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Article

What Drives Infrastructure Participants to Adopt Digital Technology: A Nexus of Internal and External Factors

by
Zhixia Qiu
1,
Shifa Wang
2,
Yaxin Hou
1 and
Sheng Xu
1,3,*
1
School of Economics and Management, Chang’an University, Xi’an 710064, China
2
Chengdu Branch of Sichuan Chengmian Cangba Expressway Co., Ltd., Chengdu 641400, China
3
Engineering Research Center of Digital Transportation Infrastructure, Ministry of Education of the People’s Republic of China, Xi’an 710064, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(23), 16229; https://doi.org/10.3390/su152316229
Submission received: 25 September 2023 / Revised: 12 October 2023 / Accepted: 19 November 2023 / Published: 23 November 2023
(This article belongs to the Special Issue Building Information Modeling (BIM) in the Perspective of Green Building)
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Abstract

:
Given the potential of digital technologies in value-adding and decision support in infrastructure projects, the promotion of digital technologies, how factors of government promotion, technological development, and the organization’s technology capability and capacity influence digital technology adoption is necessary but still unclear. This research aims to identify the environmental, technological, and organizational factors, as well as the interactive relationships among them, for infrastructure participants’ intention to adopt digital technologies. The extended Technology Acceptance Model (e-TAM) was used as a theoretical base to develop a hypothesis of the influencing paths of internal and external factors, with perceived usefulness (PU), perceived ease of use (PEU), and perceived image improvement (PII) as critical internal factors, and technological and environmental factors as external factors. The questionnaire survey collected 172 valid responses and structural equation modeling was applied for the hypothesis testing. The model fitting results indicated that intentions of digital adoption are directly influenced by PU as internal factors and environmental factors, while PEU, PII, and technological factors play indirect roles. As the interaction of external and internal factors, environmental factors directly affect PEU, and technological factors significantly correlate with PU. The findings supported most of the hypothesis and contributed to providing guidance for infrastructure participants’ digital adoption practice.

1. Introduction

Digital technologies such as cloud computing, big data, Internet of Things, artificial intelligence, and blockchain have enabled decision support and value creation to improve construction efficiency [1,2]. Mega infrastructure projects have witnessed the rapid development of digital technologies to meet higher development and service requirements [3]. Digital adoption in infrastructure can improve energy efficiency by optimizing energy use and optimizing the utilization of urban resources through intelligent infrastructure systems, thereby reducing energy waste and carbon emissions, and improving urban sustainability. The United Nations General Assembly [4] proposed building secure and inclusive digital public infrastructure to establish sustainable global digital partnerships, and nations worldwide launched digitalization roadmaps and policies to enhance the digitization of infrastructure planning, construction, and maintenance, such as the Digital Built Britain [5] and Digital China from 2019 [6].
However, the digital adoption in infrastructure projects is staggering due to several conflicts and dilemmas. Firstly, government incentives and mandates push for the rapid adoption of cutting-edge information technologies, while many organizations lack a clear understanding of the focus and strategies of digital adoption. This lack of clarity hinders the efficient allocation of additional financial, human, and technological resources, and the policy interventions are often implemented without adequate supervision and guidance [7]. Secondly, digital platform users frequently encounter difficulties in using the technology, such as complex interfaces and heavy manual data input requirements. These challenges result in passive digital adoption, with low overall application rates and insufficient awareness among infrastructure participants. Additionally, the lack of professional talents and lagging education and training programs further hinder the effective adoption and utilization of digital technology [8,9,10]. As a consequence, the digital adoption process is characterized by low efficiency, and the potential benefits of cutting-edge technologies are not effectively realized in infrastructure projects [11]. Moreover, the driving forces of the environment and technology have not effectively stimulated and matched the internal adoption factors of infrastructure participants. This misalignment between external incentives and internal factors creates a significant barrier to the successful implementation of digital technology in infrastructure projects [12,13].
Therefore, incoordination has risen between external impetus including software tools, digital workflows [14], policy support [14,15], financial support [15], management leadership and commitment [16] and organizational adoption factors such as their understanding of digital adoption, relevant technology experience [17,18,19], and discovering and dissolving the incoordination can substantially facilitate the digital adoption in infrastructure projects. More importantly, external factors have been reported to affect individuals’ digital adoption intentions [20,21]. However, it is still being determined how different environmental and technological factors influence individual factors in their decision of digital adoption, and which factors have more significant influence.
This research used the extended technology acceptance model on two layers of factors to examine how different external factors, including technological and environmental factors, impact internal factors and eventually the intentions to adopt digital technology. In addition, the proposed model provides theoretical support and decision-making reference for infrastructure participants to adjust their digital adoption framework. In this way, the following three questions are to be answered to provide a reference for infrastructure digital adoption:
  • What are the direct and indirect factors affecting the digital adoption intentions of infrastructure participants?
  • What are the relationships between the external and internal factors that affect participants’ behavioral intentions for digital adoption?
  • How should infrastructure participants improve their digital transformation capabilities?
The remainder of this paper is arranged as follows. Section 2 briefly overviews the literature, focusing on the digital adoption of infrastructure participants and the development and application of TAM. The following three sections describe the model and hypothesis of this study, the data collection process, and the results of empirical analysis using structural equation modeling. Finally, Section 6 discusses the results, and Section 7 presents the conclusion.

2. Literature Review

2.1. The Needs and Challenges in Digital Adoption in Infrastructure Projects

As the leading capital of society, infrastructure plays a pivotal role in economic and social development [22]. However, infrastructure construction takes longer than most residential buildings; the operation stage requires a reasonable scale of profitability; and the primary pursuit includes social benefits [23]. Therefore, careful planning and prioritization are essential to maximize resource utilization efficiency and ensure long-term sustainability. Infrastructure projects involve a greater degree of involvement from various participants throughout their lifecycle [24], which requires adopting more advanced digital technologies. In recent years, advances in big data, cloud computing, and artificial intelligence have been implemented throughout the entire lifecycle of infrastructure projects [25]. Digital adoption in the infrastructure industry has broad benefits. By adopting digital technologies, tools, and processes in infrastructure projects, infrastructure participants can enhance on-site safety, quality, productivity, error detection, and communication [26,27]. Additionally, they can improve transparency and efficiency, and ensure maximum data security [25]. Integrating multiple digital technologies, such as digital twins, can support better decision-making regarding intelligent infrastructure assets [28,29].
However, several challenges remain in the digital adoption of infrastructure projects. For instance, many countries mandate that the primary participants in large-scale infrastructure projects adopt digital technologies such as Building Information Modelling (BIM) [30], while deviations are observed in the regulatory requirements among regions due to the decentralization of investors and regulators to local institutions. The demands of regulatory entities vary across different stages of the lifecycle, posing challenges to data sharing [31]. Additionally, since projects are one-off, each infrastructure project involves new participants and establishes cooperation relationships based on different contract logic, creating information silos [32]. The constantly changing market environment, unclear corporate transformation goals, and a lack of technology adoption culture and technical talents are common obstacles to digital adoption in infrastructure projects [29,33,34]. Thus, it is crucial to study the factors influencing digital adoption in the face of these challenges.

2.2. Factors Affecting Technology Acceptance in Infrastructure Projects

Researchers have studied the determinants of digital adoption from various perspectives and aspects to facilitate the digital adoption process. Park, Choi, and Ryu [35] identified six dimensions that determine users’ digital adoption: digital technology, organizational culture, organizational operability, operations, business environment, and perceived risk. As the core digital technology in infrastructure projects, factors influencing BIM adoption have been identified as BIM innovation characteristics, internal environment characteristics, and external environment characteristics in extensive studies [36]. Qin et al. [37] highlighted the government incentives in the BIM adoption.
Previously identified factors can be grouped as internal factors and external factors. Internal factors included an individual’s technical capability [38] and interest [39], and external factors included top management support [16], technical advantages [40], and digital culture [38] have also been identified. Previous studies have shown that digital adoption is susceptible to internal and external forces [41], and the opportunity for achieving digitalization and sustainable development will be lost if the challenges posed by internal and external factors are not addressed [42]. Therefore, it is crucial to utilize technology adoption theories to further investigate the relationship between internal and external factors and explore the influencing paths of digital adoption.

2.3. Application of Technology Acceptance Model (TAM)

The adoption intentions can be explained with models of technology acceptance, one of which is the Technology Acceptance Model (TAM) proposed by Davis [43] to elucidate the users’ behavior when accepting new technologies [44,45]. The TAM was derived from the Theory of Reasoned Action (TRA), focusing on elucidating individuals’ acceptance of information technology. TAM suggests that “perceived usefulness” and “perceived ease of use” are used to predict individuals’ attitudes towards adopting specific new technologies. Subsequent studies have made modifications to the original version of TAM. The attitude was eliminated because of its weak mediating effect between behavioral intention and perceived usefulness and ease of use [46]. As further research on the model was conducted, more extended models such as TAM2 [47]; UTAUT [48], and TAM3 [49] were subsequently proposed.
TAM and its extended models have made significant contributions to the examination of the acceptance behavior towards various technologies in both personal [50,51] and organizational contexts [52,53] in diverse contexts [54,55]. TAM also has been used to explain digital adoption such as BIM [56], digital twins [57], AI [58], and intelligent surveillance systems [59].
The literature has shown that the development of the TAM has evolved from solely focusing on individual perception to considering external factors that impact internal factors. Internal factors primarily capture micro factors associated with personal perception, while external factors encompass macro factors such as the environment and technology. Therefore, TAM with extensions is a suitable theoretical framework to explore the interactions between external and internal factors in digital adoption. Table 1 provides an overview of various internal and external factors discussed in the existing literature.
Based on the aforementioned review, it is evident that the TAM has been widely applied and its effectiveness in studying the adoption of digital technology has been validated. Furthermore, the current research trend of TAM focuses on the expansion of external and internal variables, making it highly suitable for investigating the relationship between external and internal factors in the context of digital adoption in infrastructure projects.

3. Development of Hypotheses

This research applied the extended TAM to explore the factors influencing digital adoption behavior among infrastructure participants. Factors include perceived usefulness, perceived ease of use, perceived image improvement, environmental factors, and technical factors.

3.1. Behavioral Intention

This study focused on investigating the impact of internal and external factors on the intention to adopt digital technology, with behavioral intention serving as the dependent variable. Behavioral intention, defined as “an indication of the readiness to adopt digital technology”, plays a crucial role in the TAM. According to the TAM, adoption intentions directly influence the willingness to embrace new technologies [47]. Therefore, this research employed the TAM framework to examine how internal and external factors affect digital adoption intention.

3.2. Internal Factors

Internal factors include perceived usefulness (PU), perceived ease of use (PEU), and perceived image improvement (PII). Perceived usefulness is defined as “the extent to which a person believes that using digital technology or a digital system would enhance his/her performance in carrying out specific tasks” [43]. On the other hand, perceived ease of use is defined as “the degree to which a person perceives the process of using a specific digital technology or digital system to be effortless” [43], meaning that individuals find digital technology easy to understand and operate. As the central components of the TAM model, perceived usefulness and perceived ease of use serve as the primary predictors of behavioral intentions [43]. In other words, users’ intention to use digital technology largely depends on their perceptions of its usefulness and ease of use. Additionally, simple and user-friendly technology can help them complete tasks more efficiently and improve their work performance. Therefore, when individuals believe that digital technology is easy to use, they are more likely to perceive it as useful for their work or tasks. The simpler the operation of a specific digital technology, the more useful it is perceived to be. Then the following hypotheses were formulated:
H1. 
Perceived usefulness will positively affect behavioral intention.
H2a. 
Perceived ease of use will positively affect behavioral intention.
H2b. 
Perceived ease of use has a positive effect on perceived usefulness.
The concept of image has been explored as an external variable in TAM2 [47]. It refers to employees within an organization holding certain perceptions of their image and influencing their perceived usefulness of digital technology. Engaging in digital transformation offers individuals the opportunity to enhance their position and influence within their team. This paper proposed that respondents’ perceptions of self-image improvement are often subjective and introduced perceived image improvement as an additional variable. Perceived image improvement is defined as “one’s desire to maintain a positive position for themselves”. Perceived image improvement plays a crucial role in motivating individuals to participate in digital adoption actively. Therefore, the following hypothesis is proposed:
H3. 
Perceived image improvement will positively affect perceived usefulness.

3.3. External Factors

External factors were categorized into environmental factors and technological factors in this research. Environmental factors are further divided into subjective norm (SN) and support (S), while technological factors include technical benefit (BI) and task-technology fit (TTF).
Subjective norm is defined as “important people in the organization think whether new technologies are important necessary for adoption”. It is the direct determinant of behavior intention in TRA [72]. Subjective norm directly impact intention because participants might be under pressure from important referents and engage in digital adoption even if it does not directly benefit them. Similarly, subjective norm also impact perceived image improvement, as discussed in TAM2 [47]. The following hypotheses were proposed based on these points:
H4a. 
Subjective norm will positively affect perceived image improvement.
H4b. 
Subjective norm will positively affect perceived usefulness.
H4c. 
Subjective norm will positively affect perceived ease of use.
H4d. 
Subjective norm will positively affect behavioral intention.
Support refers to “the degree of support provided by the organization and policy environment for the digital adoption”. Previous research has shown that support from the organization, government, and legislation can positively impact employees’ perceived usefulness and perceived ease of use of digital technology [73,74]. Furthermore, support plays a crucial role in determining users’ intention to use technology [75]. Based on the points as mentioned above, the following hypotheses were proposed:
H5a. 
Support will positively affect perceived usefulness.
H5b. 
Support will positively affect perceived ease of use.
H5c. 
Support will positively affect behavioral intention.
Technical benefit is defined as “the tangible outcomes that digital technology can provide”. In the context of infrastructure digital adoption, the scope of these outcomes can be further specified to infrastructure project benefits in terms of cost, schedule, quality, and safety. When digital technology demonstrates work-related benefits, users are likely to perceive improvements in their performance and image [76]. Based on these considerations, the following hypotheses are proposed.
H6a. 
Technical benefit will positively affect perceived image improvement.
H6b. 
Technical benefit will positively affect perceived usefulness.
H6c. 
Technical benefit will positively affect perceived ease of use.
The concept of task-technology fit is based on the task-technology fit (TTF) theory [77]. It is defined as “the degree to which the digital technology used aligns with the requirements of the work task”, primarily used to assess the matching degree between the work task and the digital tool. This study proposed that the higher the matching degree between digital technology and work tasks, the more positively employees perceive the effectiveness of digital adoption. When digital technology seamlessly fulfills the needs of daily work processes, it is perceived to have more favorable usage effects. Therefore, the following hypothesis was formulated:
H7a. 
Task-technology fit will positively affect perceived usefulness.
H7b. 
Task-technology fit will positively affect perceived ease of use.

3.4. Hypothetical Framework

Based on the above analysis, the proposed theoretical framework is illustrated in Figure 1. The factors are divided into two aspects: internal factors and external factors. All the hypothesized paths proposed in this study are indicated in the figure.

4. Questionnaire Design and Data Collection

4.1. Questionnaire Design

A structured questionnaire was developed for data collection. It consists of two parts. The first part encompassed respondents’ demographic information, such as gender, age, educational background, position, and organization. The second part explored respondents’ utilization and perceptions of digital technology adoption. Questions in the second part used the 5-point Likert-type scales with 1–5 expressing “strongly disagree”, “disagree”, “general”, “agree” and “strongly agree” respectively, to evaluate participants’ digital adoption.
The measurement items were derived from existing TAM-related research and modified to adjust to the Chinese context of infrastructure participants’ digital adoption. The questions were tailored to the unique aspects of infrastructure participants’ digital adoption. For instance, the items related to perceived image improvement (PII) examined whether respondents experienced improvements in personal image when utilizing digital technology. The items related to task-technology fit (TTF) primarily aimed to investigate whether respondents’ use of digital tools at work met their data needs when performing daily tasks, drawing upon the task-technology fit theory.
After the initial completion of the questionnaire design based on existing research, a focus group filled out the questionnaire and discussed the wording and translation. This process ensured that each item was non-redundant and had clear meanings and explanations. After revisions and modifications, the second part of the questionnaire comprised 21 measurement items, as presented in Table 2.

4.2. Data Collection

The data collection focused on employees from Chinese infrastructure participants, including owners, contractors, designers, and supervisors. Through on-site visits with personnel from dozens of enterprises, data was collected through both web-based surveys and paper-based questionnaires during on-site visits to more than 10 relevant enterprises and establishing online contacts. Qualified participants were informed about the research objectives, procedures, and the confidentiality and security of their responses. After respondents agreed to participate in the questionnaire survey, they were asked to complete it, with each survey taking approximately 10 to 15 min to complete. From September 2021 to November 2022, four rounds of surveys collected a total of 240 responses, of which 68 were excluded due to missing values or bias with the same answers to every item. Ultimately, 57 (33%) hard copies of questionnaires and 115 (67%) online questionnaires (in total, 172 valid questionnaires) were received for data analysis.
Determining the appropriate sample size is a crucial consideration in SEM research. However, there is currently no consensus in the literature regarding the optimal sample size for SEM studies. Nevertheless, evidence suggests that even with a small sample size (N ≥ 100), meaningful testing of simple SEM models is possible [90,91]. Typically, a minimum sample size of N = 100–150 is recommended for SEM [92,93,94]. Empirical methods commonly employ a criterion of having a minimum of 5 cases per variable (N:q = 5) to determine the appropriate sample size [95]. In the present study, since there are 21 observed variables (q = 21), the N:q ratio exceeds 8, indicating that a sample size of 172 is deemed acceptable.

5. Data Analysis and Results

5.1. Descriptive Statistics

Among these respondents, 86.63% were male, and 13.37% were female. The age group with the highest proportion is 25–34 years old, accounting for 67.44%, and more than 85% of respondents have at least 5 years of work experience. Most respondents had bachelor’s degrees; this group accounts for 79.07% of total respondents. In addition, in terms of companies, the number of respondents from owners and contractors was higher, accounting for 32.56% and 36.05% respectively. The demographics of the respondents are presented in Table 3.

5.2. Reliability and Validity Analysis

The collected data were first analyzed for reliability and validity using SPSS 25 and AMOS 24. The overall KMO value of the questionnaire is 0.855, while Cronbach α is 0.929, suggesting the data was reliable and effective. The results of the reliability and validity analysis are presented in Table 4. It is worth mentioning that during the initial stages of model development, it was proposed that technological factors have a direct impact on behavioral intention (BI). However, upon further investigation, we were unable to establish the specific pathways through which these technical factors influence BI. Moreover, the inclusion of these uncertain pathways would compromise the reliability and validity of the model. As a result, we have made the decision to remove these assumptions from our analysis.
During the analysis, the standardized factor loadings of TTF3 found the item was below 0.5, so it was deleted [96]. After deletion, the standardized factor loading for TTF1 and TTF2 were 0.928 and 0.768, respectively. Additionally, Cronbach’s α, CR, and AVE of TTF increased were 0.832, 0.835, and 0.718, respectively. Cronbach’s α for all eight variables remained above 0.700 after adjustment, indicating well-structured reliability [97].
The validity of the measurement model was assessed through confirmatory factor analysis, taking into account convergence and discriminant validity. All variables met the requirements for convergence validity, with comprehensive reliability (CR) above 0.7 (0.726~0.970) and average variance extracted (AVE) above 0.5 (0.513~0.942). Additionally, the standardized factor loading for all projects exceeded 0.5. These indicators met the acceptable level of convergence validity [98,99]. At the same time, all structures showed a p-value less than 0.001. The discriminant validity was confirmed by comparing the square root of the average variance extracted from each construct with the correlation between the respective constructs [97]. Table 5 demonstrates that all diagonal values were greater than the inter-structure correlation, confirming the measurement model has discriminant validity.

5.3. Model Fit

The fitting degree primarily assesses the consistency between the variable relationships depicted in the established model and those reflected in the collected data. A good fit between the collected data and the theoretical model is determined when the difference between the covariance matrices of the theoretical model and the actual data falls below a predetermined threshold value.
The structural equation model (SEM) in AMOS 24 utilizes the maximum likelihood estimation (MLE) method to assess the compatibility between the proposed model and the collected data. Various indicators, including the chi-square model and degrees of freedom ratio (χ2/df), goodness-of-fit (GIF), root mean square error approximation (RMSEA), normed fit index (NFI), comparative fit index (CFI), incremental fit index (IFI), and Tacker–Lewis index (TLI), are used to evaluate the goodness of fit of measurement models. Table 6 presents the criteria and evaluation results of the overall fitness of the conceptual model. The NFI value is close to the recommended threshold, and the other indicators align with the recommended values. Overall, the measurement model developed in this study demonstrates a strong fit.

5.4. Hypotheses Testing

Next, the path analysis is conducted with AMOS 24 to test the hypothesis, and the results are shown in Table 7. The results showed that H1 was supported but H2a was not. Thus, perceived usefulness positively impacts behavioral intention, while perceived ease of use has no significant impact on behavioral intention. Meanwhile, H2b and H3 were supported. Perceived ease of use and perceived image improvement have a significantly positive influence on perceived usefulness. Subjective norm has a positive impact on perceived image improvement (H4a). However, its impact on perceived usefulness and perceived ease of use was not supported because H4b and H4c were invalid. And support has a significant impact on perceived ease of use (H5b), while it has no significant impact on perceived usefulness (H5a). Subjective norm and support can directly positively affect behavioral intentions (H4d and H5c). Besides, H6b and H7a were supported. The two digital technology feature variables, TTF and TB, positively impact perceived usefulness.
Finally, the pathway that the hypothesis holds was retained, and the results are presented in Figure 2. The results confirmed that all four external factors, including technological and environmental factors, have a direct influence on internal factors. Specifically, the technological factors primarily affect perceived usefulness rather than perceived ease of use. The pathway from environmental factors to behavioral intention has also been confirmed. With these findings, which specific internal factors are affected by various external factors and which factors can directly affect behavioral intention can be further discussed.

6. Discussion

This paper applied the extended TAM model to explore the impact paths of external and internal factors to explain the infrastructure participants’ intention for digital adoption. The results of the hypothesis testing suggested that the adoption intentions were directly affected by internal factors and environmental factors; and internal factors were further affected by technological and environmental factors. By establishing the interactions between external and internal factors, the proposed framework can facilitate organizations to strengthen the driving force and promote digital adoption ultimately.

6.1. Internal Factors

The results showed that perceived usefulness (PU) directly and positively affects behavioral intention, while perceived ease of use (PUE) and perceived image improvement (PII) indirectly affect behavioral intention through their positive impact on perceived usefulness. Consistent with previous research, PU has been confirmed to be the direct antecedent of behavioral intention [106]. Digital adoption is a goal-oriented behavior. Perceived usefulness directly affects users’ acceptance because of their rational reactions and expectations for product effectiveness. Therefore, perceived usefulness is considered the most critical and direct factor in determining the intention of adoption behavior [43,107,108].
Unlike the original TAM, the results did not support the effect of PEU on behavioral intention (BI). Some previous studies have discussed the weak role of perceived ease of use in predicting user acceptance [109,110]. A reasonable explanation for this finding is that the respondents did not prioritize the ease of use because in many cases, digital adoption is mandated. Most of the respondents passively accepted relevant digital technologies under the mandates of the government and organization, rarely considering whether the technology is easy to use as a determinative factor of adoption. Thus, the perceived ease of use did not impact behavioral intention directly; instead, it indirectly affected behavioral intention through perceived usefulness.
Similar to perceived ease of use, PII also indirectly affects behavioral intention by influencing perceived usefulness. It shows that individuals tend to perceive digital technologies as beneficial for their work when they believe it upgrades their personal image. This improvement in perceived usefulness can further stimulate adoption intention. This result is consistent with the conclusion drawn in TAM2 [47]. When adopting digital technologies, the learning and application of new technologies can equip individuals with better professional knowledge and skills, enabling them to perform effectively in the workplace.

6.2. External Factors

This study expanded the original TAM by dividing external factors into environmental and technological factors and explored their influence on internal factors. As expected, all four variables were essential predictors. Environmental factors have a direct and indirect impact on behavioral intention, while technological factors only have an indirect impact on behavioral intention.
Both environmental factors, subjective norm and support, have shown direct effects on behavioral intention (BI), indicating that they are critical in the digital adoption process. Subjective norm and external support for digital adoption can directly promote the intention to adopt digital technology. Environmental factors also indirectly affect BI through two paths: (1) support (S) positively affects PEU and further indirectly affects BI; and (2) subjective norm (SN) positively affects PII, and further indirectly affects BI. In summary, environmental factors not only affect BI directly, but also impact perceived ease of use and perceived image improvement, which further indirectly drive the adoption through perceived usefulness.
Both technological factors had an indirect impact on behavioral intention through internal factors. Additionally, they did not significantly impact perceived ease of use either. Contradicting the common belief that improved technology leads to increased perceived ease of use, results showed that respondents did not believe that enhancing the adaptability of technology functions improved the ease of use of the technology. This may be attributed to external mandates that lead to indifference towards ease of use, given that the mean value of perceived ease of use (3.107) was lower than that of perceived usefulness (3.899). Furthermore, ease of use did not directly influence respondents’ decision to adopt digital technology.
Both technological factors affect the user’s perceived usefulness. A higher level of technology-task fit results in digital technology meeting work task requirements more effectively, leading to higher perceived usefulness. Additionally, the more demonstrable benefits generated by digital adoption, the more users perceive it as useful. The technical benefits also indirectly enhance users’ perceived ease of use. Moreover, compared to technical benefit, the correlation of technology-task fit on perceived usefulness is relatively weak. This may be attributed to the goal-oriented nature of the digital adoption. The measurement results of technical benefits clearly show the dominant benefits of using digital technology, but the technology-task adaptation does not. Besides, technical benefit also positively impacts perceived image improvement.

6.3. Theoretical Implications

The research considered technological, environmental, and internal factors as three types of significant variables that collectively affect infrastructure participants’ digital adoption intention. The analysis demonstrated the applicability of the proposed model, thereby expanding the theoretical foundation for the study of digital adoption by infrastructure participants. Structural equation modeling ascertained the role of subjective norm, task-technology fit, perceived image improvement, and other influencing factors in the digitalization atmosphere of the infrastructure industry. This contributes to the understanding of decision-making mechanisms of digital adoption and provides a broadly applicable framework for explaining infrastructure participants’ digital adoption behavior.
This research summarized the interactions between external and internal factors in infrastructure participants’ digital adoption. All four external have an influence on internal factors and indirectly determine behavioral intentions through this influence. External technological factors primarily indirectly affect behavioral intention by influencing the perceived usefulness among internal factors. Similarly, external environmental factors indirectly influence behavioral intention by affecting the perceived ease of use. Furthermore, the study confirmed the direct impact of environmental factors on behavioral intentions, including the impact of SN on BI and the impact of S on BI.

6.4. Practical Implications

This study provides valuable insights for infrastructure participants seeking to enhance digital adoption in infrastructure projects. The insights are presented from three key perspectives: individual, environmental, and technological. By providing a comprehensive understanding of these perspectives, this study provides a reference for promoting effective digital adoption strategies.
Firstly, this study highlights the significance of environmental factors as direct influencers of digital adoption intention. However, the level of support provided to the respondents (average 3.31) in this study was relatively insufficient. Therefore, it is crucial for relevant organizations to enhance their support for creating a conducive atmosphere for digital adoption. Organizations should increase their support for digital adoption by providing adequate resources, training, and policies. This will help participants overcome initial challenges during the initial stages of digital adoption, such as high learning costs and risks. Additionally, clarifying data ownership and introducing relevant regulations through the appropriate departments will help establish a favorable environment for digital adoption.
Secondly, perceived usefulness has emerged as the most significant factor impacting impression behavior intention (β = 0.41, p < 0.01). Thus, when introducing new digital technologies, it is crucial to prioritize enhancing the perceived usefulness of the technology to foster the development of usage intentions. Moreover, although perceived ease of use may have a limited impact on behavioral intention, its influence on perceived usefulness should not be disregarded. Infrastructure participants should not overlook factors affecting perceived usefulness, including perceived ease of use, perceived image improvement, and technology. These factors also contribute to the positive impact of digital technology on individual performance, thereby establishing its usefulness and encouraging further adoption. Based on the above discussion, it is crucial to focus on enhancing the perceived usefulness of digital technologies. This can be achieved by clearly demonstrating the benefits and advantages of the technology to infrastructure participants. Meanwhile, highlighting how the technology can improve their performance and overall image can further encourage their intention to adopt it.

7. Conclusions

Digital transformation provides opportunities for all infrastructure participants and necessitates active adoption and promotion by these participants. The results from hypothesis testing demonstrated that the TAM provides valuable insights into the formation mechanism of infrastructure participants’ digital adoption intention. Specifically, the adoption intention is related to individuals’ perceived usefulness of digital technology and is directly influenced by environmental factors. The usefulness of digital technology is primarily assessed by considering the compatibility between participants’ daily work tasks and the functions offered by digital technology, as well as the visible benefits derived from its use. Meanwhile, ease of use is influenced by two environmental factors: leaders’ perspectives on digital transformation adoption and the support available during the adoption process. The findings also reveal that perceived ease of use does not significantly impact behavioral intention, but it does have a noteworthy effect on perceived usefulness. Additionally, the inclusion of perceived image improvement in the model strongly influences perceived usefulness and is also influenced by technical benefit and subjective norm. Therefore, the overall results of this research provide robust support for the applicability of TAM in this particular study. Based on the proposed mechanism, infrastructure participants can concentrate on controlling key factors to facilitate the adoption and promotion of digital transformation.
This research bears several limitations. Firstly, although valid respondents were adequate for the analysis, it could be further expanded to include more participants and provide for possible longitude analysis. Secondly, this research did not compare the factors of digital adoption between owners and other participants, and a deeper comparison could lead to further theoretical insights in promoting digital adoption.

Author Contributions

Conceptualization, S.X. and S.W.; methodology, Z.Q.; software, Z.Q.; validation, S.X., S.W. and Z.Q.; formal analysis, Z.Q.; investigation, Z.Q. and S.W.; resources, S.W.; data curation, S.W.; writing—original draft preparation, Z.Q.; writing—review and editing, S.X. and Y.H.; visualization, Z.Q. and Y.H.; supervision, S.W.; project administration, S.W.; funding acquisition, S.X. and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Project of the Sichuan Transportation Department (2022-ZL-03), National Social Science Fund of China (22AZD099) and Social Science Foundation of Xi’an City (23GL66).

Institutional Review Board Statement

Ethical review and approval were waived for this study, as the university do not have review requirements and do not have an ethics review committee. We clearly outlined the research purpose and scope of data collection in the introduction section of the questionnaire and obtained informed consent from the participants verbally.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

Author Shifa Wang was employed by the company Chengdu Branch of Sichuan Chengmian Cangba Expressway Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Hypothetical model.
Figure 1. Hypothetical model.
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Figure 2. Results of structural equation modeling.
Figure 2. Results of structural equation modeling.
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Table 1. Internal and external factors in existing literature.
Table 1. Internal and external factors in existing literature.
Factors Author & Year
Internal Factors Perceived Usefulness Almarzouqi, Aburayya, and Salloum [60]; Joo, So, and Kim [61]; Vahdat et al. [62]
Perceived Ease of Use Almarzouqi, Aburayya, and Salloum [60]; Joo, So, and Kim [61]; Vahdat et al. [62]
Technological Anxiety Kamal, Shafiq, and Kakria [63], Pillai and Sivathanu [64]
Perceived Risk Singh, Sinha, and Liebana-Cabanillas [65]; Kamal, Shafiq, and Kakria [63]; Zhang et al. [66]
Self-Determination Joo, So, and Kim [61]
Performance Expectancy Pillai and Sivathanu [64]
Trust in Technology Kamal, Shafiq, and Kakria [63]; Xu et al. [67]
External Factors Technology Amenities Yang et al. [68]
Skills Sayaf et al. [69]
Facilitating Conditions Kamal, Shafiq, and Kakria [63]
Compatibility Almarzouqi, Aburayya, and Salloum [60]; Gholami, Abdekhoda, and Gavgani [70]
Complexity Gholami, Abdekhoda, and Gavgani [70]
Resistance to Use Kamal, Shafiq, and Kakria [63]
Social Influence Singh, Sinha, and Liebana-Cabanillas [65]; Kamal, Shafiq, and Kakria [63]; Vahdat et al. [62]
Group Norm Singh, Sinha, and Liebana-Cabanillas [65]
Organizational Competency Gholami, Abdekhoda, and Gavgani [70]
Organizational Support Na et al. [58]; Gholami, Abdekhoda, and Gavgani [70]
Safety Xu et al. [67]; Kaur and Rampersad [71]
Privacy Security Kamal, Shafiq, and Kakria [63]; Zhang et al. [66]
Table 2. Measurement items.
Table 2. Measurement items.
VariablesItemsMeasurement QuestionsReferences
Subjective Norm (SN)SN1Senior leaders in my organization believe that digital adoption is necessary and urgent.Nguyen et al. [78]; Salloum et al. [79]
SN2Senior leaders in my organization believe that digital technology is the future.
SN3Senior leaders encourage new technologies.
Support (S)S1The enterprise has provided me with suitable training for digital adoption.Katebi, Homami, and Najmeddin [80]; Hewavitharana et al. [81]
S2Existing laws and regulations have defined data ownership and responsibility.
S3There is sufficient government support currently.
Task-Technology Fit (TTF)TTF1I can collect and upload information from the existing business process to the digital platform.Zhao and Bacao [82]; Hung et al. [83]
TTF2I can maintain information in time on the platform.
TTF3I can access the necessary information for my work in a timely manner from the digital platform.
Technical Benefit (TB)TB1Digital technology has provided cost and schedule advantages in my work.Chan, Olawumi, and Ho [84]; Olawumi and Chan [85]
TB2Digital technology has yielded benefits in terms of quality, safety, and other aspects in my work.
Perceived Image Promotion (PIP)PII1Digital adoption is conducive to shaping the positive image of the enterprise.Karahoca, Karahoca, and Aksöz [86]; Yuen et al. [87]
PII2Using digital technology improves our image of contribution to the project.
PII3Acquiring new digital skills improves organization’s performance in the project.
Perceived Usefulness (PU) PU1Using digital technology makes my work more convenient.Lai and Lee [88]
PU2Using digital technology makes my work more efficient.
Perceived Ease of Use (PEU)PEU1Digital technology is easy to use.Gamil and Rahman [89]
PEU2I can easily use digital technology to complete my work.
Behavioral Intention (BI)BI1Our company is willing to adopt digital technology.Lai and Lee [88]
BI2Our company is applying more digital technology.
BI3In the future, our company will continue to engage in digital transformation.
Table 3. Demographics of the respondents (N = 172).
Table 3. Demographics of the respondents (N = 172).
Variables Category Frequency Percentage (%)
Gender Male 149 86.63%
Female 23 13.37%
Age 25 and below 4 2.33%
25–34 116 67.44%
35–44 47 27.33%
45 and above 5 2.91%
Work Experience 0–4 years 24 13.95%
5–9 years 74 43.02%
10–14 years 51 29.65%
15 and above 23 13.37%
Education Diploma and below 4 2.33%
Bachelor’s degree 136 79.07%
Master’s degree and above 32 18.60%
Position Manager 55 31.98%
Professional technician and others 117 68.02%
Company Owner 56 32.56%
Contractor 62 36.05%
Designer 26 15.11%
Supervisor and others 28 16.27%
Table 4. Standardize factor loadings, Cronbach’s α, CR, and AVE values before adjustment.
Table 4. Standardize factor loadings, Cronbach’s α, CR, and AVE values before adjustment.
VariablesItemsStandardized
Factor Loadings		pCronbach’s αCRAVE
Subjective Norm (SN)SN10.903***0.9290.9290.814
SN20.948***
SN30.860***
Support (S)S10.569***0.7200.7260.513
S20.713***
S30.767***
Task-Technology Fit (TTF)TTF10.787***0.7220.7670.550
TTF20.906***
TTF3 0.394***
Technical Benefit (TB)TB10.934***0.9150.9160.845
TB20.903***
Perceived Image Promotion(PIP)PII 10.869***0.8080.8120.593
PII 20.575***
PII 30.922***
Perceived Usefulness (PU)PU10.996***0.9690.9700.942
PU20.944***
Perceived Ease of Use (PEU)PEU10.873***0.8840.8840.792
PEU20.908***
Behavioral Intention (BI)BI10.823***0.8900.8980.748
BI20.963***
BI30.799***
Note: *** p < 0.001.
Table 5. The average variance extracted and estimated correlations among constructs.
Table 5. The average variance extracted and estimated correlations among constructs.
PIISNTTFTBSPUPEUBI
Perceived Image Promotion (PIP)0.770
Subjective Norm (SN)0.6410.902
Task-Technology Fit (TTF)0.5140.6650.847
Technical Benefit (TB)0.5150.5060.6770.919
Support (S)0.4550.5630.6480.6510.716
Perceived Usefulness (PU)0.5890.3680.5370.5840.3470.971
Perceived Ease of Use (PEU)0.2240.2800.3460.3130.4430.4180.890
Behavioral Intention (BI)0.5140.5300.5590.5470.5700.6030.4000.865
Note: The diagonal numbers in bold represent the square of the average variance extracted.
Table 6. Evaluation of overall fitness of the conceptual model.
Table 6. Evaluation of overall fitness of the conceptual model.
Fitness Indexχ2/dfGIFRMSEACFINFIIFITLI
Recommended Value≤3 a≥0.8 b<0.08 c≥0.9 d≥0.9 e≥0.9 f≥0.9 g
Value1.7530.8390.0770.9430.8790.9440.927
Note: a Gefen, Straub and Boudreau [100]. b Seyal, Rahman, and Rahim [101]. c Hair et al. [102]. d Bentler and Bonett [103]. e Hair et al. [102]. f Nargundkar [104]. g Marsh, Balla and Hau [105].
Table 7. Results of the tested hypotheses.
Table 7. Results of the tested hypotheses.
HypothesisRelationshipβStandardized ErrorCritical Ratio
(t-Value)		pResults
H1BI←PU0.410.0855.046**Supported
H2aBI←PEU0.040.0930.4550.649Not Supported
H2bPU←PEU0.290.0813.559**Supported
H3PU←PII0.460.0984.468**Supported
H4aPII←SN0.510.0815.419**Supported
H4bPU←SN−0.220.098−1.8250.068Not Supported
H4cPEU←SN0.000.1130.0001.000Not Supported
H4dBI←SN0.200.0802.127*Supported
H5aPU←S−0.290.121−2.4990.053Not Supported
H5bPEU←S0.380.1812.058*Supported
H5cBI←S0.300.1152.954**Supported
H6aPII←TB0.360.0862.736**Supported
H6bPU←TB0.340.1052.929**Supported
H6cPEU←TB0.000.135−0.0170.986Not Supported
H7aPU←TTF0.290.1352. 072*Supported
H7bPEU←TTF0.100.1810.5640.573Not Supported
Note: ** p < 0.01; * p < 0.05.
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Qiu, Z.; Wang, S.; Hou, Y.; Xu, S. What Drives Infrastructure Participants to Adopt Digital Technology: A Nexus of Internal and External Factors. Sustainability 2023, 15, 16229. https://doi.org/10.3390/su152316229

AMA Style

Qiu Z, Wang S, Hou Y, Xu S. What Drives Infrastructure Participants to Adopt Digital Technology: A Nexus of Internal and External Factors. Sustainability. 2023; 15(23):16229. https://doi.org/10.3390/su152316229

Chicago/Turabian Style

Qiu, Zhixia, Shifa Wang, Yaxin Hou, and Sheng Xu. 2023. "What Drives Infrastructure Participants to Adopt Digital Technology: A Nexus of Internal and External Factors" Sustainability 15, no. 23: 16229. https://doi.org/10.3390/su152316229

APA Style

Qiu, Z., Wang, S., Hou, Y., & Xu, S. (2023). What Drives Infrastructure Participants to Adopt Digital Technology: A Nexus of Internal and External Factors. Sustainability, 15(23), 16229. https://doi.org/10.3390/su152316229

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