Convergence Accelerator Phase I (RAISE): Prepare the US labor Force for Future Jobs in the Hotel and Restaurant Industry: A hybrid Framework and Multi-Stakeholder Approach

融合加速器第一阶段 (RAISE)：为美国劳动力在酒店和餐饮业的未来就业做好准备：混合框架和多利益相关者方法

• 批准号: 1937833
• 负责人: Yan Huang
• 金额: $ 97.76万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937833&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Prepare

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact of this Convergence Accelerator Phase I project will support future research and knowledge growth on Artificial Intelligence (AI) and future jobs by developing transdisciplinary methods. This project will demonstrate how to integrate advances in data mining and analytics, qualitative analysis, survey research, and visualization techniques in predicting and visualizing the impact of AI on future jobs and proposing contextual reskill training strategies. This project will provide critically needed data on the evolutionary paths of jobs, trends of job tasks, required skills and tools, and workers' adaptation capabilities in the hotel and restaurant industry, which are significantly under-represented in the public labor databases. The research team will create an online, open-access repository of the forecasting model's outputs, description of future job content and required skills, and policy recommendations. All the findings and developed training modules will be integrated into a web-based Expert Recommendation System, which allows any user to access future job task descriptions, required skills, and customized reskill training modules. The models developed in this project can also be evaluated, scaled, and applied across different industries in the future. The proposed methodology and research findings will be used to enrich undergraduate and graduate courses in hospitality management.This Convergence Accelerator Phase I project contributes to the understanding of the intertwined relationships among AI, jobs, and workers from a spectrum of angles. This project will combine the most recent advances in deep learning, semi-structured interviews, surveys, and work-life journal data analysis in building a hybrid framework to predict the multi-dimensional impact of AI on future jobs in the HR industry. This project will also contribute to identifying various social-economic factors, family backgrounds, and personal experiences that may influence workers' adaptation capabilities. This project bridges the gap between our understanding of the workers' current conditions and customized reskilling strategies for meeting the needs of future jobs. This project will result in: (1) more complete documentation and analysis of the multi-faceted evolution of job contents influenced by AI in the HR industry (hospitality management), (2) a more complete understanding of the triangular relationships among technology, jobs, and labor force (sociology), (3) advances in heterogeneous data mining methods for human subject research (computer science and engineering), (4) an enhanced understanding of how to design effective reskilling programs in a complex system consisting of rapid technological advances, job task evolution, and individuals' backgrounds and experiences (human resources management).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.

NSF 融合加速器支持基于团队的多学科努力，以解决国家重大挑战并在不久的将来展示交付成果的潜力。 该融合加速器第一阶段项目的更广泛影响将通过开发跨学科方法来支持人工智能（AI）和未来就业的未来研究和知识增长。该项目将展示如何整合数据挖掘和分析、定性分析、调查研究和可视化技术的进步来预测和可视化人工智能对未来工作的影响，并提出情境再技能培训策略。该项目将提供酒店和餐饮业工作演变路径、工作任务趋势、所需技能和工具以及工人适应能力等急需的数据，而这些数据在公共劳动力数据库中的代表性严重不足。研究团队将创建一个在线、开放访问的预测模型输出存储库、未来工作内容和所需技能的描述以及政策建议。所有调查结果和开发的培训模块将集成到基于网络的专家推荐系统中，该系统允许任何用户访问未来的工作任务描述、所需技能和定制的重新技能培训模块。该项目开发的模型将来还可以在不同行业进行评估、扩展和应用。所提出的方法和研究成果将用于丰富酒店管理本科生和研究生课程。这个融合加速器第一阶段项目有助于从多个角度理解人工智能、工作和工人之间相互交织的关系。该项目将结合深度学习、半结构化访谈、调查和工作生活期刊数据分析方面的最新进展，构建一个混合框架，以预测人工智能对人力资源行业未来工作的多维影响。该项目还将有助于确定可能影响工人适应能力的各种社会经济因素、家庭背景和个人经历。该项目弥补了我们对工人当前状况的了解与满足未来工作需求的定制再培训策略之间的差距。该项目将带来：(1) 对人力资源行业（酒店管理）中受人工智能影响的工作内容的多方面演变进行更完整的记录和分析，(2) 对技术、工作之间的三角关系有更完整的理解和劳动力（社会学），（3）人类学科研究（计算机科学与工程）的异构数据挖掘方法的进步，（4）加深对如何在由快速技术进步组成的复杂系统中设计有效的再培训计划的理解、工作任务演变和个人背景和经验（人力资源管理）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

An Employee Sharing Model for the Tourism and Hospitality Industry

旅游和酒店业的员工共享模式

• DOI: 10.3390/tourhosp2020011
• 发表时间: 2021-06
• 期刊: Tourism and Hospitality
• 作者: De la Mora Velasco, Efrén;Huang, Arthur;Haney, Adam
• 通讯作者: Haney, Adam

Examining Instructional Technologies in Hospitality and Tourism Education: A Systematic Review of Literature

检验酒店和旅游教育中的教学技术：文献的系统回顾

• DOI: 10.1080/10963758.2022.2109480
• 发表时间: 2022-07
• 期刊: Journal of Hospitality & Tourism Education
• 影响因子: 2.9
• 作者: Huang, Arthur;de la Mora Velasco, Efrén;Haney, Adam
• 通讯作者: Haney, Adam

Customers’ Behavioural Immune System Responses to the COVID-19 Pandemic: A conceptual framework

客户行为免疫系统对 COVID-19 大流行的反应：概念框架

• DOI: 10.54055/ejtr.v30i.2264
• 发表时间: 2022-03
• 期刊: European Journal of Tourism Research
• 影响因子: 2.3
• 作者: Huang, Arthur;Farboudi Jahromi, Melissa;Marquez, Julia
• 通讯作者: Marquez, Julia

Resilience building in service firms during and post COVID-19

COVID-19 期间和之后服务公司的复原力建设

• DOI: 10.1080/02642069.2020.1862092
• 发表时间: 2021-01
• 期刊: The Service Industries Journal
• 作者: Huang, Arthur;Farboudi Jahromi, Melissa
• 通讯作者: Farboudi Jahromi, Melissa

Exploring skill-based career transitions for entry-level hospitality and tourism workers

探索入门级酒店和旅游从业人员基于技能的职业转型

• DOI: 10.1016/j.jhtm.2021.07.007
• 发表时间: 2021-09
• 期刊: Journal of Hospitality and Tourism Management
• 影响因子: 8.3
• 作者: Huang, Arthur;Baker, Mark
• 通讯作者: Baker, Mark