My research falls along three lines of inquiry: corporate sustainability and environmental complexity; artificial intelligence, cyber-physical systems, and responsible innovation; and data science and digital data. Specifically, I examine corporate sustainability and sustainable energy transitions and I investigate the regulatory and economic transformations that cyber-physical systems are causing, with a focus on energy and transportation. In researching these topics, I extensively use digital data (i.e., web-scraped data) and data science (i.e., advanced quantitative methods).




My research engages with literatures on corporate sustainability and environmental complexity. My dissertation research encompasses three different research topics on corporate sustainability. It focuses on the application of the United Nations Sustainable Development Goals (SDGs) to corporate sustainability behavior. The SDGs have had significant global implications in state level environmental actions. However, there has been limited research on directly applying the SDGs to corporations. The first research chapter of my dissertation constructed a new corporate sustainability index using the SDGs. Not only does this index incorporate one the of most important international goals into quantifiable measures for corporations, it also allows for granular data analysis by providing a corporate score for each goal. Using data from Bloomberg, ASSET4, and the Carbon Disclosure Project, this study then identifies corporate leaders and laggards for each SDG. Although many studies emphasize the importance of corporations in environmental actions, few studies directly connect the SDGs to corporations. In this sense, this paper contributes to the understanding of global environmental governance and expands the frameworks around corporations. The paper is currently going through the revise and resubmit process in Environmental Sociology.


The second research topic uses web-scraped data from the U.S. Securities Exchange Commission and other sources to assess whether central corporations’ behavior on CSR differs from less central corporations. Relying on Sklair’s theory of global corporate citizenship, this paper uses social network analysis, Bayesian multiple imputation, and linear regression analysis to empirically show that central corporations score higher in SDGs compared to less central corporations, showing a greater SDG compliance. Additionally, this study also shows that central corporations are interested in social and environmental issues equally. This study is theoretically important because it combines theories of corporate sustainability and corporate interlocks. Moreover, it also indicates the significance of corporate networks in determining the success of global environmental governance. This paper is currently under review in the Journal of Business Ethics.


The third research topic uses institutional logics theory to investigate the extent to which the logic of profit and the logic of sustainability can coexist. Here, the logic of sustainability is measured using the SDG scores. Using structural equation modeling, this paper constructs the two logics as latent variables and show that in smaller and non-consumer facing companies, the environmental logic and the profit logic do not coexist. However, regarding the social logic and profit logic, the evidences of coexistence are clear in all types of companies: larger, smaller, consumer facing, and non-consumer facing. This paper shows that companies can pursue both goals, profit and sustainability. In other words, it shows that corporations do not have to sacrifice profitability in following global environmental governance.


In addition to the three dissertation chapters, I am also working on a paper with my colleagues on how diversity leadership and sustainability leadership can affect financial outcomes. Using original data collected from LinkedIn and publicly available data from Bloomberg and ASSET4, preliminary findings indicate that having diversity or sustainability representative on leadership have positive effects on financial outcomes of a corporation.


I am also interested in sustainable energy transitions and the complexities that follow these economic and social changes. Energy is responsible for over 70% of greenhouse gas emissions according to the World Resources Institute. Consequently, sustainable energy transition is an important field of study that contributes to climate change mitigation. In paper published in Renewable and Sustainable Energy Reviews (SCIE, impact factor: 12.1), my co-authors and I show that market structures and government policies can have a determining effect on the success of new infrastructure and energy systems, such as community solar and community choice aggregation. The findings of this paper can be directly applied to studies on the adaptation of newly emerging technologies, which require social, political, and economic supports from governments and other stakeholders to succeed and advance.




Artificial intelligence is placed at the core of cyber-physical systems. Cyber-physical systems, as a concept is defined as technologies that integrate digital capabilities, such as artificial intelligence, with physical devices and systems. I am currently funded by the National Science Foundation to study cyber-physical systems and automated technologies. I work with engineers, computer scientists, policy scholars, and social scientists to investigate the social and legal risks that these cyber-physical systems are posing and suggest ways to harmonize international regulatory practices. I have a publication in Transportation Research Part A: Policy and Practice (SSCI, SCI, impact factor: 4.0) on the safety and liability regulatory challenges of connected and automated vehicles (CAVs, self-driving vehicles). With a wider spread of CAV testing, this paper provides safety guidelines for conducting on-road testing of CAVs, and it shows that data reporting and collection are crucial components in ensuring CAV safety.


Despite the importance of safety that comes with digital data collection, many emerging technologies pose privacy challenges. This is particularly the case in smart meters, which collect household energy consumption data. Such data have been found to show individual lifestyle, religious affiliation, and even income level. A paper under review in Utilities Policy directly addresses these issues of digital data in the energy industry. In this paper, my co-author and I assess privacy regulatory challenges for personal energy consumption data and data management and we propose ways to improve privacy while maintaining the benefits that come with energy automation. Similarly, transactive energy, which addresses both price responsive controls and load management, is pertinent in the discussion of digital data.


I also have a paper forthcoming in The Electricity Journal. This study focuses on the challenges proposed by transactive energy transitions. Automating load management and energy use in real time, transactive energy is considered as one of the promising ways to reduce energy demand. However, the findings of this study show that the expected energy reduction from the adoption of transactive energy may not be significant. Therefore, to further test this finding, I am currently working on a paper on simulating the energy demand when solar and transactive energy systems are adopted. A group of social and computer scientists and I are estimating the energy use by creating a virtual community that adopts solar energy and transactive energy. Preliminary findings indicate there is a significant energy reduction in solar transitions but not in transactive energy transitions. This is a significant finding that may change the frameworks of energy decentralization movement to exclude transactive energy as a sustainable energy transition.




My research also uses extensively utilizes digital data and strives for data-driven communication. I have a minor in quantitative methods, through which I have gained a number of advanced methods training, including but not limited to Bayesian statistics, structural equation modeling, latent growth curve modeling, social network analysis, categorical data analysis, and multilevel analysis. I have used my skills in digital data management and statistical analysis to evaluate economic and environmental issues.


I have used digital media data for a quantitative analysis of solar energy transition in a paper published in a leading sustainability transitions journal, Environmental Innovation and Societal Transitions (SSCI, SCIE, impact factor 8.4). In this paper, I had a group of undergraduate research assistants who helped me to collect relevant news articles in order to assess whether different social and economic actors’ positions on solar energy changed over time. Using inferential statistics, my co-author and I found that investor owned utilities and other relevant social actors tend to change the frequency and framing of their arguments on solar transition depending on political climate.


In the future, I envision merging my two research interests, namely corporate sustainability and AI and new technologies. The literature on CSR is highlighting the importance of smaller vendors, developers, and small and medium size enterprises. Combining this with the literature on AI and new technologies, I plan to investigate how AI is changing the frameworks of corporate sustainability. In this project, I plan to rely on two digital datasets: (1) the data that I have collected, cleaned, and analyzed for my dissertation as the foundational data; and (2) large-scale survey that investigates how smaller companies are investing in AI and cyber-physical systems in comparison to larger companies. For the survey data, the questions will focus not only on AI’s impacts on CSR discourses but also on whether smaller companies’ approaches to AI and cyber-physical systems are different from the larger transnational companies. I predict that smaller companies’ approach to the development of AI and cyber-physical systems are more sustainable and ethical compared to the larger companies, particularly in the energy industry. This project will successfully combine the two main research interests of mine, corporate sustainability and AI and emerging technologies.

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