---
_id: '15256'
abstract:
- lang: eng
text: This paper deals with online customer reviews of local multi-service providers.
While many studies investigate product reviews and online labour markets with
service providers delivering intangible products “over the wire”, we focus on
websites where providers offer multiple distinct services that can be booked,
paid and reviewed online but are performed locally offline. This type of service
providers has so far been neglected in the literature. This paper analyses reviews
and applies sentiment analysis. It aims to gain new insights into local multi-service
providers’ performance. There is a broad literature range presented with regard
to the topics addressed. The results show, among other things, that providers
with good ratings continue to perform well over time. We find that many positive
reviews seem to encourage sales. On average, quantitative star ratings and qualitative
ratings in the form of review texts match. Further results are also achieved in
this study.
author:
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Kersting J, Geierhos M. What Reviews in Local Online Labour Markets Reveal
about the Performance of Multi-Service Providers. In: Proceedings of the 9th
International Conference on Pattern Recognition Applications and Methods.
Setúbal, Portugal: SCITEPRESS; 2020:263--272.'
apa: 'Kersting, J., & Geierhos, M. (2020). What Reviews in Local Online Labour
Markets Reveal about the Performance of Multi-Service Providers. In Proceedings
of the 9th International Conference on Pattern Recognition Applications and Methods
(pp. 263--272). Setúbal, Portugal: SCITEPRESS.'
bibtex: '@inproceedings{Kersting_Geierhos_2020, place={Setúbal, Portugal}, title={What
Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service
Providers}, booktitle={Proceedings of the 9th International Conference on Pattern
Recognition Applications and Methods}, publisher={SCITEPRESS}, author={Kersting,
Joschka and Geierhos, Michaela}, year={2020}, pages={263--272} }'
chicago: 'Kersting, Joschka, and Michaela Geierhos. “What Reviews in Local Online
Labour Markets Reveal about the Performance of Multi-Service Providers.” In Proceedings
of the 9th International Conference on Pattern Recognition Applications and Methods,
263--272. Setúbal, Portugal: SCITEPRESS, 2020.'
ieee: J. Kersting and M. Geierhos, “What Reviews in Local Online Labour Markets
Reveal about the Performance of Multi-Service Providers,” in Proceedings of
the 9th International Conference on Pattern Recognition Applications and Methods,
Valetta, Malta, 2020, pp. 263--272.
mla: Kersting, Joschka, and Michaela Geierhos. “What Reviews in Local Online Labour
Markets Reveal about the Performance of Multi-Service Providers.” Proceedings
of the 9th International Conference on Pattern Recognition Applications and Methods,
SCITEPRESS, 2020, pp. 263--272.
short: 'J. Kersting, M. Geierhos, in: Proceedings of the 9th International Conference
on Pattern Recognition Applications and Methods, SCITEPRESS, Setúbal, Portugal,
2020, pp. 263--272.'
conference:
location: Valetta, Malta
name: International Conference on Pattern Recognition Applications and Methods (ICPRAM)
date_created: 2019-12-06T13:09:42Z
date_updated: 2022-01-06T06:52:19Z
ddc:
- '000'
department:
- _id: '579'
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2020-09-18T09:27:41Z
date_updated: 2020-09-18T09:27:41Z
file_id: '19577'
file_name: Kersting & Geierhos (2020c), Kersting2020c.pdf
file_size: 963370
relation: main_file
success: 1
file_date_updated: 2020-09-18T09:27:41Z
has_accepted_license: '1'
keyword:
- Customer Reviews
- Sentiment Analysis
- Online Labour Markets
language:
- iso: eng
page: 263--272
place: Setúbal, Portugal
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: Proceedings of the 9th International Conference on Pattern Recognition
Applications and Methods
publisher: SCITEPRESS
status: public
title: What Reviews in Local Online Labour Markets Reveal about the Performance of
Multi-Service Providers
type: conference
user_id: '58701'
year: '2020'
...
---
_id: '4691'
abstract:
- lang: eng
text: Analysts have estimated that more than 80 percent of today’s data is stored
in unstructured form (e.g., text, audio, image, video)—much of it expressed in
rich and ambiguous natural language. Traditionally, to analyze natural language,
one has used qualitative data-analysis approaches, such as manual coding. Yet,
the size of text data sets obtained from the Internet makes manual analysis virtually
impossible. In this tutorial, we discuss the challenges encountered when applying
automated text-mining techniques in information systems research. In particular,
we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation,
an unsupervised text-mining technique, with a LASSO multinomial logistic regression
to explain user satisfaction with an IT artifact by automatically analyzing more
than 12,000 online customer reviews. For fellow information systems researchers,
this tutorial provides guidance for conducting text-mining studies on their own
and for evaluating the quality of others.
author:
- first_name: Stefan
full_name: Debortoli, Stefan
last_name: Debortoli
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Iris
full_name: Junglas, Iris
last_name: Junglas
- first_name: Jan
full_name: vom Brocke, Jan
last_name: vom Brocke
citation:
ama: 'Debortoli S, Müller O, Junglas I, vom Brocke J. Text Mining for Information
Systems Researchers: An Annotated Tutorial. Communications of the Association
for Information Systems. Published online 2016:555-582. doi:10.17705/1CAIS.03907'
apa: 'Debortoli, S., Müller, O., Junglas, I., & vom Brocke, J. (2016). Text
Mining for Information Systems Researchers: An Annotated Tutorial. Communications
of the Association for Information Systems, 555–582. https://doi.org/10.17705/1CAIS.03907'
bibtex: '@article{Debortoli_Müller_Junglas_vom Brocke_2016, title={Text Mining for
Information Systems Researchers: An Annotated Tutorial}, DOI={10.17705/1CAIS.03907},
journal={Communications of the Association for Information Systems}, author={Debortoli,
Stefan and Müller, Oliver and Junglas, Iris and vom Brocke, Jan}, year={2016},
pages={555–582} }'
chicago: 'Debortoli, Stefan, Oliver Müller, Iris Junglas, and Jan vom Brocke. “Text
Mining for Information Systems Researchers: An Annotated Tutorial.” Communications
of the Association for Information Systems, 2016, 555–82. https://doi.org/10.17705/1CAIS.03907.'
ieee: 'S. Debortoli, O. Müller, I. Junglas, and J. vom Brocke, “Text Mining for
Information Systems Researchers: An Annotated Tutorial,” Communications of
the Association for Information Systems, pp. 555–582, 2016, doi: 10.17705/1CAIS.03907.'
mla: 'Debortoli, Stefan, et al. “Text Mining for Information Systems Researchers:
An Annotated Tutorial.” Communications of the Association for Information Systems,
2016, pp. 555–82, doi:10.17705/1CAIS.03907.'
short: S. Debortoli, O. Müller, I. Junglas, J. vom Brocke, Communications of the
Association for Information Systems (2016) 555–582.
date_created: 2018-10-12T08:30:04Z
date_updated: 2026-03-10T09:44:54Z
department:
- _id: '196'
doi: 10.17705/1CAIS.03907
extern: '1'
keyword:
- Latent dirichlet allocation
- Online customer reviews
- Text mining
- Topic modeling
- User satisfaction
language:
- iso: eng
page: 555-582
publication: Communications of the Association for Information Systems
publication_identifier:
isbn:
- '9781615679119'
issn:
- 1529-3181
status: public
title: 'Text Mining for Information Systems Researchers: An Annotated Tutorial'
type: journal_article
user_id: '14972'
year: '2016'
...