Guest editorial Free

The Fourth Industrial Revolution, or Industry 4.0, is poised to radically transform the architecture, engineering and construction (AEC) industry. In 2018, the World Economic Forum (WEF) initiated an action plan for shaping the future of construction as it called the attention of senior executives to exploit the capabilities that new digital, sensing and fabrication technologies can bring to improve the productivity and sustainability of the sector (WEF, 2018). There has also been growing academic interest on this topic, with a number of reviews and thought-pieces that explore the (potential) implications of Industry 4.0 on the AEC industry (see, e.g. Oesterreich and Teuteberg, 2016; Dallasega et al., 2018; Newman et al., 2020, and; Edwards et al., 2021). Special issues have and continue to be edited as well, for example Sherratt (2020) and more recently in this journal by Kumar and Rahimian (2021). Thus, the concept of Industry 4.0 has gradually seeped into the imagination and discourse of academic research and industry practice.

We are, as Kumar and Rahimian (2021, p. 453) put it, “experiencing a kind of paradigm shift” that integrates developments of building information modelling (BIM) with technological advances in cyber-physical tools (e.g. the Internet of Things and augmented reality), data analytics and advanced manufacturing techniques (e.g. robotics and autonomous systems). This paradigm shift is also evident in the rhetoric of shifting the AEC industry to The Next Normal (see the report by the McKinsey Institute; Ribeirinho et al., 2020), where the disruption caused by the COVID-19 pandemic has created the impetus to think about technological solutions that can keep production going to mitigate loss productivity due to safe distancing measures.

To date, the discourse on Industry 4.0 and the AEC industry has been one that is dominated by technological innovation and determinism (Chan, 2020a; Sherratt et al., 2020). Consider this quote by a recent review by Newman et al. (2020, p. 5),

Thus, in this techno-centric account of developments in Industry 4.0 for the AEC industry, the prevailing assumption is that technological advancements can radically change the industry and its practices for the better. Indeed, Newman et al. (2020, p. 5) cited a litany of (potential) benefits that included “raising the bar for productivity, performance and/or safety [… and] the potential to automate the whole organization thereby alleviating human managerial burdens.” Therefore, the problem lies firmly in the resistance of people and companies working in the industry, for their notoriety in being slow to change and technological deployment stymies the industry's path to progress. Yet, if the exhortations of the promised benefits were true, the interesting question to ask is why then are firms not embracing these developments at scale?

In this special issue, we contend that the explanation goes beyond the simplistic answer of resistance to change. Instead, we argue that evidencing the benefits of Industry 4.0 for the AEC industry is far from straightforward, and that there are immense challenges still in demonstrating the benefits (or otherwise) at scale (see also Darko et al., 2020 and Edwards et al., 2021). Against this backdrop, the need for this special issue was conceived. In the call for papers, we raised a number of key questions, including: (1) where do we measure productivity outcomes of Industry 4.0 for the AEC industry, and how and why? (2) How do we study and evaluate the true impact of technologies on managerial practices, and what qualitative and quantitative evidence can enable such substantiation? In so doing, the aim of this special issue was to repurpose the use and expectations of the performance outcomes of adopting Industry 4.0 technologies in the AEC industry by providing, we hoped, compelling evidence to support claims of their benefits.

A total of 21 submissions were received for this special issue. Following the peer-review process, a collection of seven articles eventually made it into this special issue. In the first four papers, attention is placed on the key factors that need to be considered when organisations in the AEC industry decide to embrace Industry 4.0. These papers highlight that Industry 4.0 is not simply a problem of technological implementation but one that also entails the challenge of social change. In the final three papers, we then turn to practical examples of how integration of different aspects of Industry 4.0 can change perceptions and practices in the industry.

In the first paper, Sepasgozar et al. (2021) synthesised, from a systematic review of 21 studies, the key metrics of concern that need to be considered when approaching the adoption of mixed reality and digital twin approaches in the AEC industry. Drawing on the technology acceptance model, which places the ease of use and usefulness of technological change as the central focus, Sepasgozar et al. (2021) found that numerous surveys have highlighted the importance of the social and behavioural aspects that apply in addressing the uptake of mixed reality and digital twin approaches. Thus, social influences whether in terms of encouraging and spreading practices among workers, or through knowledge sharing in (increasingly online) social networks are ways in which experiences of using new technologies can be exchanged. Through exchanges of (human) experiences, it is also important to highlight how users enjoy the sense of being in the virtual environment.

The ability to influence social interactions and human experience is also a theme that features in the second paper. In this paper, Alade and Windapo (2021) addressed the question: how will organisational leadership change in an Industry 4.0 enabled AEC industry? Based on 416 responses to an online survey administered to senior executives in the South African Construction Industry Development Board (CIDB) register of contractors, their findings suggest that leaders of construction organisation will likely take on the role of a visionary, coach, champion, facilitator and driver in an Industry 4.0-enabled AEC industry. The command and control style of leadership gives way to leadership intelligence that focuses on the creative impulses that can help produce agile responses to a dynamically changing world. In this transformation, the ability to develop effective interpersonal relationships and to tap into knowledge from across disciplinary boundaries is ever more crucial.

Strategy-making is also a key aspect that is often tied to leadership thinking. Despite all the benefits that Industry 4.0 technologies promise to bring, uptake in the construction industry is cautiously slow. In the third paper, Bhattacharya and Momaya (2021) present an integrative review, drawn from the organisational literature, on how companies in the industry can create actionable strategies for enabling digital transformation. In the proposed framework, Bhattacharya and Momaya (2021) argued that strategies have to be both simple yet flexible, focus on crafting dynamic capabilities that link organisational routines with non-routine and disruptive transformation, and balance change with business continuity. Clearly, strategising for digital transformation in the AEC industry transcends beyond the adoption of technology. As Bhattacharya and Momaya (2021) acknowledged in their review, there are multiple paradoxes that need to be addressed: how can strategies be simplified so that practitioners have clarity of what needs to be done, yet address the complexities of interdisciplinary working? How can companies build dynamic capabilities that are responsive to change, yet do not alienate the customer base that have for long provided business continuity? Indeed, the ultimate challenge in any strategy-making for embracing Industry 4.0 in the AEC industry (and beyond) lies in addressing the question of contradictions. As Bhattacharya and Momaya (2021) noted, “Contradictions are grounded within the internal dynamics of the organization, arising from dilemmas over past, and projected future range of activities.” Thus, there is scope for more reflection on how these dilemmas are framed, understood and addressed in practice.

The fourth paper in this special issue focuses on how practices in construction businesses will likely change as a result of Industry 4.0. In this paper, Das et al. (2021) reported the outcomes of an expert forum on what business model transformation when construction businesses embrace Industry 4.0. Based on the input from 30 academics in Australia and New Zealand, and adapting the Business Model Canvas, projections were put forward to describe how key partners, activities and resources, client relationships, supply chain configurations, value propositions, cost structure and revenue streams might change in an Industry 4.0-enabled sector. These highlight the proliferation of technological solutions aimed at providing more data-driven designs and production solutions, increasing partnerships that are globally dispersed and a challenge to the conventional client-designer-contractor relationship as the role of the modular builder that integrates design and fabrication in a data-rich environment takes hold. Managing the logistical process is then of paramount importance.

In the final three papers, the focus shifts towards more practical applications of Industry 4.0 technologies in the AEC Industry, including virtual reality, computer-aided facilities management and machine learning. These papers share a common thread that Industry 4.0 technologies have the potential to bring different stakeholders together to co-produce the future. In the fifth paper of this special issue, for instance, Prabhakaran et al. (2021) combined BIM and virtual reality in a quasi-experiment involving 12 professionals, designers and end-users in furniture, fixture and equipment design. They found that significantly less time was needed for decision-making, due to the virtual reality platform offering a synchronised collaborative opportunity for designers and end-users to test and review options. The participants also reported higher levels of design satisfaction since the virtual environment enabled them to experience what changes to the design would look and feel like, something which is almost always limited in a 2D drawing. By integrating the information models with virtual reality platform, designers and end-users also perceived the usefulness of being able to come together to co-produce designs even if individuals were physically located at a distance.

While there is already a vast body of scholarship that attends to the power of Industry 4.0 to integrate design and construction, far less has been done to examine how Industry 4.0 tools can be used to manage and maintain the built asset in the operational phase. In the sixth paper of this special issue, Ismail (2021) reviewed existing computer-based maintenance management models and found that these do not adequately support the detection and control of defects. By developing a computer-based maintenance model that couples with BIM in an industrialised building setting in Malaysia, Ismail (2021) investigated user perceptions of the impacts of an ICT-enabled environment for managing defects. Ismail (2021) found that the computer-based model facilitated more effective communication between designers, contractors and facilities managers. Furthermore, such a model aided the planning of corrective maintenance and provided support for making the business case for a reliability-centred approach to managing maintenance.

While much attention has been placed on how Industry 4.0 technologies can yield higher productivity levels so that we can produce much faster and cheaper, less is examined on what influences and impacts on cash flow and profitability. In the final paper of this special issue, Adinyira et al. (2021) analysed data from 150 completed projects in Ghana from 2014 to 2018, in order to develop and test the sensitivity of a support vector regression algorithm. The resultant model was found to achieve 73.66% predictive accuracy, highlighting the significance of loan acquisition and utilisation as well as labour allocation to be significant factors in predicting the cash flow performance and profitability of projects. As the AEC industry enters the realm of (big) data analytics, there are opportunities for applying and testing the algorithm to inform forecasts of profitability and cash flow in other project contexts outside of Ghana.

Returning back to the key questions that we posed in the call for papers – i.e. where do we measure the productivity outcomes of Industry 4.0 technologies in the AEC industry, and how do we capture the impacts on managerial practices – the seven papers included in this special issue go some way to address these questions. In terms of measuring productivity impacts, these tended to be activity based as we saw in Prabhakaran's et al. (2021) study of designing furniture, fixtures and equipment, as well as in Ismail's (2021) study of detecting and correcting defects. The implications for managerial practices were somewhat broader, with the authors generally acknowledging that embracing Industry 4.0 in the AEC industry requires strategic and social change. Indeed, as Alade and Windapo (2021) suggested, the industry will need a new kind of leadership, one that is not characterised by command and control, but rather leaders who can orchestrate learning and working across disciplines in their agile responses to change.

In sketching out what it takes to transform the business model, it is clear from Das et al. (2021) that embracing Industry 4.0 in the AEC sector is not simply a question of the adoption of technologies. Rather, there will be transformations necessary on working practices, on models of collaboration and on how we navigate through an increasingly data-rich world. As we already indicated in our initial call for papers, the predicted economic (and, we add here as well, social and environmental) gains are predisposed upon a coalescence of technological, process and information communication innovation that needs to transcend. Thus, for the promises of technological innovation in Industry 4.0 to be realised, there is a need to consider the human factor too. The social is as, if not more, critical as the technical.

Of course, the importance of the social is not new. It is worth remembering that Industry 4.0 was a concept that was popularised in Germany, at a time when the German manufacturing industry was trying to maintain their global competitiveness amidst social and demographic change (see Kagermann et al., 2013). Industry 4.0 was therefore borne out of inter alia, a repositioning of the economy following the global financial crisis of the late 2000s and to deal with an increasingly flexible and ageing workforce. What is noteworthy is that those who conceptualised Industry 4.0 were not strictly motivated by technology but also by the human factor. As Kagermann et al. (2013, p. 53) stressed,

In deconstructing the core principles of Industry 4.0, Lasi et al. (2014) also emphasised the need for self-organisation, adaptation to human needs and corporate social responsibility, transcending the narrow techno-centric focus on smart factories and cyber-physical systems.

In any case, the papers in this special issue have raised more questions than we had expected ^[1]. Here, we reflect on three key areas that deserve further attention. First, there is a need for more engaged scholarship that examines more holistically (and beyond discrete activities) the performance impacts of Industry 4.0 on the AEC industry. Furthermore, the emphasis to date has been on demonstrating the benefits of adopting Industry 4.0 technologies and improving the skills of (human) workers and managers. Yet, taking this focus means that the question of material agency is obscured. Therefore, the second area that requires further attention is to examine what Industry 4.0 technologies do to materially change what we humans do, and in what ways and with what outcomes. The third and final point of attention lies in the need to be mindful of the potentially negative impacts of Industry 4.0 as well. Simply promoting the benefits and obscuring the possible downsides will, we argue, be counterproductive to scaling up Industry 4.0 in the AEC industry. These will now be elaborated in turn.

Despite our intention to feature studies that capture a variety of places where the performance impacts of Industry 4.0 technologies can be evaluated, the papers included in this special issue seemed only to focus on activity-based measurements and often from the viewpoint of established actors (e.g. designer, contractor and facilities manager) in the field. The use of questionnaire surveys remains dominant in capturing perceptions of whether and to what extent the use of Industry 4.0 technologies have led to improvements. While these studies go some way to identify the benefits of Industry 4.0 technologies, perhaps in a bid to persuade incumbents in the industry that adopting such technologies is a good thing, capturing perceptions falls short of the depth required to understand and improve the actualities of practice. Furthermore, studies such as Prabhakaran et al. (2021) and Ismail (2021) base their evaluations of whether the technology works or not on the basis of asking those “experts” who are already participating in the change. Thus, this bears the methodological limitations of capturing only the perceptions of a self-selecting group, i.e. the converted. The challenge remains as to how we can transcend preaching to the converted so that we move beyond small-scale pilot projects to make such transformation happen at a wider scale.

This is not to say that collecting the perceptions of practitioners is not useful. But, it is important to note that understanding what people think is not sufficient to drive the kinds of change that is needed if the widespread adoption of Industry 4.0 was desired. Moreover, it is worth noting that what people think, what people do and what people think or say they do are very different things (see, e.g. Marshall, 2014). To go beyond capturing perceptions and to change and improve what we do in the industry requires engaged scholarship. This involves “active participation in testing and exploring new ways of working” (Voordijk and Adriaanse, 2016, p. 549; see also Van de Ven, 2007). As Industry 4.0 is still emerging for the AEC industry, exploring new ways working together with the practitioners to co-produce actionable strategies (as Bhattacharya and Momaya would put it) would seem appropriate. To some extent, a number of papers in this special issue have covered that change that resulted from active participation between the researchers and practitioners, whether in terms of designing furniture or creating a computer-based maintenance management model. At the same time, while it may be useful for Das et al. (2021) to map out the business model transformation required for Industry 4.0 in the AEC industry, this was still based mainly on the views of academic “experts”. How these considerations and pathways translate in practice, and how to overcome the contradictions and challenges remains under-explored.

A further limitation lies in where the performance of Industry 4.0 is evaluated. These tended to focus on the incumbents, and mainly on the contractor. On the one hand, this focus on the incumbents is understandable and can perhaps be explained by the fixation with using Industry 4.0 technologies to boost productivity. On the other hand, while disruptors like Google have been mentioned (and certainly in Das et al., 2021), it is rare to find studies in our field that deepen our understanding of how these disruptors work in practice (for a recent exceptional case, see, e.g. Hall et al., 2020). Gaining access to these disruptors may, however, be easier said than done. The workings of some of these disruptors can at times be quite opaque since, in the increasingly data-driven world we live today, the ability to preserve the confidentiality of the algorithmic tools of the trade is a key to securing and sustaining one's competitive advantage (see, e.g. Zuboff, 2019). Still, as much as it is important to track the performance outcomes across the lifecycle of a built asset, from design to construction to managing the built facility, it is also important to trace the performance impacts across the whole ecosystem that includes both established incumbents and emerging disruptors.

In accounts of developments of Industry 4.0 in the AEC industry, the prevailing academic discourse can be characterised as one that emphasises what we humans can and/or must do to yield to Industry 4.0 technologies (see, e.g. Newman et al., 2020; Darko et al., 2020). This emphasis is also mirrored in the papers included in this special issue. In so doing, Industry 4.0 technologies are seen just as another tool that we humans use to produce the built environment. Yet, this downplays the question of material agency, and how technologies can also play an active part in challenging and changing our practices. In moving to a cyber-physical world where data collected and stored in the virtual space can automate and drive decisions with ramifications for the physical world, there is an increasing and urgent need to also pay attention to what non-humans can do to us humans and our built environment. In a study of how construction firms can change its business model from a product-based logic to a whole-life service-orientation, for example, Robinson et al. (2016, p. 16) argued that it is important to focus our attention on smart technologies, both hardware and software, not simply as tools but as a “constellation of emergent and existing technological features” that actively makes a difference.

Robinson's et al. (2016) study is notable since it addresses the question of novelty afforded by material agency. Put another way, they ask two interrelated questions of (1) what can the new technological features (in their case, sensors that afford intelligence on equipment failures) enable us humans to do that we cannot do or have not done before and (2) what can these features help us do what we have done before in different and better ways? On balance, and as demonstrated by the papers included in this special issue, construction management researchers have tended to focus on asking how new technologies can improve what we currently do. Yet, a key limitation lies with this focus is that studies have tended to narrowly analyse particular technologies in isolation. So, in Prabhakaran's et al. (2021) case, the focus is on virtual reality, and in Adinyira et al. (2021), the focus is on machine learning. This therefore raises the question of how we evaluate what happens when a constellation of technologies is at play? What configurations of different Industry 4.0 technologies work “best”, and in what contexts? Singling out particular technologies to analyse can lead to two problems. First, we miss the dynamic interactions between different technologies and how these interactions can yield better or worse results. Second, by overlooking the constellation of technologies available at hand, we may end up focussing on technologies or technological features that are (becoming) well-entrenched in the AEC industry, e.g. BIM (see Oesterreich and Teuteberg, 2016; Chan, 2020b).

Another consequence of treating technology as an active participant in shaping our practices (instead of just being another tool for humans to use) is that this can challenge our underlying assumptions about what it is we do. This deepening of the understanding of our underlying rationality is crucial if we were indeed motivated to address the question of what can technologies allow us to do that we cannot do or have not done before. Without understanding and addressing our underlying rationalities of practice, then the adoption of Industry 4.0 is unlikely to deliver the transformative benefits it promises. And there is some evidence to demonstrate this argument. In analysing the effects of the new digital economy, Van Ark (2016) found what he termed as the “productivity paradox”. To his surprise, he found that the most intensive users of digital technologies have accounted for a majority of productivity decline in the United States of America, the United Kingdom and Germany. Reflecting on this counter-intuitive finding, Van Ark (2016) surmised that technology itself can only bring about productivity improvements up to a point; unless the underlying rationality and business model changes, if we continue to do what we have always done, using new technologies will only make us do the same thing slightly faster for a period of time, but this does not guarantee sustained growth. Therefore, we need more studies that focus on the agency of technologies or technological features that challenge our prior assumptions and rationalities of what we do, in order to capture and make sense of how we are doing something that is truly novel.

Van Ark's (2016) analysis also highlights that Industry 4.0 has the potential to create negative impacts, a point that is often neglected (also in this special issue) but increasingly considered (see, e.g. Sherratt, 2020, and; Sherratt et al., 2020). Perhaps in the “installation phase” (Van Ark, 2016) of Industry 4.0, there is the push to find positive outcomes from adopting new technologies, so that incumbents can be persuaded to invest and deploy. Yet, this drive to emphasise the benefits may lead to the omission of two concerns. First, by making a case for technological deployment and writing off its low uptake as simply the result of the industry's resistance to change, we overlook the points on how and why the incumbents are so resilient. The fact that disruptors have struggled to strengthen their foothold on the industry beyond niche experiments and pilot case studies thus highlights a blind spot that is worth investigating – that is, how did the practices of the industry become so institutionalised that to change the regime of the incumbents is so difficult? Could this resilience be regarded as a strength rather than a weakness?

Machiavelli once remarked that the reason why it is so difficult to change the order of things is because those who stand to gain are not certain of their benefits, and those who stand to lose are most certain of their losses. Therefore, the second question that needs to be brought under the spotlight is: what and who is at stake here? Thus, it is interesting to note that in Ismail (2021), in comparison with the other responses, the eight experts surveyed reported that they were less comfortable in relinquishing their decision-making powers to the machine. In a similar vein, a quantity surveyor interviewed in Newman's et al. (2020, pp. 17–18) study also believed that “the role of quantity surveyor in managing tenders and contracts will be important as it is less autonomous”. These therefore hint at the potential losses that established actors might face when adopting Industry 4.0 in the AEC industry, and discounting these as simply resistance to change would not do much to help mitigate against these (perceived) negative impacts.

There is no doubt that the introduction of new technologies will have an impact on everyday practices in any industry. When asking the question of how susceptible human jobs are to computerisation, Frey and Osborne (2017) were surprised to find that even in a craft-based industry like construction that labour substitution is potentially high as jobs are automated away. In a recent essay, though, Fleming (2019) asked about the realities of automating jobs as he questioned the distribution of these (potential) impacts:

And for those whose jobs are not automated away in Industry 4.0, what are we left with? In a dystopian account of the machine taking over, Lindebaum et al. (2020, p. 248) argued that in the data-driven world of today, algorithms are “supercarriers of formal rationality.” This means that the calculative and calculable overwrite the sensibilities of human judgement in seeking efficiencies of standardisation. In the quest for greater convenience, our ability to exercise our human judgement yields to the ever-standardising control of the machine. In Qian and Papadonikolaki's (2021) study of how blockchain technologies can change the ways of building trust in construction supply chains, for instance, they found relational trust becomes less important as what matters more to their participants is cognition-based and system-based trust. The system may trump our human fragilities and foibles, but what these scenarios paint of the power of the machine runs counter to the founding principles of Industry 4.0 that Kagermann et al. (2013) had conceived. We are left thus with the question: is automating away the sensibilities of human judgement and of relational trust the desirable future that we imagine in an Industry 4.0 enabled AEC industry? One thing is for certain, at least for now, and that is we will still need both human and machine power to evidence the impacts of Industry 4.0 on the AEC industry.