Accountability in the Blue-Collar Data-Driven Workplace

TY - GEN

T1 - Accountability in the Blue-Collar Data-Driven Workplace

AU - Kristiansen, Kristian Helbo

AU - Valeur-Meller, Mathias A.

AU - Dombrowski, Lynn

AU - Holten Moller, Naja L..

PY - 2018/4/20

Y1 - 2018/4/20

N2 - This paper examines how mobile technology impacts employee accountability in the blue-collar data-driven workplace. We conducted an observation-based qualitative study of how electricians in an electrical company interact with data related to their work accountability, which comprises the information employees feel is reasonable to share and document about their work. The electricians we studied capture data both manually, recording the hours spent on a particular task, and automatically, as their mobile devices regularly track data such as location. First, our results demonstrate how work accountability manifests for employees' manual labor work that has become data-driven. We show how employees work through moments of transparency, privacy, and accountability using data focused on location, identification and time. Second, we demonstrate how this data production is interdependent with employees' beliefs about what is a reasonable level of detail and transparency to provide about their work. Lastly, we articulate specific design implications related to work accountability.

AB - This paper examines how mobile technology impacts employee accountability in the blue-collar data-driven workplace. We conducted an observation-based qualitative study of how electricians in an electrical company interact with data related to their work accountability, which comprises the information employees feel is reasonable to share and document about their work. The electricians we studied capture data both manually, recording the hours spent on a particular task, and automatically, as their mobile devices regularly track data such as location. First, our results demonstrate how work accountability manifests for employees' manual labor work that has become data-driven. We show how employees work through moments of transparency, privacy, and accountability using data focused on location, identification and time. Second, we demonstrate how this data production is interdependent with employees' beliefs about what is a reasonable level of detail and transparency to provide about their work. Lastly, we articulate specific design implications related to work accountability.

U2 - 10.1145/3173574.3173906

DO - 10.1145/3173574.3173906

M3 - Article in proceedings

SN - 978-1-4503-5620-6

SP - 1

EP - 12

BT - CHI '18 Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

T2 - 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018

Y2 - 21 April 2018 through 26 April 2018

ER -