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Article

Developing a Novel Architectural Technology Adoption Model Incorporating Organizational Factors and Client Satisfaction

by
Hesham Algassim
1,*,
Samad M. E. Sepasgozar
1,
Michael J. Ostwald
1 and
Steven Davis
2
1
Faculty of Arts, Design and Architecture, University of New South Wales, Sydney 2052, Australia
2
Faculty of Engineering, University of New South Wales, Sydney 2052, Australia
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(10), 1668; https://doi.org/10.3390/buildings15101668
Submission received: 24 September 2024 / Revised: 30 January 2025 / Accepted: 11 February 2025 / Published: 15 May 2025
(This article belongs to the Section Construction Management, and Computers & Digitization)
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Abstract

:
Despite some high-profile exceptions, the architecture service industry has typically adopted new digital technologies slowly. Previous research has examined the influence of user-friendliness and ease of technology use to explain the slow adoption rate, but contextual factors associated with the operations of architectural organizations—such as result demonstrability, training needs, cost factors, environmental expectations, project factors, and client satisfaction—have been largely overlooked. This paper presents a novel architectural technology adoption model (ATAM) encompassing multiple architecture-service-specific factors and their relationships. The key hypotheses embodied in ATAM are that the digital technology adoption process is shaped by industry-specific factors that directly affect perceived usefulness and perceived ease of use by architects and, subsequently, their intention to employ the technology. Furthermore, the impacts of user satisfaction, user behavior, and client satisfaction on technology acceptance are examined. This paper describes the development of an ATAM, drawing on a set of original data collected from 452 participants from a case study country (Saudi Arabia). The ATAM is then validated through extensive hypothesis testing performed using maximum likelihood structural equation modeling in AMOS. The outcomes show that result demonstrability, training needs, cost factors, environmental expectations, project factors, and client satisfaction are significant factors affecting technology adoption. The development of the ATAM addresses the lack of empirical knowledge about technology adoption in architecture. The ATAM offers a novel and rigorous approach to helping organizations understand and overcome the factors that affect technology adoption in the architecture service sector. Fundamentally, the ATAM supports a new understanding of the factors that are critical for organizations to increase user and client satisfaction with technology adoption. The ATAM contributes to the literature on architectural innovation, but it can be modified and replicated in various sectors associated with architectural services where clients’ satisfaction is critical.

1. Introduction

The global architecture industry is expected to grow at a compounded rate by 2030. Such worldwide growth in the sector is reliant on efficiencies achieved using new digital technologies. In response, researchers and software companies have introduced a wide range of technologies to the architectural service sector to support streamlined work processes, enhance organizational productivity, and improve the overall outcomes of the design process [1,2,3,4,5]. There are also growing numbers of Artificial Intelligence (AI) applications for the Architecture Engineering and Construction (AEC) sector, and it has been argued that these services can be enhanced to optimize a design based on the maximum likelihood of customer satisfaction [6]. The continued evolution of technology in the architecture service industry is currently in a phase known as “Industry 4.0”.
Industry 4.0, a name reflecting the existence of a “fourth industrial revolution”, is a concept that refers to the rapid digitalization and automation taking place across a wide variety of industries. Construction Industry 4.0 (CI 4.0) is a subset of this concept, encapsulating the continued digitalization and use of a wide range of technologies that are intended to improve construction objectives such as safety, productivity, and delivery time [7,8,9]. According to Oesterreich and Teuteberg [10], using advanced CI 4.0 technologies could potentially enhance the efficiency of material planning, quantity surveying, and waste control while also improving aspects of the design process. Two core components of CI 4.0 are building information modeling (BIM) and the Internet of Things (IoT). They provide centralized systems for automating architectural processes from 3D modeling to material specification, construction simulation, and facilities management [11,12,13,14,15]. Furthermore, the IoT, enabled by the proliferation of portable sensing devices, offers opportunities for improving communication and decision-making in the architectural value chain [1]. According to Lawal and Rafsanjani [12], the use of IoT in the process of designing and building can improve security and safety concerns by providing access to the remote control of systems and appliances. Machine Learning (ML) and AI are also components of CI 4.0, and their perceived potential to optimize any task or workflow is central to meeting the future needs of the AEC sector. However, past research has repeatedly identified the slow adoption of emerging technologies as an impediment to achieving CI 4.0 [1,16].
Shonbeck et al. [17] argue that research is needed to determine how CI 4.0 can be accelerated in pertinent industries. Specifically, the success of CI 4.0 across the AEC sector relies on removing technology’s barriers to adoption [17]. Another factor in the success of CI 4.0 is the adoption of AI, which past research has identified as being shaped by perceptions of culture, trust, and organizational competence. For example, Tjebane, Musonda, and Okoro [18] suggest that an innovative and organizational culture is the main component impacting adoption. An effective organizational culture that supports technology adoption can arise in an environment where innovation is welcome. While organizational culture plays a significant role in technology adoption, it is essential to contextualize this further within the unique dynamics of the architecture service sector and consider client-related factors. Architectural firms often operate within a highly collaborative, design-centric environment where creative autonomy and professional identity play pivotal roles. Organizational culture in this sector is shaped by a firm’s openness to technological change and its ability to balance innovation with the client’s needs and desires, including the preservation of traditional design elements. Thus, a deeper understanding of how architectural firms in various regions cultivate a culture that may support or resist technological adoption is crucial to addressing the barriers specific to this field.
Organizational culture can also explain how some firms are willing to experiment with new technology despite barriers. Furthermore, Kelly et al. [19] identify the role of psychosocial factors such as trust and attitudes. Algassim et al. [20] identify competence and associated capacity-building costs as major contributors. However, Kelly, Kaye, and Oviedo-Trespalacios [19] point out the understudied role of bias caused by job security concerns and identified a gap in the literature concerning their effect on the acceptance of AI.
While there are examples of architects who have been celebrated for their rapid adoption of technology—such as Frank Gehry, Zaha Hadid, and Norman Foster, to name a few—the sector is, in many parts of the world, considered a slow adopter. The precise reasons for this are complex. For example, research shows that digital technologies can significantly improve the architecture field, but there is a considerable gap in the literature about factors influencing technology adoption in the architecture field and in various regions. Past research argues that architectural firms in particular countries and cultures have resisted using new technology and, as a result, are falling behind the needs of CI 4.0 [21,22,23]. It has been proposed that regional differences in technology adoption rates may be due to a poor understanding of the technology, its benefits, and risks [24,25]. Any failure in technology adoption may result in low productivity and reduced quality of building designs [26,27].
While the broader context of Industry 4.0 and its impact on the AEC sector has been well established, it is crucial to explore the unique challenges and opportunities in adopting these emerging technologies in the context of architectural services. Unlike other construction sectors, architectural firms often operate in more creative, client-driven environments where design priorities, organizational culture, and resource constraints can significantly influence strategic decisions at the organizational level, including decisions to utilize new digital tools. For example, integrating technologies like BIM, IoT, and AI in architecture affects workflow automation and requires shifts in how design decisions are made, communicated, and executed. Understanding these sector-specific dynamics is essential for identifying the barriers and enablers to technology adoption in architecture.
Considering the factors affecting technology adoption research within the building industry, which in many parts of the world has close professional ties to architecture, suggests that technology adoption depends on the perceived ease of use [28], complexity, and cost of technology systems [29], along with customer- and vendor-related attributes [30]. However, despite these examples in a related sector, relatively few attempts have been made to predict and understand the technology adoption process in architecture. The few examples that exist are either not used [28], or they borrow general acceptance models from IT, such as TAM (technology adoption model) [31]. TAM was developed to offer guidelines for new technology development strategies and to explore perceptions about technology adoption at the user level. Additionally, Davis [32] suggested that the model would offer a way to gauge how well users would accept new technology. As such, TAM provides an important foundation for research on barriers to technology adoption. It is one of the most influential models since it is user-friendly and affordable to implement [33,34,35].
The overarching aim of the present research is to explore factors influencing technology adoption in the architectural service industry. The present paper aims to develop a sector-specific TAM, an architectural technology adoption model (ATAM). ATAM intends to improve architects’ capacity to understand and overcome organizational barriers to adopting new digital technologies. The principles underlying the development of ATAM are founded on international research and validated using new survey and interview data from a case study of the architectural service industry in Saudi Arabia. The proposed framework, ATAM, differs from the general technology acceptance model (TAM). The general model is used as a base for modeling technology adoption in various fields and has been modified by multiple scholars to address unique needs in the selected sectors and countries. The present study intends to develop a technology adoption framework that addresses the unique needs of the architecture service sector. While TAM focuses broadly on two constructs—perceived ease of use and perceived usefulness—as key factors of technology adoption, ATAM considers additional factors that are specifically tailored to the architectural industry. It incorporates industry-specific elements such as client satisfaction, user satisfaction, and the intricacies of architectural workflows, which are influenced by creative design processes and organizational dynamics. By adapting TAM to account for these distinct factors, ATAM aims to provide a more context-specific framework for understanding the barriers and drivers of technology adoption in architecture.
Within the context of this aim, the present research had three objectives: (1) To understand the industry-specific factors affecting the stakeholders involved in organizational decision-making about technology in the architecture service industry in Saudi Arabia. (2) To distinguish the organizational determinants of technology adoption in terms of perceived usefulness, client satisfaction, user satisfaction, and perceived ease of use of technology. (3) To empirically test the significance of the proposed relationships between key factors of the technology adoption model.
To address the gaps in the terminologies used in this paper, this study introduces the concept of ‘brief preparation,’ defined as the structured process of creating guiding documents that communicate a project’s scope, objectives, and expectations clearly to stakeholders. Additionally, ‘result demonstrability’ refers to the ability of a technology to visually or practically showcase its benefits and outcomes, enabling stakeholders to assess its value effectively.

2. Technology Acceptance Framework and Hypothesis Development

According to Fishbein [36], the behaviors of technology adopters depend on their intentions to use the technology, something that TAM assists users in understanding in different industries [37]. While TAM is one of the most influential models for this purpose, past research suggests that one of its limitations is that it needs to incorporate organizational knowledge as a key factor and also be localized to specific industries [38]. Thus, this paper develops ATAM to assist architectural practices in understanding when, how, and to adopt a new technology. Key factors were identified in the literature to achieve this goal, and, consequently, the hypothetical relationships between these factors were formulated. Specifically, past research has argued that organizational factors affecting technology adoption in the architectural sector remain undetermined [39]. The industry-specific organizational factors and needs that have been hypothesized as shaping technology adoption are brief preparation (BF), result demonstrability (RD), cost-effectiveness (CE), project time considerations (TC), service delivery (SD), training considerations (TCs), and environmental considerations (ECs) [24,40]. As such, the adoption of technology at the managerial level is the dependent variable. In contrast, independent variables are the organizational factors identified from previous research:
The hypothesis related to user satisfaction (US): According to Seghezzi et al. [41], end-user satisfaction plays a crucial role in technology adoption. The chances of successful adoption and acceptance of a technology increase when users are satisfied with its performance. Further, if digital technology users believe they are likely to be more cost-effective, then technology adoption will be easier. The hypothesis on CE is thus related to the US.
The hypothesis related to user behavior (UB): UB and attitude have been found to directly affect user satisfaction when adopting technology. In developing TAM, Venkatesh et al. [38] identified user behavior as a contributing factor to technology adoption. Users with a positive attitude toward adopting technologies may derive more utility and satisfaction from it, and, in turn, management can be more confident to adopt new technologies as users are motivated to use them. As such, it is hypothesized that UB will influence US and the managerial decision to adopt the technology.
Hypothesis Related to CS: Past research about information systems identifies that user-friendly design and cost have a significant impact on client satisfaction [42]. The customer’s happiness can shape an application’s success and be affected by the services it receives and technological and financial objectives. Architectural services are mainly concerned with how a brief is prepared, how details are demonstrated to the client, how long this process takes, and its quality. Thus, the hypothesis is that CE, RD, BP, PT, SQ, and EC positively affect the satisfaction levels of architectural clients.
Hypothesis related to PU: Organizations that perceive technology as useful are more likely to adopt technology [43]. Organizations will only perceive technology to be useful if it directly influences the production of projects to, for example, improve completion times. The quality of services offered by architecture firms is an important factor as it helps improve the firm’s image. Further, as environmental concerns are increasingly shaping the architecture service sector, this is now a factor in technology adoption. In addition, if a company adopts technology without having the right training available, it is likely that the technology will not be as useful to the company as there are no employees with relevant skills to utilize it. Thus, the hypothesis is that CE, RD, BP, PT, SD, EC, and TC positively affect perceived ease of use.
Hypothesis related to PEU: Perceived ease of use has been considered a very important aspect of technology adoption. Venkatesh et al. [38], for example, offered the general conclusion that technology that is easy to use is more likely to be adopted and actively used. Within the context of the architecture service sector, it is hypothesized that there was a significant relationship between PEU, UF, and UB. A summary of the hypotheses is listed in Table 1.
The new knowledge developed by testing these fifteen hypotheses will be embedded in ATAM and provide a novel contribution to the body of knowledge in the field.

3. Methods

A quantitative research method was utilized for this research. Such methods emphasize the use of objective measurements, or the numerical analysis of statistical and mathematical data collected via surveys [44,45]. Three strategies were used to identify participants for the present research: criterion-based selection, comparative sampling, and chain sampling. The criterion-based strategy identified participants who fit the following criteria: (1) Involved in at least one significant technology adoption process for the last year to ensure they had relevant experience. (2) Working in the present organization for at least six months to ensure that they have good knowledge about the factors affecting the organization’s adoption process. Comparative sampling [46] was used to select the research participants (clients and users) from a range of different architectural firms and locations in Saudi Arabia. Chain sampling was used to select extra participants based on the recommendation of previous respondents to cover any gaps in the sample. The combination of the three sampling techniques ensured that different participants from diverse architectural firms and in different locations were selected to increase variations in data collection. According to Urquhart et al. [47], variation is a significant aspect of building theory as it increases the broadness of the concept being studied, giving a broad structure to the theoretical scope. The combination of the three strategies—the criterion-based strategy, comparative strategy, and chain strategy—helped maximize the value and quality of the survey data. Data was collected using structured surveys, each containing 45 questions. The survey was conducted in accordance with the conditions of a formal Human Ethics Approval process. A total of 475 responses were received; 23 were incomplete and eliminated, resulting in the use of 452 responses for statistical analysis. The responses were divided into two categories: 245 clients and 207 architecture professionals. The survey utilized a five-point Likert scale: 1—strongly disagree; 2—moderately disagree; 3—neither disagree nor agree; 4—moderately agree; and 5—strongly agree. Consequently, each indicator’s score is equivalent to its attributes’ arithmetic values. To account for sequential bias, the order of the factors was randomized in the survey. It is worth noting that the structure of the survey questions was highly influenced by the survey conducted by Venktash [48], which utilized short, direct sentences to measure people’s perception regarding technology. The ten organizational factors that formed the survey’s key questions offer high-level indicators of digital technology adoption. Below are some examples of questions used to measure each construct.
  • Brief Preparation: DMAT helps me prepare informative briefs (detailed guiding documents that communicate the project’s scope to the client) for the client.
  • Result Demonstrability: DMAT helps me translate the client’s needs and requirements into the expected results.
  • Project Time: DMAT helps me manage the project time effectively.
  • Environmental Considerations: DMAT allows me to embrace and predict the environmental impact on the building.
  • Service Delivery: DMAT improves service delivery.
  • Cost-Effectiveness: DMAT improves project cost-effectiveness.
  • Training Considerations: I prefer DMAT, which meets the users’ training considerations.
  • User-Friendliness: I prefer DMAT, which has a friendly interface.
  • User Satisfaction: I am satisfied if a client is satisfied with the project.
  • Client Satisfaction: The cost estimations of my projects are accurate and optimal.
The analysis approach of the present paper is mainly statistical, using structural equation modeling (SEM) tests, which are widely adopted in social sciences, marketing, management, and technology adoption research to examine complex relationships between observed (measured) and unobserved (latent) variables. In the context of the present study, SEM was employed to analyze the survey data and explore organizational factors affecting technology adoption in the architecture industry. The first step in applying SEM in this study was identifying the key variables based on a systematic literature review [20]. This review helped pinpoint the constructs relevant to technology adoption, such as organizational factors, environmental considerations, and perceived usefulness. These variables formed the theoretical basis of the model and served as the foundation for the subsequent SEM analysis.
Before running SEM, we conducted a reliability analysis to ensure that the measurement scales used for the variables were consistent and dependable. Reliability analysis typically involves calculating Cronbach’s Alpha, which measures internal consistency. This step is essential to verify that the survey items accurately reflect the latent constructs they are designed to measure, thereby increasing the validity of the results. Once the reliability of the measurement scales was confirmed, factor analysis was performed. Factor analysis helps reduce data complexity by identifying underlying factors (latent variables) that explain the patterns of correlations among observed variables. This step is critical in SEM because it allows the researcher to confirm that the observed variables are appropriately grouped and reflect the constructs they are intended to measure.
With the identified latent and observed variables, the researcher specified the structural equation model. SEM combines elements of factor analysis, regression analysis, and path analysis. This study designed the model to explore the relationships between organizational factors (e.g., cost-effectiveness and perceived usefulness) and technology adoption in the architecture industry. The hypotheses were tested by examining how well the model fits the data, using Chi-square and Root Mean Square Error of Approximation (RMSEA) to assess goodness of fit. SEM allows the researcher to test direct and indirect relationships between variables and validate theoretical models. Using IBM AMOS (Version 2) software, the path relationships between the latent and observed variables were tested. This involved estimating the strength and direction of the relationships between variables, allowing for an understanding of how one variable influences another within the context of the model. The path analysis also helps identify unobserved (latent) variables that cannot be measured directly but are inferred from observed variables.
After conducting the SEM analysis, the study validated ATAM by comparing the results with the hypotheses. The validation process ensured that the theoretical framework accurately reflected the factors affecting technology adoption in the architecture industry. This helped achieve Objective 3 of the study, which was to formulate a technology adoption framework based on empirical findings. The SEM process helped identify the most significant organizational factors influencing technology adoption, contributing to developing a structured framework to guide future decision-making in the industry. Previous studies [44,49] have used SEM in similar contexts, particularly in technology adoption, where latent variables are prevalent and cannot be measured directly. SEM’s ability to model complex relationships among variables makes it an ideal technique for exploring how different factors influence technology adoption in the construction and architectural sectors.
SEM combines factor analysis, path analysis, and regression analysis, enabling the study to achieve its objectives and validate the ATAM framework for technology adoption in the architecture industry.

4. Data Analysis and Results

4.1. Factor Analysis

The SEM process commences with a factor analysis of the data collected, with the intention of reducing the observed variables into fewer analyzable variables. To determine if the model is to proceed with analysis, the values to the right and left of the correlation matrix diagonally are considered if they are very small (less than 0.05). Factors with values more than 0.05 are removed. Bartlett’s Spherical and Kaiser–Meyer–Olkin (KMO) tests are then performed using IBM SPSS Statistics (Version 2 of AMOS), as shown in Table 1, indicating that the data collected are reliable for further analysis. The KMO of 0.852 is between the recommended values of 0.8 and 1 for reliable datasets. Bartlett’s test of sphericity shows that the model is significant as it is less than 0.05, and it is 0.000, meaning the correlation matrix is not an identity matrix. Furthermore, the table of commonalities indicates that more than 50% of all the variables were accounted for, allowing for the factor analysis to proceed as the variance or value of commonalities is more than 0.5.

4.2. Reliability Measurement

The observed variable factors from factor analysis are then tested for reliability. This study utilized the Cronbach coefficient method to analyze the reliability of the sample data collected. In general, a Cronbach Alpha greater than 0.7 indicates a high level of confidence and is acceptable [50]. A Cronbach Alpha of less than 0.5 is considered untrustworthy. All but one of the Cronbach’s coefficient values in this study are greater than the threshold of 0.5, indicating good measurement reliability (Table 2). The one factor that scored less than 0.05 is user satisfaction, with a validity of 0.475, and the two factors are relatively close to the threshold—cost-effectiveness (0.503) and client satisfaction (0.654)—confirming their validity is acceptable but lower than the other factors. The standardized factor loadings are then used to assess the validity of the measurement model. The standardized factor loadings (Table 2) are all above the recommended threshold of 0.5, demonstrating the validity of the measurement model.

4.3. Hypothesis Testing

With the aid of AMOS 22.0, the maximum likelihood estimate (MLES) method was employed to derive the SEM in Figure 1, to validate the hypotheses and the fitness between the proposed model and the collected data. For ATAM, technology adoption is affected jointly by all nine factors, namely CE, RD, BP, PT, SE, EC, TC, UF, PU, PE, and UB, as indicated in Figure 1.
Figure 1 presents the SEM for ATAM. In general, the results for path coefficients and path significance reflect those reported in prior research [32,38,43], suggesting further validity. The fitness of a proposed model is validated by the mean square error approximation (RMSEA), which was 0.129; the normed fit index (NFI), which was 0.610; the comparative fit index (CFI), which was 0.663; Tucker–Lewis Index (TLI), which was 0.587; and the Incremental fit index (IFI), which was 0.635 [51]. The indices obtained from the current study are illustrated in Table 3, which indicates that SEM is an effective model as most indices are close to the recommended values, except the TLI, which is a bit too far from the recommended value. This limitation should be addressed in future studies, but a potential reason can be the overlay of the complex model proposed initially. A path analysis (Table 4) was then conducted to test the postulated hypothesis, resulting in the acceptance of the hypothesis based on the results in Table 4.
All the factors in Table 4 are significant as the p-values are less than alpha 0.05. The beta coefficients indicate the level of influence that each factor has on technology adoption. The bigger the coefficient, the larger the influence. CE affects PU, for example, and has a significant impact at a coefficient of 0.643; similarly, the impact of CE on CS is 0.643. However, SD has a greater significance value of 0.713 for clients who prefer technology use in their projects. In fact, the direct effect of RD on perceived ease of use for users is significant, with the beta coefficient being 0.643. In addition, the impact of RD (H.3.1b) on CS is slightly higher (0.64) than the direct effect of RD on the user groups. BP significance on users and clients is the same, with a beta coefficient of 0.643. Consideration of project time is an antecedent to both the perceived usefulness of technology by users and the extent of client satisfaction, with a positive impact on the technology of 0.643. Service quality beta coefficients are 0.643 on users’ perceived usefulness, with a higher increase in impact on client satisfaction at 0.713. This is an indication that service delivery was much more important to clients than users. Although significant, there appears to be a lesser effect on perceived usefulness and client satisfaction from environmental considerations with beta coefficients of 0.443 and 0.316, respectively.
The ATAM results are most significant for hypotheses at the user and client levels. The training’s relationship with the perceived usefulness is supported by a beta coefficient of 0.643. Further, user-friendliness had the highest impact on technology adoption as its influence on perceived ease of use was seen to increase at 0.707 for every change in perceived ease of use. If clients were satisfied, users were able to attain a certain level of satisfaction with adopting and using technology in clients’ projects; however, this occurred at a lower significance and at a beta coefficient of 0.403. User behavior was a significant indicator of user satisfaction, with a coefficient of 0.662. Jointly, user satisfaction, user behavior, and client satisfaction have a significant impact on managerial decisions to adopt digital technology. SEM also identified a strong relationship between perceived ease of use and user behavior, with a value of 0.554. However, users were found to be more receptive to technology adoption if they perceived technology to be useful than if the technology was easy to use, as indicated by beta coefficients of 0.608 and 0.554, respectively. In essence, all ATAM hypotheses are supported but with different levels of significance.

5. Discussion

5.1. Result-Oriented Factors

Result-oriented factors in this study include any factors that deal with a project’s outcomes at any stage. The result-oriented factors include brief preparation. Result-oriented factors in this study include any factors that deal with a project’s outcomes at any stage. The result-oriented factors include BP and RD. From the analysis of the survey results, the authors found that BP was a significant factor as the p-value is less than alpha (0.000 < 0.05) in influencing technology adoption in Table 3, with an influence of 0.643 on the adoption of digital technology. The results suggest that, in general, without exploring any contributing factors in the technology adoption process, respondents indicate that the brief preparation was a significant factor in choosing technology, as the p-value was. This suggests that firms that invest time and resources in preparing for technology adoption—through activities like assessing the technology’s fit, conducting preliminary training, and engaging stakeholders—are more likely to successfully adopt and integrate new digital tools. The BP in an architectural services practice involves a series of strategic steps, including conducting an initial technology assessment, providing foundational training, and creating a pilot implementation plan. These activities help mitigate the uncertainty and resistance often associated with adopting new technologies, fostering a smoother transition. In addition, early engagement with key stakeholders ensures that all relevant parties are aligned and prepared for the changes.
The analysis further identified that respondents might perceive technology as useful and easy to use when it helps them demonstrate project results effectively. The analysis found that result demonstrability is the key pillar supporting organizations’ initiative to attain success, particularly in using and applying digital technology in both the construction and the architectural industries. For instance, results suggested that technological adoption must be considered in building sustainable architectural work to reduce the time and cost of building buildings in modern society [11]. The results indicated that clients are more likely to perceive technology as essential when anticipating its usefulness and focusing on achieving positive outcomes [52]. Specifically, technologies such as BIM or energy simulation software were cited as tools that help architects and construction companies improve the efficiency and sustainability of buildings. These technologies help visualize and predict project outcomes and support the integration of sustainable design principles, thus enhancing client satisfaction and project success.
The results showed that the targeted clients in the market are more likely to consider technology essential, particularly in circumstances where they have the platform to anticipate the usefulness of technology and focus on attaining positive results [52]. The data analysis confirmed that the objective of using innovative techniques, such as software, in the architectural industry is useful for improving the effectiveness of establishing sustainable buildings. Hence, the level of satisfaction from both the clients and the construction companies is attained based on the perception that technology would be safe and highly sustainable. The study’s results outlined that computerization is intended to promote and improve digitalization in the construction and architecture fields [53]. Hence, the focus on affirming the essentiality of result-oriented factors, such as result demonstrability, relies on the effective and accurate adoption of digital technology in the field of construction management.

5.2. Cost-Related Factor

The study’s cost-related factors include cost-effectiveness, which encompasses fixed, variable, and benefit costs associated with a particular technology. The quantitative analysis identified costs as a significant factor in technology adoption, as illustrated in Table 3, where the p-value is less than alpha (0.00 < 0.05) with an influence of 0.643 in the decision to adopt digital technologies. The focus on adopting cost-effective technologies to establish secure and safe buildings would derive a level of satisfaction regarding the perceived usefulness of advanced technologies. The results of this study defended the use and application of digital technologies as the most cost-effective approach to constructing a building in an era in which humanity is quite sensitive about the overall cost of initiating and completing an entire construction project [54,55,56]. According to Liu et al., the target would consider the usefulness of the arch foundation technology [57]. A comparative analysis of the cost implications between traditional and digital approaches in construction reveals significant differences in efficiency and overall expenses. Traditional construction methods often involve higher labor costs, material waste, and longer project timelines. Manual labor is a major cost driver, as workers are needed for tasks like rework, measuring, and correcting errors [54]. Additionally, traditional methods can lead to material waste due to inaccuracies in design or construction [55].
In contrast, digital technologies like BIM and 3D printing help optimize design accuracy, streamline communication, and reduce material waste. Studies show that BIM can reduce project costs by as much as 20% through better planning, fewer errors, and enhanced collaboration [57]. These technologies also shorten project timelines, reducing labor costs and minimizing delays [58]. Thus, digital methods are more cost-effective by improving efficiency and reducing both direct and indirect costs. Consequently, the viewpoints of the study participants denoted the focal objective, which is to attain the desired level of customer satisfaction, which is embedded in an accurate choice of technologies that align themselves to safety procedures, alongside incurring the lowest cost throughout the project execution process.

5.3. Services and Labor-Oriented Factors

The service factor analyzed in the study is service quality. Service quality encompasses the ability to deliver accurate, reliable, and high-quality services. The study analyzed the relationship between the factor of service quality and perceived usefulness. The analysis identified that service quality is a significant factor as the p-value is less than alpha (0.000 < 0.05), meaning that organizations perceived service quality as a useful factor in deciding to adopt a technology.
According to Raes [59], the objective of delivering quality and standard services in the market is the epitome of deriving the highest level of client satisfaction in the market. The perceived usefulness of technology is defined by the nature of services guaranteed by the architectural and construction companies in the market [60]. The quantitative analysis conducted in the study defended the objective of adopting building information models as a key pillar that supports quality service provision in the construction sector. The study’s perceptions acknowledged that adopting a user-friendly interface is one of the key factors that affirms the objective of construction firms to deliver quality service in the market [61].
Many tools help practitioners enhance their performance and task quality. For example, the One World Trade Center project in New York used BIM to create detailed 3D models, allowing teams to identify design issues early, streamline coordination, and ensure high-quality outcomes [61]. Project management software such as Procore helps manage project timelines, budgets, and communication, improving overall efficiency. In the case of a hospital expansion project, Procore facilitated real-time updates and better collaboration, leading to the timely delivery of a high-quality facility [59]. Lastly, drones and 3D scanning technologies are increasingly used to monitor construction sites, ensuring accuracy and progress. The Shenzhen Ping An Finance Center utilized drones to capture aerial footage for site monitoring, improving safety and project quality [60]. These technologies collectively enhance service quality by improving precision, collaboration, and efficiency.
In this context, a significant percentage of the targeted clients would accept technology that is perceived as useful and reliable. These are deemed more effective in the market and might better guarantee sustainable service quality.

5.4. Environment-Related Factor

This research empirically explores the relationship between environmental considerations and technology adoption. The study found that technology adoption was significantly impacted by environmental considerations that both users and clients had regarding a given project, as the p-value of 0.000 was less than 0.05. The research findings align with other findings suggesting that environmental factors may not be as important as cost and safety factors [2,57,62]. The analysis and interpretation of the present research affirms that the clients did not show interest in technology adoption in the construction sector based on the need to support sustainability. Hence, the study’s results justify the focus on green building technologies as the epitome of deriving the level of client satisfaction in the market. The analysis of the study findings revealed that environmental considerations such as environmental impact, sustainability, and environmental safety are major factors that most construction companies consider as the foundation for designing and constructing sustainable buildings [1,57,62].
Technologies such as green building materials, energy-efficient design software, and solar-powered construction tools are leading the way in reducing the environmental impact of construction projects. BIM, for instance, now includes tools for optimizing energy performance and material efficiency, helping companies design structures that minimize energy consumption and waste. Additionally, 3D printing in construction offers the potential to reduce material waste and energy usage by using precise, sustainable materials for each project. Solar panels and smart building technologies are also gaining traction as they lower operational costs in the long run, appealing to clients who prioritize both financial savings and sustainability. These innovations align with the increasing demand for environmentally responsible building practices, highlighting how integrating sustainability into technology adoption can meet both economic and ecological goals [1,57]. The results of past research [2,57,62] identify a growing need among organizations committed to environmental sustainability for better strategies and technologies to be adopted in the building industry.

5.5. Technology-Related Attributes

The technology-related attribute analyzed in the study is user-friendliness. The analysis identifies user analysis to be a significant factor in technology adoption, with a p-value (0.000) of less than 0.05. In addition, identifying the positive effect of user-friendliness on technology adoption in the construction sector plays an essential role in deriving client satisfaction. For instance, the viewpoints of most of the study respondents asserted that a significant percentage of the targeted clients would accept technology that is cost-effective and easy to use. The opinions and viewpoints presented by the professionals who took part in the study are in line with previous findings that show that the adoption of digital technology is based on the perception that they create a highly interactive field between and amongst designers from different disciplines [1]. This outlines the core underlying focus of assessing the advantages of implementing parametric software in construction and engineering. The user-friendliness factor in ATAM is consistent, with user-friendliness being a key factor in general technology adoption models and the application of TAM models in the architecture and construction industry [33,59].
This study highlights the significant role of user-friendliness in technology adoption within the construction industry, with user feedback playing a crucial part in improving technology interfaces. The analysis reveals that a user-friendly interface is a key factor in client satisfaction and technology acceptance, as a majority of respondents emphasized that clients are more likely to adopt cost-effective and easy-to-use technologies. Feedback from users provides valuable insights into the practical challenges and preferences of technology interfaces, leading to continuous improvements. For example, professionals in the study suggested that technologies that promote interaction among multidisciplinary teams—such as parametric design software—are perceived as more effective. By incorporating user feedback, technology developers can refine interfaces to ensure ease of use and functionality, thus enhancing adoption. This aligns with the technology acceptance model (TAM) and the theory of reasoned action, which stress the importance of user perceptions in the successful integration of new technologies [1,30].
Hence, the focus on TAM in the construction industry is embedded in consideration of humanity’s conscious approaches to designing buildings through the affirmation of the theory of reasoned action [30]. In addition, the quantitative data analysis in the study outlined that ATAM is intended to scrutinize the level of technology acceptance in the construction and architectural fields. In this regard, the study participants agreed with the fact that the consideration of the quantitative approaches through the application of TAM has been widely used in the construction fields to assess the level of user acceptance as one of the most reliable technological innovations, further reinforcing the application of ATAM.

6. Conclusions

The research aimed to explore factors influencing technology adoption at the organizational level. Exploring the relationships among these factors led to the development of an architectural TAM, called ATAM. ATAM includes nine verified factors that offer a modified version of the well-known model of technology acceptance. Through SEM statistical analysis, the fitness of this model was validated and found to be a fit model for understanding the influence of organizational factors on digital technology adoption in the architecture industry. The hypotheses were tested using maximum likelihood in structural equation modeling. The two main hypotheses—(1) that the digital technology adoption process is influenced by industry-specific factors that directly impact “perceived usefulness” and “perceived ease of use” factors and (2) that user satisfaction, user behavior, and client satisfaction have an impact on the process of technology adoption—were accepted.
The paper contributes to the body of knowledge by extending the general technology acceptance modeling practices in the information systems field and applying the extended version to the context of architectural services. A set of original data and first-hand field observations supported and tested the modified model. Based on the survey findings, the data collected fit the proposed ATAM in alignment with Objective 3, which was to develop a model to inform the technology adoption process. Significant relationships were identified concerning perceived ease of use and perceived usefulness. Result demonstrability, training consideration, project time, service delivery, brief preparation, cost-effectiveness, and environmental considerations were found to significantly impact the perceived usefulness of technology in line with the objective. (2) On the other hand, user-friendliness significantly impacted perceived ease of use, aligning with Objective 2 of the study. The relationship between user satisfaction and user behavior postulated in the proposed ATAM was validated as a significant relationship and was identified through the structural equation model. (3) For the first time, the relationship between user satisfaction and client satisfaction in the architecture industry was tested, and a significant relationship was also identified between client satisfaction and user satisfaction.
Through statistical analysis, the novelty of the research was established. Eleven factors emerged that affected technology adoption in the architecture industry and validated the proposed ATAM. The study identified 11 factors that impacted the process of technology adoption. Each of the 11 factors was hypothesized within the ATAMs to validate the model. The application of structural equation modeling determined that the model was found to fit and that the factors significantly impact digital technology. Some factors, such as service delivery and user-friendliness, influence technology adoption much more than the other nine factors.
The genesis of ATAM can be traced back to TAM. However, to achieve ATAM, context-based modifications were made to the previous general TAM, as shown in Figure 1, which increased the novelty of the research. The research contributed to the literature on architecture and enriched it by first seeking to include organizational factors specific to the architecture industry that affect technology adoption both at the user and managerial level instead of just concentrating on the user level, as is the case in TAM. It was achieved by (1) involving users, clients, and managers during empirical model testing; (2) establishing the influence of both user and client satisfaction on technology adoption; (3) identifying the link between organizational variables, user behavior, and user satisfaction; (4) tracing the influence of client and user satisfaction on the decision-making process; and (5) proposing ATAM, which represents the novelty of the research. The research developed the ATAM, a combination of the relationships between organizational factors, aspects of TAM, and the factors clients consider satisfying during the adoption process. The model includes diverse factors tested by an extensive survey and an in-depth dataset that can be viewed as a robust theoretical framework for the architecture industry.
The findings of the study have a variety of practical implications in the architecture industry. The research is geared toward providing a framework that will help software vendors and innovators better understand the architecture industry’s needs so that they can match their products with industry needs, increasing the chances of adoption. As the first to develop a model for architecture, the study forms the foundations for researchers to create better and more advanced models to inform the technology implementation process in the architecture sector. The framework will support managerial executives in making more informed decisions about the opportunities and challenges of adopting new technology.
While the research achieved its objectives, the investigation had some limitations. Like other international studies, the research data collection was conducted in dual languages, Arabic and English; this affected the time of translations and was one of the investigation challenges. Re-translating the questionnaire and the content to both languages enhanced the dependability of the data and ensured that there was no loss or misinterpretation. However, this practice was carefully managed to collect and analyze a set of first-hand industry data to contribute to the body of research available to scholars analyzing the global trends and challenges of taking up new technologies. The model from a selected country was examined in the current paper. Still, future researchers are encouraged to replicate this model in various countries and examine details that may offer generalizability of the model in the future.
These translation challenges could impact the applicability of the findings to other regions or contexts, as the perceptions and behaviors of technology adopters might vary based on language and cultural differences. For instance, terms related to technology adoption and innovation might have different connotations or significance in various languages, which could influence how respondents interpret the questions. As a result, while the study’s findings are valuable for the specific context in which the research was conducted, caution should be taken when attempting to generalize the results to other countries or cultural settings. Future research that replicates the model in different linguistic and cultural contexts would be beneficial to assess the broader applicability of the findings and identify any regional variations that could impact the adoption of new technologies in the architecture industry.
This paper provides insight into adopting new technologies, such as AI, as a trending technology that may significantly affect architectural services in the near future. While this paper was limited to examining a wider range of current technologies being used by architects in a selected country, it explores the value of contextual factors such as client satisfaction. Also, it confirms the measures of the usefulness of the architectural services, such as the demonstrability of results and brief preparation. The findings of this paper can guide innovation leaders in the industry to engage with evolving AI applications tailored to architectural services. This ensures that both practitioners and clients benefit from current trends while addressing the urgent need to explore AI-based technologies scientifically. Additionally, it helps raise awareness of new technological advancements.
It is recommended that future researchers include the attributes of managers that impact technology adoption. These attributes are essential in managerial decision-making and are often the final determinants of organizational technology adoption. Future research needs to propose a model that can be tested empirically to determine its functionality in fully understanding the technology adoption process in the architecture industry. Further, future research needs to be conducted to determine a clear relationship between genders and technology adoption in different firm sizes, ranging from small to large. More research on how digital technologies related to big data can use IoT and information modeling tools in information management to aid processes such as brief preparation needs to be performed. It also emerged that more research needs to be conducted to identify if firms consider training costs before purchasing technology or, rather, as a maintenance cost once the technology is purchased.

Author Contributions

Methodology, S.D.; Writing—original draft, H.A. and S.D.; Writing—review & editing, S.M.E.S. and M.J.O.; Supervision, S.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in the study are included in the article.

Conflicts of Interest

There is no conflict of interest.

References

  1. Lee, J.H.; Ostwald, M.J.; Kim, M.J. Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture. Sensors 2021, 21, 3417. [Google Scholar] [CrossRef] [PubMed]
  2. Chen, Y.; Yin, Y.; Browne, G.J.; Li, D. Adoption of Building Information Modeling in Chinese Construction Industry: The Technology-Organization-Environment Framework. Eng. Constr. Archit. Manag. 2019, 26, 1878–1898. [Google Scholar] [CrossRef]
  3. Ostwald, M.J. Systems and Enablers: Modeling the Impact of Contemporary Computational Methods and Technologies on the Design Process. In Computational Design Methods and Technologies: Applications in CAD, CAM and CAE Education; Gu, N., Wang, X., Eds.; IGI Global: Hershey, PA, USA, 2012; pp. 1–17. [Google Scholar] [CrossRef]
  4. Vannoy, S.A.; Palvia, P. The Social Influence Model of Technology Adoption. Commun. ACM 2010, 53, 149–153. [Google Scholar] [CrossRef]
  5. Zhang, J.; Zhao, L.; Ren, G.; Li, H.; Li, X.; Wang, Q. Special Issue “Digital Twin Technology in the AEC Industry”. Adv. Civ. Eng. 2020, 2020, 8842113. [Google Scholar] [CrossRef]
  6. El-Masri, M.; Al-Yafi, K.; Kamal, M.M. A Task-Technology-Identity Fit Model of Smartwatch Utilisation and User Satisfac-tion: A Hybrid SEM-Neural Network Approach. Inf. Syst. Front. 2023, 25, 835–852. [Google Scholar] [CrossRef]
  7. Begić, H.; Galić, M. A Systematic Review of Construction 4.0 in the Context of the BIM 4.0 Premise. Buildings 2021, 11, 337. [Google Scholar] [CrossRef]
  8. Forcael, E.; González, V.; Vargas, J.; Pino, R.; Ushassi, R. Construction 4.0: A Literature Review. Sustainability 2020, 12, 9755. [Google Scholar] [CrossRef]
  9. Hansen, S.; Rostiyanti, S.F.; Setiawan, A.F.; Koesalamwardi, A.B. Developing a Work-Integrated Learning Model Adjust-ing to Construction 4.0 Concepts. Int. J. Work-Integr. Learn. 2022, 23, 65–80. [Google Scholar]
  10. Oesterreich, T.D.; Teuteberg, F. Understanding the Implications of Digitisation and Automation in the Context of Industry 4.0: A Triangulation Approach and Elements of a Research Agenda for the Construction Industry. Comput. Ind. 2016, 83, 121–139. [Google Scholar] [CrossRef]
  11. Lin, S.-H.; Zhang, H.; Li, J.-H.; Ye, C.-Z.; Hsieh, J.-C. Evaluating Smart Office Buildings from a Sustainability Perspective: A Model of Hybrid Multi-Attribute Decision-Making. Technol. Soc. 2022, 68, 101824. [Google Scholar] [CrossRef]
  12. Lawal, K.; Rafsanjani, H.N. Trends, Benefits, Risks, and Challenges of IoT Implementation in Residential and Commercial Buildings. Energy Built Environ. 2022, 3, 251–266. [Google Scholar] [CrossRef]
  13. Zhang, F.; Boukamp, F.; Wang, P.; Beetz, J. Integrated Applications of Building Information Modeling and Artificial Intel-ligence Techniques in the AEC/FM Industry. Autom. Constr. 2022, 139, 104289. [Google Scholar] [CrossRef]
  14. Panya, D.S.; Kim, T.; Choo, S. An Interactive Design Change Methodology Using a BIM-Based Virtual Reality and Aug-mented Reality. J. Build. Eng. 2023, 68, 106030. [Google Scholar] [CrossRef]
  15. Saka, A.B.; Oyedele, L.O.; Akanbi, L.A.; Ganiyu, S.A.; Chan, D.W.M.; Bello, S.A. Conversational Artificial Intelligence in the AEC Industry: A Review of Present Status, Challenges, and Opportunities. Adv. Eng. Inform. 2023, 55, 101869. [Google Scholar] [CrossRef]
  16. Yin, X.; Liu, H.; Chen, Y.; Al-Hussein, M. Building Information Modelling for Off-Site Construction: Review and Future Directions. Autom. Constr. 2019, 101, 72–91. [Google Scholar] [CrossRef]
  17. Schönbeck, P.; Löfsjögård, M.; Ansell, A. Quantitative Review of Construction 4.0 Technology Presence in Construction Project Research. Buildings 2020, 10, 173. [Google Scholar] [CrossRef]
  18. Tjebane, M.M.; Musonda, I.; Okoro, C. Challenges for the Implementation of Sustainable Construction Practices in Developing Countries: A Bibliometric Review. In Proceedings of the 19th Int’l Conference on Computing in Civil and Building Engineering, Cape Town, South Africa, 26–28 October 2022; Springer: Cham, Switzerland, 2022; pp. 1–12. [Google Scholar] [CrossRef]
  19. Kelly, S.; Kaye, S.-A.; Oviedo-Trespalacios, O. A Multi-Industry Analysis of the Future Use of AI Chatbots. Hum. Behav. Emerg. Technol. 2022, 2022, 2552099. [Google Scholar] [CrossRef]
  20. Algassim, H.; Sepasgozar, S.M.E.; Ostwald, M.; Davis, S. A Qualitative Study on Factors Influencing Technology Adoption in the Architecture Industry. Buildings 2023, 13, 1100. [Google Scholar] [CrossRef]
  21. Maqsoom, A.; Zulqarnain, M.; Irfan, M.; Ullah, F.; Alqahtani, F.K.; Khan, K.I.A. Drivers of, and Barriers to, the Adoption of Mixed Reality in the Construction Industry of Developing Countries. Buildings 2023, 13, 872. [Google Scholar] [CrossRef]
  22. Haider, H. Calligraphic Architecture: Stroke to Form, Space, and Surface. How Does Arabic Calligraphy Influence the De-sign Process of Zaha Hadid in Her Creation of Architectural Forms? Fields J. Huddersfield Stud. Res. 2021, 7, 1–21. [Google Scholar] [CrossRef]
  23. Jung, C.; Al Qassimi, N.; Awad, J. The Evolution of Dynamicity in Architecture of Frank Gehry. Int. J. Adv. Res. Eng. Innov. 2021, 3, 18–30. [Google Scholar]
  24. Chen, Z.; Agapiou, A.; Li, H. A Benefits Prioritization Analysis on Adopting BIM Systems Against Major Challenges in Megaproject Delivery. Front. Built Environ. 2020, 6, 26. [Google Scholar] [CrossRef]
  25. Doan, D.T.; GhaffarianHoseini, A.; GhaffarianHoseini, A.; Naismith, N.; Zhang, T.; Tookey, J. Examining Critical Perspec-tives on Building Information Modelling (BIM) Adoption in New Zealand. Smart Sustain. Built Environ. 2021, 10, 594–615. [Google Scholar] [CrossRef]
  26. Zakaria, S.A.S.; El-Abidi, K.M.A. Economic Effects of Migrant Labor on Industrialized Building System (IBS) Adoption in the Malaysian Construction Industry. Archit. Eng. Des. Manag. 2021, 17, 50–66. [Google Scholar] [CrossRef]
  27. Berlak, J.; Hafner, S.; Kuppelwieser, V.G. Digitalization’s Impacts on Productivity: A Model-Based Approach and Evalua-tion in Germany’s Building Construction Industry. Prod. Plan. Control 2021, 32, 335–345. [Google Scholar] [CrossRef]
  28. Liu, D.; Lu, W.; Niu, Y. Extended Technology-Acceptance Model to Make Smart Construction Systems Successful. J. Constr. Eng. Manag. 2018, 144, 04018035. [Google Scholar] [CrossRef]
  29. MacLennan, E.; Van Belle, J.-P. Factors Affecting the Organizational Adoption of Service-Oriented Architecture (SOA). Inf. Syst. E-Bus. Manag. 2014, 12, 71–100. [Google Scholar] [CrossRef]
  30. Sepasgozar, S.M.; Bernold, L.E. Factors Influencing the Decision of Technology Adoption in Construction. In ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction; American Society of Civil Engineers: Fort Worth, TX, USA, 2013; pp. 654–661. [Google Scholar] [CrossRef]
  31. Godoe, P.; Johansen, T.S. Understanding Adoption of New Technologies: Technology Readiness and Technology Acceptance as an Integrated Concept. J. Eur. Psychol. Stud. 2012, 3, 38–52. [Google Scholar] [CrossRef]
  32. Davis, F.D. A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Ph.D. Thesis, Massachusetts Institute of Technology, Sloan School of Management, Cambridge, MA, USA, 1985. [Google Scholar]
  33. Katebi, A.; Homami, P.; Najmeddin, M. Acceptance Model of Precast Concrete Components in Building Construction Based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) Framework. J. Build. Eng. 2022, 45, 103518. [Google Scholar] [CrossRef]
  34. Lai, Y.L.; Lee, J. Integration of Technology Readiness Index (TRI) into the Technology Acceptance Model (TAM) for Explaining Behavior in Adoption of BIM. Asian Educ. Stud. 2020, 5, 10. [Google Scholar] [CrossRef]
  35. Lin, C.-Y.; Xu, N. Extended TAM Model to Explore the Factors That Affect Intention to Use AI Robotic Architects for Ar-chitectural Design. Technol. Anal. Strateg. Manag. 2022, 34, 349–362. [Google Scholar] [CrossRef]
  36. Fishbein, M. A Behavior Theory Approach to the Relations Between Beliefs About an Object and the Attitude Toward the Object. In Readings in Attitude Theory and Measurement; John Wiley & Sons, Inc.: New York, NY, USA, 1967. [Google Scholar] [CrossRef]
  37. Davis, F.D.; Venkatesh, V. A Critical Assessment of Potential Measurement Biases in the Technology Acceptance Model: Three Experiments. Int. J. Hum.-Comput. Stud. 1996, 45, 19–45. [Google Scholar] [CrossRef]
  38. Venkatesh, V.; James, Y.L.T.; Xin, X. Unified theory of acceptance and use of technology: A synthesis and the road ahead. J. Assoc. Inf. Syst. 2016, 17, 328–376. [Google Scholar] [CrossRef]
  39. Lines, B.C.; Vardireddy, P.K.R. Drivers of Organizational Change within the AEC Industry: Linking Change Management Practices with Successful Change Adoption. J. Manag. Eng. 2017, 33, 04017031. [Google Scholar] [CrossRef]
  40. Stang Våland, M.; Svejenova, S.; Clausen, R.T.J. Renewing Creative Work for Business Innovation: Architectural Practice in the Trading Zone. Eur. Manag. Rev. 2021, 18, 389–403. [Google Scholar] [CrossRef]
  41. Seghezzi, E.; Caccavella, V.M.; Dell’Ovo, M.; Brambilla, A.; Bonomo, P.; Capolongo, S. Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment. Appl. Sci. 2021, 11, 3108. [Google Scholar] [CrossRef]
  42. Lu, J.; Zhang, G. Cost Benefit Factor Analysis in E-Services. Int. J. Serv. Ind. Manag. 2003, 14, 570–595. [Google Scholar] [CrossRef]
  43. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
  44. Wang, Q.; Zhao, M.; Li, R.; Li, X.; Zhang, W. Exploring the Influences of Green Industrial Building on the Energy Con-sumption of Industrial Enterprises: A Case Study of Chinese Cigarette Manufacturers. J. Clean. Prod. 2019, 231, 370–385. [Google Scholar] [CrossRef]
  45. Walliman, N. Qualitative Data Analysis. In Research Methods: The Basics, 2nd ed.; Routledge: London, UK, 2017; pp. 148–166. [Google Scholar]
  46. Doody, O.; Noonan, M. Preparing and Conducting Interviews to Collect Data. Nurse Res. 2013, 20, 28–32. [Google Scholar] [CrossRef]
  47. Urquhart, C.; Lehmann, H.; Myers, M.D. Putting the ‘Theory’ Back into Grounded Theory: Guidelines for Grounded Theory Studies in Information Systems. Inf. Syst. J. 2010, 20, 357–381. [Google Scholar] [CrossRef]
  48. Venkatesh, V.; Bala, H. Adoption of Interorganizational Business Process Standards in Business-to-Business Integration: An Exploratory Study. Syst. Inf. Manag. 2007, 12, 53–78. [Google Scholar] [CrossRef]
  49. Alamoudi, A.K.; Abidoye, R.B.; Lam, T.Y. An Evaluation of Stakeholders’ Participation Process in Developing Smart Sus-tainable Cities in Saudi Arabia. Smart Sustain. Built Environ. 2024, 13, 1074–1095. [Google Scholar] [CrossRef]
  50. Hair, J.F.; Anderson, R.E.; Tatham, R.L.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis; Pearson Education Ltd.: London, UK. [CrossRef]
  51. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  52. Agarwal, R.; Prasad, J. The Antecedents and Consequents of User Perceptions in Information Technology Adoption. Decis. Support Syst. 1998, 22, 15–29. [Google Scholar] [CrossRef]
  53. Van Tam, N.; Diep, T.N.; Toan, N.Q.; Le Dinh Quy, N. Factors Affecting Adoption of Building Information Modeling in Construction Projects: A Case of Vietnam. Cogent Bus. Manag. 2021, 8, 1918848. [Google Scholar] [CrossRef]
  54. Jørgensen, M.; Gruschke, T. Industrial Use of Formal Software Cost Estimation Models: Expert Estimation in Disguise? In Proceedings of the Conference on Evaluation and Assessment in Software Engineering (EASE’05), Keele, UK, 11–12 April 2005; pp. 1–7. [Google Scholar]
  55. Safiki, A.; Solikin, M.; Sahid, I.H.N. Analysing Cost Implications of Building Design Variables as Areas of Modelling to Achieve Value for Money: A Study on Selected Buildings in Surakarta and Surrounding Areas. Master’s Thesis, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia, 2016. [Google Scholar]
  56. Alwisy, A.; Bouferguene, A.; Al-Hussein, M. Factor-Based Target Cost Modelling for Construction Projects. Can. J. Civ. Eng. 2018, 45, 393–406. [Google Scholar] [CrossRef]
  57. Liu, H.; Kong, F.; Yin, H.; Middel, A.; Zheng, X.; Huang, J.; Xu, H.; Wang, D.; Wen, Z. Impacts of Green Roofs on Water, Temperature, and Air Quality: A Bibliometric Review. Buildings 2021, 11, 794. [Google Scholar] [CrossRef]
  58. Alwisy, A.; Barkokébas, B.; Bu Hamdan, S.; Gül, M.; Al-Hussein, M. Energy-Based Target Cost Modelling for Construction Projects. J. Build. Eng. 2018, 20, 387–399. [Google Scholar] [CrossRef]
  59. Raes, L.; Michiels, P.; Adolphi, T.; Tampère, C.; Dalianis, A.; McAleer, S.; Kogut, P. DUET: A Framework for Building In-teroperable and Trusted Digital Twins of Smart Cities. IEEE Internet Comput. 2021, 26, 43–50. [Google Scholar] [CrossRef]
  60. Sunindijo, R.Y.; Hadikusumo, B.H.W.; Phangchunun, T. Modelling Service Quality in the Construction Industry. Int. J. Bus. Perform. Manag. 2014, 15, 262–276. [Google Scholar] [CrossRef]
  61. Tseng, M.-L.; Lin, S.; Chen, C.-C.; Sarmiento, L.S.C.; Tan, C.L. A Causal Sustainable Product-Service System Using Hierar-chical Structure with Linguistic Preferences in the Ecuadorian Construction Industry. J. Clean. Prod. 2019, 230, 477–487. [Google Scholar] [CrossRef]
  62. Yu, R.; Gu, N.; Ostwald, M.J. Computational Design: Technology, Cognition and Environments; CRC Press: Boca Raton, FL, USA, 2021. [Google Scholar] [CrossRef]
Buildings 15 01668 g001
Figure 1. The output of the SEM for the ATAM consists of constructs in common with TAM. Note: blue = key factors of the proposed model, orange = independent factors, and reference numbers indicate the question numbers in the survey.
Figure 1. The output of the SEM for the ATAM consists of constructs in common with TAM. Note: blue = key factors of the proposed model, orange = independent factors, and reference numbers indicate the question numbers in the survey.
Buildings 15 01668 g001
Table 1. A summary of constructs of the ATAM and the hypothetical relationships proposed in literature.
Table 1. A summary of constructs of the ATAM and the hypothetical relationships proposed in literature.
ConstructsVariables Relevant Hypotheses Past Research
USCEH.3.1c. The organization’s cost-effectiveness has a significant effect on user satisfaction.Seghezzi et al. [41]
UBUS and ATH3.12. User behavior has a significant effect on user satisfaction, which in turn affects the decision to adopt technology.
H3.13. User behavior has a significant effect on the managerial decision to adopt the technology.		Venkatesh et al. [38]
Client Satisfaction (CS)CE, RD, BP, PT, SD, and ECH.3.1b: The organization’s cost-effectiveness has a significant effect on client satisfaction.
H.3.3b: The organization’s Brief Development has a significant effect on client satisfaction.
H.3.4b: The organization’s project time has a significant effect on client satisfaction.

H.3.5b: The organization’s service quality has a significant effect on client satisfaction.
H.3.2b: The organization’s result demonstrability has a significant client satisfaction.
H3.9: Client satisfaction has a significant effect on user satisfaction in adopting technology. 		Lu and Zhang [42]
PUCE, RD, BP, PT,
SD, EC, and TC		H.3.3a: The organization’s Brief Development has a significant effect on the perceived usefulness.
H.3.1a: An organization’s cost-effectiveness significantly affects the perceived usefulness of a technology.
H.3.4a: The organization’s project time has a significant effect on the perceived usefulness.
H.3.5a: The organization’s service quality has a significant effect on the perceived usefulness.
H.3.6a: Organizations will perceive technology to be useful if environmental considerations are considered.
H3.7. Organizations will perceive technology to be useful if training considerations are considered.		Venkatesh et al. 2003 [43]
Perceived Ease of Use (PEU)UF and USH3.8. Organizations will perceive a technology to be easy to use if the User Interface is easy to understand and deal with.
H3.14. Perceived ease of use has a significant effect on user behavior.		Venkatesh et al. [38]
Table 2. Reliability measurement.
Table 2. Reliability measurement.
FactorsCronbach’s AlphaStandardized Factor Loading Items
Brief preparation0.9180.919
Result demonstrability0.8570.864
Project time0.8470.845
Environmental considerations0.9430.943
Service quality0.7980.800
Cost-effectiveness0.5030.539
Training considerations0.7700.772
User-friendliness0.8840.883
User satisfaction0.4750.520
Client satisfaction0.6980.654
User behavior0.8470.865
Table 3. Evaluation of the overall fitness of the conceptual model.
Table 3. Evaluation of the overall fitness of the conceptual model.
Fitness IndexRecommended ValueValue
RMSEA<0.080.129
CFI0.90.663
NFI0.90.610
IFI0.90.635
TLI0.90.587
Table 4. Results of path analysis. Key: PU = perceived usefulness, PEU = perceived ease of use, CS = client satisfaction, UB = user behavior, US = user satisfaction.
Table 4. Results of path analysis. Key: PU = perceived usefulness, PEU = perceived ease of use, CS = client satisfaction, UB = user behavior, US = user satisfaction.
PathEstimateS.E.C.R.pp < 0.05
PU <- Result Demonstrability (RD)0.6430.12527.5640.000Significant
PU <- Training Considerations (TCs)0.6430.11629.0130.000Significant
PU <- Project Time (PT)0.6430.12031.6260.000Significant
PU <- Service Delivery (SD)0.6430.12528.1230.000Significant
PU <- User-Friendliness (UF)0.7070.12724.1700.000Significant
PU <- Cost-Effectiveness (CE)0.6430.14321.4150.014Significant
PU <- Brief Preparation (BF)0.6430.12724.9920.000Significant
PU <- Environmental Considerations (ECs)0.4430.12724.7390.000Significant
CS <- Cost-Effectiveness (CE)0.6430.12732.9580.023Significant
CS <- Result Demonstrability (RD)0.6430.12631.5300.021Significant
CS <- Brief Preparation (BF)0.6430.12730.2710.000Significant
CS <- Service Delivery (SD)0.7130.12630.1160.000Significant
CS <- Environmental Considerations (ECs)0.3160.12631.3700.000Significant
CS <- Training Considerations (TCs)0.6430.12531.4170.000Significant
CS <- Project Time (PT)0.6430.12931.0150.000Significant
UB <- Perceived Usefulness (PU)0.6080.10640.9770.000Significant
UB <- Perceived Ease of Use (PEU)0.5540.11932.4510.000Significant
US <- Client Satisfaction (CS)0.4030.10838.3160.000Significant
US <- User Behavior (UB)0.6620.11037.5680.000Significant
US <- Cost-Effectiveness (CE)0.2590.10439.0130.000Significant
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MDPI and ACS Style

Algassim, H.; Sepasgozar, S.M.E.; Ostwald, M.J.; Davis, S. Developing a Novel Architectural Technology Adoption Model Incorporating Organizational Factors and Client Satisfaction. Buildings 2025, 15, 1668. https://doi.org/10.3390/buildings15101668

AMA Style

Algassim H, Sepasgozar SME, Ostwald MJ, Davis S. Developing a Novel Architectural Technology Adoption Model Incorporating Organizational Factors and Client Satisfaction. Buildings. 2025; 15(10):1668. https://doi.org/10.3390/buildings15101668

Chicago/Turabian Style

Algassim, Hesham, Samad M. E. Sepasgozar, Michael J. Ostwald, and Steven Davis. 2025. "Developing a Novel Architectural Technology Adoption Model Incorporating Organizational Factors and Client Satisfaction" Buildings 15, no. 10: 1668. https://doi.org/10.3390/buildings15101668

APA Style

Algassim, H., Sepasgozar, S. M. E., Ostwald, M. J., & Davis, S. (2025). Developing a Novel Architectural Technology Adoption Model Incorporating Organizational Factors and Client Satisfaction. Buildings, 15(10), 1668. https://doi.org/10.3390/buildings15101668

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