Nan Jia

Do artificial intelligence (AI) technologies, by enhancing firms’ data analysis capabilities, broaden participation in corporate political activities (CPA), thus “democratizing” CPA? We develop a theoretical framework examining how AI shapes CPA, focusing on the overlap between AI’s capabilities and firms’ political capabilities. Drawing on dynamic capabilities theory and CPA literature, we classify political capabilities into political sensing, access, and exchange—each linked to a distinct policymaking stage. AI enhances firms’ “know what” by improving information acquisition for these capabilities, potentially encouraging broader engagement. However, because AI does not directly support activities reliant on political connections, its effect on firms’ “know who” remains limited. Thus, AI does not erode the advantage of politically connected firms. Instead, it reinforces it: by enhancing informational capabilities, AI enables these firms to deploy their connections more effectively, deepening their advantage.

Leaders shape firm strategy not only through the decisions they make directly but also through influencing employees’ workplace behavior. We argue that leaders’ formative life experiences, which shape personal traits, can influence employee behavior and work performance. Using detailed data from Chinese securities firms, we examine the impact of chairpersons’ experiences as “sent-down youths” (SDYs) during China’s Cultural Revolution, which instilled in them a strong work ethic in the face of adversity. We argue that SDY chairpersons encourage security analysts to increase efforts to improve the accuracy of earnings forecasts for the publicly listed firms they follow. We show that when the same analyst experiences a change in chairperson within the same securities firm, from one without any SDY experience to one with SDY experience (both within the same cohort), their forecast accuracy for the same listed firm increases. Specific actions, such as conducting site visits to gather company information firsthand, contribute to improving forecast accuracy. We find that analysts led by SDY chairpersons were more likely to conduct site visits, visit more companies for the first time, visit companies located in less accessible areas, ask more targeted questions during site visits, and update forecasts promptly after major events. To strengthen causal inference, we employ fixed effects, examine various types of chairperson turnovers, and employ instrumental variable approaches and placebo tests. Our study suggests that, through influencing employee behavior, the “style" of a firm leader spreads far within an organization.

Constituency-building is an important corporate political strategy that expands the scope of conflict by mobilizing stakeholders to pressure policymakers. Although prior studies of constituency-building have highlighted how enlisting stakeholders in political battles creates unique opportunities and risks, what has been overlooked is the importance of firms assembling “strange bedfellows” of economically- and morally-motivated constituents, sometimes called “bootlegger-and-Baptist” coalitions. Drawing on theories developed in research on regulatory economics, political processes, social movements, and corporate political strategy, we advance a theoretical framework to examine the tradeoffs of firms creating blended coalitions consisting of parties who each have distinct economic and moral motives as opposed to uniform coalitions. We argue that blended coalitions often generate greater benefits by increasing policymakers’ support. However, we highlight higher costs at each of four key stages in blending coalitions – searching for partners, coordinating with partners, mobilizing partners, and monitoring partners – and discuss mitigation strategies. We further develop theory on how to implement blended coalitions, including formulating messages for constituents and choosing policymakers to target. We develop propositions to advance theory development on constituency-building, linked to stakeholder mobilization and social movement approaches. We offer wide-ranging real-world examples to provide guidance for empirical research.

Multimodal data, comprising interdependent unstructured text, image, and audio data that collectively characterize the same source, with video being a prominent example, offer a wealth of information for strategy researchers. We emphasize the theoretical importance of capturing the interdependencies between different modalities when evaluating multimodal data. To automate the analysis of video data, we introduce advanced deep machine learning and data fusion methods that comprehensively account for all intra- and inter-modality interdependencies. Through an empirical demonstration focused on measuring the trustworthiness of grassroots sellers in live streaming commerce on Tik Tok, we highlight the crucial role of interpersonal interactions in the business success of microenterprises. We provide access to our data and algorithms to facilitate data fusion in strategy research that relies on multimodal data.

People want to “feel heard,” to perceive that they are understood, validated, and valued. Can artificial intelligence (AI) serve the deeply human function of making others feel heard? Our research addresses two fundamental issues: Can AI generate responses that make human recipients feel heard, and how do human recipients react when they believe the response comes from AI? We conducted an experiment and a follow-up study to disentangle the effects of actual source of a message and the presumed source. We found that AI-generated messages made recipients feel more heard than human-generated messages, and that AI was better at detecting emotions. However, recipients felt less heard when they realized that a message came from AI (vs. human). Lastly, in a follow-up study where the responses were rated by third-party raters, we found that compared with humans, AI demonstrated superior discipline in offering emotional support, a crucial element in making individuals feel heard, while avoiding excessive practical suggestions, which may be less effective in achieving this goal. Our research underscores the potential and limitations of AI in meeting human psychological needs. These findings suggest that while AI demonstrates enhanced capabilities to provide emotional support, the devaluation of AI responses poses a key challenge for effectively leveraging AI’s capabilities.