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1. Evolutionary Co-Mention Network Analysis via Social Media Mining

   https://doi.org/10.1115/DETC2023-115114  

   Gavino, Phillip AO; Xiao, Yinshuang; Cui, Yaxin; Chen, Wei; Sha, Zhenghui (November 2023, American Society of Mechanical Engineers)

   The immense volume of user-generated content on social media provides a rich data source for big data research. Comentioned entities in social media content offer valuable information that can support a broad range of studies, from product market competition to dynamic social network mining and modeling. This paper introduces a new approach that combines named entity recognition (NER) and network modeling to extract and analyze co-mention relationships among entities in the same domain from unstructured social media data. This approach contributes to design for market systems literature because little research has investigated product competition via co-mention networks using large-scale unstructured social media data. In particular, the proposed approach provides designers with a new way to gain insight into market trends and aggregated customer preferences when customer choice data is insufficient. Moreover, our approach can easily support the evolution analysis of co-mention relationships beyond cross-sectional analysis of co-mention networks in a single year due to the abundance of social media data in multiple years. To demonstrate the approach to supporting multi-year product competition analysis, we perform a case study on mining co-mention networks of car models with Twitter data. The result shows that our approach can successfully extract the co-mention relationships of car models in multiple years from 2016 to 2019 from massive Twitter content; and enables us to conduct evolutionary co-mention network analysis with temporal network modeling and descriptive network analysis. The analysis confirmed that the co-mention network is capable of identifying frequently discussed entities and topics, such as car model pairs that often involve in competition and emerging vehicle technologies such as electric vehicles (EV). Furthermore, conducting evolutionary co-mention network analysis provides designers with an efficient way to monitor shifts in customer preferences for car features and to track trends in public discussions such as environmental issues associated with EVs over time. Our approach can be generally applied to other studies on co-mention relationships between entities, such as emerging technologies, cellphones, and political figures. 

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2. Product Design Incorporating Competition Relations: A Network-Based Design Framework Considering Local Dependencies

   https://doi.org/10.1115/1.4066426  

   Xiao, Yinshuang; Cui, Yaxin; Koskinen, Johan; Contractor, Noshir; Chen, Wei; Sha, Zhenghui (February 2025, Journal of Mechanical Design)

   Abstract System design has been facing the challenges of incorporating complex dependencies between individual entities into design formulations. For example, while the decision-based design framework successfully integrated customer preference modeling into optimal design, the problem was formulated from a single entity’s perspective, and the competition between multiple enterprises was not considered in the formulation. Network science has offered several solutions for studying interdependencies in various system contexts. However, efforts have primarily focused on analysis (i.e., the forward problem). The inverse problem still remains: How can we achieve the desired system-level performance by promoting the formation of targeted relations among local entities? In this study, we answer this question by developing a network-based design framework. This framework uses network representations to characterize and capture dependencies and relations between individual entities in complex systems and integrate these representations into design formulations to find optimal decisions for the desired performance of a system. To demonstrate its utility, we applied this framework to the design for market systems with a case study on vacuum cleaners. The objective is to increase the sales of a vacuum cleaner or its market share by optimizing its design attributes, such as suction power and weight, with the consideration of market competition relations, such as inter-brand triadic competition involving three products from different brands. We solve this problem by integrating an exponential random graph model (ERGM) with a genetic algorithm. The results indicate that the new designs, which consider market competition, can effectively increase the purchase frequency of specific vacuum cleaner models and the proposed network-based design method outperforms traditional design optimization. 

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3. Spot Markets for Spectrum Measurements

   https://doi.org/10.1109/DySPAN.2019.8935666  

   Ghosh, Arnob; Berry, Randall (November 2019, IEEE DySPAN)

   The recent framework for spectrum sharing in the 3.5 GHz band allows for Environment Sensing Capability operators (ESCs) to measure spectrum occupancy so as to enable commercial use of this spectrum when federal incumbent users are not present. Each ESC will contract with one or more Spectrum Access Systems (SASs) to provide spectrum occupancy data. Commercial firms using the band will in turn contract with a SAS to determine when it can access the spectrum. Initially, the decisions of which ESC and SAS to partner with will likely be based on long-term contracts. In this paper, we consider an alternative framework, in which an ESC sells its spectrum management information via a spot market so that from periodto- period a commercial user can select a different ESC from which to acquire spectrum measurements. We develop a game theoretic model to analyze such a market and show that using such a spot market may better enable multiple commercial firms to operate in a given spectrum band. We also show that this increased competition may not benefit consumer surplus unless firms adopt a non-stationary strategy profile. 

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4. Trials and Terminations: Learning from Competitors’ R&D Failures

   https://doi.org/10.1287/mnsc.2020.3775  

   Krieger, Joshua L. (September 2021, Management Science)

   I analyze project continuation decisions where firms may resolve uncertainty through news about competitors’ research and development (R&D) failures, as well as through their own results. I examine the tradeoffs and interactions between product-market competition and technological learning from parallel R&D projects. Leveraging the biopharmaceutical industry’s unique characteristics to overcome barriers to measuring project-level responses, I use a difference-in-differences strategy to evaluate how competitor exit news alters a firm’s own project discontinuation decisions. The findings reveal that technological learning dominates competition effects. Firms are most sensitive to competitor failure news from within the same market and same technology area—more than doubling their propensity to terminate drug development projects in the wake of this type of information. Finally, I explore how levels of competition, uncertainty, and opportunities to learn moderate the response to competitor failure news. This paper was accepted by Joshua Gans, business strategy. 

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5. Imperfect Competition, Compensating Differentials, and Rent Sharing in the US Labor Market

   https://doi.org/10.1257/aer.20190790  

   Lamadon, Thibaut; Mogstad, Magne; Setzler, Bradley (January 2022, American Economic Review)

   We quantify the importance of imperfect competition in the US labor market by estimating the size of labor market rents earned by American firms and workers. We construct a matched employer-employee panel dataset by combining the universe of US business and worker tax records for the period 2001–2015. Using this panel data, we identify and estimate an equilibrium model of the labor market with two-sided heterogeneity where workers view firms as imperfect substitutes because of heterogeneous preferences over nonwage job characteristics. The model allows us to draw inference about imperfect competition, worker sorting, compensating differentials, and rent sharing. (JEL D24, H24, H25, J22, J24, J31, J42) 

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