When Systems Fail: Remote Worker Accuracy and Operational Transparency

26 Pages Posted: 23 Apr 2021

See all articles by Jorge Mejia

Jorge Mejia

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Chris Parker

American University - Kogod School of Business

Date Written: April 22, 2021

Abstract

There is an increasing dependence on remote work systems in many industries, including service operators---a trend that is likely to accelerate in the coming years due to demands for higher flexibility in our workforce. However, workers are unlikely to be successful working remotely when the systems they rely on are unreliable. Our primary research questions are 1) to what extent can system failures impact worker performance after a system fails and is restored, and 2) what remedies exist that can reduce the impact of these failures on worker performance. To answer these questions, we conduct eight experiments (four at a large US university and four on Amazon Mechanical Turk) in which subjects are asked to perform tasks commonly used to train data used for an artificial intelligence (AI) model. In one set of experiments, subjects classify images, which is the most used classification tool in AI. In another set of experiments, subjects train a chatbot, which is a tool expected to make a significant impact among service operators. Consistently, our results show that a system failure leads to a decrease in task accuracy after the system recovers from failure and comes back online. Furthermore, providing employees with operational transparency about the failure restoration status brings accuracy back to pre-failure levels, performing just as well as performance-based pay, a common tool to motivate high-accuracy work. Finally, we use mediation analysis to test for four plausible mechanisms behind our main effect and find that worker confidence is an important mediating factor.

Keywords: operational transparency, service operations, lab experiment, artificial intelligence, future of work

Suggested Citation

Mejia, Jorge and Parker, Chris, When Systems Fail: Remote Worker Accuracy and Operational Transparency (April 22, 2021). Kelley School of Business Research Paper No. 2021-33, Available at SSRN: https://ssrn.com/abstract=3832154 or http://dx.doi.org/10.2139/ssrn.3832154

Jorge Mejia

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Business 670
1309 E. Tenth Street
Bloomington, IN 47401
United States

Chris Parker (Contact Author)

American University - Kogod School of Business ( email )

4400 Massachusetts Avenue NW
Washington, DC 20816-8044
United States

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