PUB - Publikationen an der Universität Bielefeld

# Extended Abstract

## Introduction
In the course of the uprising of new work forms like gig-economy and crowdwork (CW) the nature of the work is changing and challenges both work design theory (Bergman & Jean, 2016; Brawley, 2017; Kuhn, 2016) and the research methods on this topic (Schulte, Schlicher, & Maier, 2020). Recent research in work design in CW targeted the work on the level of the whole job. Mostly the motivation for participating was investigated (Hossain, 2012; Kaufmann, Schulze, & Veit, 2011; Spindeldreher & Schlagwein, 2016) and motivational theories like the self-determination theory were successfully adopted to CW to determine job satisfaction (Brawley, 2017; Durward, Blohm, & Leimeister, 2020). Also characteristics of CW tasks and the circumstances when they are perceived as pleasant were investigated. Schulze, Seedorf, Geiger, Kaufmann and Schader (2011) therefore identified fourteen task characteristics like a high reward, simplicity or the availability of multiple tasks of the same kind. Brawley and Pury (2016) accessed unpleasant tasks and the role of work design and affect on satisfaction. However, we know little about the appraisal of common tasks in CW and how they are experienced.

## Scope of the presentation
This leads to the paradox situation to know much about the whole work design of the job “crowdworker” but not its components i.e. task design. However it seems important to take a closer look on single tasks because previous research demonstrated that slightly changes of task characteristics in single tasks like the significance/meaningfulness can affect the crowd worker’s satisfaction and performance (Chandler & Kapelner, 2013). Additionally, work design goes beyond mere motivational aspects but can be split into task and social characteristics as well as the work context (Humphrey, Nahrgang, & Morgeson, 2007; Morgeson & Humphrey, 2006) and different CW platforms differ in work design (Leimeister, Zogaj, Durward, & Blohm, 2016). These two aspects are less researched (Schulte et al., 2020). In this presentation we want to bridge this gap in CW research by a) investigating the work design of single tasks and how they influence the satisfaction and b) how they vary on different platforms. We hypothesized that work design features of task, social and contextual characteristics are related to satisfaction on the level of single tasks. Additionally, we assumed differences in task design on the platforms. Platforms offering a more specialized focus on specific tasks e.g. texting or coding support a higher perceived specialization and meaningfulness in contrast to less specific platforms.

## Method
We investigated two different platforms. One participating platform offers a broad range of different micro tasks (n = 107) and one platform has a focus on texting and content creation (n = 119). Participating crowd worker were introduced to revive and describe the last task they performed on the platform. We used the event-reconstruction-method (ERM; Grube, Schroer, Hentzschel, & Hertel, 2008), a technique in which participants are instructed to remember specific situation and were then asked to evaluate or rate this situation. Afterwards crowd worker rated the task design with the Work Design Questionnaire (WDQ; Morgeson & Humphrey, 2006) and the task characteristics of Schulz and colleagues (2011) as well as the task satisfaction (BIAJS; Thompson & Phua, 2012).

## Results
Task design characteristics in the domain of the task were related to task satisfaction and affect. However social and context characteristics were not associated with task satisfaction – except for ergonomics. The relation of the task characteristics and satisfaction differed significantly between both platform types. Task identity, feedback and complexity were important on the micro task platform but not on the texting platform. Assumptions about specialization and meaningfulness could not be approved.

## Conclusion
Our research gives detailed insights into common tasks experienced in CW. Beside motivational aspects at least ergonomic aspects should be considered in CW. This is important as crowd workers are responsible for ergonomics on their own. Future task and work design should follow a broader scope of characteristics. Additionally, our findings are contradictory to previous research where no differences were found between platforms on the level of the job (Durward et al., 2020). Our data provide a more differentiated pattern between two platforms. However, this research is limited by two important aspects: a survey design cannot proof causality and characteristics were observed on the level of single tasks where other research is focused on the level of the whole job. Further research is necessary to investigate the interplay of these two levels as well as causal relations in task design.

## References
Bergman, M. E., & Jean, V. A. (2016). Where have all the “workers” gone? A critical analysis of the unrepresentativeness of our samples relative to the labor market in the industrial–organizational psychology literature. Industrial and Organizational Psychology, 9(1), 84–113. https://doi.org/10.1017/iop.2015.70
Brawley, A. M. (2017). The big, gig picture: We can't assume the same constructs matter. Industrial and Organizational Psychology, 10(4), 687–696. https://doi.org/10.1017/iop.2017.77
Brawley, A. M., & Pury, C. L.S. (2016). Work experiences on MTurk: Job satisfaction, turnover, and information sharing. Computers in Human Behavior, 54(1), 531–546. https://doi.org/10.1016/j.chb.2015.08.031
Chandler, D., & Kapelner, A. (2013). Breaking monotony with meaning: Motivation in crowdsourcing markets. Journal of Economic Behavior & Organization, 90, 123–133. https://doi.org/10.1016/j.jebo.2013.03.003
Durward, D., Blohm, I., & Leimeister, J. M. (2020). The nature of crowd work and its effects on individuals’ work perception. Journal of Management Information Systems. (37), 1–30. https://doi.org/10.1080/07421222.2019.1705506
Grube, A., Schroer, J., Hentzschel, C., & Hertel, G. (2008). The event reconstruction method: An efficient measure of experience-based job satisfaction. Journal of Occupational and Organizational Psychology, 81(4), 669–689. https://doi.org/10.1348/096317907X251578
Hossain, M. (2012). Users' motivation to participate in online crowdsourcing platforms. In R. S. R. Kasim (Ed.), International Conference on Innovation, Management and Technology Research (ICIMTR) (pp. 310–315). Piscataway, NJ: IEEE. https://doi.org/10.1109/ICIMTR.2012.6236409
Humphrey, S. E., Nahrgang, J. D., & Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: A meta-analytic summary and theoretical extension of the work design literature. The Journal of Applied Psychology, 92(5), 1332–1356. https://doi.org/10.1037/0021-9010.92.5.1332
Kaufmann, N., Schulze, T., & Veit, D. (2011). More than fun and money. Worker motivation in crowdsourcing - A study on Mechanical Turk. AMCIS 2011 Proceedings. (11), 1–11.
Kuhn, K. M. (2016). The rise of the “gig economy” and implications for understanding work and workers. Industrial and Organizational Psychology, 9(1), 157–162. https://doi.org/10.1017/iop.2015.129
Leimeister, J. M., Zogaj, S., Durward, D., & Blohm, I. (2016). Systematisierung und Analyse von Crowd-Sourcing-Anbietern und Crowd-Work-Projekten. Reihe Praxiswissen Betriebsvereinbarungen: Nr. 324. Düsseldorf: Hans-Böckler-Stiftung. Retrieved from http://hdl.handle.net/10419/142715
Morgeson, F. P., & Humphrey, S. E. (2006). The Work Design Questionnaire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work. The Journal of Applied Psychology, 91(6), 1321–1339. https://doi.org/10.1037/0021-9010.91.6.1321
Schulte, J., Schlicher, K. D., & Maier, G. W. (2020). Working everywhere and every time?—Chances and risks in crowdworking and crowdsourcing work design. Gruppe. Interaktion. Organisation. Zeitschrift Für Angewandte Organisationspsychologie (GIO), 43(3). https://doi.org/10.1007/s11612-020-00503-3
Schulze, T., Seedorfer, S., Geiger, D., Kaufmann, N., & Schader, M. (2011). Exploring task properties in crowdsourcing-an empirical study on mechanical turk. In Proceedings of the 19th European Conference on Information Systems. Symposium conducted at the meeting of ECIS, Helsinki, Finland.
Spindeldreher, K., & Schlagwein, D. (2016). What drives the crowd? A meta-analysis of the motivation of participants in crowdsourcing. PACIS 2016 Proceedings.
Thompson, E. R., & Phua, F. T. T. (2012). A Brief Index of Affective Job Satisfaction. Group & Organization Management, 37(3), 275–307. https://doi.org/10.1177/1059601111434201