Gig work business process improvement
Loading...
Author (Corporation)
Publication date
2018
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Wong, Ka Chun
Editor (Corporation)
Supervisor
Parent work
ISCBI 2018. 6th International Symposium on Computational and Business Intelligence. Proceedings
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
10-15
Patent number
Publisher / Publishing institution
CPS
Place of publication / Event location
Basel
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
We collaborate with a gig work platform company (GPC) in Switzerland. The project aims to improve the business by influencing process management within the GPC, providing automated matching of jobs to workers, improving worker acquisition and worker commitment, and particularly focusing on the prevention of no shows. One expects to achieve financial, organizational and efficiency gains. As research tools we use a combination of text mining and sentiment analysis, Business Process Modeling and Notation (BPMN), interviews with workers and employers, and the design of sociotechnical improvements to the process, including platform improvements and prototypes. Here, we focus on the successful combination of BPMN modelling with sentiment analysis in the identification of problems and generation of ideas for future modifications to the business processes.
Keywords
sentiment analysis, Business Process Modeling and Notation, business process improvement, gig work
Subject (DDC)
Event
Exhibition start date
Exhibition end date
Conference start date
27.08.2018
Conference end date
29.08.2018
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Pustulka, E., Telesko, R., & Hanne, T. (2018). Gig work business process improvement. In K. C. Wong (Ed.), ISCBI 2018. 6th International Symposium on Computational and Business Intelligence. Proceedings (pp. 10–15). CPS. https://doi.org/10.26041/fhnw-1764