Strategic Management

Description of the video:

My research interest lies in the intersection of international business and sustainability. So basically, I study how firms tackle the complexity of operating globally while simultaneously pursuing making a positive impact on social and environmental space. So this challenge really comes from the heterogeneity of institutional environments across different countries and also the completing stakeholder demands worldwide. For instance, global investors looking for shareholder returns and ESG compliance, while local communities looking for social and environmental justice and the governments looking for the national industry security. So that firms really need to tackle this complexity. But meanwhile, this provides opportunity for them to develop competitive advantage by developing innovative sustainability strategies. So let me talk about two projects I'm working on. So the first projects, we look at how CEOs say during their earning calls about their global climate risks and what actions follow. So what we find that is very interesting that their international divestment and investment decisions are not only just driven by what actual physical risks they face, for instance, the chronic risks such as rising sea levels or the acute risks such as hurricanes and wildfires, But actually how they disclose and frame and how leaders and global shareholders perceive those risks are very important. So in other words, it's not only what risks they face, but also what they say and frame will really affect their decisions. So the second project we study is how S&P 500 companies, they lead the green transitions in their global supply chains, particularly in the space of CO2 emission reduction. So this is not just one buyer and one supplier sorting things out. We find that this is really dozens of relationships all happening at once, and each with their own agenda, deadlines, and demands. So therefore, these suppliers, they are really facing different demands, sometimes conflicting demands, coming from different buyers and also the different competitive pressures coming from the peers across the globe. So in the process, the buyers really need to learn and learn and relearn different green standards with a moving target for green standards across the globe, which is really exciting. Thank you.

Description of the video:

I'm a strategy professor, so ultimately, at the end of the day, my goal is to understand anything that would be a reason or an explanation for why one organization outperforms another. So specifically within that realm, I'm looking at firm governance, I'm looking at firm resources, especially human capital. Research ideas can come from several different directions. Most of my early research projects were inspired by things that I'd observed when I was working for multinational organizations in different places around the globe, especially with my background in accounting and finance. Sometimes research ideas come from when you're reading the scholarly literature and you you read something that you wonder is this still true or is there something else that I would need to add to this. And then finally a lot of my research ideas come from just observing what's going on around us. So by reading the financial press I can identify a phenomenon or a trend and wonder if we have the right scholarly theories to explain what we're observing. So one of my current research projects right now is studying firms that have survived long enough to move from that entrepreneurial stage to conducting an IPO, an initial public offering. And what kept happening as I was reading the financial press was that some firms that had recently been lauded as some of the exemplar examples of what a company should be doing, they'd grown so rapidly and were doing so well, turned around and started laying people off. And I recognized this multiple times over, And I began to ask myself if I could conduct some research to understand if there were certain things that were more likely to predict which post-IPO firms would end up conducting large layoffs. We know, obviously, from people around us who've been affected by layoffs, that this is an important problem that would be important to society if we can better predict when these layoffs would occur or recognize firms who exhibit greater commitment to human capital. So this project that I'm interested in right now is really about the antecedents of the layoffs in post-IPO firms. This project is several years in the making, and I would say we're still only probably in about the middle of the process. Because essentially, initially I needed to build a sample of firms that had reached an IPO for which I could also identify other variables that could be affecting their human capital decisions. So we built out a data set with a number of governance variables, a number of factors about the founding team and the individuals who are part of these IPO firms. And we were able to test some initial hypotheses or some initial hunches of what might be affecting that layoff propensity and that layoff timing. Once we had some initial results, we then went and tried to grow our data set even further. So going from several hundred firms to twice that many, trying to get closer and closer to 1,000 firms that would be in the total data set. At this point, we're lined up to present that research at an upcoming major strategy conference, and then we'll proceed to participate in the submission to the publication process. So in describing that research, this is just an indicative project of what we can study in the strategy arena. And it evidences my key interest in the idea of firm resources, especially human capital, in the interface with other governance factors in the organization. There's one other project I'd want to share about, and that's recently published work, where we were able to exploit the fact that the SEC began to require an additional disclosure in firms' annual reports. And this is really at the interface of natural language processing, AI, and governance research. What we did in this paper was to actually extract that very specific disclosure, in this case about the relationship between the CEO and a board chairman. We began to create variables that would describe whether that relationship was more control-based in nature or whether it was a more collaborative relationship. And this is something that firms were required to reveal. But most governance research occurs on just the largest echelon of companies. And one of the things I've been really interested in expanding is the availability of governance variables on a broader swath of organizations. So what we did in this project was to draw on natural language processing techniques to extract the right text and conduct the classification just in the same manner that a human would have done. And in doing so, we're able to make contributions on the one hand to the governance literature of describing and looking for predictors of when that relationship between the CEO and chairman is most effective, but also in this machine learning, natural language processing realm to say what are the best in class approaches to us taking human coded data and processing it at scale so that we can make sure that our observations are generalizable to a much larger set of organizations. So to the scholarly community, this is a place where we're going to continue to build, especially in predicting those governance relationships. In the more applied arena, this is a place where there may be uses within your organization for identifying places where there's data that is relatively unstructured and asking, are there ways that we can now use AI or machine learning to turn that data set into something that's more usable into more predictive models.