In this study, digitalization is framed as a socio-technical change, as evidenced by multiple studies, rather than just a technological shift (Ghosh et al., 2025; Lundgren et al., 2023; Paul et al., 2022). This indicates that digitalization involves significant social change in organization’s culture as well. This aligns with the socio-technical systems theory, which stresses the interplay between social and technical elements in organizational change (Ghosh et al., 2025). In this context, digitalization includes changes in business models, work environments and the need for new skills in addition to adopting new technologies (Benharkat and Aslan, 2023). Implicitly, this means that isolated solutions can easily lead to siloed effects: in some parts of the process improvements can be made, but in other parts new problems and challenges, even disbenefits, may arise. The end result is a back-and-forth going dynamic process where the aggregate system is under constant change. Digital tools are argued to create potential for productivity gains when they are complemented by interoperability, stakeholder engagement, standardization and other organizational inputs (Paskoff et al., 2024; Manzoor et al., 2021). In other words, change in technology level and organizational structure should be conducted jointly, if not return of investment in technology may be neutral or even negative for productivity. This complementarity logic is consistent with prior construction research that reports large variation in realized productivity gains (Ma and Liu, 2018; Cheng et al., 2023).

In this study, the socio-technical framing is operationalized by structuring digitalization into dimensions that are examined later in the analysis: (1) digital tools and data practices, (2) process integration and interoperability, (3) people/skills and organizational change, and (4) value-chain governance and incentive alignment. These dimensions informed the development of the survey items and the interview guide. As a result, the theoretical background shaped what is measured and how results are interpreted in the subsequent sections.

Digitalization can be linked to productivity through the following: i) coordination and information processing, ii) standardisation, and iii) value distribution along AEC chain (Saraiva et al., 2025; Karunaratne et al., 2025). Construction projects are information-intensive and fragmented by multiple stakeholders. Digital tools increase capacity to process information and lower transaction frictions between stakeholders (Saraiva et al., 2025). The expected immediate outcomes are fewer errors and clashes, faster error detection, and better adherence to schedule (Musarat et al., 2024). Once information is structured and processes are routinised, knowledge has higher potential of reusing. Here interoperability and data quality are very decisive enablers (Malykhin et al., 2024). In the absence of standardisation, digitalization can add more work and even decrease productivity gains. In addition, even when the overall productivity rises, gains can be uneven throughout the value chain. Without contractual or incentive alignment, asymmetric value capture can discourage some of the key stakeholders from engaging in digitalization.

Overall, the productivity effects of digitalization are anticipated to be conditional on organizational complements and incentive alignment along the AEC value chain, which explains heterogeneous outcomes reported in prior work (Krutova et al., 2022; Abdel-Wahab and Vogl, 2011; Lu et al., 2021).

The concept of productivity is generally defined as the relationship between input and output, where higher output with lower input reflects higher productivity (Mackensen, 2008). Productivity serves as a key performance indicator in the construction industry (Rathnayake and Middleton, 2023). In construction productivity, output refers to the construction industry’s results, while input refers to resources used (Ayele and Fayek, 2019). Productivity measurement can be divided into two different areas: macro and micro levels (Rathnayake and Middleton, 2023). The macro level considers productivity from the economic perspective, whereas the micro level considers it from the construction management perspective (Gerami Seresht and Fayek, 2018). In other words, macro success is more about ultimate operation/functions or long-term benefits of the project, while micro success is about profitability or short-term benefits (Toor and Ogunlana, 2010). Empirically, this study is about micro-level (project and organizational level) productivity-related impacts as perceived by professionals, and macro-economic productivity using national accounts are not studied.

While micro-micro distinction is conceptually useful, it also creates a persistent measurement tension in construction. Macro-level productivity indicators often aggregate heterogeneous projects, contractual arrangements, and quality differences, which can mask firm- or project-level efficiency changes (Rathnayake and Middleton, 2023). Conversely, micro-level productivity measurement is frequently operationalized through partial indicators but these can be difficult to standardize across projects and are sensitive to scope changes, risk allocation, and reporting practices (Ayele and Fayek, 2019). As a result, productivity may be measured, proxied, or perceived differently by stakeholders depending on their position in the value chain and which outcomes they observe directly. This measurement ambiguity helps explain why empirical findings on digitalization–productivity relationships remain heterogeneous across studies and contexts. In this study, we therefore treat productivity as a multi-dimensional construct and interpret reported impacts as productivity-related outcomes rather than direct productivity metrics.

Measuring productivity is challenging in construction industry due to the uniqueness of projects, variability of outputs, and difficulty in standardization (Turk, 2023). Measures aimed at improving the performance of construction projects have been identified as critical and challenging issues (Dixit et al., 2019). A project that appears successful to the client may be a complete failure to contractors or end users, meaning that project success means different things to different stakeholders (Toor and Ogunlana, 2010). From a traditional perspective, the iron triangle criteria have been used to assess the success of construction projects, i.e., delivery on time, budget and quality/scope (Locatelli et al., 2023). However, a broader view of project performance of project performance has long been discussed in construction management research that emphasize safety, sustainability, efficient use of resources, effectiveness, satisfaction of stakeholders are playing a more prominent role (Toor and Ogunlana, 2010; Borges et al., 2025).

Productivity in construction is influenced by various factors, including the efficient use of input resources such as labour, materials, capital and energy (Ayele and Fayek, 2019). In addition, a shortage of material, rework, tools and equipment, and worker motivation are significant factors affecting productivity (Dixit et al., 2019). Rathnayake and Middleton (2023) argues that factors affecting construction productivity can be categorized into six main categories: labour, equipment and technology, construction site, schedule, supervisors and materials. Overall, addressing these factors is crucial for effective utilization of assets and cost savings in construction (Dixit et al., 2019). These main factors affecting productivity are summarized in Table 1, which is used in this study as an analytical map for interpreting how different digitalization initiatives are expected to influence productivity via specific leverage points, and it informs the structure of the subsequent empirical synthesis.

The labour is one of the most significant factors affecting construction productivity (Dixit et al., 2019). Employee health, safety, skills, and motivation directly impact performance, while education and training are essential for long-term improvements (Naoum, 2016; Vigneshwar and Shanmugapriya, 2023). Leadership style and project organization also shape workforce effectiveness (Naoum, 2016). Equipment and technology offer opportunities to streamline work processes and improve accuracy (Rathnayake and Middleton, 2023). Building information modelling (BIM), automation, robotics, and digital project management tools can reduce errors, improve coordination, and increase safety (Barbosa et al., 2017; Chowdhury et al., 2019). However, successful implementation requires appropriate equipment availability, maintenance, and employee training to fully utilize these technologies (Fonseca et al., 2024). The construction site environment significantly affects productivity through factors such as logistics, planning, supervision and environmental conditions (Dixit et al., 2019; Naoum, 2016). Early integration of construction expertise into design improves site performance (Rathnayake and Middleton, 2023), while effective communication systems and supervision minimize errors and delays (Naoum, 2016).

The project schedule is also important factor influencing construction productivity (Dixit et al., 2019). Realistic and adaptable schedules can optimize resource utilization, prevent waste, and reduce costs by up to 15% (Vigneshwar and Shanmugapriya, 2023). Effective planning is therefore crucial to maintaining productivity in changing conditions (Geiger et al., 2023). Supervisors play a crucial role in coordinating labour and materials (Dixit et al., 2019). Strong communication, timely input, and supportive leadership reduces waiting times and increase motivation, which in turn improves efficiency (Naoum, 2016). Finally, the availability and management of materials are vital (Dixit et al., 2019). Just-in-time deliveries, adequate storage, and tracking systems prevent delays and rework (Rathnayake and Middleton, 2023). On the other hand, shortages or design changes can cause waste and inefficiency, highlighting the importance of strategic materials management (Naoum, 2016).

While digital tools, e.g., BIM, digital twins, IoT, AI, cloud computing, automation & robotics, RFID & GPS tracking, virtual reality & augmented reality, are widely expected to mitigate labour shortages and improve efficiency in the construction industry (Lu et al., 2021), its impact on productivity is neither automatic nor uniform (Krutova et al., 2022). ICT and related technologies can improve project management and resource utilization (Isayev, 2023), but research shows that their impact on productivity growth has diminished in advanced economies and in some cases even produced negative results (Abdel-Wahab and Vogl, 2011). The benefits appear to be stronger in less developed contexts, where the introduction of computers and technologies has more directly increased labour productivity (Lu et al., 2021). However, some ICT investments may prove unproductive if implemented without appropriate process redesign or workforce adaptation (Krutova et al., 2022). Overall, the literature suggests that digitalization will only improve productivity if it is supported by organizational change, management commitment, and continuous skills development (Lu et al., 2021).

Efficient information flow and coordination are significant benefits of digitalization in the construction industry (Moshood et al., 2024). As the Architecture, Engineering and construction (AEC) industry is highly data-intensive and project-based, effective communication and collaboration between different stakeholders is crucial to the success of the project (Beach et al., 2013). Traditionally, construction has been a fragmented industry, which has made fluent cooperation between stakeholders difficult (Alashwal et al., 2011). Digitalization can help address these challenges by enabling unified data environments that improve transparency, support informed decision-making, and strengthen collaboration (Fonseca et al., 2024).

In addition to improving information flow, digitalization has the potential to improve project planning and implementation. Studies highlight that digital tools reduce design errors, minimize duplication, and support proactive detection of potential problems, which together improve quality and reduce work (Naoum, 2016; Ametepey et al., 2024). Decision-making based on reliable, real-time data helps align project goals and outcomes, which in turn improves efficiency and reduces waste (Chowdhury et al., 2019; Fonseca et al., 2024). In addition, the potential of digitalization is also seen in process optimization and resource efficiency. Integrating traditional lean strategies with modern digital technologies, the construction industry is increasingly reframing operational efficiency (Ali Berawi et al., 2024). By enabling more accurate planning and streamlined workflows, digitalization can reduce waste, support on-time delivery, and improves overall project management (Barbosa et al., 2017). In addition to boosting productivity, these advances can also promote sustainability as resource use and environmental impacts may be more effectively managed (Musarat et al., 2023). Table 2 provides illustrative examples of prominent digital technologies discussed in the construction digitalisation literature and summarizes their commonly reported productivity-related benefits. The technologies were selected because they recur frequently in prior reviews and empirical studies on AEC digitalisation, and the list is not intended to be exhaustive.

While digitalization holds promise for productivity improvements in construction industry, its adoption is hampered by the fragmented nature of the industry, project-based workflows and variable supply chains, resulting in productivity growth lagging behind other industries (Brozovsky et al., 2024; Van Tam et al., 2024). Overcoming these barriers is seen as essential to fully exploiting digital technologies (Machado et al., 2019). These challenges are broadly categorized as technological, economic, organizational, human and regulatory/contractual factors (Dauda et al., 2024; Van Tam et al., 2024). These challenges are summarized in Table 3.

In the construction industry, many organizations lack technological infrastructure (Van Tam et al., 2024). Companies are still using outdated systems, which hinders the integration of modern technologies such as BIM, AI, and the Internet of Things (IoT) (Chen, 2023). The lack of industry-wide standards and interoperability frameworks further hinders technology adoption, causing compatibility issues and inefficiencies in data transfer between different software platforms (Chen, 2023; Van Tam et al., 2024). In addition, concerns about cybersecurity and data protection make stakeholders hesitant to adopt new tools, as construction projects often involve sensitive information that can be vulnerable to data breaches (Dauda et al., 2024; Turk, 2023).

The adoption of digital technologies typically requires large initial investments in software, hardware and training (Van Tam et al., 2024). This financial burden is particularly burdensome for small and medium-sized enterprises (SMEs) with limited margins (Turk, 2023). These financial constraints lead to hesitation among stakeholders, as they are uncertain about the return on investment and often lack evidence (e.g., case studies of cost savings or productivity gains) to justify the costs of digital tools (Dauda et al., 2024). Researchers suggest that financial support encourage companies to invest in digital solutions (Van Tam et al., 2024).

Within organizations, the problem is often the lack of a clear digitalization strategy or strong management support (Chen, 2023). As a result, decision-makers are often reluctant to invest in new technologies without clear evidence of benefits and are concerned about implementation risks (Dauda et al., 2024). In addition, communication and collaboration are often fragmented: stakeholders use different, incompatible systems due to a lack of consistent practices (Chen, 2023). Conservative organizational culture and hierarchical decision-making further slowdown innovation, as companies often prefer to stick to traditional methods rather than try new solutions (Zulu and Khosrowshahi, 2021).

A significant human related challenge is the lack of digital skills among construction workers, and companies often struggle to upskill workers due to time constraints, resource constraints and the need to maintain traditional skills (Chen, 2023; Van Tam et al., 2024). In addition, the sector faces widespread resistance to change leading to a reluctance to adopt new technologies (Turk, 2023; Dauda et al., 2024).

The slow pace of development of legislation and standards has emerged as a significant institutional barrier to digitalization in the construction sector (Van Tam et al., 2024). The most significant shortcomings include the lack of interoperability standards (e.g., for BIM) (Turk, 2023) and legal uncertainty regarding data ownership and responsibilities, which creates confusion in multi-stakeholder projects (Chen, 2023; Van Tam et al., 2024). Therefore, researchers are calling for clear legal frameworks and policies to support digital integration, including government-led initiatives, such as updated guidelines or incentives such as tax breaks to accelerate innovation in the sector (Chen, 2023).

The research process of this study is structured so that the effects of digitalization on construction productivity can be examined both broadly and in depth. A mixed-methods approach was therefore chosen for the study, combining a quantitative online survey and qualitative semi-structured interviews. The use of the mixed-methods approach requires the formation of a deeper and closer interaction between the methods rather than the simple coexistence of two different methodologies (Matović and Ovesni, 2023). A survey allows for obtaining information about a specific phenomenon by formulating questions that reflect the opinions, perceptions, and experiences of a group of individuals, while semi-structured interviews allow for more in-depth exploration of individual issues (Almeida et al., 2017). Such a combination enables complementarity of methods, where the strengths of one material support the limitations of the other and improve the overall understanding of phenomenon under study (Jain, 2021). This research method should therefore lead to added value in knowledge about the research topic that would not be available if only one of the above methods were used (Matović and Ovesni, 2023). Both the survey and interview protocols were derived from the literature-based dimensions introduced in Section 3, with the survey providing breadth through standardised measures and the semi-structured interviews providing depth and contextual explanation to interpret patterns observed in the quantitative results. Figure 1 below illustrates the research process of this study.

Overall, the research process followed a systematic order ensure both a theoretical foundation and high-quality empirical data. It began with identifying the research problem and limiting the research and defining the research questions. This was followed by the literature review, synthesizing previous research and identifying relevant frameworks. To develop analytical scaffolding for the study. We conducted a targeted literature review to identify commonly reported productivity factors, prominent digital technologies and their reported productivity-related benefits, and widely discussed challenges to digitalization. Searches were conducted in Scopus and Google Scholar using combinations of keywords such as construction productivity factors, digitalization OR digital transformation, BIM, IoT, digital twin, AI, barriers, challenges, and AEC. We prioritized recent peer-reviewed journal articles and review papers, complemented sources where relevant. The categories in Tables 1 and 3 were derived by consolidating recurring themes across the identified sources and harmonizing terminology to avoid duplicates; the resulting categories were used as a structured input for the subsequent empirical instruments (survey and interview guide). This review was designed as a scoping to support instrument development rather than as a comprehensive systematic literature review. Based on the theoretical knowledge obtained, a survey and interview questionnaire were designed to collect both quantitative and qualitative perspectives. The survey aimed to collect broad-based information from construction professionals, and the interviews provided more in-depth information on expectations, experiences and challenges related to digitalization. Once the empirical research design was completed, the questionnaire was distributed, and interviews were arranged with selected experts.

Data collection was followed by a data analysis phase, where the results of the questionnaire and interviews were examined. Integration of survey and interview results occurred at the interpretation stage. First, the survey data were used to establish broad patterns in perceived productivity-related impacts and challenges (e.g., relative salience of items and differences across respondent groups). Second, semi-structured interviews were used primarily for explanation and expansion: they provided contextual mechanisms (why a pattern may occur), clarified implementation conditions (when benefits materialise or fail), and supplied more concrete workflow examples that structured survey items may not capture. Interviews also served a complementary validity check by assessing whether the most salient survey patterns were consistent with experts’ accounts, while explicitly noting instances where interview narratives qualified or contradicted survey tendencies. Findings are reported by presenting survey patterns alongside interview evidence that confirms, explains, expands, or challenges those patterns. This dual approach allowed for the exploration of both general trends and individual experiences. Finally, the results from both data sources were compiled and synthesized. At this stage, the empirical findings were combined with a theoretical framework, which allowed the identification of the most important themes and drawing conclusions.

The data collection phase consisted of literature review, survey, and interviews. The literature review was used to create a theoretical framework for construction productivity and the role of digitalization, based on which both the survey and interview questionnaire were created. The survey aimed to collect quantitative data from a broad range of construction professionals, focusing on the adoption of digital tools, the perceived impacts of digitalization, and the associated challenges. Surveys are well suited for collecting standardized information from a wide range of participants, although the collection of personal experiences may be limited in some places (Jain, 2021). To complement this, semi-structured interviews were conducted to gain deeper insight into the benefits and challenges of digitalization, as well as practical indications of the impacts. This method, based on open-ended but flexible questions, allows respondents to refine their perspectives and provide more detailed information than survey data alone (DiCicco-Bloom and Crabtree, 2006). The primary advantage of a semi-structured interview is that it combines pre-directed targeting with the flexibility to explore new ideas that emerge, which deepens understanding of the topic (Adeoye-Olatunde and Olenik, 2021). By comparing the two datasets to identify overlapping findings and complementary insights, the validity of the results is strengthened (Jain, 2021).

The empirical research data was collected in two simultaneous phases. In the first phase, a quantitative online survey was conducted, targeting various actors in the Finnish construction industry, from experts to top management. The survey was distributed through two primary industry channels: the Arctic Construction Cluster (https://www.rakennusklusteri.fi/about) and Finnish Association of Construction Industries (https://rt.fi/en/) mailing lists and networks, which were also used to support interview recruitment. These channels reach hundreds of construction professionals, however because distribution occurred via mailing lists and onward sharing within networks, the total number of recipients cannot be determined reliably, and therefore a formal response rate cannot be calculated. The survey was open for responses approximately for 4 weeks, and a total of 40 responses were received. The resulting sample (n = 40) should be interpreted as a non-probabilistic, self-selected sample and is not intended to be statistically representative of the Finnish construction industry; accordingly, the quantitative analysis is limited to descriptive statistics. Prior to launch, the questionnaire was reviewed and piloted internally to ensure clarity, terminology consistency, and face validity, and minor wording adjustments were made before distribution. After closure, responses were screened and cleaned by removing incomplete submissions and checking for duplicate entries and out-of-range values; no additional exclusion criteria were applied. We acknowledge potential selection and non-response biases, and we treat these as limitations when interpreting the findings. Interviewees were recruited purposively via the same networks to ensure coverage across design, construction, and consulting roles. Interviews were continued until diminishing returns were observed (i.e., additional interviews largely reiterated previously identified themes and added examples rather than new categories); on this basis, 10 interviews were deemed sufficient for the study’s explanatory purpose.

The Standard Industrial Classification (Statistics Finland, 2025) was used to classify organizations. The largest groups were F41 Construction of buildings and M71 Architectural and engineering services. When examining the size of organizations, a clear majority of respondents (70%) worked in large organizations (more than 250 employees), suggesting that the results are influenced by organizations that are typically further along in their digital transformation and have more resources to invest in technology. When examining the respondents’ roles in the organization: middle management (e.g., site/project managers) formed the largest group, top management the second, and specialists (e.g., architects/designers) the smallest. The data thus reflects both strategic and operational perspectives. Finally, the respondent group was highly experienced: 48% reported having been in the industry for over 20 years and overall, 90% had over 10 years of experience. Figure 2 presents more detailed information about the respondents’ backgrounds.

Other than collecting background information, the survey consisted of three sections. The first section focused on the use of ICT in organizations, including questions about the maturity of digital transformation, BIM adoption, and the use of other digital tools. The frameworks used were from established models in the literature, such as the Digital construction company maturity model for digital transformation (Jäkel et al., 2024), BIM maturity levels (Pinnacle Infotech, 2024), and a technology list based on Brozovsky et al. (2024). The second section, which formed the core of the survey, examined the impacts of digitalization at three levels: industry, organizational, and project levels. At the industry level, respondents rated claims about the benefits of digitalization. At the organizational level, the impacts were examined using the DuPont model to assess the impact on financial performance (Filatov and Bunkovsky, 2020) and a question on challenges compiled from previous studies (Chen, 2023; Dauda et al., 2024; Van Tam et al., 2024). At the project level, the questions addressed the “iron square” of cost, time, quality and sustainability (Locatelli et al., 2023), as well as later life cycle stages such as circular economy and material recovery. In the final section, respondents were asked to identify the digital technologies they considered most important in the next 5 years, using the same list of technologies as before (Brozovsky et al., 2024). It is worth noting that when surveying current use of digital tools, respondents were allowed to select as many as they wanted. However, for the future use of technology, respondents were limited to select up to three most important technologies.

In the second phase of research data collection, qualitative data were collected through 10 semi-structured interviews, with professionals from design, construction and consulting sectors. The interviews were conducted via Microsoft Teams video calls. Contact was mainly by call, but some were also contacted by email. The interviews were recorded with the consent of the interviewees for transcription and to ensure the quality of the analysis. The interview recordings were transcribed into text documents using standard word-processing software and subsequently imported into NVivo for qualitative analysis. The qualitative data were analysed using thematic coding; results are therefore reported as themes and illustrative excerpts rather than statistical analysis. All interviews were conducted in Finnish, anonymized, and processed in accordance with research ethics principles. Table 4 summarizes the roles, industries, and experiences of the participants.

The interview questionnaire divided into four sections. The first section focused on the interviewee’s background and their role in their organization. The second section consisted of questions about general expectations about the benefits of digitalization, the tools used in organization, and challenges and drivers regarding the adoption of technologies. The third section focused on questions about how digitalization impacts productivity and efficiency, and practical examples related to this. The last section included questions regarding the challenges in utilizing digitalization and the prospects of digitalization. The aim of the interviews was to gain depth and practical examples to support the broader survey data.

The empirical research data was analyzed as two separate entities according to the nature of the data: quantitative survey data and qualitative interview data. The survey data consisted mainly of closed-ended questions with Likert scales and multiple-choice options. The data was examined using descriptive statistics to identify distributions and key trends. The results were presented using percentage distributions and graphs to illustrate respondents’ views on the impacts of digitalization. In Likert scale questions, the results were presented as mean values of the responses and standard deviations were added to the graphs. Given the sample size (n = 40) and the non-probabilistic, self-selected sampling frame, the quantitative analysis is intentionally limited to descriptive statistics; inferential testing is not used because the data are not intended to support population-level inference.

The qualitative data consisted of semi-structured interviews which were fully transcribed for analysis. The interviews were analysed using thematic content analysis, a flexible and systematic method for identifying recurring themes and meaning structures in the data. This involved coding the material to identify recurring themes, based on both the theoretical framework and the topics raised in the interviews. Particular attention was paid to expectations related to digitalization, perceived productivity impacts and practical examples of professional practice. The analysis highlighted experiential perspectives that complemented the broader survey results. The coding followed an iterative, hybrid approach combining deductive codes derived from the literature-based framework (e.g., productivity impacts, organisational complements, and challenges) with inductive codes emerging from the interview material. First, transcripts were imported into NVivo and coded at the segment level. Second, codes were refined through repeated reading and merging/splitting to reduce overlap and improve internal consistency. Third, related codes were grouped into higher-order themes, which were then reviewed against the full dataset to ensure that themes were coherent and distinct. To enhance credibility, coding decisions and theme definitions were discussed among the authors, and disagreements were resolved through consensus.

Overall, the survey and interview questionnaire were created based on the literature review. The key themes of the topic were identified from the literature, based on which it was possible to smoothly carry out the empirical research. At the end of the study, it was possible to compare data from literature, survey, and interviews and identify the most important themes and possible differences regarding expectations of the benefits of digitalization, perceived productivity impacts, and challenges related to digitalization.

Throughout this section, Likert-scale means are reported to summarise overall tendencies in perceptions; however, they should be interpreted cautiously given the non-probabilistic sample and the presence of “I cannot say” responses for some sections.

This section revisits expectations for digitalization in the construction industry by interpreting and comparing perspectives from the literature, survey responses, and interviews. Interpreted through a socio-technical systems lens, expectations reflect anticipated technology gains and the need for complementary changes in work practices, skills, and inter-organisational coordination. In this sense, expectations can be read as signals of where socio-technical alignment is perceived to be feasible versus still aspirational. The Venn diagram below (see Figure 9) synthesizes the findings, highlighting both areas of overlap and divergence.

All three sources agreed on the expectation that digitalization will primarily improve productivity through increased efficiency, reduced errors and smoother processes. There was also strong consensus on the importance of collaboration, data standardization and the growing role of AI, while sustainability was identified as an emerging but not yet fully realized benefit. In addition to these shared views, the literature highlighted long-term enablers such as increased R&D (Agarwal et al., 2016) and the adoption of off-site construction (OFC) (Rathnayake and Middleton, 2023) but evidence was not found from survey and interviews. The literature and interviews together highlighted the need for improved information transparency, productivity measurement and data-driven decision-making. The interviews alone raised practical expectations such as the automation of routine design tasks, a move away from 2D drawings and the wider use of BIM modelling in maintenance and warranty phases. The three perspectives provide a comprehensive picture: researchers emphasize strategic developments and systemic enablers, and Finnish professionals emphasize concrete workflow innovations. Importantly, none of the sources contradicted each other, but rather highlighted different expectations about how digitalization can impact construction productivity. Overall, expectations indicate achievable short-term efficiency gains but also highlight that systemic enablers (standards, skills, incentives) remain prerequisites for sustained productivity improvements.

In this section, the focus shifts to the actual and perceived impacts of digitalization on construction productivity. Figure 10 summarizes convergence and divergence across the three sources. Based on this, the discussion will examine the main themes in more depth and consider how these impacts indicate the role of digitalization in shaping the productivity of the Finnish construction sector. The mixed impacts across actors indicate potential value-capture asymmetries: some benefits materialise downstream (construction/owners) while costs and effort may concentrate upstream (design), which can weaken incentives for full adoption.

The convergence across sources suggests that the most consistently realised productivity mechanisms are coordination gains (information flow) and quality-related gains (less rework), which are the classic pathways through which digital tools translate into performance improvements in fragmented project environments. BIM and cloud-based platforms were consistently identified as key enablers of smoother information flow and coordination. However, the interviews revealed that the use of digital tools is still lacking in some areas, such as in data-driven decision-making. This highlights that while technologies exist, their value depends on how effectively people and organizations adopt them. In every source, quality improvements emerged as one of the clearest productivity benefits. Time and cost impacts were also confirmed, particularly in scheduling and budgeting, where early detection of delays and overruns has strengthened project management. However, perspectives varied: contractors benefit from increased efficiency, while design teams often face higher workloads and increased costs, revealing structural imbalances in how benefits are distributed across the value chain.

Direct and indirect cost savings were widely acknowledged and supported by survey results on material and overhead cost management, although the impact on labour costs was weaker. Meanwhile, sustainability and circular economic practices are increasingly seen as emerging areas where digital tools can support long-term efficiency, even if immediate productivity improvements remain modest. Finally, all sources noted that systematic methods for measuring productivity impacts are still rare. While the benefits are widely observed, the lack of consistent frameworks can underestimate the value of digitalization and limit knowledge across projects. It is therefore important to develop standardized measurement practices to fully capture and communicate the productivity impacts of digital transformation in the construction industry. Overall, perceived benefits cluster around coordination and quality, while uneven value capture and weak measurement practices remain key constraints on scaling gains.

While evidence from literature, survey and interviews shows clear perceptions of productivity benefits of digitalization, it also shows that these benefits are not achieved without challenges. This section interprets the main challenges identified in this study using the perspectives of the literature, survey respondents and interviewees, and considers their implications for productivity. Many challenges identified here can be interpreted as socio-technical misalignment, where technological capabilities (e.g., BIM, platforms) outpace organisational routines, skills, standards, and contractual arrangements required to realize productivity gains. By examining these challenges in detail, this section aims to identify where actions should be directed to accelerate digital transformation and ensure that digitalization can be fully exploited. The Venn diagram below (see Figure 11) synthesizes the findings across the three sources.

The persistence of interoperability and data-quality issues indicates that the productivity problem is not merely “tool adoption” but the lack of system-level integration (shared data structures, standards, and governance) across organisational boundaries. The most common challenge was the lack of interoperability and standardization of digitalization, combined with issues related to data quality and management. These systemic barriers hinder the exchange of information between tools and stakeholders and turn technologies intended to improve efficiency into sources of additional work. In addition, the lack of clear industry standards and the fragmented structure of the industry were significant challenges, which were particularly highlighted in the literature (Dixit et al., 2019; Geiger et al., 2023) but also reflected in the survey results.

Cultural and organizational barriers also emerged in every source, with resistance to change and conservative attitudes identified as key reasons why digital initiatives often stall. Encouragingly, interviews suggested a shift in attitudes as awareness grows, but the slow pace of cultural change remains a key bottleneck. This is compounded by limited awareness of the benefits of digitalization and a lack of digitally literate professionals, which can make it challenging to fully utilize the tools available. Tight project schedules and resource constraints exacerbate these challenges, as organizations rarely have time to experiment or train staff. From a productivity perspective, this creates a paradox: tools that could save time in the long term are underutilized due to short-term capacity constraints. Financial and systemic barriers were also identified. The literature particularly highlighted high initial costs and uncertainty about the return on investment as barriers, especially among SMEs with limited financial capacity (Dauda et al., 2024). While larger companies may embrace these investments, smaller companies are at risk of being left behind, creating a digital divide that makes it difficult to collaborate across the industry.

In summary, the barriers identified in the literature, surveys and interviews reveal that while digitalization offers significant productivity potential, its adoption and exploitation is not that simple. The foundational challenges mentioned in all sources, such as interoperability issues, cultural resistance, skills shortages and lack of standards, represent structural barriers that affect the entire industry. At the same time, more context-specific issues such as industry fragmentation, financial issues, regulatory issues, and security concerns further complicate the pace and scale of adoption. Together, these findings suggest that the productivity gains of digitalization depend strongly on how effectively these barriers are addressed. Without coordinated action at both the organizational and industry levels, the benefits of digital tools may remain unevenly distributed and partially unrealized. Beyond reinforcing well-established mechanisms in the literature, the study adds value by triangulating literature, survey patterns, and practitioner interviews to identify where benefits and costs concentrate along the value chain in the Finnish context. In particular, the results highlight a recurring gap between tool availability and data-driven use, and perceived upstream workload burdens in design versus downstream benefits in construction and operations, which helps explain uneven uptake despite broadly positive expectations.

From a critical perspective, it is important to note that the perceived benefits of digitalization are largely process benefits: fewer errors, smoother workflows, better coordination, and more accurate schedules. These are genuine improvements, but they do not automatically translate into direct productivity metrics, such as lower unit costs or shorter overall project durations. In some places, these productivity metrics are certainly being achieved, but there is very little concrete evidence of actual productivity development. This explains why global construction industry has historically reported modest productivity growth despite decades of technological innovation. Digitalization is therefore better understood as a long-term enabler rather than a short-term productivity enhancer.

This raises the question of what the real contribution of digitalization is to productivity development. As defined at the beginning of the study, productivity is the relationship between input and output (Mackensen, 2008). So does digitalization enable lower input or higher output, or perhaps both. Based on the most common benefits examined in the study, digitalization specifically enables lower input, but there is no measured evidence for this either. However, the lack of such evidence may reflect measurement challenges rather than a lack of real benefits. Indirect impacts, such as improved quality, reduced errors and faster information flow, may gradually affect both input and output, but these are not yet systematically recorded. Realizing these benefits also requires integrating digital tools into processes and supporting organizational change.

Another key reflection is related to the distribution of productivity gains in the value chain, i.e., the uneven distribution of costs and benefits across stakeholders. Designers often face increased workflows, which include a lot of time-consuming manual work, while contractors and owners benefit from reduced rework and better forecasts. Without mechanisms to balance these impacts, such as fairer contracts or shared incentives, the willingness of design industry to invest in digitalization may remain limited. This imbalance is not just an operational problem, but a strategic barrier to industry-wide change. In the long term, such differences may threaten to slow down the overall pace of digital transformation. Possible solutions to this uncertain profitability of design work could include not only contractual arrangements but also technological innovations that would enhance design work, such as the much-discussed AI. Further research is needed to determine how AI and other efficiency-enhancing technologies can be utilized in the automation of design work, which would reduce the increased manual work in the design phase.

Another important finding emerging from the study concerns the gap between literature and practice regarding the use of digital technologies. Although research emphasizes the role of advanced tools such as the IoT, VR, and 3D printing, the Finnish professionals still primarily rely on more established solutions such as BIM, mobile technologies, and cloud services to improve productivity. This discrepancy reflects the fact that literature is often based on global precedents and pilot projects, while the survey and interviews represent mainstream industry practices. Thus, the perceived productivity impacts remain concentrated in widely used tools, while the potential of new technologies remains largely untapped. This gap highlights the need for future research on how new technologies can be scaled and integrated into routine construction processes.

Finally, there are some limitations to this study. The survey sample was including only Finnish companies, and most respondents were from the building construction sector. In addition, most survey respondents and interviewees represented large organizations. As a result, the experiences of SMEs are reflected less directly, even though they play a crucial role in the sector in general and incorporate digitalization as well (Turk, 2023). Therefore, findings cannot be generalized beyond similar contexts. Moreover, although the material offers a diverse range of perspectives, it does not necessarily fully cover all aspects of construction. For example, the interviews would have benefited from broader expertise in infrastructure construction and the public sector, which would have made the results more reliable in terms of industry coverage and diversity. Furthermore, as digitalization is a rapidly evolving field, the technologies and practices discussed in this study may look different in just a few years, limiting the generalizability of the results. Last it should be noted that the results are based on perceptions and self-reported experiences rather than direct productivity measurements.

This study contributes to both academic research and practice by examining perceived impacts and experiences of digitalization on construction productivity through three complementary perspectives: literature, survey data and expert interviews. From a theoretical perspective, the study aligns with widely reported mechanisms and perceived benefits such as improved collaboration, quality, and scheduling, but is also adds important nuances from the Finnish context. Rather than simply copying international frameworks, the findings highlight how local conditions, such as uneven distribution of costs and benefits, reduced resistance to change, and the lack of BIM use in the maintenance phase, shape the real-world impacts of digitalization. These findings extend existing theory by showing that global expectations cannot be applied uniformly but must be interpreted through local practices and industry structures.

In practice, the results highlight that while digitalization is perceived to be delivering tangible benefits in terms of collaboration and error reduction, their full realization depends on leadership, process standardization and systematic training. Considering the imbalance in how benefits are distributed across the value chain, as contractors benefit from increased efficiency, while design industry often face higher workloads and increased costs. Procurement models and incentives should be adapted to ensure that all stakeholders, not just contractors and owners, are motivated to invest in digital tools. Industry-wide standards and interoperability remain critical bottlenecks, and without progress in these areas, potential efficiency gains risk being lost. Furthermore, developing more systematic ways to measure productivity impacts would help companies demonstrate value, build trust, and justify additional investments.

Future research directions that emerge from these results can be highlighted. One clear gap is the lack of systematic frameworks for measuring productivity impacts of digitalization. While benefits such as reduced errors and smoother workflows are widely recognized, they remain difficult to measure using traditional productivity metrics. Developing such methodologies should be a priority for both researchers and practitioners. In addition, the distribution of costs and benefits across the construction value chain should be explored in more depth and contractual or organizational mechanisms identified that would balance these impacts more fairly. The results also highlight the need to better understand the role of SMEs. As the empirical data in this study focused on large organizations, the challenges and opportunities of smaller companies are less visible. Given their central role in the sector, future research should explore how SMEs are embracing and benefiting from digitalization, and what targeted support they may need.