The US construction industry stands at a crossroads. The aging infrastructure of the United States is in dire need for renewal as we face a major shortage of labor and craft workers (Construction Industry Institute, 2021). Recent trend data indicates that the construction workforce is aging fast causing an industry wide labor shortage (Construction Industry Institute, 2016). The consensus by industry experts is that the construction industry will look radically different over the next 20 years (Ribeirinho et al., 2020). With the increasing level of sophistication in the construction industry, companies are turning to innovative technologies and approaches to gain a competitive advantage (Renz et al., 2016). At the same time, construction technologies are being developed faster than the rate of their implementation, and the industry is yet to benefit from their full potential (Construction Industry Institute, 2021). An analysis by McKinsey and Company of Venture Capitalist (VC) investments trends has found that VC investment growth in construction technologies has outpaced VC investment growth in other industries by approximately 15-fold, with clear indications of continuing momentum (Bartlett et al., 2020).

Construction workers are the backbone of the construction industry, as they make up a sizable portion of overall production costs. Therefore, their performance and productivity is critical to the successful execution of construction projects (Okoro et al., 2017). An important issue facing the construction industry is the declining rate of worker productivity and performance (Shehata and El-Gohary, 2011). Because construction workers play a huge role in overall productivity, it is essential to understand the determinants of what impacts their performance. Factors involving tools and construction equipment, materials, and technologies were found to have a major impact on worker performance (Dai et al., 2009). Other determinants of worker performance and productivity include: increased level of supervision (Yates, 2006), communication and efficient information flow (Lingard et al., 2007), adequate financial compensation (Yates, 2006), proper work planning of construction tasks, materials, and methods (Gatti and Migliaccio, 2013), continuous work training and education, (Yates, 2006), and implementation of effective safety measures (Enshassi et al., 2009)

In addition to the importance of people on a construction project, information is considered the lifeblood of the modern construction industry (Nassereddine et al., 2022). Given the sheer volume of knowledge produced throughout the lifecycle of a construction project, the proper management of this information is critical due to the compartmentalized nature of the industry (Kazi and Koivuniemi, 2006; Dave and Koskela, 2009). Adequate information access has been found to add numerous benefits to the construction industry. Information access and information technologies enhance employee satisfaction, and improve external and internal collaboration and communication (Klinc et al., 2008). Information access and management improves work quality, simplifies complicated tasks, and increases worker productivity (Vasista and Abone, 2018). Within an organization, information management increases overall efficiency, and eases the process of disseminating necessary information to related projects, which in turn facilitates project completion on schedule per specification guidelines. Among team members, it allows the effective communication between workers and supervisors, and promotes collaboration, coordination and effective decision making (Prasanna and Raja Ramanna, 2014).

In the construction industry, while many attribute low productivity to craft workers, any problems are more likely to be caused by the lack of adequate supervision and failure of frontline supervisors to provide the necessary planning, information, support, and motivation (Howell, 1999). Frontline supervisors have a critical responsibility of ensuring safe and healthy work practices, and they create the missing communication link between management and the craft workforce (Uwakweh, 2005; Oswald and Lingard, 2019; Ramadan et al., 2022). Multiple studies have extensively highlighted the importance and major influence frontline supervisors have on construction productivity (Liberda et al., 2003; Hewage et al., 2011; Gerami Seresht and Fayek, 2019).

Meanwhile, the construction industry is plagued with numerous workforce challenges. The skilled craft labor shortage has been a consistent and unfortunate recurring struggle for the past 3 decades (Construction Industry Institute, 2016). Moreover, recent trends show that the construction workforce has been aging rapidly, aggravating the labor shortage challenge and negatively impacting project performance in terms of cost, schedule, and safety (Taylor et al., 2016). Additionally, the construction industry lacks workforce diversity, particularly gender diversity, where women make up only 4% of the construction craft workforce in the US (US Bureau of Labor Statistics, 2021; Ramadan et al., 2022). Furthermore, the industry has not been able to consistently attract and retain skilled workers due to industry image problems, insufficient training, and an aging workforce (Tucker et al., 1999).

To understand where the literature stands on the potential solutions and recommendations to address construction workforce challenges, the top 100 search results on this topic from “Google Scholar” were analyzed. While not all of these 100 academic papers contained relevant information, 23 academic papers were identified that included recommendations to mitigate the impact of workforce challenges. Some of these papers discussed single issue recommendations, while others discussed multiple recommendation. In total, these papers made 12 unique recommendations that were mentioned for 45 times across this sample of the literature. Figure 1 shows a word cloud of the main topics discussed in the literature to address workforce challenges in the construction industry.

Technology adoption and pursuing workforce diversity were the two leading topics discussed in the literature to minimize workforce challenges. Technology adoption was discussed 12 times while workforce diversity was mentioned 9 times. The overwhelming sentiment from the literature is that technology adoption can potentially increase productivity and workforce output While technology adoption is discussed in the literature as avenues to minimize workforce challenges, these academic studies do not attempt to study impact or potential benefits of technology adoption. For this reason, this research will focus on analyzing the impact of on-site technology use on construction workforce performance and information access.

Researchers have studied and discussed construction 4.0 technologies at length (Forcael et al., 2020; Ammar and Nassereddine, 2022), including the most recent developments in blockchain technologies (Li et al., 2019; Sadeghi et al., 2022a; Sadeghi et al., 2022b). In this study, based on a review of existing work on new technologies, and a workshop discussion among industry experts on technology use on construction sites, 13 technologies were identified for this research. The existing literature has highlighted the numerous benefits of such construction technologies. For instance, barcode scanning technology has been found to be one of the most effective methods of object recognition and information collection with high speed acquisition and low production cost (Yan et al., 2013). Augmented or Virtual reality allows for real time project visualization and data collection, improving spatial cognition, detection of design errors, enhancing productivity and quality, and improving communication and collaboration (Nassereddine, 2019; Nassereddine et al., 2020). Radio Frequency Identification (RFID) chip tracking facilitates the control of construction processes throughout the lifecycle of a project using the efficient tracking of construction materials, resources, equipment, components, and systems (Valero et al., 2015). Building Information Modeling (BIM) has been found to allow faster, more efficient, and more accurate planning and construction, increasing productivity as well as cost and time savings, and enabling simultaneous contribution to the system from all project stakeholders (Guo et al., 2017; Torres et al., 2018; Dadi et al., 2022). Artificial intelligence has helped contribute to the improvement of services and business operations, and improved automation processes of companies to give them a competitive advantage (Abioye et al., 2021). Drones have contributed to overall cost reduction and fewer project delays, improving the safety record, and provided the capability of generating high resolution aerial imagery used for more accurate data collection and surveying (McGuire et al., 2016; Hatoum and Nassereddine, 2022). Robotics and autonomous machinery use has facilitated construction processes, and has been found to considerably reduce costs and time spent on construction tasks, as well as improve the quality of executed product (Prasath Kumar et al., 2016; Hatoum and Nassereddine, 2020). Battery powered tools were shown to increase worker productivity and decrease workplace hazards and injuries. (Welch et al., 2015). Prefabrication/Modularization allows for faster on-site construction, reduced on-site work, improved quality, reduced energy and emissions, and reduced overall construction costs (Lopez and Froese, 2016). Finally, quick connection systems have been found to reduce work hours, decrease the number of necessary work packages, improve quality, and facilitate construction processes (Shan et al., 2012).

While the importance and benefits of technology use have been evaluated and highlighted in existing work, no research has yet studied how on-site technology use impacts the worker’s performance and ability to access needed information. This study attempts to bridge this gap of knowledge using a survey of construction workers that investigates the types of technologies used on site, the respondent’s performance record, and the difficulty of the information access process. The aim of this paper is to analyze and understand the impact of on-site technology use on 1) the self-evaluated personal performance record, and 2) the difficulty of the process to access information, of construction workers and frontline supervisors in the construction industry.

The objective of this paper is to analyze and understand the impact of on-site technology use on the worker’s performance (self-reported performance record including safety, attendance, quality, productivity, and initiative), and information access (the difficulty of the process to receive or get access to required information) for construction workers and frontline supervisors in the construction industry. To achieve the research objective, an online questionnaire was developed in “Qualtrics” and distributed to US construction workers. Among other questions, the survey asks construction workers about the types of technologies used on their construction sites, their performance, and how hard it is to gain access to information. The survey received 2,780 responses from all 50 states. Figure 2 details the geographic distribution of the responses. States contributing most to the survey were large population states including New York, California, Texas, Pennsylvania, and Illinois.

Overall, the responses have a gender breakdown of 94.8% male and 4.9% female. This breakdown is roughly representative of the US construction craft workforce, where, according to the bureau of labor statistics, females constituted 3.9% of the construction craft workforce in 2021 (US Bureau of Labor Statistics, 2021).

The breakdown of the respondents by age showed that 30% of them are over the age of 55, 25% are in the 45–54 age group, 25% are in the 35–44 age group, 17% are in the 25–34 age group, and 3% are under the age of 25. Of the survey respondents, 41% are craftspersons/journeymen, 14% are foremen, 13% are apprentices/helpers, 13% are superintendents, 7% are general foremen, 3% are project managers, and 9% indicated that they hold another title. By education, 42% of the respondents indicated that they have received some college education, 29% have a high school equivalent education or less, 16% have completed a technical program, 11% hold a bachelor’s degree or a graduate/post graduate degree, and 2% indicated that they have another type of education.

In this study, based on a review of existing work on new technologies, and a workshop discussion among industry experts on technology use on construction sites, 13 technologies were identified for this research. The survey asked the construction workforce to select all the identified information, material, and equipment technologies that they are using on their construction sites. The following is a list of technologies that can be selected.

• Information Technologies included the following: Barcode Scanning, Virtual or Augmented Reality (VR or AR), RFID Chip Tracking, Building Information Modeling (BIM), Artificial Intelligence, Robotics, and Drones. The respondents were allowed to list “Other” information technologies used on site

Finally, the survey asked construction workers the two following questions relating to their performance record and the level of difficulty to gain access to information.

• For the last year, please rate your personal performance record (including safety, attendance, quality, productivity, and initiative) on a scale from 0 to 10, 0 being “weak”, and 10 being “superior”.

A higher value on the response to the personal performance record indicates a more positive outcome, while a lower value on the response to how hard the process to receive or get access to required information indicates a more positive outcome.

The data collected through “Qualtrics” was downloaded in an Excel format. The dataset was secured and protected using a password to ensure the data is not manipulated or accessed by unauthorized personnel to ensure data integrity. A copy of the dataset was stored separately as a backup. The data was cleaned, sorted and analyzed using Python code. To assess the reliability and internal consistency of the survey data, a Cronbach’s alpha was calculated. The survey had a Cronbach’s alpha of 0.735 with a 95% confidence interval of (0.719,0.750), which is above the 0.7 acceptable threshold.

To compare 1) the personal performance record and 2) the difficulty of the process to receive or get access to information based on whether each technology is used on site, the data was grouped based on whether the technology is used on site or not. The corresponding average of the performance record and difficulty level of accessing information is calculated and compared for the two groups.

To test if the difference between two groups is statistically significant, the non-parametric Mann-Whitney U- test was performed to obtain the p-value. This test was used to err on the conservative side of the statistical analysis due to large discrepancies in sample sizes of two groups being compared (Gibbons and Chakraborti, 1991). The students’ t-test was avoided because of the large discrepancies in sample sizes. If sample sizes in both conditions are equal, the t-test is very robust against unequal variances. However, if sample sizes are unequal, unequal variances can influence the Type 1 error rate of the students’ t-test by either increasing or decreasing the Type 1 error rate from the nominal alpha significance level. In such cases, the Mann-Whitney U-test is better behaved and performs better than the t-test (Gibbons and Chakraborti, 1991). For this reason, the Mann-Whitney U-test is more appropriate in this statistical analysis. A significance level, α, of 0.1 is considered for statistical significance (i.e., 90% confidence level).

Moreover, the total number of technologies used on site for each respondent is calculated. The average personal performance record, and the average difficulty of the process to receive or get access to information is calculated for each total. The Kendall tau-b correlation coefficient was used to test if there is a relationship between the total number of technologies uses on site and 1) the average personal performance record and 2) the average difficulty of the process to receive or get access to information. The p-value of the Kendall tau-b correlation coefficient is obtained and compared to the significance level, α, of 0.1.

This analysis will be performed for 1) the survey population as a whole representing the overall construction workforce, 2) among frontline supervisors as one group, and 3) among craft workers as another group.

Finally, a k-means cluster analysis is performed to identify which technologies had the highest impact in improving the 1) personal performance record and 2) easing the process to receive or gain access to information for the overall construction workforce. Ranking the technologies according to their scores of “personal performance record” and the “difficulty of the process to receive or gain access to information” does not necessarily mean that the first score is statistically higher or lower than the second. Therefore, cluster analysis was used to organize the data into categories or levels. Cluster analysis is a statistical method used to group data by comparing each candidate technology to the other technologies already in the cluster. If the difference between the candidate technology and the other technology already in the cluster is significant, then the candidate technology is assigned to a different cluster.

Having a better understanding of the impact of different technologies on worker performance, and ability to access information, is critical to helping construction industry leaders identify avenues that improve construction productivity as we face the challenges of a decades long labor shortage in the industry. This study presents an in-depth statistical analysis of the impact the use of 13 technologies has on the construction workforce of the United States. Using the data collected from frontline supervisors and craft professionals, respondents identified different kinds of technologies being used, and then self-evaluated their own personal performance record, and the difficulty of the process to receive or get access to needed information. Worker performance and the ability to access information was compared among those who indicated specific technologies are used on their construction sites, and those indicated that it is not used. The results of this analysis for the overall construction workforce clearly show that technology use is overwhelming beneficial for worker performance, where 12 of the 13 of the technologies, on average, caused an increase in performance, nine of which had a statistically significant positive impact, including five information technologies, both material technologies, and two equipment technologies. Moreover, 11 of the 13 technologies, on average, eased the process of information access, seven of which had a statistically significant positive impact, including five information technologies, one material technology, and one equipment technology.

This analysis was broken down among frontline supervisors and craft workers. For frontline supervisors, 12 of the 13 technologies resulted in an increase in performance. Yet, only four of those had a statistically significant positive impact, including two information technologies, one material technology, and one equipment technology. However, frontline supervisors mostly benefited from the use of technologies, specifically, information technologies, in the area of information access, where nine of the 13 technologies eased the process to receive or get access to information, seven of which had a statistically significant positive impact, including five information technologies, both material technologies, and none of the equipment technologies.

As for craft workers, nine of the 13 technologies resulted in an increase in performance. However, only three of those had a statistically significant positive impact, including two information technologies, one material technology, and none of the equipment technologies. On the other hand, craft workers did not greatly benefit from the use of technologies to ease the process of information access. Specifically, only four technologies, on average, eased the process to receive or get access to information, but only one had a statistically significant positive impact. Only two of the information technologies, on average, had a slightly positive impact on easing the process of information access, but none were statistically significant. None of the information technologies that indicated a negative impact were statistically significant either. All of the information technologies listed are sophisticated and advanced technologies that require training and expertise to operate. Potentially, the lack of training and familiarity with such systems that are not widely adopted across the industry may have hampered the realization of any benefits among craft workers. Also, information captured by such technologies are usually handled and operated at a higher level than craft workers (foremen, general foremen, superintendents, etc.), and therefore the benefit of such systems was realized at the level of frontline supervisors as the previous section indicated, where five of the seven listed information technologies yielded a statistically significant positive impact on the process of information access.

Moreover, the analysis clearly shows that there is a statistically significant strong correlation between the total number of technologies implemented and used on site and the average respondent’s personal performance record. This means that on average, the personal performance record improves as the number of technologies used increases. This is true for the overall construction workforce, frontline supervisors, and craft workers. On the other hand, there is a decent negative correlation between the total number of technologies implemented and used on site and the average difficulty to receive or get access to required information. This means that on average, the difficulty of information access decreases as the number of technologies used increases. This correlation is statistically significant for the overall construction workforce and the craft workers, but not for frontline supervisors. While the previous analysis showed that the use of specific technologies does not greatly ease the process of information access among craft workers, the use of multiple or an increasing number of technologies does. Industry leaders aiming to enhance worker performance and ease information access should consider implementing an increasing number of technologies to gain the desired benefits.

A cluster analysis of technologies identified three technologies with great potential for a strong performance record (cluster 1), eight technologies for some potential for performance (cluster 2) and one technology with limited potential for performance (cluster 3). As for information access, the cluster analysis identified four information technologies that had a great potential for decreasing the difficulty of information access (cluster 1), seven technologies for some potential for decreasing the difficulty of information access (cluster 2), and one technology with limited potential for decreasing the difficulty of information access (cluster 3). Of note, Virtual or Augmented Reality, RFID Chip Tracking, and Drones, are three technologies that are common in cluster one for performance and cluster one for information access. These three technologies are likely to have a huge positive impact for worker performance and information access on a project site. Leaders in the construction industry should consider implementing these technologies before others to maximize wanted benefits if the investment resources are limited.

Over the past decade, technological innovations have played a key role in the growth and advancement of an increasingly diverse construction industry. While, research has extensively studied the use of technology, little existing work has studied its direct impact on worker performance. The objective of this paper is to analyze and understand the impact of on-site technology use on the worker’s self-evaluated personal performance record, and the difficulty of the process to receive or get access to information in the construction industry. Using data from a survey of 2,780 construction workers, statistical analysis showed there are substantial benefits to the use and implementation of several technologies. This study found that on average, for the overall construction workforce, nine of the 13 technologies had a statistically positive impact on worker performance, and seven of the 13 technologies had a statistically positive impact on the process to receive or get access to needed information. Furthermore, increasing the number of technologies used is statistically correlated with improving the average performance and decreasing the difficulty of information access.

While this study presents a robust statistical analysis of the impact of technology use on personal performance and information access, the study does have certain limitations. The survey of construction workers does not ask any multiple-choice questions or open-ended questions that discuss specific benefits, challenges, or perceptions of the used technologies. Therefore, while this analysis can empirically measure which technologies have a positive impact on performance and information access, the study can not answer the questions related to why certain technologies yield improvements and others do not. Future research can build on this study and attempt to answer these specific questions, to build a roadmap for the leaders of the construction industry to identify critical technologies that will maximize their benefits based on their specific construction needs.