Journal of Construction Engineering and Management

Megaproject management has continuously faced new challenges that require new theories and methods. The network perspective has been an increasing trend in this context. However, there is a lack of consensus concerning the applicability of network thinking to megaprojects and the status of network analytic methods with respect to the facilitation of megaproject management. This paper aims to present the full status of the network perspective in the context of megaproject management, integrate network thinking, clarify the application of network analytic methods, and accelerate the network paradigm in the context of megaproject management. This research included a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analyses framework. A total of 152 relevant studies were retrieved from three major databases and reviewed. The findings of this study were based on a novel integrated framework that included the constitutive and the analytic perspectives of the network paradigm. The constitutive perspective sheds light on the megaproject network, its definition, characteristics pertaining to network thinking, and megaproject governance through network logic. The analytic perspective illuminates the applicability of network methods to megaproject issues in detailed subknowledge areas, including stakeholder management, risk management, communication, coordination and collaboration, knowledge management, and interface management. Furthermore, these two perspectives were generalized and converged into pure categories of network research, including network mechanisms and antecedents, network patterns and dynamics, network functions and outcomes, and network controls and optimization. Based on meticulous discussions concerning the integrated framework, limitations and future directions for progress in this area were proposed to facilitate a deeper exchange of ideas in the context of any future research efforts.

In recent years, offsite construction has attracted increasing attention, which has led to the broader use of precast concrete (PC) construction methods. Accordingly, the need for safety management in PC production plants is increasing. However, despite the potentially hazardous tasks performed in PC production plants, such as the handling and transportation of heavy materials, well-established safety management guidelines are lacking. Therefore, this study aims to propose a safety management guideline tailored to PC production plants. To this end, this study examined the existing safety manuals and extracted safety management items that need to be monitored to manage safety risks in PC production plants. In addition, this study investigated critical safety management items by administering a questionnaire-based survey to plant workers and managers on the perceived importance and management performance of the items in the guideline by using the importance-performance analysis method. The results indicated that safety management is relatively weakly implemented compared with workers’ perceived importance of safety management. In addition, the results indicated that the focus should be on work- or worker-related safety management items, such as the continuous monitoring of workers’ access to hazardous areas and the proximity between workers and dynamic risk factors, such as the loading of materials and equipment operation. The results of this study contribute to the development of a more systematic and detailed guideline for the safety management of PC production plants to continuously manage safety risks and prevent safety accidents.

In an environment of fierce market competition, companies commonly implement several projects simultaneously and employ programs to manage these multiprojects. The program is scheduled at the tactical layer to coordinate multiple projects, each scheduled independently by its project leader. The discrete time-cost trade-off problem (DTCTP) is one of the most significant project scheduling problems, and numerous studies have been conducted on DTCTP for several decades. However, there is still a notable gap in research on DTCTP in multiproject scenarios. A program is a popular approach for managing multiple projects that are related to each other. This paper proposes a new hierarchical discrete time-cost trade-off problem (HDTCTP) for the program that integrates the scheduling problems at the different layers. We clarify the interactions between different layers in program scheduling and introduce the model of a program cascading scheduling process. The program at the upper layer allocates budgets and targets to projects at the lower layer, where each project is scheduled independently. The scheduling results at the lower layer are fed back to the program for further coordination and optimization. This program iteratively coordinates, redistributes budgets, and assigns targets until the overall optimization is achieved. Then the HDTCTP is described and formulated based on analytical target cascading (ATC). To solve the presented HDTCTP, a top-down iterative algorithm is proposed according to the characteristics of HDTCTP in the frame of ATC. A real-life program case verifies the usability of the presented approach, and the results of experiments show that the presented algorithm based on ATC can efficiently solve the presented HDTCTP for programs.

Robotic teleoperation has shown great potentials in various construction applications. With the advancements of virtual telepresence and motion capture technologies, bilateral teleoperation has been tested in precision construction operations, where a human operator can drive the motion of a remote robot with their natural body motions. A significant challenge is that because of the mismatch between robot mechanic design and the human body, such as a different number of joints of a robotic arm and a human arm, the recovered robot motions driven by human hand motions may not be desired, leading to unintended consequences including collision. This study presents a proactive collision avoidance system based on the real-time prediction of human hand motions. The proposed method, Feature-based Human-Motion Prediction (FHMP), stores streaming motion data into a data pool, quantifies the spatiotemporal relationship between gaze focus and hand movement trajectories, and segments and clusters the streaming data into different pattern groups based the motion pattern similarity. Different machine learning (ML) models are trained for each of the pattern groups. During the real-time prediction, whenever a pattern change is detected, the ML model is transitioned to a new model that matches the new pattern. A data buffering approach is used to reuse the old data and old ML model for a certain period of time before the new ML model is well trained, to ensure an uninterrupted real-time prediction of human hand motions. The gaze and hand motion data of a human subject experiment ($\mathrm{n}=120$) for pipe skid maintenance was used to test the system in a virtual reality (VR) environment. The result shows that FHMP can support anticipatory collision avoidance in bilateral teleoperation with a better prediction performance. Future research could enable testing the method on real robots for more believable results.

Visual management (VM) is a strategy for information management that aims to increase process transparency, one of the main principles of the lean production philosophy. It has been widely used in manufacturing, but the use of VM in construction is still very limited. As the development of VM is mostly based on trial and error efforts, it is useful to produce taxonomies of VM practices, which can lead to typologies to support the understanding of concepts and the development of theories. The main contribution of this paper is a typology of concepts related to VM that extends the contribution of taxonomies previously proposed in the literature, by emphasizing the role of collaboration and communication, as well as the need to integrate VM practices into managerial routines and to other practices. This typology has been used to assess the implementation of VM in 16 construction sites located in Brazil. Moreover, it has identified improvement opportunities, based on the comparisons of VM practices used in those sites to a sample of manufacturing plants.

Collusive bidding has been a major concern for both antitrust authorities and clients because it can inflate winning prices to artificially high levels. Deciding to initiate such an illegal business competition is subject to external environmental factors (EEFs). Understanding the EEFs’ impacts on collusive bidding decisions allows institutional arrangements to encapsulate free and fair competition. Although previous studies have examined the EEFs separately, few have explored their synthesis in determining collusive bidding decisions. This study decomposed the EEFs into three categories: economic, industrial, and geographical (EIG), and detected their effects on collusive bidding decisions by using 254 collusive bidding cases gathered from the Chinese construction industry. Results of the data analysis indicated that as a key EEF, industrial competition has a positive effect on bidders’ collusive willingness and collusive team number. However, its impacts on collusive bid prices are negative. In addition, the coupling of economic development and industrial competition positively affects bidders’ collusive prices. These findings provide new insight into the essence of the EEFs and support the formulation of countermeasures to deter bidders from seeking a to-collude decision.

Theory of operations research is applied in this paper. The minimum total cost of steel reinforcement work was used as the objective function, and a three-step mathematical model to optimize the rebar cutting-stock was developed for building the finished rebars. Model I was applied to determine the whole raw material rebars that build the finished rebar. Models II and III were then applied to obtain the cut rebars needed to form the remaining rebar length of the finished rebar. The optimization results of the three-step model were combined to obtain a rebar cutting-stock list and method. Note the following results: (1) Considering comprehensively the effects of labor and material costs in the models led to the least rebar waste and number of welding spots, and thus the minimum total cost was reached. (2) In Model I, the minimum of cost sum of the rebar waste and welding spot count was taken as the objective to use the longer whole rebars first to minimize cutting and welding operations, and reducing the remaining rebar count reduced the subsequent calculation workload of Models II and III. (3) In model II, a constraint that every rebar section length be restricted to three nonoverlapping cases was imposed, which ensured that welding spots were not located in the midspan of a finished rebar. (4) In the practical case study considered, the rebar waste rate was 0.44%, and the finished rebars including 1 or 2 welding spots accounted for 90% of all finished rebars, which shows that the overall number of welding spots was small. The optimization not only clearly saved the rebar material cost but also decreased the labor cost of cutting and welding.

Although control strategies are widely used to mitigate opportunistic behaviors, their implementation in consulting projects with intangible outputs can be challenging. Whether or not control strategies are effective to curb consultants’ opportunistic behaviors remains unknown. To fill the knowledge gap, this study examines the effects of clients’ control strategies on consultants’ opportunistic behaviors in consulting projects, with requirement risks and information asymmetry as moderators. The results from a questionnaire survey of 360 architectural and engineering design consultants showed that outcome control breeds opportunistic behaviors. This effect is exacerbated in projects with high levels of requirement risks or information asymmetry. It was also found that behavior control constrains opportunistic behaviors. The findings complement the project management literature by providing empirical evidence for the role of control strategies in curtailing design consultants’ opportunistic behaviors and by revealing that the tangibility of project outputs is an important contextual factor for mitigating opportunistic behaviors.

The literature on destructive styles of leadership usually suggests the negative consequences at the individual and firm level, whereas the combined effect of dark dyads with susceptible followers lacks attention. Drawing on the cognitive appraisal theory, we examined the underlying psychological mechanism through which tyrannical leadership undermines employees’ task performance via work withdrawal behaviors in construction projects. Moreover, the moderating effect of subordinates’ Machiavellian personality trait on the relationship between tyrannical leadership and supervisor-rated subordinates’ task performance was investigated. We collected data using questionnaires from 215 leader-subordinate dyads working in the construction industry. The results supported the negative effect of tyrannical leadership on task performance as well as the mediating effect of work withdrawal. This study provides evidence that tyrannical leadership induces work withdrawal behaviors, which in turn reduces subordinates’ task performance. Moreover, the interactive effect of subordinates’ Machiavellian personality trait with tyrannical leadership style positively impacts the subordinate’s task performance, confirming the effectiveness of leaders-follower dark dyads in construction projects. The study expands theoretical as well as empirical evidence on destructive leadership in construction projects and demonstrates the relationship among psychological variables and their effects on task performance through the unique dynamics of the follower’s personality trait in the construction industry.

The use of unmanned rollers for the compaction of the earth-rockfill dams is of great significance. However, collaborative control of multiple unmanned rollers at the construction site, enabling each unmanned roller to operate on the optimal rolling path with optimal rolling parameters to improve compaction efficiency, is an unaddressed challenge. There are three technical barriers, i.e., how to plan the optimal rolling path, how to determine the optimal rolling parameters, and how to control the unmanned roller fleet (URF) based on the optimal rolling plan. This study aims to solve these technical barriers. First, a Stripe-genetic algorithm (GA) path planning method that can balance the task load of multiple unmanned rollers is proposed. Second, a spatial global rolling parameter decision (SGRPD) method is proposed to maximize the stripe compaction efficiency. Finally, a URF-based intelligent compaction system composed of the control module, communication module, remote server, and cloud platform integrated with Stripe-GA and SGRPD is proposed. Field experiments conducted on the construction site of a 295-m high earth-rockfill dam have verified the effectiveness and advantages of the proposed system. The results demonstrated that the proposed system could operate multiple unmanned rollers to achieve higher compaction quality and efficiency by compaction with optimal rolling path and parameters. Compared with the monitoring-based manual compaction method, its compaction quality and efficiency are improved by 2.57% and 24.18%, respectively. The proposed system has the potential to be applied to earthwork constructions, such as roads and airports.

This study examines how prior relationships between construction industry project teams influence work outcomes. Drawing on social network literature, we theorize that factors like the frequency of partner interactions, resource requirements, and level of competition impact performance. Using a dataset of public infrastructure projects in the State of California, we test how network structures (structural holes and Simmelian ties) affect project performance. Longitudinal network analyses reveal a strong positive effect of cliques on project performance in the construction industry. Even when tested under varying conditions of stress—competition and access to valuable resources—the positive effect of cliques remained intact. This study links the social networking theory to construction cost overruns and shows that cliques between contractors and subcontractors affect project performance.

This paper presents the study of the execution speed influence on displacements of soil-nailed structures with a vertical face in urban areas, intended as the basements of buildings. Fifteen structures in the Metropolitan Region of São Paulo, Brazil, were evaluated. The main objective was to demonstrate the evolution of the construction technique, making its application possible in conditions considered by the technical community to be unfeasible due to the inevitable resulting displacements. The studied structures ranged from 6.8 to 22.7 m high. Soil-nailed walls were instrumented with displacement gauges (deflectometers) positioned near the top, which monitored the structures’ horizontal displacements. The execution time of each work was evaluated in terms of the sectoral excavation advancement, which was 3, 4, or 5 days, as well as the production in square meters per day. The results showed works that were less displaceable than the estimates proposed in the literature. The impact of execution speed also was evidenced, showing that the opening interval of new work fronts resulted in significant differences, with displacements ranging from 20 to 23 mm, from 10 to 15 mm, and from 0 to 6 mm, respectively. Two equations proposed to predict horizontal displacement based on the retaining structure height and daily work productivity are presented. The study can enable works that may be considered impractical if analyzed from the displacement premises of the literature; moreover, this study disseminates design and execution standards to obtain less-displaceable works.

Deep learning image captioning methods are able to generate one or several natural sentences to describe the contents of construction images. By deconstructing these sentences, the construction object and activity information can be retrieved integrally for automated scene analysis. However, the feasibility of deep learning image captioning in construction remains unclear. To fill this gap, this research investigates the feasibility of deep learning image captioning methods in construction management. First, a linguistic schema for annotating construction machine images was established, and a captioning data set was developed. Then, six deep learning image captioning methods from the computer vision community were selected and tested on the construction captioning data set. In the sentence-level evaluation, the transformer-self-critical sequence training (Tsfm-SCST) method has obtained the best performance among six methods with the bilingual evaluation (BLEU)-1 score of 0.606, BLEU-2 of 0.506, BLEU-3 of 0.427, BLEU-4 of 0.349, metric for evaluation of translation with explicit ordering (METEOR) of 0.287, recall-oriented understudy for gisting evaluation (ROUGE) of 0.585, consensus-based image description evaluation (CIDEr) of 1.715, and semantic propositional image caption evaluation (SPICE) score of 0.422. In the element-level evaluation, the Tsfm-SCST method achieved an average precision of 91.1%, recall of 83.3%, and an F1 score of 86.6% for recognition of construction machine objects by deconstructing the generated sentences. This research indicates that deep learning image captioning is feasible as a method of generating accurate and precise text descriptions from construction images, with potential applications in construction scene analysis and image documentation.

Because existing buildings occupy most of our built environment, there is an urgent need to upgrade them considering building sustainability criteria. Therefore, many optimization models were proposed to find the optimum upgrading solution that improves the building sustainability while minimizing its costs using traditional sustainability rating tools [e.g., Leadership in Energy and Environmental Design (LEED) (US), Building Research Establishment Environmental Assessment Methodology (BREEAM) (UK), and others]. The variations among these tools hinder their application outside their original countries, calling for global tools. Therefore, this study contributes to the knowledge by developing a novel multiobjective optimization model to solve the life cycle cost (LCC)-sustainability trade-off for building upgrading using a generic sustainability rating tool. This tool includes seven sustainability criteria and 29 subcriteria, resulting in 134 decision variables. The proposed model finds the near-optimum upgrading solutions that minimize their LCC while improving the building sustainability using the multiobjective artificial immune system algorithm. The model was applied to a real case study of a large building in Montreal, Canada. The obtained solutions covered almost all the ratings ranges from pass to outstanding and showed the trade-offs between the building sustainability and LCC. This research is a step toward adopting a global sustainability rating tool to find the optimum building upgrading solutions that can address the regional limitations of the traditional rating tools.

Fall from height (FFH) is the most significant cause of occupational fatalities in the construction industry, accounting for approximately 54% of all accidents. Such fatalities have decreased considerably due to the use of personal protective equipment (PPE). However, the manual monitoring of compliance to PPE is complex and challenging for site managers. Automation in construction safety presents multiple solutions for monitoring safety at sites. In this study, a smart safety hook (SSH) monitoring method is proposed to eliminate the risk associated with FFH accidents by integrating computer vision [closed-circuit TV (CCTV)-imagery] and Internet-of-Things (IoT)-based [inertial measurement unit (IMU)IMU and altimeter] monitoring technologies. The proposed monitoring approach is validated through five real-time scenarios: (1) attached to the scaffolding and $h>1.82\mathrm{m}$ (6 ft), (2) attached to the worker and $h>1.82\mathrm{m}$, (3) unattached and $h>1.82\mathrm{m}$, (4) $h<1.82\mathrm{m}$, and (5) outside of the risk zone. The proposed technique aims to relieve the site manager’s or safety engineer’s workload by smartly and instantaneously alerting of workers’ unsafe behavior (via alarm, LED blinking, and bounding box on live camera feed). Moreover, the IoT-based hardware setup goes to low power to extend the battery life when there is no unsafe behavior. The experimental results demonstrate that the proposed solution exhibits more than 98% accuracy for real-time detection and classification. Furthermore, it can be extended to monitor several workers and their location data in the future.

Various studies developed models and decision support tools to assess the feasibility and optimize the use of modularization. However, none has explored the schedule benefits of modular construction. This paper fills this knowledge gap. To this end, the authors completed the following: (1) analyzed the criticalities of the various modular risk factors on potential schedule savings using data collected from 48 industry professionals, (2) investigated the schedule savings associated with the use of modularization using data collected from 68 modular construction projects, and (3) developed an interrelated assessment model to calculate the schedule savings of using modularization. The provided model was verified using extreme conditions, surprised behavior tests, and sensitivity analysis. Also, it was validated by industry experts. The results show that design and engineering issues, regulatory and organizational matters, and resources and technology aspects are among the top parameters affecting schedule savings of modularized construction projects. This research adds to the body of knowledge by developing a decision-making benchmark that can assist project stakeholders in making proactive decisions, suitable mitigating strategies, and early corrective actions to ensure maximized capitalization on the schedule benefits of modularization in the construction industry.

Railways are an efficient transport mode, but building and maintaining railway tracks has a significant environmental impact in terms of ${\mathrm{CO}}_2$ emissions and use of raw materials. This is particularly true for slab tracks, which require large quantities of concrete. They are also more expensive to build than conventional ballasted tracks, but require less maintenance and have other advantages that make them a good alternative, especially for high-speed lines. To contribute to more sustainable railways, this paper aims to optimize the design of one of the most common slab track typologies: RHEDA 2000. The main objective is to reduce the amount of concrete required to build the slab without compromising its performance and durability. To do so, a model based on finite-element method (FEM) of the track was used, paired with a kriging metamodel to allow analyzing multiple options of slab thickness and concrete strength in a timely manner. By means of kriging, optimal solutions were obtained and then validated through the FEM model to ensure that predefined mechanical and geometrical constraints were met. Starting from an initial setup with a 30-cm slab made of concrete with a characteristic strength of 40 MPa, an optimized solution was reached, consisting of a 24-cm slab made of concrete with a strength of 45 MPa, which yields a cost reduction of 17.5%. This process may be now applied to other slab typologies to obtain more sustainable designs.

Construction projects are cost-intensive, complex, and challenging ventures that often place employees in a harsh work environment filled with safety hazards. Employees facing such rigid characteristics must also adhere to strict time schedules to complete construction tasks; as a result, construction employees experience high stress that can deplete their psychological resources. Consequently, construction employees develop intentions to quit the organization, commonly known as turnover intentions. This study evaluates the role of ethical leadership in reducing employee turnover intentions in the construction industry. In this study, work exhaustion and psychological contract breach are considered prime antecedents of employee turnover in the construction industry. We collected data from satellite town development projects in Pakistan and performed regression path analysis to assess hypothesized relationships between construction employee turnover intention and its antecedents. The moderating effects of ethical leadership in reducing the impacts of work exhaustion and psychological contract breach were studied. Results revealed that work exhaustion and perceived psychological contract breach positively correlate with construction employee turnover intention; however, ethical leadership significantly reduced the impacts of work exhaustion and psychological contract breach on employee turnover intention. The findings and practical implications of this study can be used to help construction managers act ethically in their managerial duties to retain employees and continue to motivate employees to complete project tasks.

Although some researchers and practitioners have perceived that the current reliance on offsite construction methods is high, other studies have hypothesized that the use of offsite construction techniques is still considered to be somehow limited. To this end, this paper aims to quantify the state of practice of offsite construction in terms of current trends and future prospects for the overall industry as well as the following main sectors: industrial, building and commercial, and infrastructure. First, a questionnaire was formed, pilot-tested, distributed, and completed by 100 construction practitioners. Second, the questionnaire’s internal and external validity and reliability were examined using statistical analysis. Third, the research findings were validated. The results showed that the future offsite construction operations will be different from the current operations by shifting from single-trade fabrication to modularization, shifting from customized offsite construction components to standardized offsite construction components, shifting from permanent offsite construction structures to relocatable or portable offsite construction structures, and shifting the reliance on single-skilled labor to multiskilled labor. In addition, 87% of industry practitioners perceive that the future offsite construction growth rate in the coming decade will be higher than that of the previous decade. This research also showed that offsite construction will become the norm rather than the exception because (1) the current average offsite construction percentage of 33.64% will substantially grow to reach an average of 54.9% in the future, (2) the offsite construction industry will grow 4.33 times, on average, in the coming decade, (3) companies are planning to increase their offsite construction utilization rate by an average of 5.03-fold, and (4) the offsite construction automation percentage will increase by 7% in the future. The research outcomes also provided guidance on the key technologies that the industry shall currently invest in and consider leveraging in the future.

Offsite construction has showed great potential in addressing many of the industry’s problems. While multiple research efforts have been directed at examining different offsite construction aspects, few studies investigated the workforce-related aspects. Therefore, this study aims to address this knowledge gap by examining the impact of offsite construction on the workforce, as represented by the following five workforce categories: offsite, onsite, engineering and design, construction and fabrication, and administrative workforce. To this end, this paper (1) determined the impact of offsite construction on the skillset (reskilling or upskilling) and the demand (shrink or growth) for the offsite and onsite construction workforce occupations; (2) evaluated the impact of offsite construction on the technical and managerial skills of the engineering, construction, and administrative workforce occupations; and (3) identified the specific required skills that need to be incorporated into the offsite construction training programs for the workforce. The results showed that the skillsets for all offsite and onsite workforce occupations need to be upskilled. While the demand for the offsite construction workforce will increase, the demand for around 79% of the onsite workforce occupations will decrease. The findings also reflected that a total of 20 main skills are needed for the offsite construction engineering and design workforce, a total of 24 key skills are needed for the construction and fabrication workforce involved in offsite construction operations, and 22 skills are needed for the offsite construction administrative workforce. This study adds to the body of knowledge by helping offsite construction industry practitioners in workforce planning and management, in the prioritization of training needs and programs, and in identifying the required skillset to improve the quality of the workforce involved in the offsite construction operations.

Tower cranes are very common at construction sites. As workers focus most of their attention on their own tasks, their ability to detect changes in the surrounding environment is reduced, and it is difficult to avoid the collision risk of heavy falling objects. To solve this problem, this study establishes a dynamic collision prewarning mechanism for tower crane construction based on vision and trajectory analysis by tracking and predicting the trajectories of loads and workers. Specifically, the proposed dynamic collision prewarning mechanism consists of three parts. First, Fairmultiple object tracking (FairMOT), a multiple object tracking algorithm based on deep learning, is used to detect and track workers and loads, and time-series data of their positions are obtained. Then a trajectory prediction model based on a transformer is applied to predict the trajectories of objects in the future (10 s) based on the historical data. Finally, safety rules are established by considering the locations, speeds, shapes, and sizes of loads and workers and their trajectories over a period of time. Risk levels for each worker are assigned to reduce the risk of collisions between workers and loads. Finally, the performance of the models is evaluated at a construction site. FairMOT has good tracking performance and can continuously track objects with short occlusion (2 s). Transformer-based trajectory prediction model has higher accuracy than other methods [e.g., social generative adversarial network (GAN), social long short-term memory (LSTM)]. The results of the study show that the proposed method can accurately predict the unsafe approach of workers and loads. The safety prewarning mechanism proposed in this study can help improve the safety of tower crane construction.

Construction is a dynamic sociotechnical process, consisting of ongoing interdependencies between people and the built environment. Accordingly, finding solutions to construction challenges when they arise requires understanding the interactions between social and technical factors. Over the past three decades, qualitative methods have been increasingly applied in construction engineering and management (CEM) research to understand challenges within this industry. However, there remains a lack of resources in the CEM literature on qualitative method selection and implementation specifically applicable to this domain. Without such guidance, it can be challenging to choose the most appropriate research methods, which can limit theoretical and practical contributions. To begin to address this gap, this paper offers an overview and comparison of three qualitative data analysis techniques—ranging in their use of induction, prevalence in CEM research, and ability to answer different types of research questions. These analysis techniques are applied to the same semi-structured interview data drawn from a case study on water infrastructure in rural Alaska. Insights gained from each method are compared to illustrate the utility of each technique. To the authors’ knowledge, this study is the first qualitative method-comparison paper published for a CEM audience. Based on the comparison findings, choosing a deductive content analysis can allow for full characterization and quantification of a data set and discussion of results in relation to a predefined framework, such as a framework based on design and construction standards. A hybrid content analysis can expose new, detailed insights for an existing framework by allowing emergent themes to arise and be quantified. Conversely, a constant comparative analysis can reveal emergent trends and uncover the reasons why these trends occur based on connections between prominent themes, which can help CEM researchers develop new theories. Overall, this study helps advance the sociotechnical side of CEM research by enabling the discipline to better address the industry’s complex challenges.