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: 2005665
• 负责人: Zhenghui Sha
• 金额: $ 15.44万
• 依托单位: University of Arkansas
• 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: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005665&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.

本研究的目的是调查什么产品的客户认为，他们最终购买使用分层，多维网络为基础的设计方法。出于对工程设计中社会技术交互建模的需求，本研究将设计理论与网络科学相结合，探索三个相互关联的主题：1）考虑产品关联和社会影响的客户偏好建模的两阶段多维网络模型; 2）预测多竞争者战略决策影响的动态网络模型，3）知识转移，以展示可推广性和创建共享数据资源，使研究社区受益。该项目将推进复杂系统的设计理论，并开发用于工程设计中社会技术相互作用建模的定量方法。结合企业驱动的设计，开发的方法将提高美国工业在不断变化的市场中的竞争力。测试案例包括电动汽车和小型SUV设计的主要案例研究和家用产品设计的次要案例研究。该项目还将促进学生在数据科学、网络科学和人工智能方面的培训，特别强调代表性不足的群体、女性和本科生的参与。这项研究的智力价值体现在四个方面。首先，层次网络模型将顾客的考虑和选择作为不同的、但又是综合的行为来研究。它确定了考虑和选择阶段的独特驱动因素。其次，本研究克服了客户社交网络数据缺失的实际挑战。该解决方案依赖于一种创新的方法来评估个人的偏好是如何影响他们自己的自我为中心的社会接触，通过协同集成的自动化演员属性模型（ALAAM）与多维客户产品网络（MCPN）的框架。第三，使用时间指数随机图模型（t-ERGM），动态网络建模方法将允许预测未来的市场竞争，考虑到目前的竞争结构和多个竞争者的设计决策。最后，将开发一个基于众包的数据收集平台，整合在线产品数据和评论，以在多阶段决策中获取客户偏好。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

Modeling Multi-Year Customers’ Considerations and Choices in China’s Auto Market Using Two-Stage Bipartite Network Analysis

使用两阶段双向网络分析对中国汽车市场的多年客户考虑因素和选择进行建模

• DOI: 10.1007/s11067-021-09526-9
• 发表时间: 2021
• 期刊: Networks and Spatial Economics
• 影响因子: 2.4
• 作者: Bi, Youyi;Qiu, Yunjian;Sha, Zhenghui;Wang, Mingxian;Fu, Yan;Contractor, Noshir;Chen, Wei
• 通讯作者: Chen, Wei