Transformative role of big data through enabling capability recognition in construction

Abstract Big data application is a significant transformative driver of change in the retail, health, engineering, and advanced manufacturing sectors. Big data studies in construction are still somewhat limited, although there is increasing interest in what big data application could achieve. Through interviews with construction professionals, this paper identifies the capabilities needed in construction firms to enable the accrual of the potentially transformative benefits of big data application in construction. Based on previous studies, big data application capabilities, needed to transform construction processes, focussed on data, people, technology, and organisation. However, the findings of this research suggest a critical modification to that focus to include knowledge and the organisational environment along with people, data, and technology. The research findings show that construction firms use big data with a combination strategy to enable transformation by (a) driving an in-house data management policy to rolling-out the big data capabilities; (b) fostering collaborative capabilities with external firms for resource development, and (c) outsourcing big data services to address the capabilities deficits impacting digital transformation.


Introduction
Big data application has been referred to as a technology to "revolutionize the art of management" (Wamba et al. 2015), "a management revolution" (McAfee and Brynjolfsson 2012), "an important driver of business success" (Wegener and Sinha 2013) and "next frontier for innovation" (Manyika et al. 2011). In simple terms, big data application uses insight from large datasets to manage and improve business processes (Bradlow et al. 2017). Examples from other industries include improving processes in detecting banking transaction frauds (Melo-Acosta et al. 2017), identifying trends from insights in healthcare (Wang and Hajli 2017), and monitoring store inventory to inform manufacturing (Ji and Wang 2017). Businesses in all industries are now operating using data-driven decision-making (Lu et al. 2020, Troisi et al. 2020. Big data encompasses both generation, storage, and analysis of large volumetric data with technology and is used to solve organisational problems and improve processes through analytics and algorithms (Yang et al. 2021). The introduction of big data in a business firm can transform and enhance business improvement, provided the firms can host and manage the big data technologies. The characteristics of big data are defined by velocity, volume, variety, veracity, and value (Davenport 2014, Bilal et al. 2016bGolparvar-Fard 2017, Yu et al. 2020). Volume describes large datasets that traditional analytics technologies are not able to handle. Variety refers to the heterogeneous data formats generated from different sources, and velocity is the rate of generating data. Veracity is the trustworthiness and authenticity of the data, while the relevance of the data to an entity is its value. Wang et al. (2018) and Zhang et al. (2017) developed similar architectures for big data analysis built upon increasing levels of sophistication in the degrees of analytics undertaken, from a data layer, which includes the data sources to be used for supporting operations and problem-solving; through a data aggregation layer, which is in charge of acquiring, transforming and storing data; to an analytics layer using any of cluster analysis, data mining, forecasting models, Monte Carlo Simulations, Artificial intelligence or machine learning to develop representational models, or prediction models or behavioural models to inform business practice including strategy, financial planning, operations and marketing.
The call for digital technologies and big data in the construction industry's transformation is increasing (Guo et al. 2016, Li et al. 2016, Han and Golparvar-Fard 2017, Ribeirinho et al. 2020. There have been studies on the potential benefits of big data application to construction (Bilal et al. 2016b). Data generating technologies like Unmanned Aerial Vehicles (UAV), Unmanned Ground Vehicles (UGV), time-lapse cameras, smart sensors, location sensors, inventory and supply chain data (KPMG China 2016, Han andGolparvar-Fard 2017), smart metre data (Funde et al. 2019); bridge inventory datasets (Galvan-Nunez and Attoh-Okine 2017) are all in everyday use in construction. The rise of big data is attributed to the influx of these digital technologies that can generate heterogeneous and large volumes of data quickly, based on its use. Therefore the generation of data, the storing and processing, and the analysis and application of the data to everyday problems provide a holistic overview of big data. These data are difficult to analyse because the construction industry lacks the skills and resources to analyse these big datasets (KPMG China 2016) leading to financial burden (Gray 2007). Transformation is needed for construction firms to accrue the benefits embedded in these datasets through the use of big data application.
Numerous existing studies in construction have explored the potential and actual benefits of big data in the various stages of the construction life cycle: for example at the preconstruction stage, big data has been used to facilitate the selection of subcontractors on construction projects (Atuahene et al. 2022); or used for analysing and monitoring building permits (Lai and Kontokosta 2019); at the construction stage, big data has been shown to be helpful for project monitoring, reducing litigation amongst project stakeholders, construction defects prediction and for construction site safety (Guo et al. 2016, Han and Golparvar-Fard 2017, Lin and Fan 2019, Atuahene et al. 2022; and at the post-construction stage, improvement in asset performance, indoor comfort and behaviour (Kim et al. 2019). Outside the construction life cycle zone, big data has been used to develop a model to examine the profitability of construction projects (Bilal et al. 2019); to identify the potential of using big data to manage stakeholders on projects; an example is the sentiment analysis of social media data relating to the Sydney Light Rail Project (Kanjanabootra et al. 2019); and to both identify the drivers, awareness, challenges, opportunities and benefits using big data for facilities management and develop a methodological framework to implement big data applications into facilities management organisations (Konanahalli et al. 2018. These transformational benefits provide reasonable grounds to speculate about the potential of big data in construction, especially in this era where digitalisation is endorsed by governments, for instance, the Australian government's efforts to encourage business across all sectors of the economy to drive productivity through big data (Australian Government 2018).
Transformation is argued to be "a process that engenders a qualitatively different organization" (Orlikowski 1996, Crowston and Myers 2004, Besson and Rowe 2012. Big data offers transformative effects in business through asset sharing, through creating a collaborative business ecosystem (collaboration with supply chain partners) and helps allocate business risks more appropriately, making cost reductions possible, and through enabling agility (Kavadias et al. 2016).
Digital transformation (re)defines the value proposition and is characterised by the emergence of a new organisational identity (Eden et al. 2019, Wessel et al. 2021. This is achieved through understanding and developing capabilities to support and sustain the transformation. Capabilities are meta-level constructs (Osmundsen 2020) underpinned by different competencies embedded in organisational processes and rooted in employee skills and knowledge (Peppard and Ward 2004). Amit and Schoemaker (1993, p. 35) defined capability as "a firm's capacity to deploy resources, usually in combination, using organizational processes, to effect a desired end". Moreover, Ngo et al. (2020) define "capability" as the condition of having the capacity to do something. Within this condition, there is a potential for improvement of skills. A firm's capacity, resources and organisational processes are the stand-out features of organisational capabilities. The firm's capacity could be through tangible and intangible resources available to the firm or sourced from outside, which could be enablers or capabilities for achieving the digital transformation, big data in this case. This study defines capabilities as anything designed and efficiently employed by people in the organisation to achieve its intended mission. Therefore, a digital transformation often requires a transformation of the workforce (Eden et al. 2019) and is associated with, among other structural changes, changes in employee roles and skills (Vial 2019). This paper then will address the following research question through interviews with construction professionals: what capabilities are needed by construction firms to enable big data transformation benefits to accrue? This is done within the frameworks of Wang et al. (2018) and Zhang et al. (2017). As noted above, their argument represents a gradient for big data application from initial data capture and storage to advanced analytics, Artificial intelligence and Machine Learning.

Theoretically positioning big data application in construction
Digital strategy exploits digital technology to achieve organisational objectives, which is an antecedent to digital transformation (Gobble 2018). The rapid application of digital technology and the resultant accumulation of big data also drives digital transformation (Correani et al. 2020); this makes big data a crucial element and a bridge between digital strategy and digital transformation. The transformational role of big data in construction can be realised through the nature and dynamics of capabilities, using both resource-based view and dynamic capabilities theories (Dosi et al. 2000). The resource-based view of the firm (Wernerfelt 1984, Barney 1991 directs the management of firms to focus on internal resources, which could potentially lead to gaining a competitive advantage. Barney (1991) posits that firms can sustain a competitive advantage based on the assumption that firms are heterogeneous based on their strategy, which is valuable, rare, inimitable, and non-substitutable. These assumptions were later revised to value, rare, imitability, and organisation (Barney 1995). In essence, this theory considers the superiority of one firm over another in the same industry through differential availability of the resources to the firm.
These resources include "all assets, capabilities, organisational processes, firm attributes and knowledge" (Barney 1991, p. 101). Resource-based view has now become the basis for further discussion on the importance of IT as bringing competitive advantage resources to the firm (Kim et al. 2011). To Grant (1991), applying a strategy that exploits firms' resources and capabilities helps identify the potential resource gap, which could be replenished, augmented, and upgraded. Resource-based view is used herein to identify big data capabilities. The limitations of the resource-based view approach are addressed in a more agile approach to resourcing and capability development through dynamic capabilities theory.
As resource-based view focuses on using internal organisational resources and capabilities to develop a competitive advantage over other firms, dynamic capabilities theory emphasises developing new forms of competitive advantage by integrating, building, and reconfiguring internal and external capabilities (Teece et al. 1997). Eisenhardt andMartin (2000, p. 1107) defined dynamic capabilities as "the firm's processes that use resources -specifically the processes to integrate, reconfigure, gain and release resources -to match and even create market change … " This emphasises the positioning of firms to adapt to changes in the business environment by developing new capabilities using both internal and external resources.
To Teece (2007) technological opportunities contribute to process improvement by developing capabilities using external and internal resources to leverage opportunities sensed from the external environment (Gajendran et al. 2014). These resources -tangible and intangible -are strategically aligned and realigned to the firm's processes to make it competitive in the market.
Dynamic capabilities theory has been used as a theoretical lens in researching big data in facilities management by Konanahalli et al. (2018) who argued that the theory provides a deeper understanding of how complex knowledge is explored and exploited in organisational settings and enables organisations to extract value from technology and to maximise profits. Konanahalli et al. (2018) argued that the application of dynamic capabilities can allow facilities management organisations to innovate and respond to dynamic market conditions using big data. Wamba et al. (2017) proposed a three-order hierarchical model that assesses the impact of big data analytics and business analytics capabilities on firm performance and process-oriented dynamic capabilities -showing that big data analytics capabilities affect firm performance directly. Similarly, Braganza et al. (2017) suggest that the organisation scans the environment to identify potential products to help the organisation develop the right capabilities. Scanning is in response to a capability gap identified in the organisation. The organisation then seises the product or capabilities and reconfigures the organisational systems by integrating the new capabilities into the organisational processes.
The need for identifying capability gap(s) becomes instrumental for the development of the digital strategy. In order to understand the extant literature on this study, systematic literature on big data capabilities was conducted, and the synthesis of the screened and selected full reviewed articles is summarised in Table  1. Three levels of consideration were made in identifying the capabilities: (i) theoretical lenses (Table 1 and also discussed above), (ii) literature and (iii) reflection on big data definition.
Resource-based view and dynamic capabilities, as described earlier, were used as the theoretical lenses in this study (Table 1) for other big data research (Wamba et al. 2017, Wang and Hajli 2017, Atuahene et al. 2018) and organisational capabilities research (Amit andSchoemaker 1993, Teece 2007). As described earlier, the two lenses consider the capabilities readily and currently used by the firm or yet to be employed and adapted in the firm's processes. For example, the Australian government's strategy for building a data-driven and digital economy led to assessing their current strength through research and realising the need for digital skills, digital infrastructure, data, and an enabling environment (Australian Government 2018). Notwithstanding, each of the identified capabilities could belong to either of the theoretical lenses. Bharadwaj (2000) and Wamba et al. (2017) were used as reference points in identifying and synthesising big data capabilities from these articles because these studies explored information technology and big data capabilities, respectively, on firms' performance. For example, Bharadwaj (2000) identified IT infrastructure, human IT resources and IT-enabled intangibles as IT capabilities. On the other hand, big data analytics infrastructure, management, and personnel expertise capability were identified by Wamba et al. (2017). By comparison, the former study appears generic while the latter is big data specific. Notwithstanding, these capabilities reveal the same ideas relating to people (human IT resources/  ( personnel expertise), technology (IT infrastructure/big data infrastructure), and organisation strategy (ITenabled intangibles/management). On reflection, the definition of big data application emphasises features of the data and the technology for capturing, storing, analysing and applying the data (Zhang et al. 2017, Wang et al. 2018, Yu et al. 2020, Atuahene et al. 2022. This means that data should be considered a capability on its own, though it was not considered in the study of Wamba et al. (2017) because that study focussed on analytics. In addition, using big data analytics infrastructure ignores the role of digital technologies in the generation and storing of data; however, using generic terms as technology simplifies and exemplifies all the tools used in the big data process.
Based on the theoretical lenses, literature and the reflection on the definition of big data, the following four main capabilities emerged: data, people, technology, and organisation strategy. The constituent capabilities identified from the review were mapped to the main capabilities, as shown in Table 1.

Data
Data is one of the essential elements for digital transformation. Data quality, standardisation, availability of data (Table 1) are all essential qualities of data capabilities because, without these, the data become useless and would not create value for the firm. For instance, the UK HM Government (2013) paper on Construction 2025 identified intelligent construction as one of the strategies to transform the UK construction industry. The report demonstrated the essential role of data in achieving this strategy. Wegener and Sinha (2013) indicated that the data collected should conform to the firm's strategy. This is when adequate, appropriate, valuable data are collected and applied in the firms' processes.

People
Implementing big data is meaningless if there is no big data expert. The competencies of the analyst should include technical knowledge, technology management skills, business skills, and relational knowledge (Table 1). This capability might be outsourced; however, firms can have in-house personnel to manage big data technologies (Silva et al. 2019, Bolonne andWijewardene 2020). The ability of the big data experts to understand the construction processes together with the big data knowledge as an added advantage (Gupta and George 2016).

Technology
Big data-driven business requires digital infrastructure assets to become essential and desired capabilities (Chaurasia and Verma 2020). The technology capability comprises the hardware and software to generate, manage and process the datasets (Kim et al. 2011). Different infrastructures are made available by developers of big data analytics ( Table 1). The adapted platform should be compatible with the data gathered. Wamba et al. (2017) added that big data technologies must be compatible with the firms' operations. The ability to apply machine learning techniques to the datasets should be an essential feature of the technology. The presentation of the knowledge generated through the data analysis process like visual analytics is also vital in having a robust technology (Jayasingh et al. 2016).

Organisation strategy
Organisation strategy becomes inseparable from the business's digital strategy when there is continuous integration and interconnectivity of digital technologies that rely on data to function (Bharadwaj et al. 2013). The scope and speed of the digital strategy are dependent on the combined efforts of investment, top management commitment, culture and business agility (Table 1), and external factors that inform firms to sanction research and development goals, which subsequently lead to innovation in a firm or industry.
These identified capabilities, summarised in Table 1 and represented in Figure 1, serve as a framework in this research to explore the big data enabling capabilities needed for digital transformation in construction from the perspective of construction professionals. As earlier studies have reiterated the importance of big data applications in industries, this is also depicted in the framework (Figure 1). However, achieving the impact of big data applications in the framework requires identifying the enabling capabilities, which is a gap to be addressed in this study.

Methodology
Big data application is considered in this research context as a social-technical system (Hevner et al. 2004) because of the harmonious integration of people and technologies, working together to achieve an objective. For example, the big data process stream comprises three stages: big data sources, big data storage and processing, and big data analytics. These stages require people to use digital technologies to capture, store, analyse big data, and make decisions from these insights to address organisational problems. In essence, for a socio-technical system to exist, people and technology should be considered monolithic, dependent on each other (Orlikowski 2007). This study of construction professionals adopts a constructivist approach in exploring big data application in the construction process. The constructivist philosophy (Petersen and Gencel 2013) was adopted because (i) this research explores the socio-behavioural and technical elements of big data application based on the reality (truth) of the construction practitioners; (ii) this research seeks an in-depth understanding of the experiences of construction practitioners and their interactions with big data in the construction process; (iii) this research focuses on understanding the meaning of big data application from multiple participants, and (iv) big data applications are seen in the context of this study as a social-technical system like in many ways similar to how researchers in construction management view the project environment.
Phenomenology seeks to describe a phenomenon with as much richness of detail as possible, with the unique goal of describing the "essences" of the phenomenon that contribute to understanding the meaning (Randles 2012). It offers a method for understanding complex experiences rather than being an arbitrary measure of consistency and uniformity. The end result of phenomenological research is to arrive at a nonreductive, non-generalised (Converse 2012) structure that unites the elements of an experience into a description of the experience reflecting the core elements shared by the participants (Conklin 2007, Creswell 2014). This description consists of "what" they experienced and "how" they interpret their experiences. This research is a search to identify the capabilities needed for construction firms to adopt and effectively use big data. From the experiences of construction professionals, these capabilities are described through their interpretation of their experience. Those descriptions are used here to identify the core elements they share about the capabilities needed for construction firms to adopt big data in their business operations. Figure 2 shows the simplified research method flow chart used for this study.
This study used purposive sampling (non-probability sampling) (Guest et al. 2006, Palinkas et al. 2015 which involves selecting a group of people with specific skills or experiences of a particular issue. In this study, fifteen (15) construction professionals, who undertake roles relating to various stages of big data process stream in the construction process, and employees from large construction and consultant firms in Australia participated in the interviews. The sample size for this study  was more than six (Morse 1994) and within the range of 5-25 (Creswell 1998) as reported in earlier phenomenological studies. In order to avoid bias and ensure a better understanding (Papadonikolaki and Wamelink 2017), both junior staff and senior staff were interviewed because most technology roll-outs in construction firms are used by these staff on-site (Table 2). Interviews (semi-structured) were used to allow researchers to understand other people's perspectives (Patton 2002). After receiving consent from the firms in Australia, fifteen semi-structured interviews were conducted: one video conferencing, seven face-to-face, and seven on the telephones. Each interview was recorded on multiple devices (voice recorder, mobile phone, and video conferencing) and was conducted within an hour timeline. Big data was introduced to the construction professionals, and background, experience, and sensory question options were then asked in the interviews (Patton 2002). The questions included: Could you please take me through your professional journey? (background); Could you please give examples of the technologies you use in your role? How often are these technologies used? What type of data is collected? How do these generated data help you in your role? (experience); With the experience you have gained in the construction process as far as the generation, storage, and application of a lot of data (big data) is concerned, what capabilities do we need to leverage data to improve the construction process (transformative)? (sensory). The experience questions enabled the authors to ask questions based on their experiences in the big data process stream: big data sources, big data storage and processing, and big data analytics (benefits). The sensory questions enabled the interviewees to highlight the capabilities they perceived as being needed for transformation benefits to accrue based on their experiences.
The first author administered and transcribed the interviews. Table 2 shows the information about the participants of this study. The respondents included people performing diverse roles with varying years of experience in the construction industry.
In all, the participants were from seven firms. For instance, the junior staff are directly involved in the capturing and storing of data and the use of data capture technologies like drones, captures defects, defect sign-ins and sign-outs with tablets, checks for digital/ manual attendance by site personnel, the middle staff ensures that junior staff captures and stores the data, and use the captured data for their assigned responsibilities like monitoring and controlling projects/procurement of subcontractors and management of subcontractors claims, the senior level staff daily reviews the captured and stored data in the firm's system to direct the activities of the junior/middle staff.
Thematic analysis was used to analyse the data through the use of NVivo software. Thematic analysis is structured and helps to identify, analyse and report themes in data (Braun and Clarke 2006). Structural coding is useful in analysing semi-structured interviews involving multiple participants (Saldaña 2016). The emphasis is not just about the frequencies of the ideas identified from the raw data but the context of the applicability of big data ideas to the construction

RaƟonale for study
Big data was considered as social-technical system, and phenomenology was used to understand and idenƟfy capabiliƟes of social-technical system like big data, through the experiences of construcƟon professionals

What data was collected
Semi-structured interview from 15 construcƟon professionals selected through purposive sampling: process. All identified items were considered equally important, irrespective of whether it occurred once or more from the interviews. The formats of the interview questions contributed to the selection of structural and In Vivo coding. In Vivo coding captures the participant's voice, in other words, using the exact words spoken by the respondents as a code (Saldaña 2016), "which results in rich data" (Rogers 2018, p. 890). After the first cycle coding, a second cycle coding of the interview transcripts was performed to "reorganize and reanalyse data that was coded in the first level coding" following Rogers (2018, p. 891). Pattern coding was employed for the second cycle; it reduces first-level codes into smaller categories and themes; it aids in theme development and identifies the connections between the codes (Saldaña 2016). Undertaking this process was essential to identify and then understand what capabilities are needed by construction firms to enable big data transformation benefits to accrue.

Results: identification of enabling capabilities transformation of big data in construction
Thematic analyses of the research data led to the emergence of a set of twenty-eight (28) enabling capabilities for construction organisation transformation, summarised into five (5) dimensions Table 3: People, Knowledge, Technology, Data and Environmental capability dimensions. An analysis of each dimension is given below.

People capabilities dimension (PCD)
This research identified people as essential capabilities for construction firms; this capability was identified in the review of big data research (Table 1). As seen in Table 3, the people dimension has five constituent capabilities, this research shows that construction firms need to develop to achieve the transformative benefits with the implementation of big data: The research indicates the need for collaborations between people from two domain areas (data and practice), working together to achieve the same purpose. The realisation occurs when the construction professionals and the data experts understand their roles in this arrangement. An example is when R2 stated, "I know that we generate the data, it goes somewhere and if I want something, I go to the dashboard". This is evident in making clear rules of engagement, where construction professionals who might feel threatened with their jobs will be relieved of their fears and make it easier for collaboration since earlier studies on technologies adoption in construction have demonstrated experienced resistance (Chan et al. 2019). This capability concerns encouraging collaboration between construction professionals and data analytic experts in the construction firm (PCD1).
The construction firms need to realise that the benefits of big data require the services of big data experts; without them, the construction firm would not be able to leverage data in its operations. Some participants indicated that "they employ people that are really good in technologies" [R5], and "people managing our digital intelligence systems are employees of our firm" [R7]. These exemplar responses indicate that construction firms can assemble an in-house team of big data experts, something noted in some participants' firms -a capability of employing people with data analytic expertise (PCD2).
It became evident in the research that construction firms equip and appoint individuals responsible for the big data process stream. For instance, [R2] stated that "so this current project is a 16/17 million billed, so there is one engineer and one cadet who are just there for a lot of data collection as part of their day and who have been trained for it". This is to avoid the inherent risk of duplication or compromising on the quality of There should be people with construction domain capabilities in the construction industry context, which is not different from Wamba et al. (2017) findings that business domain skills are crucial capabilities required by the firm.
[R13] and [R15] indicated that people like project managers serve as an "interface" because "the project delivery team will discuss and get some decisions on technical themes then capture data through experience, processing and getting a conclusion". The research data shows that the construction experts become the bridge for dictating the data collected in the construction process and applying the insights to achieve project deliverables, a capability of having people with construction process expertise (PCD4).
Understanding the research data suggests that the construction firm can focus its expertise on its core competencies of delivering construction projects and therefore outsource the big data roles to analytic firms. This is on the opposite spectrum from having an in-house big data expert in-house. The capabilities required in such an arrangement are not entirely different from construction firms making a pact with subcontractors or suppliers. The capability is one of potentially out-sourcing data analytic role to experts from analytics firms (PCD5).
This research has confirmed the essential need for people capabilities for big data application in construction, shown in previous research (Table 1 -managerial and technical skills), and included in Table 3 as PCD2 and PCD4 in the transformative framework. The research interviews highlight the need for these capabilities in big data applications in construction to achieve their digital strategy for transformation ( Figure 1). The research identifies specific requirements in construction firms for people with construction and big data expertise. What this research has added to this understanding is the need for an in-charge person for the data processing (PCD3) and collaboration (PCD1), noting that for construction, outsourcing of these capabilities (PCD5) is needed. These capabilities are not seen in the current literature, possibly because most of these studies assumed that the firm already People dimension Encouraging collaboration between construction professionals and data analytic experts in the construction firm (PCD1) Employing people with data analytic expertise (PCD2) Getting people in charge of the data processes (PCD3) People with construction process expertise (PCD4) Out-sourcing data analytic role to experts from analytics firms (PCD5) Knowledge dimension Construction firms collaboration with universities on developing data management curriculum for students in construction related programs (KCD1) Ensuring the documentation of lessons learnt in the construction process (KCD2) Formulating data storage and processing guidelines (KCD3) Having regular discussion on data strategy for projects (KCD4) Training for construction professionals on using digital technologies to capture data (KCD5) Technology dimension Collaborating with digital technology firms to capture and store construction data through service agreement (TCD1) Collaborating with internet service providers to provide good connectivity on projects (TCD2) Designing a robust system to protect the safety and security of data (TCD3) Performing diagnostic/predictive/ prescriptive analytics on all kinds of construction dataset (TCD4) Providing digital management (intranet) reporting platform (TCD5) Providing electronic database system (TCD6) Providing organisational/project based server (TCD7) Making digital technologies available in the construction firm (TCD8) Data dimension Analysing visual (image and videos) data in the construction process (DCD1) Co-creating universal industrial performance databases amongst construction firms on suppliers and subcontractors (DCD2) Ensuring that accurate, enough and right data are captured and accessible in the construction process (DCD3) Properly filing and organising data in the appropriate place (DCD4) Requesting for specific data in the construction process (DCD5) Environmental dimension Developing cost models on big data through R & D (ECD1) Enforcing the use of digital technologies and application of data (ECD2) Government/construction industrial bodies' sponsored workshop on data management for construction firms (ECD3) Inculcating the data-driven culture and using digital technologies in the construction process (ECD4) Investing in digital technologies in the construction firm (ECD5) possessed these capabilities, or the study was conducted in an IT firm, or the theoretical lenses used for the study omitted them (Wamba et al. 2017, Wang et al. 2018, Bolonne and Wijewardene 2020. These new capabilities and their function in the big data transformative context have been used to revise Figure 1 as Figure 3.

Knowledge capabilities dimension (KCD)
Knowledge emerged from the research data as a distinct capability needed for big data application in construction (Table 3). This dimension had not been specified in previous research (Table 1). Knowledge is required of the people who will be critical to big data application in the construction process. These people will be expected to know about construction processes, about digital technologies, and about data management and analytics. The knowledge capabilities dimension identifies and frames the strategies needed to acquire the correct and relevant knowledge for big data application in the construction process.
The research data showed two divergent ideas from the participants; one indicated that "some of our young engineers who are coming out of University now basically learned about those stuff (sic) at University and they came to do it as well" [R2]. The other suggested way of developing big data capabilities in the long term is when "we should start with the Universities before coming to the industry. I think there should be something on a University level or tertiary education, especially in the construction industry, on model or something like data management and data control and data sharing idea in university education so that people  will know the advantage and all" [R14]. These responses and others demonstrate a perception that big data application is transformative and therefore necessary and that these are practical means of construction firms getting well-equipped graduates who are both technology and data-oriented to contribute to the digital transformation of construction firms. This represents a needed capability of collaborating with universities on developing data management curriculum for students in construction related programs (KCD1). The data shows that documenting past experiences in the construction process is also a capability needed in construction firms. [R13] indicated that "we need to record what we have done … and deal with how we are going to say and deal with issues. There is an accident … we need to record all that … every action contributes to the generation of data". Such capability as practiced in these firms helps co-create customised institutional knowledge for the firm. Most importantly, it enables the big data experts to compare and contrast past projects with ongoing projects to spot potential red flags. This process of knowledge management and its recognised transformative effects are well researched in the construction literature (e.g. Dave and Koskela 2009, Wang and Meng 2019, Adi et al. 2021) and highlights knowledge management's importance as a key capability for big data, one of ensuring the documentation of lessons learnt in the construction process (KCD2).
Poor data handling is a significant problem confronting the construction industry (Ahmed et al. 2018). From the participants' responses, their firms have guidelines to direct employees on managing the high streams of data from the construction process, e.g.
[R14] categorically stated that "we do have a data management policy". Furthermore, R1 further described what their guidelines dictate, i.e. "So every project location have a local server, and so a lot of data will be held on the server. Those servers are updated daily at night when the site is still down and the data to location A where our head office is". These guidelines equip the individuals to execute their roles responsibly, emergent from having a capability of formulating data storage and processing guidelines (KCD3). Part of enabling this better data handling relates to communication. Some participants, for instance, [R13] recounted that "before a project commence there is a workshop called project launch and in the project launch. There will be a discussion on how, when, and where we will generate data and how do we manage the data. Moreover, there is a process in recording this data". This capability becomes a reminder and instils in the project team the need to collect huge data streams, even at projects' commences through having regular discussion on data strategy for projects (KCD4).
Facilitating better data handling also requires training. Another capability identified from the interviews is the need to train construction professionals on specialised technologies to reap the benefits of big data technologies.
[R7] stated that "to be able to fly the drones in areas like … .the Central Business District, you do need to have a licence, so I have to do my commercial drone licence, to be able to fly in this area because [location] … . is a no-fly zone being so close to Royal Air Force and airport". Another participant [R1] indicated that "it is one thing to capture the data, and that is pretty easy, capturing the data but the ability to use the data and manipulate the data to get the maximum benefit out of it that is a training thing". These responses reiterate the importance of training for construction professionals' especially capturing data using digital technologies, storing and then using and manipulating that data -a capability of training for construction professionals on using digital technologies to capture data (KCD5) The separation of the Knowledge capabilities dimension, reported in Figure 3 and Table 3, represents a distinct capability not identified previously (Table 1) as a significant set of capabilities for big data application in construction. Prior research considered these knowledge skills to be associated with people capabilities (Wamba et al. 2017) without specific reference to the knowledge capabilities identified in these interviews. The results from this study highlight that for big data application to be achieved in construction and be useful to the firm, then there is a capability requirement for documentation of lessons learnt from big data application in specific projects (KCD2); the need for formulation of specific guidelines on knowledge application related to data storage and processing, including analytics (KCD3); requirement for regular discussion on data strategy for projects (KCD4); and the need for specific knowledge development and training on big data and construction (KCD5). This, too, was added as an essential capability for digital transformation via big data application in a construction context (Figure 3).

(c) Technology capabilities dimension (TCD)
Technology emerged as another capability dimension from the research data (Table 3). Separating technologies from big data is inevitable because the drive to digitise has led to big data (Kitchin 2013). In addition the research shows that each stage of the big data process needs significant levels of technology to be complete.
The research data showed that some construction firms might not be in the best position to house digital technologies and manage them but have to contract third parties to capture the data or hire the digital technologies from external sources. For instance, [R1] recounted that "in terms of the cloud the third party host stuff, I think the storing of the data is managed by the corporate agreements that we have in place with that business. I am talking about businesses like Aconex, BIM360, and Autodesk". Participant [R4] also responded that "time-lapse camera data is stored with the subcontractor cloud base". These show that, for some construction firms, a third-party arrangement becomes useful in managing big data from construction projects within the project lifecycle. This dimension forms the first of a number of capabilities where the focus is on collaboration -collaborating with digital technology firms to capture and store construction data through service agreement (TCD1). Collaboration is defined as a "process in which information, activities, responsibilities and resources are shared to jointly plan, implement, and evaluate a program of activities to achieve a common goal, and a joint generation of value" (Camarinha-Matos et al. 2009, pp. 47-48). In the examples of TCD 1 and TCD 2, there is a mutual expectation of benefits accruing from the collaboration and unique adaptation of the software packages cited to solve the issues identified by the construction partner. This also aligns with Mattessich and Monsey (1992), who defined collaboration as a dynamic and mutually beneficial and well-defined relationship entered into by two or more organisations to achieve common goals. Papadonikolaki et al. (2019) argue that not all collaborations are well-defined, well-structured and truly mutual, or indeed working towards the same goals. They also argue, citing Malone and Crowston (1994, p. 4), that collaboration can be represented in a simpler form as "working together on an intellectual endeavour". In this case, there is a mutual "intellectual endeavor" adapting various software applications to resolve one organisation's problem/issue. That collaboration is quite exclusive to the organisation's arrangement and is thus a collaboration to create an enabling infrastructure. These are identified as customer-specific service level agreements in IT (SLA) (De Kinderen and Gordijn 2008) developed as an individual adaptive collaboration between the service provider and the requesting organisation. Furthermore, as Brocke et al. (2010) argued, these are rarely applicable generally and reflect a collaboration rather than the development of a universal solution.
Secondly collaboration is needed with service providers. The interview responses identify that construction firms need to select internet providers to get the best services for their projects. Data is not stagnant in the construction process but moves from one stream to another. This requires stable hardware, stable software, and a stable internet connection on the project site, and people capable of managing that.
[R1] noted that "a lot of it relies on wireless capability and so whether it is a local network or it is a mobile network to draw data from the clouds, you know absolutely from an infrastructure perspective. One of the things we have learned is that if we do not have good connectivity when we start the project because we are such technology dependents that is one of the first hurdles we need to overcome". There is then a capability need for collaborating with internet service providers to provide good connectivity on projects (TCD2) The integrity and safety of big data are also capabilities associated with handling data, part of a set of system capabilities needed for big data application in construction. The construction firm needs to ensure that the data is adequately protected from individuals who have no business with the data. [R15] said "not all team members can have access, so when we give access to some people the sensitive data might leak to other people that is the risk involved in that data". Other participant stated that "I do not have too much to do with the site management dashboard other than giving blokes access to it. And they do all the data gathering and drives itself pretty well". It suggests that robust systems need to be in place to protect the trustworthiness of the data, a capability of designing a robust system to protect the safety and security of data (TCD3) The interview data also identified the need to have staff who can analyse captured data. However, it appears that current capabilities focus mostly on descriptive analysis of data which inform construction firms about what is happening on-site; for example, "the report primarily just capture what is happening on site. It does not make predictions but what we do" [R1]. Others suggested their attempt to use artificial intelligence in its decision making, i.e. "currently, we do not have AI, we just look at it to make a decision on it" [R8]. The responses show that higher levels of big data analytics competencies are needed in construction firms, a capability to perform diagnostic/ predictive/prescriptive analytics on all kinds of construction datasets (TCD4).
To enable the most efficient method of perform diagnostics, the interviewees noted the importance of integrated data management systems were often mentioned in the research data as being needed by construction firms to develop their organisational capabilities for big data application, e.g. "so we have an integrated management system that draws on the data collected at the project level and brings that into our reporting platform that provides statistics and information on program quality, safety and even site management" [R1]. Such systems allow close-to real-time monitoring of projects on-site by head-office, a capability necessary with the organisation providing digital management (intranet) reporting platform (TCD5).
The interviewees also highlighted the need for well-designed database systems to hold both current and historical data which could become useful for analytics and for the firm's use in the future. For instance [R15] said that "we have an electronic database system where all the data are stored, so historical data will be there in the system, and you can access it in the future". Data based systems and associated hardware were identified as significant capabilities needed, one a capability to provide electronic database systems (TCD6) and the other a capability to providing an organisational/project based server (TCD 7). The organisational and project-based server is a capability identified from the research data like "project only server" [R3] and software-based storage like "Prism will store all the data and it is a system based data collection" [R14]. These divergent approaches enable project data management from a common environment, as suggested by Ahmed et al. (2018).
Finally, the research data indicated that digital technologies should be made available for use in construction firms. Common technologies mentioned included "who is on location" [R3], "time lapse camera" (R7), "drones" [R11], and "swipe card access" [R13]. Due to the availability of these technologies, construction firms can generate and apply insights from this data in their processes. These reinforce the assertion that digital technologies are the pivot of the big data process (Konanahalli et al. 2018), a capability of making digital technologies available in the construction firm (TCD 8).
Achieving digital strategies for transformation, in this case, big data applications (Figures 1 and 3), obviously focuses on technology. Technology was identified from the systematic review through its constituent capabilities (Table 1). Previous research in other industries has focussed on the high-level analytics end of big data adoption, a process that took retailers and others over a decade to accomplish. The interviewees in this study were aware of the potential advantages of these high-level analytical methods and could express their perceptions of the value that would accrue. However, like the early adopters in other industries, the construction interviewees were also aware that they needed to build their capabilities from simpler levels to reach those objectives (Table 3) eventually. The interviewees identified enabling capabilities concerning those analytics they deemed necessary to perform the diagnostic, predictive and prescriptive analytics on all construction datasets (TCD4). The contextual settings of this study and the construction professionals interviewed skew the results from just a focus on analytics to their focus on the entire big data process because of their experiences. They themselves noted this was because of their own experiences. This expressed best by them summarised in Table 3 as "making digital technologies" available in the construction firm' (TCD8). This technology capabilities dimension was also added as an essential capability for digital transformation via big data application in a construction context (Figure 3).

Data capabilities dimension (DCD)
The driving force in big data in the construction process is generating, processing, and applying large volumes of data. The interviewees identified several issues relating to data capabilities: The interviewees identified the need for capabilities to analyse visual data since most analyses undertaken currently are not performed, e.g. [R2] said: "particularly any file like video and pictures and expressions … yeah, it is really just knowing what to do with all the footages at the end of the job which at the moment we are not doing any with it". This is a capability that construction firms urgently need because there is an overwhelming amount of available visual data, e.g. "24/7 use of time lapse camera on projects" [R4]. Moreover, there is the common use of visual data in legal disputes, i.e. "if we get photos we can show when they were installed and if they get removed, it becomes legal issue for someone else" [R12]. Analysing visual (image and videos) data in the construction process (DCD1) is a needed capability for big data application.
Participants in the research indicated the need for construction firms to contribute to a universal industrial database system. For instance, [R2] said, "in a way where the builder has a system or platform where they report all bad subcontractors on. That is what I think we should share some data amongst builders". From a design perspective, [R10] stated that "If we do have a universal database somewhere to have all those models and everybody has a free access that will be wonderful like a socialist idealistic society". These are significant points, but construction firms will need to develop data sharing capabilities since these unreported "bad" subcontractors could negatively affect the contractors' reputation. The participants identified a capability for co-creating universal industrial performance databases amongst construction firms on suppliers and subcontractors (DCD2).
Several participants indicated the need for accurate, sufficient, and appropriate data collected from a construction project. Exemplar statements from the participants include "collect enough data and the right data" [R12], "the data is enough to capture" [R1], and "you can't provide inaccurate data. You have to make sure of accuracy" [R3]. While these might be argued as not part of the technical construction project delivery, it could tend to affect the progress of the project and create hostile relationships amongst project stakeholders due to inaccurate, insufficient or incorrect, or incomplete data capture. There is a need for a capability of ensuring that accurate, enough and right data are captured and accessible in the construction process (DCD3).
The interviewees also identified that properly filing and organising inappropriate data places is another important capability for big data application in construction firms. As [R15] noted that "the project team members are to save the data in a proper place". Furthermore, the rationale, as recounted by some participants' includes "but if they save it in a different location and makes it a bit difficult to get the data" [R15] or "one of the staff members has left and someone picks that and it is like where was the data saved" [R12]. This evidence aims to prevent new staff members or other exiting colleagues from accessing the project data easily. There is then a capability need for properly filing and organising data in the appropriate place (DCD4).
Another identified capability from the research participants relating to the data dimension is the need to be able to request specific data. It is evident that different data sets perform specific responsibilities, and specifying a dataset could be helpful to the construction organisation. For example, if the construction firm wants to improve efficiency and cut down on waste, data on both labour and materials movements on-site and materials would need to be specified. However, according to the participants, collecting such data requires experience. [R15] said, "specific data are important to projects because there are so many data available but to get the correct data which is applicable to a specific project that will come from our experience". There is then a capability for construction organisations to request specific data in the construction process (DCD5).
Data is the essential link between digital strategy and transformation (Bharadwaj et al. 2013, Correani et al. 2020. Therefore, data unsurprisingly was highlighted in the interviews as a significant capability for big data applications in construction and an essential inclusion in the framework (Figures 1 and 3), data capabilities for construction firms identified in this study included the need to analyse visual (image and videos) data (DCD1); create universal industrial performance databases amongst construction firms on suppliers and subcontractors (DCD2); ensure that accurate and sufficient are captured and accessible in the construction process (DCD3), etc. (see Table 3). Most of these are identical to capabilities identified in previous research (Table 1). The interviewees here, in a construction context, emphasised that the data capabilities dimension had to include recognition of the importance and efficiency of using diverse, heterogeneous and notably, large datasets to achieve their digital transformation strategy (Figure 1).

Environmental capabilities dimension (ECD)
Environmental capabilities also emerged as an important set for big data application in construction firms (Table 3). These environmental capabilities comprise the construction firms' internal structures and contributions from the external environment. These are described further below; The participants in this research identified R&D cost models as a required capability to gain benefits from transformation from adoption of big data. Digital strategies like big data are capital intensive, and the respondents in the research noted that construction firms need to explore the short, medium, and longterm cost models relating by holistically assessing the cost ramification. [R14] stated that "R&D was needed in terms of how much the construction companies can afford to pay for doing all these initiatives". The capability identifies was in developing cost models on big data through R & D (ECD1).
Digital technologies use are central to big data application, and it is vital to enforce their use in the construction process. Through these digital technologies, big data becomes useful in the transformational agenda of the construction organisation. Participant [R1] said that "the project manager is accountable to me to make sure his team is using the technology to incorporate in the day-to-day management of the projects". This is contrary to another participant, [R5], who indicated the former firm had access to the technologies "but no one knows how to use and it is not beneficial". However, there was a general consensus that to gain benefits from technology they need to be used and there needs to be a capability of enforcing the use of digital technologies and application of data (ECD2).
The respondents in the research strongly stated that government and construction professional associations are instrumental in developing big data capabilities in construction firms. In Australia, there are no government plans for big data in the construction industry, except the generic plan to promote a datadriven business in Australia (Australian Government 2018). Notwithstanding, "a government grant or sort of financial help for the company to collect some data and manage it" [R12] was cited as to how the Government or professional bodies could help construction firms. This fund could be used as incentives (Yu et al. 2020) and be channelled through workshop training in creating awareness on big data applications. As a general proposition from the research participants there is the need for a capability for government/construction industrial bodies' sponsored workshop on data management for construction firms (ECD3).
The interviewees all noted that big data can bring changes to the organisational culture in construction firms. Culture shapes the firm's values, objectives, and business decisions, and construction firms need such drive (Yu et al. 2020). It was commonly stated by the interviewees that their respective firms had cultures oriented towards digital technologies and a datadriven culture. For example, participants represented culture with phrases like "the way we do business" [R1], "established by the directors of the company" [R12], and "company has culture of everyone using technology" [R5]. It was noted that this was essential for both transformation developing big data capabilities in construction firms through a capability of inculcating a data-driven culture and using digital technologies in the construction process (ECD4).
The commitment of top management to big data application represents the firm's ability to invest in digital technologies (Kim and Park 2017). This research shows that these investments have been used to procure digital technologies and big data infrastructure, i.e. servers, digital management platforms, etc. The evidence in this research contributes to demonstrating that digital technologies can make the construction firms "efficient" [R1] in their processes and this requires a capability of investing in digital technologies in the construction firm (ECD5).
The environment capabilities dimensions have most elements relating to the firm (Table 3). The findings of this research are similar to those already existing in literature as far as big data capabilities are concerned (Atuahene et al. 2018, Sun et al. 2018, Alaskar et al. 2020. The construction firm's objective of digital transformation through big data application means that the firm should have a strategy which could rely on internal managerial commitment, culture and enforcement of strategy as well as external support through government sponsorships. The capabilities identified in this research to achieve that goal included "enforcing the use of digital technologies and application of data" (ECD2) and the need for "government/construction industrial bodies' sponsored workshops on data management for construction firms" (ECD3). In the internal organisational context, the research showed there was a need for organisational-based capabilities to "develop cost models on big data through R & D" (ECD1); "inculcating a datadriven culture of using digital technologies in the construction process" (ECD4); and "investing in digital technologies (including analytics) in the construction firm itself" (ECD5). This big data application enabling dimension is as important as others, people, technology, data and knowledge capabilities identified by the construction professionals in this research (Figure 3). Kavadias et al. (2016) argued that big data offers transformative effects in business through asset sharing, creating a collaborative business ecosystem and helps allocate business risks more appropriately, making cost reductions possible and enabling agility. Wamba et al. (2017) argue that big data analytics directly affects firm performance. In essence, it is a digital transformation (Wessel et al. 2021) that creates a qualitatively different organisation (Orlikowski 1996) underpinned by different capabilities embedded in the workforce (Peppard and Ward 2004). A proposed set of capabilities needed for big data use in construction organisations have been specifically identified in this research. In Table 3, we summarise the key enabling capabilities for transformative action through adopting a digital strategy using big data applications in construction firms.

Discussion
The research data analysis identified five (5) key enabling capabilities needed for big data application in Construction (Table 3). The focus of these capabilities is on construction and big data expertise, which Wamba et al. (2017) referred to as both business and technical knowledge. The resource-based view model adopted here emphasises the importance of aligning assets, including people, within organisations with their strategic needs (Grant 1991), and identifying gaps dynamically in capabilities needed to use those assets (Teece et al. 1997). The enabling peoplefocussed capabilities identified by the respondents for construction firms in this research each contribute to both alignment of technology with business strategy and the dynamic nature of construction through encouraging collaboration between construction professionals and data analytic experts; through the construction organisation employing people with data analytic skills when they do not exist in the organisation; through better managing data and having a person in charge of the data processes; through aligning data experts with people who have construction expertise to ensure accuracy and reliability of data; and if a strategic position is reached, then outsourcing the data analytics role to outside experts.
The construction organisation respondents in this research identified how the people-focussed capabilities are applied. The construction firms would employ a big data expert and create a division (in-house team), outsource the big data duties to third parties, or employ a hybrid in-house and outsourcing, needsbased, flexible arrangement. Irrespective of the strategies, the respondents emphasised the crucial role of co-existence and cooperation of construction professionals and big data domain experts to deliver the big data agenda of the construction firms. The research data showed that the construction personnel interviewed believed that the right people in the construction organisations contribute to knowledge transfer and the co-creation of knowledge between the groups, especially if the purpose of their roles is welldefined and well understood. The synergies in the working relationships between the construction professionals and big data experts were identified as central to big data application bridging the gap and enabling alignment between technology adoption and the attainment of a digital strategy in a construction context. However, the respondents did note that there could be an adversarial relationship between the two groups of professionals or resistance because the construction professionals might feel threatened. This resistance is common in construction since there have been instances where construction professionals have vehemently resisted technology adoption in their processes (Davies et al. 2017). Nevertheless, the respondents noted that friction has to be managed by the construction firm through awareness creation and constant assurance to the construction professionals.
The respondents viewed the Technology dimension and its corresponding enabling capabilities as just as important as the people skills needed. They noted that the alignment of technology, people and the construction organisation's digital strategy was reliant on collaboration with both digital firms to capture and store construction data through service agreements and internet service providers for secure and reliable connectivity; on efficiently managing data internally and ensuring data safety utilising well-planned data management systems; and on having appropriate hardware (e.g. servers) available in the organisation. Bilal et al. (2016a) had proposed a big data architecture for construction waste analytics for the construction industry, also recognising earlier studies about the need for construction firms to have their internal arrangements for big data through a data management policy. The responses in this research not only endorsed this conclusion but also identified the specific enabling capabilities needed to make it happen (Table 3). In addition, the construction professionals interviewed in this research emphasised that construction firms must have access to appropriate digital technologies, especially servers. There was a clear recognition in the interviews that outsourcing becomes important when firms become cognisant of its limitations (Bhimani and Willcocks 2014) and engage the services of other competing firms. For instance, [R7] stated "some clients in Australia require contractors to fix time-lapse cameras on construction projects 24/7". It was recognised in the interviews that it would not be economically prudent for all contractors to have such highly intensive technology because it could become a liability. Regarding collaboration, the respondents noted that digital technologies like cloud servers could be accessed and utilised for project data through corporate agreements. However, the respondents did note that if a construction organisation uses outsourced digital technologies to either generate data and/or store data on cloud servers, the safety and ownership of the data could be problematic, especially if the construction firm uses a third-party server on highly sensitive projects.
Just as the research data highlighted the importance of people and technology hardware as important enablers of big data application in construction, they also focussed on the crucial role of the data itself. Without data, there would be nothing like big data. The interviews highlighted the importance of two elements: the availability of data and data quality. Previous research has shown that data is a central concern for organisations requesting specific data (Gupta and George 2016), performing analytics on all kinds of datasets (Wang and Hajli 2017) and ensuring accuracy and appropriateness of data (Kim and Park 2017). To enable these processes, the interviewees identified five key capabilities through ensuring that accurate, sufficient and correct data are captured and made accessible throughout the construction process; properly managing data through standardised filing and organising appropriately; enabling requests for specific data throughout the construction process and enabling the analysis of visual as well as numeric data; then through collaboratively co-creating and implementing a standardised universal industrial performance database amongst construction firms on suppliers and subcontractors. The respondents noted that when data is properly considered and implemented in a construction organisation, it can provide a competitive edge to the firm. One striking characteristic from the responses in the interviews, not previously noted in any research, was the need to create a universal industrial performance database. The respondents believed such a database would encourage data sharing and, in the broader perspective, could be very important in contributing to the organisation's competitiveness since problematic subcontractors and suppliers would be made known across the construction industry. Such action, they believed, would foster improvement in the construction industry's reputation. Creating a reporting database about shoddy subcontractors could avoid the bad reportage associated with the construction industry. Several respondents noted that such an action would enable better appraisal of and knowledge about "shoddy" work and disregard for health and safety. This capability need identified here could possibly reflect a key local issue. However, collaboration with institutions to gather evidential defects data on newly constructed projects on waterproofing, fire safety systems, structure, enclosure, and key services collected from the recently constructed residential building was seen as significant but did demonstrate that big data capabilities can extend beyond one firm.
The analysis of the interviews identified that the respondents also considered that the specific roles and enabling capabilities of people, technology and data in big data application in construction organisations were of lesser value without the knowledge created and accrued being captured and utilised. The enabling capabilities identified for big data application in construction were characterised the respondents as training, proper documentation, and the development of detailed guidelines. Unlike the assumption that all people involved in big data or information technology applications are knowledgeable already (Bharadwaj 2000, Sun et al. 2018, the interview data showed that the demands of and for knowledge as a stand-alone capability create the opportunity for personnel development. Because construction professionals need guidance and training to facilitate big data applications enabling alignment of investment in technologies with the transformation as part of the strategic expectations of the organisation. For example, the respondents noted that the big data experts would be deficient in the activities involved in the execution of construction. While they can suggest and plan the big data process stream, they would not necessarily collect appropriate data based on technical construction knowledge. This was made clear in the interviews by the construction professionals, who noted that the knowledgeable construction project manager could be the interface between the stipulated guidelines and the data collected in the construction process. There were statements in the interview data that there needed to be a correlation between knowledge and skills acquisition, a previously noted perspective (Dreyfus 2004). The respondents argued that an inhouse strategy would encourage the cross acquisition of knowledge between the construction and big data professionals. The expected outcome will lead to welldeveloped guidelines that integrate the two domains. They argued that this knowledge would guide the construction professionals to look for cues about using the right technology to capture the right data. The big data professionals benefit by understanding or acquiring knowledge about potential hotspots on projects. In the case of an outsourcing strategy, the respondents noted that the construction organisation's management would have to opt to be an advisory board member of university programs relating to the built environment and make inputs into the curriculum based on their experience with digital technologies and big data-driven construction business.
The final set of enabling capabilities for big data application in construction organisations capability encompasses the construction firm as an organisation and the impact on it from the external environment, which have been previously defined in the organisational transformation literature as organisational perspective (Chaurasia and Verma 2020), and organisation structure (Yu et al. 2020). The respondents noted that the importance of a construction organisation having a data-driven culture shows a firm's commitment to big data application through investing in digital technologies. They identified this commitment culture can be enabled by investing in, developing and using cost models for construction embedded in big data; through senior managers enforcing the use of digital technologies and application of data across all business and construction processes within the organisation; and through the provision of construction industry-wide professional development and the establishment of standards. The respondents viewed these capabilities as inseparable and made possible through an organisation's digital strategy. In practice, the respondents noted that there needed to be three strategies: (a) establishment of an in-house data management policy, (b) collaborative capabilities with external firms, and, if needed, (c) establishing an outsourcing big data strategy. To facilitate the interaction of the environmental context of any construction organisation with an internally driven digital strategy to transform their processes, specifically using big data applications, the respondents were categorical about the need for a data-driven culture, well-planned investment decisions in technology, and management commitment, all oriented to the competitive demands of the construction organisation's external/market context. A digital strategy is argued in the research literature to transcend and affect all activities and processes of the firm and is instrumental in the survival of businesses in this digital age (Correani et al. 2020). The transformational impact of this digital strategy relies on creating, curating, sharing, communicating, and applying data (Bharadwaj et al. 2013, Becker andSchmid 2020) and by building skills (Kane et al. 2015). These skills for big data application in construction organisations have been made more explicit through this research (Table 3). Their role as part of a revised and expanded big data transformative context framework is shown in Figure 3.

Implications
By identifying these big data capabilities, this study informs construction business managers that developing big data capabilities is more important than just purchasing digital technologies (Atuahene et al. 2018). This analysis shows that developing big data capabilities is a complex and multi-faceted process, requiring deliberate and strategic effort to pull both intangible and tangible assets within and without the firm, as reported in earlier capabilities studies (Teece 2007). Each construction firm is unique, and the capacity for developing and employing these capabilities might differ. This study has shown that owning these capabilities is not always the case, but alternative arrangements can be deployed for the firm's benefit through outsourcing, for example. These capabilities can be developed and implemented gradually because they are capital intensive and create extra responsibilities, aside from the technical construction responsibilities. This research identified that big data application is a multi-layered process, recognising the need for construction firms to develop their capabilities building upon increasing levels of sophistication in the degrees of analytics undertaken, from a data layer, used for supporting operations and problem-solving; through data aggregation layers; to an analytics layer using cluster analysis, data mining, forecasting models, or prediction models or behavioural models to inform construction business practice. In this study, the respondents from the construction companies were only operating in the initial levels of these models with expectations that once learning more and accruing greater benefits, their application of big data analysis would increase the use of more sophisticated tools. Their responses reflected some understanding that they were not engaged in data collection and management alone; rather, they were searching to get value from applying simple analytics that would improve business practice in their construction context. Their use of trend analysis, albeit simple, represented their attempts beyond big data capture and storage. The interviewees recognised that any application of big data analytics in construction will be a somewhat slow process of building skill levels and learning which big data applications will best serve their firm's individual needs.
To effectively develop or borrow these capabilities, the resource-based view principle of self-assessment of the firm is needed (Grant 1991) in identifying and understanding the construction firm's own strengths and weaknesses in big data application and identifying the possible inherent threats and opportunities. This appraisal must not be limited to the management level but be a firm-wide exercise (Barney 1995).
Previous studies have researched the awareness (Reyes et al. 2019) and potential benefits of big data application to the construction industry (Bilal et al. 2016b, Han andGolparvar-Fard 2017) but have not empirically explored the contextual transformation capabilities for the industry according to those actually involved. This study has gone some way to address the capabilities gap for big data transformation in the construction industry identified initially in this paper. The capabilities framework developed from the construction professionals' perspective here extends our understanding of what is needed for construction firms to adopt big data analytics within their business operations and how this can be used to extend their traditional data management, if used at all, in the construction industry. Unlike the introduction of digital technologies in construction, where research has espoused the need for a construction firm to own them, this study has further identified that outsourcing and collaborating offer real potential to accrue value and better support the adoption and implementation of digital technologies in the construction industry. In addition, this study identified that deep knowledge of digital innovation, especially big data applications is an essential standalone capability needed. The knowledge capabilities elaborate on the need to understand the creation of knowledge on big data interspersed with construction through formal education and other training modes.
Big data application is complex but potentially offers construction firms significant potential to accrue value and improve process and business performance. However, in the Australian context, this research has also identified that individual firms cannot do this alone. They need government and industry-wide assistance. There is a common belief among the construction professionals that there is a need to create a universal industrial performance database to assist them in meeting a capability to identify whom they can best partner or collaborate with and avoid inadequate service provision, including technology provision as well as sub-contractor work. Ngo et al. (2020) define "capability" as the condition of having the capacity to do something. Within this condition, there is a potential for improvement of skills. Having a universal database of industrial performance is perceived by the construction professionals as an enabler capability, giving them, they believe, the capacity to implement big data, in this case, or take other actions with some degree of certainty about the veracity of their business partners, sub-contractors, or technology providers. Such an expectation on the part of the construction professionals might be considered to be a naïve proposition. However, there was significant mention in the independent interviews of the need for them to have the capability to identify potential inefficient or ineffective or deleterious partnerships. In any phenomenological research, the focus is on the "what and how" expressed by those engaged in interviews or focus groups and accepting that is their perception here of a capability they believe they need "to do something" or enable their "potential for improvement of skills". The interviewees see this as a capability associated with data in this research. We would argue, however, that it might also be a knowledge capability. This represents an element from this study which begs further investigation.

Conclusion
Although several studies have explored big data application in the construction industry (Bilal et al. 2016b, Chaurasia and Verma 2020, Atuahene et al. 2022, none to our knowledge have empirically derived the specific big data enabling capabilities for transforming the construction industry through the experiences of construction professionals. This paper has identified a set of twenty-eight big data application enabling capabilities needed to transform processes in construction companies. These have been classified across 5 dimensions (people, knowledge, technology, data, and environment) ( Table 3) and modelled ( Figure 3) to demonstrate their relationship to the alignment of a digital strategy with technology adoption in construction organisations, specifically focussed on big data application. From observation and analysis of the interviews conducted with construction professionals, the authors posit that each capability appears to be equally important. For experiencing the transformative role of big data in construction, these capabilities, we argue, should be employed as a composite enabling capability set. These notions need to be further explored in a much larger-scale study in the future.
Our findings contribute to the discourse on big data capabilities development in businesses and construction (Wamba et al. 2017, Wang and Hajli 2017, Konanahalli et al. 2018. The paper offers a big data transformative context framework grounded in the twenty-eight enabling capabilities identified in the research. These transformative enabling capabilities were shown to be implemented by construction firms using any/all of a strategy including (a) driving an inhouse data management policy to rolling-out the big data capabilities; (b) fostering collaborative capabilities with external firms for resource development, and (c) outsourcing big data services to address the capabilities deficits impacting big data transformation. The research highlighted the relevance of construction transformation focussing on both their internal and external resources. Transformation is a process that engenders a qualitatively different organisation. In having and utilising the set of enabling capabilities identified in this research, a construction firm will have the basis to enable the transformation to a more efficient operation.
The next stage of this research is to validate the findings of this study through a quantitative study on construction firms and professionals in the UK and Australia. Using a stochastic model will enable the project to be used to build a predictive model of capabilities and develop a measuring instrument to assess the preparedness of construction professionals on big data application from first steps through to advanced analytics.
In terms of limitation, as with all qualitative studies, it is important to recognise that making definite conclusions or inferring the applicability of the research outcomes to all construction firms and contexts is inappropriate.