Publication database

Artificial intelligence (AI) is diffusing rapidly throughout the economy, raising questions regarding its impact on human workers. Current theories predict that the degree to which humans are replaced (automation) or supported (augmentation) will depend on characteristics of the task such as its degree of routinization and manual vs. cognitive nature. Yet, existing empirical evidence tends to come from aggregate occupation data or individual case studies, with limited research using larger samples of projects or tasks. Moreover, whether and how AI is implemented may not only depend on task characteristics but also on managers’ objectives, which may go beyond narrow efficiency to include benefits resulting from human employment itself (“employment goals”). We provide novel empirical evidence using data from more than 1,200 research projects that involve crowd members in different tasks such as data collection, data analysis, or creative problem solving. We confirm that the use of AI is associated with task characteristics, although the patterns are more nuanced than those shown in work on prior automation technologies such as computers and robots. Moreover, we find that managers pursuing employment goals are less likely to use AI for automation, while they are just as likely as others to use AI to augment the work of human workers. We contribute to the literature on AI adoption in organizations by providing rare evidence from project-level data, and by highlighting that the path towards “good AI” or “bad AI” is, partly, a matter of human choice.

With permission of the Academy of Management

Recently, organizations started exploring the use of crowdsourcing not only to solve pre-defined problems, but also to identify novel problems worth solving – not least in the hopes of more effectively aligning research and innovation agendas with issues of societal relevance. Yet, a key challenge is that many crowd-identified problem statements are not novel and simply re-state well-known problems, resulting in an ineffective way to organize problem identification. In this study, we theorize that the extent of “user knowledge” among crowd members, which can be acquired both through one's own experience as someone affected by a particular situation (e.g., as a patient) and through engagement with the experiences of others (e.g., as a caretaker or medical professional), increases the novelty of problem statements. In addition, we explore whether the novelty of crowd-identified problem statements might be improved by providing crowd members with two types of complementary knowledge related to the problem space: declarative and procedural. Our preliminary results from a large-scale online experiment show a significant positive relationship between “user knowledge” of crowd members and the novelty of the problem statements they submit. Providing crowd members with complementary knowledge related to the problem space does not lead to significant novelty improvements, according to our preliminary analysis. Nevertheless, these initial findings indicate that the impact of experiential knowledge in problem-solving also applies to problem finding. This has significant implications for organizations aiming to effectively utilize crowdsourcing for establishing their research and innovation agendas.

With permission of the Academy of Management

In this symposium, we will examine a wide array of questions and hypotheses that focus on the people who conduct science -- as a complement to more established research traditions that focus on the publications and patents that people produce. Talks will cover topics that relate to a variety of career stages and background characteristics such as: What are the characteristics of scientists who are also inventors? How is Artificial Intelligence being integrated into crowd science projects? How does media coverage about research variably impact the authors of the research? Talks will also feature innovative data resources including one presentation that is able to examine the ways in which External Letters variably influence academic careers with respect to tenure and promotion decisions (particularly in relation to faculty who seek and gain one or more patents).

With permission of the Academy of Management

Peer evaluations place organizational members in a dual role: they evaluate their peers and are being evaluated by their peers. We theorize that when evaluating their peers, they anticipate how their evaluations will be perceived and adjust their evaluations strategically to be evaluated more positively themselves when their peers assess them. Building on this overarching claim of role duality resulting in strategic peer evaluations, we focus on a dilemma that evaluating members face: they want to leverage their evaluations of peers to portray themselves as engaged and having high standards, but at the same time, they must be careful not to offend anyone as doing so may cause retaliation. We suggest that organizational members about to be evaluated resolve this dilemma by participating in more peer evaluations but carefully targeting in which evaluations they participate. We test our theory by analyzing peer evaluations on Wikipedia, supplemented by in-depth semi-structured interviews. Our study informs research on peer evaluation and organizational design by revealing how being an evaluator and evaluated can make evaluations more strategic.

© 2023, INFORMS

Early-stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet, they compete with large technology firms to hire the best workers. Theories of ability sorting predict that high ability workers will choose jobs in established firms that offer greater complementary assets and higher pay, leaving low ability workers to take lower-paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enable startups to hire talented workers who have a taste for a startup environment, even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school into industrial employment, we overcome common empirical challenges by observing ability and stated preferences prior to first-time employment. We find that both ability and career preferences strongly predict startup employment, with high ability workers who prefer startup employment being the most likely to work in a startup. We show that this is due in part to the dual selection effects of worker preferences resulting in a large pool of startup job applicants, and startups “cherry picking” the most talented workers to make job offers to. Additional analyses confirm that startup employees earn approximately 17% lower pay. This gap is greatest for high ability workers and persists over workers’ early careers, suggesting that they accept a negative compensating differential in exchange for the non-pecuniary benefits of startup employment. This is further supported by data on job attributes and stated reasons for job choice.

© 2022, INFORMS

In 2013, Trumpf, a global market leader machine tools from Germany, acquired the majority shares in JFY, a smaller machine tool manufacturer from China. With this acquisition, Trumpf wanted to enter the fast-growing low-cost segment of the market. Until then, JFY had performed very well on the Chinese market, but the company's success increasingly waned after the acquistion. Other Chinese competitors performed significantly better. After JFY even had to report losses for the first time in 2019, Trumpf changed the management at JFY. Under the new management, initial successes were achieved, but even two and a half years later, JFY still did not reach the profit targets which all business units at Trumpf had to meet. In a Trumpf management meeting in October 2022, a decision was therefore to be made as to whether JFY should remain part of Trumpf or be sold off again.