Leveraging Sustainable Housing through Artificial Intelligence Enabled Dynamic Capability, Technological Self Efficacy, Digital Twin Adoption and Data Driven Culture in Netherlands

Leveraging Sustainable Housing through Artificial Intelligence Enabled Dynamic Capability, Technological Self Efficacy, Digital Twin Adoption and Data Driven Culture in Netherlands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Leveraging Sustainable Housing through Artificial Intelligence Enabled Dynamic Capability, Technological Self Efficacy, Digital Twin Adoption and Data Driven Culture in Netherlands Muhammad Hussain, Usman Ahmad, Ngan Thi Luong, Abdul Rauf, Arsalan Mujahid Ghouri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8969460/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Drawing on dynamic capability theory, social cognitive theory, and socio-technical system theory, this research aims to examine the mediating roles of technological self-efficacy and digital twin adoption in the relationship between AI-enabled dynamic capability and sustainable housing in the Dutch housing sector. Furthermore, this study examines the moderating effect of data-driven culture on the relationships among AI-enabled dynamic capability, technological self-efficacy, and digital twin adoption. The structured, closed-ended questionnaire was distributed to the senior manager of housing projects using a time-lagged, three-wave (T1, T2, T3) research approach, in which data were collected on AI-enabled dynamic capability and data-driven culture at T1. After four weeks, data work was collected on technological self-efficacy and digital twin adoption. Similarly, at T3, after 4 weeks, data collection on sustainable housing was conducted. The final number of complete questionnaires was 246, which was utilised for the final analysis. Partial least squares structural equation modeling (PLS-SEM) was employed for data analysis and hypothesis testing. The results reveal a significant and positive effect of AI-enabled dynamic capability on sustainable housing. Furthermore, the mediating role of technological self-efficacy and digital twin adoption between AI-enabled dynamic capability and sustainable housing is also established. In addition, a data-driven culture moderates the relationships among AI-enabled dynamic capability, technological self-efficacy, and digital twins in the Dutch housing sector. sustainability dynamic capability artificial intelligence sustainable housing Dutch Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The decarbonization of the housing sector is one of the most pressing issues the global world is currently facing to ensure sustainability. The housing sector contributes a significant portion of global carbon emissions; therefore, it has become a central focus of climate-neutrality and circular-economy policies (Huang, Krigsvoll, Johansen, Liu, & Zhang, 2018 ). The housing sector is radically changing across Europe, especially in the Netherlands, where ambitious climate targets, the development of digital infrastructure, and the adoption of sustainable construction policies are evident (J. van Oorschot et al., 2023 ). Nevertheless, even with the technological investment and regulatory pressure, most organizations in the housing sector are unable to transform digital initiatives into sustainable performance due to less adoption of artificial intelligence-driven tools and techniques in the construction setting (Anumba & Madubuike, 2022). This paradox raises a core issue: how can artificial intelligence (AI) and organizational capabilities be utilized in the housing sector to ensure sustainable housing? Recent research focuses on AI as a revolutionary technology that can improve predictive analytics, energy efficiency, construction lifecycle management, and data-driven decision making. However, a plethora of literature views AI as a technological artifact or a result of adoption rather than an internal organizational capability. Therefore, a dynamic capabilities perspective (Teece, 2007 ) indicates that to respond to technological turbulence, firms need to develop higher-order capabilities to sense opportunities in the external environment, seize digital innovations, and reconfigure internal resources. So, AI-enabled dynamic capability (AIEDC) is an enhanced organizational capability that leverages AI analytics, machine learning systems, and real-time data infrastructures in strategic renewal and sustainability-oriented innovation processes. Although the notion of dynamic capabilities has been extensively discussed in the literature on digital transformation (K. S. Warner & M. Wäger, 2019 ), its application in the sustainable housing domain is underexplored. Specifically, the prior literature has failed to detail the processes by which AI-driven dynamic capabilities are translated into sustainability performance in multifaceted socio-technical systems such as the housing sector. To fill this theoretical gap, this research posits that sustainable performance depends on both micro and meso-level factors (Winston, 2022 ), through which AIEDC may achieve sustainable housing as it encompasses various stakeholders (Opoku, Perera, & Osei-Kyei, 2021 ). At the micro level, drawing on social cognitive theory, technological self-efficacy acts as a mediating factor, establishing an underlying mechanism/pathway linking AIEDC and sustainable housing. Technological self-efficacy is an individual's confidence in their ability to use advanced digital tools and systems powered by AI (Sharma, Singh, & Singh, 2021 ). Self-efficacy is a key factor in the use of technology at the workplace for innovation and the successful implementation of digital transformation (Pan, 2020 ). AI's dynamic capabilities can develop learning-related settings, cross-functional experimentation, and knowledge-sharing practices that increase employees' technological confidence (K. S. R. Warner & M. Wäger, 2019 ). In turn, employees having greater technological self-efficacy find it easier to use AI-based energy management systems, predictive analytics, and sustainability dashboards more effectively, thus enhancing sustainable housing performance (Permana, Fitriani, & Aulia, 2023 ). Technological self-efficacy is therefore a key foundation for the organization's AI capability to support sustainability outcomes. Although the individual cognition factor (i.e., technological self-efficacy) is pivotal to achieving sustainability in housing, technological adoption and implementation are also crucial in the housing sector (J. A. Van Oorschot, Halman, & Hofman, 2020). Likewise, this study proposes digital twin adoption as a second middle-level mediating factor, providing a technology-driven mechanism to achieve sustainable housing. Virtual simulations of a real building that incorporate real-time feedback via digital twins can be used for design optimization, predictive maintenance, and lifecycle energy efficiency (Bibri & Huang, 2025 ; Fritz, 2023). Digital twin improves resource optimization, emission reduction, and asset sustainability in the housing sector (Wang, Chen, & Li, 2024 ). Nonetheless, digital twins need to be adopted through data integration, AI analytics, and the reconfiguration of organizational processes (Lu, Xie, Parlikad, & Schooling, 2022 ), which are based on organizational AIEDC. So, adopting digital twins is a way to use technology to help AIEDC achieve measurable, sustainable housing performance. This paper describes a multi-level mediation rationale that balances between personal cognition and technological application by combining technological self-efficacy and digital twin adoption. However, the mediating mechanisms for translating AIEDC into sustainable housing may not be fully operationalized in the absence of a conducive culture. This research postulates that a data-driven culture serves as a boundary condition that shapes the relationship among AIEDC, technological self-efficacy, and digital twin adoption. A data-driven culture focuses on evidence-based decision-making, transparency, analytics orientation, and experimentation (Bose & Luo, 2022 ; Gupta & George, 2016 ; Mikalef, Krogstie, Pappas, & Pavlou, 2020 ). Data-driven norms and values support AI-driven strategies, activities, and capabilities, helping employees feel confident in adopting digital technologies and systems and speeding up work progress (Mikalef et al., 2020 ). This moderation logic adds context to the dynamic capability framework, recognizing that the organizational culture contributes to the success of AI-based sustainability transformation. Thus, based on the above discussion and highlighting the theoretical gaps, this research will answer the following research questions: Does AIEDC have any effect on sustainable housing? Does technological self-efficacy mediate the relationship between AIEDC and sustainable housing? Does digital twin adoption mediate the relationship between AIEDC and sustainable housing? Does data-driven culture moderate the relationship between AIEDC and technological self-efficacy? Does data-driven culture moderate the relationship between AIEDC and digital twin adoption? This research extends the existing literature in the fields of sustainability, artificial intelligence, and the housing sector. Firstly, this research contributes to the existing literature on the dynamic capability theory by theorizing AIEDC as a critical factor of sustainable housing, particularly in the Dutch housing sector. Secondly, develops multi-level approaches for AIEDC and sustainable housing through technological self-efficacy and the use of digital twins as intermediaries. Third, it presents a data-driven culture as a key condition for strengthening capability-based factors to achieve sustainable housing. 2. Literature review 2.1 Theoretical Underpinnings and Hypotheses Development The research is grounded in the dynamic capability theory, social cognitive theory, and socio-technical systems theory to describe AI driven organizational capabilities transforming into sustainable housing. The dynamic capability theory underscores crucial role of sustainable competitive advantage to generate higher-order capabilities to sense opportunities, acquire innovations, and reorganize resources in dynamic and complex environment (Helfat & Peteraf, 2009; Teece, 2007; K. S. Warner & M. Wäger, 2019). These capabilities are dependent on analytics infrastructures, AI integration, and data orchestration in the digital transformation setting (Mikalef & Gupta, 2021). Nevertheless, dynamic capabilities are not sufficient to reveal how the organizational capabilities are implemented at the individual level (Akter, Bandara, & Hani, 2020). Therefore, social cognitive theory deals with the individual motivations, capabilities and self-confidence (Schunk & DiBenedetto, 2020). The social cognitive theory asserts that self-efficacy influence the readiness of people to become familiar with and use complex technologies (Bandura, 1997; Schunk & DiBenedetto, 2020). This self-belief not only helps in performing the daily activities but also ensures the use of modern technology within organization (Haque & Akter, 2023). This self-efficacy enables employees to believe that they have suffice skills and capabilities that can easily understand complex data structures and use them better to perform any task (L. Chen & Zhang, 2023). This technological self-efficacy plays a key role in improving strategies and processes in the organization, including AI-driven analytics, algorithms and data-driven structures (Nguyen, Newby, & Macaulay, 2022). Furthermore, at the system level, the socio-technical systems theory posits that changes in technology require the correspondence between technological artifacts, human actors, and institutional arrangements (Vial, 2021). Digital twins are the examples of such socio-technical integration, integrating AI analytics, IoT infrastructures and organizational routines (Boje et al., 2020; Opoku et al., 2021). Combining the above insights, the current research contributes to a multi-level model connecting AIEDC to technological self-efficacy, digital twin adoption, and sustainable housing in the context housing sector of developed world like Netherlands. 2.2 AI-integrated Dynamic Capability and Sustainability Housing. Sustainable housing refers to the energy efficiency, less carbon emissions, lifecycle optimization, and the use of the circular in housing construction (Huang, Krigsvoll, Johansen, Liu, & Zhang, 2018). To ensure these sustainable house characteristics, organizations keenly indulge in environmental sensing, adaptive decision-making, and strategic reconfiguration of core processes through cutting edge technologies (Almeida, 2023; Helfat & Peteraf, 2009; Zhang, Xu, Li, & Teece, 2023). The sensing, seizing, and reconfiguring functions are reinforced with AI-based dynamic capability, which improves the predictive analytics and real-time monitoring of building performance (Dwivedi & et al., 2021), data-driven investment and decisions (Mikalef, Krogstie, Pappas, & Pavlou, 2020; K. S. Warner & M. Wäger, 2019), and digital workflow integration and process automation (Vial, 2021). Notably, existing studies indicate that sustainability effect of digital technologies can only be created through better its integration into supplementary organizational capabilities (Dubey, Gunasekaran, Childe, Papadopoulos, & Luo, 2020). Organizations with greater AIEDC have a better chance of coordinating AI tools with improvements in sustainable housing (Zhang, Xu, & Li, 2023) through digital infrastructure and ambitious climate policies. Therefore, AIEDC is not only a technological contribution but a strategic driver of sustainable housing(Bibri, Omar, Kenawy, & Huang, 2025). Therefore following hypothesis is developed: H 1: AI-enables dynamic capabilities are positively related to sustainable housing 2.3 Technological Self-Efficacy as a Mediator The conversion of AIEDC into sustainability outcomes relies on the engagement of employees with current and cutting edge technologies despite its organization wide capabilities. The cognitive theory underscores that self-efficacy is the key to give confidence to people to start, continue and complete technology-related tasks successfully (Bandura, 1997; Schunk & DiBenedetto, 2020). The prior literature provides ample evidence that technological self-efficacy is a significant predictor of technology adoption, digital innovation behavior, and performance outcomes (McDonald & Siegall, 1992; Pan, 2020; Sharma, Singh, & Singh, 2021). Based on the micro-foundational view of dynamic capability, organizational capabilities are applied using individual skills, cognition, behavioral involvement (Felin, Foss, Heimeriks, & Madsen, 2012; Helfat & Peteraf, 2009). AIEDC also cultivates digital education, cross-functional education, and experimentation habits that raise the technological confidence of employees (Mikalef, Boura, Lekakos, & Krogstie, 2019; K. S. Warner & M. Wäger, 2019). Likewise, employees with greater technological self-efficacy are more apt to take a correct interpretation of predictive analytics, take advantage of AI-based energy systems, and involving digital dashboards into sustainability planning during all of housing stages (Adediran et al., 2025; Shehab, Khaidzir, Shehab, & Kılıç, 2025). As a result, the organizational AI capability is transformed into sustainable housing outcomes via the cognitive medium of technological self-efficacy. Thus, following hypothesis is generated: H2: Technological self-efficacy mediates the relationship between AI-enabled dynamic capability and sustainable housing 2.4 Digital Twin Adoption as a Mediator Sustainable housing requires multifaceted interaction of stakeholders and organizational level due to its complexity and wicked nature (Adediran et al., 2025). Therefore, considering solely cognitive elements to achieve satiability is not sufficient rather technological integration is also a critical factor across all stages (Gan, Yan, & Wen, 2023). The virtualization of real buildings is facilitated through digital twins, which are useful in optimizing the lifecycle, predictive maintenance, and simulating energy (Bibri & Huang, 2025; Opoku, Perera, & Osei-Kyei, 2021). Such technologies work efficiently in the context of the socio-technical systems theory only in the case of their correspondence to organizational routines and human capabilities (Vergragt & Brown, 2012; Vial, 2021). Digital twin adoption needs sophisticated data coordination, interoperability requirements, and AI analytics that entrench by organizational dynamic capabilities (Teece, 2007; Helfat et al., 2007). Strong AIEDC in organizations allows to identify the opportunities of digital twin, make investment decisions, and reorganize the processes to implement the systems housing construction process(Mikalef et al., 2019; K. S. Warner & M. Wäger, 2019). As soon as they are adopted, digital twin improve transparency in sustainable construction by decreases energy waste, and sustain circular housing strategy (Permana, Fitriani, & Aulia, 2023; Pomponi & Moncaster, 2017). Thus, the digital twin acceptance is a meso-level process converting AIEDC to quantifiable sustainable housing performance. Thereby, below is the relevant hypothesis: H3: Digital twin adoption mediates the relationship between AI-enables dynamic capability and sustainable housing 2.5 Data-Driven Culture as a Moderator Organizational culture is one of the widely researched constructs in the management literature (Akpa, Asikhia, & Nneji, 2021). The previous literature unfolds various types of organizational culture such as innovative culture (Bendak, Shikhli, & Abdel-Razek, 2020), supportive culture (Stephan & Uhlaner, 2010), strong and weak culture (Thokozani & Maseko, 2017) and most currently data driven culture (Bose & Luo, 2022). Data-driven culture promotes analytics, institutionalizing norms based on evidence, transparency, experimentation and data based approaches (Bose & Luo, 2022; Gupta & George, 2016). Data driven culture strengthens and aligned the organizational capabilities developed based on the cutting edge technologies like artificial intelligence(Elgendy & Elragal, 2020). The implementation of AI initiatives in the organization is justified and becomes a part of organizational routine, which in turn enhances the level of technological self-efficacy of employees with a strong data-driven culture (Maruping & Magni, 2015). On the other hand, poor data-based cultures can lead to resistance restricting trust and connectivity with AI technology (Dwivedi & et al., 2021). Similarly, the use of digital twin demands cross-departmental data combination and long-term commitment to data driven norms, analytics and agorithms(Hosamo & et al., 2022). The integration of such kind is more probable in the organizations in which the transparency of data and evidence-based reasoning are highly entrenched (Mikalef & Gupta, 2021; Warner & Wager, 2019). In this way, the translation of AIEDC into both mediating pathways is increased by the presence of data-driven culture. H4: Data-driven culture moderates between AI-enabled dynamic capability and technological self-efficacy, where the relationship is strong in the strong data-driven culture H5: Data-driven culture moderates between AI-enabled dynamic capability and digital twin adoption, where the relationship is strong in the strong data-driven culture Based on the above discussion comprised of underpinnings theories and hypotheses development, following is the conceptual framework of the current study 3. Methodology To achieve the research objectives, this study employed quantitative, time-lag (three-wave) research design that empirically examines the moderated-mediation framework of AI-enabled dynamic capability (AIEDC) and sustainable housing, technological self-efficacy, digital twin adoption and data-driven culture. The Dutch housing industry provides good research setting as it offers a theoretically and practically applicable environment due advanced digital infrastructure, strong sustainability policies and the strategic focus on smart and circular residential development (van Oorschot et al., 2023 ). In line with the plethora of prior studies, the unit of analysis is the construction projects and the key informants are senior managers as they are key personnel to execute digital transformation, sustainability strategy, innovation management, and smart housing strategies (Moleka, 2023 ; USMAN, 2018 ; Winston, 2022 ). A three-wave time-lagged (T1, T2, T3) approach was employed with four-week intervals between each wave to gather data to enhance causal inference and reduce common method variance (Rasheed, Hameed, Kaur, & Dhir, 2024 ). It is commonly recommended to do temporal separation between predictor, mediators, moderator variables, and outcome variables to minimize the common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003 ). At Time 1, data were gathered on AIEDC and data driven culture. After 30 days at time 2, data were gathered on technological self-efficacy and digital twin adoption. Finally after 30 days at time three, data on sustainable housing were gathered. Identification codes were generated which identified respondents across the waves to ensure the anonymity. Reminder emails were also administered in the course of every wave to increase the response rate and decrease the issue of non-response bias in line with the previous studies (Akram, Saeed, Bresciani, Rehman, & Ferraris, 2022 ; Rasheed et al., 2024 ). The final analysis was carried out by retaining only the complete responses in all the three waves constituted a sample size of 246. 3.1 Measurements All latent constructs were utilized from the prior literature. Sustainable housing is second order constructs consisting two dimension namely physical components and environmental components derived from (Nasrabadi & Hataminejad, 2019 ) where physical component dimension having 11 items and environment components render 10 items. AI-enabled dynamic capability is operationalized as a first order construct consisting nine items derived from (Abou-Foul, Ruiz-Alba, & López-Tenorio, 2023 ). In addition technological self-efficacy was measured through five items utilized from the work of (Saville & Foster, 2021 ). Similarly, digital twin adoption was measure with nine 9 items scale used from (Singh, Singh, Daultani, & Chouhan, 2023 ). Finally data driven culture was measured with 4 items utilized from (Chatterjee, Chaudhuri, & Vrontis, 2024 ). 4. Results and Findings Partial least square structural equation modeling (PLS-SEM) technique employed for data analysis and hypothesis testing. A two-stage model has been employed for data analysis namely measurement model and structural model (Hair, Risher, Sarstedt, & Ringle, 2019 ). Table 1 indicates the results of the measurement model, where reliability, validity and multicollinearity have been examined. Table 1 Reliability and Validity Latent Constructs Items Factor loadings CR AVE AI Enabled Dynamic Capability AIEDC2 0.757 0.834 0.556 AIEDC3 0.766 AIEDC4 0.731 AIEDC5 0.728 Data Driven Culture DDC1 0.864 0.912 0.723 DDC2 0.870 DDC3 0.859 DDC4 0.806 Digital Twin Adoption DTA1 0.772 0.904 0.574 DTA2 0.714 DTA3 0.824 DTA4 0.753 DTA5 0.747 DTA6 0.776 DTA9 0.713 Sustainable Housing SH1 0.785 0.959 0.530 SH1 0.730 SH12 0.749 SH13 0.763 SH13 0.826 SH14 0.810 SH15 0.817 SH16 0.787 SH17 0.820 SH18 0.825 SH19 0.812 SH2 0.793 SH3 0.752 SH4 0.743 SH5 0.756 SH6 0.757 SH7 0.761 SH8 0.746 Technical Self-Efficacy TSE1 0.811 0.860 0.605 TSE2 0.773 TSE3 0.795 TSE4 0.730 Table 1 shows that all factor loadings are greater than 0.70, therefore inter-item consistency is established (Hair Jr, Matthews, Matthews, & Sarstedt, 2017 ). All factor loadings less than 0.70 were deleted from the hypothesized model recommended by the prior literature in PLS-SEM (Hair Jr et al., 2017 ). Furthermore, values of composite liability (CR) of all latent constructs are greater than 0.70, thereby internal consistency is ensured (Hair et al., 2019 ). Average variance extracted (AVE) was employed to analyse the convergent validity. Table 2 Multicollinearity (VIF) AIEDC DTA SH TSE 1.272 1.193 1.272 DDC 1.268 1.268 DTA 2.412 TSE 2.373 The results indicate in Table 1 that the AVE values of all latent constructs are greater than 0.50 which proves that convergent validity exists in the hypothesized model (Memon et al., 2021 ). Similarly, the variance inflation factor (VIF) was employed to check multicollinearity. Table 3 HTMT Ratio AIEDC AIEDC DDC DTA SH TSE DDC 0.540 DTA 0.467 0.710 SH 0.604 0.851 0.834 TSE 0.477 0.658 0.914 0.793 Table 2 reveals that the values of all latent constructs are less than 5 which established that no issue of multicollinearity exists in the hypothesized model (Daoud, 2017 ; Hair et al., 2019 ). Furthermore, Table 3 shows the HTMT ratio and it depicts that that all values are less than 0.90 which ensures that all latent constructs are distinct from each other on empirical standards (Hair Jr et al., 2017 ). Furthermore, Table 4 and Fig. 2 reveal the results of measurement model. It depicts the results of hypothesis testing through bootstrapping technique on 5000 sub sample (Hair Jr et al., 2017 ). Results show that AIEDC has a significant and positive direct effect on SH (β = 0.242, t = 3.480, p = 0.001), therefore supported. Moreover, H2 reveals the significant mediating role of TSE between AIEDC and SH (β = 0.040, t = 1.995, p = 0.046), it is also supported. Similarly, H3 confirms that DTA is a significant mediator (β = 0.086, t = 2.346, p = 0.019) between AIEDC and SH, thus supported. Table 4 Structural Model Hypo No. Hypothesized Path β Standard deviation T statistics P values Decision H1 AIEDC -> SH 0.242 0.069 3.480 0.001 Supported H2 AIEDC -> TSE -> SH 0.040 0.020 1.995 0.046 Supported H3 AIEDC -> DTA -> SH 0.086 0.037 2.346 0.019 Supported H4 DDC x AIEDC -> TSE 0.209 0.081 2.569 0.010 Supported H5 DDC x AIEDC -> DTA 0.195 0.085 2.289 0.022 Supported Likewise, Table 4 and Figs. 4 and 5 depict the results of moderation in the hypothesized model. H4 reveals the significant positive moderating effect of DDC between AIEDC and TSE (β = 0.209, t = 2.569, p = 0.010), hence supported. Finally, H5 confirms that DDC is also a significant and positive moderator between AIEDC and DTA (β = 0.195, t = 2.289, p = 0.022), thus it is also supported. The moderation results implicate that in the presence of data driven culture the relation between AIEDC, technological self-efficacy and digital twin adoption is stronger. 4.1 Co-efficient of Determination (R 2 ) Table 5 shows the results of co-efficient of determination (R 2 ). It indicates that Sustainable Housing (SH) reveals strong explanatory power R²=0.660 in the hypothesized model. Digital Twin Adoption shows R²= 0.448 indicating moderate explanatory power. Finally, technological Self-Efficacy (TSE) demonstrates an R²=0.372 suggesting a moderate level of explanatory power. Table 5 R Square DTA R-square R-square adjusted Explanatory power 0.448 0.442 Moderate SH 0.660 0.656 Strong TSE 0.372 0.364 Moderate 4.2 Effect Size (f 2 ) Table 6 depicts the results of effect size (f 2 ). It show that AIEDC has a small effect on DTA (0.041) and TSE (0.045), and a medium effect on SH (0.144), suggesting its strongest impact is directly on sustainable housing. DDC demonstrates a large effect on DTA (0.502) and a medium-to-large effect on TSE (0.328), highlighting its substantial moderating influence. DTA exerts a large effect on SH (0.317), indicating its strong mediating contribution, whereas TSE has a small effect on SH (0.056). Table 6 Effect Size AIEDC DTA SH TSE 0.041 0.144 0.045 DDC 0.502 0.328 DTA 0.317 TSE 0.056 5. Discussion The aim of the research was to empirically test the impact of AI-enabling dynamic capability (AIEDC) on sustainable housing the Dutch housing industry via technological self-efficacy and digital twin adoption depending on interaction effect of data-driven culture. The results support and expand the theoretical foundations of dynamic capability theory, social cognitive theory and socio-technical system theory, will ingrained in the current research of artificial intelligence and sustainability (Abou-Foul et al., 2023 ; Al-Husain et al., 2025 ; Mikalef et al., 2019 ; Wamba et al., 2017 ). The empirical findings of H1 show that AIEDC has a significant and positive impact on sustainable housing(b = 0.242, p = 0.001), which in accord with the dynamic capability theory. It postulates that companies can perform better when they are capable of sensing opportunities, capturing them by making an investment and reconfiguring resources to keep them aligned with the ecological change (K. S. R. Warner & M. Wäger, 2019 ; Zhang, Xu, Li, et al., 2023). Within the setting of Dutch housing contingent on the energy transition requirements, circular construction strategies, and the maturity of digital infrastructure, AI-based sensing (predictive analytics), seizing (AI-based decision systems), and reconfiguring (process automation and smart retrofitting) are collectively aimed at improving sustainability (K. S. Warner & M. Wäger, 2019 ). Empirical studies in the prior literature underscores that AIEDC are beneficial to environmental and operational performance, as they allow data-intensive decision-making and responsive reconfigurations (Bag, Gupta, Kumar, & Sivarajah, 2021 ; Dubey et al., 2020 ; Mikalef et al., 2019 ). Dynamic capability theory highlights that AIEDC is high-level, inimitable component that coordinates data, algorithms, and managerial routines to deliver sustainability-enhancing results (Pournader, Kaur, Akter, & Dubey, 2022 ; Susanti & et al., 2022). The direct effect remains even in the context of mediators implies that AIEDC is not just an enabler but a strategic meta-capability that has a positive contribution to sustainable housing performance through systemic optimization and strategic foresight (Xu, Li, & Tan, 2023 ). The second hypothesis proves the significant mediating role of technological self-efficacy between AIEDC and sustainable housing (b = 0.040, p = 0.046). As stated by Bandura ( 1997 ), self-efficacy determines technology usage and intensity to indulge in to overcome the complexity employee confronted with. Housing sector requires the use of AI driven technologies implies predictive and analytical knowledge that would demand human analysis and implementation (Shehab et al., 2025 ). Therefore, dynamic capabilities are not sufficient unless organizational actors have a belief in the utilization of these technologies. The recent research findings within the digital transformation contexts prove that technological self-efficacy has a profound positive impact on AI utilization, innovation behavior, and sustainability-focused performance (Akter et al., 2020 ; Dwivedi & et al., 2021; Sharma et al., 2021 ). Theoretically, the result adds value to the dynamic capability theory by making individual cognition and action as the micro-foundations of capabilities by which capabilities generate performance impacts (Al-Husain et al., 2025 ; Felin et al., 2012 ; Pavlou & El Sawy, 2011 ). It also shows that the way that AIEDC leads to sustainable housing is partly behavioral and psychological and thus it connects the macro level capability theory with the individual level systems of beliefs. Therefore, socio-technical systems theory can be used to explain the significant mediation effect digital twin adoption between AIEDC and sustainable housing (b = 0.086, p = 0.019). Digital twin adoption is structural implementation of reconfiguring capabilities, that is, the integration of AI into real-time monitoring, lifecycle simulation, and predictive optimization systems (Lu, Xie, Parlikad, & Schooling, 2022 ; Wang, Chen, & Li, 2024 ). According to dynamic capability theory, digital twin adoption reflects the dimension of seizing and reconfiguring, in which organizations convert sensing insights into structural investor, which changes operational structures (Pavlou & El Sawy, 2011 ; Teece, 2007 ). Recent sustainability and construction studies reveal that the digital twin adoption significantly effect to optimize the energy efficiency of the building, predictive maintenance precision, and the reduction of carbon in the lifecycle (Opoku et al., 2021 ; Zhang, Xu, & Li, 2023 ). This implies that meso-level technological infrastructure is a pivotal transmission process between the higher-order capability and the sustainability results. The technological self-efficacy works at the cognitive level, whereas digital twin adoption works at the structural level that supports the multi-level character of capability enactment. Moreover, moderating effect of the data-driven culture (DDC) also reinforces the theoretical soundness of the model. The strong interaction effects on technological self-efficacy (b = 0.209, p = 0.010) on digital twin adoption (b = 0.195, p = 0.022) identify that cutting edge technologies, data driven approaches and algorithms based capabilities are critical to achieve sustainable housing via cognitive and technological process. The theory of dynamic capability highlights that capabilities are path-conditioned and integrated into the organizational processes and norms (Teece, 2018 ). Evidence-based decision-making, analytics-use transparency, and being open to experimentation constitute the characteristics of a data-driven culture that facilitates the flourishing of AI-enabled routines (Mikalef et al., 2020 ; Gupta and George, 2016 ). Higher DDC provides the legitimization of AI outputs, support the interest of employees in the use of analytics, and reduce the level of resistance to technological investments (Xu et al., 2023 ). In the recent studies of digital strategy and analytics capability, it is highlighted that the returns on AI investments are greatly increased by the cultural orientation towards the data (H. Chen, Chiang, & Storey, 2021 ; Kraus et al., 2021 ; Wamba et al., 2017 ). 6. Theoretical Implications The current research provides various theoretical contributions to dynamic capability theory, digital transformation, and sustainability research. First contribution this study is to extend the dynamic capability theory by integrating AI-enabled dynamic capability (AIEDC). The AIEDC is a higher-order digital capability that directly and indirectly increases sustainable housing performance. Although previous studies have already confirmed that dynamic capabilities are associated with firm performance (Teece, 2007 ; Teece, 2018 ), there is still little empirical evidence that establish a direct relationship between AIEDC and sustainability performance. It thus reacts to the recent academic recommendations of considering the capabilities of digital transformation alongside the formation of environmental values (Mikalef et al., 2019 ; Wamba et al., 2017 ). Secondly, the paper adds to the body of micro-foundations literature of cognitive theory by using technological self-efficacy as a mediating variable. Technological self-efficacy embraces as a cognitive driver at the individual level that contributes to enhance performance (Felin et al., 2012 ). This study also shows that AIEDC do manifest as sustainability outcomes partly due to psychological capability of technology usage. This result also corroborates the that underscores the critical role of self-image, concept and capability to enhance individual performance that lead to greater organizational achievements (Bandura, 1997 ). This integration at multiple levels adds depth to the theoretical discourse as it links the macro-level strategic competencies with the micro-level behavioral processes. Third, the research contributes to the growing body of knowledge on digital twin adoption and sustainability by theorizing digital twin adoption as a meso-level structural mediator between AIEDC and sustainable performance. Although the usage of digital twin in construction and infrastructure management has been emphasized as an operationally beneficial concept through recent studies (Lu et al., 2022 ; Opoku et al., 2021 ), there has been little empirical literature that has put digital twin adoption into a capability-based context. This study offers an explanation of the way digital infrastructure operationalizes AI capabilities in measurable gains of sustainability by demonstrating that digital twin adoption as a key mediator. Lastly, this study adds into socio-technical system theory by empirically examining data driven culture between cognitive and technology driven factors as boundary condition which is elusive in the prior literature (Brown & Vergragt, 2008 ; Vergragt & Brown, 2012 ). It validates that the value-generating potential of AI capabilities is enhanced by the presence of intangible data driven cultural resources. Results of the moderation process prove that the efficiency of AIEDC in promoting technological self-efficacy and the adoption of digital twin is conditional upon an organizational culture that justifies the use of analytics in decision-making. The result is a continuation of the previous studies on analytics culture (Gupta and George, 2016 ; Mikalef et al., 2020 ). Together, these theoretical input contributions offer a multi-level account of AI-enabled sustainability transformation, and they combine strategic, structural, and cognitive processes in a consistent framework. 7. Practical Implications This study also provides practical guidelines to policymakers and strategists of housing associations and real estate developers who have an interest in enhancing sustainable housing. First, policy makers should understand that AIEDC they should investment in AI-controlled sensing, predictive analytics and reconfiguration capabilities to make it a strategic prerequisite to meet sustainability goals. The housing organizations should focus on the development of AI based decision system supporting the lifecycle optimization, energy prediction and predictive maintenance. Second, technological self-efficacy as a mediating variable highlights the significance of human capital development. The managers, in addition to investing in AI, ought to integrate formal training, digital literacy, and cross-functional learning systems to boost the employee confidence in using AI tools. Due to the lack of such cognitive preparation, the opportunities of AI systems may be unexploited. Third, the more powerful mediation with the adoption of digital twins emphasizes the importance of introducing AI into the technological framework. Practitioners/policy makers should stop pilot AI projects and proceed to integrated digital twin systems linking IoT devices, simulation applications, and real-time monitoring to housing projects. The effectiveness of such integration can be effective in terms of resource efficiency and sustainability performance. Fourth, the significant moderating effect of data-driven culture accentuates that the technological investments are not sufficient, leaders should also establish a culture of evidence-based decision-making, foster experimentation, and mitigate the opposition to the analytics-based change. This can include the redesign of performance evaluation, motivating data use, and transparency between departments. Therefore, sustainable housing transformation should be achieved through a concomitant capability development, technological infrastructure and cultural orientation. 8. Limitations and Future Research Directions Despite its several theoretical and practical contribution to the literature and industry, this paper carries a few limitations. First, despite the three-wave time-lagged design helps to overcome the common method bias and enhance the internal validity, the research is still observational and not establish causal inferences. Longitudinal panel designs or quasi-experimental designs could be used in future research to elicit the dynamic evolution of capability over time. Second, the research is conducted in the Dutch housing industry, which is the highly developed digital infrastructure and well-developed sustainability laws. Although this enriches the context, it can curtail the generalizability to emerging economies or lower digitally mature housing markets. Comparative cross-country research would be able to examine how much research model is robust in terms of the institutional environment. Third, the research is based on perceptual assessment of sustainable housing. Despite its popularity in the field of management studies, perceptual indicators may not provide accurate way to measure objective metrics on sustainable housing. The triangulation method may be employed by including the archival information like energy consumption reports, lifecycle carbon assessment, or the green certification rates in future studies. Fourth, although the model has key mediators and a moderator, other boundary conditions can be used to further understand the applicability of the model such as the nature of leadership, digital governance structure, or regulatory compliance. Lastly, the future studies might examine other micro-foundational processes other than technological self-efficacy, including digital mindfulness, absorptive capacity, or AI trust perceptions. A further exploration of the interaction between these cognitive and relational factors and organizational capabilities can provide a deeper understanding of AI-based transformation of sustainability. Further, the qualitative or mixed-method research might reveal the process-level dynamics and contextual subtleties which might be used to supplement the quantitative results described here. Declarations Clinical Trial Number not applicable Consent to Participate: Informed consent was obtained from all participants prior to the commencement of data collection. Participants were clearly informed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw at any time without any negative consequences. They were assured that their responses would remain confidential and anonymous, and that all collected data would be used exclusively for academic and research purposes. Consent to Publish: not applicable Ethics approval: The research protocol for this study was reviewed and approved by the Institutional Research Ethics Committee of Wittenberg University of Applied Sciences, Netherlands, in accordance with the ethical guidelines and regulations of the committee and the principles outlined in the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All procedures involving human participants were performed following these approved guidelines. Funding Declaration: The study has not received any external funding. Author Contribution M.H. conceptualized the study, developed the theoretical framework, and led the manuscript writing. U.A. contributed to research design, instrument development, and supervised data collection. N.T.L. contributed to the theoretical grounding, particularly the integration of dynamic capability, social cognitive, and socio-technical system perspectives. A.R. supported methodology development, questionnaire design, and data validation procedures. A.M.G. conducted the data analysis, interpreted the statistical results, and prepared tables and figures.All authors contributed to refining the manuscript, reviewed and approved the final version, and agree to be accountable for all aspects of the work. Data Availability Data are available from the corresponding author upon reasonable request References Abou-Foul, M., Ruiz-Alba, J. L., & López-Tenorio, P. J. (2023). The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective. Journal of Business Research, 157 , 113609. Adediran, A. O., Ajibade, S.-S. M., Jasser, M. B., Bashir, F. M., Dodo, Y. A., Yassin, Y. N. H. M., & Issa, B. (2025). Artificial intelligence adoption in housing: A bibliometric analysis. 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Sustainable Cities and Society, 92 (13), 104503-101350. doi: 10.1016/j.scs.2023.104503 10.1002/smj.640 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 21 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 18 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8969460","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612823289,"identity":"b55bb42c-08c2-437f-b735-71acb33bf3fe","order_by":0,"name":"Muhammad Hussain","email":"","orcid":"","institution":"DHA Suffa University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Hussain","suffix":""},{"id":612823290,"identity":"61348109-1e66-4964-8392-69c6b5881446","order_by":1,"name":"Usman Ahmad","email":"","orcid":"","institution":"Wittenborg University of Applied 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10:23:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1518649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8969460/v1/148adb7c-209e-420f-b05a-843ae38bafae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leveraging Sustainable Housing through Artificial Intelligence Enabled Dynamic Capability, Technological Self Efficacy, Digital Twin Adoption and Data Driven Culture in Netherlands","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe decarbonization of the housing sector is one of the most pressing issues the global world is currently facing to ensure sustainability. The housing sector contributes a significant portion of global carbon emissions; therefore, it has become a central focus of climate-neutrality and circular-economy policies (Huang, Krigsvoll, Johansen, Liu, \u0026amp; Zhang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The housing sector is radically changing across Europe, especially in the Netherlands, where ambitious climate targets, the development of digital infrastructure, and the adoption of sustainable construction policies are evident (J. van Oorschot et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, even with the technological investment and regulatory pressure, most organizations in the housing sector are unable to transform digital initiatives into sustainable performance due to less adoption of artificial intelligence-driven tools and techniques in the construction setting (Anumba \u0026amp; Madubuike, 2022). This paradox raises a core issue: how can artificial intelligence (AI) and organizational capabilities be utilized in the housing sector to ensure sustainable housing?\u003c/p\u003e \u003cp\u003eRecent research focuses on AI as a revolutionary technology that can improve predictive analytics, energy efficiency, construction lifecycle management, and data-driven decision making. However, a plethora of literature views AI as a technological artifact or a result of adoption rather than an internal organizational capability. Therefore, a dynamic capabilities perspective (Teece, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) indicates that to respond to technological turbulence, firms need to develop higher-order capabilities to sense opportunities in the external environment, seize digital innovations, and reconfigure internal resources. So, AI-enabled dynamic capability (AIEDC) is an enhanced organizational capability that leverages AI analytics, machine learning systems, and real-time data infrastructures in strategic renewal and sustainability-oriented innovation processes. Although the notion of dynamic capabilities has been extensively discussed in the literature on digital transformation (K. S. Warner \u0026amp; M. W\u0026auml;ger, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), its application in the sustainable housing domain is underexplored. Specifically, the prior literature has failed to detail the processes by which AI-driven dynamic capabilities are translated into sustainability performance in multifaceted socio-technical systems such as the housing sector.\u003c/p\u003e \u003cp\u003eTo fill this theoretical gap, this research posits that sustainable performance depends on both micro and meso-level factors (Winston, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), through which AIEDC may achieve sustainable housing as it encompasses various stakeholders (Opoku, Perera, \u0026amp; Osei-Kyei, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the micro level, drawing on social cognitive theory, technological self-efficacy acts as a mediating factor, establishing an underlying mechanism/pathway linking AIEDC and sustainable housing. Technological self-efficacy is an individual's confidence in their ability to use advanced digital tools and systems powered by AI (Sharma, Singh, \u0026amp; Singh, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Self-efficacy is a key factor in the use of technology at the workplace for innovation and the successful implementation of digital transformation (Pan, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI's dynamic capabilities can develop learning-related settings, cross-functional experimentation, and knowledge-sharing practices that increase employees' technological confidence (K. S. R. Warner \u0026amp; M. W\u0026auml;ger, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In turn, employees having greater technological self-efficacy find it easier to use AI-based energy management systems, predictive analytics, and sustainability dashboards more effectively, thus enhancing sustainable housing performance (Permana, Fitriani, \u0026amp; Aulia, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Technological self-efficacy is therefore a key foundation for the organization's AI capability to support sustainability outcomes.\u003c/p\u003e \u003cp\u003eAlthough the individual cognition factor (i.e., technological self-efficacy) is pivotal to achieving sustainability in housing, technological adoption and implementation are also crucial in the housing sector (J. A. Van Oorschot, Halman, \u0026amp; Hofman, 2020). Likewise, this study proposes digital twin adoption as a second middle-level mediating factor, providing a technology-driven mechanism to achieve sustainable housing. Virtual simulations of a real building that incorporate real-time feedback via digital twins can be used for design optimization, predictive maintenance, and lifecycle energy efficiency (Bibri \u0026amp; Huang, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Fritz, 2023). Digital twin improves resource optimization, emission reduction, and asset sustainability in the housing sector (Wang, Chen, \u0026amp; Li, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nonetheless, digital twins need to be adopted through data integration, AI analytics, and the reconfiguration of organizational processes (Lu, Xie, Parlikad, \u0026amp; Schooling, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which are based on organizational AIEDC. So, adopting digital twins is a way to use technology to help AIEDC achieve measurable, sustainable housing performance. This paper describes a multi-level mediation rationale that balances between personal cognition and technological application by combining technological self-efficacy and digital twin adoption.\u003c/p\u003e \u003cp\u003eHowever, the mediating mechanisms for translating AIEDC into sustainable housing may not be fully operationalized in the absence of a conducive culture. This research postulates that a data-driven culture serves as a boundary condition that shapes the relationship among AIEDC, technological self-efficacy, and digital twin adoption. A data-driven culture focuses on evidence-based decision-making, transparency, analytics orientation, and experimentation (Bose \u0026amp; Luo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gupta \u0026amp; George, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mikalef, Krogstie, Pappas, \u0026amp; Pavlou, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Data-driven norms and values support AI-driven strategies, activities, and capabilities, helping employees feel confident in adopting digital technologies and systems and speeding up work progress (Mikalef et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This moderation logic adds context to the dynamic capability framework, recognizing that the organizational culture contributes to the success of AI-based sustainability transformation. Thus, based on the above discussion and highlighting the theoretical gaps, this research will answer the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes AIEDC have any effect on sustainable housing?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes technological self-efficacy mediate the relationship between AIEDC and sustainable housing?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes digital twin adoption mediate the relationship between AIEDC and sustainable housing?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes data-driven culture moderate the relationship between AIEDC and technological self-efficacy?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes data-driven culture moderate the relationship between AIEDC and digital twin adoption?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis research extends the existing literature in the fields of sustainability, artificial intelligence, and the housing sector. Firstly, this research contributes to the existing literature on the dynamic capability theory by theorizing AIEDC as a critical factor of sustainable housing, particularly in the Dutch housing sector. Secondly, develops multi-level approaches for AIEDC and sustainable housing through technological self-efficacy and the use of digital twins as intermediaries. Third, it presents a data-driven culture as a key condition for strengthening capability-based factors to achieve sustainable housing.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003e\u003cstrong\u003e2.1 Theoretical Underpinnings and Hypotheses Development \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research is grounded in the dynamic capability theory, social cognitive theory, and socio-technical systems theory to describe AI driven organizational capabilities transforming into sustainable housing. The dynamic capability theory underscores crucial role of sustainable competitive advantage to generate higher-order capabilities to sense opportunities, acquire innovations, and reorganize resources in dynamic and complex environment (Helfat \u0026amp; Peteraf, 2009; Teece, 2007; K. S. Warner \u0026amp; M. W\u0026auml;ger, 2019). These capabilities are dependent on analytics infrastructures, AI integration, and data orchestration in the digital transformation setting (Mikalef \u0026amp; Gupta, 2021). Nevertheless, dynamic capabilities are not sufficient to reveal how the organizational capabilities are implemented at the individual level (Akter, Bandara, \u0026amp; Hani, 2020). Therefore, social cognitive theory deals with the individual motivations, capabilities and self-confidence (Schunk \u0026amp; DiBenedetto, 2020). The social cognitive theory asserts that self-efficacy influence the readiness of people to become familiar with and use complex technologies (Bandura, 1997; Schunk \u0026amp; DiBenedetto, 2020). This self-belief not only helps in performing the daily activities but also ensures the use of modern technology within organization (Haque \u0026amp; Akter, 2023). This self-efficacy enables employees to believe that they have suffice skills and capabilities that can easily understand complex data structures and use them better to perform any task (L. Chen \u0026amp; Zhang, 2023). This technological self-efficacy plays a key role in improving strategies and processes in the organization, including AI-driven analytics, algorithms and data-driven structures (Nguyen, Newby, \u0026amp; Macaulay, 2022). Furthermore, at the system level, the socio-technical systems theory posits that changes in technology require the correspondence between technological artifacts, human actors, and institutional arrangements (Vial, 2021). Digital twins are the examples of such socio-technical integration, integrating AI analytics, IoT infrastructures and organizational routines (Boje et al., 2020; Opoku et al., 2021). Combining the above insights, the current research contributes to a multi-level model connecting AIEDC to technological self-efficacy, digital twin adoption, and sustainable housing in the context housing sector of developed world like Netherlands.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 AI-integrated Dynamic Capability and Sustainability Housing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSustainable housing refers to the energy efficiency, less carbon emissions, lifecycle optimization, and the use of the circular in housing construction (Huang, Krigsvoll, Johansen, Liu, \u0026amp; Zhang, 2018). To ensure these sustainable house characteristics, organizations keenly indulge in environmental sensing, adaptive decision-making, and strategic reconfiguration of core processes through cutting edge technologies (Almeida, 2023; Helfat \u0026amp; Peteraf, 2009; Zhang, Xu, Li, \u0026amp; Teece, 2023). The sensing, seizing, and reconfiguring functions are reinforced with AI-based dynamic capability, which improves the predictive analytics and real-time monitoring of building performance (Dwivedi \u0026amp; et al., 2021), data-driven investment and decisions (Mikalef, Krogstie, Pappas, \u0026amp; Pavlou, 2020; K. S. Warner \u0026amp; M. W\u0026auml;ger, 2019), and digital workflow integration and process automation (Vial, 2021). Notably, existing studies indicate that sustainability effect of digital technologies can only be created through better its integration into supplementary organizational capabilities (Dubey, Gunasekaran, Childe, Papadopoulos, \u0026amp; Luo, 2020). Organizations with greater AIEDC have a better chance of coordinating AI tools with improvements in sustainable housing (Zhang, Xu, \u0026amp; Li, 2023) through digital infrastructure and ambitious climate policies. Therefore, AIEDC is not only a technological contribution but a strategic driver of sustainable housing(Bibri, Omar, Kenawy, \u0026amp; Huang, 2025). Therefore following hypothesis is developed:\u003c/p\u003e\n\u003cp\u003eH 1: AI-enables dynamic capabilities are positively related to sustainable housing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Technological Self-Efficacy as a Mediator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conversion of AIEDC into sustainability outcomes relies on the engagement of employees with current and cutting edge technologies despite its organization wide capabilities. The cognitive theory underscores that self-efficacy is the key to give confidence to people to start, continue and complete technology-related tasks successfully (Bandura, 1997; Schunk \u0026amp; DiBenedetto, 2020). The prior literature provides ample evidence that technological self-efficacy is a significant predictor of technology adoption, digital innovation behavior, and performance outcomes (McDonald \u0026amp; Siegall, 1992; Pan, 2020; Sharma, Singh, \u0026amp; Singh, 2021). Based on the micro-foundational view of dynamic capability, organizational capabilities are applied using individual skills, cognition, behavioral involvement (Felin, Foss, Heimeriks, \u0026amp; Madsen, 2012; Helfat \u0026amp; Peteraf, 2009). AIEDC also cultivates digital education, cross-functional education, and experimentation habits that raise the technological confidence of employees (Mikalef, Boura, Lekakos, \u0026amp; Krogstie, 2019; K. S. Warner \u0026amp; M. W\u0026auml;ger, 2019). Likewise, employees with greater technological self-efficacy are more apt to take a correct interpretation of predictive analytics, take advantage of AI-based energy systems, and involving digital dashboards into sustainability planning during all of housing stages (Adediran et al., 2025; Shehab, Khaidzir, Shehab, \u0026amp; Kılı\u0026ccedil;, 2025). As a result, the organizational AI capability is transformed into sustainable housing outcomes via the cognitive medium of technological self-efficacy. Thus, following hypothesis is generated: \u003c/p\u003e\n\u003cp\u003eH2: Technological self-efficacy mediates the relationship between AI-enabled dynamic capability and sustainable housing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Digital Twin Adoption as a Mediator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSustainable housing requires multifaceted interaction of stakeholders and organizational level due to its complexity and wicked nature (Adediran et al., 2025). Therefore, considering solely cognitive elements to achieve satiability is not sufficient rather technological integration is also a critical factor across all stages (Gan, Yan, \u0026amp; Wen, 2023). The virtualization of real buildings is facilitated through digital twins, which are useful in optimizing the lifecycle, predictive maintenance, and simulating energy (Bibri \u0026amp; Huang, 2025; Opoku, Perera, \u0026amp; Osei-Kyei, 2021). Such technologies work efficiently in the context of the socio-technical systems theory only in the case of their correspondence to organizational routines and human capabilities (Vergragt \u0026amp; Brown, 2012; Vial, 2021). Digital twin adoption needs sophisticated data coordination, interoperability requirements, and AI analytics that entrench by organizational dynamic capabilities (Teece, 2007; Helfat et al., 2007). Strong AIEDC in organizations allows to identify the opportunities of digital twin, make investment decisions, and reorganize the processes to implement the systems housing construction process(Mikalef et al., 2019; K. S. Warner \u0026amp; M. W\u0026auml;ger, 2019). As soon as they are adopted, digital twin improve transparency in sustainable construction by decreases energy waste, and sustain circular housing strategy (Permana, Fitriani, \u0026amp; Aulia, 2023; Pomponi \u0026amp; Moncaster, 2017). Thus, the digital twin acceptance is a meso-level process converting AIEDC to quantifiable sustainable housing performance. Thereby, below is the relevant hypothesis:\u003c/p\u003e\n\u003cp\u003eH3: Digital twin adoption mediates the relationship between AI-enables dynamic capability and sustainable housing \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Data-Driven Culture as a Moderator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOrganizational culture is one of the widely researched constructs in the management literature (Akpa, Asikhia, \u0026amp; Nneji, 2021). The previous literature unfolds various types of organizational culture such as innovative culture (Bendak, Shikhli, \u0026amp; Abdel-Razek, 2020), supportive culture (Stephan \u0026amp; Uhlaner, 2010), strong and weak culture (Thokozani \u0026amp; Maseko, 2017) and most currently data driven culture (Bose \u0026amp; Luo, 2022). Data-driven culture promotes analytics, institutionalizing norms based on evidence, transparency, experimentation and data based approaches (Bose \u0026amp; Luo, 2022; Gupta \u0026amp; George, 2016). Data driven culture strengthens and aligned the organizational capabilities developed based on the cutting edge technologies like artificial intelligence(Elgendy \u0026amp; Elragal, 2020). The implementation of AI initiatives in the organization is justified and becomes a part of organizational routine, which in turn enhances the level of technological self-efficacy of employees with a strong data-driven culture (Maruping \u0026amp; Magni, 2015). On the other hand, poor data-based cultures can lead to resistance restricting trust and connectivity with AI technology (Dwivedi \u0026amp; et al., 2021). Similarly, the use of digital twin demands cross-departmental data combination and long-term commitment to data driven norms, analytics and agorithms(Hosamo \u0026amp; et al., 2022). The integration of such kind is more probable in the organizations in which the transparency of data and evidence-based reasoning are highly entrenched (Mikalef \u0026amp; Gupta, 2021; Warner \u0026amp; Wager, 2019). In this way, the translation of AIEDC into both mediating pathways is increased by the presence of data-driven culture.\u003c/p\u003e\n\u003cp\u003eH4: Data-driven culture moderates between AI-enabled dynamic capability and technological self-efficacy, where the relationship is strong in the strong data-driven culture\u003c/p\u003e\n\u003cp\u003eH5: Data-driven culture moderates between AI-enabled dynamic capability and digital twin adoption, where the relationship is strong in the strong data-driven culture\u003c/p\u003e\n\u003cp\u003eBased on the above discussion comprised of underpinnings theories and hypotheses development, following is the conceptual framework of the current study\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eTo achieve the research objectives, this study employed quantitative, time-lag (three-wave) research design that empirically examines the moderated-mediation framework of AI-enabled dynamic capability (AIEDC) and sustainable housing, technological self-efficacy, digital twin adoption and data-driven culture. The Dutch housing industry provides good research setting as it offers a theoretically and practically applicable environment due advanced digital infrastructure, strong sustainability policies and the strategic focus on smart and circular residential development (van Oorschot et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In line with the plethora of prior studies, the unit of analysis is the construction projects and the key informants are senior managers as they are key personnel to execute digital transformation, sustainability strategy, innovation management, and smart housing strategies (Moleka, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; USMAN, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Winston, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A three-wave time-lagged (T1, T2, T3) approach was employed with four-week intervals between each wave to gather data to enhance causal inference and reduce common method variance (Rasheed, Hameed, Kaur, \u0026amp; Dhir, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is commonly recommended to do temporal separation between predictor, mediators, moderator variables, and outcome variables to minimize the common method bias (Podsakoff, MacKenzie, Lee, \u0026amp; Podsakoff, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). At Time 1, data were gathered on AIEDC and data driven culture. After 30 days at time 2, data were gathered on technological self-efficacy and digital twin adoption. Finally after 30 days at time three, data on sustainable housing were gathered. Identification codes were generated which identified respondents across the waves to ensure the anonymity. Reminder emails were also administered in the course of every wave to increase the response rate and decrease the issue of non-response bias in line with the previous studies (Akram, Saeed, Bresciani, Rehman, \u0026amp; Ferraris, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rasheed et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The final analysis was carried out by retaining only the complete responses in all the three waves constituted a sample size of 246.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Measurements\u003c/h2\u003e \u003cp\u003eAll latent constructs were utilized from the prior literature. Sustainable housing is second order constructs consisting two dimension namely physical components and environmental components derived from (Nasrabadi \u0026amp; Hataminejad, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) where physical component dimension having 11 items and environment components render 10 items. AI-enabled dynamic capability is operationalized as a first order construct consisting nine items derived from (Abou-Foul, Ruiz-Alba, \u0026amp; L\u0026oacute;pez-Tenorio, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition technological self-efficacy was measured through five items utilized from the work of (Saville \u0026amp; Foster, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, digital twin adoption was measure with nine 9 items scale used from (Singh, Singh, Daultani, \u0026amp; Chouhan, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally data driven culture was measured with 4 items utilized from (Chatterjee, Chaudhuri, \u0026amp; Vrontis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Findings","content":"\u003cp\u003ePartial least square structural equation modeling (PLS-SEM) technique employed for data analysis and hypothesis testing. A two-stage model has been employed for data analysis namely measurement model and structural model (Hair, Risher, Sarstedt, \u0026amp; Ringle, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates the results of the measurement model, where reliability, validity and multicollinearity have been examined.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReliability and Validity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLatent Constructs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFactor loadings\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Enabled Dynamic Capability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAIEDC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAIEDC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAIEDC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAIEDC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Driven Culture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDDC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDDC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDDC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDDC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Twin Adoption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e\n \u003cp\u003e\u003cstrong\u003eSustainable Housing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnical Self-Efficacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTSE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTSE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTSE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTSE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that all factor loadings are greater than 0.70, therefore inter-item consistency is established (Hair Jr, Matthews, Matthews, \u0026amp; Sarstedt, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All factor loadings less than 0.70 were deleted from the hypothesized model recommended by the prior literature in PLS-SEM (Hair Jr et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, values of composite liability (CR) of all latent constructs are greater than 0.70, thereby internal consistency is ensured (Hair et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Average variance extracted (AVE) was employed to analyse the convergent validity.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMulticollinearity (VIF)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIEDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTSE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.193\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe results indicate in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e that the AVE values of all latent constructs are greater than 0.50 which proves that convergent validity exists in the hypothesized model (Memon et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, the variance inflation factor (VIF) was employed to check multicollinearity.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHTMT Ratio\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eAIEDC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAIEDC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDDC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDTA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eTSE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals that the values of all latent constructs are less than 5 which established that no issue of multicollinearity exists in the hypothesized model (Daoud, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hair et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the HTMT ratio and it depicts that that all values are less than 0.90 which ensures that all latent constructs are distinct from each other on empirical standards (Hair Jr et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. 2 reveal the results of measurement model. It depicts the results of hypothesis testing through bootstrapping technique on 5000 sub sample (Hair Jr et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Results show that AIEDC has a significant and positive direct effect on SH (\u0026beta;\u0026thinsp;=\u0026thinsp;0.242, t\u0026thinsp;=\u0026thinsp;3.480, p\u0026thinsp;=\u0026thinsp;0.001), therefore supported. Moreover, H2 reveals the significant mediating role of TSE between AIEDC and SH (\u0026beta;\u0026thinsp;=\u0026thinsp;0.040, t\u0026thinsp;=\u0026thinsp;1.995, p\u0026thinsp;=\u0026thinsp;0.046), it is also supported. Similarly, H3 confirms that DTA is a significant mediator (\u0026beta;\u0026thinsp;=\u0026thinsp;0.086, t\u0026thinsp;=\u0026thinsp;2.346, p\u0026thinsp;=\u0026thinsp;0.019) between AIEDC and SH, thus supported.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStructural Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypo No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHypothesized Path\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eT statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eP values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eDecision\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIEDC -\u0026gt; SH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e3.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIEDC -\u0026gt; TSE -\u0026gt; SH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e1.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIEDC -\u0026gt; DTA -\u0026gt; SH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDC x AIEDC -\u0026gt; TSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDC x AIEDC -\u0026gt; DTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e2.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupported\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Figs. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and 5 depict the results of moderation in the hypothesized model. H4 reveals the significant positive moderating effect of DDC between AIEDC and TSE (\u0026beta;\u0026thinsp;=\u0026thinsp;0.209, t\u0026thinsp;=\u0026thinsp;2.569, p\u0026thinsp;=\u0026thinsp;0.010), hence supported.\u003c/p\u003e\n\u003cp\u003eFinally, H5 confirms that DDC is also a significant and positive moderator between AIEDC and DTA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.195, t\u0026thinsp;=\u0026thinsp;2.289, p\u0026thinsp;=\u0026thinsp;0.022), thus it is also supported. The moderation results implicate that in the presence of data driven culture the relation between AIEDC, technological self-efficacy and digital twin adoption is stronger.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Co-efficient of Determination (R\u003csup\u003e2\u003c/sup\u003e)\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of co-efficient of determination (R\u003csup\u003e2\u003c/sup\u003e). It indicates that Sustainable Housing (SH) reveals strong explanatory power R\u0026sup2;=0.660 in the hypothesized model. Digital Twin Adoption shows R\u0026sup2;= 0.448 indicating moderate explanatory power. Finally, technological Self-Efficacy (TSE) demonstrates an R\u0026sup2;=0.372 suggesting a moderate level of explanatory power.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eR-square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eR-square adjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eExplanatory power\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eStrong\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Effect Size (f\u003csup\u003e2\u003c/sup\u003e)\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the results of effect size (f\u003csup\u003e2\u003c/sup\u003e). It show that AIEDC has a small effect on DTA (0.041) and TSE (0.045), and a medium effect on SH (0.144), suggesting its strongest impact is directly on sustainable housing. DDC demonstrates a large effect on DTA (0.502) and a medium-to-large effect on TSE (0.328), highlighting its substantial moderating influence. DTA exerts a large effect on SH (0.317), indicating its strong mediating contribution, whereas TSE has a small effect on SH (0.056).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIEDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eDTA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTSE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe aim of the research was to empirically test the impact of AI-enabling dynamic capability (AIEDC) on sustainable housing the Dutch housing industry via technological self-efficacy and digital twin adoption depending on interaction effect of data-driven culture. The results support and expand the theoretical foundations of dynamic capability theory, social cognitive theory and socio-technical system theory, will ingrained in the current research of artificial intelligence and sustainability (Abou-Foul et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Al-Husain et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mikalef et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wamba et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe empirical findings of H1 show that AIEDC has a significant and positive impact on sustainable housing(b\u0026thinsp;=\u0026thinsp;0.242, p\u0026thinsp;=\u0026thinsp;0.001), which in accord with the dynamic capability theory. It postulates that companies can perform better when they are capable of sensing opportunities, capturing them by making an investment and reconfiguring resources to keep them aligned with the ecological change (K. S. R. Warner \u0026amp; M. W\u0026auml;ger, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang, Xu, Li, et al., 2023). Within the setting of Dutch housing contingent on the energy transition requirements, circular construction strategies, and the maturity of digital infrastructure, AI-based sensing (predictive analytics), seizing (AI-based decision systems), and reconfiguring (process automation and smart retrofitting) are collectively aimed at improving sustainability (K. S. Warner \u0026amp; M. W\u0026auml;ger, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Empirical studies in the prior literature underscores that AIEDC are beneficial to environmental and operational performance, as they allow data-intensive decision-making and responsive reconfigurations (Bag, Gupta, Kumar, \u0026amp; Sivarajah, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dubey et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mikalef et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Dynamic capability theory highlights that AIEDC is high-level, inimitable component that coordinates data, algorithms, and managerial routines to deliver sustainability-enhancing results (Pournader, Kaur, Akter, \u0026amp; Dubey, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Susanti \u0026amp; et al., 2022). The direct effect remains even in the context of mediators implies that AIEDC is not just an enabler but a strategic meta-capability that has a positive contribution to sustainable housing performance through systemic optimization and strategic foresight (Xu, Li, \u0026amp; Tan, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second hypothesis proves the significant mediating role of technological self-efficacy between AIEDC and sustainable housing (b\u0026thinsp;=\u0026thinsp;0.040, p\u0026thinsp;=\u0026thinsp;0.046). As stated by Bandura (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), self-efficacy determines technology usage and intensity to indulge in to overcome the complexity employee confronted with. Housing sector requires the use of AI driven technologies implies predictive and analytical knowledge that would demand human analysis and implementation (Shehab et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, dynamic capabilities are not sufficient unless organizational actors have a belief in the utilization of these technologies. The recent research findings within the digital transformation contexts prove that technological self-efficacy has a profound positive impact on AI utilization, innovation behavior, and sustainability-focused performance (Akter et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dwivedi \u0026amp; et al., 2021; Sharma et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Theoretically, the result adds value to the dynamic capability theory by making individual cognition and action as the micro-foundations of capabilities by which capabilities generate performance impacts (Al-Husain et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Felin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pavlou \u0026amp; El Sawy, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It also shows that the way that AIEDC leads to sustainable housing is partly behavioral and psychological and thus it connects the macro level capability theory with the individual level systems of beliefs.\u003c/p\u003e \u003cp\u003eTherefore, socio-technical systems theory can be used to explain the significant mediation effect digital twin adoption between AIEDC and sustainable housing (b\u0026thinsp;=\u0026thinsp;0.086, p\u0026thinsp;=\u0026thinsp;0.019). Digital twin adoption is structural implementation of reconfiguring capabilities, that is, the integration of AI into real-time monitoring, lifecycle simulation, and predictive optimization systems (Lu, Xie, Parlikad, \u0026amp; Schooling, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang, Chen, \u0026amp; Li, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to dynamic capability theory, digital twin adoption reflects the dimension of seizing and reconfiguring, in which organizations convert sensing insights into structural investor, which changes operational structures (Pavlou \u0026amp; El Sawy, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Teece, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Recent sustainability and construction studies reveal that the digital twin adoption significantly effect to optimize the energy efficiency of the building, predictive maintenance precision, and the reduction of carbon in the lifecycle (Opoku et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang, Xu, \u0026amp; Li, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This implies that meso-level technological infrastructure is a pivotal transmission process between the higher-order capability and the sustainability results. The technological self-efficacy works at the cognitive level, whereas digital twin adoption works at the structural level that supports the multi-level character of capability enactment.\u003c/p\u003e \u003cp\u003eMoreover, moderating effect of the data-driven culture (DDC) also reinforces the theoretical soundness of the model. The strong interaction effects on technological self-efficacy (b\u0026thinsp;=\u0026thinsp;0.209, p\u0026thinsp;=\u0026thinsp;0.010) on digital twin adoption (b\u0026thinsp;=\u0026thinsp;0.195, p\u0026thinsp;=\u0026thinsp;0.022) identify that cutting edge technologies, data driven approaches and algorithms based capabilities are critical to achieve sustainable housing via cognitive and technological process. The theory of dynamic capability highlights that capabilities are path-conditioned and integrated into the organizational processes and norms (Teece, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Evidence-based decision-making, analytics-use transparency, and being open to experimentation constitute the characteristics of a data-driven culture that facilitates the flourishing of AI-enabled routines (Mikalef et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gupta and George, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Higher DDC provides the legitimization of AI outputs, support the interest of employees in the use of analytics, and reduce the level of resistance to technological investments (Xu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the recent studies of digital strategy and analytics capability, it is highlighted that the returns on AI investments are greatly increased by the cultural orientation towards the data (H. Chen, Chiang, \u0026amp; Storey, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kraus et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wamba et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. Theoretical Implications","content":"\u003cp\u003eThe current research provides various theoretical contributions to dynamic capability theory, digital transformation, and sustainability research. First contribution this study is to extend the dynamic capability theory by integrating AI-enabled dynamic capability (AIEDC). The AIEDC is a higher-order digital capability that directly and indirectly increases sustainable housing performance. Although previous studies have already confirmed that dynamic capabilities are associated with firm performance (Teece, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Teece, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), there is still little empirical evidence that establish a direct relationship between AIEDC and sustainability performance. It thus reacts to the recent academic recommendations of considering the capabilities of digital transformation alongside the formation of environmental values (Mikalef et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wamba et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, the paper adds to the body of micro-foundations literature of cognitive theory by using technological self-efficacy as a mediating variable. Technological self-efficacy embraces as a cognitive driver at the individual level that contributes to enhance performance (Felin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This study also shows that AIEDC do manifest as sustainability outcomes partly due to psychological capability of technology usage. This result also corroborates the that underscores the critical role of self-image, concept and capability to enhance individual performance that lead to greater organizational achievements (Bandura, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This integration at multiple levels adds depth to the theoretical discourse as it links the macro-level strategic competencies with the micro-level behavioral processes.\u003c/p\u003e \u003cp\u003eThird, the research contributes to the growing body of knowledge on digital twin adoption and sustainability by theorizing digital twin adoption as a meso-level structural mediator between AIEDC and sustainable performance. Although the usage of digital twin in construction and infrastructure management has been emphasized as an operationally beneficial concept through recent studies (Lu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Opoku et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there has been little empirical literature that has put digital twin adoption into a capability-based context. This study offers an explanation of the way digital infrastructure operationalizes AI capabilities in measurable gains of sustainability by demonstrating that digital twin adoption as a key mediator.\u003c/p\u003e \u003cp\u003eLastly, this study adds into socio-technical system theory by empirically examining data driven culture between cognitive and technology driven factors as boundary condition which is elusive in the prior literature (Brown \u0026amp; Vergragt, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vergragt \u0026amp; Brown, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It validates that the value-generating potential of AI capabilities is enhanced by the presence of intangible data driven cultural resources. Results of the moderation process prove that the efficiency of AIEDC in promoting technological self-efficacy and the adoption of digital twin is conditional upon an organizational culture that justifies the use of analytics in decision-making. The result is a continuation of the previous studies on analytics culture (Gupta and George, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mikalef et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Together, these theoretical input contributions offer a multi-level account of AI-enabled sustainability transformation, and they combine strategic, structural, and cognitive processes in a consistent framework.\u003c/p\u003e"},{"header":"7. Practical Implications","content":"\u003cp\u003eThis study also provides practical guidelines to policymakers and strategists of housing associations and real estate developers who have an interest in enhancing sustainable housing. First, policy makers should understand that AIEDC they should investment in AI-controlled sensing, predictive analytics and reconfiguration capabilities to make it a strategic prerequisite to meet sustainability goals. The housing organizations should focus on the development of AI based decision system supporting the lifecycle optimization, energy prediction and predictive maintenance. Second, technological self-efficacy as a mediating variable highlights the significance of human capital development. The managers, in addition to investing in AI, ought to integrate formal training, digital literacy, and cross-functional learning systems to boost the employee confidence in using AI tools. Due to the lack of such cognitive preparation, the opportunities of AI systems may be unexploited.\u003c/p\u003e \u003cp\u003eThird, the more powerful mediation with the adoption of digital twins emphasizes the importance of introducing AI into the technological framework. Practitioners/policy makers should stop pilot AI projects and proceed to integrated digital twin systems linking IoT devices, simulation applications, and real-time monitoring to housing projects. The effectiveness of such integration can be effective in terms of resource efficiency and sustainability performance. Fourth, the significant moderating effect of data-driven culture accentuates that the technological investments are not sufficient, leaders should also establish a culture of evidence-based decision-making, foster experimentation, and mitigate the opposition to the analytics-based change. This can include the redesign of performance evaluation, motivating data use, and transparency between departments. Therefore, sustainable housing transformation should be achieved through a concomitant capability development, technological infrastructure and cultural orientation.\u003c/p\u003e"},{"header":"8. Limitations and Future Research Directions","content":"\u003cp\u003eDespite its several theoretical and practical contribution to the literature and industry, this paper carries a few limitations. First, despite the three-wave time-lagged design helps to overcome the common method bias and enhance the internal validity, the research is still observational and not establish causal inferences. Longitudinal panel designs or quasi-experimental designs could be used in future research to elicit the dynamic evolution of capability over time. Second, the research is conducted in the Dutch housing industry, which is the highly developed digital infrastructure and well-developed sustainability laws. Although this enriches the context, it can curtail the generalizability to emerging economies or lower digitally mature housing markets. Comparative cross-country research would be able to examine how much research model is robust in terms of the institutional environment.\u003c/p\u003e \u003cp\u003eThird, the research is based on perceptual assessment of sustainable housing. Despite its popularity in the field of management studies, perceptual indicators may not provide accurate way to measure objective metrics on sustainable housing. The triangulation method may be employed by including the archival information like energy consumption reports, lifecycle carbon assessment, or the green certification rates in future studies. Fourth, although the model has key mediators and a moderator, other boundary conditions can be used to further understand the applicability of the model such as the nature of leadership, digital governance structure, or regulatory compliance. Lastly, the future studies might examine other micro-foundational processes other than technological self-efficacy, including digital mindfulness, absorptive capacity, or AI trust perceptions. A further exploration of the interaction between these cognitive and relational factors and organizational capabilities can provide a deeper understanding of AI-based transformation of sustainability. Further, the qualitative or mixed-method research might reveal the process-level dynamics and contextual subtleties which might be used to supplement the quantitative results described here.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eClinical Trial Number\u003c/h2\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003ch2\u003eConsent to Participate:\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all participants prior to the commencement of data collection. Participants were clearly informed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw at any time without any negative consequences. They were assured that their responses would remain confidential and anonymous, and that all collected data would be used exclusively for academic and research purposes.\u003c/p\u003e\n\u003ch2\u003eConsent to Publish:\u003c/h2\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003ch2\u003eEthics approval:\u003c/h2\u003e\n\u003cp\u003eThe research protocol for this study was reviewed and approved by the Institutional Research Ethics Committee of Wittenberg University of Applied Sciences, Netherlands, in accordance with the ethical guidelines and regulations of the committee and the principles outlined in the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All procedures involving human participants were performed following these approved guidelines.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study has not received any external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eM.H. conceptualized the study, developed the theoretical framework, and led the manuscript writing. U.A. contributed to research design, instrument development, and supervised data collection. N.T.L. contributed to the theoretical grounding, particularly the integration of dynamic capability, social cognitive, and socio-technical system perspectives. A.R. supported methodology development, questionnaire design, and data validation procedures. A.M.G. conducted the data analysis, interpreted the statistical results, and prepared tables and figures.All authors contributed to refining the manuscript, reviewed and approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are available from the corresponding author upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAbou-Foul, M., Ruiz-Alba, J. L., \u0026amp; L\u0026oacute;pez-Tenorio, P. J. (2023). The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective. \u003cem\u003eJournal of Business Research, 157\u003c/em\u003e, 113609.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdediran, A. O., Ajibade, S.-S. M., Jasser, M. B., Bashir, F. M., Dodo, Y. A., Yassin, Y. N. H. M., \u0026amp; Issa, B. 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AI and digital twin adoption for smart sustainable buildings\u003c/p\u003e\n\u003cp\u003eExplicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. \u003cem\u003eSustainable Cities and Society, 92\u003c/em\u003e(13), 104503-101350. doi: 10.1016/j.scs.2023.104503\u003c/p\u003e\n\u003cp\u003e10.1002/smj.640\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sustainability, dynamic capability, artificial intelligence, sustainable housing, Dutch","lastPublishedDoi":"10.21203/rs.3.rs-8969460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8969460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrawing on dynamic capability theory, social cognitive theory, and socio-technical system theory, this research aims to examine the mediating roles of technological self-efficacy and digital twin adoption in the relationship between AI-enabled dynamic capability and sustainable housing in the Dutch housing sector. Furthermore, this study examines the moderating effect of data-driven culture on the relationships among AI-enabled dynamic capability, technological self-efficacy, and digital twin adoption. The structured, closed-ended questionnaire was distributed to the senior manager of housing projects using a time-lagged, three-wave (T1, T2, T3) research approach, in which data were collected on AI-enabled dynamic capability and data-driven culture at T1. After four weeks, data work was collected on technological self-efficacy and digital twin adoption. Similarly, at T3, after 4 weeks, data collection on sustainable housing was conducted. The final number of complete questionnaires was 246, which was utilised for the final analysis. Partial least squares structural equation modeling (PLS-SEM) was employed for data analysis and hypothesis testing. The results reveal a significant and positive effect of AI-enabled dynamic capability on sustainable housing. Furthermore, the mediating role of technological self-efficacy and digital twin adoption between AI-enabled dynamic capability and sustainable housing is also established. In addition, a data-driven culture moderates the relationships among AI-enabled dynamic capability, technological self-efficacy, and digital twins in the Dutch housing sector.\u003c/p\u003e","manuscriptTitle":"Leveraging Sustainable Housing through Artificial Intelligence Enabled Dynamic Capability, Technological Self Efficacy, Digital Twin Adoption and Data Driven Culture in Netherlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 17:07:04","doi":"10.21203/rs.3.rs-8969460/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T13:27:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T21:20:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T10:40:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246641485574444097076895404709015810655","date":"2026-04-02T06:38:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118626762877256987160364844744566532842","date":"2026-03-31T14:42:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T16:52:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-21T09:13:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T07:01:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-18T13:39:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ad8c585-05f6-4a1a-9668-5d652a5840b6","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T20:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 17:07:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8969460","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8969460","identity":"rs-8969460","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}