Exploring the Acceptance of the Technical Disclosure Method Based on 3D Digital Technological Process by Construction Workers through the Perspective of TAM

Abstract

Effective pre-control methods for construction workers during the construction phase are important means to ensure the final quality of the construction product. The 3D digital technological process introduces model-based definition (MBD) to the construction industry and enhances construction process management to some extent by combining it with building information modeling (BIM). However, the proper application of the 3D digital technological process requires a good knowledge of the factors associated with its acceptance. This study introduced the 3D digital technological process into technical disclosure and further explored the factors that may influence its acceptance among the construction worker population. Several new extrinsic variables, cognitive level, subjective norms, technology anxiety, and enjoyment, were added and evaluated in the technology acceptance model (TAM), and a total of 314 data samples were collected to verify the hypothesized paths. The results showed that the proposed model was effective in predicting the attitude of the worker population towards this method.

1. Introduction

As one of the largest industries in the world, the construction industry still faces many serious issues, such as construction safety, costs, and quality control. It is well known that quality is paramount to the success of a construction project [1]. Fragmented and sloppy construction modes are a major cause of poor building product quality reflected in all phases of the project process, of which the construction phase has the most significant impact on the overall quality of the engineering entity [2]. Therefore, it is important to adopt effective quality control methods for this phase.

Building quality is a collaborative effort involving many parties. From this perspective, quality can be expressed as the degree of refinement of deliverables accomplished within a fixed time and cost and measured against recognized standards and the needs of the various parties involved [1]. The nature of the construction project determines that quality management is a key aspect throughout the project. Previous research explored the applicability and possibility of a variety of technological approaches in promoting quality management in the construction industry, such as Total Quality Management (TQM) [3] and ISO 9000 quality management system [4]. However, the aforementioned research [3,4,5] on quality management mainly centered on the implementation possibilities and challenges, with a lack of targeted application and case validation during the construction phase. In addition, with the continuous improvement of quality management theory, the focus of its attention has gradually shifted from after-control to in- and before-control [6]. Real-time monitoring and assessment of project quality [7], acquisition and management of quality inspection data [8], and monitoring and alarming of workers’ safety behavior [9] are currently hot topics in the field of in-process control. Moreover, it is worth noting that workers are the actual executors of engineering projects, and the processes and details of workers’ operations during the construction process directly determine the final quality of the project [6].

At present, the research on the standardization of construction workers’ processes during construction is still in its infancy [10]. Compared with the time-consuming and labor-intensive real-time monitoring of the quality of the project in the construction process, it is more advisable to adopt effective pre-control measures for workers to fundamentally improve construction quality. A complete construction process consists of a number of construction sections in series, each of which is divided into a number of steps. The latter is also regarded as a refinement of each construction section. The standardized implementation of each section is an important guarantee of the construction quality of the project. However, in practice, due to inefficient training methods and outdated quality management tools, etc., it is often difficult for workers to accurately understand and realize quality control requirements, which, in turn, leads to eventual quality defects. It is not hard to find that current research on pre-control in the construction industry is mainly focused on innovation and validation of the effectiveness of safety training methods, and in particular, on the use of VR [11,12]. The research on training in construction techniques and technological process training for workers is relatively limited. Therefore, it is necessary to further explore more effective process quality control methods at the pre-process stage in order to deepen the workers’ understanding of the techniques and thus improve the quality of construction.

In recent years, digital technology has flourished and is of broad application prospect. In the construction industry, digital technologies, such as blockchain, IoT, and BIM, have been widely used to remove information barriers in various segments of construction and to optimize facility management [13,14]. Although digital design technology in the construction industry has become increasingly mature with the popularity of various types of 3D building design software based on BIM, the 3D digital process technology of construction products still lags behind and deserves further research [15]. MBD [16], first applied to the aerospace industry, is a digital definition method that integrates the design and manufacturing information of a product through a 3D model. This method gives full play to the advantages of intuitive visualization of the 3D model and organizes the information on design, manufacturing, and quality requirements in a modeled form to contain both the final state of the product and the intermediate state of the production phase of the product, which can intuitively reflect the dynamics of the process. MBD is now widely used in the representation and digital management of processes in the manufacturing industry. Wang [17] explored a non-rotational part modeling method based on MBD and Pro/ENGINEER; Zhang [18] investigated MBD-based integrated product design techniques for complex and customized products to achieve efficient collaboration of different business activities; Geng [19] proposed a method for issuing 3D instructions for lightweight assembly of complex products by combining MBD techniques with lightweight models. It is evident that the effectiveness of MBD as a digital technology to assist process management and quality control has been well proven in the manufacturing industry. Though the construction industry is similar to the manufacturing industry, the research on the application of MBD in the construction industry is still in its infancy. Liu [15] introduced MBD into the construction process management and proposed a method for 3D digital technological process of construction products in combination with BIM to deepen the workers’ understanding, which provided a new approach to process management and process quality management in the construction industry. Figure 1 illustrates the flow of the technological process digitization proposed by Liu. However, the research on the acceptance of this technology among the construction worker population and the relevant influence factors is very limited.

With the acceleration of the process of scientific development, various types of information and communications technologies (ICTs) have been constantly innovated and evolved. Multiple theories and models have been used to assess the acceptance and dissemination of these technologies in different industries. The commonly used ones include the technology acceptance model (TAM) [20,21], the unified theory of adoption and utilization of technology (UTAUT) [22,23], the innovation diffusion theory (IDT) [24], the technology-organization-environment (TOE) framework [25,26], and the task-technology fit (TTF) model [27]. Among these, TAM has been widely used in studies to explore user acceptance of computer information technology and is considered to be a concise model for explaining user technology adoption behavior [28,29]. Its prediction of user acceptance can be as high as 50% [30]. Meanwhile, there have been studies [31,32,33] applying TAM to explore technology adoption in the construction industry. Therefore, TAM was chosen as the theory framework of this research. Based on the theory of reasoned action, Davis [30] proposed the technology acceptance model (TAM) to explain people’s acceptance of information technology and identified perceived usefulness (PU) and perceived ease of use (PEU) as the two main variables that determined users’ attitudes towards technology and were also directly influenced by some external variables. Several scholars have introduced a variety of factors to improve and extend the model mentioned above to explore the acceptance of different technological systems by different populations in various fields. For example, Abdullah [34] explored the impact of several of the most common factors, such as self-efficacy as well as subjective norms, on students’ use of e-portfolio systems; Ahmad [35] investigated the role of factors such as perceived irreplaceability and perceived trustworthiness in the willingness of elderly diabetic patients to continue using wearable devices. Moreover, TAM has also been widely used in the construction industry. Zhang [36] investigated the factors influencing low acceptance of VR technology by combining perceived price value, self-efficacy, and perceived playability as external variables with TAM and found that perceived ease of use had the most significant effect on intention to use; Park [31] used an extended TAM to investigate the determinants of acceptance of web-based training by professionals in the construction industry and found that user satisfaction was the most prominent indicator; Billanes [32] used TAM as the theory framework to study the acceptance of residents towards the adoption of smart energy technologies in their residential buildings. From these studies, it can be inferred that the use of TAM has provided a valuable reference for the adoption and dissemination of different ICTs in the construction industry. In these studies, the research subjects were mostly employees of construction companies, construction students, engineers, and members of trade associations, etc., while few centered on the group of construction workers. Moreover, at present, much of the research on this group focuses on areas such as personal protective equipment and wearable devices. For example, Wong [37] introduced safety management practice factors into the TAM model and investigated construction workers’ attitudes toward personal protective equipment (PPE); Man [38] built a PPE acceptance model for construction workers (PAMCW) by integrating the theory of planned behavior with TAM. Technical disclosure is an important pre-control of the project quality [39]. Traditional technical disclosure has the problems of formalization and inefficiency, and the introduction of information technology into technical disclosure has become a new tendency. Xu [39] applied BIM to technical disclosure and confirmed its advantages in improving the quality of this process by discussing real cases; Wu [40] proposed a method for automatic generation of technical disclosure documents based on the knowledge element model, which helped to reduce the workload of managers. The 3D digital technological process is an emerging technology based on MBD, but there have been few attempts to combine it with technical disclosure. Therefore, our study mainly introduced the 3D digital technological process into construction technical disclosure, aiming to explore the factors affecting workers’ acceptance of this new method through TAM. Specifically, cognitive level, subjective norms, technology anxiety, and enjoyment were set as antecedent variables for PU and PEU. Structural equation modeling (SEM) was used to identify the supporting or hindering role of the aforementioned factors in the acceptance of this approach by the construction worker group. The results of this study provided valuable information for predicting the acceptance of 3D digital technological process by construction workers in technical disclosure and technical training and may provide a relevant theoretical basis for further study of the PU and PEU in the group of construction workers.

2. Research Model and Hypotheses

The research model in this study was extended based on TAM. In TAM, PU and PEU are the two most important factors and are affected by external variables. The key to explaining user acceptance of a technology is to understand the antecedents of the core structure of TAM, which are external factors [41]. TAM with external factors can give researchers and developers specific help in technology upgradation while predicting the adoption of the technology [42]. Therefore, many scholars have extended TAM with different external factors to explain the acceptance or adoption of various technologies [21,29,31,43,44]. Among them, in studying the acceptance of web-based training (WBT) by construction professionals, Park [31] considered the external variables associated with WBT acceptance from four perspectives: individual, social, organizational support, and system issues. Herein, organizational support is understood as a personal perception of the importance of the system perceived by the organization, involving senior leadership value and resource allocation. This paper followed a similar pattern. However, since the hardware and resource part and system issue are more related to the technology than to the people themselves, we have excluded related factors. In addition to this, the management value part and social influence can be summarized as the environmental pressures exerted by others on individuals when they choose to engage in or avoid a behavior. Therefore, these two issues can be considered in combination with the external variable of subjective norms. On this basis, the emotional and practical experience of the technical disclosure was considered from the perspective of the construction workers themselves, and technical anxiety and enjoyment were chosen as external variables accordingly. In addition, the percentage of young people entering the industry has been declining due to the negative aspects of the job, which has led to a significant problem of an aging workforce in the construction industry. Ranasinghe [45] stated that increasing age directly affected a person’s cognitive level and learning ability. At the same time, the education level of the construction worker group is relatively low. Nguyen [46], in the study on the impediments to live-streaming among tea farmers, pointed out that knowledge and experience were key barriers to technology adoption and that tea farmers with higher education were more likely to be interested in adopting new technologies. Since age and education level could not be measured directly in SEM, cognitive level was added as a corresponding external variable. The research model is shown in Figure 2.

2.1. Technology Acceptance Model

Four constructs were included in the original TAM model: PU, PEU, attitude (ATT), and behavioral intention (BI). Among them, PU and PEU had been identified as key factors influencing the acceptance and continued use of technology [47]. PU described the extent to which users deem a particular technology helpful in their work [30], and PEU was defined as the extent to which users accept or use a particular technology without expending too much effort [31]. In this study, PU referred to how construction workers perceived the benefits brought to them by technical disclosure based on the 3D digital technological process, and PEU referred to the level of effort made by construction workers to participate in this technical disclosure. In addition, ATT referred to the extent to which construction workers were willing to accept this form of technical disclosure. Previous studies have shown that PU and PEU were closely related to people’s attitudes towards new technologies [36,48]. Therefore, the following hypotheses were proposed:

H1: 

PU positively affects construction workers’ attitudes toward 3D digital technological process-based technical disclosure.

H2: 

PEU positively affects construction workers’ attitudes toward 3D digital technological process-based technical disclosure.

2.2. Cognitive Level

In this study, cognitive level referred to the extent to which construction workers understood as well as were willing to try out the technical disclosure method based on the 3D digital technological process. Nguyen [46] pointed out that the lack of relevant knowledge and experience was a significant deterrent to the PU and PEU of a technology. The more people become knowledgeable about the technology and the use of the associated equipment, the more they understand the benefits that this technology can bring. On the contrary, in the case of insufficient cognitive level, people’s attitudes are more hesitant [49]. This will particularly be the case in the increasingly aging construction industry, where age affects people’s perception, learning ability, and cognitive level [50]. Older people may be less familiar with the operation of new technologies and have a lower level of awareness of new technologies than younger people, leading to a more difficult acceptance of new technologies. Therefore, the following hypotheses were proposed:

H3: 

Cognitive level positively affects PU.

H4: 

Cognitive level positively affects PEU.

2.3. Subjective Norms

Subjective norms are a kind of passive factors promoting action. Several past studies have shown that subjective norms are an important factor in explaining people’s adoption of a technology [29,33,34,41,51]. Possessing social characteristics, each individual is not independent and is in constant interaction with others. In this research, subjective norms were defined as the extent to which the individual perceived that other people, such as his workmates as well as supervisors, who exerted a significant influence on his behavior, believed that he should accept this new way of technical disclosure, which was a form of pressure and influence from others. The importance of subjective norms is more significant when the information technology to which people are exposed is completely new and if the adoption of this technology is mandatory, just as the acceptance of technical disclosure based on the new technology leaves little room for refusal for the workers [29,41]. If those around them are more supportive of a technical disclosure based on the 3D digital technological process, then the workers themselves will be more inclined to adopt an optimistic and positive attitude. Therefore, the following hypotheses were proposed:

H5: 

Subjective norms positively affect PU.

H6: 

Subjective norms positively affect PEU.

2.4. Technology Anxiety

Technical anxiety is a negative emotional response that refers to the emotions such as nervousness and helplessness that workers feel when faced with this new way of technical disclosure [52]. When individuals are exposed to new technologies, those with high levels of anxiety are prone to “absent-minded” behaviors, leading to a decrease in PU [53]. By analyzing patient acceptance of a home telecare system, Rahimpour [52] found that technology anxiety affected people’s assessment of PU and PEU. Currently, the files of the 3D digital technological process can only be presented in exe file form. While this format is free from the limitations of the CATIA platform, it still requires a computer environment to view the complete technological process. This may cause workers who are not familiar with computer operations to feel uneasy or anxious due to the fear that they may not be able to understand or use it well, thus negatively affecting the PU and PEU. Therefore, the following hypotheses were proposed:

H7: 

Technical anxiety negatively affects PU.

H8: 

Technical anxiety negatively affects PEU.

2.5. Enjoyment

Emotions have an important influence on the acceptance of new technology by individuals. This variable measured the degree of pleasure and interest aroused by the new technical disclosure method to the workers. The role of enjoyment in technology acceptance has been investigated in many studies [31,34,44,54]. The results suggest that higher levels of enjoyment lead to the promotion of better acceptance of a technology. Enjoyment reduces the perceived burden and anxiety of using new technology and influences people’s assessment of ease of use through increasing their sense of experience [55]. Park [31] confirmed the positive relationship between enjoyment and PU and PEU when investigating the influencing factors affecting the acceptance of web-based training in the construction industry. In the context of this study, workers who experience more fun from this new approach may also have higher PU and PEU. Therefore, the following hypotheses were proposed:

H9: 

Enjoyment positively affects PU.

H10: 

Enjoyment positively affects PEU.

3. Research Method

3.1. Survey Design and Data Collection

Since masonry structure is one of the most commonly used structural forms in the construction industry due to its advantages of easy material extraction, low cost, and excellent thermal insulation, and heat preservation performance, the infilled masonry wall was chosen as the research example for this study. Firstly, the researchers generated the files of the 3D digital technological process for the infill masonry wall based on the technical route proposed by Liu [15] and then contacted the project managers and builders involved in the research. Before the formal experiment, the researchers sent them the files and asked for their opinions on the content. Their suggestions led to a more rigorous linguistic description in the expression of the technology. During the experiment, the workers began by viewing the files through a projection device in a conference room, a process that lasted about 10 min. Afterwards, workers were provided with free time to use the computer. Using the interactivity of the exported files, workers could select different sections to view on their own. At the same time, the corresponding model of each process could be adjusted by using the right mouse button and long press to change the view angle, which allowed workers to fully observe the model according to their own needs, thus deepening their understanding of the technological process and especially the details of the practice. This process lasted about 15 min. The workers were then asked to fill out a questionnaire, and upon completion, the workers were thanked and dismissed.

In order to collect data for the study of the model, a two-part questionnaire was designed, consisting of a background survey of the workers interviewed and questions corresponding to the extended TAM structure. In the first part, participants were asked to inform about their gender, age, level of education, and years of participation in the workforce. In the second part, participants would answer a total of 21 questions related to the seven constructs to explore their acceptance of the technical disclosure based on the 3D digital technological process. These questions referred to the previous literature and were modified descriptively to fit the research context of this study, as shown in

Table 1. CFA results of the measurement model.

Construct	Item	Loading	CR	AVE	References
Cognitive Level (Cronbach’s α = 0.906)			0.907	0.764	[34,46]
CL1	I don’t know much about 3D digital technological process	0.911
CL2	I don’t know the benefits of participating in technical disclosure based on 3D digital technological process	0.861
CL3	I am willing to accept or attempt technical disclosure based on 3D digital technological process	0.849
Subjective Norms (Cronbach’s α = 0.910)			0.910	0.772	[31,34,56]
SN1	My colleagues think that I should try this technical disclosure method	0.904
SN2	My superiors think that I should try this technical disclosure method	0.846
SN3	People who are important to me think that I should try this technical disclosure method	0.885
Technology Anxiety (Cronbach’s α = 0.899)			0.903	0.758	[31]
TA1	This technical disclosure method makes me feel anxious	0.846
TA2	This technical disclosure method makes me feel confused	0.831
TA3	This technical disclosure method makes me feel confused	0.931
Enjoyment (Cronbach’s α = 0.940)			0.940	0.840	[31,34]
ENJ1	I think this technical disclosure method is novel	0.923
ENJ2	I think this technical disclosure method is interesting	0.886
ENJ3	I think this technical disclosure method makes me willing to participate	0.939
PU (Cronbach’s α = 0.956)			0.958	0.883	[30,31,57]
PU1	This technical disclosure method allows me to have a more direct understanding of the technological process	0.938
PU2	Compared to other methods, this technical disclosure method is more efficient	0.908
PU3	Compared to other methods, this technical disclosure method is more targeted and clearer	0.972
PEU (Cronbach’s α = 0.895)			0.894	0.738	[46,57]
PEU1	This technical disclosure method is convenient	0.867
PEU2	This technical disclosure method is easy to understand	0.876
PEU3	The computer operation required to learn this technical disclosure method is not difficult	0.834
Attitude (Cronbach’s α = 0.931)			0.931	0.819	[31,36]
ATT1	I think this technical disclosure method is a wise choice	0.903
ATT2	I am satisfied with the results of this technical disclosure method	0.886
ATT3	Compared to other methods, I prefer this technical disclosure method	0.925

. Each question was measured using a five-point Likert scale, with 1 indicating strong disagreement and 5 indicating strong agreement.

The final data samples came from workers in a total of ten projects under construction under two construction organizations in Nanchang, Jiangxi Province, China. The survey lasted from 30 June 2023 to 20 July 2023, and a total of 337 questionnaires were collected. After excluding 23 incomplete questionnaires and invalid questionnaires with exactly the same answers to the questions, a final total of 314 questionnaires were used for analysis. The majority of respondents, 84.4%, were male. More than half of the respondents were older than 40, with the largest number of workers in the 40–50 age range at 40.1%. Overall, the respondents had a low level of education, with 66.6% of the workers receiving only junior high school education or less. Meanwhile, 51.2% of the respondents had more than 10 years of working experience, indicating that the majority of the respondents had relatively rich working experience. The demographics are presented in

Table 2. Demographics of the surveyed workers.

	Frequency	Percentage (%)
Gender
Male	265	84.4
Female	49	15.6
Age
≤25	13	4.1
25–30	36	11.5
30–40	86	27.4
40–50	126	40.1
50–60	49	15.6
60–65	4	1.3
Education
Primary school or below	66	21.0
Junior high school	143	45.5
High school	54	17.2
Junior college or above	51	16.2
Length of Service
<5 years	57	18.2
5–10 years	96	30.6
10–20 years	88	28.0
20–30 years	60	19.1
>30 years	13	4.1

.

3.2. Digital Technological Process of the Infilled Masonry Wall

According to the 3D digital technological process route proposed by Liu (Figure 1), firstly, the process of infilled masonry walls was refined in the form of a flow chart. To ensure scientific accuracy, the main sections were sorted out according to a masonry construction program provided by a construction company, including base cleaning, surveying and setting out, rebar planting, hanging wire and standing height pole, preparation of mortar, staying wire and masonry and top oblique masonry. The above major sections were then divided into work steps, as shown in Figure 3, and the quality and technical control points were sorted out to form the process information model. Then, each parameterized unit, including floor slabs, structural beams, structural columns, bottom lime-sand bricks, concrete plus blocks, horizontal and vertical lime joints, height pole, and top oblique bricks was created based on the Revit platform from Autodesk [15]. On this basis, the construction-oriented process design model for the construction was created in the order of the main sections. The process design model consisted of four parts: the front model, the auxiliary process model, the key process model, and the process detail model. In this case, the front model contained the beams, columns, and floor slabs where the masonry infilled walls were located. The auxiliary process model contained the steps of drawing wall lines and control lines (as in Figure 4), hanging vertical lines and standing height poles (as in Figure 5), and hanging horizontal lines. These units would not be in the final infilled masonry wall but were very necessary during construction. The key process model expressed the quality and technical control points, such as masonry planting practices, hanging line considerations, and top oblique masonry practices. The process detail model showed specific operations in the masonry process, such as the out-and-in bond method for the bottom lime-sand bricks (as in Figure 6) and the ‘three-one masonry method’ (one brick, one shovel of mortar and one rub masonry method) for the concrete plus blocks. Afterwards, using the CATIA Composer 2021 of Dassault, the process design model was first lightened, and then, through the view function, different views of the same process were created by changing the visibility of the components in the model, thus achieving a dynamic representation of the process. Finally, the mapping from the process information model to the process design model was completed by adding the corresponding process information to the created view through text boxes, labels, and other functions. Figure 7a,b illustrates this mapping relationship using the top oblique masonry as an example. After completing the above tasks, we exported the created key views as an exe file, which could run directly in Windows without the assistance of the CATIA platform [15]. This exe file provided a complete representation of the technological process for the infilled masonry wall.

4. Results

The analysis of the collected data consisted of two main processes. First, confirmatory factor analysis (CFA) was applied to test the reliability, convergent validity, and discriminant validity of the measurement model. After the results of the measurement model met the requirements, the structural equation model was applied to test the proposed hypotheses.

4.1. Measurement Model

Tests of reliability, convergent validity, and discriminant validity of the measurement model were conducted. The results of the CFA are shown in Table 1. The basic statistical information of the questionnaire data is shown in

Table 3. Basic statistical information.

	Mean Value	Median	Standard Deviation
CL1	3.328	3	1.108
CL2	3.299	3	1.126
CL3	3.290	3	1.157
TA1	2.882	3	1.124
TA2	2.904	3	1.139
TA3	2.908	3	1.138
SN1	3.035	3	1.054
SN2	3.070	3	1.074
SN3	3.089	3	1.021
ENJ1	3.357	3	0.997
ENJ2	3.411	3	1.031
ENJ3	3.341	3	1.041
PU1	3.420	3	1.032
PU2	3.427	3	1.020
PU3	3.424	3	1.020
PEU1	3.083	3	1.156
PEU2	3.156	3	1.172
PEU3	3.121	3	1.142
ATT1	3.239	3	1.014
ATT2	3.248	3	1.032
ATT3	3.242	3	1.028

. The reliability was assessed based on Cronbach’s α coefficient, and the value for each construct was greater than 0.7, indicating that the reliability was acceptable. Moreover, the factor loadings for each item exceeded the threshold of 0.7, indicating good item reliability [58]. Convergent validity was assessed by composite reliability (CR) and average variance extracted (AVE). In the model of this study, the results of CR values ranged from 0.894 to 0.958, which exceeded the acceptable level proposed by Fornell et al. [59]. All AVE values exceeded the recommended requirement of 0.5 [58]. Finally, discriminant validity was analyzed by comparing the square root of the AVE for each construct with the correlation coefficient between the corresponding construct and the others. If the former was greater than the latter, it indicated that there was sufficient discriminant validity between the different constructs. As shown in

Table 4. Correlation matrix and discriminant validity.

	AVE	CL	SN	TA	ENJ	PU	PEU	ATT
CL	0.764	0.874
SN	0.772	0.383	0.879
TA	0.758	−0.487	−0.340	0.871
ENJ	0.840	0.614	0.473	−0.549	0.917
PU	0.883	0.531	0.426	−0.478	0.601	0.940
PEU	0.738	0.570	0.486	−0.578	0.600	0.468	0.859
ATT	0.819	0.475	0.391	−0.451	0.521	0.670	0.603	0.905

CL (Cognitive Level), SN (Subjective Norms), TA (Technological Anxiety), ENJ (Enjoyment), PU (Perceived Usefulness), PEU (Perceived Ease of Use), ATT (Attitude). The numbers in bold are the square root of the AVE for each structure.

, the values on the diagonal, the square root of the AVE for each construct, were greater than the correlation coefficients between the constructs, indicating acceptable discriminant validity.

4.2. Structural Model

The structural model was verified by using the structural equation model (SEM) to test the fit between the model and the data collected. Tests were conducted using AMOS 26. In this study, a total of six fit indices were tested, including chi-square normalized by degrees of freedom (χ²/df), incremental fit index of improved NFI (CFI), goodness-of-fit index (GFI), adjusted GFI (AGFI), the normalized fit index (NFI), and root-mean-square error of approximation (RMSEA). The criteria for a good fit of these six indexes and the corresponding calculated values are given in

Table 5. Model fit results.

Fit Index	Recommended Value	Structural Model
χ2/df	<3	1.498
GFI	>0.8	0.929
AGFI	>0.8	0.905
CFI	>0.9	0.986
NFI	>0.9	0.958
RMSEA	<0.08	0.040

. The results showed that the fit between the structural model and the data was acceptable.

Hypothesis testing was analyzed using AMOS 26 based on the data collected, and the results of the path analysis are shown in

Table 6. Hypothesis testing.

Hypothesis	Relationship	Standardized Path Coefficient	C.R. (t-Value)	Result
H1	PU→ATT	0.497	9.729	supported (p < 0.001)
H2	PEU→ATT	0.370	7.002	supported (p < 0.001)
H3	CL→PU	0.202	3.169	supported (p < 0.01)
H4	CL→PEU	0.225	3.497	supported (p < 0.001)
H5	SN→PU	0.142	2.618	supported (p < 0.01)
H6	SN→PEU	0.204	3.707	supported (p < 0.001)
H7	TA→PU	−0.151	−2.578	supported (p < 0.05)
H8	TA→PEU	−0.284	−4.724	supported (p < 0.001)
H9	ENJ→PU	0.327	4.786	supported (p < 0.001)
H10	ENJ→PEU	0.210	3.071	supported (p < 0.01)

. PU (β = 0.497, p < 0.001) and PEU (β = 0.370, p < 0.001) were both significantly positive for the attitude, which supported Hypothesis 1 and Hypothesis 2. Subsequently, the hypothesized paths of influence of external variables on the basic structure of TAM were all confirmed. The cognitive level had a significant positive effect on PU (β = 0.202, p < 0.01) and PEU (β = 0.225, p < 0.001), which supported Hypothesis 3 and Hypothesis 4. Similarly, subjective norms had a positive and significant impact on both PU (β = 0.142, p = 0.009) and PEU (β = 0.204, p < 0.001), which provided support for Hypothesis 5 and Hypothesis 6. Technology anxiety significantly and negatively affected both PU (β = −0.151, p = 0.01) and PEU (β = −0.284, p < 0.001), which confirmed Hypothesis 7 and Hypothesis 8. The results also indicated a positive and significant path between enjoyment and PU (β = 0.327, p < 0.001) and PEU (β = 0.21, p = 0.002), supporting Hypothesis 9 and Hypothesis 10. The total variance (R2) was used to measure the explanatory power of the model for the variables [60]. In this model, external variables explained 44% of the variance in PU and 52% of the variance in PEU, respectively, while PU and PEU explained 56% of the variance in the attitude.

4.3. Analysis

By simultaneously extending the TAM structure and using the SEM method, the factors influencing workers’ acceptance of the technical disclosure method based on 3D digital technological process were analyzed. The results showed that, on the one hand, both PU and PEU had a positive effect on the workers’ attitudes, while the effect of PU was stronger than that of PEU. On the other hand, with PU and PEU as mediating variables, the three external factors (cognitive level, subjective norms, and enjoyment) all had a positive effect on workers’ attitudes. In addition to this, the effect of technology anxiety on PU and PEU of the workers was significantly negative.

Consistent with the results of previous studies [31,34,44,54], enjoyment was an important determinant of PU and PEU. This indicated that when workers deem this technical disclosure method as being pleasurable, their feelings of its ease of use and usefulness will correspondingly increase, making them more likely to accept it. At the same time, the study found that cognitive level had a direct impact on PU and PEU, which was easy to understand. The technical disclosure method based on the 3D digital technological process is a new concept for workers, and without the guidance of an experienced person, it is difficult for them to identify the specific benefits that can be brought to them. When people are exposed to a new concept without understanding it, their opinions are often passive. In addition, the impact of the length of service of the workers should also be paid attention to. The majority of respondents’ length of service was concentrated in the 5–20-year range (58.6%). The experience accumulated over a long period of time in the field will lead to the formation of stereotypes in the minds of the workers. When confronted with a new method of technical disclosure, workers often easily feel that they already know it very well and will not be willing to spend time and become impatient. At the same time, workers also lack a basic comprehension of the 3D digital technological process, which is not conducive to the promotion of the technical disclosure method based on the 3D digital technological process. Our results also confirmed a positive correlation between subjective norms and PU and PEU, but the path coefficients of subjective norms were smaller than those of enjoyment and cognitive level. One possible reason for this is that technical disclosure, as a part of technology management, is mostly carried out in different forms of training. In this process, workers are passive recipients with no choice of the training form. At the same time, this kind of training is often mandatory, and there is no room for workers to refuse. This status may result in workers being pressured by their superiors to accept and participate in the technical disclosure, but it does not actually affect their perception of the usefulness and ease of use of the technical disclosure method to any significant extent.

Meanwhile, in this study, it was also found that technology anxiety was a significant negative influencer of PU and PEU, suggesting that when anxiety levels are high, workers are less likely to have a positive attitude towards the technical disclosure method based on the 3D digital technological process. This might be due to the fact that the technical disclosure documents were presented and manipulated on the computer during the experiment. Aging in the construction industry is currently a problem that cannot be ignored. In the interviewed group, 56.9% of the workers were over 40 years of age, and many of them might never have had access to a computer, which was particularly evident in the second phase of the experiment. We observed that even simple mouse clicks and long presses required constant invitations from the researchers before any workers were willing to try, and often, it was only when a few workers succeeded that the rest of the workers gradually let go of their concerns and participated. Chen [57] pointed out that increasing age reduced cognitive abilities, such as memory and attention, so older people were relatively less receptive to new things. Another possible reason is that self-efficacy assumes the role of a mediating variable between technology anxiety and PU and PEU, which means that, to some extent, technology anxiety affects the self-efficacy of a person when exposed to something new. Self-efficacy refers to an individual’s confidence in overcoming difficulties and accomplishing specific behaviors [61]. Because of unfamiliarity with computer operations, workers were less confident in accepting this new method of technical disclosure, which led to a decrease in their PU and PEU.

5. Conclusions

The aim of this article was to systematically investigate construction workers’ acceptance of a technical disclosure method based on the 3D digital technological process and to investigate the factors influencing acceptance through an extended TAM model.

It was found that the proposed TAM model, which integrated cognitive level, subjective norms, technology anxiety and enjoyment, could be used to explain and predict how construction workers accept this type of technical disclosure. In particular, enjoyment was a key factor influencing workers’ perceived usefulness, so their perceived level of pleasure should be emphasized in order to promote positive attitudes. In terms of workers’ PEU, cognitive level played a more important predictive role than other external variables. This confirmed the importance of workers’ knowledge and familiarity with new things. How to actively promote the dissemination of the technical disclosure method based on the 3D digital technological process is an issue that deserves the attention of the management of the construction industry. In this process, the effectiveness and practicability of this technical disclosure method should be fully explained. In addition, the cognitive learning ability of the workers’ group needs to be taken into account, and more appropriate as well as reasonable methods of dissemination and guidance should be chosen accordingly. Although subjective norms played a relatively smaller role in PU and PEU, it was still a significant antecedent. The management should encourage and motivate workers to participate. Furthermore, technology anxiety was the only negative factor found in this study’s model, suggesting a need for management to provide some organizational support, such as through specialized teaching tutorials and introductory computer training, to alleviate construction workers’ technological anxiety. In order to promote and successfully apply the technical disclosure method based on the 3D digital technological process in the construction industry, in addition to the diversification of export forms, 3D digital technological process developers and designers should recognize the limitation of the computer terminal view and pay more attention to the mobile phone interface and online apps, which are not subject to the aforementioned limitations and easier to use. Good interface design and clear instructions in development are very important to improve the PEU for workers. Moreover, perceived usefulness was also a key determinant of workers’ acceptance of this new technical disclosure method. Therefore, during the ensuing development and diffusion process, developers and the management group should keep frequent contact with the workers to obtain their dynamic feedback on optimization and take both timely and appropriate adjustment measures to continually improve the acceptability of this technical disclosure method. In addition, there are still relatively few construction processes that are digitally handled. How to make the 3D digital technological process fit more accurately with the construction program of different projects, especially in the detailed operation of the process, in order to apply it more efficiently in the technical disclosure also needs to be further explored.

Although this study provided some insights into the acceptance of the technical disclosure method based on the 3D digital technological process by the construction worker community, there were still some limitations. Firstly, this study only focused on the hypothesized relationships between the selected variables and never investigated moderating effects, such as gender, age, and education level, on pathways. Secondly, this study was cross-sectional, measuring perceptions and attitudes at a static point in time. A longitudinal approach should be more appropriate for the dynamics research of workers’ acceptance as they continue to learn more about and become familiar with this method of technical disclosure. Finally, this study was carried out in China and focused on the infilled masonry wall process as a case study, so the generality of the conclusions needs to be further verified.