the company. X2: Perception of workers’ attitude towards work.
Teamwork	X1: Promotion of teamwork in the company. X2: Evaluation and perception of employee teamwork. X3: Frequency of employees in teamwork in solving problems.
Adaptability	X1: The extent to which the company can adapt to evolving technology and work procedures. X2: Employees’ degree of flexibility regarding new work practices.
Employee satisfaction	X1: Perception and level of employee satisfaction. X2: Employee satisfaction with the tasks.
Values	X1: Quality of values promoted within the company and among employees.
Cost	X1: An assessment of the business’s ability to cover project costs. X2: The degree of cost efficiency of the company.
Technology (AI, Cloud Technology, 3D, Virtuality)	X1: Level of investment in augmented reality, cloud computing, 3D printing, and AI technology. X2: The extent to which initiatives employ technology and how that affects their goals and objectives. X3: The extent to which employees have every technological tool they need to succeed.
Employee use of technology	X1: Employees’ degree of technology usage for project solutions. X2: Employee technology use: availability and efficiency.
Innovation	X1: Innovative ideas and methods proposed by employees and managers. X2: Support for fresh concepts or suggestions from employees, supervisors, or managers.
Productivity	X1: Productivity level of employees in conformity with the managers’ stated goals and objectives.

.

Five architectural and construction industry experts were surveyed, and pilot interviews were held to carefully validate the questionnaire on Industry 4.0, Industry 5.0, and organizational culture. These specialists, comprising a business owner and two construction managers, were well-versed in their organization’s strategic goals and vision. Their professional expertise ensured that the questions and variables were carefully considered and verified, utilizing real-world knowledge and industry insights.

This focused strategy made it easier to gather high-quality data that reflected the complex viewpoints of seasoned professionals in the field on the incorporation and effects of Industry 4.0 and 5.0 technologies within the AEC’s organizational culture. Using expert validation and strategic dissemination, the questionnaire underwent refinement to comprehensively capture the constructs under study, thereby enhancing its reliability and relevance.

4.4. Sample and Data Collection

The study population consists of professionals (architects and civil engineers) employed by mid-sized Mexican construction companies. The questionnaire was sent to managers, directors, and owners of construction firms to enhance the reliability of the research findings. Only participants who comprehend the company’s vision and mission concerning technological changes and trends in the AEC were included in the study. To ensure the inclusion of pertinent and competent responses, the questionnaire was mainly circulated via LinkedIn and Mexican construction chambers, using a selective sampling approach. This approach enabled exact filtering by years of experience, position level, responsibility, and age.

A total of 450 construction enterprises in the construction field received questionnaires, and 120 responded, which is considered a mid-sized sample and is appropriate for PLS-SEM, a simplified tool for running models with small sample sizes [49]. On the other hand, the strength of any sample relies more on carefully choosing respondents than on its overall size [50]. That is why the selection of participants was carefully considered, and their academic and professional profiles are shown in

Table 3. Demographic summary of survey participants (N = 120).

Category		Sample Size	Percentage
General Sample size		120	100.0
Gender	Male	93	77.5
	Female	27	22.5
Education level	Primary school or below
	Middle School
	High school
	College or University	120	100.0
Job position	Construction business owner	24	20.0
	Management personnel	71	59.1
	Director	25	20.8
Years of experience	0–10	64	53.3
	10–20	44	36.6
	20–30	12	10.0

.

In PLS-SEM, sample size adequacy is commonly assessed using the “ten-times rule”, which holds that the minimum sample size should be at least ten times the maximum number of structural paths directed toward any latent construct in the model [51]. In the proposed model (previous Figure 2 and Figure 4 shown later in Section 5.2), Productivity is the construct with the most incoming paths and is influenced by three antecedent variables: Innovation, Teamwork, and Commitment. On the other hand, the minimum required sample size is 30 observations. Accordingly, the present study includes a sample of 120 respondents, which is four times this minimum threshold, thereby ensuring sufficient statistical power for the analysis.

4.5. Data Analysis

The PLS-SEM technique was employed to examine the relationship between variables. Once the data were collected, the questionnaire’s validity and reliability were evaluated. Subsequently, the significance and the determination coefficient $R^2$ of the path coefficients were examined. The PLS approach, which has garnered considerable attention lately for its numerous benefits in latent-variable prediction, is employed in the analysis of this model. Structural equation modeling is a method for building a model that connects criteria and concepts and uses arrows to show how different aspects interact. Modeling the variation between parameters and their indicators, PLS, as a structural equation modeling technology, primarily focuses on predictive causal analysis and carefully assesses the cause-and-effect relationships [52].

PLS-SEM, a statistical method for modeling intricate relationships among latent constructs, is noteworthy because it departs from conventional paradigms that require larger sample sizes to ensure statistical validity [49]. A medium sample size is recommended to maximize the accuracy and dependability of predictive modeling results, providing a stronger basis for clarifying latent variable interactions and improving the integrity of prediction evaluations [53,54]. SmartPLS 4 Software was used to examine the data.

This research employed the PLS-SEM approach to assess the reliability of the constructs. Evaluating the measurement model included analyzing item reliability, internal consistency (scale reliability), as well as convergent and discriminant validity. In a PLS model, the reliability of each item is evaluated through its loadings (λ) or correlations with its respective construct. Loadings (λ) represent the correlations between a latent variable and its observed measures or indicators. A PLS model determines variable reliability by analyzing these loadings or simple correlations with the corresponding latent variable [55].

5. Analysis of Results

5.1. Measurement Model

A PLS model evaluates the reliability of each item by analyzing the loadings (λ). Since loadings are correlations, a value of 0.7 or higher indicates that the construct explains more than half of the variance in the observed variable. As illustrated in

Table 4. Loads.

ADAP1 ► Adaptability	0.934
ADAP2 ► Adaptability	0.857
COMM1 ► Commitment	1.000
COST1 ► Cost	0.862
COST2 ► Cost	0.868
INNOV1 ► Innovation	0.905
INNOV2 ► Innovation	0.933
MENT1 ► Mentality	0.887
MENT2 ► Mentality	0.924
PRODU ► Productivity	1.000
REW1 ► Rewards	0.941
REW2 ► Rewards	0.942
REW3 ► Rewards	0.931
SATIS1 ► Employee Satisfaction	0.955
SATIS2 ► Employee Satisfaction	0.955
STAFTECH1 ► Employee Technology	0.917
STAFTECH2 ► Employee Technology	0.880
TEAMW1 ► Teamwork	0.907
TEAMW2 ► Teamwork	0.872
TEAMW3 ► Teamwork	0.907
TECH1 ► Technology	0.906
TECH2 ► Technology	0.925
TECH3 ► Technology	0.910
VAL1 ► Values	1.000
LEAD1 ► Leadership	1.000
LEAD2 ► Leadership	−0.334

, most variables satisfy these criteria.

When some loadings are below 0.707, this typically indicates that the formative factors and the latent variables have a weak relationship, suggesting poor representation of the latent variable [56]. To improve the model and produce a more robust and better-fitting model, some authors recommend moving these variables or even removing them [57]. Accordingly, as part of the debugging process, the formative factor of Leadership LEAD 2 (−0.334) was removed as it stands out among the weak correlations. This removal improved the model’s behavior, placing greater emphasis on the Leadership variable, and reaffirming that leadership is, in fact, an important driver in navigating uncertainty and adopting Industry 4.0 and 5.0 technologies.

5.2. Structural Model

The PLS model provides a structural framework that illustrates how latent variables are interconnected by proposing a conceptual model. To create accurate predictions between variables, the PLS model essentially functions as a predictive model based on variance [58]. Therefore, the validation method for the PLS model is primarily based on its predictive ability, which determines how well it predicts and explains the observed data [59,60]. The PLS model is shown in Figure 4. Rectangular shapes in this paradigm represent manifest variables, the measurements or signs that have been observed. Latent variables are the unobserved constructs inferred from the manifest variables [61]. Ovals represent them. Straight arrows illustrate the links between these variables, showing the paths or potential causal interactions within the model.

Figure 4. PLS-SEM Diagram for Industry 4.0, Industry 5.0, and Organizational Culture variables.

Figure 4. PLS-SEM Diagram for Industry 4.0, Industry 5.0, and Organizational Culture variables.

Table 5. Model evaluation parameters, adapted from Forcael et al. [48].

Evaluation of the Measurement Model				Evaluation of the Structural Model
Criteria	Reliability of the item	Construct reliability	Convergent reliability	Discriminant reliability	$R^2$	β
	>0.7	>0.7	AVE > 0.5	$\sqrt{AVE}>CORREL$	>0.1	>0.2

presents the thresholds for the measurement and structural models, along with the standards used to assess the quality of the models and their suggested interrelationships.

The complex linkages and key indicators between the variables related to Industry 4.0, Industry 5.0, and Organizational Culture are depicted in Figure 4. For example, productivity has a substantial impact on costs, as evidenced by its path coefficient of 0.506. Similarly, Mentality influences Adaptability (0.567), and Leadership influences Rewards (0.562). The variable Technology has a stronger impact on the Utilization of Employee Technology, with a coefficient of 0.702, underscoring its critical role. While Values and Rewards also affect Employee Satisfaction, their coefficients of 0.551 and 0.554, respectively, suggest moderate but meaningful associations. The analysis highlights the substantial impact of organizational culture variables, including Rewards, Employee Satisfaction, and Mentality, on key elements of Industry 4.0 and Industry 5.0. Understanding the extent of these effects is crucial, as it highlights the varying strengths of these relationships and can inform strategic decisions and interventions within organizations.

A low path coefficient indicates a negligible or nonexistent direct effect of one latent construct on another, and when negative, it may suggest an inverse relationship [57]. Within the model, these results suggest weak correlations among certain latent variables, indicating limited or no meaningful association between them. The PLS analysis highlights the complex interplay of the primary constructs in the construction industry, revealing varying levels of influence across relationships. While some linkages demonstrate moderate strength, others, such as Leadership with Technology (0.077) and Teamwork with Innovation (−0.048), appear weak, indicating a minimal direct impact.

Regarding the previous low or negative path coefficients, Technology can weaken Leadership by replacing human connection with digital impersonality, as overusing gadgets may diminish empathy and nonverbal cues, fostering reactive responses to data overload, clouding strategic vision, and leading to micromanagement or control, which erodes trust and autonomy. Similarly, in traditional and regulated construction environments, Teamwork primarily ensures compliance with Standard Operating Procedures (SOPs), safety, and contracts, while focusing less on creative problem-solving. These practices prioritize efficiency, risk reduction, and following set workflows, which can limit new ideas and foster groupthink, suppressing dissent and reducing Innovation.

In contrast, several relationships stand out as more relevant. For example, Employee Satisfaction with Commitment (0.106) and Teamwork with Productivity (0.384) suggest moderate contributions to productivity outcomes. Notably, the employee’s use of technology has a substantial effect on Innovation (0.729), which, in turn, exerts a strong influence on Teamwork. Likewise, Technology significantly affects Employee Technology (0.702), while Innovation exerts a marked impact on Productivity (0.729). These findings underscore the central role of technology and innovation in shaping performance within the sector.

Theoretically, specific constructions may have little or no link with one another. Some authors suggest removing these routes or coefficients; however, doing so would improve the model’s fit but would not accurately reflect the realities of the building business [62].

In PLS analysis, latent variable correlations provide essential insights into the direction and intensity of interactions between unobserved components within the model [63]. The degree to which one latent variable influences another is quantified by these correlations, represented by path coefficients, which help researchers understand the underlying structural dynamics. Strong associations are indicated by high correlation values, which suggest that changes in one latent variable significantly affect changes in another [64]. The correlation matrix of the latent variables shown in

Table 6. Latent Variables Correlations.

	Adaptability	Mentality	Innovation	Leadership	Employee Satisfaction	Technology	Rewards	Values	Employee Technology	Teamwork	Productivity	Commitment	Cost
Adaptability	1.000	0.581	0.552	0.201	0.038	0.036	0.033	0.015	0.005	0	−0.056	−0.087	−0.13
Mentality	0.581	1.000	0.585	0.151	0.023	0.05	0.053	0.022	0.028	0.084	−0.114	−0.129	−0.12
Innovation	0.552	0.585	1.000	0.113	0.115	0.014	0.039	0.028	0.062	0.002	0.093	0.013	−0.01
Leadership	0.201	0.151	0.113	1.000	0.021	0.077	−0.013	0.214	0.095	0.02	−0.096	−0.02	0.092
Employee Satisfaction	0.038	0.023	0.115	0.021	1.000	0.661	0.694	0.731	0.605	0.653	0.482	0.609	0.524
Technology	0.036	0.05	0.014	0.077	0.661	1.000	0.659	0.64	0.701	0.532	0.474	0.613	0.582
Rewards	0.033	0.053	0.039	−0.013	0.694	0.659	1.000	0.569	0.575	0.488	0.352	0.438	0.468
Values	0.015	0.022	0.028	0.214	0.731	0.64	0.569	1.000	0.545	0.553	0.489	0.599	0.508
Employee Technology	0.005	0.028	0.062	0.095	0.605	0.701	0.575	0.545	1.000	0.554	0.589	0.591	0.549
Teamwork	0	0.084	0.002	0.02	0.653	0.532	0.488	0.553	0.554	1.000	0.541	0.57	0.499
Productivity	−0.056	−0.114	0.093	−0.096	0.482	0.474	0.352	0.489	0.589	0.541	1.000	0.564	0.506
Commitment	−0.087	−0.129	0.013	−0.02	0.609	0.613	0.438	0.599	0.591	0.57	0.564	1.000	0.492
Cost	−0.127	−0.115	−0.009	0.092	0.524	0.582	0.468	0.508	0.549	0.499	0.506	0.492	1.000

illustrates the relationships among the constructs in the model.

As expected, the strongest correlations are observed among constructs associated with organizational outcomes and employee perceptions. For instance, Employee Satisfaction shows strong positive correlations with Rewards (0.694), Values (0.731), Technology (0.661), Employee Technology (0.605), Teamwork (0.653), and Commitment (0.609). Similarly, Technology is positively correlated with Employee Technology (0.701), Rewards (0.659), and Values (0.640), highlighting the interdependence of technological adoption and employee-related outcomes.

Moderate correlations are also observed, such as between Innovation and Mentality (0.585) and between Adaptability and Mentality (0.581), suggesting conceptual alignment among Adaptability, Innovation, and the organizational mindset. By contrast, weak or near-zero correlations exist between Leadership and most constructs, except for Values (0.214), indicating a relatively limited statistical association between leadership and other dimensions in this dataset. Negative correlations, although weak, were detected for variables such as Productivity and Mentality (−0.114) and Leadership (−0.096), suggesting possible inverse relationships.

The R² value shows how effectively the independent latent variables predict the endogenous latent variable. For instance, an R² of 0.55 indicates that 55% of the variance in the endogenous latent variable is accounted for by the independent variables [54]. The R² criterion, a predictive measure within the model, shows that any value above 0 suggests the model’s predictability is meaningful. On the other hand, the model might explain some of the variations in the dependent variable if its R² value is higher than 0 [52]. A $R^2$ value of 0 does not necessarily mean that it is worthless or unimportant. It simply means that the model cannot account for any variation in the data. In parallel, a model that accurately predicts the dependent variable from the independent variable or variables has an R² value of 1 [65].

Table 7. Path coefficients R².

Variable	R-Square
Adaptability	0.410
Commitment	0.724
Cost	0.692
Employee Satisfaction	0.481
Employee Technology	0.510
Innovation	0.739
Mentality	0.290
Productivity	0.692
Teamwork	0.704
Technology	0.571
Rewards	0.660
Values	0.394

illustrates how each dependent variable has a construct that meets acceptable conditions for explaining it. This finding indicates that the dependent variables and their corresponding manifest variables provide a sufficient explanation for the model.

The results indicate that constructs such as Innovation (0.739), Commitment (0.724), Teamwork (0.704), Productivity (0.692), and Cost (0.692) exhibit strong explanatory power, suggesting that the predictors included in the model adequately capture the main determinants of these variables. Constructs including Technology (0.571), Employee Technology (0.510), Employee Satisfaction (0.481), and Rewards (0.660) show moderate explanatory power, indicating that the model accounts for a reasonable portion of their variance. Conversely, Adaptability (0.410), Values (0.394), and, particularly, Mentality (0.290) display weaker explanatory power, indicating that the current predictors only partially explain their variance.

Low R² values do not necessarily undermine the validity of the model, particularly in exploratory research or when analyzing complex organizational and behavioral constructs, where explained variance is often modest. Instead, these results suggest that specific constructs may be influenced by additional factors not captured in the model, or that further refinement of the measurement may be necessary. Overall, the findings demonstrate satisfactory explanatory power for most constructs, while highlighting opportunities for further model development, particularly regarding Adaptability, Values, and Mentality.

To assess unidimensionality, the variables that constitute the constructs should accurately measure the intended concept rather than other variables. In this article, Cronbach’s Alpha and Average Variance Extracted (AVE) are employed for this evaluation.

Table 8. Cronbach’s Alpha.

Variable	Cronbach’s Alpha
Leadership	0.749
Adaptability	0.721
Cost	0.732
Employee Satisfaction	0.903
Employee Technology	0.764
Innovation	0.817
Mentality	0.783
Commitment	0.867
Productivity	0.711
Teamwork	0.877
Technology	0.901
Values	0.794
Rewards	0.805
Overall Cronbach’s Alpha Value	0.925

presents the reliability test results based on Cronbach’s alpha. Cronbach’s alpha is a helpful indicator of internal consistency and dependability. It helps with an accurate assessment of the underlying component by measuring the degree to which scale or survey questions relate to one another. According to widespread consensus, most research demands can be met with a Cronbach’s alpha of 0.70 or higher [53].

The instrument’s reliability analysis, assessed using Cronbach’s alpha, indicates excellent internal consistency (alpha = 0.925). This result confirms that the survey items as a whole reliably measure the intended constructs. At the individual variable level, most dimensions demonstrated acceptable to very high reliability, ranging from 0.711 to 0.903. Constructs such as Employee Satisfaction (0.903), Technology (0.901), Teamwork (0.877), and Commitment (0.867) achieved exceptionally high scores, reflecting strong consistency among their measurement items. Innovation (0.817), Values (0.794), Rewards (0.805), Mentality (0.783), and Leadership (0.749) also fall within the good reliability range, further supporting the instrument’s robustness. Overall, the Cronbach’s alpha result demonstrates that the instrument is statistically reliable and suitable for subsequent analyses.

On the other hand, Average Variance Extracted (AVE) is used to evaluate a construct’s convergent validity. The degree to which indicators, or observed variables, that are conceptually connected to the same construct are related in reality is known as convergent validity [65]. A construct’s AVE must be 0.50 or greater to have acceptable convergent validity. This finding indicates that the construct accounts for at least 50% of the variance in its indicators, implying that, on average, it explains more than half of that variance [62].

Table 9. Average Variance Extracted (AVE).

Variable	AVE
Leadership	0.818
Adaptability	0.837
Cost	0.749
Employee Satisfaction	0.912
Employee Technology	0.808
Innovation	0.845
Mentality	0.821
Commitment	0.854
Productivity	0.796
Teamwork	0.802
Technology	0.835
Values	0.722
Rewards	0.885

shows that all variables satisfy the previously stated requirement. The variables under analysis are associated with the corresponding constructs.

The AVE values range from 0.722 (Values) to 0.912 (Employee Satisfaction), reflecting strong convergence of items toward their respective constructs. Employee Satisfaction (0.912), Rewards (0.885), and Commitment (0.854) achieved the highest values, suggesting very high explanatory power. Similarly, constructs such as Innovation (0.845), Adaptability (0.837), and Technology (0.835) also demonstrated excellent convergent validity. Even the lowest AVE value, corresponding to Values (0.722), remains well above the acceptable threshold, further confirming the robustness of the measurement model. The results provide strong evidence of convergent validity across all dimensions.

On the other hand, the PLS model does not have a universal criterion for optimality, resulting in a single comprehensive fit function used to evaluate the overall goodness-of-fit (GoF) of the model [60]. Additionally, since it is a variance-based model built on predictive criteria, its validation primarily assesses predictive performance [66]. The GoF, a criterion that evaluates the model by considering both measurement and structural models, is the final method used for assessment [62]. This criterion assesses the model’s quality by examining the constructs and their interrelationships. There is no hard-and-fast rule for this ratio; it is considered better when the value is higher. Given the number of latent variables, their correlations, and the model’s complexity, the current study’s GoF of 0.734 is satisfactory.

Finally, a collinearity assessment was conducted for all formative indicators to ensure the robustness and validity of the measurement model. Specifically, Variance Inflation Factors (VIFs) were calculated to assess multicollinearity among the indicators. The results indicated that all VIF values were below the conservative threshold of 3.3, suggesting that collinearity did not threaten the stability or interpretability of the estimated parameters. This finding confirms that each formative indicator contributes uniquely and meaningfully to the construct without redundant overlap.

In addition, the mediating role of Organizational Culture was examined to better understand the underlying mechanism linking Industry 4.0 adoption to project cost reduction. The results confirmed a statistically significant indirect effect (p < 0.05), demonstrating that Organizational Culture partially mediates the relationship between Industry 4.0 adoption and project cost reduction. This finding suggests that the benefits of Industry 4.0 technologies on cost performance are strengthened when supported by a conducive organizational culture that facilitates innovation, collaboration, and digital integration.

6. Discussion

This study’s primary contribution is to examine the relationship between Industry 4.0 factors, centered on technology, augmented reality, the Internet of Things, and 3D printing, and Industry 5.0 factors, centered on the human factor and organizational culture. The results support the development and application of these variables in the construction sector, which is characterized by low human resource development, high variability, a high risk of errors, and the use of artisanal processes. Organizational culture is one such crucial element. The primary contribution of this study is to examine the role of organizational culture in enabling repurposing and innovation strategies in the AEC sector under conditions of high uncertainty. The proposed model’s visual structure in this research places the human element and Industry 5.0 values at the center by illustrating that Industry 4.0 technologies serve as enablers that enhance human capabilities, rather than as ends in themselves.

By focusing on how firms adapt to crises through the adoption and integration of Industry 4.0 technologies and Industry 5.0’s human-centric approaches, the study highlights the cultural mechanisms that support resilience and competitiveness. The interdependence model reveals that productivity, technology, innovation, and mentality are the most significant factors, displaying the strongest and most meaningful relationships. These variables not only drive immediate adaptation but also shape organizations’ long-term capacity to repurpose resources, reconfigure processes, and sustain innovative practices in volatile environments.

A significant portion of the findings stemmed from a literature review that identified the components of each idea and their correlations, supporting the theories presented by relevant authors in the field. It encompasses innovations such as cyber-physical systems, big data analytics, and the IoT [6]. The adoption of Industry 4.0 presents both challenges and opportunities for the AEC, which has historically been averse to rapid technological advances. For this adoption to occur, organizational culture is essential. Effective adoption of Industry 4.0 technology requires a culture that values creativity, ongoing education, and adaptability [4].

Another significant contribution is the development of a conceptual model that articulates how Industry 5.0 principles integrate with organizational culture in construction companies. Rather than merely linking technological components, this model reframes firms’ operational foundations by positioning human-centered value creation as the core driver of innovation and competitiveness. Complementing the human-centered approach of the present study, cost efficiency and productivity are secondary operational outcomes of a healthy, worker-centered organizational culture.

From an Industry 5.0 perspective, leadership is responsible not only for implementing technological strategies but also for shaping a culture that harmonizes advanced technologies with human capabilities. While Industry 4.0 emphasized digital transformation and automation, Industry 5.0 marks a paradigm shift toward reintegrating the human factor: prioritizing collaboration between humans and intelligent systems [30]. In this sense, technology is no longer an end in itself but a means to enhance creativity, decision-making, and overall human performance. This transition is especially relevant in the AEC sector, where complexity, interdisciplinary collaboration, and contextual problem-solving demand a strong human component. Industry 5.0 strengthens the role of talent by promoting well-being, continuous learning, and the cognitive and creative development of professionals, ensuring that technological adoption amplifies rather than replaces human expertise.

Consequently, organizational culture becomes a strategic enabler of Industry 5.0. Construction companies must foster environments that prioritize human-centric innovation, ethical responsibility, and social impact. This includes empowering employees, encouraging participatory decision-making, and embedding sustainability and resilience into everyday practices. In this framework, culture is not a passive backdrop but an active system that aligns human values with technological advancement, enabling a more adaptive, inclusive, and sustainable construction industry.

Additionally, the relationship between the critical components in the context of Industry 4.0, Industry 5.0, and organizational culture is the PLS conceptual model’s most important contribution. Specifically, adaptability is the most relevant component, both in the company’s technological integration (e.g., 3D printing, IoT, and AR) and in how personnel utilize these technologies. Due to technology’s flexibility, businesses can continually develop and enhance their operations while maintaining their competitiveness. Similarly, employee interaction and teamwork significantly influence productivity, which in turn affects project cost reduction. To fully realize the advantages of cutting-edge technologies within the context of Industry 4.0 and the soon-to-be Industry 5.0, organizations must cultivate a culture that values cooperation and technological efficiency [67]. This research emphasizes the significance of this effort.

Organizational culture and industrial evolution interact dynamically. The construction sector is shifting from Industry 4.0 to Industry 5.0 [4]. The cultural characteristics that enabled technology adoption must change to promote a more human-centered approach [68]. A conscious change in organizational practices, attitudes, and values is necessary for this progress [69]. A company with a culture that excels at process optimization and technological proficiency may have effectively embraced Industry 4.0 technologies [31]. To fully realize the benefits of Industry 4.0 and make a seamless transition to Industry 5.0, an innovative, flexible, and human-centered culture is necessary [70].

Industry 5.0 in the AEC sector should not be understood simply as the reintegration of humans into automated systems. Rather, it represents a paradigm shift toward human-centric synergy, where advanced digital technologies developed under Industry 4.0, such as BIM, IoT, AI, robotics, and data analytics, are reinterpreted and repurposed through human creativity, judgment, and experiential knowledge. In this framework, technology does not replace the worker; instead, it becomes an adaptive tool shaped by human agency.

7. Conclusions

The analysis of the measurement and structural models provides strong evidence of reliability, validity, and predictive power within the context of the construction industry. The findings highlight that organizational culture is a decisive driver of crisis-driven innovation strategies such as repurposing, bricolage, and exaptation. In volatile environments shaped by pandemics, economic shocks, and disruptive technologies, organizational culture becomes pivotal in enabling firms to adapt quickly, mobilize resources effectively, and sustain innovation beyond immediate survival needs.

The study demonstrates that technology, innovation, and productivity are the most influential factors in fostering resilience in the AEC industry. When these concepts are embedded in a culture that values adaptability, collaboration, and problem-solving, firms are better able to turn crises into opportunities. In particular, repurposing strategies supported by a strong culture of experimentation and trust allow organizations to reconfigure resources and processes, ensuring continuity while also laying the foundation for long-term competitiveness. On the other hand, although leadership has a low influence on variables such as Technology and Mentality, its influence increases when it is linked to variables such as Values and Rewards. Conversely, mentality appears less impactful when considered in isolation, underscoring the importance of systemic cultural alignment rather than individual attributes alone.

At a broader level, organizational culture is revealed as a critical driver of innovation in the era of Industry 5.0. It defines how technology is embraced, how virtuality and cyberspace are integrated, and how collaborative and remote work models are encouraged. A strong, adaptive culture that values innovation, collaboration, and the adoption of technology is essential for organizations seeking to thrive in this rapidly evolving landscape. Industry 5.0 has revolutionized how people communicate, transforming the virtuality of processes into a source of global connectivity and competitive advantage. In this context, markets, companies, institutions, and research must continuously adapt to remain resilient and relevant. Together, these insights underscore that the convergence of robust organizational culture, technological adoption, and innovative practices lays the foundation for sustainable performance and competitiveness in the construction sector and beyond.

Nevertheless, the study has limitations. First, the sample of 120 AEC professionals primarily comes from mid-sized companies in Mexico’s construction industry, which may limit the generalizability of the results to other regions or company sizes. Second, although the PLS-SEM method is appropriate for this sample, the relatively low R² values for some constructs, such as Mentality, Values, and Adaptability, suggest that other factors (perhaps related to external market pressures or specific regulatory frameworks) could better explain these cultural aspects. Furthermore, the study does not fully explore the ethical, sustainability, and inclusivity aspects of crisis-driven innovation. Future research should examine how AEC sector strategies for repurposing can achieve a balance between quick adaptation and societal needs, including environmental concerns, fairness, and ethical issues related to AI and digital technologies. Specifically, exploring the legal aspects of climate change and its effects on urban planning is crucial for meeting the needs of environmental migrants and maintaining socially responsible innovation [71]. Finally, longitudinal studies are recommended to assess how these cultural and technological interrelations evolve beyond immediate crisis response to institutionalize long-term transformation. Such research would deepen the understanding of how organizations can pursue resilience in high-uncertainty contexts while simultaneously contributing to sustainable and socially responsible futures.