Nan Jia

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.

This study examines how trade protection, particularly anti-dumping sanctions on foreign exports, impacts innovations developed by affected foreign firms. Using data on anti-dumping sanctions levied by the United States (US) government on Chinese exports and on domestic patents associated with the sanctioned products developed by Chinese firms in China, our difference-in-differences estimates show that such anti-dumping sanctions imposed by the US on Chinese products significantly increased the total number of patented innovations produced by the firms exporting from China targeted by the sanctions. This effect is boosted during the pro-innovation national policy period launched by China in 2006. Moreover, we ascertain a critical scope condition that targeted exporting firms increase more-substantive innovations, rather than more-marginal ones, that likely increase the value-added of their products. Overall, these findings suggest that anti-dumping sanctions could lead to an unintended consequence of prompting targeted exporting firms to enhance their innovations in order to elevate the value-added of their products, thus enabling these firms to escape the price competition of lower value-added products and mitigate the risk of future anti-dumping sanctions. This outcome means that, in an effort to protect lower-end domestic manufacturers, protectionist policies could actually intensify the competition faced by higher-end domestic manufacturers via increasing the innovations of affected foreign firms. These outcomes might not only alter the structure of product-market competition, but also contribute to greater technological competition in the future.

Human managers are increasingly challenged by artificial intelligence (AI) technologies in performing managerial functions. We undertook a field experiment that used AI vis-à-vis human managers to perform structured, data-intensive evaluations of employee performance. We generate two sets of insights. First, employees considered AI to be both fairer and more accurate in evaluating their performance than the average human manager. Second, to catch up with AI, human managers’ fairness perceived by employees played a first-order role by (a) helping human managers, to a greater extent than those managers’ evaluation accuracy, to close the performance gap of the employees evaluated by them compared with that of those evaluated by AI, and (b) constraining the effect of human managers’ perceived accuracy of evaluations on employees’ performance. Thus, facing the competition from AI, it is all the more important for human managers to treat employees fairly and build positive interpersonal relationships with employees.