中文摘要
我们研究流程自动化对与自动化任务互动的人类员工的行为影响。我们引入一个风格化的规范模型，其中两名员工依次完成各自任务，共同推进一个联合项目，以获得一笔固定报酬加上一笔取决于项目完成早晚的可变奖金。规范模型给出的预测是：如果员工完全理性，且提前完成奖金足够高，他们将尽快完成各自任务。然而，遵循已有文献，我们假设员工会次优地延迟项目完成。基于一个行为模型的洞见，我们进一步预测，自动化将通过降低策略不确定性来缓解这一问题，从而在更高的项目完成率和更短的项目工期这一直接收益之外，带来员工生产率提高这一间接的行为收益。为检验这些预测，我们开展了一项复现该理论模型的实验，改变(i)员工是与同事还是与机器人协作，以及(ii)在与机器人协作的情形下，被自动化的是上游任务还是下游任务。首先，我们发现员工在很大程度上偏离最优策略，他们完成任务所需时间比规范理论所规定的更长，或者未能完成项目。其次，我们表明流程自动化提高了项目完成率并缩短了项目完成时间，证实了流程自动化的收益。有趣的是，与机器人协作的员工完成任务所需时间更长，这与我们最初的假设——流程自动化对人类员工的生产率具有正向影响——相矛盾。此外，我们发现上游自动化比下游自动化更有利。我们还表明，社会偏好是这些结果的重要驱动因素，因为亲社会被试在与人类同事协作时比与机器人协作时往往更具生产率。最后，我们表明我们的发现在连续处理情境下依然稳健。本文由 Felipe Caro 接受，属于“人与算法连接”特刊。资助：作者谨此感谢纽约市立大学巴鲁克学院 Zicklin 商学院以及德克萨斯大学达拉斯分校的资助。本项目得到 PSC-CUNY 奖项的支持，该奖项由 Professional Staff Congress 与纽约市立大学联合资助。补充材料：在线附录和数据文件可在 https://doi.org/10.1287/mnsc.2024.05408 获取。
Abstract
We study the behavioral effects of process automation on human workers interacting with automated tasks. We introduce a stylized normative model with two workers who complete their tasks sequentially, working toward a joint project to obtain a fixed payment plus a variable bonus that depends on how early the project is completed. The normative model prescribes that, if workers are fully rational, they will complete their tasks as soon as possible if the early completion bonus is high enough. However, following the literature, we hypothesize that workers will suboptimally delay project completion. Following the insights from a behavioral model, we further predict that automation will alleviate this problem by reducing strategic uncertainty, resulting in an indirect behavioral benefit of a higher worker productivity, in addition to the direct benefit of a higher project completion rate and a shorter project duration. To test these predictions, we conduct an experiment replicating the theoretical model, varying (i) whether a worker collaborates with a coworker or a robot, and (ii) in the case of collaborating with a robot, whether the upstream or downstream task is the one automated. First, we find that workers largely deviate from the optimal policy, as they take longer than what the normative theory prescribes to complete their tasks or do not complete the project. Second, we show that process automation increases the project completion rate and reduces the project completion time, confirming the benefits of process automation. Interestingly, workers who collaborate with robots take longer to complete their tasks, contradicting our initial hypothesis that process automation has a positive effect on the productivity of human workers. In addition, we find that upstream automation is more beneficial than downstream automation. We also show that social preferences are an important driver of these results because prosocial subjects tend to be more productive when collaborating with a human coworker than with a robot. Finally, we show that our findings remain robust in a continuous processing setting. This paper was accepted by Felipe Caro, Special Issue on the Human-Algorithm Connection. Funding: The authors gratefully acknowledge financial support from Zicklin School of Business, Baruch College, the City University of New York, and the University of Texas at Dallas. Support for this project was provided by a PSC-CUNY Award, jointly funded by the Professional Staff Congress and the City University of New York. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05408 .