Exploring Perceptions of the Adoption of Prefabricated Construction Technology in Pakistan Using the Technology Acceptance Model

Abstract

Prefabricated construction is being pursued globally as a critically important sustainable construction technology. Prefabricated construction technology (PCT) provides opportunities to effectively manage construction waste and offers venues to address the poor productivity and lackluster performance of construction projects, which are often expected to miss their budget and schedule constraints. Despite the significant benefits inherent in the adoption of PCT, research has shown an unimpressive exploitation of this technology in the building sector. A modified version of the popular technology acceptance model (TAM) was used to understand Pakistan’s building construction industry stakeholder’s acceptance of PCT and the factors that influence its usage. Data were collected from 250 building construction experts in the industry to test the hypotheses derived from the proposed model. Data analysis using covariance-based structural equation modeling revealed that construction industry stakeholders’ perceptions of perceived ease-of-use, perceived usefulness, trust, and satisfaction all strongly influenced PCT acceptance behavior. Moreover, results also confirmed the total direct and indirect effects of the perceived usefulness and perceived ease-of-use of behavioral intention toward using PCT, with trust and user satisfaction as mediators. The results of this research are expected to serve as a guide for the construction industry stakeholders to effectively plan, strategize, encourage, and increase the adoption of PCT to achieve sustainable construction outcomes in the building construction sector.

1. Introduction

The construction industry contributes significantly to national and global economies. Construction is one of Pakistan’s most neglected industries. Comparing the construction industry to other industries, such as manufacturing, the construction industry is considered backward [1]. The industry lacks regulations, standards, mechanization, advanced technology, and a waste management plan [2]. Construction processes have always been criticized for project time overruns, low productivity, a lack of security, and waste generation [3]. There are still issues with traditional onsite construction practices, such as a lack of regulations and high construction waste generation [4]. Therefore, there is a need to change the construction industry environment through technological advancements for sustainable growth in the construction industry. To improve overall quality and efficiency, the process of construction must be improved based on sustainability parameters [5]. The key is to innovate and remove the many obstacles that prevent the sector from creating a sustainably built environment. Increasing construction demand, rising costs, and critical environmental issues have prompted a worldwide search for innovative sustainable alternatives, such as prefabricated construction. Prefabricated construction has been identified as a vital means of overcoming these issues and ensuring sustainable development and green buildings in the construction industry. In today’s world, prefabricated buildings are associated with modern and innovative ecological qualities. Environmentally friendly building materials are simple to use in prefabricated buildings. The customization of prefabricated buildings based on location, layout, and materials used is possible. All of this gives the end user more options and flexibility [6]. Thus, it is critical to assess the current landscape to properly discuss the benefits of prefabrication and other construction methods. Therefore, it is indispensable to study advanced construction techniques.

Previously, there are a number of studies that have been published relating to the acceptance of prefabricated construction technology. Lee and Kim (2018) highlighted the variables that are driving the construction industry to adapt modular construction and they give some recommendations for future vertical extension [7]. Karthik et al. (2020) investigated the benefits and limitations of modular construction and compared benefits with conventional construction [8]. Similarly, Seo and Lin (2020) conducted a case study to examine the environmental implications of prefabricated construction and its characteristics, and also shed light on some advantages of prefabricated construction [9]. The current status of prefabrication adoption in small-scale construction projects was evaluated by Khahro et al. (2019) [10]. Similarly, Adindu et al. (2020) experimentally explored the understanding, adoption, possibilities, and difficulties of applying the prefabricated construction technology method to construction infrastructure projects [11]. Attempts were made by El-Abidi et al. (2019) to investigate the current state of prefabricated construction systems and the potential of these systems to satisfy the housing needs of the people of Libya [12]. Wu et al. (2019) examined the impact of technology promotion and cleaner production on the use of prefabricated construction technology in China to improve its application [13].

In prefabricated construction technology related previous studies, various research gaps were found; first, prefabricated construction technology acceptance has not been studied quantitatively [14]. Second, no study investigated the effect of trust and satisfaction as mediators in technology acceptance literature related to the construction industry [15]. Third, no study investigated the technology acceptance model (TAM) to establish a theoretical framework to account for prefabricated construction technology acceptance in the construction industry. To fill these research gaps, the present study proposed a prefabricated construction technology-based technology acceptance model for construction stakeholders that integrates the technology acceptance model with trust and user satisfaction as mediators. Therefore, developing and presenting a proposed model for prefabricated construction technology adoption based on the technology acceptance model is the prime objective of this research. The technology acceptance model was selected because it has been widely used to explain technology acceptance and human behavior in different research areas; for example, BIM and AR integration in the construction industry [16], and construction safety [17]. Relationships between the perceived usefulness (PU) and perceived ease-of-use (PEOU) of prefabricated construction technology are explored and developed in the presence of certain influencing factors. After reviewing the literature on these technology acceptance model variables and external variables, stakeholders’ behavioral intention toward prefabricated construction technology adoption is being studied using an extended technology acceptance model. An analysis of the hypothesized relationships is conducted, with conclusions drawn.

The theoretical and empirical contributions of this study are unique and essential. To explain behavioral intentions, the technology acceptance model uses only two variables, even though it is used as a unique and robust concept by other researchers [18]. As a basic research approach, the technology acceptance model does not inform us what variables might impact customers’ intentions to use prefabricated construction technology. Behavioral intentions [19,20] and performance have been considered to be influenced by trust [21]. Attitudes of people in the tourism sector are strongly influenced by trust, according to research by Kaushik et al. (2015) [20]. Similarly, there has been a considerable influence of customer satisfaction on behavioral intention, as argued by Durdyev et al. (2018) [22]. The combination of satisfaction, and trust (technology acceptance model) is good in this regard. Because of this, the primary goal of this study is to examine the elements that influence people’s intentions to use prefabricated construction technology. This new technology acceptance model based integrated model contends that trust and satisfaction mediate the relations between perceived ease-of-use, perceived usefulness, and behavioral intention to adopt prefabricated construction technology. To author’s knowledge, this study is one of the first to use trust and satisfaction to explore perceived usefulness and perceived ease-of-use in behavioral intention related to the construction industry. Furthermore, this research findings provide valid evidence in favor of utilizing the technology acceptance model in conjunction with additional constructs (external variables) to anticipate prefabricated construction technology acceptance.

The paper is organized as follows. According to previous studies, prefabricated construction technology adoption in construction has been substantiated. Second, a detailed set of hypotheses is presented based on a literature assessment of each measured item in the prefabricated construction technology acceptance model. Third, the survey’s methodology and findings are discussed. Finally, recommendations for further research and the implications are discussed. The research model was tested using data from a sample of construction industry stakeholders (consultants, contractors, architects/engineers, etc.), who were familiar with prefabrication. To generalize the outcomes, participants were selected from different construction project sites. Structural equation modeling (SEM) using AMOS software was employed for hypothesis testing. Based on Anderson and Gerbing’s (1988), research, a two-phased strategy was employed [23]. First, a measurement model was estimated using exploratory (EFA) and confirmatory factor analysis (CFA) to assess overall model fit, validity, and reliability. Second, the structural model was used to test the hypotheses.

2. Literature Review and Model Development

2.1. Literature Review

2.1.1. Technology Acceptance-Related Theories

User perceptions of technology are key factors in adopting, accepting, and using new technologies, and targeting these perceptions can help avoid resistance and increase the chances of success [12]. Many studies have described the role of user perceptions in accepting new technology in terms of the theory of reasoned action [24], such as the technology acceptance model (TAM) [25], the technology acceptance model 2 (TAM2) [26], and the unified theory of acceptance and use of technology (UTAUT) [27].

The theory of reasoned action was created by Fishbein and Ajzen (1975) [24]. It was the basis for most later theories, such as TAM, TAM2, TAM3, and UTAUT. It clarifies human behavior for technology adoption from a social psychology standpoint. The theory asserts that behavioral intention is affected by two main constructs: subjective norms and attitude toward behavior. The technology acceptance model proposed by Davis was modified from the TRA of Fishbein and Ajzen (1975) [24] and is one of the most widely used research models to predict the acceptance and use of information technology systems. The technology acceptance model was developed to overcome the various weaknesses of the TRA. According to F. Davis (1986), the deletion of subjective norms was justifiable because participants did not have enough information regarding the social influence at the acceptance testing stage [28].

The technology acceptance model is an extension of the TRA as a general psychological theory for individual behavior prediction in information systems [25]. Both perceived usefulness (PU) and perceived ease-of-use (PEOU) were used as technology acceptance model external constructs. A learner’s perception of the usefulness of technology is measured by perceived usefulness. Perceived ease-of-use refers to the assumption that learning using technology requires no intellectual effort. The technology acceptance model is widely used in information systems, electronics, and construction to describe technology acceptance.

Venkatesh and Davis (2000) developed TAM2 to overcome the drawbacks of the technology acceptance model and enhance the model’s explanatory power (R²) [26]. TAM2 has the primary determinants of the original TAM, namely perceived usefulness and PEU. It also considers social impact, including subjective norms, images, and the cognitive instrumental processes, which include output quality, job relevance, and result demonstrability. TAM2 and TAM are widely utilized for explaining an individual’s adoption and technology acceptance in various settings and contexts [29].

The unified theory of acceptance and use of technology (UTAUT) was constructed by Venkatesh et al. (2003) to address the various weaknesses of previous theories [27]. The UTAUT integrates eight of the most well-known previous theories and includes four determinant constructs: social influence, facilitating condition, effort expectancy, and performance expectancy, all of which affect BI [27].

TAM3 reveals the moderating effect of experience, which Venkatesh (2000) [30] and Venkatesh and Davis (2000) [26] did not empirically test. The relationships between (a) PEOU and PU; (b) computer anxiety and PEOU; and (c) PEOU and behavioral intention [31] are moderated by experience. Through the determinants of PU and PEOU, TAM3 has made significant theoretical contributions. In TAM3, there are complementary elements of context, content, process, and individual differences [31].

2.1.2. Prefabricated Construction

Structures that are built onsite using prefabricated components are called prefabricated construction. It is the method of construction in which construction is performed with the help of separate components of structures, e.g., walls or roofs already being constructed in an established offsite factory-based environment before their fabrication at the construction site [32]. PCT modules come in many shapes and sizes for usage in the construction industry. Prefabricated construction is considered a technology in this study, which includes all of the prefabricated components such as stairs, façades, slabs, air-conditioning panels, and balconies. Prefabrication can be divided into three categories: semi-prefabrication, comprehensive prefabrication, and volumetric modular building [33]. On the contrary, modular construction includes transforming a structure into a panel or volumetric-style unit [34]. Modular construction is the practice of construction in which a whole part of a building or structure, such as a room of a building or a whole house, is transported to the construction site for assembling, after finalizing its construction at an offsite facility.

The topic of green construction is primarily focused on the practice of prefabrication [35]. The implementation of prefabricated assembly techniques can significantly reduce the ecological footprint of the construction process while also optimizing the allocation of project resources. Prefabricated buildings have a low environmental impact due to their effective conservation of project resources and significant potential for future growth. This has the potential to facilitate the progression of society toward a more sustainable mode of development [36]. Prefabricated buildings have been advocated as a sustainable development strategy in the construction sector due to the traditional construction method’s lack of suitability for cleaner production [37,38]. Ai et al. (2023) investigated the limits of government regulation of prefabricated buildings and concluded that such structures had the potential to not only exceed previous growth projections for developers but also to fully embody the idea of green development and significantly impact China’s future sustainable development [36]. Prefabricated buildings can boost the sustainability performance of construction initiatives, which in turn encourages the sustainable development of society [39]. Research conducted by Rahardjo and Dinariana (2016) using Bantul and Bandung in Badan city as case studies demonstrated that precast systems were a form of green building since they conserved wood, lowered construction costs, and safeguarded the environment [40].

PCT can help the construction industry in achieving lower costs [41], better HSE [10], improved productivity [41], efficiencies of material and labor resources [41], sustainability [41], quality [10], and a reduction in construction duration [41]. The advantages of PCT in the construction industry show that fully automated production, modernization control, and industrialized production are achievable goals. PCT not only improves quantity, cost, schedule, and material utilization in construction projects but also helps to achieve high mechanization and increased work efficiency. Despite these benefits, the use of PCT in the construction industry has been slow because of higher initial costs [13,42], dominated project processes [43], inadequate policies and regulations [43], a lack of knowledge and expertise [44], a lack of social climate and acceptance [45], and ineffective logistics ([39] and a limited availability of design options/complicated designing [46]).

Buildings in the housing sector are responsible for most of the new construction. Data on the attitudes of Australian builders concerning prefabrication is provided by Steinhardt and Manley (2016) using the theory of planned behavior (TPB) and the technology acceptance model (TAM), resulting in a clarification of beliefs that can guide efforts to enhance the market share of prefabrication [14]. However, despite a challenging stakeholder network and an industrial setting, their views on prefabrication for Australian housing are positive. Due to a lack of industry infrastructure, prefabrication adoption has been slow and is almost entirely unsupportive. The study on offsite technologies in housing by Nanyam et al. (2017) defines a holistic selection framework with a set of offsite-specific attributes along with a set of standard attributes that are necessary and favored for the acceptance of offsite technologies for affordable housing [47]. They also tested and validated the framework in an offsite case study.

PCT is still in its infancy in China, but it will undoubtedly be the future of Chinese construction industrialization. Jiang et al. (2020) focus on the interrelationships of factors affecting PCT promotion [48]. The overall relationship of each factor was quantitatively modeled (SEM). The results show that the policy factor dominates, followed by the management and market factors. In another study on rural residential buildings, Zhou et al. (2019) aim to design a model for determining the suitable strategy for prefabrication implementation [49]. Similarly, Imran et al. (2019) studied the level of adoption of prefabrication in the construction industry of Pakistan [1]. Buildings, roads, and bridges were selected to form a literature review and questionnaire survey.

In the PCT literature, many previous research efforts have tried to investigate user acceptance, establishing frameworks and models for the development of PCT technologies. However, they lack the consideration of the context of establishing a theoretical framework that determines the extent of end-user acceptance. Therefore, using the technology acceptance model may fill this gap by explaining the variance between factors and discussing the significance of external factors to predict and explain the adoption of PCT in developing countries’ construction industry. Additionally, based on this model, construction professionals’ acceptance of PCT will be predicted and explained in terms of perceived usefulness, perceived ease-of-use, and related variables. Therefore, there is a need to identify additional factors (if any) related to prefabricated construction and its validation by the technology acceptance model in the current scenario.

Nonetheless, there is a scarcity of research in Pakistan on PCT acceptance models based on the perspectives of construction industry stakeholders; as a result, the mechanisms for achieving or accepting PCT have yet to be defined.

2.2. Model Development

2.2.1. Overview of Proposed Model

Despite widespread agreement on PCT’s potential applicability and advantages, it remains unclear how PCT could be employed and what its advantages are. As a result, construction research and practice continue to focus on how people perceive PCT acceptance. Therefore, the goal of the study is to understand how PCT is accepted based on empirically validated and proven research models, such as technology acceptance model-related concepts [26,31,50,51].

There is a theoretical basis for each component in the proposed model, as well as additional factors based on previous research on PCT use. A research model for PCT acceptance is provided based on the above concepts. The model comprises (1) project resources, site management, project coordination, and technological factors as an external variable for PCT acceptance, and (2) technology acceptance model-related factors (PEOU, PU) and user trust and satisfaction as mediation factors for the intention to adopt PCT.

2.2.2. External Variables for Prefabricated Construction Acceptance

Extending the standard technology acceptance model, this research proposes that the influence of external variables (e.g., project resources, site management, project coordination, and technological factors) on the intention to use are mediated by user trust, satisfaction, perceived usefulness, and perceived ease-of-use. As a result of selecting external variables, theory development and technological adoption are both enhanced. The existence of external variables directs the steps required to influence increased use by providing a better understanding of what drives perceived usefulness and perceived ease-of-use. The most important variables influencing the adoption of PCT in construction firms are the external variables.

A total of 54 critical factors for PCT adoption were considered from previous studies for this study. The factors were classified into four categories: project resources, site management, project coordination, and technological factors.

In an attempt to develop a technology acceptance model for PCT adoption, this research identified the external factors by exploring the constructs that can influence PCT adoption by diverse stakeholders. As previously stated, this research examines the details of enablers and drivers in the literature. Even though numerous studies have looked at what makes PCT usable, these earlier studies have limitations since they do not account for the potential effects of factors related to the execution stage and may, therefore, only partially explain the reason why actual technology use is insufficient. One of the most common obstacles to PCT cited is the execution stage, policies, etc. Since the successful completion of a building project is a crucial component, these particular types of construction present unique challenges. As a result, this research focuses on identifying external factors that could affect perceived usefulness and perceived ease-of-use from an execution perspective. It could be categorized into the content of project resources, site management, project coordination, and technological features.

Project resources are related to low-cost and sustainable approaches undertaken in prefabricated building construction. Building construction performance depends on the performance of parties and resource availability. Consequently, the optimized use of resources and materials seems to be another indicator for the prolonged advancement of project sustainability performance. An organization’s willingness to embrace the notion of a sustainable economy and take constructive efforts toward sustainable development can be shown in its propensity to organize its activities and resource use strategy to respect the rights of subsequent generations to environmental resources. So, to comprehend the mechanism of PCT adoption, this research includes project resources.

Site management is related to time, quality, safety, and logistics/site operations. Creating a strong organizational culture is a powerful tool to influence employees’ behavior and improve their performance. Successful site management includes time management, improved quality of construction, safer construction, and better site operations in a factory-controlled environment. The site management factors that are involved and the implementation of the variables in those factors that lead to the improvement of the prefabricated construction building performance results in the inclusion of this construct in our model.

Project coordination is related to the coordination of staff and tasks and the simplification of activities. Collaboration has been a facilitator of PCT, helping to change problem-addressing behavior, and as a crucial component in PCT practitioners’ relationships. Simply moving the building process inside a factory was noted for the benefit of a central coordination point for organizing staff across multiple projects. The construction sector is said to be highly disorganized, dependent on cooperation, and reliant on communication. The success of a project depends on timely, precise communication among all involved stakeholders. All of these promote collaboration in PCT procurement. In this respect, the concept of project coordination was included in the proposed research model.

Technological features are related to technology and innovation, industry/market culture and knowledge, improved productivity, and the efficiency of materials and labor. The availability of locally manufactured plants and equipment, skilled personnel resources, the breadth and depth of local material resources, and the depth of use of such local construction resources are all indicators of suitable construction technology, resulting in alleviating the industry’s performance. Similarly, the importance and role of innovation in construction and its future are also very much evident in the literature. Based on this, the construct of technological features was included in the research model. These factors were employed to establish hypotheses to comprehend the psychological mechanism of PCT acceptance. This led to the creation of an extended technology acceptance model that accounts for external factors.

2.2.3. Mediating Variables

In construction management research, the role of mediating factors and their mechanisms remains relatively understudied. Little is known about how perceived usefulness and perceived ease-of-use lead to user trust and satisfaction and how they interact to facilitate PCT adoption. That is why it is being argued that mediating factors are important to help us understand the prefabricated building processes that influence PCT adoption.

When discussing technology, “trust” is synonymous with confidence in the system’s ability to function as intended. To be more precise, it is the belief that a piece of technology will aid one in accomplishing a task because of its usefulness, dependability, and functionality. In the early stages of adopting new technology, this can be a decisive component in overcoming the risk and skepticism that users may feel. The incorporation of trust in the technology acceptance model revealed its importance in predicting customers’ intention to use new technologies in many studies, which why it has been in the research model as a mediator that is considered along with satisfaction.

Customer satisfaction is defined as “the degree to which a customer is pleased or dissatisfied with the performance or outcome of a product as compared to the customer’s expectations.” Satisfaction is considered an important variable due to its high effects on customers’ future behavior and attitudes about certain products or services. The level of client satisfaction an organization achieves is inextricably linked to the quality, pricing, timeliness, and accessibility of the items it offers. Therefore, to examine the influence of satisfaction of users in the context of prefabricated building construction, it has been incorporated in the proposed model as a mediator.

According to the concept of the technology acceptance model, it is, therefore, argued that satisfaction and trust mediate the effect of perceived ease-of-use and perceived usefulness on behavioral intention. Perceived ease-of-use could elicit behavioral intention along the first mediating path (through trust). We argue that clients who consider the PCT technologies as beneficial and simple to use and have high trust have a higher intention to employ the technologies. The second mediating path emphasizes satisfaction as a vital foundation to enhance behavioral intention. It is anticipated that people with a high level of satisfaction with a certain technological product are more inclined to adopt it.

According to the literature review, the success of a construction project can be evaluated by a set of criteria specified by various scholars. According to Sue and Ritter (2012), when the terminology and terms used in the survey are inaccurate, the survey’s validity is compromised [52]. As a result, the content of the designed survey was evaluated before the study’s final version. Experts are asked to assess whether the constructed survey measures the necessary content, which is known as face validity [53,54]. This will help to elicit recommendations from reviewers based on prior knowledge and expertise [55]. In this research, the developed questionnaire was tested in collaboration with three academic experts. The face validity test was successful, and several different versions of the questionnaire were created before the final one. Several grammatical issues were raised. Factors were reduced to 36. Various scale items were replaced and rephrased. An overall positive response was received with some remarks on the questionnaire layout design and question wording. The questionnaire was modified because of these suggestions.

As a result, before the data collection step, the questionnaire items were clear and understandable. As stated in

Table 1. Literature review for the research hypotheses.

No.	Hypotheses	Description of Category	Relevant Literature
H1	Project resources affect perceived usefulness positively.	External factors	[37,56]
H2	Site management affects perceived usefulness positively.		[42,57]
H3	Project coordination and collaboration affect perceived ease-of-use positively.		[14,58]
H4	Technological features affect perceived ease-of-use positively.		[57,59]
H5	Perceived ease-of-use affects perceived usefulness positively.	Technology acceptance model factors	[50]
H6	Perceived usefulness affects behavioral intention positively.		[50]
H7	Perceived usefulness affects user trust positively.		[60]
H8	Perceived usefulness affects user satisfaction positively.		[61,62]
H9	Perceived ease-of-use affects user trust positively.		[60,63]
H10	Perceived ease-of-use affects user satisfaction positively.		[64,65]
H11	Perceived ease-of-use affects behavioral intention positively.		[50]
H12	User trust affects behavioral intention positively.	Mediating factors	[66]
H13	User satisfaction affects behavioral intention positively.		[22,67]
H14a	Perceived usefulness mediates the relationship between PEOU and the intention to use PCT.		[68]
H14b	Perceived usefulness mediates the relationship between PEOU and user satisfaction.		Proposed by the authors
H14c	Perceived usefulness mediates the relationship between PEOU and user trust.		Proposed by the authors
H15a	User trust mediates the relationship between PEOU and the intention to use PCT.		[69]
H15b	User trust mediates the relationship between PU and the intention to use PCT.		[69]
H16a	User satisfaction mediates the relationship between PU and the intention to use PCT.		[70,71]
H16b	User satisfaction mediates the relationship between PEOU and the intention to use PCT.		[70]

, the hypotheses are scientifically formulated based on some rationale and supporting research. The relevant literature is the outcome of studies that are related to every research variable.

2.2.4. Proposed Model

In this paper, a research model for empirical analysis of the intention to accept PCT is proposed, based on the technology acceptance model’s previous literature review (Figure 1). Project resources, site management, project coordination, technological factors, perceived ease-of-use, perceived usefulness, and behavioral intention to accept PCT are among the 36 observed indicators in the proposed model, and 9 latent constructs are described here (assessment items and factors).

Based on the proposed research model, hypotheses are formulated (

Table 2. Constructs with measurement items.

Construct	Definition	Items	Measured Variables
Project Resources (PR)	Project resources are related to low-cost and sustainable approaches undertaken in prefabricated building construction.	PR1	I find prefabricated construction technologies affordable/low costly.
		PR2	I think using prefabricated construction yields savings due to on-site labor reduction.
		PR3	I find reduction in construction waste while adopting and using prefabricated construction.
		PR4	Using prefabricated construction reduces energy consumption.
Site Management (SM)	Site management is related to time, quality, safety, and logistics/site operations.	SM1	I find more control of construction quality while using prefabricated construction.
		SM2	I find it feasible to adopt and use prefabricated construction as it enhances easy transport of heavy components (size restrictions) and increases site accessibility.
		SM3	Using prefabricated construction enhances speed of construction.
		SM4	I find reduction in elevated work and dangerous activities by adopting and using prefabricated construction.
Project Coordination and Collaboration (PCC)	Project coordination is related to the coordination of staff and tasks and the simplification of activities.	PCC1	Using prefabricated construction enhances logistics processes coordination/Supply chain coordination.
		PCC2	It is desirable to use prefabricated construction involving information transparency.
		PCC3	I find prefabricated construction to enhance simplification of the construction process.
		PCC4	Given the ease of handling, I intend to use prefabricated construction.
Technological Features (TF)	Technological features are related to technology and innovation, industry/market culture and knowledge, improved productivity, the efficiency of materials, and labor.	TF1	Given the industry knowledge, experience, awareness, availability modular producers and suppliers, I expect that I would use it.
		TF2	Using prefabricated construction enhances reduction of material use.
		TF3	I think adopting and using prefabricated construction to be feasible due to improved flexibility and adaptability.
		TF4:	It is desirable to use prefabricated construction due to work on-site continues irrespective of whether the product is being made elsewhere.
Trust (T)	Trust is the belief that a piece of technology will aid one in accomplishing a task because of its usefulness, dependability, and functionality.	T1	Based on my experience with the prefabricated construction technologies will have integrity.
		T2	Centered on my experience with the prefabricated construction will be reliable.
		T3	Overall, prefabricated construction techniques will be trustworthy.
		T4	Based on my experience with prefabricated construction, I believe that this technology will provide good services.
Satisfaction (SAT)	Customer satisfaction is defined as “the degree to which a customer is pleased or dissatisfied with the performance or outcome of a product as compared to the customer’s expectations”.	SAT1	I am satisfied with the performance of prefabricated construction technologies.
		SAT2	I am pleased with the experience of using the prefabricated construction technologies.
		SAT3	I am satisfied with the prefabricated construction efficiency and effectiveness.
		SAT4	Overall, I am satisfied with the prefabricated construction.
Perceived Usefulness (PU)	Perceived usefulness is defined as the extent to which stakeholders believe that utilizing PCT is useful.	PU1	Using prefabricated construction would improve construction industry performance.
		PU2	Using prefabricated construction would increase construction productivity.
		PU3	Using prefabricated construction would enhance the effectiveness of the construction industry.
		PU4	I would find prefabricated construction useful in the construction industry.
Perceived Ease-of-Use (PEOU)	Perceived Ease-of-use is defined as the extent to which construction industry stakeholders perceive that using PCT does not involve substantial effort.	PEOU1	Learning to operate prefabrication techniques would be easy for me.
		PEOU2	I would find it easy to get prefabricated construction to do what I want it to do.
		PEOU3	It would be easy for me to become skillful at using prefabricated construction.
		PEOU4	I would find prefabricated construction easy to use.
Behavioral Intention (BI)	Behavioral intention is the probability or a measure of the strength of one’s intention to perform a specific behavior toward using technology and, therefore, it determines technology acceptance.	BI1	I support the adoption and use of prefabricated construction in construction industry projects.
		BI2	Intend to increase use of prefabricated construction to perform construction activities in the future.
		BI3	Given that I had access to prefabricated construction (technology, materials and equipment’s), I predict that I would use it in construction industry projects.
		BI4:	I will recommend others to use prefabricated construction in performing construction activities.

). The basis for the developed hypotheses was described in the previous section. Hypotheses were tested using AMOS 23.0 and SEM. The measurement model was first estimated to test the overall fit of the model, as well as its validity and reliability. Second, using the structural model, the hypotheses were tested between constructs.

The constructs, definitions, and measurement items, along with the respective codes used in this research, are shown in following Table 2.

3. Research Methodology

The relationships between the research model’s constructs were investigated using a quantitative cross-sectional design. Nine constructs were derived from previously validated instruments used in similar circumstances to produce research instruments. There are references in Appendix A to prior studies from which the items in the questionnaire for these constructs were obtained. There were multiple items for each construct, measured on a five-point Likert scale (1–5), i.e., (1) strongly disagree to (5) strongly agree.

The pilot study is used to remedy any lack of required quality, assure the clarity of the questionnaire items, and eliminate phrasing errors, according to [54]. Few researchers used small sample sizes in their studies [72,73]. Before collecting full-scale data, a 30-person pilot study was conducted to test instrument reliability and validity [74]. The questionnaire was sent to a random sample of construction industry stakeholders (engineers, contractors, consultants) in Pakistan. The value of Cronbach’s alpha calculated for the pilot study sample considering the entire questionnaire (36 items) was 0.890. The pilot study gave acceptable results for the measurement items through Cronbach’s alpha test.

3.1. Sampling and Procedure

The population of interest for this study was stakeholders of all construction companies in Pakistan. This research study’s sampling frame consisted of only major cities in Pakistan due to time and cost constraints. Companies from both the public and private sectors were involved. Researchers, project managers, contractors, and employees make up the unit of analysis. A sample of at least 200 participants was necessary for the data analysis technique and the analysis of moment structures (AMOS) [75,76]. A total of 350 questionnaires were distributed. The sample was recruited from different construction companies. This sample was selected to meet specific criteria: participants who were 18+ years old, owned or used offsite construction techniques and other prefab-related technologies, and involved in the construction industry.

The current study utilized the Statistical Package for the Social Sciences (SPSS) and analysis of moment structures (AMOS) software to perform statistical analyses. The statistical software package SPSS was utilized for data coding, data cleaning, the verification of assumptions, and conducting exploratory factor analysis. In contrast, AMOS was used to evaluate a measurement model’s validity, reliability, discriminant validity, and goodness-of-fit indices. The structural model in AMOS was utilized to conduct hypothesis testing. Finally, mediation analysis was carried out using PROCESS Macro v4.0.

3.2. Common Method Bias

Harman’s single-factor test is a prevalent technique used in academic literature that serves as a simple and frequently utilized approach to identify and mitigate common method bias (CMB). CMB is a plausible origin of measurement error that has the potential to distort the associations between variables and compromise the credibility of research outcomes [77]. The present study involved the implementation of Harman’s single factor test utilizing an un-rotated single factor constraint within the SPSS v21 software. The initial component extracted was found to explain a variance of less than 22.472%. The study’s results indicate that the presence of common method bias was not a significant issue.

Common method bias (CMB) [77] was tested on the measurement items used in this study, which represents the variance attributed to the method of measurement rather than the variance explained by the constructs in this study. Harman’s single-factor test [77,78], which uses an unrotated factor analysis using a single component, is a standard method to detect CMB. The test revealed that the first factor explained only 22.47% (below 50%) [78] of the variance in the data, indicating the lack of dominance of a single factor, and CMB was not a substantial issue in this study. The research method consisted of two phases. The measurement model was first validated. A structural model and path analysis were used to analyze the relationship between the constructs for hypothesis testing. IBM SPSS and AMOS were used to analyze data.

4. Results and Discussion

The results of the analysis are revealed in this section. The assumption of linearity and normality of constructs was made before the statistical analysis of the obtained data. The scale’s reliability and validity were tested using a dataset of n = 250. For the hypotheses that were developed for this study, the following are the measurements, structural models, and path analysis results.

4.1. Descriptive Statistics

The questionnaires were distributed via databases of organizations throughout Pakistan that are related to the construction industry. Out of 300 distributed questionnaires, 260 responses (representing 86% of the total distributed) were valid, and 3% of the survey responses were invalid or missing, resulting in 250 remaining responses. Most participants (n = 102) were architects/engineers, and these constituted about 41% of the sample. A total of 57 respondents were project/construction managers, 22 respondents were consultants, 40 were contractors, and 29 respondents were academics/ researchers. The percentages of the sample size constitute about 22.8%, 8.8%, 16%, and 11% of the sample, respectively. A 5-point Likert scale ranging from “strongly disagree” to “strongly agree” was used for each question.

4.2. Reliability of Constructs

Cronbach’s reliability test was utilized to determine the robustness of the measurement model used for the final SEM evaluation. For Cronbach’s alpha, a cutoff value of 0.7 was used to indicate acceptable levels of initial consistency [75]. There was good reliability (0.890) for each of the items in the final SEM that measured all latent variables.

4.3. Exploratory Factor Analysis

As far as current knowledge permits, this study represents an initial attempt to investigate the factor structure underlying the adoption of PCT. Thus, exploratory factor analysis was used because the author’s could not make a confident estimate of the number of factors contained in this measure. To ascertain the dimensions that underlie the various variables incorporated in this investigation, we employ the maximum likelihood approach, with the Promax and Kaiser normalization rotation methods, for optimal results. [79]. The cutoff criterion that is acknowledged in the literature for its practical consequences is maintaining items with factor loading minimums that are less than 0.35 [79].

To determine whether the data on the respondents is suitable for factor analysis before the factors are extracted, many tests need to be performed [80]. Two statistical tests commonly used in research are the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. In factor analysis, the KMO index is utilized with a threshold of 0.5, and its values fall within the range of 0 to 1. The application of factor analysis is deemed suitable solely when the statistical significance of Bartlett’s test of sphericity is established (p < 0.05).

4.4. Measurement Model Assessment

Purification of items is vital. Thus, EFA was used to assess the observed variables’ transparency. The 36-item survey was subjected to exploratory factor analysis (EFA) to see if the underlying structure in the data was present. Initially, EFA was used to assess the validity of the measurement model using maximum likelihood in SPSS 21. Using the Kaiser–Meyer–Olkin (KMO) sample adequacy method, its value is 0.830, and any value above 0.5 is considered acceptable [81]. Using nine constructs, 60.97 percent of the total variance is explained, which is quite high. In these tests, all nine constructs and responses were sufficient for factor analysis. EFA uses the maximum likelihood method, with Promax rotation and Kaiser normalization for better results [79]. After EFA, a confirmatory factor analysis (CFA) was performed by SEM using AMOS to verify recognized factors.

The measurement model was evaluated using CFA, and validation of the measurement model was performed with discriminant and convergent validity and reliability [75]. Construct validity was established by following the criteria specified by [82] and, which states the following: When performing a CFA, convergent and discriminant validity must be established. Testing a causal model is useless if factors do not show adequate validity and reliability. Measures of validity and reliability include composite reliability (CR), average variance extracted (AVE), maximum shared variance (MSV), and average shared variance (ASV). The thresholds [75] for these values are as follows: reliability (CR > 0.7), convergent validity (AVE > 0.5), discriminant validity (MSV < AVE), and the square root of AVE greater than inter construct correlations. AVE > 0.50 indicates adequate convergent validity. All AVE values in

Table 3. Validity and reliability.

	PR	PU	SAT	PEOU	T	PCC	TF	SM	BI
Cronbach’s Alpha	0.732	0.912	0.858	0.877	0.837	0.851	0.85	0.783	0.87
CR	0.755	0.861	0.812	0.854	0.84	0.853	0.852	0.757	0.821
AVE	0.515	0.756	0.59	0.661	0.569	0.593	0.59	0.51	0.606
MSV	0.088	0.497	0.57	0.364	0.28	0.021	0.088	0.063	0.57
MaxR (H)	0.83	0.863	0.813	0.862	0.852	0.861	0.857	0.765	0.825
PR	0.718	0.275	0.049	0.171	0.091	0.056	0.297	0.209	0.109
PU		0.87	0.622	0.469	0.344	−0.028	0.067	0.218	0.705
SAT			0.768	0.508	0.382	0.087	0.003	0.251	0.755
PEOU				0.813	0.353	0.144	0.285	0.081	0.603
T					0.754	0.078	0.108	0.11	0.529
PCC						0.77	0.052	−0.057	0.114
TF							0.768	−0.066	0.142
SM								0.714	0.146
BI									0.778

Note: Values in bold are the square root of the AVE for each construct.

are above this threshold, indicating convergent validity. Construct reliability (CR) > 0.70 indicates internal consistency or adequate convergence. As shown in Table 3, all CR values are above this threshold, indicating internal consistency.

The utilization of SPSS was initially employed to assess the reliability and validity of the data. As shown in Table 3, Cronbach’s alpha values for the variables ranged from 0.732 to 0.912, while the CR values ranged from 0.755 to 0.861. These values surpass the standard threshold of 0.7, signifying the credibility and reliability of the scale data. Additionally, the measured items displayed strong internal consistency and overall reliability.

Construct validity was evaluated via convergent and discriminant validity. Convergent validity was established by making sure the average variance extracted (AVE) values of the variables exceeded 0.5, and the composite reliability (CR) values were above 0.7, as illustrated in Table 3. These findings show acceptable convergence validity. By confirming that the square root of the AVE values for each variable was greater than the correlation coefficients between the variables, discriminant validity was established. Table 3 shows that for all variables, the square root of the AVE values was greater than the correlation coefficients, demonstrating good discriminant validity.

The measurement model indicates covariance between latent variables and standardized weights or indicator loadings for the variables. Two variables from PU, and one each from SAT, PEOU, SM, PR, and BI were removed to improve model fit. The values of the standard error of the coefficient imply more reliable predictions and smaller confidence intervals [82]. As a result, there is no need to reduce the variables, and they can be used as predictors of their respective latent variables. In this study, the measurement model has a high level of reliability, convergent validity, and discriminant validity.

Table 4. Model fit indices of the measurement model (adopted from Hanif et al. (2018) [84]).

Model Fit Indices	Reference Range	Measurement Model Estimate	Structural Model Estimate
CMIN		488.233	452.133
DF		338	328
CMIN/DF	Between 1 and 3	1.444	1.378
CFI	>0.95	0.953	0.959
SRMR	<0.08	0.049	0.058
RMSEA	<0.06	0.042	0.039
PCLOSE	>0.05	0.943	0.985

provides a mix of absolute and incremental measurement model fit indices commonly reported in SEM-related literature. The results show that the model fit is good, as none of the fit indices are outside the acceptable range. In the absence of any issues with fit indices, no further model modifications were made. The chi-square and values of CFI and SRMR indicate excellent model fit [82,83].

The study determined the fit indices of the proposed model, which yielded root mean square error of approximation (RMSEA) values of 0.042 and 0.039, as well as a comparative fit index (CFI) of 0.953 and 0.959. The presence of these indices indicates an adequate level of fitness. Table 4 shows the goodness-of-fit indices, which serve as an indicator of the degree to which the data align with the model. The SEM path analysis did not violate the thresholds of the fit indices, as reported in previous studies.

4.5. Structural Model Assessment

This study conducted a multicollinearity test to see if there was a problem with multicollinearity. The value of VIF for multicollinearity assessment should be around 1.0 and less than 3.0. There was no evidence of multicollinearity because all VIF values were less than 3.0 and around 1.0.

Because relations are allocated between constructs based on studies, the structural model attempted to identify dependencies between model constructs. The structural model was evaluated using a two-step approach, as suggested by Cheng, (2001) [85]. The structural model’s goodness-of-fit indices (GOF) are evaluated first, and then standardized parameter estimates are utilized to support causal relationships and testing hypotheses. The first step is to evaluate and test the overall model, GOF, using the same criteria as the measurement model. It is preferable to have a structural model GOF nearer to the measurement model’s fit values. The hypothesized structural model is presented in Table 4.

The standardized coefficients and hypothesis testing results are in

Table 5. Parameter estimates and results of the hypotheses. (*** Indicating significance at p < 0.001).

			Estimate	S.E.	C.R.	p
PEOU	<---	PCC	0.182	0.096	1.891	0.059
PEOU	<---	TF	0.342	0.089	3.842	***
PU	<---	SM	0.306	0.133	2.306	0.021
PU	<---	PR	0.206	0.099	2.090	0.037
PU	<---	PEOU	0.386	0.067	5.723	***
SAT	<---	PU	0.480	0.078	6.196	***
T	<---	PU	0.219	0.080	2.726	0.006
SAT	<---	PEOU	0.229	0.065	3.553	***
T	<---	PEOU	0.195	0.071	2.757	0.006
BI	<---	PU	0.284	0.075	3.785	***
BI	<---	SAT	0.406	0.090	4.515	***
BI	<---	T	0.221	0.058	3.814	***
BI	<---	PEOU	0.171	0.057	3.015	0.003

. AMOS results show a strong significant effect for all factors except project coordination and collaboration (PCC), which is not significant (H3). It appears that the hypothesized model is adequately fitted, as the GOF statistics are within acceptable parameters. Considering these results (Table 5), Figure 2 summarizes the proposed model.

The summary of the hypotheses testing is presented in Table 5, which shows that twelve hypotheses are accepted and only one is rejected. The output of the structural equation model revealed that the hypothesized constructs, including PR, SM, TF, PU, PEOU, T, and SAT, have a significant positive effect (p < 0.05). PU was significantly influenced by two constructs, i.e., PR (p = 0.037) and SM (p = 0.021). Thus, H1 and H2 are supported. Similarly, PCC had a significant influence on PEOU (p = 0.059) but not TF (p < 0.001). Therefore, H4 was supported, However, PCC (H3) tends to be not significant and rejected as a result of this analysis. Trust was associated with PU (p = 0.006) and PEOU (p = 0.006), and both of these were also predictors of satisfaction, with statistics supporting their significance (p < 0.001). Thus H7, H8, H9, and H10 were supported. PU (p < 0.001), PEOU (p = 0.003), T (p < 0.001), and SAT (p < 0.001) were four predictors of BI, supporting H6, H11, H12, and H13, respectively.

4.6. Mediation Analysis

Recommendations described by Preacher et al. (2007) were followed to test the hypothesized meditation model, including a bootstrapping procedure [86]. PROCESS macro developed by Hayes (2017) was used, which was based on the analytical conceptualization of Preacher et al. (2007) [86,87]. To test the hypotheses, the first hierarchical multiple models (mediated relationships) were developed, as suggested by hypotheses H14a,b,c-H15a,b-H16a,b, and then in SPSS, the independent variables, mediators, and dependent variables were analyzed in separate steps.

Table 6. Mediation analysis.

Mediation Path	Total Effect (β)	Direct Effect (β)	Indirect Effect (β)	S.E	Indirect Effect and 95% Confidence Interval
					LL	UL
PEOU-PU-BI	0.5535	0.2960	0.2576	0.0391	0.1860	0.3380
PEOU-SAT-BI	0.5535	0.2289	0.3246	0.0404	0.2483	0.4062
PEOU-T-BI	0.5535	0.4364	0.1171	0.0300	0.0630	0.1807
PU-SAT-BI	0.7593	0.3612	0.3981	0.0461	0.3097	0.4894
PU-T-BI	0.7593	0.6419	0.1174	0.0275	0.0654	0.1738
PEOU-PU-SAT	0.4528	0.2156	0.2372	0.0349	0.1714	0.3091
PEOU-PU-T	0.3034	0.1945	0.1089	0.0412	0.0357	0.1951

shows the total, direct, and indirect effects of the perceived ease-of-use (PEOU) on behavioral intention (BI) toward using PCT, with perceived usefulness (PU) as a mediator. PEOU had a significant effect on BI = 0.5535, p = 0.000, according to the mediation results. PEOU had a significant and positive impact on BI β = 0.2960, p = 0.000, revealing that the increase of one unit in PEOU will result in a change of 0.2960 units in BI. The findings also reveal that the indirect effect of PEOU on BI across PU is also substantial β = 0.2576 (0.1860, 0.3380). This shows that PU acts as a mediator between PEOU and BI. The results of this study strongly confirm hypothesis H14a, which states that PU partially mediates PEOU and BI.

Similarly, the role of PU as a mediator between PEOU and SAT in the form of hypothesis 14b, β = 0.2372 (0.1714, 0.3091) and PU as a mediator between PEOU and T in the form of hypothesis 14c, β = 0.1089 (0.0357, 0.1951) is also significant, supporting hypotheses H14b and H14c, respectively.

The outcomes indicate total, direct, and indirect effects of the perceived ease-of-use (PEOU) and perceived usefulness (PU) on behavioral intention (BI) toward using PCT, with user trust (T) as a mediator. The findings reveal substantial indirect effects of PEOU on BI, with T as a mediator β = 0.1171 (0.0630, 0.1807), and PU on BI, with T as a mediator β = 0.1174 (0.0654, 0.1738), which strongly supporting hypotheses H15a and H15b, respectively. Similarly, results also show total, direct, and indirect effects of the perceived usefulness (PU) and perceived ease-of-use (PEOU) on behavioral intention (BI) toward using PCT, with user satisfaction (SAT) as a mediator. As per the results of Table 6, user satisfaction plays a significant role as a mediator between PU and BI β = 0.3981 (0.3097, 0.4894), and between PEOU and BI β = 0.3246 (0.2483, 0.4062), supporting hypotheses H16a and H16b, respectively.

4.7. Discussion

This study examines the association between the perceived usefulness and perceived ease-of-use of hypothesized external factors and the behavioral intention toward using PCT. Table 5 demonstrates that most of the hypotheses are supported. The goodness-of-fit (GOF) measurements of the proposed model confirm that it can adequately reflect the obtained data and assist in understanding the behavioral intention of construction practitioners toward adopting PCT. The relevance of every model construct is then addressed, as revealed by hypothesis testing.

The study’s findings show that several hypotheses (H1, H2, H4, H5, H6, H7, H8, H9, H10, H11, H12, and H13) are supported and only one (H3) is not supported. The proposed model hypothesized that PR directly affects PCT’s perceived usefulness (H1). It means stakeholders are more likely to consider PCT technologies useful if they believe they have adequate project resources. Other researchers have empirically found [56,58] that the project resources of PCT are a determinant of perceived usefulness. This result also validated the claim of M. Li et al. (2017) [88] that prefabricated construction is the way of the future for China’s construction sector, as it is a green building type that offers energy savings and environmental protection. Similarly, researchers assumed site management has a direct positive impact on prefabricated construction’s perceived usefulness (H2). As a result, stakeholders are more likely to perceive PCT technologies as useful if they perceive competent site management. Other researchers have also empirically found [42,57,89] that the site management of prefabricated construction is a determinant of perceived usefulness. The findings of this study support the hypothesis that PCT in Pakistan has a good influence on site management and perceived usefulness. Construction practitioners who know that employing technological approaches in the construction industry can boost their benefits tend to project resources and management. These findings also suggest that stakeholders tend to go for innovative approaches in the construction industry when they can benefit from those technologies.

Contrary to expectations, project coordination and collaboration did not affect PCT’s perceived ease-of-use (H3). This finding contradicts many previous studies that found the same belief system persists. Refs. [14,58,90] hypothesized and found that project coordination and collaboration play a significant role in affecting perceived ease-of-use based on many past research findings. However, this study found no significance for project coordination and collaboration in perceived ease-of-use prediction. This conclusion is surprising since it seems to disagree with the broadly accepted idea that projects with a higher level of simplification and ease of management are more likely to influence stakeholders’ preferences.

The findings support hypothesis H4, which states that technological features directly influence PCT’s perceived ease-of-use. Among the external variables, the path coefficient signifies the strongest significant relation between technological features and perceived ease-of-use. Thus, it confirms the importance of technological features in PCT usage in Pakistan and shows the importance of technological factors in ease-of-use. The numerous features of this technology, such as flexibility and adaptability, are the reason for the significance of this construct. Hence, they also find ease in their use while using these technologies because of their flexibility and usage irrespective of weather conditions, which in turn increases their motivation to accept further automation in construction. Prefabrication is a new technology in Pakistan’s construction sector, so stakeholders need easy-to-use PCT. This finding is consistent with many construction-related empirical studies [42,61]. Technological features have a considerable favorable effect on the perceived ease-of-use of PCT in Pakistan, according to this research.

Trust was assumed to have a positive effect on PCT behavioral intention (H12). The result shows that trust and behavioral intention have the strongest direct relationship. This research is in line with the previous research carried out by Gefen and Straub (2004), which found that consumers were primarily motivated by the technology’s ability to give trust [91]. On the other hand, user satisfaction was hypothesized to influence PCT behavioral intention positively (H13). The results show that user satisfaction improves behavioral intention. Thus, the SAT–BI relationship is one of the strongest direct relationships. Many studies imply that the SAT–BI relationship is strongest [22,92]. These findings suggest that user trust and satisfaction are strong predictors of user satisfaction toward using PCT due to the fact that they find PCT useful and easy to use. Thus, construction industry professionals who have trust and satisfaction when using PCT will be more likely to accept and recommend it to others.

Perceived usefulness is defined as the extent to which stakeholders believe that utilizing PCT is useful (Davis, 1986) [28]. Prefabricated construction is more useful for promoting sustainable development regarding the economy, society, and the environment when compared to conventional construction methods [93], which also aligns with the intention to attain sustainable development on a global scale. These benefits align with the principles of sustainable and green building, which prioritize minimizing the environmental impact of building projects while creating healthy and functional spaces for people to live and work. The study hypothesized that perceived usefulness directly affects PCT behavioral intention (H6). The results reveal that perceived usefulness positively affects behavioral intention, which is consistent with previous literature [26,50], where the technology’s usefulness and functionalities were the primary motivators for users. Thus, this study confirms the favorable influence of perceived usefulness on behavioral intention to use PCT in Pakistan. Correspondingly, this study concludes a significant positive influence of perceived usefulness on the satisfaction of PC (H8), which is also consistent with previous literature [61], where the satisfaction and features provided by the technology were the primary motivators for users. Thus, our study confirms the strong and favorable influence of perceived usefulness on PCT customer satisfaction in Pakistan. Similarly, the positive impact of perceived usefulness on the trust of PCT was also hypothesized (H7), which was followed previous literature [69,94] where the technology’s usefulness and trustworthiness were the primary motivators for individuals. As a result, the findings of this study show the existence of a strong and favorable influence of perceived usefulness regarding trust to use PCT in Pakistan.

For the current study, perceived ease-of-use is defined as the extent to which construction industry stakeholders perceive that using PCT does not involve substantial effort [28]. Perceived ease-of-use was hypothesized to have a direct impact on PCT’s perceived usefulness in this study (H5). The results show that perceived ease-of-use positively affects perceived usefulness, confirming that people perceive PCT as useful when it is easy to use. With less effort required to use these technologies, stakeholders perceive them as more useful. Prefabrication reduces the amount of time and effort required for construction workers to learn new skills. In line with other technology models, such as the TAM [25]. On the other hand, the perceived ease-of-use of PCT is assumed to directly impact behavioral intention (H11). This research shows that perceived ease-of-use has a favorable effect on behavioral intention. This result may be explained by the fact that prefabrication in construction is new in Pakistan, and thus user-friendliness is critical. This study’s findings support previous research on models such as TAM, TAM2, the PEOU determinants model, and TAM 3. This study confirms the positive effect of perceived ease-of-use on PCT intention in Pakistan. This study hypothesized that perceived ease-of-use has a direct positive influence on user satisfaction with PCT (H10). According to this study, Perceived ease-of-use has a direct impact on PCT user satisfaction (H10). This indicates that perceived ease-of-use has the strongest direct influence on user satisfaction. These findings support prior research [64,65] that found that users were largely motivated by the level of satisfaction they had with the technology and its features. Thus, this study supports a strong and favorable influence of perceived ease-of-use on PCT user satisfaction in Pakistan. Similarly, perceived ease-of-use was assumed to have a positive influence on the trust of PCT (H9). The results demonstrate that perceived ease-of-use had a favorable influence on trust, as anticipated [60,69,95], because individuals were mostly driven by the ease-of-use and trust provided by the technology. So, this study shows that perceived ease-of-use has a favorable effect on trust when using PCT.

An important contribution of this research is the addition of user trust and satisfaction to the technology acceptance model to explain the behavioral intention toward adopting PCT. This study’s findings demonstrated that the intention to embrace PCT was strongly influenced by both trust and satisfaction (H12, H13). The findings provide insight to the role of perceived usefulness and perceived ease-of-use in influencing PCT adoption by identifying the mediator variables. It is observed that trust and satisfaction (H15a,b, and H16a,b) mediated the relations between perceived usefulness, perceived ease-of-use, and the intention to utilize PCT. Prior researchers who used these constructs did not study how trust and satisfaction function as mediating factors in influencing the intention to use [22]. As a result, this article proved a model by developing mediation between perceived usefulness, perceived ease-of-use, and intention to use. A further benefit of the model is that it accounts for 73% of the variance in the dependent variable (BI). Further study is required to update the technology acceptance model in light of the constant evolution of construction technology and the emergence of newer technologies to automate the construction industry. As a first step, the proposed model is likely to inspire subsequent research in a variety of domains. Figure 3 illustrates the final model.

5. Conclusions and Recommendations

The first objective of the study was met by proposing an extended version of the TAM-based hypothesized model duly supported by previous researchers. The usage of technology in Pakistani construction is rising, and stakeholders are expected to take advantage of it. To account for factors affecting PCT uptake in developing countries, an extended version of the technology acceptance model is proposed. Project resources, site management, and technological factors have a significantly favorable effect on using PCT technologies. Project coordination and collaboration do not seem to influence the use of these technologies by stakeholders. Similarly, user trust and satisfaction play a significant role as mediators in the proposed model.

The second objective of the study was met by empirically validating the extended version of the TAM-based hypothesized model, which was duly supported by the goodness-of-fit and other statistical parameters. The projected result of this study is expected to function as a guide for forthcoming research on construction technology in Pakistan. The provision of assistance will aid professionals in the construction industry, consultants, and contractors with international operations in Pakistan to efficiently strategize their project planning. The recently developed 36-item PCT scale, based on the established hypothesis, can serve as a valuable tool for construction stakeholders in Pakistan to help prioritize their attempts toward boosting prefabrication. The outcomes of the research are limited to the construction sector of Pakistan; however, they have the potential to be generalized to other civil engineering ventures and emerging markets that share comparable working conditions. Moreover, it is anticipated that the PCT scale and the technology acceptance model that have been formulated will possess validity in developing countries that exhibit comparable work settings, customary practices, and geographical principles. Similarly, the methodologies adopted for this study, i.e., identification of the PCT variables, SEM analysis, and investigation of the mediation relationship using PROCESS can be used in every sector and any part of the world.

The findings of this research emphasize the potential of accepting sustainable PCT via the technology acceptance model. According to Jin et al. (2018) [96], the endorsement of prefabricated construction is deemed a significant advancement toward attaining sustainable development within the construction sector. Prefabricated construction has been deemed a significant advancement toward attaining sustainable development in the construction sector, as it facilitates more efficient use of materials and resources, reduces waste, shortens construction times, and lowers costs while encouraging sustainable construction practices. The close interconnection between sustainable PCT and sustainable and green building practices points out the promising pathway that prefabricated construction offers for accomplishing sustainability goals in the building sector. In addition, the controlled environment of a factory setting enables better quality control, which leads to buildings that are more energy efficient and environmentally friendly. As such, the adoption of sustainable PCT can play a key part in advancing sustainability in the construction industry and contributing to a more sustainable built environment.

A new model based on the technology acceptance model (TAM) is proposed in this research, which includes trust and satisfaction as mediating factors to better explain the factors that contribute to the acceptance and utilization of PCT in the construction industry in Pakistan. As the paper concludes, “the findings can be used to help construction companies plan for Prefabricated Construction Technology adoption by identifying relevant influential factors and providing a theoretical framework for future research.” Despite this, the study does have a few limitations, such as the fact that it relies solely on quantitative data, employs a cross-sectional study approach, and focuses primarily on Pakistan’s construction industry. To better understand the factors influencing PCT acceptance and usage in developing countries, the study suggests conducting additional research employing longitudinal studies, qualitative approaches, and other external variables and mediating factors.