Green ties, media eyes, and financial tides: exploring the dynamics of corporate environmental sustainability through networks, innovation spillover, and media influence

This dissertation examines how external actors shape firms’ environmental strategies through mechanisms of media scrutiny and supply chain relationships. Drawing on institutional theory, the relational view, and resource-based perspectives, it employs large-scale text analytics, structural econometrics, and graph-based deep learning frameworks to study three related topics: In the first study, “No News about Climate Action Is Good News for Low-Polluting Firms,” I examine how media coverage of firms’ climate actions impacts their operational and financial performance. Using a machine learning-based approach to capture the intensity of media attention on climate actions, the study reveals that increased media scrutiny can lead to higher operational costs and adverse financial performance for low-polluting firms. These findings highlight the need for corporate leaders to strategically manage media engagement around their climate initiatives. In the second study, “The Green Ripple Effect: Unraveling Green Innovation Spillover in Supply Chain Networks,” I examine the spillover of green innovation across supply chains, highlighting how the intensity of environmental patents by customers and suppliers influences the focal firm. Using a structural econometric model on a comprehensive dataset of supply chain linkages, the study uncovers a significant peer influence channel through which the green innovation intensity of customers and suppliers directly impacts that of the focal firm. This spillover effect highlights the interconnected nature of firms in their pursuit of sustainability. In addition to unveiling the significant role of peer influence in the spillover of green innovations, this research also demonstrates the positive impact of a shift toward more green innovations on a firm’s financial performance, further elucidating the competitive advantage it brings. In the final study, “Predicting Firm Greenwashing through Dynamic Supply Chain Graphs and Linguistic Cues in Job Postings,” I propose a novel methodology for forecasting corporate greenwashing by leveraging structural features from firms’ supply chain networks and linguistic patterns in their green job postings. The study employs a Dynamic Graph Neural Network (GNN) framework to capture evolving inter-firm relationships and time-varying firm attributes, enabling more accurate prediction of greenwashing tendencies based on network structure and firm-level cues. Additionally, I analyze job posting language, finding that firms with higher predicted greenwashing propensities tend to use vaguer and less concrete terms in their sustainability-related postings. This integrated approach highlights how combining dynamic network modeling with natural language processing can enhance our ability to detect greenwashing, offering valuable tools for promoting corporate accountability and transparency. Collectively, these studies contribute to a critical understanding of the complex interplay between corporate strategies, environmental sustainability, and external influences. By integrating insights from relational view theory of the firms, resource-based views, and institutional pressures, this dissertation offers theoretical corroboration of several organizational management theories through data-driven approaches and provides practical guidance for businesses striving to align their operations with sustainability goals. The research highlights the importance of strategic network formation, effective media management, and innovation spillover as pivotal elements that can significantly impact a firm’s ability to successfully adopt sustainable practices and reap economic benefits.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Business Administration

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Safaei, Nima

Contributors dc:contributor

• Pant, Gautam
• Anand, Gopesh
• Subramanyam, Ramanath
• Ahsen, Mehmet

• Media Spotlight
• Green Innovation
• Graph Neural Network
• Text Analytics
• Corporate Environmental Sustainability
• Greenwashing
• Econometrics
• Peer Effect

Statement dc:rights

• Copyright 2025 Nima Safaei

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130203

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130203