# Investigating major challenges for industry 4.0 adoption among construction companies

Sevilay Demirkesen (Civil Engineering, Gebze Technical University, Kocaeli, Turkey)
Algan Tezel (Art, Design and Architecture, University of Huddersfield, Huddersfield, UK)

ISSN: 0969-9988

Article publication date: 29 April 2021

Issue publication date: 24 March 2022

1387

## Abstract

### Purpose

The purpose of this study is to explore the challenges hindering the adoption of Industry 4.0 (I4.0) among construction companies.

### Design/methodology/approach

The construction industry needs innovative technologies due to its complex and dynamic nature. In this respect, the latest trends such as digitalization, building information modeling (BIM), Internet of things (IoT) are of utmost importance in terms of fostering the change in managing projects and encouraging industry practitioners to adopt the change for better performance. This paper focuses on I4.0adoption among construction companies. In this respect, a questionnaire was designed and administered to construction professionals to reveal the challenges in I4.0 adoption among construction firms. The respondents were requested to fill in the questionnaire on the I4.0 efforts of their companies. The questionnaire was intended to collect the perceptions of industry practitioners working at large construction companies. Based on these, the challenges listed were ranked based on their relative importance and success indices. Finally, the Mann–Whitney U test was conducted to test whether statistically significant responses exist among groups of respondents (i.e. young and old companies, large and small, high and low revenue and main area of expertise).

### Findings

The results of the study indicated that resistance to change, unclear benefits and gains and cost of implementation are the major important challenges in terms of I4.0 adoption in construction projects. On the other hand, the data analysis implied that the majority of construction organizations successfully deal with the problems arising from lack of standardization, legal and contractual issues and cost of implementing in terms of promoting I4.0 adoption.

### Research limitations/implications

The study is expected to guide construction practitioners in terms of benefitting from I4.0 applications and deliver projects with better outcomes. This study might be used as a guide for the companies aiming to start their I4.0 transformation knowing the challenges and develop strategies for how to handle them. A concrete plan would help them achieve greater performance and benefit from the I4.0 implementation at the maximum level. Finally, the study implies that construction firms shall prepare action plans for handling each challenge listed and monitor their performance based on the planned and actual data of their projects.

### Originality/value

This study investigates the major challenges of I4.0 among construction companies. This is one of the important studies, which puts I4.0 focus forefront of the construction industry with a clear identification of challenges that construction organizations have to address to transform their organizations into construction 4.0. The study has the potential to guide both industry practitioners and researchers to develop awareness for the benefits of using the latest technology and fostering innovation. This is expected to create value for construction clients in terms of achieving the product with serious gains such as time and cost.

## Citation

Demirkesen, S. and Tezel, A. (2022), "Investigating major challenges for industry 4.0 adoption among construction companies", Engineering, Construction and Architectural Management, Vol. 29 No. 3, pp. 1470-1503. https://doi.org/10.1108/ECAM-12-2020-1059

## Publisher

:

Emerald Publishing Limited

## 1. Introduction

The manufacturing industry has already taken a step forward to create more effective production processes and increased customer satisfaction through adopting a fully digital approach (Osunsanmi et al., 2018). This digitalization process, interconnection, information transparency and technical assistance for human operators are part of the Industry 4.0 (I4.0) principles, which are expected to positively affect today's production processes (Hermann et al., 2016; Axelsson et al., 2019). I4.0 is a term used to represent a high-technology strategy articulated first by the German government referring to the development of “cyber-physical systems (CPSs) and dynamic data processes that use massive amounts of data to drive smart machines” (Strange and Zucchella, 2017; Sirkin et al., 2015). The latest technological developments and innovations fostered the evolution of I4.0, leading to growth and development in company performance (Maskuriy et al., 2019).

I4.0 is recognized as the reference point for the Fourth Industrial Revolution and several terms such as smart factory, smart production and smart manufacturing are used to define I4.0 in a broader sense (Drath and Horch, 2014; Oesterreich and Teuteberg, 2016). The main contribution of I4.0 is to facilitate computerization and interconnection in industries resulting in a production chain automatically and flexibly adapted as well as to come up with new service types and business models for the value chain (Liao et al., 2017; Lu, 2017). Having been adopted in various industries such as manufacturing, healthcare and software (Frank et al., 2019; Kumar et al., 2020; Demel et al., 2017), I4.0 also inspired the construction industry, which needs more efficient production chains and business models (Axelsson et al., 2018). This transformation is identified as “Construction 4.0” representing the digitalization of the construction industry (FIEC, 2015). I4.0 has already promoted the use of various digital technologies such as smart materials, sensor systems and intelligent machines in the construction industry. Among those, building information modeling (BIM) took the key role for the digital information of a project for creating and managing the digital information of an asset (Craveiro et al., 2019; King, 2018). However, the construction industry is conservative toward benefitting from the innovative technologies of I4.0 despite the benefits mentioned in previous studies (Hampson et al., 2014). Previous studies indicated that the construction industry is way behind in implementing new technologies on time (Hargaden et al., 2019; Klinc and Turk, 2019). Moreover, it was further mentioned that the construction industry is changing its target from mass production to consumer-specific products, which are easier to control with the use of I4.0 principles (Klinc and Turk, 2019). Also, the construction industry has a fragmented nature consisting mostly of small to medium enterprises (SMEs), which necessitates a considerable effort for coordination. Furthermore, SMEs have limited resources to foster themselves for innovative technologies (Arayici and Coates, 2012; Dallasega et al., 2018). The studies in the construction industry reveal that only a small portion of construction companies are capable of achieving the complete use of digital tools (Dallasega et al., 2018). This stems from the fact that the industry is slow to uptake innovations (Newman et al., 2020). Another reason is that the construction companies have recently been practicing BIM and focusing on BIM applications thanks to knowledge gained through digital models. Therefore, industrialization in the industry is mostly shaped around BIM technology (Li and Yang, 2017). It was further mentioned that there is no sound organizational strategy for implementing I4.0 (Sony and Naik, 2020) such as training and supporting employees with their professional development (Agostini and Filippini, 2019). In the lack of such practices at the organizational level, employees fail to sustain information flow and develop skills for innovation. Even though the manufacturing industry has recently proven serious effort toward implementing and benefitting from digitalization (Zhong et al., 2017), the construction industry still suffers from the reluctance toward such adaptations (Newman et al., 2020). Therefore, the construction industry is not in a comparable position to some other industries such as manufacturing, which greatly benefits from the digitalized value chains enabled by I4.0 (Lasi et al., 2014). This proves that the construction companies need guidance for transformation to I4.0.

## 2. Literature review

### 2.1 I4.0 definition and its adoption among industries

I4.0 is a broad term presenting a new stage in the Industrial Revolution, which focuses on automation, real-time data, machine learning, interconnectivity and smart digital technologies (EPICOR, 2019). Baur and Wee (2015) define I4.0 as a “confluence of trends and technologies promises to reshape the way things are made”. It is also defined as “a new technological age for manufacturing that uses CPSs and Internet of things (IoT), data and services to connect production technologies with smart production processes” by the German government (Kagermann et al., 2013; MacDougall, 2014). I4.0 helps improve manufacturing organizations in terms of their business models and production processes through cyber-physical technologies. Hence, it is perceived as a way of revolutionizing industries such as manufacturing, energy, healthcare and urban areas/frameworks like the built environment. Fargnoli and Lombardi (2020) mentioned that I4.0 technologies such as the IoT, autonomous robots and vehicles, simulation, blockchain, cybersecurity and virtual reality have a considerable impact on competitiveness regarding production technologies, financial performance and workforce empowerment. Considering the benefits provided by I4.0, the construction industry also has the opportunity to create more efficient production processes, business models and value chains through I4.0. This is possible through the transforming technologies and trends brought by I4.0. The technologies promoted by I4.0 such as BIM, prefabrication, wireless sensors, 3D printing and automated and robotic equipment (Buehler et al., 2018) might act as a catalyst for the more industrialized and automated construction industry (Sawhney et al., 2020).

### 2.2 I4.0 adoption in the construction industry

The construction industry is less controlled and fragmented compared to the manufacturing industry (Harvey, 2003). The temporary nature of construction projects also lacks standardization of the processes, which leads to lower levels of productivity (Dubois and Gadde, 2002; Stehn and Höök, 2008). However, increasing the productivity in construction projects and reducing the uncertainty is possible through adopting the I4.0 principles. Within this context, digital access, automation, connectivity and digital data are key to overcome the challenges posed by the industry (Dallasega et al., 2018). Alaloul et al. (2020) study the opportunities and challenges brought by the I4.0 implementation in the construction industry from a stakeholders' perspective. They mentioned that social factors such as cultural habits and technical factors are the main barriers hindering the adoption of I4.0 in construction. They further implied that the construction industry is still lacking the implementation of I4.0 despite its numerous benefits proven so far. García de Soto et al. (2019) worked on the implications of Construction 4.0 on the organizational structures and workforce. They highlighted that transforming organizations for Construction 4.0 has the potential to reduce the workforce but leads to a safer and less labor-intensive work environment for the construction workers.

Given this background, this study defines Construction 4.0 as the translation of I4.0 into construction with introducing digital technologies. However, as indicated by several studies, the implementation of new technologies in construction is slow (Klinc et al., 2010; Hargaden et al., 2019; Klinc and Turk, 2019). Since a major portion of studies focus on heavily implemented technologies such as BIM or construction automation, some other technologies such as IoT, CPSs or cloud computing are not well considered in the construction industry (Hossain and Nadeem, 2019), and there is not yet a systematic framework or analysis of potential challenges for I4.0 transformation that might encourage construction companies to start the smooth transition to I4.0 technologies. The literature still lacks providing a complete assessment of factors limiting the adoption of I4.0 in the construction industry, and yet an approach is not available for use. Hence, it is apparent that the industry needs a clear guide to facilitate this transformation and fasten the adaptation to I4.0, considering the proven benefits. This clear guide should include a complete set of challenges for I4.0 that the companies recognize them to revise their strategies accordingly for a full and successful transformation. Thus, it is of utmost importance to reveal the challenges of adopting I4.0 for improved performance in construction projects. To fill this gap, this study presents a comprehensive list of challenges hindering construction companies from adopting I4.0. The study also discusses these challenges in detail, so that companies can develop actions to improve processes, increase productivity and smoothen process integrations.

### 2.3 Challenges of I4.0 adoption among construction companies

Based on an extensive literature review, an initial list comprising 17 challenges was identified regarding the I4.0 adoption among construction firms. These challenges determined were “cost of I4.0 transition”, “resistance to change”, “lack of management support for I4.0 transition”, “lack of skilled labor”, “lack of standardization”, “data protection and cybersecurity”, “uncertainties in benefits and gains in terms of labor and workforce”, “fragmented and conservative nature of the construction industry”, “poor knowledge management”, “poor planning and programming practices”, “high accident rates in the construction industry”, “poor communication with project parties”, “legal and contractual issues”, “reluctance in investing in research and development (R&D) projects”, “cost of training for technology adoption”, “low technical know-how of construction professionals” “lack of performance standards for employees”. However, it was observed that some of the challenges are either referring to common problems or explaining similar situations. Hence, some challenges were either merged or eliminated to come up with a concise and clear list of challenges. In the final list, a total of nine challenges were identified for I4.0 adoption among construction companies. Table 1 lists these nine challenges as the major barriers to I4.0 adoption at construction companies.

The first challenge identified is the cost of implementation, which is troublesome for companies aiming to achieve maximum benefits from the I4.0 transformation. Alaloul et al. (2020) implied that the implementation of an innovative technology brings along a considerable cost burden. Moreover, the cost of training and equipment maintenance, which are hidden costs, also leads to a challenging implementation process along with the uncertainty associated with the return of investment. This hinders the investment in innovative technologies by the construction companies, which hesitate to adopt I4.0 due to the high investment costs and uncertainties of the benefits (Oesterreich and Teuteberg, 2016).

Construction is a fragmented and conservative industry (Ahmad et al., 1995; Nam and Tatum, 1997). This leads to a lack of willingness to adopt new technologies and innovations, which in return creates resistance to change. Chan et al. (2019) implied that the major barriers for BIM adoptions stem from the fact that construction stakeholders are resistant to change emphasizing that the lack of standards for BIM hinders companies to adopt change in the Hong Kong construction industry. Hemström et al. (2017) further indicated that the contractors in Sweden are particularly resistant to change to remain successful in the industry, which is dominated by a few large contractors. Therefore, the companies fail to fully adopt and understand the benefits of the I4.0 transformation due to resistance to change toward such adaptation. Hence, the resistance to change is an important challenge that the companies must address before starting the I4.0 transformation.

Lack of labor force is a serious concern for the construction companies aiming to invest in new technologies and use digitalization confidently. Hewage et al. (2008) investigated the IT usage in the building projects in Alberta, Canada. They emphasized that a shortage of labor force leads managers to doubt whether the available labor force is confident in utilizing modern information technologies. They further explained that construction companies are unwilling to welcome the change driven by new technologies when the labor force is not sufficient. Doloi et al. (2012) mentioned that lack of skilled labor leads to poor labor productivity, which oftentimes results in delays in Indian construction projects. Hence, it becomes troublesome for construction companies to start a transformation process for I4.0 where the labor force is unstable and lacking.

Unclear benefits and gains from I4.0 are a major cause for the unwillingness toward welcoming change and investing in innovative technologies. Hence, the construction companies are reluctant to adopt new technologies due to uncertainties in benefits and gains to be brought by this adoption (Davies and Sharp, 2014; Oesterreich and Teuteberg, 2016). Luthra and Mangla (2018) further mentioned that most industries are hesitant in adopting I4.0 due to ignorance of the potential benefits. Therefore, it is a serious concern for the construction companies not to have a definite plan for the unknown benefits and gains. This necessitates the construction companies to develop strategies, after thoroughly evaluating the potential gains and losses after the I4.0 adoption.

The budget spared for research and development (R&D) in the construction industry is relatively low compared to other industries (Zhang et al., 2010). Ofori (2003) mentioned that Singapore's construction industry invests in construction R&D less than other developed countries and other sectors. In another study conducted by Ling et al. (2006), it was emphasized that architecture–engineering–construction (AEC) firms investing in R&D are more likely to experience budget overruns for the fact that the R&D investment generates cost increases in the short term and does not yield returns fast. On the other hand, Kim et al. (2009) implied that interdisciplinary R&D programs conducted by academia and other entities are promising for promoting the use of advanced technologies in construction. This indicates that the construction companies are still struggling with the decision of investing in R&D and have not become yet sure whether the investment cost will be compensated with the exclusive benefits in the long term. This leads to the discussion that the lack of investment in R&D is still a challenge for the industry aiming to increase benefits with the adoption of I4.0.

The fragmented and project-based nature of the construction industry hinders many companies from investing in digitalization and welcoming innovations (Elmualim and Gilder, 2014; Chowdhury et al., 2019). Lavikka et al. (2018) mentioned that the fragmented nature of the industry creates knowledge boundaries leading to challenges in communication and collaboration. This results in poor adoption of new technologies and unperceived benefits of the I4.0 implementation. Yap et al. (2019) explored the criticality of the construction industry problems in Malaysian construction projects. They concluded that the fragmentation in projects leads to poor project performance, low productivity and reluctance toward implementing innovative solutions. One might assert that construction companies are less likely to change their focus toward becoming more technological organizations adopting the innovative solutions driven by I4.0. Hence, the fragmentation and project-based nature of construction projects are major challenges in terms of the I4.0 adoption.

Lack of standardization is a major problem in construction projects. Wang et al. (2016) mentioned that engineering construction standards have not been successfully adopted in the Chinese construction industry due to a lack of standardization talents. Moreover, Thunberg and Fredriksson (2018) implied that temporary organizations suffer from a lack of standardization in processes and lack of information sharing. The temporary nature of construction projects is therefore leading to unstandardized processes hindering construction companies from setting up standard procedures for operations. Gamil and Rahman (2019) listed lack of standardization as a challenge for the BIM implementation in the Yemenese construction industry. It is apparent that the construction companies struggle with standardizing processes, and this leads to an unwillingness to adopting I4.0. Therefore, lack of standardization is listed as an important challenge for the I4.0 adoption among construction companies.

Data protection and cybersecurity is a major concern for the construction companies. Mantha and de Soto (2019) implied that the AEC industry has already been experiencing cyberattacks such as stealing private information, accessing unauthorized files and remove records. The expansion of digital platforms on construction sites and transformation toward I4.0 are also expected to increase the risk of cyberattacks in the construction industry (Patel and Patel, 2020). Therefore, construction companies must work toward developing their infrastructure and organizational structure in terms of handling cybersecurity risks. However, there is not yet an available standard to develop a procedure for identifying such risks in the construction industry (Mantha and de Soto, 2019). Hence, data protection and cybersecurity are listed as an essential challenge for the I4.0 implementation in the construction industry because the construction companies do not still have a definite plan or procedure to follow for protecting digital assets.

Legal and contractual procedures might be problematic for some construction projects for the fact that contracts either do not explicitly indicate project terms and clauses or there are vague statements in terms of legal aspects. For example, Oesterreich and Teuteberg (2016) emphasized that there are several legal and contractual uncertainties in regards to the use of BIM indicating that legal ownership of the BIM model and legal responsibility of errors with the model remain unanswered. In another study, Abubakar et al. (2014) implied that legal and contractual constraints lead to reluctance toward BIM adoption in the Nigerian construction industry. Jo et al. (2018) further revealed that legal and contractual issues are among the critical barriers for BIM implementation in the Malaysian construction industry. Li et al. (2019) stated that there are several aspects associated with untested legal issues and clear contract terms in the construction industry to reduce the likelihood of risks leading to unintended obligations and disputes. This reveals that the construction companies must first work on strategies and sound plans for removing the conflicts in terms of legal and contractual issues for the I4.0 adoption. This would lead to a more successful transformation process and digitalization within the organization.

Given this background, Table 1 summarizes the challenges identified for the I4.0 adoption in the construction industry.

## 3. Research methodology

In this study, a mixed-method of research was assessed to collect both qualitative and quantitative data. Figure 1 summarized the research process developed for this study.

### 3.1 Identification of challenges for industry 4.0 adoption

As the first step, the study identified major challenges for the I4.0 adoption in the construction industry. In the first stage, a total of 17 challenges for I4.0 in construction were identified from the literature. However, after conducting a pilot study with experts from the industry, some of the challenges were either merged or removed as they represent similar challenges. The final list consists of nine major challenges (as seen in Table 1).

### 3.2 Pilot study and questionnaire design

The questionnaires are valuable and reliable methods of data collection when questionnaire items are properly operationalized. Using both qualitative and quantitative approaches in data collection is a recommended method for research involving human actions, behavior in construction processes, leadership and planning (Abowitz and Toole, 2010). Baker (2003) mentions that questionnaires are effective ways of data collection thanks to the speed of completion, analysis and accuracy. Fife-Schaw (2000) implied that the questionnaires are the most common research tools with their advantages provided to researchers such as simplicity, versatility and low cost. Therefore, data collection through a questionnaire was preferred over other methods in this research.

The specified challenges are evaluated through a questionnaire designed and administered to industry practitioners. The results are presented and discussed in the sections below. In the first stage, a questionnaire was developed and administrated to construction professionals operating in the US construction industry. Before designing the final questionnaire, a pilot study was conducted with five practitioners from the construction industry, who have experience with the I4.0 adoption within their organizations and three university professors working on the I4.0 transformation at construction companies. Conducting a pilot test is an effective means of establishing the reliability of a questionnaire (Radhakrishna, 2007). A pilot study is also utilized to test whether any flaws exist in the measuring instrument leading to validity assessment (Srinivasan and Lohith, 2017). Based on the analysis of the pilot studies, the questionnaire items were redesigned, where some of the questions were either deleted or revised as per the feedback provided by the experts. This exploratory approached led to an explanatory questionnaire design.

### 3.3 Semistructured interviews

In the second part, semistructured interviews were conducted with industry practitioners, who are experienced in the I4.0 adoption in the construction industry. The basis for utilizing the mixed-method approach is to guarantee participant enrichment, ensuring instrument fidelity, assessing treatment integrity and enhancing significance (Collins et al., 2006). Conducting semistructured interviews provide a well-set interaction between a researcher and participant focusing on the research items effectively, and these interviews are among the primary methods utilized in qualitative research (Schultze and Avital, 2011; Oltmann, 2016; Mahat-Shamir et al., 2019).

The sampling entails the general contractors listed in the 400 Top Contractors by Engineering News-Record (ENR) in 2019. Different construction sector groups are operating in the United States having different roles in the projects such as clients, consultants, independent contractors, general contractors and subcontractors. Among these, ENR presents a top owners, top contractors and top design firms list every year. ENR is a weekly news magazine published in the United States covering the news and data regarding the construction industry worldwide (Jones et al., 2010). The reason why the 400 Top Contractors list was selected as the sampling is that ENR lists the contractors based on their contracting revenue. According to data presented on the ENR website, the companies listed in the 400 Top Contractors in 2018 generated a revenue of $405 bn in 2018, where this was$373.98 bn in 2017 (ENR, 2020). This indicates that these companies on the list generate most of the national construction value and shows their extensive presence in the industry. The Global Powers of Construction report also listed 13 companies from the United States in 2019 in the Top 100 ranking emphasizing the largest contractors' increase in sales, which were also listed in the ENR's list (Deloitte, 2020). Moreover, Lu (2014) assessed the reliability of ENR data in his research and concluded that the ENR data might be confidently used in international construction research for the fact that there are no systemic errors in the data.

### 3.4 Assessment of questionnaire validity and reliability

A nonprobability sampling approach was adopted along with the convenience and snowball techniques with stratification as mentioned in various studies (Bagaya and Song, 2016; Ling and Khoo, 2016; Yap et al., 2019). These techniques are commonly utilized in construction research to obtain significant responses from industry practitioners (Bagaya and Song, 2016; Abowitz and Toole, 2010; Yap et al., 2019). Semistructured interviews were conducted with the industry practitioners to increase the response rate. Initially, a total of 111 responses were received, but it was detected later that there were some nonresponse items in the returned questionnaires. Therefore, the questionnaires having nonresponse items were eliminated for a more reliable analysis. A two cycled data collection was conducted. In the first cycle, a total of 59 responses were collected by the cutoff date of December 17th, 2019. However, the response rate was not evaluated to be satisfactory for analysis and generalizability. Therefore, a second cycle was commenced. Finally, 89 responses were collected by the final cutoff date of January 17th, 2020 out of the 400 surveys sent out, resulting in a response rate of 22%. This response rate was found to be satisfactory after a careful review of the questionnaire data since mostly high revenue-generating companies had responded to the questionnaire without nonresponse items. Considering the high volume of work undertaken by high revenue-generating companies, the results could be generalizable for the US contractors since low revenue-generating companies generally adapt their strategies through benchmarking large companies. Moreover, similar studies reported approximately similar response rates emphasizing that the response rate is acceptable and satisfactory for a reliable analysis (Chen et al., 2010; Demirkesen and Arditi, 2015).

A one to five-point Likert scale (1 = not important, 2 = slightly important, 3 = moderately important, 4 = very important, 5 = extremely important) was adopted for the assessment of the challenges in terms of evaluating their level of importance. Moreover, the level of success in tackling those challenges was also assessed based on a one to five-point Likert scale, where 1 represents “poor” and 5 represents “excellent”. To assess the reliability of the survey, the Cronbach's alpha value was investigated as the most common measure used for internal consistency and reliability (Litwin, 1995). Values of Cronbach's alpha greater than 0.7 represent acceptable reliability in SPSS (Bolarinwa, 2015). The Cronbach's alpha value was calculated as 0.911 utilizing SPSS leading to the conclusion that the questionnaire is reliable. A pilot study was also employed before distributing the survey to reinforce the reliability. Content validity was assessed by examining the skewness of the distributions. The skewness ranged between −0.11 and + 0.02 indicating that the frequency distribution of scores is quite symmetrical and not considerably skewed. The values of kurtosis were also assessed, and it was observed that the kurtosis values ranged between −1.03 and 2.13. Skewness and kurtosis values are calculated to observe the nonnormality of data distribution, and it was revealed that the data are normally distributed since the values of skewness and kurtosis lying between −2 and +2 are acceptable to prove a univariate normal distribution (George and Mallery, 2003). Convergent and discriminant validity were also assessed. The average variance extracted (AVE) values were all above 0.7 indicating the questionnaire items were adequately convergent valid measures (Fornell and Larcker, 1981).

### 3.5 Data analysis based on different respondent groups

Furthermore, the questionnaire data were analyzed based on different response groups utilizing the Mann Whitney U test in SPSS. The Mann–Whitney U test was preferred over a parametric test such as analysis of variance (ANOVA) since the parametric tests consider that the observations in the samples follow a normal distribution. Zimmerman (1987) mentioned the comparative power of the Mann–Whitney U test for unequal sample sizes and variances compared to other tests for data analysis. Erceg-Hurn and Mirosevich (2008) stated that the Mann–Whitney U test is a robust statistical method to increase the accuracy and power of the research studies. Hence, this test was utilized for analyzing the subgroups for the fact that it provides accurate results for comparison purposes.

Since the sample of 89 observations in this study is divided into smaller sub-samples (i.e. old vs young, building vs infrastructure contractors and small and large employers) for comparison purposes, it is safer to assume that the fewer observations in the smaller sub-samples are not normally distributed. Therefore, the Mann–Whitney U test, which is conducted when samples are not normally distributed, was utilized in this study as a nonparametric test. Moreover, the sub-samples might justify why small employers or large employers struggle more with the I4.0 adoption in case there are significant differences in responses. The age of the companies and their main business area might also be considered significant parameters in terms of comparing either the perceptions of these firms about the I4.0 adoption or detecting the different approaches in terms of handling the I4.0 challenges. A sample questionnaire can be found in Appendix.

The respondent characteristics were analyzed to better interpret the results. Figure 2 presents the percentages of respondents by their positions, and Figure 3 presents the companies' main business activity. According to Figure 2, the majority of the respondents are project managers working at large-size companies. According to Figure 3, a major portion of the responding companies are executing building projects.

More respondent characteristics were sought such as the companies' years of experience in construction, annual turnover and the number of employees. Table 2 reflects the respondent and company characteristics.

The responses were grouped according to the companies' years of experience in the construction industry, annual turnover, company employment size (number of employees), respondents' age and respondents' years of experience in the construction industry. The average years of experience of the responding companies were found to be 26 years, which is a significant amount of time in terms of operating in the industry. Years of experience is also a key factor for the growth of a construction company. The average annual turnover of the companies was found to be 403 m. This is not surprising because the companies selected for the questionnaire are in the ENR Top 400 Contractors list, which is an indication of high returns. The average number of employees was found to be 3703, representing a high employee volume. The respondents' average age and years of experience in the construction industry were found to be 34 and 7.4 years, respectively. The type of construction projects undertaken by the companies was also considered to be an essential variable for investigating the companies' I4.0 adoption. It was found that 57% of the responding companies are undertaking building construction projects, whereas 39% are pursuing infrastructure facilities. The remaining 4% indicated that they mostly construct water structures. ## 4. Data analysis and results In this study, a total of 17 challenges were identified for the adoption of I4.0 among construction firms through an in-depth literature review. After conducting pilot studies with industry experts, the challenges were reviewed, and nine challenges were identified in the final list. The industry experts also provided their feedback during pilot studies for the questionnaire, which was administered to construction professionals. To increase the response rate and content validity of the questionnaire, semistructured interviews were conducted with construction professionals aiming to promote their I4.0 adoption and practice I4.0 technologies in their companies. Semistructured interviews provided that construction professionals have a hard time adopting I4.0 technologies due to several reasons such as lack of management support for I4.0 transformation, budget concerns and lack of trained employees for such transition. The reliability and validity of the questionnaire were assessed by evaluating Cronbach's alpha and a set of validity and reliability measures such as skewness, kurtosis and AVE, which all resulted in the acceptable values. The first section of the questionnaire administered in the study was intended to collect information regarding the importance level of each challenge identified for the I4.0 adoption. To rank the challenges, the relative importance index (RII) method was used to quantify the relative importance of the I4.0 challenges in the US construction industry. This method has already been applied in various construction research studies to determine the relative importance of different items and was mentioned as an effective way of ranking factors (Kometa et al., 1994; Sambasivan and Soon, 2007; Gündüz et al., 2013). The RIIs for the I4.0 challenges were calculated following Eqn (1) presented below. (1)RII= W(AN) In Eqn (1), RII represents the relative importance index, where W = weighting given to each challenge by the respondents (from 1 to 5; 1 refers to the lowest and 5 refers to the highest); A = highest weight and N = total number of respondents. The RII values ranged between 0 and 1, where the values approaching 1 represent more importance. The RIIs were then ranked, and the results are presented in Table 3. According to Table 3, “resistance to change” is ranked as the most important challenge hindering the I4.0 adoption. Moreover, “unclear benefits and gains” and “cost of implementation” are also ranked as very important challenges for the I4.0 adoption by the respondent companies. “Lack of standardization”, “fragmented and project-based nature of the industry” and “lack of labor force” are ranked as important and moderately important challenges based on the responses. Finally, “lack of investment in research and development” is ranked as moderately important, whereas “data protection and cybersecurity” and “legal and contractual problems” are ranked as less important, compared to the other challenges based on the assessment of relative important indices. As provided above, the semistructured interviews with the industry professionals have provided a priori evaluation regarding the importance rank of challenges. As stated by several industry experts, resistance to change especially by the management level was articulated as an important barrier to start I4.0 transformation within an organization. An interviewed expert explained that I4.0 adoption is difficult in their organization because they have a hard time standardizing their internal and external documents along with the quality management processes. He further added that organizations are oftentimes reluctant toward investing in I4.0 technologies due to unforeseen conditions and uncertainties in gains. Even though “data protection and cybersecurity” and “legal and contractual problems” challenges were ranked less important by the respondents, this cannot be translated as these challenges are not important to consider instead it reflects that organizations are more concerned with creating an environment open to change and innovations. The questionnaire also assessed the level of success in terms of tackling the above-listed challenges. This assessment was done by considering the success level index using the mean score approach. Similar methodological approaches were adopted in various studies in construction research before (Yeung et al., 2009; Ahadzie et al., 2008; Osei-Kyei and Chan, 2018). To compute the success indices, the “mean score” method was utilized as perceived by the contractors. The five-point Likert scale (1 = unsuccessful and 5 = very successful) was used to calculate the mean scores for each challenge, which were then used to determine its success ranking in descending order. The mean score (MS) for the challenges was computed by Eqn (2), where s = score given to each challenge by the respondents, ranging from 1 to 5 (1 = unsuccessful and 5 = very successful); f = frequency of each rating (1–5) for each challenge and N = total number of responses concerning a particular challenge. (2)MS= (f×s)N,(1MS5) The success level indices of the challenges are presented in Table 4, reflecting the contractors' success level in handling each challenge based on the responses provided. An evaluation scale of 1–5 was used to comment on the responses. According to the research conducted by Ahadzie et al. (2008), a success criterion is critical if it has a mean score of 3.5 or more. The research also implied that when two or more criteria have the same mean, the one having the lowest standard deviation must be assigned the highest importance ranking. Based on the results, Table 4 indicates that the responding companies are relatively more successful in handling the lack of standardization challenge for their I4.0 adoption. Moreover, the companies also reported that they are successful in overcoming the legal and contractual problems, where that challenge is not as critical as lack of standardization. The companies responded that they are fairly successful in tackling the cost of implementation, data protection and cybersecurity, lack of investment in research and development and unclear benefits and gains challenges for the I4.0 adoption. On the other hand, they reported that they are unsuccessful in handling the resistance to change and lack of labor force. Even though the fragmented and project-based nature of the industry was found to be an important challenge for the I4.0 adoption based on the RIIs, the companies are reported to be least successful in handling this challenge by the success index. In the semistructured interviews, the experts stated that their organizations are considering standardization more importantly than other barriers of I4.0 adoption. The success level indices of I4.0 challenges provided that the organizations are showing a stronger reaction toward standardization and develop ways to handle the lack of standardization. Therefore, they were found to be more responsive to standardization. Second, respondents provided that they are successfully dealing with legal and contractual problems for the fact that construction projects require a detailed analysis of legal and contractual issues especially when they are overseas. As disclosed in the majority of semistructured interviews, the organizations expressed that they are not as successful as in cost of implementation, data protection, cybersecurity, lack of investment in R&D and unclear benefits and gains challenges for the I4.0 adoption compared to standardization, and legal and contractual issues. This is because most organizations still consider that they are not ready for I4.0 transformation in the existence of resistance to change and budget concerns along with the unclear benefits and gains. In the final stage of data analysis of the questionnaire, the respondents and companies were grouped into smaller sub-samples such as old vs young, building vs infrastructure contractors and small and large employers to test the differences between the groups. The differences in ratings were analyzed using SPSS. The Mann–Whitney U test was applied to test whether the differences between the groups are statistically significant at α = 0.05. The Mann–Whitney U test was selected as the statistical testing tool for the fact that it is a nonparametric test (Rees, 2011). Considering the parametric tests' limitation in assuming the observations in samples follow a normal distribution, the nonparametric Mann–Whitney U test was determined to be the most appropriate test to compare the groups. Hence, it is safer to assume that the fewer observations in the smaller sub-samples are not normally distributed. The companies were also grouped by their level of experience/years of operation (old vs young), by their operational area (building vs infrastructure) and by their size (small vs large) and turnover (high annual turnover vs low annual turnover) to test whether there are statistically significant differences among the identified groups by the Mann–Whitney U test. Table 5 shows the results of the Mann–Whitney U test based on the response groups set. Table 5 shows the companies' success level in tackling those challenges by the analysis of the groups. The success level was analyzed for different groups as recommended by several experts in the semistructured interviews. This was proposed for the fact that I4.0 perception might differ significantly based on organizational measures such as turnover, the experience level of the company in the industry and the main business area. According to the table, there is a significant difference between the younger and older companies in terms of handling the lack of standardization, where younger companies are more successful in handling lack of standardization with an average value of 4.2. Moreover, a significant difference in the responses is observed between the companies having higher and lower annual turnover for the cost of implementation challenges, where high annual turnover generating companies are more successful to meet the cost of implementing for I4.0 with an average value of 3.7. The cutoff value for dividing the old and young companies into two groups was determined to be 50 years in operation with the analysis of average values for company age. Since there was no threshold proposed in the literature for such categorization, the average age (years of operation of the company in the construction industry) was calculated for the responding companies as 50.36 years. Hence, the responding companies having an age of less than 50 were evaluated as young companies, whereas the responding companies having an age of higher than 50 were evaluated as old. On the other hand, young companies were further grouped into ages of 0–10, 10–20, 20–30, 30–40 and 40–50 for more rigorous analysis and accurate evaluation of different age groups' experience toward I4.0 adoption due to dynamic nature of the industry. A similar division criterion was applied for the other groups identified. Finally, the success in tackling the resistance to change challenge is responded to differently by the younger and older companies, where the younger companies report that they better handle this challenge than the older companies. The results indicate that there are some significant differences among different response groups in overcoming I4.0 adoption barriers. A detailed discussion regarding the significant differences among the different response groups is provided in the findings and discussion section. ## 5. Findings and discussion The analysis of the questionnaire revealed interesting results worth further discussion. The respondents reported that the resistance to change is the most serious concern for the adoption of I4.0 with an RII of 0.892. This stems from the fact that companies either perceive I4.0 transformation as challenging or consider the high cost of implementation as a serious burden for creating an environment open to change and innovations. Several studies have already reported that resistance to change is a critical barrier for technology adoption specifically for BIM tools, digitalization and automation in the construction industry (Stewart et al., 2004; Khosrowshahi and Arayici, 2012; Oesterreich and Teuteberg, 2016; Matarneh and Hamed, 2017). Considering the conservative nature of the industry, it is not surprising that companies put resistance to change as the most important barrier in front of I4.0 adoption. Moreover, the respondents indicated that they cannot effectively tackle the resistance to change at their organizations (success level index: 2.14). Especially, the resistance to change has been reported to be a common significant challenge for different construction industries such as Hong Kong and Sweden. For example, Chan et al. (2019) implied that the resistance to change is heavily observed in the Hong Kong construction industry in terms of BIM adoption by construction stakeholders emphasizing that proper standards are lacking for successful adoption. Hemström et al. (2017) implied that the resistance to change exists in the Swedish construction industry for the fact that companies are reluctant toward adopting the change brought by the technology for staying competitive within the market with a traditional structure. Hence, it is apparent that the resistance to change is also a critical barrier for US construction companies. Therefore, the companies first need to address this barrier to prepare their organization for a successful I4.0 transformation (Ozumba and Shakantu, 2018). To resolve this issue, the companies must work toward developing a changing culture or offer training to prepare themselves for a smooth transition. In this context, changing organizational culture toward adopting innovations is a critical task for the companies aiming to transform their organizations following I4.0 principles. The analysis of the statistical tests indicated that the responses for the resistance to change differed between the younger and older companies. The younger companies indicated that they perform better while tackling the resistance to change than the older companies. This stems from the fact that the young individuals in an organization better respond to changes and adopt the process of change more easily than older do (Neiva et al., 2005). The younger organizations are more able to welcome change and take the risk in terms of investing in new technologies. Hence, it is not surprising that younger companies reported that they handle resistance to change than older ones. Moreover, the companies were further divided into subgroups in terms of their age since younger companies might perceive I4.0 differently than older companies. The companies were grouped based on their ages for the intervals of 0–10, 10–20, 20–30, 30–40 and 40–50. Some significant differences were observed in responses between companies having an age of between 0 and 10 and 30 and 40. The younger companies (age of 0–10) reported that they better handle the resistance for change, lack of standardization and fragmented and project-based nature of the industry, which hinder the I4.0 adoption. This is because younger companies are more open to change in terms of innovating their practices when they are aiming for high growth (Czarnitzki and Delanote, 2013). Younger companies are also more on the side of creating younger teams eager to learn new things, invest in promising technologies and turn risk into gain. Hence, younger companies resist changes lesser than older companies, which in return results in relatively easier change management for I4.0 at younger companies. On the other hand, Hemström et al.'s (2017) study revealed that Swedish contractors are not willing to adopt I4.0 regardless of the firm age, where the industry is dominated by a few contractors. Unclear benefits and gains were listed as another important challenge for I4.0 by the respondents with an RII of 0.878. One clear statement that was made during semistructured interviews in this study pointed out that most organizations do not want to invest in new technologies unless they are certain about benefits and gains. The fragmented nature of the construction industry also hinders a complete assessment of net gains and benefits, where most stakeholders are more concerned with tight schedules and budgets leading to reluctance toward innovations. As evidenced by several studies, the companies are unwilling to invest in new technologies and welcome change unless they are certain or better informed about the benefits and gains brought by the potential investment (Davies and Sharp, 2014; Lee and Lee, 2015). As revealed by Luhtra and Mangla (2018), ignorance of the potential benefits brought by the adoption of I4.0 is a serious problem not only for the construction industry but also for several other industries. However, the construction industry itself has a project-based nature, which is rendering this even more challenging for careful consideration of the benefits and gains achieved through the I4.0 adoption since project teams and project characteristics are dynamic. Hence, the I4.0 adoption becomes challenging when the companies are not capable of foreseeing or forecasting its benefits and gains. Moreover, the respondents reported that they are not quite successful in handling the unclear benefits and gains in terms of the I4.0 adoption (success level index: 2.64). This stems from the fact that the industry is reluctant toward adopting innovations and creates an environment open to change. This hinders individuals in organizations from focusing on real benefits and gains brought by the new technologies and systems. Moreover, the uncertainties and changes in construction projects hinder the practitioners' capability of estimating those potential benefits and gains (Lechler et al., 2012). Therefore, the companies and researchers first need to develop ways for eliminating the uncertainties and better estimating the benefits and gains from the I4.0 adoption. A successful forecast of the benefits and gains toward the I4.0 transformation could help construction companies devise new strategies in time, cost or quality management practices. This would in turn result in enhanced performance in processes and experience less wasteful activities. Cost of implementation is rated as another important challenge for the I4.0 adoption by the construction companies with an RII of 0.792. Since the construction industry is dynamic and complex in nature, the cost of implementing new technology is a risk for the majority of companies. The perception of the high implementation cost of investing in new technology is the major barrier in front of construction companies to adapt to advanced technologies. A significant comment by the industry experts during semistructured interviews was that construction organizations are not sure about whether they are going to experience higher gains from adopting new technologies. Moreover, it was further mentioned that the organizations are skeptical of high returns when investing in new technologies. Hence, the companies approach innovative technologies with caution due to the cost burden that the technology may cause. The cost of implementation as a barrier for the I4.0 adoption has already been highlighted in various studies (Oesterreich and Teuteberg, 2016; Uhlemann et al., 2017). Even though the study of Alaoul et al. (2020) indicated that innovative technology causes a significant cost burden for organizations, the return on investment is oftentimes disregarded. However, the adoption of I4.0 in the construction industry, also called Construction 4.0, is estimated to generate significant cost and time savings (Hofmann and Rüsch, 2017; Osunsami et al., 2018). Khosrowshahi and Arayici (2012) stated that the UK construction industry is experiencing slow progressive changes in the BIM implementation due to the belief that adoption of BIM might cause higher additional project costs. Hence, it is essential that construction organizations beware that the cost of implementation for I4.0 technologies might compensate the costs with a high revenue generated through utilizing new technologies. The results of the success level analysis by the respondents indicated that the companies are ready to undertake the implementation costs, hoping to realize the I4.0 benefits and gains (success level index: 2.94). Contrary to the reluctance toward investing in new technologies as highlighted in the past studies (Ruikar et al., 2007; Henderson and Ruikar, 2010; Sköld et al., 2018), the US contractors reported that they are successful in terms of investing in new technologies considering the potential benefits and gains. This stems from the fact that US firms are willing to involve in R&D activities, where they perceive the potential benefits and gains easier than other contractors operating in other countries. Moreover, the majority of US contractors get a chance to reaching to new technology faster than other contractors overseas for the fact the country is supportive of innovations. This results in that US contractors succeed in making investments more bravely than their competitors. Godin (2004) highlighted that United States constitutes a high proportion of R&D activity compared to other countries, which makes it competitive in science-based industries. The Mann–Whitney U test results revealed that there is a significant difference between the responding companies having a high and low annual turnover. The results show that the companies generating higher revenues are more welcoming in terms of the I4.0 implementation costs. This finding also highlights that the companies having larger revenues are more successful in handling the cost of implementation challenges for the I4.0 adoption. This might stem from the fact that larger employers are more able to spare a seizable budget for new technologies and adopt those within their organizations. Lack of standardization is ranked as another important challenge with an RII of 0.695. Standardization is among the most important steps toward creating an effective working organization and satisfying high-quality targets. Most companies are beware that achieving high standards lies behind setting up standardized procedures and processes. Therefore, it is understandable that responding organizations listed lack of standardizations as a critical challenge toward adopting I4.0 in the construction organizations. The respondents of the questionnaire in the study further stated that they are quite successful in standardizing their I4.0 adoption with a success index of 3.46. Standardization is critical to creating an effective workflow and production environment (Akbar et al., 2015). However, various research studies conducted in different regions have already highlighted that the construction companies fail to adopt standardization and lack information sharing due to lack of standardization (Wang et al., 2016; Thunberg and Fredriksson, 2018; Gamil and Rahman, 2019). This might be attributed to the project-based nature of the industry, where it is difficult to create standardized procedures for work processes since every project is unique in nature. Lack of standardization was also shown to be a critical challenge for I4.0 by US contractors. On the other hand, the contractors reported that they are wisely handling this challenge in their projects. This is associated with the lessons learned from projects, where most problems stem from the lack of standardized procedures. Since lack of standardization leads to low project performance and inefficient processes, firms should create efforts toward developing standardized workflows and processes to benefit more from the I4.0 transformation. Hence, creating standardized workflows at the organizations strongly contributes to the adoption of a new technology, which in return leads to a higher I4.0 awareness (Trappey et al., 2017). Moreover, the Mann–Whitney U test results show that there are significant differences in responses between the younger and older companies. According to these results, the younger companies are better at handling the lack of standardization challenge for the I4.0 adoption than the older companies. This is not surprising because the younger companies aiming to grow their business as a primary objective and target are one of the well-recognized industry leaders. Hence, they are more welcoming toward the utilization of new technologies or the adoption of new concepts and methods (Premkumar and Roberts, 1999). The efforts for standardization become more significant for younger companies since young organizations are rather at the beginning of the journey, and they are more adaptive toward setting up new standards for the organization. The reason behind the younger companies' higher success for the standardization may also stem from the fact that they consider the standardization in that regard as a more important matter than the older companies do because of the stronger growth desire and motivation. The fragmented and project-based nature of the industry is ranked as another important challenge in terms of the I4.0 adoption with an RII of 0.688. Especially, the project-based nature of the industry requires dynamic teams, knowledge and project data where it is hard to have settled practices. The fragmentation also creates complexity, where information flow is not smooth. Moreover, the fragmented and project-based nature of construction projects renders the adoption of new technologies or standardized work harder due to the changing conditions and dynamic environment (Jacobsson and Linderoth, 2010). The fragmented nature of the industry was also reported to result in serious problems such as low performance, low productivity and unwillingness toward developing innovative solutions as in the Malaysian case (Yap et al., 2019). The responding companies in the US construction industry reported that they mostly fail to overcome the challenge associated with the fragmented and project-based nature of the industry for their I4.0 adoption with a success level rating of 1.34. This is the lowest rated success level item of all the other challenges, indicating that the companies are struggling with the industry conditions and temporary nature of construction projects and their complexity. The fragmented nature of the industry is a serious concern for the construction industry in general in terms of welcoming change and innovation. The changing dynamics of every project makes the I4.0 adoption harder since projects teams and conditions are not stable. Shen et al. (2010) further underlined that structuring a collaborative environment and interoperability in practice is a serious concern due to the fragmented nature of the industry. This leads to low awareness of the innovative approaches and adoption of new technologies. Hence, the temporary nature of construction projects is a major problem in terms of preparing the construction companies to adapt to new technologies or concepts. Even though not ranked very high in terms of importance concerning the other challenges, the lack of labor force is also listed among important challenges for the I4.0 adoption in the construction industry (RII: 0.658). The lack of labor force leads to insufficient training and motivation for I4.0 adoption since a skilled labor force is a must for a successful technology transformation. In Hewage et al.'s (2008) study, it was implied that lack of labor force renders managers concerned about whether the available labor force is sufficient to adopt innovative technologies. Doloi et al. (2012) revealed that it is likely to experience low productivity when there is a labor shortage. This eventually leads to poor performance and reluctance toward adopting change and developing innovative solutions. Moreover, the respondents reported that they fail to overcome the labor force barrier to facilitate the I4.0 adoption in their organizations (success level index: 1.74). Since the labor force and more importantly skilled labor force is the key to improve productivity and sustain improved processes, it is challenging for organizations to start a successful technology transformation in the lack of in-house trained personnel to achieve this transformation. On the other hand, the I4.0 adoption is expected to remove some problems in the industry such as material, labor and long set-up times (Dalenogare et al., 2018). Therefore, companies succeeding in handling the labor force problem in their organizations are likely to better promote the I4.0 adoption. Lack of investment in R&D, data protection and cybersecurity and legal and contractual issues are rated as the relatively less important challenges in terms of the I4.0 adoption in the construction industry with the RIIs of 0.641, 0.627, 0.599, respectively. R&D budgets are already low in the construction industry compared to other industries such as healthcare, safety and manufacturing. Therefore, the industry has been experiencing low interest in R&D projects. Therefore, lack of R&D in the industry is not yet perceived as a critical challenge of I4.0 adoption among construction firms. Similarly, data protection and cybersecurity are not yet perceived as an important challenge of I4.0 adoption by construction companies knowing that changing the culture toward adopting innovations is of utmost importance toward securing the data and improve cybersecurity. As another less important challenge of I4.0 adoption, legal and contractual issues are most of the time associated with the project itself. Considering that every project is unique in construction, legal and contractual issues are studied at the very beginning of the projects requiring a more sensitive evaluation. The responding companies reported that they are relatively successful in dealing with the legal and contractual problems (success level index: 3.35), data protection and cybersecurity (2.84) and the lack of investment in R&D (2.77), compared to the other challenges listed. These might stem from the fact that construction organizations are more familiar with these challenges and know how to approach these challenges. Oesterreich and Teuteberg (2016) implied that the R&D investments in the construction industry are relatively low when compared to other industries. As emphasized by several studies, R&D expenditures are proven to provide long-term benefits than short-term gains (Ling et al., 2006; Kim et al., 2009). However, the companies should be aware of the potential of setting up collaborations with academia and to benefit from R&D to the fullest. This explains the fact that the responding companies find it as a less important challenge in terms of implementing I4.0 as the I4.0 adoption is thought to have more important challenges such as resistance to change and lack of standardization. Moreover, data protection and cybersecurity are of utmost importance if I4.0 is to be fully adopted in an organization. As implied by Mantha and de Soto (2019), the AEC industry is vulnerable to cyberattacks such as stealing private information, accessing unauthorized files and remove records, which makes the industry open to threats. The increase in the number of digital platforms used in the industry is also increasing the risk of cyberattacks in construction (Patel and Patel, 2020). Hence, the construction companies are advised to reinforce their infrastructure for handling the cyberattacks and risks of cybersecurity. Even though data protection and cybersecurity were perceived to be a less important challenge among the I4.0 adoption challenges, the companies must develop ways to set up secure digital structures to ensure the protection of data. Although there is no standard procedure available to identify cybersecurity risks in the construction industry (Mantha and de Soto, 2019), the companies might work toward developing their procedures for data protection and cybersecurity, where the responding companies indicated that they are not quite successfully handling such attacks and risks. Finally, the legal and contractual issues pose serious challenges not specifically for the I4.0 adoption only but more generally, for the adoption of new technologies and innovative ways of work. Various studies have already shown that legal and contractual problems lead to poor adoption of new technologies and digitalization (Abubakar et al., 2014; Jo et al., 2018; Li et al., 2019). Even though this challenge was ranked as the least important among other challenges, the responding companies reported that they successfully handle this barrier for their I4.0 adoption. The relatively low ranking of importance for the legal and contractual issues might stem from the fact that the companies are mostly struggling with the resistance to change and implementation costs for I4.0 than legal and contractual concerns. On the other hand, the responding companies indicated that they are relatively successful in handling the problems that arise due to legal and contractual issues. The reason behind the high rating for success maybe for the fact that the companies have already developed ways to deal with the legal and contractual challenges since this appears to be a common concern for almost all construction projects. This study aimed to reveal the challenges for the I4.0 adoption for the construction companies in the US since a major portion of studies have focused mostly on the opportunities for I4.0 instead of determining and discussing the challenges. One of the main objectives of the study was to provide the construction companies with what challenges that they need to handle before starting their I4.0 transformations. In this context, a comprehensive list of challenges was identified, and the challenges identified were ranked based on their order of importance as per the responses from the construction companies participating in a questionnaire study. The study showed that the construction companies are still struggling with instilling a changing culture toward the I4.0 adoption as the most important challenge. Moreover, it was found that the construction companies operating in the United States are not still fully aware of the benefits and gains for the I4.0 adoption and implementation. One other important finding is that the companies participating in the survey are doubtful about the cost of implementing I4.0, which hinders the adoption of I4.0. The companies also indicated that they are not quite successful in handling these challenges in terms of transforming for I4.0. Contrary to a significant portion of studies indicating that the construction companies are welcoming toward digitalization and implementing innovative technologies, this study revealed that the construction companies in the United States are still not ready for the full implementation of I4.0 due to the challenges listed. Hence, this study might be used as a reference guide for the companies aiming to start their I4.0 transformation to understand the I4.0 challenges and to develop strategies for how to handle them. A concrete plan would help them achieve greater performance and benefit from the I4.0 implementation Moreover, the study might encourage researchers in terms of developing strategies for handling the challenges listed for I4.0 and conducting similar studies in different regions, where the results could differ enabling comparative discussions. Finally, the study might be used in practice by considering the challenges and preparing action plans for handling each challenge ## 6. Conclusions This study investigated the challenges for the I4.0 adoption at the construction companies in the United States. To reveal these challenges, an extensive review of the previous studies and pilot studies with experts were conducted. As a result, a total of nine major challenges for the I4.0 adoption for the construction companies were listed. Then, a questionnaire was designed and administered to construction professionals to observe how these challenges are perceived by their importance and how the contractors are performing in terms of handling those challenges. The questionnaire yielded a high response rate, revealing notable results. The analysis of the questionnaire data indicated that the majority of the responding companies see resistance to change, unclear benefits and gains and cost of implementation as the major challenges for the adoption of I4.0 at the construction companies. The companies' level of success in handling the identified challenges was also assessed. According to data analysis, the respondents think that they are successful in overcoming the lack of standardization, legal and contractual problems and cost of implementing challenges for their I4.0 adoption. The results of the questionnaire were further analyzed by the different responding groups; old and young companies, large and small contractors, the heavy focus of contractors on building vs civil works and high and low revenue-generating contractors. Some significant differences were observed for the lack of standardization, cost of implementing and resistance to change challenges based on the level of success achieved in handling them by the responding companies. The younger companies were found to be more successful in handling the lack of standardization than the older companies. Companies having higher annual turnover seem to be dealing better with the cost of implement challenge for the I4.0 adoption. Finally, the younger companies seem to be better at coping with the resistance to change challenge in terms of the I4.0 adoption. The main contribution of this study is to provide construction researchers with a comprehensive list of challenges toward I4.0 adoption. The researchers might benefit from the findings of this research to study and compare other locations and propose new barriers for companies operating in different countries. The researcher might also contribute from this study to propose strategies of how to successfully transform for I4.0, where listed barriers exist for US contractors. Moreover, this study with its mixed methodology provides insights toward opinions and recommendations of industry experts to succeed in I4.0 adoption in construction organizations. The industry practitioners might benefit from the barriers and a detailed evaluation of these barriers depending on different measures to devise and implement their strategies regarding a successful I4.0 adoption and transformation. Apart from the theoretical and practical contributions, the study has some limitations. The main limitation of this study is that it is based on data gathered from a portion of the ENR Top 400 Contracting companies, which reflects the thoughts and opinions of a relatively small study group. The results may differ by sample groups but considering there are top companies in the list, and the results are deemed to be generalizable mostly for the companies aiming to start an I4.0 transformation. To validate the questionnaire results, studies in construction sites might be conducted to observe the experience level of respondents in terms of the I4.0 adoption. Future work can be conducted to reveal the enablers for the I4.0 adoption, and the results can be compared alongside the challenges. A generic list of key enablers and barriers for I4.0 would help contractors revise their strategies and better align their organizations toward adopting I4.0. Moreover, case studies might be conducted to observe the practical implementation of I4.0 to better evaluate the challenges, opportunities and enablers for I4.0.Appendix ## Figures ### Figure 1 Research process ### Figure 2 Respondents by position ### Figure 3 Companies' main business activity ## Table 1 Challenges for I4.0 adoption in the construction industry ChallengeDefinitionReference Cost of implementationConstruction companies are prejudiced against adopting I4.0 in construction projects since they are not clear with its benefits in cost savings as well as its investment requirements. Hence, companies perceive I4.0 as costly to implementZhou et al. (2015), Oesterreich and Teuteberg (2016), Dallasega et al. (2018) Resistance to changeThe construction industry is conservative in terms of embracing change. However, I4.0 requires change, which appears as a significant challenge for the I4.0 adoption by the industryOesterreich and Teuteberg (2016), Trstenjak and Cosic (2017), Woodhead et al. (2018) Lack of labor forceThe construction industry is competing against the lack of a skilled workforce in I4.0 due to the complexity and dynamic nature of projects. Introducing the I4.0 principles in the industry might require the utilization of new technologies and creating new departments. Therefore, the lack of labor force is a serious challenge for the successful adoption of I4.0 in the construction industryAllmon et al. (2000), Schneider (2018) Unclear benefits and gainsTechnology investment and innovation adoption require a complete understanding of value generation for construction projects. Presently, the I4.0 benefits and gains are not clear for the construction industry. This vagueness poses a serious challenge for the I4.0 investmentBarlish and Sullivan (2012), Lee et al. (2015), Oesterreich and Teuteberg (2016) Lack of investment in research & development (R&D)The construction industry has traditionally lacked commitment to R&D activities and investment. This also puts a barrier before the R&D necessary for I4.0 in the industryDulaimi (1995), Blayse and Manley (2004), Oesterreich and Teuteberg (2016) Fragmented and project-based nature of the industryThe construction industry is fragmented and project-based. Therefore, the conditions are dynamic and variable in every project, which hinders construction practitioners from developing structures to enable technology innovation and adoption. This eventually leads to reluctance for the I4.0 adoption. Hence, the fragmentation and project-based nature are key challenges for I4.0Ofori (1994), Nitithamyong and Skibniewski (2004), Golizadeh et al. (2014) Lack of standardizationConstruction companies need to keep up with global dynamics. However, several companies are still struggling with the lack of standardization, which results in serious time losses and increased costs. Even though there are efforts toward creating standardized processes, there is still a need for setting up the standardsGoodrum et al. (2006), Li and Yang (2017), Axelsson et al. (2018) Data protection and cybersecurityConstruction companies suffer from data protection and adaptation issues for new technologies. These cause serious challenges for the majority of companies while implementing I4.0. Hence, the companies are seeking ways to improve their data protection policiesLove et al. (2001), Patel and Patel (2020) Legal and contractual issuesThe legal and contractual processes are often troublesome for construction companies due to unclear statements in the contracts and the difficulties in contract management. This leads to vulnerability in adopting new technologies and innovation within companies. Hence, legal and contractual issues are major barriers hindering construction companies from adopting the I4.0 principlesChan and Suen (2005a, b), Eadie et al. (2015) ## Table 2 Respondent characteristics MeanMedianMinimumMaximum Company's years of experience in the construction industry2640780 Annual turnover (million US)40379412,127
Number of employees37037905317,000
Respondent's age34292671
Respondent's years of experience in the construction industry7.49236

## Table 3

Relative importance indices for challenges for I4.0 adoption

ChallengeRIIRank
Resistance to change0.8921
Unclear benefits and gains0.8782
Cost of implementation0.7923
Lack of standardization0.6954
Fragmented and project-based nature of the industry0.6885
Lack of labor force0.6586
Lack of investment in research and development0.6417
Data protection and cybersecurity0.6278
Legal and contractual problems0.5999

## Table 4

Success level indices of challenges for I4.0 adoption

ChallengeSuccess level index
Lack of standardization3.46
Legal and contractual problems3.35
Cost of implementation2.94
Data protection and cybersecurity2.84
Lack of investment in research and development2.77
Unclear benefits and gains2.64
Resistance to change2.14
Lack of labor force1.74
Fragmented and project-based nature of the industry1.34

## Table 5

Average ratings for “What is your success level for overcoming I4.0 adoption challenges in the construction industry?” by control variables (1 = poor, 5 = excellent)

Questionnaire itemsYoung companies <50 yearsOld companies >50 yearsLarge contractor >500Small contractor <500BuildingInfrastructureHigh annual turnover >$200 mLow annual turnover <$200 m
Lack of standardization4.2*2.5*3.63.23.43.43.63.2
Legal and contractual problems3.23.43.43.23.33.33.43.2
Cost of implementation2.63.23.32.53.12.73.7*2.1*
Data protection and cybersecurity2.92.82.82.82.82.82.92.8
Lack of investment in research and development2.82.72.72.72.72.72.82.7
Unclear benefits and gains2.62.62.62.62.72.62.72.6
Resistance to change2.8*1.4*2.31.92.12.12.41.8
Lack of labor force1.81.71.71.81.71.71.81.7
Fragmented and project-based nature of the industry1.41.31.41.31.31.31.41.3

Note(s): Statistically significant difference at α = 0.05

## References

Abowitz, D.A. and Toole, T.M. (2010), “Mixed method research: fundamental issues of design, validity, and reliability in construction research”, Journal of Construction Engineering and Management, Vol. 136 No. 1, pp. 108-116.

Abubakar, M., Ibrahim, Y.M., Kado, D. and Bala, K. (2014), “Contractors' perception of the factors affecting building information modelling (BIM) adoption in the Nigerian construction industry”, Computing in Civil and Building Engineering (2014), pp. 167-178.

Agostini, L. and Filippini, R. (2019), “Organizational and managerial challenges in the path toward Industry 4.0”, European Journal of Innovation Management, Vol. 22 No. 3, pp. 406-421, doi: 10.1108/EJIM-02-2018-0030.

Ahadzie, D.K., Proverbs, D.G. and Olomolaiye, P.O. (2008), “Model for predicting the performance of project managers at the construction phase of mass house building projects”, Journal of Construction Engineering and Management, Vol. 134 No. 8, pp. 618-629.

Ahmad, I.U., Russell, J.S. and Abou-Zeid, A. (1995), “Information technology (IT) and integration in the construction industry”, Construction Management and Economics, Vol. 13 No. 2, pp. 163-171.

Akbar, A.N., Mohammad, M.F., Ahmad, N. and Maisyam, M. (2015), “Adopting standardization in construction environment: standard method of measurement (smms)”, Procedia-Social and Behavioral Sciences, Vol. 170, pp. 37-48.

Alaloul, W.S., Liew, M.S., Zawawi, N.A.W.A. and Kennedy, I.B. (2020), “Industrial revolution 4.0 in the construction industry: challenges and opportunities for stakeholders”, Ain Shams Engineering Journal, Vol. 11 No. 1, pp. 225-230.

Allmon, E., Haas, C.T., Borcherding, J.D. and Goodrum, P.M. (2000), “US construction labor productivity trends, 1970–1998”, Journal of Construction Engineering and Management, Vol. 126 No. 2, pp. 97-104.

Arayici, Y. and Coates, P. (2012), “A system engineering perspective to knowledge transfer: a case study approach of BIM adoption”, Virtual Reality–Human Computer Interaction, Vol. 2006, pp. 179-206.

Axelsson, J., Fröberg, J. and Eriksson, P. (2018), “Towards a system-of-systems for improved road construction efficiency using lean and Industry 4.0”, 2018 13th Annual Conference on System of Systems Engineering (SoSE), IEEE, pp. 576-582.

Axelsson, J., Fröberg, J. and Eriksson, P. (2019), “Architecting systems‐of‐systems and their constituents: a case study applying Industry 4.0 in the construction domain”, Systems Engineering, Vol. 22 No. 6, pp. 455-470.

Bagaya, O. and Song, J. (2016), “Empirical study of factors influencing schedule delays of public construction projects in Burkina Faso”, Journal of Management in Engineering, Vol. 32 No. 5, 05016014.

Baker, M.J. (2003), “Data collection–questionnaire design”, The Marketing Review, Vol. 3 No. 3, pp. 343-370.

Barlish, K. and Sullivan, K. (2012), “How to measure the benefits of BIM—a case study approach”, Automation in Construction, Vol. 24, pp. 149-159.

Baur, C. and Wee, D. (2015), Manufacturing's Next Act, McKinsey & Company, available at: www.mckinsey.com/business-functions/operations/our-insights/manufacturings-next-act.

Blayse, A.M. and Manley, K. (2004), “Key influences on construction innovation”, Construction Innovation, Vol. 4 No. 3, pp. 143-154.

Bolarinwa, O.A. (2015), “Principles and methods of validity and reliability testing of questionnaires used in social and health science researches”, Nigerian Postgraduate Medical Journal, Vol. 22 No. 4, p. 195.

Buehler, M., Buffet, P. and Castagnino, S. (2018), “The fourth industrial revolution is about to hit the construction industry. Here's how it can thrive”, available at: https://www.weforum.org/agenda/2018/06/construction-industry-future-scenarios-labour-technology/.

Chan, E. and Suen, H.C.H. (2005a), “Legal issues of dispute management in international construction projects contracting”, The Construction Law Journal, Vol. 21 No. 4, pp. 291-305.

Chan, E.H.W. and Suen, H.C.H. (2005b), “Dispute resolution management for international construction projects in China”, Management Decision, Vol. 43 No. 4, pp. 589-602.

Chan, D.W.M., Olawumi, T.O. and Ho, A.M.L. (2019), “Critical success factors for building information modelling (BIM) implementation in Hong Kong”, Engineering, Construction and Architectural Management, Vol. 26 No. 9, pp. 1838-1854, doi: 10.1108/ECAM-05-2018-0204.

Chen, Y., Okudan, G.E. and Riley, D.R. (2010), “Sustainable performance criteria for construction method selection in concrete buildings”, Automation in Construction, Vol. 19 No. 2, pp. 235-244.

Chowdhury, T., Adafin, J. and Wilkinson, S. (2019), “Review of digital technologies to improve productivity of New Zealand construction industry”, Journal of Information Technology in Construction, Vol. 24, pp. 569-587.

Collins, K.M., Onwuegbuzie, A.J. and Sutton, I.L. (2006), “A model incorporating the rationale and purpose for conducting mixed methods research in special education and beyond”, Learning disabilities: A Contemporary Journal, Vol. 4 No. 1, pp. 67-100.

Cooper, S. (2018), “Civil engineering collaborative digital platforms underpin the creation of ‘digital ecosystems’”, Proceedings of the Institution of Civil Engineers – Civil Engineering, Vol. 171 No. 1, p. 14, doi: 10.1680/jcien.2018.171.1.14.

Craveiro, F., Duarte, J.P., Bartolo, H. and Bartolo, P.J. (2019), “Additive manufacturing as an enabling technology for digital construction: a perspective on Construction 4.0”, Automation in Construction, Vol. 103, pp. 251-267.

Czarnitzki, D. and Delanote, J. (2013), “Young innovative companies: the new high-growth firms?”, Industrial and Corporate Change, Vol. 22 No. 5, pp. 1315-1340.

Dalenogare, L.S., Benitez, G.B., Ayala, N.F. and Frank, A.G. (2018), “The expected contribution of Industry 4.0 technologies for industrial performance”, International Journal of Production Economics, Vol. 204, pp. 383-394.

Dallasega, P., Rauch, E. and Linder, C. (2018), “Industry 4.0 as an enabler of proximity for construction supply chains: a systematic literature review”, Computers in Industry, Elsevier, Vol. 99 August 2017, pp. 205-225.

Davies, A. and Sharp, D. (2014), RICS Strategic Facilities Management. Case Studies, International Workplace, Cambridge, Report.

Deloitte (2020), Global Powers of Construction 2019, available at: https://www2.deloitte.com/content/dam/Deloitte/at/Documents/presse/Deloitte-Global-Powers-of-Construction-2019.pdf.

Demel, J., Bockelmann, C. and Dekorsy, A. (2017), “Evaluation of a software defined GFDM implementation for industry 4.0 applications”, 2017 IEEE International Conference on Industrial Technology (ICIT), IEEE, pp. 1283-1288.

Demirkesen, S. and Arditi, D. (2015), “Construction safety personnel's perceptions of safety training practices”, International Journal of Project Management, Vol. 33 No. 5, pp. 1160-1169.

Doloi, H., Sawhney, A., Iyer, K.C. and Rentala, S. (2012), “Analysing factors affecting delays in Indian construction projects”, International Journal of Project Management, Vol. 30 No. 4, pp. 479-489.

Drath, R. and Horch, A. (2014), “Industrie 4.0: hit or hype?”, IEEE Industrial Electronics Magazine, IEEE, Vol. 8 No. 2, pp. 56-58, doi: 10.1109/MIE.2014.2312079.

Dubois, A. and Gadde, L.E. (2002), “The construction industry as a loosely coupled system: implications for productivity and innovation”, Construction Management and Economics, Vol. 20 No. 7, pp. 621-631.

Dulaimi, M. (1995), “The challenge of innovation in construction: investing in R&D does not guarantee a company's ability to innovate, however innovative organization is an essential element for successful, challenging and motivating activities”, Building Research and Information, Vol. 23 No. 2, pp. 106-109.

Eadie, R., McLernon, T. and Patton, A. (2015), “An investigation into the legal issues relating to building information modelling (BIM)”, Rics Cobra Aubea 2015.

Elmualim, A. and Gilder, J. (2014), “BIM: innovation in design management, influence and challenges of implementation”, Architectural Engineering and Design Management, Vol. 10 Nos 3-4, pp. 183-199.

Engineering News-Record (ENR) (2020), “ENR 2019 top 400 contractors: the market keeps growing”, available at: https://www.enr.com/articles/46942-enr-2019-top-400-contractors-the-market-keeps-growing.

EPICOR (2019), “What is Industry 4.0—the industrial internet of things (IIoT)?”, available at: https://www.epicor.com/en-us/resource-center/articles/what-is-industry-4-0/.

Erceg-Hurn, D.M. and Mirosevich, V.M. (2008), “Modern robust statistical methods: an easy way to maximize the accuracy and power of your research”, American Psychologist, Vol. 63 No. 7, p. 591.

Fargnoli, M. and Lombardi, M. (2020), “Building information modelling (BIM) to enhance occupational safety in construction activities: research trends emerging from one decade of studies”, Buildings, Vol. 10 No. 6, p. 98.

FIEC (2015), Construction 4.0, FIEC - European construction industry federation website, pp. 1-9, available at: http://www.fiec.eu/en/themes-72/construction-40.aspx (accessed 10 September 2020).

FIEC (2017), “Safeguarding in the next industrial revolution”, Construction Europe, European Construction Industry Federation.

Fife-Schaw, C. (2000), “Questionnaire design”, in Breakwell, G.M., Hammond, S. and Fife-Schaw, C. (Eds), Research Methods in Psychology, pp. 158-174.

Forgues, D., Rivest, L., Danjou, C. and Meyer, J. (2019), “De l'Industrie 4.0 à la Construction 4.0: Des exemples concrets!”, Congrès 2019: influencez l’avenir, 15-16 March 2019.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.

Frank, A.G., Dalenogare, L.S. and Ayala, N.F. (2019), “Industry 4.0 technologies: implementation patterns in manufacturing companies”, International Journal of Production Economics, Vol. 210, pp. 15-26.

Gamil, Y. and Rahman, I.A.R. (2019), “Awareness and challenges of building information modelling (BIM) implementation in the Yemen construction industry”, Journal of Engineering, Design and Technology, Vol. 17 No. 5, pp. 1077-1084.

García de Soto, B., Agustí-Juan, I., Joss, S. and Hunhevicz, J. (2019), “Implications of construction 4.0 to the workforce and organizational structures”, International Journal of Construction Management, pp. 1-13.

George, D. and Mallery, P. (2003), “SPSS for windows step by step: a simple guide and reference. 11.0 update”, p. 549, George 4 answers pdf, available at: https://wps.ablongman.com/wps/media/objects/385/394732/george4answers.pdf.

Godin, B. (2004), “The obsession for competitiveness and its impact on statistics: the construction of high-technology indicators”, Research Policy, Vol. 33 No. 8, pp. 1217-1229.

Golizadeh, H., Alfareh, M.A.M., Ata, S.S.M. and Mohamad, M.I. (2014), “Adoption to online collaboration in the construction sites of developing countries”, Journal of Basic and Applied Scientific Research, Vol. 4 No. 5, pp. 29-35.

Goodrum, P.M., McLaren, M.A. and Durfee, A. (2006), “The application of active radio frequency identification technology for tool tracking on construction job sites”, Automation in Construction, Vol. 15 No. 3, pp. 292-302.

Hampson, K., Kraatz, J.A. and Sanchez, A.X. (2014), “The global construction industry and R&D”, R&D Investment and Impact in the Global Construction Industry, 1st ed., Routledge, Oxon, p. 364.

Gündüz, M., Nielsen, Y. and Özdemir, M. (2013), “Quantification of delay factors using the relative importance index method for construction projects in Turkey”, Journal of Management in Engineering, Vol. 29 No. 2, pp. 133-139.

Hargaden, V., Papakostas, N., Newell, A., Khavia, A. and Scanlon, A. (2019), “The role of blockchain technologies in construction engineering project management”, 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, pp. 1-6.

Harvey, M. (2003), “The United Kingdom: privatization, fragmentation and inflexible flexibilization in the UK construction industry”, in Bosch, G. and Philips, P. (Eds), Building Chaos: A International Comparison of Deregulation in the Construction Industry, Routledge Studies in Business Organization and Networks, Routledge, pp. 188-209, ISBN 9780415260909.

Hemström, K., Mahapatra, K. and Gustavsson, L. (2017), “Architects' perception of the innovativeness of the Swedish construction industry”, Construction Innovation, Vol. 17 No. 2, pp. 244-260.

Henderson, J.R. and Ruikar, K. (2010), “Technology implementation strategies for construction organisations”, Engineering Construction and Architectural Management, Vol. 17 No. 3, pp. 309-327.

Hermann, M., Pentek, T. and Otto, B. (2016), “Design principles for Industrie 4.0 scenarios”, 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE, pp. 3928-3937, doi: 10.1109/HICSS.2016.488.

Hewage, K.N., Ruwanpura, J.Y. and Jergeas, G.F. (2008), “IT usage in Alberta's building construction projects: current status and challenges”, Automation in Construction, Vol. 17 No. 8, pp. 940-947.

Hofmann, E. and Rüsch, M. (2017), “Industry 4.0 and the current status as well as future prospects on logistics”, Computers in Industry, Vol. 89, pp. 23-34.

Hossain, A. and Nadeem, A. (2019), “Towards digitizing the construction industry: state of the art of construction 4.0” , in Ozevin, D., Ataei, H., Gurgun, A.P., Modares, M., Yazdani, S. and Singh, A. (Eds), ISEC 2019 - 10th International Structural Engineering and Construction Conference (ISEC 2019 - 10th International Structural Engineering and Construction Conference), ISEC Press.

Jacobsson, M. and Linderoth, H.C. (2010), “The influence of contextual elements, actors' frames of reference, and technology on the adoption and use of ICT in construction projects: a Swedish case study”, Construction Management and Economics, Vol. 28 No. 1, pp. 13-23.

Jo, T.M., Ishak, S.S.M. and Rashid, Z.Z.A. (2018), “Overview of the legal aspects and contract requirements of the BIM practice in Malaysian construction industry”, MATEC Web of Conferences, EDP Sciences, Vol. 203, p. 02011.

Jones, T., Shan, Y. and Goodrum, P.M. (2010), “An investigation of corporate approaches to sustainability in the US engineering and construction industry”, Construction Management and Economics, Vol. 28 No. 9, pp. 971-983.

Kagermann, H., Wahlster, W. and Helbig, J. (2013), Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0 – Final Report of the Industrie 4.0 Working Group Industrie, German Academy of Science and Engineering, Frankfurt.

Khosrowshahi, F. and Arayici, Y. (2012), “Roadmap for implementation of BIM in the UK construction industry”, Engineering Construction and Architectural Management, Vol. 19 No. 6, pp. 610-635, doi: 10.1108/09699981211277531.

Kim, C., Kim, H., Han, S.H., Kim, C., Kim, M.K. and Park, S.H. (2009), “Developing a technology roadmap for construction R&D through interdisciplinary research efforts”, Automation in Construction, Vol. 18 No. 3, pp. 330-337.

King, M. (2018), “How industry 4.0 and BIM are shaping the future of the construction environment”, GIM Int.Worldw. Mag. GEOMATICS, Vol. 31, pp. 24-25.

Klinc, R. and Turk, Ž. (2019), “Construction 4.0-digital transformation of one of the oldest industries”, Economic and Business Review, Vol. 21 No. 3, pp. 393-410.

Klinc, R., Turk, Ž. and Dolenc, M. (2010), “ICT enabled communication in construction 2.0”, Pollack Periodica, Vol. 5 No. 1, pp. 109-120, doi: 10.1556/Pollack.5.2010.1.8.

Kometa, S.T., Olomolaiye, P.O. and Harris, F.C. (1994), “Attributes of UK construction clients influencing project consultants' performance”, Construction Management and Economics, Vol. 12 No. 5, pp. 433-443.

Kumar, A., Krishnamurthi, R., Nayyar, A., Sharma, K., Grover, V. and Hossain, E. (2020), “A novel smart healthcare design, simulation, and implementation using healthcare 4.0 processes”, IEEE Access, Vol. 8, pp. 118433-118471.

Lasi, H., Fettke, P., Feld, T. and Hoffman, M. (2014), “Industry 4.0”, Business and Information Systems Engineering, Vol. 6 No. 4, pp. 239-242.

Lavikka, R., Kallio, J., Casey, T. and Airaksinen, M. (2018), “Digital disruption of the AEC industry: technology-oriented scenarios for possible future development paths”, Construction Management and Economics, Vol. 36 No. 11, pp. 635-650.

Lechler, T.G., Edington, B.H. and Gao, T. (2012), “Challenging classic project management: turning project uncertainties into business opportunities”, Project Management Journal, Vol. 43 No. 6, pp. 59-69.

Lee, I. and Lee, K. (2015), “The Internet of things (IoT): applications, investments, and challenges for enterprises”, Business Horizons, Vol. 58 No. 4, pp. 431-440.

Lee, S., Yu, J. and Jeong, D. (2015), “BIM acceptance model in construction organizations”, Journal of Management in Engineering, Vol. 31 No. 3, p. 04014048.

Li, J. and Yang, H.A. (2017), “Research on development of construction industrialization based on BIM technology under the background of industry 4.0”, MATEC Web Conferences, Vol. 2017 No. 100, p. 02046.

Li, J., Greenwood, D. and Kassem, M. (2019), “Blockchain in the built environment and construction industry: a systematic review, conceptual models and practical use cases”, Automation in Construction, Vol. 102, pp. 288-307.

Liao, Y., Deschamps, F., Loures, E.D.F.R. and Ramos, L.F.P. (2017), “Past, present and future of industry 4.0 – a systematic literature review and research agenda proposal”, International Journal of Production Research, Taylor & Francis, Vol. 55 No. 12, pp. 3609-3629, doi: 10.1080/00207543.2017.1308576.

Ling, F.Y.Y. and Khoo, W.W. (2016), “Improving relationships in project teams in Malaysia”, Built Environment Project and Asset Management, Vol. 6 No. 3, pp. 284-301, doi: 10.1108/BEPAM-04-2015-0014.

Ling, F.Y., Ibbs, C.W. and Hoo, W.Y. (2006), “Determinants of international architectural, engineering, and construction firms' project success in China”, Journal of Construction Engineering and Management, Vol. 132 No. 2, pp. 206-214.

Litwin, M. (1995), How to Measure Survey Reliability and Validity, Sage Publications,  Thousand Oaks, CA.

Love, P.E., Irani, Z., Li, H., Cheng, E.W. and Tse, R.Y. (2001), “An empirical analysis of the barriers to implementing e-commerce in small-medium sized construction contractors in the state of Victoria, Australia”, Construction Innovation, Vol. 1 No. 1, pp. 31-41.

Lu, W. (2014), “Reliability of Engineering News-Record international construction data”, Construction Management and Economics, Vol. 32 No. 10, pp. 968-982.

Lu, Y. (2017), “Industry 4.0: a survey on technologies, applications and open research issues”, Journal of Industrial Information Integration, Elsevier, Vol. 6, pp. 1-10, doi: 10.1016/j.jii.2017.04.005.

Luthra, S. and Mangla, S.K. (2018), “Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies”, Process Safety and Environmental Protection, Vol. 117, pp. 168-179.

MacDougall, W. (2014), Industrie 4.0: Smart Manufacturig for the Future, Germany Trade and Investment, Berlin, available at: www.gtai.de/GTAI/Navigation/EN/Invest/industrie-4-0.html.

Mahat-Shamir, M., Neimeyer, R.A. and Pitcho-Prelorentzos, S. (2019), “Designing in-depth semi-structured interviews for revealing meaning reconstruction after loss”, Death Studies, Vol. 45 No. 2, pp. 1-8.

Mantha, B.R. and de Soto, B.G. (2019), “Cyber security challenges and vulnerability assessment in the construction industry”, Creative Construction Conference, June - 2 July 2019, Budapest.

Maskuriy, R., Selamat, A., Ali, K.N., Maresova, P. and Krejcar, O. (2019), “Industry 4.0 for the construction industry—how ready is the industry?”, Applied Sciences, Vol. 9 No. 14, p. 2819.

Matarneh, R. and Hamed, S. (2017), “Barriers to the adoption of building information modeling in the Jordanian building industry”, Open Journal of Civil Engineering, Vol. 7 No. 3, pp. 325-335.

Nam, C.H. and Tatum, C.B. (1997), “Leaders and champions for construction innovation”, Construction Management and Economics, Vol. 15 No. 3, pp. 259-270.

Neiva, E.R., Ros, M. and Paz, M.G.T. (2005), “Attitudes towards organizational change: validation of a scale”, Psychology in Spain, Vol. 9 No. 1, pp. 81-90.

Newman, C., Edwards, D., Martek, I., Lai, J., Thwala, W.D. and Rillie, I. (2020), “Industry 4.0 deployment in the construction industry: a bibliometric literature review and UK-based case study”, Smart and Sustainable Built Environment.

Nitithamyong, P. and Skibniewski, M.J. (2004), “Web-based construction project management systems: how to make them successful?”, Automation in Construction, Vol. 13 No. 4, pp. 491-506.

Oesterreich, T.D. and Teuteberg, F. (2016), “Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry”, Computers in Industry, Vol. 83, pp. 121-139.

Ofori, G. (1994), “Construction industry development: role of technology transfer”, Construction Management and Economics, Vol. 12 No. 5, pp. 379-392.

Ofori, G. (2003), “Preparing Singapore's construction industry for the knowledge-based economy: practices, procedures and performance”, Construction Management and Economics, Vol. 21 No. 2, pp. 113-125.

Oltmann, S.M. (2016), “Qualitative interviews: a methodological discussion of the interviewer and respondent contexts”, Forum Qualitative Sozialforschung, Vol. 17 No. 2, pp. 1-16.

Orzes, G., Rauch, E., Bednar, S. and Poklemba, R. (2018), “Industry 4.0 implementation barriers in small and medium sized enterprises: a focus group study”, 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, pp. 1348-1352.

Osei-Kyei, R. and Chan, A.P. (2018), “Evaluating the project success index of public-private partnership projects in Hong Kong”, Construction Innovation, Vol. 18 No. 2.

Osunsanmi, T., Aigbavboa, C. and Oke, A. (2018), “Construction 4.0: the future of the construction industry in South Africa”, World Academy of Science, Engineering and Technology, International Journal of Civil and Environmental Engineering, Vol. 12 No. 3, pp. 206-212.

Ozumba, A.O.U. and Shakantu, W. (2018), “Exploring challenges to ICT utilisation in construction site management”, Construction Innovation, Vol. 18 No. 3, pp. 321-349.

Pacchini, A.P.T., Lucato, W.C., Facchini, F. and Mummolo, G. (2019), “The degree of readiness for the implementation of Industry 4.0”, Computers in Industry, Vol. 113, p. 103125.

Patel, T. and Patel, V. (2020), “Data privacy in construction industry by privacy-preserving data mining (PPDM) approach”, Asian Journal of Civil Engineering, Vol. 21 No. 3, pp. 505-515.

Patel, T. and Patel, V. (2020), “Data privacy in construction industry by privacy-preserving data mining (PPDM) approach”, Asian Journal of Civil Engineering, Vol. 21 No. 3, pp. 505205-515225, doi: 10.1016/j.compind.2018.03.039.

Premkumar, G. and Roberts, M. (1999), “Adoption of new information technologies in rural small businesses”, Omega, Vol. 27 No. 4, pp. 467-484.

Radhakrishna, R.B. (2007), “Tips for developing and testing questionnaires/instruments”, Journal of Extension, Vol. 45 No. 1, pp. 1-4.

Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A.B.L. and Rajak, S. (2020), “Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective”, International Journal of Production Economics, Vol. 224, p. 107546.

Rees, C. (2011), An Introduction to Research for Midwives E-Book, Elsevier Health Sciences, Cardiff.

Ruikar, K., Anumba, C.J. and Egbu, C. (2007), “Integrated use of technologies and techniques for construction knowledge management”, Knowledge Management Research and Practice, Vol. 5 No. 4, pp. 297-311.

Sambasivan, M. and Soon, Y.W. (2007), “Causes and effects of delays in Malaysian construction industry”, International Journal of Project Management, Vol. 25 No. 5, pp. 517-526.

Sawhney, A., Riley, M. and Irizarry, J. (2020), Construction 4.0: An Innovation Platform for the Built Environment, Routlegde, New York, NY.

Schneider, P. (2018), “Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field”, Review of Managerial Science, Vol. 12 No. 3, pp. 803-848.

Schultze, U. and Avital, M. (2011), “Designing interviews to generate rich data for information systems research”, Information and Organization, Vol. 21 No. 1, pp. 1-16, doi: 10.1016/j.infoandorg.2010.11.001.

Shen, W., Hao, Q., Mak, H., Neelamkavil, J., Xie, H., Dickinson, J., Thomas, R., Pardasani, A. and Xue, H. (2010), “Systems integration and collaboration in architecture, engineering, construction, and facilities management: a review”, Advanced Engineering Informatics, Vol. 24 No. 2, pp. 196-207.

Sirkin, H.L., Zinser, M. and Rose, J.M. (2015), Why Advanced Manufacturing Will Boost Productivity, Boston Consulting Group, Boston, MA.

Sköld, D., Fornstedt, H. and Lindahl, M. (2018), “Dilution of innovation utility, reinforcing the reluctance towards the new: an upstream supplier perspective on a fragmented electricity industry”, Energy Policy, Vol. 116, pp. 220-231.

Sony, M. and Naik, S. (2020), “Critical factors for the successful implementation of Industry 4.0: a review and future research direction”, Production Planning and Control, Vol. 31 No. 10, pp. 799-815.

Srinivasan, R. and Lohith, C.P. (2017), “Pilot study—assessment of validity and reliability”, Strategic Marketing and Innovation for Indian MSMEs, Springer, pp. 43-49.

Stehn, L. and Höök, M. (2008), “Lean principles in industrialized housing production: the need for a cultural change”, Lean Construction Journal, pp. 20-33.

Stewart, R.A., Mohamed, S. and Marosszeky, M. (2004), “An empirical investigation into the link between information technology implementation barriers and coping strategies in the Australian construction industry”, Construction Innovation, Vol. 4 No. 3, pp. 155-171.

Strange, R. and Zucchella, A. (2017), “Industry 4.0, global value chains and international business”, Multinational Business Review, Vol. 25 No. 3, pp. 174-184.

Thunberg, M. and Fredriksson, A. (2018), “Bringing planning back into the picture–How can supply chain planning aid in dealing with supply chain-related problems in construction?”, Construction Management and Economics, Vol. 36 No. 8, pp. 425-442.

Trappey, A.J., Trappey, C.V., Govindarajan, U.H., Chuang, A.C. and Sun, J.J. (2017), “A review of essential standards and patent landscapes for the Internet of Things: a key enabler for Industry 4.0”, Advanced Engineering Informatics, Vol. 33, pp. 208-229.

Trstenjak, M. and Cosic, P. (2017), “Process planning in Industry 4.0 environment”, Procedia Manufacturing, Vol. 11, pp. 1744-1750.

Uhlemann, T.H.J., Lehmann, C. and Steinhilper, R. (2017), “The digital twin: realizing the cyber-physical production system for industry 4.0”, Procedia CIRP, Vol. 61, pp. 335-340.

Wang, W., Zhang, S. and King, A.P. (2016), “Research on the adoption barriers of the engineering construction standards in China”, Structural Survey, Vol. 34 Nos 4-5, pp. 367-378.

Woodhead, R., Stephenson, P. and Morrey, D. (2018), “Digital construction: from point solutions to IoT ecosystem”, Automation in Construction, Vol. 93, pp. 35-46, doi: 10.1016/j.autcon.2018.05.004.

Yap, J.B.H., Chow, I.N. and Shavarebi, K. (2019), “Criticality of construction industry problems in developing countries: analyzing Malaysian projects”, Journal of Management in Engineering, Vol. 35 No. 5, p. 04019020.

Yeung, J.F., Chan, A.P. and Chan, D.W. (2009), “Developing a performance index for relationship-based construction projects in Australia: Delphi study”, Journal of Management in Engineering, Vol. 25 No. 2, pp. 59-68.

Zhang, X., Skitmore, M., Wu, Y. and Ye, K. (2010), “A regional construction R&D evaluation system for China”, Construction Management and Economics, Vol. 28 No. 12, pp. 1287-1300.

Zhong, R.Y., Xu, X., Klotz, E. and Newman, S.T. (2017), “Intelligent manufacturing in the context of industry 4.0: a review”, Engineering, Vol. 3 No. 5, pp. 616-630.

Zhou, K., Liu, T. and Zhou, L. (2015), “Industry 4.0: towards future industrial opportunities and challenges”, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), IEEE, pp. 2147-2152.

Zimmerman, D.W. (1987), “Comparative power of student t test and Mann-Whitney U test for unequal sample sizes and variances”, The Journal of Experimental Education, Vol. 55 No. 3, pp. 171-174.

## Corresponding author

Sevilay Demirkesen can be contacted at: demirkesen@gtu.edu.tr