Collaborative Research: A Hierarchical Multidimensional Network-based Approach for Multi-Competitor Product Design

协作研究：基于分层多维网络的多竞争对手产品设计方法

• 批准号: 2203080
• 负责人: Zhenghui Sha
• 金额: $ 15.44万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203080&HistoricalAwards=false
• 关键词: Collaborative; Research; Hierarchical; Multidimensional; Network

The objective of this research is to investigate what product customers consider and what they eventually purchase using a hierarchical, multidimensional network-based design approach. Motivated by the need to model socio-technical interactions in engineering design, this research combines design theory with network science to explore three interrelated topics: 1) two-stage multidimensional network models for customer preference modeling that consider product associations and social influence; 2) dynamic network models for predicting the impact of multi-competitor strategic decisions, and 3) knowledge transfer to demonstrate generalizability and creation of shared data resources to benefit research community. This project will advance design theories of complex systems and develop quantitative methods for modeling socio-technical interactions in engineering design. Integrated with enterprise-driven design, the methods developed will enhance US industry’s competitiveness within changing markets. The test cases include a primary case study on the design of electric vehicles and small SUVs and a secondary case study on the design of household products. The project will also foster student training in data science, network science and Artificial Intelligence, with particular emphasis on the participation of underrepresented groups, females, and undergraduates.The intellectual merit of this research is manifested in four aspects. First, the hierarchical network model studies customers’ consideration and choice as distinct, but integrated, behaviors. It identifies distinctive driving factors underlying the consideration and choice stages. Second, this research overcomes the practical challenges of missing data on customers' social networks. The solution relies on an innovative approach to assess how individuals’ preferences are influenced by their own egocentric social contacts through a synergistic integration of autologistic actor attribute model (ALAAM) with the Multidimensional Customer-Product Network (MCPN) framework. Third, using temporal Exponential Random Graph Model (t-ERGM), the dynamic network modeling approach will allow the prediction of future market competition considering the present competition structure and multi-competitor design decisions. Finally, a crowdsourcing-based data collection platform integrating online product data and reviews will be developed for eliciting customer preferences in multi-stage decision making.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.

这项研究的目的是调查客户考虑的产品以及他们有时使用基于多维网络的设计方法购买的产品。由于需要在工程设计中建模社会技术互动的需要，该研究将设计理论与网络科学结合在一起，探讨了三个相互关联的主题：1）两阶段的多维网络模型，用于客户偏好模型，以考虑产品关联和社会影响； 2）动态网络模型，用于预测多竞争者战略决策的影响，以及3）知识转移，以证明可普遍性和创建共享数据资源以使研究社区受益。该项目将推动设计复杂系统的设计理论，并开发用于建模工程设计中社会技术互动的定量方法。与企业驱动的设计集成在一起，开发的方法将增强美国行业在不断变化的市场中的竞争力。测试案例包括有关电动汽车设计和小型SUV设计的主要案例研究，以及有关家用产品设计的次要案例研究。该项目还将培养学生在数据科学，网络科学和人工智能方面的培训，特别着重于代表性不足的群体，女性和本科生的参与。这项研究的知识分子在四个方面表现出来。首先，分层网络模型将客户的考虑和选择视为不同但综合的行为。它确定了考虑因素和选择阶段的独特驱动因素。其次，这项研究克服了丢失客户社交网络数据的实际挑战。该解决方案依赖于一种创新的方法来评估个人的偏好如何通过自动化行为者属性模型（ALAAM）与多维客户产品（MCPN）框架的自动化行为者属性模型（ALAAM）的协同整合来影响自己的以自我为中心的社会接触。第三，使用临时指数随机图模型（T-ERGM），动态网络建模方法将允许考虑当前的竞争结构和多竞争者设计决策的未来市场竞争。最后，将开发基于众包的数据收集平台集成在线产品数据和评论，以引起多阶段决策中的客户偏好。该奖项反映了NSF的法定任务，并通过使用该基金会的智力优点和更广泛的影响来评估NSF的法定任务。

项目成果

A Graph Neural Network Approach for Product Relationship Prediction

• DOI: 10.1115/detc2021-69462
• 发表时间: 2021-05
• 期刊: ArXiv
• 作者: Faez Ahmed;Yaxin Cui;Yan Fu;Wei Chen

Information Retrieval and Survey Design For Two-Stage Customer Preference Modeling

两阶段客户偏好建模的信息检索和调查设计

• DOI: 10.1017/dsd.2022.000
• 发表时间: 2022
• 期刊: 17th International Design Conference
• 作者: Y. Xiao, Y. Cui

Bayesian analysis of social influence

• DOI: 10.1111/rssa.12844
• 发表时间: 2020-06
• 期刊: Journal of the Royal Statistical Society: Series A (Statistics in Society)
• 作者: J. Koskinen;G. Daraganova

A Weighted Statistical Network Modeling Approach to Product Competition Analysis

• DOI: 10.1155/2022/9417869
• 发表时间: 2022-01
• 期刊: Complex.
• 作者: Yaxin Cui;Faez Ahmed;Zhenghui Sha;Lijun Wang;Yan Fu;N. Contractor;Wei Chen

A network-based discrete choice model for decision-based design

用于决策设计的基于网络的离散选择模型

• DOI: 10.1017/dsj.2023.4
• 发表时间: 2023
• 期刊: Design Science
• 影响因子: 2.4
• 作者: Sha, Zhenghui;Cui, Yaxin;Xiao, Yinshuang;Stathopoulos, Amanda;Contractor, Noshir;Fu, Yan;Chen, Wei
• 通讯作者: Chen, Wei