Exploring professional attitudes on automated construction progress monitoring: a Q methodology approach Open Access

Progress monitoring vitally impacts the overall performance of construction project management. Accurate and efficient progress monitoring is crucial for avoiding project schedule delays and cost overruns (Golparvar-Fard et al., 2015; Hamledari et al., 2017). However, traditional construction progress assessment is performed manually through site inspections and comparisons between site construction and project plans, resulting in a time-consuming, laborious and error-prone process (Braun et al., 2020; Golparvar-Fard et al., 2015). This can be especially inefficient when the construction scale and the number of subcontractors become too large (Braun et al., 2020). Therefore, more advanced techniques have been utilised to achieve the automation of construction progress monitoring (CPM). Recent academic attempts to improve the performance of CPM have mainly focused on the integration of computer vision techniques and data collection technologies, such as digital cameras and three-dimensional (3D) laser scanners. These advancements enable the construction of point cloud models based on the captured as-built data and allow progress information evaluation by comparing the resulting 3D models with as-designed building information modelling (BIM) data. The integrated approaches effectively improve project performance by shortening the project schedule to 20% and reducing project costs by up to 10% (Alaloul et al., 2021; Azhar, 2011; McCulloch, 1997). The ease of collecting visual information (Ekanayake et al., 2021a, b) also encourages researchers to contribute further in the field of automated construction progress monitoring (ACPM).

Despite the competitive benefits of ACPM technologies in low-cost, time-efficient and easier site data collection (Ekanayake et al., 2021a, b), existing studies (Ekanayake et al., 2021a, b; Zhang et al., 2024) identified and summarised the challenges that may resist the development and use of these techniques. For instance, the operational and environmental limitations that may impact the quality of captured data, the limited types of detectable structures and the potential failure of structure recognition (Alaloul et al., 2021; Zhang et al., 2024). While understanding the technical challenges during the development of ACPM technologies can be vitally important for future improvement, the factors influencing their adoption in the industry also need to be investigated. So far, several commercialised applications have become available in the market, such as Cupix (2023), OpenSpace (2023) and GeoSLAM (2023), where Cupix and OpenSpace utilise 360 images for comparison with BIM models, and GeoSLAM relies on laser scanning techniques. Resistance to the adoption of these technologies in the construction industry remains, due to reasons such as the popularity of traditional CPM methods among construction primary stakeholders (Qureshi et al., 2022a) and the lack of information and knowledge for implementing ACPM technologies (Qureshi et al., 2022a).

A thorough review of technology adoption studies indicates the necessity of academic research for exploring stakeholders' attitudes towards new technology adoption in the construction industry. Although similar research has been performed to study the adoption of other technologies like IoT (Zhao et al., 2023) and blockchain technology (Okanlawon et al., 2024; Wang et al., 2022), there is little effort to focus on ACPM technologies. This is also supported by Qureshi et al. (2023) and Vora et al. (2024). Qureshi et al. (2023) observed that one reason for slow ACPM implementation is the lack of a theoretical understanding of how to deploy these tools effectively in practice, which indicates a need for deeper inquiry into user acceptance factors. Vora et al. (2024) pointed out that while construction robots (including drones for progress monitoring) are emerging, analysis of industry stakeholders' viewpoints on adopting such technologies is still limited. They argued that identifying the expectations and perceptions of different construction participants is crucial for successfully integrating robotic and automated systems on project sites. While Qureshi et al. (2022a, b) attempted to identify the factors impacting the implementation of automated CPM, they only focused on the effectiveness of use rather than studying professionals' willingness to accept the adoption of CPM technology. Therefore, the attitudes of construction professionals must be studied to understand the industry's perception of the technology, which directly influences its widespread adoption. To address this gap, the current study utilises Q methodology to identify and examine the perceptions of different construction professionals towards adopting ACPM technologies in Australia. The research aims are as follows:

1. To identify and propose statements that are related to professional expectations and user experience based on the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT) model;

2. To identify professionals' perceptions on the adoption of ACPM technology in the construction industry through analysing the ranking of the statements;

3. To provide practical implications for future adoption and promotion of ACPM technology in the construction industry.

By capturing diverse professionals' perspectives, this study provides a holistic understanding of industry attitudes towards ACPM technology. Research investigating the acceptance of other innovative technologies can reference the adopted approach in this study. The findings shall shed light on the impacting factors of ACPM adoption and offer actionable insights for developers, construction companies, educators and policymakers.

Advances in ACPM technologies enable the construction of point cloud models based on the captured as-built data, allowing progress evaluation through comparisons with as-designed BIM models. Among the data acquisition technologies utilised in ACPM-related studies, 2D cameras and 3D laser scanners are the primary technologies used for site information collection (Wei et al., 2022; Zhang et al., 2024), resulting in the dominating image-based and point cloud-based ACPM approaches.

The image-based techniques consist of image-based 3D reconstruction approaches and image processing techniques (Hamledari et al., 2017). Compared with other vision-based techniques, the technology is more convenient for data acquisition and the images contain rich information, for instance, on object appearance and material texture (Alaloul et al., 2021; Wei et al., 2022). Image-based 3D reconstruction, also known as photogrammetry, creates point clouds based on the overlap of images (Hamledari et al., 2017). The procedure can be performed through photogrammetric applications like Agisoft Photoscan and VisualSFM (Wu, 2011), as well as algorithms including Structure from Motion and Multi-view Stereo (Alaloul et al., 2021). On the other hand, the image processing technique is used to improve image quality and extract relevant information from images (Ayachi et al., 2023). Image-based techniques can be adopted in both indoor and outdoor construction environments, leading to their widespread use in ACPM applications. For instance, Hamledari et al. (2017) utilised digital cameras for site data collection, integrated the computer-vision technique into the system and performed a comprehensive recognition of different interior drywall construction states, from studs and frames to painted drywalls. Based on this study, the authors further integrated BIM and drone technologies and developed a complete ACPM system, starting from data collection with drones to schedule updates in BIM models (Hamledari et al., 2021). Besides, Deng et al. (2020) developed an improved edge detection algorithm and evaluated the construction progress of tiles by identifying the boundary of tiles and calculating the corresponding area ratio. More recently, Wei et al. (2022) also focused on tracking the progress information for interior drywalls, specifically for the plastered drywall phase.

Nevertheless, there are several limitations in the image-based progress tracking methods. The quality of input images significantly impacts the results of image-based techniques. For example, the onsite lighting condition is vitally important in image-based progress tracking (Alaloul et al., 2021; Dai et al., 2010; Memon et al., 2005; Zhang et al., 2024). Excessive ambient light, whether too bright or too dark, can cause overexposed or noisy images, leading to errors when extracting information. The camera quality and the functionality of image-processing software may also influence the accuracy of information extraction (Dai et al., 2010). Besides, image resolution must also be considered for more accurate detection results (Wong et al., 2023). In addition, some image-based algorithms like SfM can be computationally intensive (Reja et al., 2022), necessitating expensive hardware and advanced computing resources. These limitations can disadvantage the accuracy and efficiency of image-based ACPM approaches.

Adopting 3D technologies for site data acquisition can achieve more precise outcomes than image-based techniques (Alaloul et al., 2021; Ali et al., 2021). Less sensitivity to extreme lighting conditions also contributes to higher data accuracy. The mainstream 3D scanners can be categorised into two groups based on size and operational requirements: handheld portable and terrestrial laser scanners (TLS). TLS are famous for their higher levels of scanning details and accuracy, while disadvantaged by the expensive costs and unportable sizes (Alaloul et al., 2021, Maalek et al., 2014). Handheld portable scanners, on the other hand, take advantage of more compact sizes and higher cost-effectiveness. However, limited by dimensions, technical parts and budget restrictions, the scanned data from portable scanners shows lower accuracy and detail levels than that from TLS. TLS are more commonly applied in studies and projects that focus on tracking the progress of larger structures and scholars tend to track the progress of more detailed and interior structures with portable scanners. For instance, Meyer et al. (2022) utilised a TLS to acquire point clouds and achieved a high-resolution detection approach for indoor construction changes. The study found that TLS point clouds are suitable for collaborating with BIM models that have a maximum level of accuracy of 40 and the proposed method is capable of documenting construction progress and verifying and updating the given BIM models. However, special attention is required for the scanning geometry and the authors also claimed that the average point density varies considerably due to the constrained geometric conditions of indoor construction (Meyer et al., 2022). On the other hand, Pučko et al. (2018) integrated low-cost Kinect V2 laser scanners into construction workers' helmets to achieve constant data collection and as-built information updates. While they focused on monitoring the as-built status of window frames and partition drywalls, the study by Ali et al. (2021) aimed to track the construction progress of column-finishing materials and HVAC pipes. The proposed system combined Kinect V2 for data capturing and virtual reality technologies for visualising progress information and showed its capability of near real-time progress monitoring.

The studies revealed a strong academic interest in integrating advanced data acquisition and processing technologies to enhance construction progress monitoring. However, the ACPM technologies have not been widely adopted in the construction industry. This situation highlights a broader issue: the cautious attitude of the construction industry towards integrating digital advancements. Understanding the factors influencing the acceptance of ACPM technology is therefore essential to bridging the gap between technological potential and practical implementation.

The fourth industrial revolution (Schwab, 2017) has accelerated digital transformation in many organisations. However, compared to other industries like manufacturing, automotive and aviation, the traditional and conservative characteristics of the construction industry drag the pace of digital transformation movement (Hewavitharana et al., 2021). The situation has been gradually changed due to the integration of digital technologies such as BIM, management information systems and the Internet of Things (IoT) in more construction projects (Ding et al., 2019; Li et al., 2022). However, the adoption of technology is not instantaneous, as it often depends on accompanying behavioural changes, some of which are non-technological (Al-Emran and Griffy-Brown, 2023; Al-Sharafi et al., 2023). Hence, understanding users' and organisations' requirements and acceptance of digital technologies is essential for successfully adopting these technologies.

Multiple models have been developed for studying the acceptance of technology adoption. For instance, the Theory of Reasoned Action (TRA) model was introduced in 1975 for sociological and psychological studies (Taherdoost, 2018). It predicts and explains human behaviour from three main cognitive components: attitudes, social norms and intentions (Taherdoost, 2018). The TRA model was expanded with the addition of perceived behavioural control (PBC), evolving into the Theory of Planned Behaviour (TPB) (Ajzen, 1985, 1991). While both TPB and TRA assume that individuals' behaviours are influenced by their behavioural intention (BI), TPB not only constitutes realistic limitations by adding PBC, but also achieves a self-efficacy type factor (Taherdoost, 2018). Consequently, PBC, subjective norm behavioural attitude are the three main factors impacting BI (Conner, 2020). Another derivative of the TRA model is the Technology Acceptance Model (TAM) (Davis and Granic, 2024). Rather than focusing on BI only, TAM also values the considerable impacts of perceived usefulness and ease of use. It is claimed to be one of the most popular models in the technology acceptance field (Wu, 2009). Other explanatory frameworks, such as the Decomposed Theory of Planned Behaviour (DTPB) (Taylor and Todd, 1995a, b), the combined model of TAM and TPB (C-TAM-TPB) (Taylor and Todd, 1995a, b), the Model of PC Utilisation (MPCU) (Triandis, 1977), the Innovation Diffusion Theory (IDT) (Rogers et al., 1995), the Motivational Model (MM) (Davis et al., 1992) and the Social Cognitive Theory (SCT) (Bandura, 1986), were also proposed to investigate the relationships between technology acceptance and its impacting factors.

While the diversity of alternatives provides researchers with flexibility in model selection for a particular context or research question, important constructs unique to each model may be overlooked, thereby weakening their explanatory power (Dwivedi et al., 2017; Hoi, 2020). Realising this, Venkatesh et al. (2003) thoroughly reviewed and compared eight TAMs, including TRA, TPB, TAM, D-TPB, C-TAM-TPB, MPCU, IDT, MM and SCT and integrated them into the UTAUT model. The UTAUT framework claims that four main factors can directly impact user behavioural intention to technology and use behaviour, including 1) performance expectancy, 2) effort expectancy, 3) social influence and 4) facilitating conditions, while these significant constructs are moderated by age, gender, experience and voluntariness (Hoi, 2020; Taherdoost, 2018). Venkatesh et al. (2012) further proposed UTAUT2 based on the UTAUT model and considered the consumer use context. New variables, including habits, price value and hedonic motivation, were added to the model (Venkatesh et al., 2012). While UTAUT obtains one of the highest capabilities for explaining user behavioural intentions and usage behaviour, many researchers have extended the UTAUT framework to better understand user behaviours in specific research fields (Gonsalves Nihar et al., 2024; Guo et al., 2023; Hewavitharana et al., 2021). These studies have repeatedly suggested the robustness of extended UTAUT in identifying user behavioural intention and usage behaviour.

Other studies have also been performed to study the resistance to new technology adoption in the construction industry, such as safety technologies and BIM. For instance, Nnaji et al. (2019) combined a literature review and expert interviews and identified 26 key predictors for safety technology adoption. The outcome revealed that technology reliability, effectiveness and durability were considered the most influential factors. It was investigated that respondents with more experience working in larger organisations valued individual- and technology-related factors significantly higher than less experienced participants who work in smaller companies. Nnaji et al. (2020) further developed a decision-making tool to aid in the adoption of safety technology in the construction industry, utilising TPB, TAM and multi-criteria decision-making models as guidance. Three predictor categories, including external-, organisation- and technology-related predictors, were covered in the tool, where the technology-related predictors were identified as the most influential factor (Nnaji et al., 2020). The findings also aligned with the outcomes from their previous study (Nnaji et al., 2019). To understand the adoption of BIM technology, Arayici et al. (2011) carried out a comprehensive and systemic assessment of relevant BIM technologies by undertaking a Knowledge Transfer Partnership project with a Small Medium Enterprise (SME) in the UK. The study revealed that focusing on people and processes is just as important as focusing on the technology. A bottom-up approach for BIM implementation was prioritised to (1) involve people in the adoption; (2) ensure the strengthened capabilities of companies and the improvement of people's skills and understanding; (3) adopt a successful change management strategy; and (4) reduce any potential resistance to change. The study also highlighted the importance of professional guidelines in the implementation strategy. Besides, Ayinla and Adamu (2018) noticed that a digital divide gap in BIM adoption speed existed among SMEs and larger organisations in the construction sector. By carrying out online questionnaires and expert interviews, the researchers developed a conceptual model and revealed that the sizes of organisations do not necessarily impact BIM adoption. They believed that aspects such as incentives, people's awareness, professional standards, client demand and technology improvements are essential for closing the digital divide gap.

Although considerable research has focused on advancing ACPM technologies, practical adoption within the construction industry remains notably limited. The reviewed literature reveals extensive academic efforts in developing and testing ACPM methods, such as image-based and point cloud-based approaches and highlights their technical capabilities and limitations. Similarly, theoretical frameworks like UTAUT and Q methodology have effectively examined technology acceptance and subjective attitudes in other domains, which underscore critical factors influencing users' adoption intentions and behaviours. However, a clear research gap persists, specifically concerning the subjective perceptions and attitudinal barriers among construction professionals towards ACPM technology adoption. The existing ACPM-related research has mainly emphasised technology development without systematically exploring the human and organisational dimensions that have a significant impact on technology acceptance. Consequently, there is a lack of sufficient empirical investigation into stakeholders' subjective viewpoints using structured and theoretically grounded approaches. While Qureshi et al. (2022a, b) attempted to identify the factors impacting the implementation of automated CPM, they focused solely on the effectiveness of use rather than studying professionals' willingness to adopt CPM technology. To address this critical gap, this study employs Q methodology integrated with an extended UTAUT framework to systematically capture, quantify and categorise construction professionals' subjective attitudes towards ACPM technology adoption. By achieving this, the study seeks to provide deeper theoretical insights and practical implications to bridge the gap between technological potential and its actual implementation within the construction industry.

In this study, the Q methodology was employed to analyse professionals' attitudes towards the adoption of ACPM technology in the construction industry. Q methodology was first proposed as an alternative method for factor analysis in psychology studies and was later adopted in other disciplines (Chang et al., 2019; Hensel et al., 2022; Stephenson, 1953). It is a scientific approach for studying human subjectivity, which can be interpreted as individuals' subjective opinions, motives, perceptions, attitudes, beliefs, priorities or views (Churruca et al., 2021; Hensel et al., 2022). This method combines the features of traditional quantitative and qualitative research, utilising only a small sample size to perform statistical analysis (Yin et al., 2023). Q methodology has been widely adopted in different research fields. For instance, it is considered a robust and systematic method in education research (Chaaban et al., 2023). Besides, it has also been used to identify different perspectives on aspects such as climate adaptation (Molenveld et al., 2020), innovation policies for new energy technologies (Chang et al., 2019) and urban planning failure (Yin et al., 2023). However, the adoption of Q methodology within the construction management sector remains largely unexplored. The goal of this study is to systematically explore and quantify subjective attitudes and diverse stakeholder perceptions towards ACPM technology adoption, which aligns closely with the unique strengths of Q methodology. In addition, considering that ACPM is an emerging technology, its adoption in practical construction projects can be limited. The capability of capturing, categorising and quantifying subjective viewpoints from relatively small samples reinforces the suitability of Q methodology for this study.

The method generally consists of five steps: (1) Developing the Q set; (2) Developing the P set; (3) Collecting Q sort data; (4) Analysing the factors; and (5) Interpreting the factors (Brown, 1980; Watts and Stenner, 2005). Figure 1 shows the overall framework of the methodology in this study. Details of the steps are described in the following sections. It is worth noting that an extended UTAUT framework is applied in the first step to help organise and generate the Q statements. Among the tasks, steps 4 and 5 are described in the Results section since they are performed in the post-data-collection phase.

A complete list of all possible statements relating to ACPM technologies was first formulated to develop the concourse, reflecting the full range of discussion and viewpoints on the particular subject (Nieuwenhuis et al., 2022). The statements were collected from sources such as academic literature reviews, technology user comments and conversations with experts. After compiling the concourse, duplicated and trivial statements were removed, resulting in a final Q set of 42 statements. The finalised Q statements were strategically categorised into the framework of extended UTAUT model. To ensure that the added factors are valuable, previous studies were studied. For instance, in the construction research field, Gonsalves et al. applied an extended UTAUT model to explore the intention of construction workers to use back-support exoskeleton (Gonsalves Nihar et al., 2024). Hewavitharana et al. (Hewavitharana et al., 2021) studied the impacting human behaviours towards the digital transformation of the construction industry by employing an extended UTAUT model, with new variables including perceived personal benefits, perceived risk and attitudes towards digitalisation. Additionally, Guo et al. (2023) employed an extended UTAUT model to identify the factors affecting the willingness of construction workers to accept metaverse safety training. Perceived trust, willingness to accept and actual behaviour were added as additional variables. Therefore, considering the conservative and cost-sensitive characteristics of the construction industry, perceived risk, price value and habit were added as additional variables to address all selected statements more adequately. Here, the Perceived Risk refers to the potential risks and challenges that may occur during the use of technology, while the Price Value represents consumers' tradeoff between the perceived benefits of the applications and the monetary cost of using them. Moreover, Habit here stands for the degree to which users' past experiences can affect their willingness of adopting new technologies. The statements were then pilot-studied by six researchers and the unclear and ambiguous statements were slightly modified based on the feedback and discussions. The employed framework and finalised statements are presented in Table 1.

The P set refers to participants involved in the Q-sorting process who are theoretically relevant to the topic and possess diverse characteristics (Yin et al., 2023). This diversity aligns with the Q methodology's aim to capture a wide range of viewpoints and perspectives (Chang et al., 2019). According to Brown (1980) and Watts and Stenner (2005), a range from 20 to 60 participants is considered suitable for Q methodology. In this study, a purposive sampling method was initially applied. Participants were required to have knowledge of ACPM technologies or, at a minimum, familiarity with the CPM process. To reflect different expertise and interests, 70 professionals from different perspectives in the construction industry were contacted and invited, including academic researchers, building developers, building designers and construction consultants. Although 45 participants accepted the invitation, the completion of their participation varied. Eventually, data from 25 respondents were included in the analysis, and the information of the selected respondents is shown in Table 2.

The data collection process in Q methodology is called Q sorting. Given the developed Q statement list, participants are asked to sort the statements based on the level of agreement or importance. The sorting can be either forced-distributed or unforced-distributed, where forced distribution is more commonly used in the existing Q studies. In studies using forced distributions, participants need to arrange the given statements in a quasi-normal distributed sorting grid, as shown in Figure 2. The restricted grid positions allow participants to treat the ranking more carefully. However, Kampen and Tamas mentioned that participants' actual thoughts about Q statements may not follow a normal distribution (Kampen and Tamás, 2013). Forcing a normal-distributed response may distort the viewpoints of participants and thus result in a distorted analysis of factors and perspectives. Besides, the shape and range of Q sort distribution have been repeatedly demonstrated to have no effect on statistical results (Giannoulis et al., 2010). Since ACPM technology is relatively new to the construction industry, most professionals have not used the technology in real construction projects. Using forced distribution may further restrict and distort their statement ranking. Therefore, this study performed an unforced Q-sorting distribution to collect research data.

In unforced Q-sorting, data collection methods are more flexible without the grid restrictions. Therefore, unlike other Q methodology studies that collect data through interviews, the Q-sorting in this study was performed through an integrated approach, embedding a recording tool into an online platform, namely Qualtrics XM (2024), and the data were collected by applying Likert scale questions. From a mechanistic perspective, both the unforced Q-sort and the Likert scaling are based on the score ranking of statements. Besides, compared to arranging statements in given Q-grids, data collection through a Likert scale survey can be more efficient and convenient since participants can evaluate the statements whenever they are available and without the pressure they might feel from interviews. With scores ranging from “−4” to “+4”, participants can freely rank the statements according to their degree of agreement, where “−4” represents “Most disagree” and “+4” represents “Most agree”. Each of the listed 42 Q statements was input as a survey question. Considering that participants may get tired when they keep answering questions with a similar focus, the provided statements were in mixed order to avoid the situation. As presented in Table 1, the fourth column shows the statement number used in the data collection, Q-sorting and factor analysis.

Rather than the exit interview in a forced Q-sorting process, several follow-up questions were presented after sorting the Q statements. Participants were asked to rank the statements with more than “+3” or lower than “−3” scores and justify the top 2 statements that they ranked on the two extremes. A voice recorder was provided with each follow-up question to facilitate participants' justifications.

With the collected data, factor analysis aims to identify participants with similar Q-sort arrangements and cluster them into the same factor, meaning that participants in the same cluster share similar perspectives. In this study, factor analysis was performed using KenQ Analysis Desktop Edition (KADE), version 1.3.1.

The first task in factor analysis is to extract the factors. There are two main approaches for factor extraction: centroid factor analysis (CFA) and principal component analysis (PCA) (Nieuwenhuis et al., 2022). Statistics show that PCA is more prevalent in existing Q method studies, with approximately half (48.5%) using PCA for factor extraction. Therefore, this study also applied PCA to extract factors. Extracted factors with eigenvalues greater than 1 are considered significant and valuable for further factor analysis (Chang et al., 2019). The PCA method resulted in 9 factors extracted in this study. The second task in factor analysis comprises factor rotation. Varimax rotation, considered the best solution for Q methodology (Watts and Stenner, 2005), was utilised to improve the interpretability of outcomes (Giannoulis et al., 2010) and maximise the amount of explained variance while maintaining consistency (Chang et al., 2019). To further determine the appropriate number of selected factors in the PCA solution, two additional criteria were applied: 1) each of the factors should have at least two Q sorts that have significant loading on that factor alone (Brown, 1980) and 2) the cross-product of the two highest loadings (absolute value) on that factor exceeds twice the standard error, which is also known as Humphrey's rule (Brown, 1980).

The factor selection process was performed using an iterative approach to define the most meaningful Q-sort clustering. Different numbers of factors were selected and rotated to examine the corresponding quantitative and qualitative data. Four main factors were finally selected, with a cumulative explained variance of 49%. The rotated factor matrix is shown in Table 3, along with the statistical value of each factor. In total, 22 of 25 Q sorts loaded significantly on one or more factors, whereas Factor 1 has the most defining Q sorts, which 10 participants loaded significantly. Factors 2 and 4, respectively, resulted in five and four significantly loaded participants, followed by three significantly loaded in Factor 3. The factor arrays are presented in Figure 3, where the light-greyed statements have P-values of less than 0.05 and the darker-greyed statements have P-values of less than 0.01. It is worth noting that although the input data was freely distributed and without a certain pattern, the visualisations of the extracted factors were presented as normally distributed due to the requirement of KADE (Shawn, 2023). However, it should not be a concern that the patterns of outcome visualisations will affect the accuracy of factor extraction since the factors were extracted based on the correlation matrix reflecting the relationship between two samples (Brown, 1980). The comparisons of statement ratings from different perspectives can be visualised in Figure 4, revealing the perspective similarities and differences.

Results indicate that Perspective 1 accounts for 23% of the overall variance and consists of responses from 10 participants. The Z-Score comparison for distinguishing statements of Perspective 1 is visualised in Figure 5, with the background information of participants provided in Table 4. Based on the factor array (a) shown in Figure 3, participants with this perspective have a generally positive attitude towards the performance and practical applications of ACPM technology. They strongly value the efficiency and technical capabilities of the technology < Statements 7, 10, 15; Scoring 3, –3, 3> and are not easily influenced by peer pressure from other companies in the construction industry <12; −4>. Previous user experiences of other technologies also contribute to their positive attitudes towards the adoption of ACPM technology <3; 3>. Additionally, the participants believe the technology is advanced and mature enough for practical adoption in real-world projects <22; −3> and consider concerns about quality information detection to be overstated <25, −3>. Moreover, Perspective 1 affirms the role of educational advancement in promoting the adoption of ACPM technology, though the moderate scoring also shows some scepticism that may temper the practical demonstration of teaching courses related to automated construction technologies <27; 2>.

Figure 6 illustrates the Z-Score comparison of Perspective 2's distinguishing statements, while Table 5 contains background details of participants who align with Perspective 2. Perspective 2, representing the attitudes of five participants, accounts for 9% of the overall variance. This group embodies a pragmatic and cautious attitude towards ACPM technology, contrasting significantly with the viewpoint in Perspective 1. They doubt the cost-saving potential of the technology, especially in reducing labour expenses <29; +3>. This is further emphasised by statements “Relying on automated construction progress monitoring can reduce the need for skilled human workers” and “Construction companies should use ACPM technologies since technology costs are typically lower than labor costs” with an agreement value of −4 on both statements. While the participants consider ACPM technology more appropriate for large construction companies <38; +4>, they are strongly concerned about its capability of ensuring quality <25; +4>. The high rating for Statement 38 also reflects that the participants are less confident that smaller companies will have enough financial resources for adoption. They remain unconvinced by factors like industry pressure, government subsidies and youthfulness as motivators for adopting ACPM technology, prioritising practical performance advantages over motivations from the external environment.

The distinguishing statements for Perspective 3, analysed through Z-Score comparison, are depicted in Figure 7, with Table 6 presenting the grouped participants' information. Different from Perspective 2, this perspective represents a group of pragmatic technology adopters primarily influenced by industry pressure <12; +4> and the practical benefits of ACPM, such as faster information collection <7; +4> and real-time updates <13; +3>. While participants with Perspective 2 are significantly concerned about the cost-saving potential of ACPM technology, experts in this group recognise the potential of ACPM technology to reduce labour expenses <42; +3>. Besides, they also believe that the technology is becoming more financially viable with a clear return on investment <8; +3>, making the adoption a more attractive option from a financial perspective. However, the group doubts the feasibility of small companies without dedicated technical departments applying such technology in practical projects, considering that smaller firms might face financial or logistical challenges. The group also reflects an attitude of resisting change and being comfortable with traditional methods. Not only do they lack enthusiasm for learning and adopting new technologies like ACPM to enhance project performance <19; −4>, but they also do not recognise the appeal of manual processes and physical progress observation <28; −3>.

Visualisation of the Z-Score comparison for distinguishing statements of Perspective 4 is shown in Figure 8, and Table 7 describes the involved participants' information. Unlike Perspective 3, which recognises the practical benefits of ACPM technology, Perspective 4 represents a group of sceptical realists who are highly cautious about the immaturity of the technology. Besides, different from the other three perspectives, participants sharing this perspective are all aged between 50 and 59 years old. Individuals in this group believe that the technology still has significant room for improvement <22; +4>. They do not think that the computational algorithms and data processing in ACPM systems are advanced enough to offer reliable real-time updates for construction progress <26; −4> and also doubt the accuracy and efficiency of the systems <17; +3>. The group also disagrees that ACPM systems are easy to use <30; −2> due to the potential requirements of training, operation and support, which may contribute to their overall reluctance towards the technology. Challenges in communication with technical support teams further contribute to their hesitancy <5; +2>, which might be tied to their doubts about the maturity of the technology as complex systems normally require robust support. While their hesitancy may not be easily affected by peer pressure in the industry <12; −3>, the group recognises the role of academia in contributing to the development of this technology. They acknowledge the importance of educational institutions in providing a talent pool for the eventual widespread use of ACPM technology <27; +4>. Considering that ACPM technology has not been widely adopted in the construction industry, the participants in this group likely prefer evidence-based research and academic studies to assess the feasibility and reliability of ACPM systems <41; −4>.

Our results indicate four contrasted viewpoints regarding the ACPM technology adoption in the construction industry. Despite the contrasted views, some statements were uniformly recognised to have significant contributions among the four perspectives. As shown in Table 8, five statements were found to have similar Z-score loadings across the four perspectives.

Four perspectives commonly agree that ACPM technologies should be capable of more detailed data collection and tracking of deviated construction progress. The agreement underscores an appreciation for data-driven approaches, provided they are sufficiently robust and flexible. On the other hand, despite variations in individual perspectives, all perspectives acknowledge negative contributions from aspects such as resistance to change, additional costs and a preference for mature technologies over partially developed innovations. Construction projects often operate within restricted financial margins, making it harder to rationalise additional expenses without a clear and immediate return on investment. This reflects the pragmatic and risk-averse nature of the construction industry, where financial constraints and operational reliability remain primary considerations. The general preference for proven and mature technologies also highlights an underlying conservatism.

Since the Q statements were categorised based on the extended UTAUT framework, analysing the outcomes from a broader view also helps stakeholders determine the focus direction in the future. Therefore, we quantified the statements that significantly contributed to the formation of different perspectives and identified their corresponding UTAUT factors. The occurrence frequencies of the factors were counted, and the comparison of factor occurrence is presented in Figure 9. A higher frequency of a factor indicates a greater diversity of participant views on that factor.

The comparison results suggest that price value, highlighted by ten occurrences, emerges as the most arguable factor in this study, underscoring the industry's cost-sensitive nature. Price Value (PV) in this context refers not only to the direct costs associated with ACPM technology acquisition and implementation but also to the broader concept of return on investment (ROI) as perceived by stakeholders. The divergence in views on PV reflects varying perceptions regarding the economic feasibility and financial benefits of the technology. The high frequency of PV across all four perspectives suggests that industry adoption will likely remain limited unless ACPM technology can demonstrate clear financial advantages and higher ROI over traditional methods. For ACPM technology, which involves considerable upfront investments in hardware, software and training, stakeholders need a compelling financial case before committing. Construction projects vary widely in budget and resource availability, meaning that the perceived ROI for adopting ACPM technology may differ significantly across contexts. Since construction companies are unable to reap economic benefits in the short term, the considerable cost of technology adoption and maintenance can lead to cash-flow distress (Zhao et al., 2023). Unlike industries with higher profit margins, construction often operates within narrow financial parameters that discourage risk-taking with unproven or costly innovations. Previous studies have also suggested that cost-related factors are vital to new technology implementation (Gambatese and Hallowell, 2011). Furthermore, the statement, “Construction companies should use ACPM technologies since technology costs are typically lower than labor costs”, has bidirectional and significant impacts on all four perspectives. While implementing construction technologies may have higher ROI and lower labour costs, it could lead to workforce displacement (Agenbag and Amoah, 2021), which has already become a serious concern in the construction industry (Ayodele et al., 2020). This can also exacerbate skill gaps and create resistance among construction workers who perceive it as a threat to job security, which could be a potential reason for experts being against this statement.

Factors including Habit, performance expectancy (PE) and social influence (SI) also attract strong conflicts among different perspective groups. Each of these three factors is mentioned eight times by the four perspectives. The disagreement about habit influencing ACPM adoption reflects the powerful pull of existing CPM routines and more mature technologies. These established routines and widely used tools not only bring comfort but are also perceived as reliable and less prone to unforeseen issues that new technology might introduce. Switching to ACPM requires professionals to learn the operations of new tools and re-consider new CPM workflows, which adds a cognitive load to the complex project management. The reliance on habit is particularly evident in Perspective 3, which lacks enthusiasm for adopting new tools with unfamiliar operational processes. This finding is also supported by previous studies (Hu et al., 2020; Venkatesh et al., 2012), recognising habit as a determinant of technology adoption together with intention.

ACPM technology has the potential to provide real-time as-built data, streamline reporting and identify potential delays or issues early, which can lead to reduced costs and enhanced project outcomes. The divergent opinions regarding PE may be caused by the varying success rates of ACPM technology applications in different project types and environments. While some participants consider the technology as a tool that could revolutionise CPM by providing real-time data and predictive insights, others may doubt its practical applicability, particularly in diverse and dynamic construction environments, where project conditions can change rapidly. Besides, the variability of construction project types, from small residential developments to large infrastructure projects, further complicates perceptions of the reliability and adaptability of ACPM. The mixed perceptions of ACPM's performance may also stem from its relative novelty in the industry. The majority of the participants in this study have never used ACPM technology. Limited exposure to successful cases or real-world applications of ACPM may also leave industry professionals unconvinced of its effectiveness. Such an argument indicates that, with examined reliability and tangible impact on productivity, professionals would be more willing to adopt ACPM technology. The finding aligns with the majority of studies that verified PE as the most potent impacting factor to behavioural intention (Dwivedi et al., 2017), affirming the necessity of performance improvement to technology adoption.

Social influence is another prominent point of contention. Considering the conservative nature of the construction industry, reputation and relationships are vitally important, making professionals wary of adopting innovations that lack widespread peer acceptance. Based on the theory of the UTAUT model, social influence involves aspects such as the impacts of peer behaviour, organisational expectations and external recognition on technology adoption (Venkatesh et al., 2003). The contention indicates that the attitudes of individuals and companies towards ACPM technology can be strongly influenced by the successful technology application in the industry or whether the technology is endorsed by respected industry bodies and professional associations.

The research findings indicate that increasing ACPM adoption requires differentiated intervention strategies rather than uniform promotion measures. For instance, when PV concerns dominate, phased investment models and verifiable ROI cases become critical. Where habit and resistance to change are prominent, gradual workflow integration and managerial endorsement become decisive. In scenarios with uncertain performance expectations, pilot implementations and transparent benchmark assessments can help build trust. These insights suggest that effective ACPM adoption relies not only on technological advancement but also on aligning improvement strategies with the attitude characteristics identified in this study. Detailed practical recommendations for ACPM development and adoption are provided in the following sections.

Despite the insights gained into ACPM technology adoption, this study has certain limitations that should be acknowledged. Firstly, as mentioned, most participants in this study lacked direct user experience with ACPM technology. Their evaluations of the Q statements were made based on their previous experiences with CPM and their knowledge about other construction technologies. Besides, this study involved a relatively small sample of 25 construction professionals. While Q methodology is well-suited for capturing diverse perspectives with small sample sizes (Brown, 1980; Watts and Stenner, 2005), a larger sample size could potentially provide more robust insights into the factors influencing ACPM adoption. Furthermore, this study focuses on the technology acceptance of ACPM technologies in the construction industry. The outcomes and methodology of this study may need to be revised to investigate other technologies in the construction industry. Moreover, since most participants involved are in the Australian construction industry/academia, the findings of this study can be less generalisable to regions with lower labour costs and less stringent safety regulations. Additionally, although the impact of each proposed UTAUT factor was investigated, the interactive relationships among the factors still need to be examined.

Based on the analysed UTAUT factors, this study provides practical recommendations to promote the adoption of ACPM technology. First, technology developers are decisive in the diffusion of the technology. Since professionals like respondents sharing Perspective 3 lack enthusiasm to learn new technologies, simplifying processes, providing clear tutorials and minimising learning efforts may greatly support adoption. Therefore, technology developers should prioritise intuitive, user-friendly interfaces and reduce the complexity of ACPM tools. Besides, to provide a more effective advertising and training method, developers may consider applying technologies like virtual reality to create interactive demos of the ACPM demonstration process. In this way, people might be more willing to get involved. Furthermore, due to the various requirements of different construction projects, some functions of ACPM tools may not be necessary for some projects. ACPM developers may consider phased integration strategies that can demonstrate incremental benefits without requiring full purchase of the tool. Focusing on specific, high-impact areas might reduce perceived risks and offer proof-of-concept examples that could eventually build wider trust and acceptance within the industry. Therefore, to accommodate various company sizes and capabilities, technology developers can offer modular ACPM packages that companies can scale over time. This approach can provide companies with entry-level options that do not demand extensive upfront investment or high maintenance costs, which addresses professionals' concerns about the price value of ACPM.

For construction companies, the primary task is to validate and highlight the performance of ACPM technologies. Companies can start by adopting ACPM technologies in smaller, less critical projects to prove the concrete efficiency improvements of progress tracking. These projects can then be applied as internal case studies to encourage other teams to adopt the ACPM system. Additionally, encouraging influential managers or senior staff to adopt and endorse ACPM technology can create a ripple effect within the company.

From another perspective, as supported by experts sharing Perspectives 1 and 4, educational institutions should incorporate ACPM technologies and digital construction management tools into their curriculum. Hands-on training and internships with ACPM tools can help prepare graduates to be familiar with these technologies. Moreover, institutions should partner with construction companies and technology developers for guest lectures, case studies or project collaborations. Exposure to real-world applications of ACPM tools can reinforce students' understanding of the value that these technologies bring to construction projects.

Another important sector is governments and policymakers. Governments can offer subsidies and tax incentives for companies that adopt ACPM technology. These financial supports could be particularly helpful for small and medium-sized construction firms that may not have the capital to invest in ACPM systems independently. In addition, governments can develop standards and best practice guidelines to assure companies of the reliability and quality of ACPM tools, potentially reducing hesitation and perceived risk around their adoption.

This study integrated an extended framework of UTAUT model and investigated the construction professionals' perspectives on the adoption of ACPM technology based on Q methodology. A total of 25 professionals from different construction disciplines participated in this study by evaluating the provided statements through a mixed online approach. The Q method analysis identified four major perspectives from the participants, namely, “Perspective 1: Enthusiasts of Efficiency and Real-World Support of ACPM Technology”, “Perspective 2: Realists Sceptical of ACPM's Impact on Labor and Adoption Potential”, “Perspective 3: Negativist in Technology Adoption Yet Can Be Influenced by Industry Pressure and Practical Benefits” and “Perspective 4: Sceptical Realists of the ACPM's Immaturity and Performance”. Experts with Perspective 1 emphasised the potential benefits of ACPM technology, particularly in enhancing progress tracking performance and reducing errors and they were optimistic and passionate about applying ACPM technology in real construction projects. In contrast to Perspective 1, Perspective 2 strongly concerned about the cost-saving potential of ACPM technology. They were also unconvinced by external recognitions like peer pressure and government incentives as motivators for adopting the technology. Perspective 3 acknowledged the benefits of ACPM adoption. However, participants sharing this perspective are unwilling to use such technology unless it is required by senior management or contractual obligation. Perspective 4, on the other hand, highlighted significant scepticism towards ACPM due to perceived inefficiencies and a lack of trust in computational analysis.

The perspectives were also analysed regarding the proposed UTAUT factors, highlighting that price value, habit, performance expectations and social influence are the key considerations of ACPM adoption. Price value emerged as the most arguable factor, with cost-related issues and expected ROI being key determinants of adoption. Habit was also notable. Many professionals resisted change, potentially due to reliance on established CPM routines and mature technologies for their reliability and familiarity, which reveals the conservativeness within the construction industry. This habitual resistance is most pronounced in Perspective 3, whose members exhibit limited enthusiasm for unfamiliar operational processes. Opinions also vary on the actual performance of ACPM technology in real construction projects. Lastly, social influence as a contentious factor suggests the importance of technology promotion by respected construction organisations and leaders. Despite the differences across the four perspectives, participants in this study reached some consensus, sharing both concerns and expectations regarding ACPM adoption. All four perspectives agree that ACPM technologies should facilitate detailed data collection and accurately track deviations in construction progress, indicating broad support for robust and flexible data-driven solutions. However, they commonly identify barriers including resistance to change, increased costs and a preference for fully mature technologies. Given the conservative nature of the construction industry, characterised by tight financial margins, additional expenses require clear justification through immediate returns on investment.

This study contributes to the research body by capturing diverse stakeholders' perspectives and providing a holistic understanding of attitudes towards ACPM technology. The findings shall shed light on critical drivers and barriers of ACPM adoption and lay a foundation for targeted initiatives by construction companies, technology developers, policymakers and educational institutions to advance the integration and acceptance of ACPM technologies.