Drivers and barriers for the traceability digitalisation in the Australian construction supply chain

Guilherme Luz Tortorella

The University of Melbourne, Australia

Universidad Austral, Argentina

Universidade Federal de Santa Catarina, Brasil

Declan Cox

The University of Melbourne, Australia

Wen Li

The University of Melbourne, Australia

Alistair Barros

Queensland University of Technology, Australia

The construction industry is expected to become a global engine for economic growth and post pandemic recovery, having a global growth by 42% and yielding US$15.2 trillion by 2030 (Oxford Economics Group, 2021). Construction supply chain is one of the critical enablers for this booming industry but also poses challenges and risks (Hackitt, 2018; Adel et al., 2022). This is mainly due to the typical make-to-order nature of construction supply chain, which is often instable, highly fragmented, and geographically dispersed (Vrijhoef & Koskela, 2000; Gharaibeh et al., 2022). Owing to the permanent inward immigration and acceleration of infrastructural investment, Australia is ranked 5^th for the construction growth among both emerging and developed economies (Oxford Economics Group, 2021).

The ability to track and trace, or called traceability, is becoming increasingly important as all materials converging to the construction site from a global supply chain. This is highlighted in the Hackitt Review after the tragic Grenfell Tower incident in London (Hackitt, 2018). The report identified that lacking product traceability is a contributory factor to fire safety systems being compromised. Apart from the compliance risks, the soaring commodity prices (e.g., steel and timber) and prolonging lead time in the building sector have further stressed the need for traceability along the complex construction supply chain so the project progress and budget can be monitored and managed efficiently. Furthermore, traceability can also contribute to improve the sustainability of the building sector through responsible sourcing and life cycle management (Glass et al., 2011). However, the adoption of traceability in the construction industry is significantly lagging behind other sectors and has been urged to accelerate (Hackitt, 2018).

Amid the digitalization era, industries across multiple sectors advance through the use of digital technologies, such as digital-twin, Internet-of-Things (IoT), cloud computing, blockchain, and artificial intelligence. There is a growing interest in the construction industry for embracing digital technologies, and technology providers (e.g., Oracle) have committed significant investments into this sector (Rogers, 2019). Nevertheless, while manufacturing sector has seen benefits of using “digital thread” for improving supply chain efficiency, the digital transition in construction industry is still slow, especially for addressing the need of material traceability (Zhong et al., 2017; Wang et al., 2020; Filippo et al., 2022). Although researchers proposed framework for encourage digitalisation in the construction industry (Hossain & Nadeem, 2019), studies that approach this topic are still scarce. Hence, there is the need to understand why construction supply chain is reluctant and the views of key stakeholders. Against this backdrop, we formulated the following research question:

“What are the main drivers and barriers for the traceability digitalisation in the construction supply chain?”

To address this gap and answer the aforementioned question, this paper aims to investigate the main drivers and barriers for the traceability digitalisation in the Australian construction supply chain. For that, we conducted semi-structured interviews with 25 experts from the academia, construction supply chain companies (i.e., contractors, sub-contractors, and material suppliers), and stakeholders (i.e., technology providers and regulatory agencies). The data collection and content analysis were grounded on the concepts from the diffusion of innovation theory (DIT) from Rogers (1995), which states that five attributes affect the rate of innovation adoption, namely: (i) relative advantage, (ii) observability, (iii) compatibility, (iv) trialability, and (v) complexity. Based on the commonalities found among interviewees arguments, we categorised the main drivers and barriers for such digitalisation. Further, following DIT’s assumptions, the perception on these attributes was used to distinguish the drivers and barriers between companies considered as early and late adopters.

The remaining of this paper is structured as follows. Section 2 presents the background on the fundamental concepts approached in our study. Section 3 describes the applied methodology, whose results are presented and discussed in section 4. Section 5 concludes the article and indicates future research opportunities.

As the digitalisation of construction supply chain traceability is still underexplored, a qualitative approach was carried out corroborating to the exploratory and descriptive nature of our study (Voss et al., 2002; Barratt et al., 2011). Following Ketokivi & Choi (2014), the study used a priori theorization to frame the research design; findings are therefore not statistically generalizable. That offered an in-depth understanding of the drivers, barriers, challenges and benefits from the digitalisation of the construction supply chain traceability, producing novel insights to the field.

The methodological design consisted of three main steps: (i) definition of selection criteria; (ii) interviews with experts; and (iii) content analysis and propositions. These steps are detailed next (see Figure 1).

Figure 1
Methodological steps of this research.

3.1. Definition of selection criteria

The following criteria were established to select interviewees. First, because we wanted to confront theoretical and practical perceptions on the subject, we involved experts from three main categories: (i) academics who have investigated the digitalisation of the construction supply chain for at least 5 years, (ii) experienced practitioners (i.e., minimum of 10 years of experience) who have played key leadership roles (e.g., manager, director, or engineer) in companies from different tiers (i.e., contractors, sub-contractors and suppliers), and (iii) stakeholders, which were composed by solution and technology providers, regulatory agencies and government institutions. The combination of different perspectives would enable a wider understanding of our research problem. To mitigate the potential bias existing in interviewees’ responses, we cross compared their opinions based on their respective category (academics, practitioners, and stakeholders). We considered arguments that were equally mentioned by experts and avoided utilizing the ones that were clearly associated with the context in which the expert is inserted. Two of the authors individually analysed interviews’ transcripts to increase the reliability and mitigate biased findings, as performed by Tortorella et al. (2021).

Finally, 26 experts were identified and invited to participate in the research. Their profiles are summarized in Table 1. Experts presented balanced characteristics in terms of experience, background, and roles, meeting the pre-determined selection criteria, and ensuring the quality and legitimacy of their opinions, as recommended by Shetty (2020).

Table 1
Interviewees’ profiles.

The data collection method that helped to achieve the shape of interviewees in Table 1 was also based on theoretical sampling. According to Corbin & Strauss (2008, p. 143), its purpose is to “collect data from places, people, and events that will maximize opportunities to develop concepts in terms of their properties and dimensions, uncover variations, and identify relationship between concepts”. The difference of theoretical sampling from conventional methods of sampling is that it is responsive to the data rather than established before the research begins, i.e., it is about discovering relevant concepts and their properties and dimensions.

Additionally, previous qualitative studies [e.g., Guest et al. (2006), Fugard & Potts (2015), Braun & Clarke (2016), Boddy (2016)] have recommended a minimum sample size of at least twelve to reach data saturation among a relatively homogeneous population, which matches with our sample size. Thus, we claim that our sample size was large enough to describe the phenomenon of interest and address the research question at hand, avoiding repetitive data, and attaining theoretical saturation (Vasileiou et al., 2018). Experts accepted to join the interviews after receiving a consent form and a plain language statement, in which they were informed that their participation was voluntary, and any information provided would be kept anonymous.

We now present the results from the semi-structured interviews. The main comments made by interviewees (Appendix B) were transcribed, coded, and analysed, leading to the consolidation of a total of 79 elements (44 drivers/benefits and 35 barriers/challenges). Those elements were grouped according to their orientation in relation to the DIT’s attributes, as indicated in Table 2. Further, the emphasis and frequency of each element were determined within each type of interviewees (i.e., academics, practitioners, and stakeholders), so that we could identify their criticality levels. In general, 22 out of the 79 elements were considered highly critical. Out of those, 13 were drivers/benefits and 9 were barriers/challenges, as displayed in Figure 2.

Table 2
Commonalities among interviewees.

Items mentioned by ≤ 33.3% of interviewees were considered lowly frequent, while items whose frequencies were > 33.3 were denoted as highly frequent; Emphasis level was qualitatively based on the strength of arguments or examples provided during the interviews; Gray cells indicate highly critical driver/benefits and barriers/challenges.

Figure 2
Distribution of criticality levels among all driver/benefits and barriers/challenges.

For relative advantage, five drivers/benefits stood out; they are: (i) greater efficiency and productivity, (ii) improved sustainability, (iii) value gained, (iv) enhanced quality, and (v) more accessible product information. Those elements were solely acknowledged as highly critical by practitioners and stakeholders, being the DIT attribute with the largest number of highly critical drivers/benefits. This result highlights the importance given by practitioners and stakeholders to the perceived advantage from the incorporation of digital technologies into the construction supply chain traceability. In turn, academics, practitioners, and stakeholders agreed that cost of investment (particularly for SMEs) should be a highly critical barrier/challenge for digitalising the construction supply chain traceability from a real advantage perspective.

From a compatibility standpoint, three drivers/benefits (i.e., introduce government mandate, enhance supply chain collaboration, and educated local workforce) were considered highly critical, while two barriers/challenges (i.e., limited data accessibility/sharing, and end-to-end supply chain requirements) were deemed as highly critical. It is worth mentioning that out of those five highly critical elements, academics pointed four of them, and practitioners and stakeholders indicated three each.

In terms of complexity, the drivers/benefits support premanufacturing strategies, provide a visualisation system of data/models, and common data environment (standardisation of data) emerged as highly critical, being the first two raised by academics and the third one suggested by stakeholders. In turn, from the ten barriers/challenges consolidated only the existence of many different systems (software interoperability) was pointed as highly critical by both academics and stakeholders. Curiously, practitioners did not indicate as highly critical any of the drivers/benefits and barriers/challenges.

Trialability was the DIT attribute with least number of elements raised from the interviews. In total, three drivers/benefits and two barriers/challenges were listed. From those, only the barrier/challenge denoted as lack of technical knowledge was regarded as highly critical by stakeholders.

Finally, with respect to observability, the drive/benefit greater supply chain transparency (better monitoring of deviations /identify opportunities for improvement) was widely deemed as critical by academics, practitioners, and stakeholders. In turn, this attributed presented the largest number of highly critical barriers/challenges, suggesting a particular concern with the visibility of the results implied by the digitalisation of the traceability in the construction supply chain. Four barriers/challenges were both emphatically and frequently mentioned; they are: (i) reactive responsiveness, (ii) short term relationships, (iii) unbalanced risk across the supply chain, and (iv) unbalanced bargaining power.

Then, the thirteen highly critical drivers/benefits had their frequency of mentioning compared between early and late adopters of digital technologies in the construction supply chain. As displayed in Figure 3, early adopters seemed to more frequently mention those drivers/benefits than late adopters. On average, early adopters mentioned these drivers/benefits 61% of the time, while late adopters only cited them in 39% of the cases. Two of the highly critical drivers/benefits were only claimed by early adopters, they are: support premanufacturing strategies, and provide a visualisation system of data/models. A similar trend was observed for the nine barriers/challenges denoted as highly critical (see Figure 4). Early adopters commented about these barriers/challenges in 63% of the cases, whereas late adopters suggested them in only 37% of the time. Further, two barriers/challenges - end-to-end supply chain requirements and existence of many different systems (interoperability) - were only mentioned by early adopters.

Figure 3
Frequency of mentioning of highly critical drivers and benefits between early and late adopters.

Figure 4
Frequency of mentioning of highly critical barriers and challenges between early and late adopters.

Now we discuss our results in light of the existing body of knowledge. The predominance of early adopters’ perceptions in the frequency of mentioning of highly critical drivers/benefits and barriers/challenges suggests a higher awareness related to the digitalisation of traceability systems in the construction supply chain. Following the concepts from hierarchy of competences proposed May & Kruger (1988), whose ideas about were later extrapolated to the organizational context (Thompson & Martin, 2010), this outcome may be associated with the existence of four competency levels: (i) unconsciously incompetent, (ii) consciously incompetent, (iii) consciously competent, and (iv) unconsciously competent. In our case, late adopters are expected to lack of proficiency and be unaware of the necessary skills to digitalise the traceability in the construction supply chain. This might explain the lower awareness level and, hence, frequency of mentioning, of the highly critical driver/benefits and barriers/challenges related to the traceability digitalisation. In this sense, late adopters could be positioned in the very first stage of the hierarchy of competences, i.e., unconsciously incompetent. On the other hand, early adopters have already been exposed to some digitalisation initiatives in the construction supply chain, which make them more familiar with the topic and aware of the drivers/benefits and barriers/challenges, although they are not yet proficient. As such, we argue that early adopters are likely to be consciously incompetent when considering the digital traceability in the construction supply chain. This finding is also somewhat aligned with the indications from Adel et al. (2022) and Gharaibeh et al. (2022), which suggested that the digitalization of the construction supply chain is still at early stages when compared to other industry sectors.

Nevertheless, it is worth highlighting that some drivers/benefits (e.g., improved sustainability, better scheduling, more accessible product information, and educated local workforce) and barriers/challenges (e.g., short term relationships limit change, unbalanced risk across the supply chain, and cost of investment - particularly for SMEs) were equally mentioned by both early and late adopters. This might indicate that the relevance of those drivers/benefits and barriers/challenges for the digitalisation of the construction supply chain traceability is equally acknowledged regardless the company’s stage in the hierarchy of competences. In other words, may be even more prominent and, hence, should be firstly addressed in the traceability digitalisation. Such outcome complements the work from Wang et al. (2020) and Filippo et al. (2022), as we provide the clear indications of which drivers/benefits and barriers/challenges are more likely to be observed in the traceability digitalization of the construction supply chain.

In this study, we aimed at identifying the drivers/benefits and barriers/challenges for the digitalisation of the construction supply chain traceability. Based on data collected through semi-structured interviews with experts (academics, practitioners, and stakeholders), we consolidated 79 elements, being 44 of them drivers/benefits and 35 barriers/challenges. Out of those, 22 elements (13 drivers/benefits and 9 barriers/challenges) were assessed as highly critical for a successful digitalisation of the traceability systems.

Experts apparently deem more prominently the drivers/benefits that promote real advantages in relation to current traceability practices and systems. When considering the barriers/challenges, the degree to which the digital traceability's results are visible to the adopters seems to be an important issue, being able to impair the digitalisation of the construction supply chain. It is worth mentioning that some highly critical drivers/benefits (e.g., enhance supply chain collaboration, and greater supply chain transparency) may only be fully achieved if the entire construction supply chain really engages in the traceability digitalisation. At the same time, some barriers/challenges (e.g., short term relationships, and unbalanced risk across the supply chain) may be inherent to the way the construction supply chain is designed and, hence, more difficult to overcome.

Furthermore, companies that already have some initiatives towards the digitalisation of the construction supply chain traceability (early adopters) may be able to understand and visualise the drivers/benefits and barriers/challenges than others that have not started yet (late adopters). This suggests that the more companies advance in the traceability digitalisation, the more aware they will become regarding its drivers/benefits and barriers/challenges. Nevertheless, some highly critical drivers/benefits and barriers/challenges were equally perceived by both early and late adopters, which may indicate their greater relevance for such digitalisation.

Some limitations of this study must be highlighted. First, from a data collection point of view, we gathered information from 26 experts. Although this sample size is reasonably sufficient for a qualitative study, it does not allow statistically generalizable findings. Thus, future studies should enlarge the sample size and diversity, enabling the utilisation of more sophisticated multivariate data analysis techniques whose results can complement the ones presented here. Second, larger samples would allow to empirically verify how companies’ contextual characteristics may influence the adoption likelihood of digital technologies in the construction supply chain traceability. Further, operational performance could also be included as one of the studied variables, leading to the identification of the relationship between the traceability digitalisation and performance improvement. Finally, the proposition of an implementation roadmap that could guide the construction supply chain agents towards the digital transformation of the traceability systems could be another opportunity for future studies. This roadmap would help to systematize and articulate the digital transformation in an organised way, minimising useless efforts and increasing the odds of a successful implementation. In the same vein, future studies could also approach the implementation of digitization and the monitoring of the construction supply chain raising the inherent benefits.