Nan Jia

Can artificial intelligence (AI) assist human employees in increasing employee creativity? Drawing on research on AI-human collaboration, job design, and employee creativity, we examine AI assistance in the form of a sequential division of labor within organizations: in a task, AI handles the initial portion which is well-codified and repetitive, and employees focus on the subsequent portion involving higher-level problem-solving. First, we provide causal evidence from a field experiment conducted at a telemarketing company. We find that AI assistance in generating sales leads, on average, increases employees’ creativity in answering customers’ questions during subsequent sales persuasion. Enhanced creativity leads to increased sales. However, this effect is much more pronounced for higher-skilled employees. Next, we conducted a qualitative study using semi-structured interviews with the employees. We found that AI assistance changes job design by intensifying employees’ interactions with more serious customers. This change enables higher-skilled employees to generate innovative scripts and develop positive emotions at work, which are conducive to creativity. By contrast, with AI assistance, lower-skilled employees make limited improvements to scripts and experience negative emotions at work. We conclude that employees can achieve AI-augmented creativity, but this desirable outcome is skill-biased by favoring experts with greater job skills.

Researchers increasingly use unstructured text data to construct quantitative variables for analysis. This goal has traditionally been achieved using keyword-based approaches, which require researchers to specify a dictionary of keywords mapped to the theoretical concepts of interest. However, recent machine learning (ML) tools for text classification and natural language processing can be used to construct quantitative variables and to classify unstructured text documents. In this paper, we demonstrate how to employ ML tools for this purpose and discuss one application for identifying artificial intelligence (AI) technologies in patents. We compare and contrast various ML methods with the keyword-based approach, demonstrating the advantages of the ML approach. We also leverage the classification outcomes generated by ML models to demonstrate general patterns of AI technological innovation development.

We develop a political path dependence model that integrates the network embeddedness perspective and the literature on corporate political strategy to understand how firms adapt their political connections when anticorruption efforts lead to the turnover of government officials. We posit that although firms that have close associations with ousted corrupt officials can benefit from both removing existing political connections (“cleaning house”) and developing new connections with their successors (“hosting new guests”), political path dependence enables firms to do the former but constrains them from doing the latter. These effects are magnified when firms are highly dependent on the government, and when the ousted corrupt officials have great political power. Evidence from anticorruption campaigns in China between 2012 and 2018 lends support for our theoretical predictions.