A Peek into the Working Day: Comparing Techniques for Recording Employee Behaviour
Šinik, Tea; Beerepoot, Iris; Reijers, Hajo A.
(2023)
Research Challenges in Information Science, volume 476 LNBIP, pp. 343 - 359
Lecture Notes in Business Information Processing, volume 476 LNBIP, pp. 343 - 359
17th International Conference on Research Challenges in Information Sciences, RCIS 2023, volume 476 LNBIP, pp. 343 - 359
(Part of book)
Abstract
Detailed recordings of employee behaviour can give organisations valuable insights into their work processes. However, recording techniques each have their advantages and disadvantages in terms of their obtrusiveness for participants, the richness of information they capture, and the risks that are involved. In an effort to systematically compare recording techniques,
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we conducted a multiple-case study at a multinational professional services organisation. We followed six participants for a working day, comparing the outcomes from non-participant observation, screen recording, and timesheet techniques. We generated 136:04 h of data and 849 records of activities. We identified 58 differences between the techniques. The results show that the use of only one technique will not produce a complete and accurate record of the activities that occur on the screen (online), in the hallway (offline), and in the extra hours (overtime). Therefore, it is vital to choose a technique wisely, taking into account the type of information it does not capture. Furthermore, this study identifies some open challenges with respect to accurately recording employee behaviour.
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Keywords: Data Collection Techniques, Employee Behaviour, Observation, Screen Recording, Timesheet, Work Patterns, Taverne, Management Information Systems, Control and Systems Engineering, Business and International Management, Information Systems, Modelling and Simulation, Information Systems and Management
ISSN: 1865-1348
ISBN: 9783031330797
Publisher: Springer
Note: Funding Information: BeCoDigital]. Financial support was also received from the European Regional Development Fund (ERDF) for the Wal-e-Cities project with award number [ETR121200003138] and from the Research Public Service of Wallonia (SPW Recherche) for the project ARIAC by DIGITALWALLONIA4AI with award number [2010235]. Funding Information: Acknowledgement. This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822). It was also co-founded by the European Union under Grant Agreement No. 101087529. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Funding Information: Acknowledgements. This research is supported by the Estonian Research Council (PRG1226) and the European Research Council (PIX Project). Funding Information: Unit 2023-2027 (CEX2021-001201-M) funded by MCIN/AEI /10.13039/501100011033, and the RCIS community for their valuable insights that helped develop this work. Funding Information: Acknowledgements. This work has been developed under the project Digital Knowledge Graph – Adaptable Analytics API with the financial support of Accenture LTD, the Generalitat Valenciana through the CoMoDiD project (CIPROM/2021/023), the Spanish State Research Agency through the DELFOS (PDC2021-121243-I00) and SREC (PID2021-123824OB-I00) projects, MICIN/AEI/10.13039/501 100011033 and co-financed with ERDF and the European Union Next Generation EU/PRTR. Funding Information: and H2020 Programmes under grant agreements 101070455 (DYNABIC), 101095634 (ENTRUST) and 101020416 (ERATOSTHENES), and the Research Council of Norway’s BIA-IPN programme under grant agreement 309700 (FLEET). Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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