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.

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.

Law-abiding firms often attempt to conceal their corporate political activity (CPA), yet the concealment of CPA has not been matched by our understanding of the phenomenon. We develop a theoretical framework consisting of three components to analyze firms’ strategy of CPA concealment. First, we provide a detailed conceptual background on CPA concealment, including what concealment of CPA is and how it can occur. Second, we develop an in-depth analysis of the key benefits and costs of concealing CPA for firms. Finally, we integrate this analysis with positive political theory to place our firm-level calculus in the context of policymaking by identifying the public policymakers whom firms are most likely to influence via CPA concealment. Based on this framework, we generate additional empirically testable propositions on how CPA concealment changes with factors at the country, institution, issue, and firm levels. This study is the first to generate systematic theory on firms’ CPA concealment strategies. Moreover, this research context highlights the particular importance of theory for investigating consequential phenomena that yield scarce data – it is theory which guides data discovery ex ante, helps assess bias ex post, and uncovers key insights that empirical analysis alone cannot generate.

Companies are increasingly using artificial intelligence (AI) to provide performance feedback to employees, by tracking employee behavior at work, automating performance evaluations, and recommending job improvements. However, this application of AI has provoked much debate. On the one hand, powerful AI data analytics increase the quality of feedback, which may enhance employee productivity (“deployment effect”). On the other hand, employees may develop a negative perception of AI feedback once it is disclosed to them, thus harming their productivity (“disclosure effect”). We examine these two effects theoretically and test them empirically using data from a field experiment. We find strong evidence that both effects coexist, and that the adverse disclosure effect is mitigated by employees' tenure in the firm. These findings offer pivotal implications for management theory, practice, and public policies.