RAPID/Collaborative Research: An Empirical Investigation of Risk Preferences of Transportation Construction Workforce Managers under COVID-19 Pandemic Uncertainties

RAPID/协作研究：COVID-19 大流行不确定性下交通建设劳动力管理者风险偏好的实证调查

• 批准号: 2035299
• 负责人: Mohsen Shahandashti
• 金额: $ 4万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035299&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Empirical; Investigation

The objective of this Rapid Response Research (RAPID) project is to identify new challenges and determine factors impacting workforce decision-making processes in the transportation construction industry under COVID-19 pandemic uncertainties. Never in the history of modern transportation infrastructure construction, has the industry had to adapt to a nationwide threat to their workforce and changing public health guidelines and constraints. How are workforce managers seeking to operate, maintain, and construct lifeline transportation infrastructure projects without risking the safety and health of their vital resources (workers, operators, engineers, fabricators, and project managers)? Workforce decision makers are dealing with a daunting task. The public expects no disruptions in receiving critical infrastructure services in these challenging times. And more importantly, the public expects limited risk of virus infection while using these critical infrastructures. COVID-19 continues to have substantial impacts on workstyle in the transportation construction industry. The pandemic challenges threaten the operations of transportation infrastructure systems that are facing ever-increasing impacts of weather-related disasters. Yet, transportation infrastructure construction and inspection cannot be delayed. Transportation infrastructure is essential to not only fulfill the daily needs of human beings but also combat the COVID-19 outbreak. The success of this research is contingent upon the collection of time-bound data on the workforce managers’ thought processes during the COVID-19 pandemic. This project will not only enhance the science of decision making under pandemic uncertainty, but also provide critical information for the transportation construction industry regarding best practices during a pandemic and effective strategies for adapting to the uncertainties emerging and evolving from this unprecedented event.Because of the exploratory nature of this research, PIs will use a combination of methods, such as surveys and follow-up interviews with key informants in state transportation agencies, to identify new challenges of workforce decision-making during the COVID-19 pandemic and determine factors impacting decision-making processes. Supporting documents will be collected from the key informants following the interviews (e.g., collecting and analyzing their revealed choices manifested in COVID-19 actions, organizational COVID-19 daily updates, internal operations memos, and public news) for further content analysis. The PIs will assemble a variety of questions that enable a thorough investigation of critical factors impacting workforce decision-making in the unique pandemic condition. These factors will include major pandemic issues, such as essential versus non-essential operations, construction site shutdown (planned or unplanned), project suspension at any stage of construction (for a designate or unknown period of time), restarting construction, project continuation, suspension, and delay, expedited construction operations to take advantage of the declined traffic and receive early completion incentives, and occupational safety policies (social distancing and wearing N95 masks). Employing the ephemeral empirical evidence rigorously collected in this RAPID project, the PIs will be able to examine and model the risk preferences of workforce decision makers under pandemic uncertainties. Successful identification of the factors impacting decision-making processes will be critical for the develop of a novel multi-attribute utility choice models that capture the risk preferences of decision makers in the transportation construction industry under pandemic uncertainties. Revealing factors impacting decision-making processes will pave the way to inform workforce policies under pandemic uncertainties. The findings will be disseminated widely through the creation of easy-to-understand and practical educational materials that are integrated with the research components so that they can improve each other. This project will also engage underrepresented students from diverse backgrounds in research activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

此快速反应研究（RAPID）项目的目标是在COVID-19疫情不确定的情况下识别新挑战，并确定影响交通建设行业劳动力决策过程的因素。在现代交通基础设施建设的历史上，该行业从未不得不适应对其劳动力的全国性威胁以及不断变化的公共卫生准则和限制。劳动力管理人员如何在不危及其重要资源（工人、操作员、工程师、制造商和项目经理）的安全和健康的情况下，运营、维护和建设生命线交通基础设施项目？劳动力决策者正在处理一项艰巨的任务。公众预计，在这个充满挑战的时期，关键基础设施服务的接收不会中断。更重要的是，公众希望在使用这些关键基础设施时病毒感染的风险有限。COVID-19继续对交通建设行业的工作方式产生重大影响。大流行的挑战威胁到交通基础设施系统的运营，这些系统正面临越来越多的天气相关灾害的影响。然而，交通基础设施建设和检查不能拖延。交通基础设施不仅对满足人类的日常需求至关重要，而且对抗击COVID-19疫情至关重要。这项研究的成功取决于在COVID-19大流行期间收集劳动力经理思维过程的有时限数据。该项目不仅将提高流行病不确定性下的决策科学性，而且还将为交通建设行业提供关键信息，以了解流行病期间的最佳做法以及适应这一前所未有的事件所产生和演变的不确定性的有效策略。由于该研究的探索性，PI将使用多种方法，例如对州交通机构的主要知情人进行调查和后续访谈，以确定COVID-19大流行期间劳动力决策的新挑战，并确定影响决策过程的因素。在面谈后，将向主要举报人收集证明文件（例如，收集和分析他们在COVID-19行动、组织COVID-19每日更新、内部运营备忘录和公共新闻中显示的选择），以进行进一步的内容分析。PI将收集各种问题，以便彻底调查在独特的大流行情况下影响员工决策的关键因素。这些因素将包括主要的流行病问题，如必要与非必要的操作，建筑工地停工（计划内或计划外），任何施工阶段的项目暂停（在指定或未知的时间段内），重新开始施工，项目继续，暂停和延迟，加快施工作业，以利用交通量下降并获得提前完工奖励，和职业安全政策（社交距离和佩戴N95口罩）。运用RAPID项目中严格收集的短暂的经验证据，PI将能够检查和建模劳动力决策者在流行病不确定性下的风险偏好。成功识别影响决策过程的因素对于开发一种新的多属性效用选择模型至关重要，该模型可以捕获交通建设行业决策者在流行病不确定性下的风险偏好。揭示影响决策过程的因素将为在疫情不确定性下制定员工政策铺平道路。将通过编写与研究组成部分相结合的易于理解和实用的教育材料，广泛传播研究结果，以便相互改进。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Impact of COVID-19 on the Diversity of the Construction Workforce

COVID-19 对建筑劳动力多元化的影响

• DOI: 10.1061/(asce)nh.1527-6996.0000560
• 发表时间: 2022
• 期刊: Natural Hazards Review
• 影响因子: 2.7
• 作者: Baral, Anil;Liang, Yunping;Li, Mingshu;Gonzalez, Marilyn;Shahandashti, Mohsen;Ashuri, Baabak
• 通讯作者: Ashuri, Baabak

Diagnosis of COVID-19 impact on the construction employment: Decline and recovery

• DOI: 10.1016/j.ijdrr.2023.104103
• 发表时间: 2023-11
• 期刊: International Journal of Disaster Risk Reduction
• 影响因子: 5
• 作者: Anil Baral;Abdullah Alnuaimi;Yunping Liang;B. Ashuri;M. Shahandashti