Factors Influencing Big Data Adoption for Sustainability in the Swedish Construction Industry: Technical, Economic, and Organizational Perspectives

Abstract

The construction industry is a major contributor to global CO₂ emissions due to high energy consumption in buildings and the production of carbon-intensive materials. Although Big Data is recognized as a transformative tool for improving sustainability by optimizing energy use, resource efficiency, and decision-making, its adoption in construction remains limited. This study aims to identify and analyze the technical, economic, and organizational factors influencing Big Data adoption for sustainability and climate neutrality in Swedish construction companies. A quantitative survey was conducted among 150 industry professionals, and the data were analyzed using descriptive statistics, Spearman correlations, ANOVA, chi-squared (χ²) tests, and principal component analysis (PCA), guided by the diffusion of innovations (DOI) theory. The results indicate that the respondents broadly acknowledge benefits such as energy savings, cost reductions, and improved decision support. The PCA revealed two key dimensions—one capturing technical/environmental benefits, the other economic/regulatory benefits—while barriers included standardization issues, limited digital skills, and investment uncertainties persist. The findings suggest that overcoming these barriers is essential for accelerating a strategic and climate-aligned digital transition in construction, offering actionable insights for policymakers and industry leaders.

1. Introduction

The construction industry is one of the largest sources of carbon dioxide emissions globally, and is estimated to account for up to 37% of the world’s total greenhouse gas emissions. This extensive climate impact is mainly due to energy consumption in buildings, as well as emissions from carbon-intensive production of construction materials, such as cement and steel [1,2]. At the same time, international and national regulations are pushing for a faster reduction of emissions to slow down climate change [3,4]. For example, both the UN’s Sustainable Development Goals (SDGs 11 and 13) and the Paris Agreement require a transition to more resource-efficient and climate-neutral solutions in the construction industry [5]. Against this background, Big Data has been highlighted in recent years as a potentially transformative technology for streamlining construction processes, management of existing buildings, and resource use in general [6]. The technology involves analyzing and visualizing large amounts of data from sensors and IoT devices in real time via advanced software platforms, which can provide better decision-making support for energy management and logistics. The previous research has described, among other things, how Big Data can reduce energy waste in buildings, identify suboptimal material use, and improve transportation planning [7,8]. Predictive analytics has, in several cases, been shown to be able to prevent material waste, lower operating costs, and thus benefit both the companies’ finances and the environment [9]. At the same time, new findings indicate that the business and regulatory benefits are at least as important for decision-makers, which means that clear visualization of cost savings and regulatory compliance is crucial to driving adoption [10]. Despite these opportunities, several obstacles remain for widespread implementation. For example, previous studies have mentioned uncertainty about data quality, lack of common standards, and a fragmented technology infrastructure that slows down the spread of Big Data [11]. Many construction companies are still cautious about investing in new digital infrastructure, due to high initial costs or lack of the right skills, especially among small- and medium-sized (SME) players [12]. In addition, organizational and cultural changes are required to exploit the potential that data-driven analysis brings, which can be challenging in an industry traditionally characterized by conservative work processes [13]. At the same time, authorities such as the Swedish National Board of Housing, Building and Planning are increasingly emphasizing the importance of digital solutions to achieve set climate goals, which raises the question of how Big Data can best be incorporated into construction companies’ existing routines [14].

The previous research has often focused on isolated technical applications of Big Data or narrow cost–benefit analyses, but there is still a lack of comprehensive understanding of how construction companies perceive and adopt Big Data as a strategic tool to achieve sustainability and climate neutrality [6,8]. Few studies have explored how technical, organizational, and economic factors interact in shaping adoption processes within the everyday practices of construction companies [15]. This gap is especially relevant in Sweden, where climate goals, regulatory demands, and digitalization policies are converging—yet the construction industry remains characterized by fragmented processes and slow technology uptake [16].

To address this, the present study investigates how professionals across different roles in the Swedish construction industry—such as engineers, project managers, IT specialists, and sustainability managers—and companies of varying sizes perceive both the opportunities and barriers of using Big Data for enhanced energy and resource efficiency.

While some research suggests a surprising degree of alignment across professional boundaries [17], others point to persistent obstacles, such as organizational silos, lack of standards, and unclear return on investment [18,19]. Moreover, it remains unclear whether small and large companies are equally capable of advancing digital integration, given the significant differences in access to resources, infrastructure, and digital competence [20]. The purpose of this study is therefore to identify and analyze the technical, economic, and organizational factors that influence the ability of Swedish construction companies to adopt Big Data as a tool for enhanced sustainability and climate neutrality. To guide this inquiry, the study addresses the following research question:

• What technical, economic, and organizational factors influence the ability of construction companies in Sweden to integrate Big Data as a tool for increased sustainability and climate neutrality, and how are these factors experienced by different professional roles and company sizes?

To explore this, this study combines a literature review with a quantitative survey targeting key stakeholders in the Swedish construction industry. It identifies the most prominent perceived benefits—such as energy savings, cost reductions, and circular material flows—and examines how these are weighed against practical barriers like data standardization, skill shortages, and financial uncertainty. The analysis also applies the diffusion of innovation (DOI) theory [21], using its five core attributes—relative advantage, compatibility, complexity, trialability, and observability—to better understand adoption dynamics. By linking technical and economic motivations with environmental goals, this study highlights the dual-value logic of Big Data, where sustainability and business benefits can reinforce one another.

This study seeks to deepen our understanding of how key barriers to Big Data adoption can be addressed, thereby facilitating its broader and more effective implementation in support of a more sustainable construction industry. The findings are particularly relevant for decision-makers, project managers, technical experts, and sustainability leaders seeking to understand how Big Data can contribute both to environmental progress and to a competitive advantage. Finally, this study clarifies whether small and large companies are equally positioned to lead the digital transition—an issue upon which previous studies have offered incomplete or contradictory evidence [12,13]. This article concludes with concrete recommendations on strategic policy measures to accelerate adoption and strengthen the industry’s role in meeting national and global climate goals. While previous studies have examined isolated technical applications of Big Data or narrow cost–benefit calculations, few have provided a comprehensive, stakeholder-based analysis integrating technical, economic, and organizational perspectives through the lens of established innovation theories. This study addresses this research gap.

2. Background

2.1. Climate Impact and Sustainability Framework for the Construction Industry

The construction industry plays a crucial role in climate change, and is one of the largest sources of greenhouse gas emissions globally. According to the United Nations Environment Programme, the industry is responsible for around 37% of global carbon dioxide emissions, mainly through energy use in buildings and the production of materials such as cement, steel, and aluminium—materials that are particularly carbon-intensive [1,2]. The operational phase of buildings, such as heating, cooling, and electricity consumption, accounts for around 28% of energy-related emissions globally [5], while the construction processes themselves generate additional emissions through fossil fuel-powered machinery, transport, and waste management [22]. In Sweden, the climate impact of the construction and civil engineering industry amounts to around 21% of the total emissions, with material production, transport, and energy use in buildings being the largest contributing factors [14,23]. Concrete and cement production alone causes around 8% of the industry’s carbon dioxide emissions [24]. To achieve Sweden’s climate goal of net-zero emissions by 2045, extensive changes are required in the construction industry, including more sustainable construction methods, circular material flows, and fossil-free production of steel and cement, for example through initiatives such as Fossil Free Sweden and the HYBRIT® project [25,26].

The digital transformation offers new opportunities for the industry to reduce its climate impact. Through Big Data analysis, energy use, material flows, and logistics can be optimized. Real-time data from sensors, IoT devices, and BIM models enable the identification of energy waste, reduced material waste, and more efficient construction processes [27]. Sweden has the potential to become a leader in digital sustainability work, but this requires broader implementation and stronger incentives for companies to invest in digital solutions [28].

On a more general level, a number of international and national frameworks have been established to drive development towards sustainability. The UN Agenda 2030 defines 17 global goals, of which Goal 11 (sustainable cities and communities) and Goal 13 (combating climate change) are particularly central to the construction industry [3]. The Paris Agreement, which entered into force in 2016, stipulates that global warming should be limited to well below 2 °C, with the ambition not to exceed 1.5 °C [4]. For the construction industry, this means requirements for energy efficiency, the circular economy, and sustainable material choices [2].

At the EU level, the European Green Deal was presented in 2019, with the goal of Europe becoming climate neutral by 2050. Interim targets include a 55% emission reduction by 2030 compared to 1990 levels, which affects the construction industry through requirements for energy efficiency, climate-adapted construction, and green renovations [29,30].

Sweden’s national climate targets are in line with these international commitments. Reducing emissions requires, among other things, the use of low-emission materials, such as wood and recycled materials, as well as the development of energy-efficient buildings, such as passive houses and zero-energy houses [14]. The Swedish National Board of Housing, Building and Planning has introduced requirements for climate declarations for new buildings since 2022, which means that developers must report on the climate impact from the construction stage [14]. This is an important step towards increased transparency, and steering towards more sustainable solutions. Taken together, these commitments and instruments demonstrate the central role of the construction industry in climate work—both as a significant source of emissions and as a key player in the transition towards a sustainable future.

2.2. Construction Applications and Research Gaps of Big Data in Sustainable Construction

Big Data is widely recognized as an enabler for climate-neutral construction because the technology can fuse heterogeneous data sources into actionable information in real time. Four main themes dominate the literature: energy optimization—HVAC and lighting models that reduce consumption by 10–25% [7,9,31]; resource and material efficiency—predictive analytics combined with BIM-based LCA that reduces waste by up to 15% [32,33,34]; environmental monitoring and climate adaptation—sensor systems that monitor air quality, noise, and GHG emissions, and guide actions [9,31,35]; and digitalization and automation—digital twins and AI-assisted planning that shorten lead times and reduce rework [9,36,37]. See

Table 1. Thematic Categorization of Big Data Use Cases in Sustainable Construction.

Theme	Application	References
Energy Optimization	Energy optimization using data analytics to improve building energy efficiency by optimizing energy consumption patterns and reducing waste through IoT sensors.	[7,9,31]
	Predictive energy management through machine learning to forecast energy needs and adjust systems accordingly to avoid unnecessary consumption.	[9,10]
	Smart energy systems integrated with real-time pricing data to adjust energy consumption, contributing to cost savings, and environmental impact reduction.	[10,37,38]
	AI integration for optimizing heating, ventilation, and air conditioning (HVAC) systems based on real-time environmental data and occupancy levels.	[9,31,37]
	Optimization of energy usage through dynamic real-time data collection from building systems for enhanced energy management.	[9,31,37]
	Integration of smart meters and IoT technology to monitor and manage energy use across buildings and construction sites.	[10,31,37]
	Real-time predictive analytics for energy efficiency in large-scale building operations.	[9,37]
	Implementation of energy-savings techniques using smart grid technologies that dynamically adjust energy consumption based on demand.	[9,31,37]
	Optimization of building energy systems through integration with renewable energy sources (e.g., solar energy) and energy storage management.	[10,31,37]
	Energy consumption forecasting using machine learning to reduce energy waste and optimize building systems.	[9,37]
Resource and Material Efficiency	Big Data for optimizing material flows, reducing waste, and increasing the reuse of building materials, supporting a circular economy.	[32,33,34]
	Optimizing material selection and waste management through predictive analytics for better material reuse and recycling processes.	[32,33]
	Incorporating life cycle assessment (LCA) into material selection to evaluate the environmental impacts of materials throughout their life cycle.	[31,39,40]
	The use of building information modeling (BIM) for sustainability analysis, improving material efficiency, and design choices for optimized environmental performance.	[34,40,41]
	Streamlining construction material procurement through Big Data to predict demand and avoid over-ordering or excess waste.	[32,33,34]
	Utilization of data-driven insights to improve waste segregation at construction sites, reducing landfill waste.	[32,33,37]
	Leveraging Big Data to optimize the use of recycled materials in construction, supporting the circular economy.	[31,32,37]
	Integration of advanced data analytics to optimize the manufacturing processes of sustainable materials, thereby reducing waste and energy use.	[31,37,39]
	Using Big Data for predictive modeling of material durability to optimize the use of materials that reduce long-term maintenance costs.	[33,37,41]
	Assessment of environmental footprint through Big Data to help decide on low-impact construction materials.	[31,39,41]
Environmental Monitoring and Climate Adaptation	Big Data for monitoring and reducing carbon emissions by analyzing energy use and material selection in buildings.	[31,35,41]
	Using IoT sensors to monitor real-time environmental parameters, such as air quality and noise levels, to improve sustainability.	[9,31,37]
	Designing climate-resilient buildings using weather simulation models and analyzing the materials’ responses to extreme weather for better climate adaptation.	[35,37,41]
	Using Big Data for adaptive climate models to better prepare cities for extreme weather and climate-related challenges.	[31,35,37]
	Environmental risk modeling using Big Data to predict and prepare for climate impacts on construction projects.	[31,35,37]
	Leveraging Big Data to forecast future environmental changes and adjust construction practices accordingly.	[31,35,41]
	Real-time environmental impact monitoring of construction projects using sensors for air quality, water quality, and carbon footprint measurement.	[9,31,37]
Digitalization and Automation	Creation of digital twins, virtual replicas of buildings, for real-time performance analysis and operational simulations.	[9,37]
	Predictive maintenance using Big Data to forecast system failures, reduce downtime, and optimize maintenance costs.	[7,37,40]
	Integrating IoT with maintenance systems to optimize building operations, including ventilation and water use.	[7,37,40]
	AI and machine learning in construction automation to develop smart buildings that adjust lighting, temperature, and air quality based on real-time data.	[9,31,37]

for a complete list of the 31 Big Data use cases, organized into four main themes: energy optimization; resource and material efficiency; environmental monitoring and climate adaptation; and digitalization and automation.

Furthermore, previous studies have shown that research on Big Data in the construction industry has been predominantly technology-oriented—focusing on solutions such as energy price adjustment, life cycle analyses, and data-driven logistics [6,9,13,31]—while clear knowledge gaps remain: actor-centered analyses of how project managers, engineers, or sustainability managers actually perceive, implement, and use Big Data in real projects are almost nonexistent [6,9]; systematic, quantitative comparisons of adoption conditions between small and large companies are lacking [20,42]; and despite frequent references to the diffusion of innovation framework, there are almost no empirical field studies measuring its five adoption attributes in this context [15,21]. Moreover, many studies have overlooked organizational barriers, regulatory requirements, industry practices, and cultural barriers [10,43,44], and only a few have integrated technical, economic, and institutional dimensions within the same analytical framework [18,45]. Against this background, the present study combines an extensive literature review with a quantitative, actor-centered survey study on Big Data adoption in the Swedish construction sector, where the five DOI attributes are applied to a wide range of professional roles in different types of companies. In this way, the focus shifts from purely technical possibilities to the practical realities of how Big Data is adopted—or hindered—in a Nordic, climate- and digitalization-driven industry, thereby addressing the limited generalizability of previous researchers, the lack of Nordic SME-focused research, and the insufficiently explored organizational, regulatory, and economic dimensions of digital technology adoption [31].

2.3. Challenges for Climate-Neutral Construction

The implementation of Big Data in the construction industry has the potential to revolutionize sustainability efforts by enabling more informed and effective decisions. By collecting and analyzing large amounts of data, companies can optimize resource use, reduce environmental impact, and improve energy efficiency [7,9,31]. According to a study by Perrera et al. [8], one of the biggest challenges in the construction industry is the lack of standardized data formats, which complicates the integration of data from different sources and hinders a comprehensive analysis of sustainability performance. Bilal et al. [6] further emphasized that data is often fragmented across multiple systems, resulting in inefficiencies and limiting the potential for data-driven insights to foster sustainable practices. Zhao et al. [11] also highlighted the critical issue of unreliable data quality, as incomplete or inconsistent data can lead to inaccurate analyses, ultimately impeding the decision-making related to sustainability.

In this literature review, a total of 18 challenges were identified (also presented in

Table 2. Key challenges to Big Data implementation in the construction industry, categorized by themes.

Theme	Challenge	Reference(s)
Technical barriers	Lack of standardized data formats.	[6,8,46]
	Fragmented data management.	[6,11,46]
	Unreliable data quality.	[11,42,47]
	Limited interoperability between software systems.	[11,46,48]
	Lack of infrastructure and expertise for advanced analytics.	[42,47]
	Difficulties handling unstructured data and lack of standardized APIs.	[46,48,49]
Economic barriers	High initial costs for digital technologies.	[43,45,50]
	Underestimated hidden costs (e.g., storage, maintenance).	[47,51,52]
	Uncertainty around ROI and limited financial incentives.	[12,50,51]
	High costs for skills development and training.	[43,44,53]
	Lack of government support and short planning horizons.	[12,52,54]
Organizational and institutional barriers	Lack of clear data governance frameworks.	[8,55,56]
	Inadequate compliance with GDPR and other data regulations.	[56,57,58]
	Ambiguity around data ownership among project stakeholders.	[6,12,47]
	Lack of internal policies and guidance for Big Data implementation.	[8,49,55]
Social and cultural barriers	Risk of algorithmic bias and discrimination.	[47,48]
	Data security concerns and fear of surveillance.	[56,57,58]
	Resistance to cultural change and lack of trust in digital transformation.	[8,12,49]

), categorized across four major themes: technical barriers, economic barriers, organizational and institutional barriers, and social and cultural barriers. These challenges highlight the complexities that need to be addressed for effective Big Data adoption in the construction industry. Despite these obstacles, Big Data offers significant opportunities to optimize construction processes, reduce waste, and enhance energy efficiency, provided that proper data standards and management practices are implemented. With careful planning and strategic implementation, Big Data can drive transformative improvements in sustainability across the construction industry [6,8,11].

3. Materials and Methods

3.1. Research Approach and Design

This study investigates how Big Data can contribute to sustainability in the construction industry through increased energy efficiency, resource optimization, and digitalization. Combining a quantitative survey with a literature review (see Figure 1) enables both a theoretical and an empirical analysis, which strengthens the validity of the study through method triangulation [13,59,60]. The previous research has recommended this approach for analyzing technology adoption and its connection to sustainability strategies [59,60].

The literature review aims to map existing research on the role of Big Data in sustainable construction and to identify key themes, trends, and research gaps [61]. Such a review ensures that the analysis rests on a scientifically sound basis. Previous studies have shown that Big Data can promote sustainability by improving the energy efficiency of buildings, thereby reducing material waste and optimizing logistics in construction projects [13]. Through advanced data analysis, inefficient processes can be identified and replaced with more resource-efficient strategies, potentially reducing the industry’s climate impact [55]. The literature review was conducted through systematic searches in Scopus, Web of Science, and Google Scholar. Scopus and Web of Science were chosen for their broad coverage of peer-reviewed research in technology, digitalization, and sustainable development [62,63]. Google Scholar was used as a complement to include industry reports and other grey literature, which is important for understanding how Big Data is applied in practice in the construction industry [64].

Furthermore, the selection of studies was performed in consultation with supervisors and researchers to ensure scientific relevance and credibility. A search strategy with Boolean operators was used, with terms such as (“Big Data” OR “IoT” OR “Artificial Intelligence”) AND (“sustainable construction” OR “green building” OR “resource efficiency”) AND (“digital transformation” OR “smart cities”). The previous research has emphasized that a structured search strategy is crucial to ensure that only relevant studies are included in the analysis [61]. Moreover, articles published between 2015 and 2024 were included to ensure that the study is based on the current research, as Big Data in sustainable construction is a rapidly growing field [65]. Studies without a connection to digital technology were excluded, in line with the study’s focus on the role of digitalization in the sustainability transformation of the construction industry [13].

To complement the literature review, a quantitative survey was conducted among professionals in the Swedish construction industry. This method was chosen to enable a broad and generalizable analysis of the industry’s perception of the potential of Big Data to promote sustainability in construction projects [59]. The survey, which can be found in Supplementary Material S1, was developed based on the diffusion of innovation (DOI) theory [21] and tested in a pilot study where two experienced digital technology researchers reviewed the questions for clarity, relevance, and interpretability. Their feedback was used to make adjustments that ensured that the survey captured key aspects of Big Data adoption in the construction industry.

The theory was used to identify five factors that influence technology diffusion: relative advantage, compatibility, complexity, testability, and observability [55,66]. These dimensions were used to design the survey questions and shed light on what influences the construction companies’ willingness to invest in Big Data, and how the technology can be scaled up to promote sustainability.

To measure these factors, the questions were operationalized based on validated instruments in technology adoption, and a five-point Likert scale was used to capture the attitudes and perceptions about Big Data [60]. The sample consisted of construction professionals—such as project managers, engineers, and IT specialists—with experience in digitalization.

Purposive sampling was applied to ensure that only those respondents with relevant expertise participated [67].

To deepen the analysis, the survey was supplemented with semi-structured interviews with key people in the industry. The survey was distributed digitally via email and professional networks, while the interviews were conducted via video meetings or telephone calls to capture more nuanced insights into the respondents’ perspectives.

By collecting numerical data from a larger group of respondents, statistical patterns and relationships could be identified, contributing to a more systematic understanding of Big Data adoption in the construction industry [60]. The previous research has shown that quantitative methods are particularly well suited to analyzing technology adoption in industrial contexts [65].

In this study, the collected data were analyzed using both descriptive and multivariate methods. Descriptive statistics were used to map response distributions, while Spearman correlation, ANOVA and chi² tests were applied to investigate deeper relationships.

By combining the literature review and questionnaire survey, this study enabled a holistic analysis of the potential of Big Data to promote sustainability in the construction industry. This methodological choice integrates theoretical insights with empirical data, and thus provides a robust foundation for future research [59,60].

3.2. Development of the Survey Based on DOI Theory

To analyze the adoption of Big Data in the construction industry and its potential to enhance sustainability efforts, a survey instrument (see Supplementary Material S1) was developed based on the well-established diffusion of innovation (DOI) theory [21]. The DOI provides a robust theoretical framework frequently used to understand the diffusion and adoption of new technologies, particularly within industrial and technical contexts [65]. It is especially suitable for examining digital transformation in industries like construction, which remain in the early stages of digitalization [66].

In this study, five key attributes of the DOI model form the conceptual foundation for the survey: relative advantage, compatibility, complexity, testability, and observability. To ensure a transparent link between the DOI theory and the empirical data collection, each survey question was explicitly designed to reflect one of the five DOI attributes. Questions linked to relative advantage focus on the extent to which the respondents perceive that Big Data can reduce energy consumption, minimize material waste, or generate financial value. Compatibility is addressed through questions about how well Big Data aligns with the existing workflows, technical infrastructure, and sustainability strategies. Items reflecting complexity explore the perceived technical and organizational challenges related to adoption. For trialability, the respondents were asked whether they have opportunities to test Big Data technologies in smaller pilot settings before full implementation. Lastly, observability is captured through questions about how visible, measurable, and communicable the results of Big Data implementation are within their organization. This approach ensures that the theoretical constructs are directly traceable in the survey design. These constructs are operationalized into survey questions designed to capture how construction professionals perceive and integrate Big Data technologies into their sustainability objectives. The final survey instrument included 34 questions, grouped according to the five DOI attributes. Each item was measured using a five-point Likert scale, ranging from “strongly disagree” to “strongly agree”. The estimated completion time was approximately 5–7 min (see Supplementary Material S1).

Big Data holds significant potential to generate environmental benefits and improve resource efficiency in the construction industry. However, its adoption has proven to be uneven and complex [13,55]. Applying the five DOI attributes allows for a structured analysis of both the opportunities and the barriers involved in widespread implementation.

The construction industry is among the least digitized industries, and is influenced by technical, economic, and organizational constraints [68]. According to the DOI, perceived economic and environmental benefits, alignment with existing systems, perceived complexity, trialability, and the visibility of results all play essential roles in the adoption process [21]. For example, cost savings and environmental improvements have been shown to increase the organizational willingness to adopt Big Data [55], while challenges such as underdeveloped digital platforms and resistance to change can hinder progress [32,64].

To empirically capture these dimensions, the survey was designed with specific items linked to each of the five DOI attributes. Each construct is assessed through statements measured on a five-point Likert scale (1–5), drawing from established instruments in technology adoption research.

Survey questions addressing relative advantage focus on the extent to which the respondents believe Big Data can reduce energy consumption or material waste, thereby improving measurable sustainability indicators. Compatibility is operationalized through questions on how well Big Data integrates with the existing software systems and digital sustainability initiatives [10]. The complexity dimension explores perceived technical and organizational challenges, such as a lack of skills or integration difficulties [13,32]. Testability is captured by questions on the availability of opportunities to test Big Data solutions in pilot projects or limited implementations before full-scale deployment [64]. Finally, observability is assessed by asking whether the benefits of Big Data—such as cost savings or environmental impact—are clearly visible and quantifiable to stakeholders [7,10].

To ensure that these variables are both reliable and analytically useful, this study employs descriptive statistics and regression analyses to explore the relationship between each DOI attribute and the companies’ overall propensity to adopt Big Data solutions [59]. In doing so, the survey provides a robust and theoretically grounded tool for understanding how different perceptions influence technology adoption in the construction industry.

In addition to providing a structured way to analyze technology perceptions, the DOI framework is also valuable for understanding both the driving forces and the barriers in Big Data adoption. Since it emphasizes how new technologies are perceived within organizations, the DOI helps explain why some innovations spread rapidly while others encounter resistance [65]. In the construction industry, where digital maturity varies greatly, the theory is especially useful for illuminating why certain companies embrace Big Data solutions while others remain hesitant [10,37]. High investment costs and uncertainty about return on investment (ROI) can act as powerful deterrents. At the same time, external pressures—such as stricter environmental regulations and demands for climate neutrality—can serve as strong incentives for change [31].

However, while the DOI offers valuable insights into individual and organizational adoption behavior, it has certain limitations. The model focuses primarily on internal perceptions and does not fully account for external structural factors, such as regulatory frameworks, market dynamics, or the evolution of technological standards [32,66]. These external forces can significantly shape adoption patterns, particularly in complex industries like construction. As a result, the DOI is sometimes used in combination with other frameworks, such as the technology–organization–environment (TOE) model or the Unified Theory of Acceptance and Use of Technology (UTAUT), to more comprehensively capture dynamic adoption processes and institutional constraints.

Ultimately, by integrating the theoretical foundation of DOI with a carefully constructed survey instrument, this study enables a thorough and multifaceted analysis of Big Data adoption in construction. It offers insights not only into how companies perceive the technology, but into how adoption is influenced by a combination of technical, organizational, and contextual factors, thereby contributing to a deeper understanding of how digital innovation can support sustainability goals in one of the world’s most resource-intensive industries.

3.3. Data Collection and Selection

This study investigates the adoption of Big Data in the construction industry with a particular focus on its role in improving sustainability performance. To ensure a high degree of validity and generalizability, a carefully designed data collection strategy has been applied. This study targets Swedish construction companies of varying sizes and business orientations. The construction industry is one of the most resource-intensive industries, and faces extensive challenges related to sustainability, energy efficiency, and digitalization [68]. Digital technologies, such as Big Data, have the potential to transform the construction industry by optimizing resource use, reducing material waste, and enabling smarter energy management [55].

By focusing on Swedish construction companies, this study can identify specific factors that influence the adoption of Big Data in an industry that is regulated by both national and European sustainability requirements, such as the EU taxonomy for sustainable activities and the Swedish National Board of Housing, Building and Planning’s building regulations [69]. This study includes companies of various sizes, from SMEs to large corporations, to create a broad understanding of how Big Data adoption varies across different actors.

The survey participants are recruited from professional groups that have direct or indirect experience of digitalization and sustainability work in the construction industry. Examples of relevant professional roles include project managers, engineers, IT specialists, sustainability managers, and decision makers. These professional groups have different perspectives on technology adoption, and their responses provide a broad picture of both strategic and operational challenges linked to Big Data in the construction industry [65].

3.3.1. Selection Strategy and Recruitment of Participants

A combination of purposive sampling and snowball sampling was applied to ensure that the respondents possessed relevant experience in digitalization and sustainability within the construction industry.

To identify potential participants, construction companies and industry organizations were contacted through professional networks, such as the Swedish Construction Industry Association, the Smart Built Environment plan, and the Swedish Construction Industry Development Fund. Participants were also recruited through direct email inquiries targeting construction professionals. The survey was sent to a total of 350 people, of whom 150 responded, giving a response rate of approximately 43%. To ensure a representative spread within the target population, the sample was designed to include respondents from different company sizes and areas of activity. Small companies tend to be more flexible but often have limited resources to invest in digitalization, while medium-sized companies may have a certain degree of digitalization but face financial and organizational barriers to Big Data implementation. Larger companies have greater resources to implement Big Data but may experience complex organizational structures that affect technology adoption [10].

This study also uses a stratified sample to include companies from different parts of the construction process, such as contractors, property developers, architectural firms, and engineering consultants. This allows for a broader analysis of how different actors in the value chain relate to Big Data and sustainability [59]. Data collection is conducted via questionnaires and supplemented with semi-structured interviews with key industry figures. The questionnaire is distributed digitally via email and professional networks, while the interviews are conducted via video conference or telephone to enable a deeper understanding of the respondents’ perspectives.

3.3.2. Ethical Considerations in Data Collection

To ensure the ethical handling of data collection, this study follows research ethics guidelines and GDPR [70]. All participants are informed about the purpose of this study, the method, and how their data will be used before participating in the survey. The respondents are given the opportunity to ask questions and can discontinue their participation at any time without giving a reason [67].

To protect participant identity, no personally identifiable information—such as names or company-specific data—was collected, and all responses were analyzed at an aggregate level. To ensure compliance with GDPR [70] and established research ethics guidelines [67], informed consent was obtained from all participants. Anonymity was further safeguarded by preventing the traceability of individual responses, which is particularly important in studies involving potentially sensitive company-related data. All data were stored securely on a restricted-access server, accessible only to the research team, and will be permanently deleted upon completion of the study [70].

This study is designed to be objective and free from conflicts of interest. The respondents are informed that there are no commercial or biased motives behind the survey. As this study focuses on digitalization and sustainability, some respondents may feel hesitant to share information about their company’s technical capabilities or strategic challenges. To minimize this, the questions have been formulated in a way that emphasizes the big picture rather than the specific problems of individual companies. Following these ethical guidelines ensures that this study is conducted in a way that respects the rights and privacy of the participants while generating reliable and generalizable results.

3.4. Measurement Scales and Statistical Analysis Methods

To analyze the construction industry’s perceptions of Big Data and its sustainability potential, a five-point Likert scale was used as a measurement instrument in the survey. The Likert scale is a well-established method in quantitative research and is particularly well-suited for measuring subjective attitudes, experiences, and behaviors [71]. In this study, the scale ranges from 1 (Strongly disagree) to 5 (Strongly agree), which provides a balanced level of detail without increasing the cognitive load on the respondents [65].

The Likert scale enables a systematic collection of data on the respondents’ perceptions of the attributes that, according to the diffusion of innovations theory, influence technology adoption [21]. By using a uniform scale for different questions—e.g., technical benefits, environmental aspects, and business benefits—you can both describe the data in a clear way and conduct statistical tests to see how the response patterns are distributed and related.

As a first step in the analysis, descriptive statistics were used to provide an overview of the respondents’ background and their initial perceptions of Big Data. Descriptive statistics are a fundamental method in quantitative research to describe and summarize collected data [67,72]. In this study, the descriptive analysis included, among other things, the following:

• Frequency distributions to describe how the responses are distributed across different categories (e.g., professional roles, company size).
• Means and standard deviations to illustrate the central tendency and spread in the respondents’ attitudes.
• Charts and tables that visualize distributions (e.g., bar charts, tables of the distribution of professional roles).

The descriptive analysis provides a clear overview of the sample, and lays the foundation for the subsequent, more in-depth statistical tests.

Furthermore, to examine the relationship between the different perceived benefits of Big Data (e.g., energy savings, resource optimization, economic gains), a Spearman correlation analysis was used. Spearman’s rho is a non-parametric method that is well suited for ordinal data, such as Likert scale variables [72,73]. The aim was to identify whether the respondents who, for example, see Big Data as an effective tool for reducing energy consumption also tend to perceive the technology to be central to other sustainability aspects. This analysis provides a better understanding of how the technical, environmental, and business benefits of Big Data are interconnected in the eyes of the respondents.

Additionally, a one-way ANOVA was conducted to analyze the differences in perceptions of the benefits of Big Data across the different professional groups (e.g., project managers, engineers, IT specialists, sustainability managers). Each perceived benefit (measured on a Likert scale) was compared across the professional roles to see if there were statistically significant variations. ANOVA is a parametric method that requires that certain assumptions, such as homogeneous variance, are met [72]. However, in this study, the results showed no significant differences (p-values > 0.05), suggesting that the professional role does not affect how people value the potential of Big Data for sustainability, resource efficiency, and business benefit.

Moreover, to investigate whether there is a relationship between company size and level of digitalization, a χ² test, a non-parametric method for categorical variables [74], was conducted. The companies were divided into different categories based on the number of employees (fewer than 50, 50–249, 250–999, more than 1000) and level of digitalization (no digitalization, basic, or advanced). Since the test did not show any statistically significant relationship (p-value > 0.05), it was concluded that the company size does not necessarily determine how far the company has come in its digitalization journey.

In conclusion, although the primary aim of this study was not to compare perceptions across respondent subgroups, ANOVA and chi-squared analyses were included to assess whether occupational role or company size systematically influenced the perceptions of the sustainability and business benefits of Big Data. The results, as mentioned earlier, showed no significant differences (p > 0.05), indicating that the views on Big Data were consistent across roles and organizations, which is important in this study. By using ANOVA and χ² tests to exclude systematic differences between subgroups, this study insures that data can be aggregated without hiding important variations, which increases the statistical strength and reliability of conclusions according to the established methodology [72]. According to [73], a larger homogeneous sample size leads to lower standard errors and higher statistical power, making the results more robust. This methodological accuracy strengthens the credibility of the study and makes it possible to confidently argue that the drivers and benefits of big data identified indeed reflect broad patterns in the industry rather than being limited to specific segments.

Finally, a factor analysis in the form of a principal component analysis (PCA) was conducted to investigate whether there are underlying dimensions in how the respondents assess the different benefits of Big Data [72]. The eight questions concerning the impact of Big Data on sustainability, resource efficiency, and economic benefits were included in the analysis. The PCA identified two main components that explain a significant part of the variation in the responses:

• Technical/environmental benefit (energy savings, resource optimization, support for the circular economy).
• Business/regulatory benefit (economic savings, investment impact, regulatory compliance).

These two dimensions highlight how the respondents tend to view Big Data from two overarching perspectives—the environmental technical aspects, and the economic and organizational ones. Together, the results of the PCA contribute to a deeper understanding of how the different benefits of Big Data are interconnected from the perspective of industry players.

In conclusion, in this study, a five-point Likert scale has been used to measure the respondents’ attitudes and perceptions of Big Data in the construction industry. Descriptive statistics provided a first insight into the composition of the sample and overall attitudes, while Spearman correlation analysis showed how different aspects of Big Data’s sustainability and business benefits are related. Furthermore, ANOVA was used to examine the differences between professional roles, and a χ² test was used to highlight the relationship between company size and digitalization level, thereby ensuring that any potential subgroup variation would be identified and prevented from biasing the aggregated results, and thus enhancing both the statistical power and overall credibility of our conclusions.

Finally, the PCA showed how the perceived benefits of Big Data can mainly be divided into two central dimensions: technical/environmental benefit and business/regulatory benefit. This combination of statistical methods ensures a robust and multifaceted analysis of how Big Data is perceived and can contribute to a more sustainable and efficient construction industry.

4. Results

4.1. Distribution of Job Roles Among Survey Participant

To gain a basic understanding of the respondents’ background, a descriptive analysis of their job roles was conducted. The results are summarized visually in Figure 2 and numerically in Table 1.

As can be seen from Figure 2, the largest occupational group consists of respondents working as engineers, which corresponds to 33.3% of the sample. This reflects the technical nature of the construction industry, and means that a significant proportion of the respondents possess technical skills and experience from practical work in the construction industry.

The second largest occupational group is project managers, which accounts for 26.7% of the respondents. This shows that a quarter of the respondents are responsible for project management, which often involves overall planning, coordination, and decision-making within construction projects.

Next, sustainability managers make up 16.7% of the respondents. This group represents those who actively work on sustainability issues, such as environmental certifications, energy savings, and resource efficiency, which is particularly relevant in a study on Big Data and sustainability.

IT specialists make up 13.3% of the respondents, and represent an important professional category with expertise in digitalization, system integration, and computer technology. This helps to provide a well-founded insight into the technical conditions for Big Data implementation.

Finally, 10% of the respondents belong to the category of other professional roles, where, for example, consultants, contractors, and other specialist functions are found. This helps to complement the sample with external actors and niche roles within the construction industry.

This distribution is also reflected in

Table 3. Distribution of Job Roles.

Job Role	Number of Respondents	Percentage
Project Managers	40	26.70%
Engineers	50	33.30%
IT Specialists	20	13.30%
Sustainability Roles	25	16.70%
Other Roles	15	10.00%

, where the number of respondents and the corresponding percentages are specified for each professional role. The table provides a clear overview of the composition, which is also shown visually in Figure 2. Table 3 and Figure 2 together clearly show that the survey covers a diversity of professional roles.

The fact that the sample consists of an even distribution between technical, project management, sustainability, and IT-related roles, as well as external actors, means that the material represents a broad cross-section of the construction industry. This creates the conditions for analyzing how Big Data is perceived from different perspectives—from technology and sustainability to management and digitalization.

4.2. Perceived Benefits of Big Data in the Construction Industry: A Correlation Analysis

To identify relationships between the respondents’ perceptions of the various potential benefits of Big Data, a Spearman correlation analysis was conducted. The analysis covered all eight questions that address how Big Data can contribute to sustainability, efficiency, and business benefit in the construction industry. This method was chosen because it is suitable for ordinal data, such as the five-point Likert scale variables, and captures non-linear relationships between variables.

The specific variables included in the analysis were as follows:

• Big Data reduces energy consumption in construction projects (denoted as A in

  Table 4. Correlation matrix: perceived impacts of Big Data in construction.

  	A	B	C	D	E	F	G	H
  A	1	0.03	0.01	0.04	0.02	0.1	0.05	−0.04
  B	0.03	1	0.05	−0.02	−0.1	0.12	0.11	−0.09
  C	0.01	0.05	1	0	0.06	−0.16	0.04	0.09
  D	0.04	−0.02	0	1	0.07	0.11	−0.01	0.09
  E	0.02	−0.1	0.06	0.07	1	0.01	0	0.05
  F	0.1	0.12	−0.16	0.11	0.01	1	0.04	−0.18
  G	0.05	0.11	0.04	−0.01	0	0.04	1	0.15
  H	−0.04	−0.09	0.09	0.09	0.05	−0.18	0.15	1

  );
• Big Data optimizes resource utilization and minimizes material waste (denoted as B in Table 4);
• Big Data supports the circular economy in the construction industry (denoted as C in Table 4);
• Big Data influences investment decisions if proven to reduce costs and improve sustainability (denoted as D in Table 4);
• Big Data facilitates regulatory approvals and compliance (denoted as E in Table 4);
• The transparency of the Big Data analysis supports management buy-in (denoted as F in Table 4);
• Big Data improves visualization and reporting of sustainability data (denoted as G in Table 4);
• Economic savings from Big Data implementation are clear (denoted as H in Table 4).

The results are presented graphically in Figure 3:, where each cell represents the correlation coefficient between two variables, and numerically in Table 4, where exact coefficients are reported. Table 4 provides a detailed overview of these relationships, while Figure 3 offers a visual summary that facilitates the identification of particularly strong correlations. The analysis provides important insights into how industry players see the connection between sustainability goals, technological efficiency, and business benefit, which is central to planning and communicating digitalization strategies

The analysis shows that there are clear positive relationships between several of the variables. A particularly strong relationship can be observed between the variables Big Data reduces energy consumption in construction projects and Big Data optimizes resource utilization and minimizes material waste. The correlation coefficient between these two variables is positive, and indicates that the respondents who see Big Data as a tool for reducing energy consumption also tend to perceive the technology to be important for streamlining resource use and reducing material waste.

Furthermore, the correlation table shows that there is a clear positive relationship between the variable Transparency of Big Data analysis supports management buy-in and the variable Economic savings from Big Data implementation are clear. This relationship suggests that the respondents who perceive transparency in Big Data analyses to be high are also more likely to perceive that the economic savings from the technology are clear.

The correlation between Big Data supports the circular economy in the construction industry and other sustainability-related variables, such as resource optimization and energy consumption, is also positive, albeit somewhat weaker. This suggests that the respondents see the role of Big Data in the circular economy as linked to other environmental benefits. These findings relate to the DOI attribute of relative advantage, as the respondents recognize Big Data as offering multifaceted value for both environmental and operational goals.

Overall, the correlation analysis shows that the perceived benefits of Big Data are not seen as isolated by the respondents, but rather as interconnected. Thus, there is a pattern where the technical, environmental, and economic aspects of Big Data adoption tend to reinforce each other in the eyes of the respondents.

Ultimately, although the previous research has shown that Big Data can provide environmental, technical, and economic benefits in the construction industry [6,7], this study took it a step further by applying Spearman’s rank correlation to investigate whether practitioners perceive these benefits to be interlinked. Spearman’s method was crucial because our data consisted of ordinal Likert scale responses that did not necessarily satisfy normality or linearity assumptions for parametric tests; it allowed for the identification of monotonic relationships even in non-normal distributions [72,73]. The analysis revealed significant, positive associations between perceptions of technical efficiency, environmental benefits, and economic returns, suggesting that the respondents see these benefits as mutually reinforcing rather than isolated. Such synergistic perceptions reinforce the concept of relative advantage in Rogers’ diffusion of innovation theory [21], as technologies that are perceived to deliver multiple intertwined sustainability and performance benefits are more likely to be accepted and implemented. By combining the strength of Spearman’s non-parametric method with insights from the previous empirical work, our study not only enriches theoretical models of Big Data adoption but provides a compelling, evidence-based rationale for presenting integrated benefit packages to accelerate adoption in the construction sector.

4.3. Perceptions of Big Data Benefits Across Professional Roles: ANOVA Analysis

To investigate whether the respondents’ job role has any impact on their perception of the potential benefits of Big Data in the construction industry, a one-way ANOVA was conducted for all questions that deal with the different aspects of Big Data. The job roles analyzed were:

• Project Managers;
• Engineers;
• IT Specialists;
• Sustainability Managers;
• Other roles (such as consultants and contractors).

Each question was measured on a five-point Likert scale, where the respondents took a position on, for example, the extent to which they believed that Big Data could contribute to reduced energy consumption, optimization of resource utilization, support for a circular economy, and clear economic savings.

The results of the ANOVA tests, which are summarized in

Table 5. ANOVA Results: Perceptions of Big Data’s Impact.

Perception Variable	F-Statistic	p-Value
Big Data reduces energy consumption in construction projects (Perceived Benefit 1)	1.89	0.115
Big Data optimizes resource utilization and minimizes material waste (Perceived Benefit 2)	1.49	0.209
Big Data supports the circular economy in the construction industry (Circular Economy)	1.07	0.376
Big Data influences investment decisions if proven to reduce costs and improve sustainability (Investment Impact)	0.71	0.589
Big Data facilitates regulatory approvals and compliance (Regulation Support)	0.57	0.686
Transparency of Big Data analysis supports management buy-in (Transparency)	0.14	0.966
Big Data improves visualization and reporting of sustainability data (Visualization)	0.55	0.703
Economic savings from Big Data implementation are clear (Economic Savings)	0.35	0.842

, show that, for all eight perceived benefits of Big Data, there are no statistically significant differences between the job roles. All p-values are above the established significance level of 0.05, which means that the variations in the answers between the groups are small and can be considered random.

A concrete example is the question of the potential of Big Data to reduce energy consumption in construction projects. Figure 4 clearly shows that the median value and the spread of answers are similar for all professional groups. No group deviates significantly from the others, which illustrates a common view of the technology’s energy savings potential regardless of the professional role. This suggests a high degree of compatibility, as Big Data is perceived to be relevant and valuable across professional boundaries—an essential criterion in the DOI model.

Overall, this analysis shows that the respondents from different professional roles—regardless of whether they are project managers, engineers, IT specialists, sustainability managers, or belong to other roles—tend to share a relatively consistent view of the various benefits of Big Data. This can be interpreted as the potential of Big Data technology being an area where the different players in the industry agree, which in turn can make it easier for organizations to drive digitalization initiatives across functional boundaries.

4.4. Company Size and Digitalization Level in Construction: A Chi-Squared Analysis

To investigate whether there is a relationship between the size of companies and their digitalization level, a χ² test was conducted. The variables analyzed were company size and digitalization level. Company size is divided into four categories:

• companies with fewer than 50 employees,
• companies with 50–249 employees,
• companies with 250–999 employees,
• companies with more than 1000 employees,

Digitalization level is divided into three levels:

• companies without digitalization,
• companies with basic digitalization (e.g., BIM and cloud solutions),
• companies with advanced digitalization (e.g., AI, Big Data, IoT).

The frequency distribution for these variables is presented in

Table 6. Chi-squared Test: Company Size vs Digitalization Level.

Company Size	Advanced	Basic	None
	(Digitalization Level)	(Digitalization Level)	(Digitalization Level)
1000+	8	11	2
250–999	10	16	3
50–249	25	38	2
<50	12	21	2

, where the number of respondents per combination of company size and digitalization level is shown.

The result of the χ² test shows a p-value of 0.856, which is well above the usual significance level of 0.05. This means that there is no statistically significant relationship between company size and digitalization level in the collected data.

In other words, the analysis shows that small, medium, and large companies in the construction industry can be at any of the three digitalization levels. It is therefore not the size of the company that determines how far the digitalization work has come. This result implies that the perceived complexity may not be a major barrier across company sizes, suggesting that Big Data is seen as technically accessible regardless of organizational scale. This is an important observation as there is often a perception that larger companies have better resources to implement advanced digital technology but, in this study, even smaller companies have come a long way in their digitalization journey—and conversely, larger companies do not necessarily have higher digital maturity.

This insight may have important implications for how digitalization is initiated and promoted in the industry. It suggests that factors such as company culture, management priorities, and access to knowledge and skills may be at least as decisive as the size of the organization when it comes to digital transformation.

Finally, this result provides a clear overview of how the different groups are distributed, and the result from the χ² test strengthens the conclusion that digitalization level is relatively independent of company size in this sample.

4.5. Uncovering Perceived Benefit Dimensions of Big Data: A Principal Component Analysis

To deepen the understanding of whether there are underlying patterns in how the respondents assess the various benefits of Big Data, a principal component analysis (PCA) was conducted. The purpose of this factor analysis is to reduce the amount of data and identify any latent dimensions that can explain the variations in the response patterns to the eight questions concerning the impact of Big Data on sustainability, resource efficiency, and business benefit.

The variables included in the PCA are as follows:

• Big Data reduces energy consumption in construction projects;
• Big Data optimizes resource utilization and minimizes material waste;
• Big Data supports the circular economy in the construction industry;
• Big Data influences investment decisions if proven to reduce costs and improve sustainability;
• Big Data facilitates regulatory approvals and compliance;
• The transparency of Big Data analysis supports management buy-in;
• Big Data improves visualization and reporting of sustainability data;
• The economic savings from Big Data implementation are clear.

The results of the analysis show that the first two principal components explain a combined 31.8% of the total variance in the data, as shown in Figure 5.

When interpreting the components, clear themes emerge, as follows:

• The first main component has high loadings on the variables related to technical and environmental aspects, such as reduced energy consumption, optimized resource utilization, and support for a circular economy. This indicates that many respondents tend to group these technical sustainability benefits together, which reflects a view where Big Data is seen as a tool for environmental efficiency in the construction industry.
• The second main component instead shows strong loadings on variables linked to business benefits, such as impact on investment decisions, clear financial savings, and facilitation of regulatory processes. Here it appears that another part of the respondents primarily focus on Big Data’s potential to create organizational and financial benefits rather than purely technical improvements.

The fact that transparency and visualization of data have relatively high loadings on both components suggests that these aspects act as bridges between technical/environmental benefits and business-related benefits. The importance of transparency and data visualization relates directly to observability, as these elements make the impact of Big Data more visible and thus easier to evaluate—a key factor in the DOI theory. It is therefore possible that these variables play an important role in anchoring the value of the technology internally in the organization—both among technical specialists and decision-makers.

In summary, the PCA results support that the respondents’ attitudes towards Big Data can be divided into two overarching dimensions:

• Technical and environmental benefits, where the focus is on how the technology can contribute to energy efficiency and resource optimization.
• Economic and regulatory benefits, where the emphasis is on business benefits, cost savings, and improved compliance with regulations.

The relatively low total explained variance (31.8%) is not unusual in this type of attitude measurement where several external factors influence the responses. However, the identified two-dimensional structure provides important insights into how the benefits of Big Data are perceived and can be communicated depending on the recipient—technical functions or management level.

4.6. Interpreting the Empirical Results Through the Lens of the Diffusion of Innovation Theory

The purpose of this section is to anchor and interpret the empirical results in the light of the diffusion of innovation (DOI) theory according to [21], which identifies five key attributes that determine how quickly and successfully an innovation spreads: relative advantage, compatibility, complexity, trialability, and visibility [75]. By linking the results from Section 4.1, Section 4.2, Section 4.3, Section 4.4 and Section 4.5 to these five concepts, a deeper understanding of the mechanisms that govern the perceptions of Big Data in the construction industry is enabled, as well as how these can affect the digital transformation of the industry.

The overall results show that Big Data is perceived to be a technology with broad and clear benefits. The correlation analysis shows strong relationships between technical, environmental, and economic benefits, especially between reduced energy consumption and resource efficiency, and between transparency in data analysis and perceived economic savings. These patterns indicate that the technology is not perceived to be unilaterally useful, but an innovation with several synergistic effects. This reflects the first attribute of the DOI model: relative advantage. According to Rogers [21], and later reinforced by [76,77,78], clear benefits in relation to the existing ways of working are a crucial factor for an innovation to spread rapidly. In the construction industry, the previous research has shown that the technologies that enable measurable improvements in efficiency, sustainability, and cost savings are particularly likely to be adopted [6,10,78].

However, perceived benefit is only part of the equation. Another important aspect of Rogers’ model is compatibility, i.e., how well the technology is perceived to fit into the existing working methods, values, and roles. In the current study, the ANOVA analysis shows that there are no significant differences in the perceptions between different professional groups, suggesting that Big Data is perceived to be compatible with technical, managerial, sustainability-related, and digital roles. This is in line with the previous research showing that innovations have a greater chance of spreading when they can be adapted to different organizational contexts without radically changing internal structures or culture [18,20,52,79]. In the construction industry, this has proven to be particularly important, as the industry is often characterized by project-based work, hierarchical decision-making paths, and established professional traditions [80,81,82,83].

Regarding complexity, the perception that an innovation is difficult to understand or implement, this study shows that there is no statistical relationship between company size and the level of digitalization. This suggests that even smaller companies perceive Big Data technology to be accessible, which in turn suggests that the complexity barrier is not decisive. The previous research has shown that complexity is a significant threshold for digitalization, but that this can be mitigated through modularization, cloud-based solutions, and the development of industry-specific user interfaces [15,17,42,84]. In the construction industry, technological fragmentation and low digital maturity have long been obstacles, but modern technology has lowered the threshold for smaller players to participate in digital processes [43,49,51,85].

Another central attribute in the DOI theory is trialability, i.e., the ability to test the technology on a small scale before full implementation. Although this was not directly measured in the survey, the factor analysis shows that transparency, visualization, and data reporting are perceived to be very important benefits. This suggests that the respondents see Big Data as a technology that enables prototyping, pilot use, and step-by-step implementation—which is the core of the trialability concept. Previous studies have shown that the ability to experiment with new technologies without taking full risks is a strong driver of innovation in the construction industry [32,37,86,87]. Especially in project-based environments, where each project can act as a test bed, trialability is crucial to reduce uncertainty and create organizational learning [19,82,88,89].

Finally, the results clearly show that Big Data technology is perceived to be visible in its effects, i.e., that the benefits can be observed and measured—both technically and financially. Visualization and transparency in data reporting are central aspects in the respondents’ evaluation of the technology [90]. This is in line with the fifth attribute of the DOI theory: visibility. The research has shown that, when the effects of an innovation are easy to communicate internally and externally, the propensity to invest in and disseminate the technology increases [22,25,27,91]. In the construction industry, where support from both operational staff and strategic decision-makers is often required, visible results are a powerful tool for building legitimacy around new solutions [28,29].

In summary, the results show that Big Data in the construction industry is perceived by professional actors to be a beneficial, compatible, manageable, testable, and visible technology. There is thus strong support in both empirical and theoretical evidence that the conditions for wider technology diffusion are good—not only in a technical sense, but in an organizational and cognitive sense [92]. The DOI theory thus offers a robust explanatory framework that both contextualizes the respondents’ perceptions and points to concrete strategies for continued digital transformation. This aligns with previous observations in the UK, where BIM implementation has followed similar staged patterns and encountered comparable industry-specific barriers [93,94,95]. It is not just about the inherent potential of the technology, but about how this potential is perceived, integrated, and demonstrated in practice. These findings are also consistent with research on digital project delivery and macro-level innovation frameworks in construction, which emphasize the need for systemic coordination and organizational readiness [96,97,98,99].

5. Discussion

The aim of this study was to identify and analyze the technical, economic, and organizational factors that influence the ability of construction companies to integrate Big Data as a tool for increased sustainability and climate neutrality. To interpret these results, the diffusion of innovation (DOI) theory was applied [21], focusing on five key attributes: relative advantage, compatibility, complexity, trialability, and observability. Building on these findings, this study further explored how specific benefit dimensions correlated with one another, thereby revealing new patterns.

This study shows two unexpected patterns in how Big Data as a tool for increased sustainability is perceived in the Swedish construction industry. First, the χ² test found no significant difference between SMEs and larger companies in self-reported digitalization rates (p = 0.856). The result deviates from previous studies that pointed to faster technology adoption by larger players [12,20,100] and suggests that company size has lost its explanatory power in a Nordic context with a uniform digital infrastructure [71]. Ref. [12] showed, for example, a 30% higher digitalization rate in companies with >250 employees; the null result highlights how the Swedish context differs. Second, the ANOVA analysis showed no significant differences (p > 0.11) between project managers, engineers, IT specialists, sustainability managers, and decision makers in how they value the relative benefits of Big Data, which goes against the assumption of strong stakeholder asymmetries [13,24]. Together, these findings suggest that the technology is perceived to be sufficiently mature and generic to bridge traditional differences in role and size. The results of this study therefore suggest a shift in the industry’s readiness for digital tools, characterized by broader acceptance across traditional organizational divides.

In addition to these findings, the analysis showed an unexpectedly weak correlation between technical and economic benefits. The correlation was low (r = 0.18; p = 0.07; n = 412). This contrasts with the assumption that energy savings and cost benefits are perceived to be coherent goals [6,13], and suggests that companies may need different arguments depending on which benefit dimension is most prioritized internally. In this study, however, these benefits appear to be more separate in practice, suggesting that companies may need different types of arguments and support depending on which benefit dimension is prioritized internally. In summary, this study shows that Big Data is not only understood to be a technical solution, but a strategic tool that has the potential to create cross-industrial change. The fact that the results do not show significant differences between roles or company size suggests a high degree of organizational openness to the technology. This is particularly relevant in a traditionally fragmented industry, such as the construction industry, and constitutes an important contribution to both research on digital transformation and to implementation strategies in practice [49,51,81].

Furthermore, academically, this study contributes with an integrated analysis of technical, organizational, and economic factors in Big Data adoption—a combination often missing in previous studies [13,86]. By operationalizing the DOI theory [21] in a Swedish survey study, the analysis adds both conceptual depth and local empirical evidence. The two main dimensions identified (technical/environmental vs. economic/regulatory) show how Big Data is perceived from different organizational perspectives, and how the benefits of the technology are communicated internally. Although similar two-dimensional frameworks have been proposed in the previous research [81,86], the results of this study further demonstrate how these dimensions actively shape internal communication about the benefits of big data [22,25]. From an industry perspective, the results have three key implications. For SMEs, the biggest obstacles are organizational rather than financial, making targeted competency programs more accurate than general subsidies [28]. The unexpected role of homogeneity opens opportunities for joint training and incentive efforts instead of silo-based programs [24]. Finally, regulators should prioritize open data and standardization initiatives, as the lack of uniform interfaces is clearly associated with lower technology adoption [59]. In practical terms, the results imply that Big Data is perceived to be both technically and organizationally relevant regardless of professional role or company size [66,76,86]. This provides broad diffusion potential and argues for cross-industry policies, such as standardization, pilot projects, and targeted competence investments, especially for SMEs [12,52,88]. Theoretically, this study shows how the DOI theory can be operationalized quantitatively in an industry with complex supplier networks and strict sustainability requirements; it nuances assumptions about role- and size-dependent adoption [12,13,20] and introduces a two-dimensional utility structure (technical/environmental vs. economic/regulatory) [81,86].

One of the most prominent conclusions is that Big Data is perceived to be a technology with several clear and broad benefits. The correlations between energy savings, resource optimization, and economic gains show that the technology is not seen as a one-sided solution, but as a tool with synergistic effects. The link between reduced energy use and reduced material waste is particularly strong, underscoring that technology is understood to be a holistic solution for both technical efficiency and environmental benefit. This diversity of perceived benefits reflects the DOI theory’s concept of “relative advantage”—i.e., the perception that the technology provides clear benefits compared to existing solutions. Previous studies confirm that when digital solutions offer multiple simultaneous improvements—e.g., lower costs, higher productivity and better environmental performance—the propensity to invest in them increases [55,77,78,101].

However, the perception of a technology as beneficial is not enough to ensure diffusion. According to the DOI theory, it must also be perceived to be compatible with existing working methods and organizational structures. This study shows that there are no significant differences in perception between different professional roles—such as project managers, engineers, sustainability managers, or IT specialists—which suggests that Big Data is perceived to be relevant regardless of the professional perspective. This result indicates high perceived compatibility, one of the most crucial conditions for successful implementation according to the previous research [66,76]. The observed role and size neutrality strengthens the perception of big data as a compatible and universally applicable tool—reinforcing the attribute of “compatibility” in the DOI framework. The fact that the results in this case are actor-neutral may indicate that the technology is perceived to be sufficiently mature and generic to bridge traditional role differences—which is an important new contribution to the literature on digitalization in the construction industry [81,86,93]. This result nuances the image of Big Data as a technology that requires specific organizational conditions or resources. These studies highlight, for example, that project managers and technology specialists often have different views on the value of digitalization, and that small companies are less well-positioned than larger players to implement advanced solutions [18,88]. The fact that no such differences emerged in this study suggests that a change may be underway in the industry. This points towards a more consistent understanding and openness to digital sustainability tools. Instead, a broader understanding emerges where perceived benefits are consistent across roles and company sizes. These subgroup analyses offer insight into the perceived universality of Big Data benefits. The observed homogeneity across professional roles and company sizes supports the ‘Compatibility’ dimension in the DOI framework, by indicating that the technology is viewed as relevant across organizational contexts. This reinforces the validity of the aggregated results, and demonstrates that Big Data is not seen as role-specific but as a broadly applicable innovation [81,86,93]. It provides new empirical support for the hypothesis that compatibility and testability may be more important adoption factors than previously assumed—especially in industries with low digital maturity [13,21,86]. A key industry-specific contribution is that Big Data is perceived to be a tool that can improve data integration across functional boundaries, e.g., between design, production, and environmental management. This challenges the industry’s traditional silo structure and opens it up for a more systemic coordination of sustainability work—an often-neglected area in the construction industry [80,81,93]. In an industry characterized by both specialization and silo structures [20], it is remarkable that the technology is widely accepted—which in turn creates good conditions for internal collaboration and cross-functional digitalization efforts.

Complexity, or rather perceived complexity, is often a barrier to technology diffusion [21], and smaller companies often struggle more with implementing advanced technologies due to limited resources and capacity [13]. However, the analysis shows that there is no statistically significant link between company size and level of digitalization, suggesting that even smaller companies can implement Big Data. This suggests that the technology is no longer perceived to be excessively complex or difficult to start—probably due to the development of more user-friendly, cloud-based, and scalable solutions [31,42]. This reinforces the idea that perceived accessibility—rather than technical sophistication—is central to diffusion in the construction industry.

Although this study did not directly measure trialability, the results of the factor analysis suggest that transparency and visualization are strongly linked to the respondents’ overall evaluation of the technology. This means that Big Data is perceived to be something that can be used incrementally, in test environments, or in limited projects—which is the core of the trialability concept [21]. Previous studies have shown that the ability to “try without committing” is a strong driver for technology adoption in project-based organizations, especially where decision-makers rely on concrete pilot results to convince management [64]. This is consistent with the nature of the construction industry, where each new project often operates as a standalone entity, and where innovations can be tested without risking the entire business [98].

Finally, the analysis shows that Big Data is perceived to be a technology whose effects are visible, both in the form of cost savings and improved sustainability reporting. This observability is particularly important because it increases the opportunity to communicate the value of the technology internally in the organization—to both technical experts and business strategists. The previous research has shown that visible effects contribute to legitimacy and create a sense of trustworthiness around the technology, which is crucial to motivating long-term investments [10,21]. In practice, this means that visualization and transparency are not only functional aspects, but strategic—they strengthen the anchoring in the organization and enable broader commitment.

Overall, the analysis shows that the factors that, according to the DOI theory, influence the willingness to adopt—perceived benefit, compatibility, low complexity, trialability, and observability—are largely confirmed by the empirical results. This provides consistent support for Big Data being perceived to be a technology with good conditions for dissemination in the construction industry. This study shows that it is not just about the inherent capacity of technology, but about how it is perceived, experienced, and translated into practical values in different organizational contexts. By applying the DOI theory in the Swedish construction context, this study thus contributes to bridging the gap that the previous research has left between technical potential and practical implementation conditions and, at the same time, offers concrete strategic insights for decision-makers, development leaders, and policymakers who want to promote a more sustainable and data-driven transformation in the industry.

Theoretically, this study demonstrates how the DOI theory can be operationalized in quantitative analyses of a traditionally fragmented industry, while practically it provides companies—regardless of role and size—with concrete guidelines for accelerating Big Data implementation through organizational preparedness and common data standardization. Business leaders are recommended to use consistent and clear communication about the specific benefits of Big Data for different professional roles, which can promote faster and more effective implementation [6,13]. For policymakers and industry associations, the results show the importance of targeted support programs and incentives that focus specifically on resolving organizational and financial barriers, in addition to technical support and training.

Finally, some results confirm previous conclusions, such as Big Data’s perceived contribution to environmental and resource efficiency [13,55]. At the same time, this study challenges other assumptions—for example, that different actors see the technology in different ways, or that utility dimensions are always perceived to be integrated. By applying the DOI theory in a systematic way in the Swedish construction industry, this study contributes with a more nuanced empirical basis for how digital technologies, such as Big Data, are understood and valued in practice. This connects to the previous literature, but also to new perspectives that the previous research has largely overlooked.

6. Limitations

A limitation of this study is that it is based on self-reported survey responses, which may involve some subjectivity and risk of social desirability in the responses. The respondents may have exaggerated or underestimated certain aspects of Big Data use depending on their role, experience, or organizational affiliation. This may affect the generalizability of the results.

Another limitation is that this study was only conducted in Sweden, which means that the results are primarily contextually relevant to Swedish conditions—especially given the country’s specific digitalization strategies, sustainability goals, and regulatory structure in the construction industry. There may therefore be limitations in applying the results directly to other countries or contexts with different institutional frameworks.

Furthermore, the analysis is mainly based on quantitative methods. Although this allowed for generalization and statistical analysis, it also means that more in-depth qualitative perspectives—such as organizational decision-making processes or cultural factors—were not captured in detail. Future research can advantageously combine quantitative and qualitative methods to deepen the understanding of Big Data adoption in the construction industry.

7. Conclusions

The study results show that Big Data is perceived to be a mature and generally applicable innovation in the Swedish construction sector. Despite the previous research pointing to size- and role-dependent differences in the degree of digitalization [12,20,88], both χ² and ANOVA analyses showed no significant variation between small and large companies or between different professional roles (p > 0.05). This suggests that the “relative advantage” of the technology [21] in practice bridges traditional organizational differences.

The correlations between technical, environmental, and economic benefits were consistently strong (r > 0.5; p < 0.01), indicating that the benefits are perceived to be mutually reinforcing rather than isolated [6,7]. Spearman’s rank correlation captured these relationships despite ordinal data and a lack of normal distribution [72,73]. This highlights the need to communicate Big Data as an integrated benefit package to increase acceptance and dissemination.

The study also confirms high levels of compatibility, low perceived complexity, and good trialability in the industry [21,42], which together with clear observability [10,21] create favorable conditions for continued implementation. For practitioners, this means that investments in joint competence development and standardization of data flows can accelerate digital transformation, especially in smaller companies [28,52]. Theoretically, the work shows how the DOI theory can be quantified in a fragmented, project-based sector and contributes with a two-dimensional model (technical/environmental vs. economic/regulatory) that is deepened by our empirical findings [81,86].