The Second Special Collection on Research Methodologies in Construction Engineering and Management is available in the ASCE Library ( special collection builds on the January 2010 special issue of this journal to provide researchers with critical insights and updated approaches to rigorously conduct research in construction engineering and management (CEM). Several of the articles included in the collection address recent technological advances such as: •extended reality simulations of humans interacting with construction situations;•unoccupied airborne systems for construction data collection and analysis;•wearable biosensors for data collection and modeling;•reinforcement learning to improve a sequence of decisions under uncertainty;•photo elicitation to enhance interview results; and•remote research charrettes to efficiently obtain group input.Other articles in the collection address how to enhance rigor when applying existing research methodologies, including topics such as: •recommendations for academia–industry collaboration;•selection of qualitative analysis techniques;•quantitative analysis for project delivery system research;•case study best practice; and•mitigating common method bias in survey (questionnaire) investigation.Articles from the 2010 special issue have been highly cited, impacting the methodological approaches of the CEM academic community. Additionally, the articles are reportedly useful as reading for introductory courses and seminars for construction engineering and management graduate students. The 2010 special issue and a 2011 article on case studies are being combined with the current special collection into the same online special collection page. We expect the additional breadth of subjects will broaden the impact of the original special issue.Impressed with the utility of the 2010 special issue, Drs. Charles T. Jahren and Patrick X. W. Zou asked to join the 2010 guest editors Drs. John E. Taylor and Edward J. Jaselskis in developing this special collection. With advice from his guest editor colleagues, Dr. Zou undertook a two-step survey process to ascertain topics of interest for the new special collection. The first step elicited unstructured responses regarding topics of interest. With input from guest editor colleagues, similar topics were combined to form a list of themes, and, in the second step, respondents were asked to rank the themes.The top-ranked themes were divided into three categories (general, specific methods, and crosscutting) included in the call for abstracts of this special collection: 1.General •What are the requirements for the proper use of surveys and questionnaires in CEM research?•What are the requirements for the proper use of case studies in CEM research, and when should a case study approach be used?•What are the requirements for validation for CEM research?•What are some typical shortcomings to avoid in quantitative CEM investigations?2.Specific methods •How should research involving the use of artificial intelligence (AI) in CEM be approached?•How should research involving augmented reality (AR), virtual reality (VR), and/or mixed reality (MR) in CEM be approached?•How can CEM research make better use of socially constructed knowledge?3.Crosscutting •How can CEM researchers reconcile qualitative or inductive theoretical approaches and findings with quantitative or deductive research approaches and findings?•What is the proper role for pragmatic (hybrid) approaches to CEM research?•What research methods properly address how construction and social value intertwine?This list reflects the generous input from a broad group of readers, authors, and the editorial board members of this journal, for which the guest editing team is deeply grateful. The call for papers was distributed through multiple channels known to the guest editing team, including social media and other distribution channels available to staff members of this journal.Prospective author teams submitted extended abstracts in response to the special collection call, and the guest editor team rated them using a matrix on responsiveness to the call, quality of the submission, experience level of the author team with the research method, and novelty. Author teams that submitted highly rated extended abstracts were invited to submit full manuscripts. The guest editor team matched the topic of each manuscript with the expertise of at least one editorial board member of this journal and asked them to provide a review in addition to requesting a review from at least one other domain expert. This process resulted in a collection of new articles evenly split between topics involving technologies that were not widely in use for construction engineering and management research in 2010, and articles that address ways to add rigor to methods that were already widely used in 2010. Each article is summarized in a separate paragraph in the following.The guest editing team gratefully acknowledges survey respondents, authors, reviewers, journal staff and editorial board members, and readers for supporting this effort. We hope the articles contained in this special issue can generate a dialogue about rigorous application of the numerous methods we use to assert our contributions to knowledge. Therefore, we close this introduction with a similar disclaimer to the previous special issue: This special collection is but a waypoint on a journey. We look forward with certainty that new technologies and better understandings about how to approach construction engineering and management research will provide new opportunities to update research methodologies that will merit examination and discourse. We hope the dialogue continues about how to improve the rigor and contributions to knowledge of our collective research endeavors; perhaps in an update to this special collection.Summaries of Articles in Special CollectionNew Technologies in Construction Engineering and Management Research•Li et al. (2022) presents a new technology that was not widely available in 2010, when the prior special issue was published. This approach can help with making assessments of human behavior under controlled (simulated) conditions. There is some similarity between this paper and another special collection contribution regarding training with biosensor data. The paper includes a detailed framework for selecting which extended reality (XR) to use, how to develop experimental methods, and how to approach validation. Two case studies are also provided to illustrate the use of the framework.•Zhang et al. (2022a) provides a newer area of investigation that was not covered in the 2010 issue. This paper provides a full review of hardware and software required for the use of unoccupied airborne systems (UAS) in construction engineering and management research, and then provides considerable emphasis on data analysis methods. The paper continues by providing a perspective on how UAS can serve as a tool for future CEM research.•Lee and Lee (2022) discusses a newer technological approach to research that was not covered in the 2010 issue. Wearable biosensors have the potential to greatly enhance our understanding of the psychophysiological state of construction workers, which can influence important aspects of performance including safety, productivity, and quality. Developing models from the data output of biosensors is challenging. Because of complicated relationships among independent variables and the dependent variable, neural networks that require training are often used to analyze the data. Selection of the correct training and testing scheme is critical to avoid overfitting or underfitting the data. This paper provides evidence regarding which training methods provide the most generalizable results.•The topic of Asghari et al. (2022) was not covered in the 2010 special issue. This article clearly defines where reinforcement learning can be applied in construction engineering and management and classifies subvariants of the same. The technique is especially useful for Markov decision process problems involving the development of optimal strategies for making a sequence of decisions that have uncertain rewards and future states. The article acknowledges that developing a reinforcing learning model requires considerable effort and gives specific recommendations regarding situations where such an investment is most likely to be worthwhile.•Simmons et al. (2022) discusses photo elicitation, which integrates the use of images during interviews to help provide focus and additional resources to the interview with the goal of providing additional rich data from the interview results. The article highlights the value and distinguishing features that photo elicitation adds to a standard interview and proposes a process for including it as a research method. A case study is provided to illustrate the use of this technique. This topic was not included in the 2010 special issue.•Gibson et al. (2022) presents research charrettes, which are structured workshops that facilitate data collection from participants combining the benefits of surveys, interviews, and focus groups in one setting. In the 2010 special issue on research methods, the lead author and a coauthor provided details of how to use this method in a CEM context. This article updates the method by describing the use of virtual meeting techniques, giving advantages and disadvantages as well as practical advice in executing remote research charrettes. Comparisons are made regarding the cost, type, and quality of input provided using both in-person and remote charrettes.Adding Rigor to Construction Engineering and Management Research•Gomes Araújo and Lucko (2022) documents the growth of case study research and compile best practice for the same from the social sciences using the tenets of the scientific method as a framework. In a review of a diverse set of construction case studies, example applications of the best practices are exposed. Finally, criteria are proposed that are intended to be helpful for readers in planning case study research in construction.•Franz et al. (2022) provides a critical review of project delivery system research with a complete discussion of the considerable challenges of working in this area. The authors review approaches to data collection and analysis with comments on which approaches are the most appropriate in varying circumstances. The article continues with suggestions about how to discuss results and points out that greater knowledge on the topic could be garnered if standardization would be increased among investigators.•Zhang et al. (2022b) examines a common issue associated with the analysis of questionnaire survey data. Common method bias occurs when the results and/or conclusions of an investigation are biased from systematic correlations caused by their common source. Such bias threatens the validity of the results and conclusions of the investigation. The authors propose procedural and statistical controls to mitigate the impact of this issue.•Spearing et al. (2022) shows how three different qualitative analysis techniques applied to the same case study can be used to extract different data that can highlight different aspects of the case leading to different conclusions. Deductive content analysis provides a somewhat quantitative approach and is useful for comparison to existing frameworks. Hybrid content analysis can reveal new insights in a detailed analysis highlighting emergent challenges. Constant comparative analysis can reveal large-scale emergent challenges and categories in a data set as well as the relationships between them.•Son et al. (2022) analyzes 11 case studies of Construction Industry Institute (CII)–funded research projects that were executed between 1988 and 2011, examining the output of 39 interviews of project participants. Comparisons are made between projects that were successful and unsuccessful. While challenges in developing initial alignment existed for both successful and unsuccessful projects, challenges with member attendance and effective leadership were only found in unsuccessful projects. A narrative that illuminates and provides context for detailed findings is provided.References Franz, B., R. Leicht, M. El Asmar, and K. Molenaar. 2022. “Methodological consistency for quantitative analysis and reporting in project delivery system performance research.” J. Constr. Eng. Manage. 148 (9): 04022080. Gibson, G. E., Jr., H. Sanboskani, M. El Asmar, and V. Aramali. 2022. “Employing technology to enable remote research charrettes as a method for engaging industry and uncovering best practices: A novel approach for a post-COVID-19 world.” J. Constr. Eng. Manage. 148 (11): 04022122. Lee, G., and S. Lee. 2022. “Importance of testing with independent subject and contexts for machine-learning models to monitor construction workers’ psychophysiological reponses.” J. Constr. Eng. Manage. 148 (9): 04022082. Simmons, D. R., M. Polmear, H. Bae, and C. McCall. 2022. “Applying a new lens: Using photo elicitation in construction engineering and management research.” J. Constr. Eng. Manage. 148 (8): 04022074. Son, J. W., J. O’Brien, and S. R. Thomas. 2022. “Recommended practices for effective management of academia–industry collaborative research teams in construction management.” J. Constr. Eng. Manage. 148 (8): 04022075. Spearing, L. A., A. Bakchan, L. C. Hamlet, K. K. Stephens, J. A. Kaminsky, and K. M. Faust. 2022. “Comparing qualitative analysis techniques for construction engineering and management research: The case of Arctic water infrastructure.” J. Constr. Eng. Manage. 148 (7): 04022058. Zhang, S., S. M. Bogus, C. D. Lippitt, V. Kamat, and S. Lee. 2022a. “Implementing remote sensing methodologies for construction research: An unoccupied airborne system perspective.” J. Constr. Eng. Manage. 148 (9): 03122055.

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