---
_id: '27507'
abstract:
- lang: eng
text: Accurate real estate appraisal is essential in decision making processes of
financial institutions, governments, and trending real estate platforms like Zillow.
One of the most important factors of a property’s value is its location. However,
creating accurate quantifications of location remains a challenge. While traditional
approaches rely on Geographical Information Systems (GIS), recently unstructured
data in form of images was incorporated in the appraisal process, but text data
remains an untapped reservoir. Our study shows that using text data in form of
geolocated Wikipedia articles can increase predictive performance over traditional
GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to
automatically extract geographically weighted vector representations for text
is established and used alongside traditional structural housing features to make
predictions and to uncover local patterns on sale price for real estate transactions
between 2015 and 2020 in Allegheny County, Pennsylvania.
author:
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location
in Real Estate Appraisal. In: 55th Annual Hawaii International Conference on
System Sciences (HICSS-55). ; 2022.'
apa: Heuwinkel, T., Kucklick, J.-P., & Müller, O. (2022). Using Geolocated Text
to Quantify Location in Real Estate Appraisal. 55th Annual Hawaii International
Conference on System Sciences (HICSS-55). Hawaii International Conference
on System Science (HICSS), Virtual.
bibtex: '@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated
Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii
International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim
and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }'
chicago: Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated
Text to Quantify Location in Real Estate Appraisal.” In 55th Annual Hawaii
International Conference on System Sciences (HICSS-55), 2022.
ieee: T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify
Location in Real Estate Appraisal,” presented at the Hawaii International Conference
on System Science (HICSS), Virtual, 2022.
mla: Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real
Estate Appraisal.” 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 2022.
short: 'T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International
Conference on System Sciences (HICSS-55), 2022.'
conference:
end_date: 2022-01-07
location: Virtual
name: Hawaii International Conference on System Science (HICSS)
start_date: 2022-01-03
date_created: 2021-11-17T07:12:03Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
keyword:
- Real Estate Appraisal
- Text Regression
- Natural Language Processing (NLP)
- Location Intelligence
- Wikipedia
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Using Geolocated Text to Quantify Location in Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '35620'
abstract:
- lang: eng
text: Deep learning models fuel many modern decision support systems, because they
typically provide high predictive performance. Among other domains, deep learning
is used in real-estate appraisal, where it allows to extend the analysis from
hard facts only (e.g., size, age) to also consider more implicit information about
the location or appearance of houses in the form of image data. However, one downside
of deep learning models is their intransparent mechanic of decision making, which
leads to a trade-off between accuracy and interpretability. This limits their
applicability for tasks where a justification of the decision is necessary. Therefore,
in this paper, we first combine different perspectives on interpretability into
a multi-dimensional framework for a socio-technical perspective on explainable
artificial intelligence. Second, we measure the performance gains of using multi-view
deep learning which leverages additional image data (satellite images) for real
estate appraisal. Third, we propose and test a novel post-hoc explainability method
called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency
of convolutional neural networks (CNNs) for predicting continuous outcome variables.
With this, we try to reduce the accuracy-interpretability trade-off of multi-view
deep learning models. Our proposed network architecture outperforms traditional
hedonic regression models by 34% in terms of MAE. Furthermore, we find that the
used satellite images are the second most important predictor after square feet
in our model and that the network learns interpretable patterns about the neighborhood
structure and density.
article_type: original
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Kucklick J-P, Müller O. Tackling the Accuracy–Interpretability Trade-off:
Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal.
ACM Transactions on Management Information Systems. Published online 2022.
doi:10.1145/3567430'
apa: 'Kucklick, J.-P., & Müller, O. (2022). Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
Appraisal. ACM Transactions on Management Information Systems. https://doi.org/10.1145/3567430'
bibtex: '@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
Appraisal}, DOI={10.1145/3567430},
journal={ACM Transactions on Management Information Systems}, publisher={Association
for Computing Machinery (ACM)}, author={Kucklick, Jan-Peter and Müller, Oliver},
year={2022} }'
chicago: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate
Appraisal.” ACM Transactions on Management Information Systems, 2022. https://doi.org/10.1145/3567430.'
ieee: 'J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off:
Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,”
ACM Transactions on Management Information Systems, 2022, doi: 10.1145/3567430.'
mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate
Appraisal.” ACM Transactions on Management Information Systems, Association
for Computing Machinery (ACM), 2022, doi:10.1145/3567430.'
short: J.-P. Kucklick, O. Müller, ACM Transactions on Management Information Systems
(2022).
date_created: 2023-01-10T05:16:02Z
date_updated: 2023-01-10T05:20:18Z
department:
- _id: '195'
- _id: '196'
doi: 10.1145/3567430
keyword:
- Interpretability
- Convolutional Neural Network
- Accuracy-Interpretability Trade-Of
- Real Estate Appraisal
- Hedonic Pricing
- Grad-Ram
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/pdf/10.1145/3567430
publication: ACM Transactions on Management Information Systems
publication_identifier:
issn:
- 2158-656X
- 2158-6578
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: 'Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning
Models for Satellite Image-based Real Estate Appraisal'
type: journal_article
user_id: '77066'
year: '2022'
...
---
_id: '36912'
abstract:
- lang: eng
text: Existing process mining methods are primarily designed for processes that
have reached a high degree of digitalization and standardization. In contrast,
the literature has only begun to discuss how process mining can be applied to
knowledge-intensive processes—such as product innovation processes—that involve
creative activities, require organizational flexibility, depend on single actors’
decision autonomy, and target process-external goals such as customer satisfaction.
Due to these differences, existing Process Mining methods cannot be applied out-of-the-box
to analyze knowledge-intensive processes. In this paper, we employ Action Design
Research (ADR) to design and evaluate a process mining approach for knowledge-intensive
processes. More specifically, we draw on the two processes of product innovation
and engineer-to-order in manufacturing contexts. We collected data from 27 interviews
and conducted 49 workshops to evaluate our IT artifact at different stages in
the ADR process. From a theoretical perspective, we contribute five design principles
and a conceptual artifact that prescribe how process mining ought to be designed
for knowledge-intensive processes in manufacturing. From a managerial perspective,
we demonstrate how enacting these principles enables their application in practice.
author:
- first_name: Bernd
full_name: Löhr, Bernd
id: '56760'
last_name: Löhr
- first_name: Katharina
full_name: Brennig, Katharina
id: '51905'
last_name: Brennig
- first_name: Christian
full_name: Bartelheimer, Christian
id: '49160'
last_name: Bartelheimer
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Löhr B, Brennig K, Bartelheimer C, Beverungen D, Müller O. Process Mining
of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing.
In: International Conference on Business Process Management. ; 2022. doi:10.1007/978-3-031-16103-2_18'
apa: 'Löhr, B., Brennig, K., Bartelheimer, C., Beverungen, D., & Müller, O.
(2022). Process Mining of Knowledge-Intensive Processes: An Action Design Research
Study in Manufacturing. International Conference on Business Process Management.
https://doi.org/10.1007/978-3-031-16103-2_18'
bibtex: '@inproceedings{Löhr_Brennig_Bartelheimer_Beverungen_Müller_2022, title={Process
Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing},
DOI={10.1007/978-3-031-16103-2_18},
booktitle={International Conference on Business Process Management}, author={Löhr,
Bernd and Brennig, Katharina and Bartelheimer, Christian and Beverungen, Daniel
and Müller, Oliver}, year={2022} }'
chicago: 'Löhr, Bernd, Katharina Brennig, Christian Bartelheimer, Daniel Beverungen,
and Oliver Müller. “Process Mining of Knowledge-Intensive Processes: An Action
Design Research Study in Manufacturing.” In International Conference on Business
Process Management, 2022. https://doi.org/10.1007/978-3-031-16103-2_18.'
ieee: 'B. Löhr, K. Brennig, C. Bartelheimer, D. Beverungen, and O. Müller, “Process
Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing,”
2022, doi: 10.1007/978-3-031-16103-2_18.'
mla: 'Löhr, Bernd, et al. “Process Mining of Knowledge-Intensive Processes: An Action
Design Research Study in Manufacturing.” International Conference on Business
Process Management, 2022, doi:10.1007/978-3-031-16103-2_18.'
short: 'B. Löhr, K. Brennig, C. Bartelheimer, D. Beverungen, O. Müller, in: International
Conference on Business Process Management, 2022.'
date_created: 2023-01-16T11:04:54Z
date_updated: 2024-01-11T11:35:54Z
department:
- _id: '196'
doi: 10.1007/978-3-031-16103-2_18
language:
- iso: eng
publication: International Conference on Business Process Management
publication_identifier:
isbn:
- 978-3-031-16103-2
status: public
title: 'Process Mining of Knowledge-Intensive Processes: An Action Design Research
Study in Manufacturing'
type: conference
user_id: '51905'
year: '2022'
...
---
_id: '25113'
abstract:
- lang: eng
text: Our world is more connected than ever before. Sadly, however, this highly
connected world has made it easier to bully, insult, and propagate hate speech
on the cyberspace. Even though researchers and companies alike have started investigating
this real-world problem, the question remains as to why users are increasingly
being exposed to hate and discrimination online. In fact, the noticeable and persistent
increase in harmful language on social media platforms indicates that the situation
is, actually, only getting worse. Hence, in this work, we show that contemporary
ML methods can help tackle this challenge in an accurate and cost-effective manner.
Our experiments demonstrate that a universal approach combining transfer learning
methods and state-of-the-art Transformer architectures can trigger the efficient
development of toxic language detection models. Consequently, with this universal
approach, we provide platform providers with a simplistic approach capable of
enabling the automated moderation of user-generated content, and as a result,
hope to contribute to making the web a safer place.
author:
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Frederik S.
full_name: Bäumer, Frederik S.
last_name: Bäumer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Caron M, Bäumer FS, Müller O. Towards Automated Moderation: Enabling Toxic
Language Detection with Transfer Learning and Attention-Based Models. In: 55th
Hawaii International Conference on System Sciences (HICSS). ; 2022.'
apa: 'Caron, M., Bäumer, F. S., & Müller, O. (2022). Towards Automated Moderation:
Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models.
55th Hawaii International Conference on System Sciences (HICSS). 55th Hawaii
International Conference on System Sciences (HICSS), Online.'
bibtex: '@inproceedings{Caron_Bäumer_Müller_2022, title={Towards Automated Moderation:
Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models},
booktitle={55th Hawaii International Conference on System Sciences (HICSS)}, author={Caron,
Matthew and Bäumer, Frederik S. and Müller, Oliver}, year={2022} }'
chicago: 'Caron, Matthew, Frederik S. Bäumer, and Oliver Müller. “Towards Automated
Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based
Models.” In 55th Hawaii International Conference on System Sciences (HICSS),
2022.'
ieee: 'M. Caron, F. S. Bäumer, and O. Müller, “Towards Automated Moderation: Enabling
Toxic Language Detection with Transfer Learning and Attention-Based Models,” presented
at the 55th Hawaii International Conference on System Sciences (HICSS), Online,
2022.'
mla: 'Caron, Matthew, et al. “Towards Automated Moderation: Enabling Toxic Language
Detection with Transfer Learning and Attention-Based Models.” 55th Hawaii International
Conference on System Sciences (HICSS), 2022.'
short: 'M. Caron, F.S. Bäumer, O. Müller, in: 55th Hawaii International Conference
on System Sciences (HICSS), 2022.'
conference:
end_date: 2022-01-07
location: Online
name: 55th Hawaii International Conference on System Sciences (HICSS)
start_date: 2022-01-03
date_created: 2021-09-29T10:06:24Z
date_updated: 2024-01-15T12:37:10Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: http://hdl.handle.net/10125/79428
publication: 55th Hawaii International Conference on System Sciences (HICSS)
publication_status: published
status: public
title: 'Towards Automated Moderation: Enabling Toxic Language Detection with Transfer
Learning and Attention-Based Models'
type: conference
user_id: '60721'
year: '2022'
...
---
_id: '41486'
abstract:
- lang: eng
text: Now accounting for more than 80% of a firm's worth, brands have become essential
assets for modern organizations. However, methods and techniques for the monetary
valuation of brands are still under-researched. Hence, the objective of this study
is to evaluate the utility of explanatory statistical models and machine learning
approaches for explaining and predicting brand value. Drawing upon the case of
the most valuable English football brands during the 2016/17 to 2020/21 seasons,
we demonstrate how to operationalize Aaker's (1991) theoretical brand equity framework
to collect meaningful qualitative and quantitative feature sets. Our explanatory
models can explain up to 77% of the variation in brand valuations across all clubs
and seasons, while our predictive approach can predict out-of-sample observations
with a mean absolute percentage error (MAPE) of 14%. Future research can build
upon our results to develop domain-specific brand valuation methods while enabling
managers to make better-informed investment decisions.
author:
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Christian
full_name: Bartelheimer, Christian
id: '49160'
last_name: Bartelheimer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Caron M, Bartelheimer C, Müller O. Towards a Reliable & Transparent Approach
to Data-Driven Brand Valuation. In: Proceeding of the 28th Americas Conference
on Information Systems (AMCIS). ; 2022.'
apa: Caron, M., Bartelheimer, C., & Müller, O. (2022). Towards a Reliable &
Transparent Approach to Data-Driven Brand Valuation. Proceeding of the 28th
Americas Conference on Information Systems (AMCIS). 28th Americas Conference
on Information Systems (AMCIS), Minneapolis, USA.
bibtex: '@inproceedings{Caron_Bartelheimer_Müller_2022, place={Minneapolis, USA},
title={Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation},
booktitle={Proceeding of the 28th Americas Conference on Information Systems (AMCIS)},
author={Caron, Matthew and Bartelheimer, Christian and Müller, Oliver}, year={2022}
}'
chicago: Caron, Matthew, Christian Bartelheimer, and Oliver Müller. “Towards a Reliable
& Transparent Approach to Data-Driven Brand Valuation.” In Proceeding of
the 28th Americas Conference on Information Systems (AMCIS). Minneapolis,
USA, 2022.
ieee: M. Caron, C. Bartelheimer, and O. Müller, “Towards a Reliable & Transparent
Approach to Data-Driven Brand Valuation,” presented at the 28th Americas Conference
on Information Systems (AMCIS), Minneapolis, USA, 2022.
mla: Caron, Matthew, et al. “Towards a Reliable & Transparent Approach to Data-Driven
Brand Valuation.” Proceeding of the 28th Americas Conference on Information
Systems (AMCIS), 2022.
short: 'M. Caron, C. Bartelheimer, O. Müller, in: Proceeding of the 28th Americas
Conference on Information Systems (AMCIS), Minneapolis, USA, 2022.'
conference:
end_date: 2022-08-14
location: Minneapolis, USA
name: 28th Americas Conference on Information Systems (AMCIS)
start_date: 2022-08-10
date_created: 2023-02-02T13:34:49Z
date_updated: 2023-02-28T08:59:38Z
department:
- _id: '195'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/amcis2022/conf_theme/conf_theme/10/
place: Minneapolis, USA
publication: Proceeding of the 28th Americas Conference on Information Systems (AMCIS)
publication_status: published
status: public
title: Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation
type: conference
user_id: '60721'
year: '2022'
...
---
_id: '21204'
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Kucklick J-P, Müller O. A Comparison of Multi-View Learning Strategies for
Satellite Image-based Real Estate Appraisal. In: The AAAI-21 Workshop on Knowledge
Discovery from Unstructured Data in Financial Services. ; 2021.'
apa: Kucklick, J.-P., & Müller, O. (2021). A Comparison of Multi-View Learning
Strategies for Satellite Image-based Real Estate Appraisal. In The AAAI-21
Workshop on Knowledge Discovery from Unstructured Data in Financial Services.
bibtex: '@inproceedings{Kucklick_Müller_2021, title={A Comparison of Multi-View
Learning Strategies for Satellite Image-based Real Estate Appraisal}, booktitle={
The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial
Services}, author={Kucklick, Jan-Peter and Müller, Oliver}, year={2021} }'
chicago: Kucklick, Jan-Peter, and Oliver Müller. “A Comparison of Multi-View Learning
Strategies for Satellite Image-Based Real Estate Appraisal.” In The AAAI-21
Workshop on Knowledge Discovery from Unstructured Data in Financial Services,
2021.
ieee: J.-P. Kucklick and O. Müller, “A Comparison of Multi-View Learning Strategies
for Satellite Image-based Real Estate Appraisal,” in The AAAI-21 Workshop
on Knowledge Discovery from Unstructured Data in Financial Services, 2021.
mla: Kucklick, Jan-Peter, and Oliver Müller. “A Comparison of Multi-View Learning
Strategies for Satellite Image-Based Real Estate Appraisal.” The AAAI-21 Workshop
on Knowledge Discovery from Unstructured Data in Financial Services, 2021.
short: 'J.-P. Kucklick, O. Müller, in: The AAAI-21 Workshop on Knowledge Discovery
from Unstructured Data in Financial Services, 2021.'
conference:
name: The Thirty-Fifth AAAI Conference on Artificial Intelligence
date_created: 2021-02-10T10:05:32Z
date_updated: 2022-01-06T06:54:49Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aaai-kdf.github.io/kdf2021/assets/pdfs/KDF_21_paper_12.pdf
oa: '1'
publication: ' The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data
in Financial Services'
status: public
title: A Comparison of Multi-View Learning Strategies for Satellite Image-based Real
Estate Appraisal
type: conference
user_id: '71922'
year: '2021'
...
---
_id: '22514'
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Jennifer
full_name: Müller, Jennifer
id: '82872'
last_name: Müller
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Kucklick J-P, Müller J, Beverungen D, Müller O. Quantifying the Impact of
Location Data for Real Estate Appraisal – A GIS-based Deep Learning Approach.
In: European Conference on Information Systems. ; 2021.'
apa: Kucklick, J.-P., Müller, J., Beverungen, D., & Müller, O. (2021). Quantifying
the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning
Approach. In European Conference on Information Systems. Virtual.
bibtex: '@inproceedings{Kucklick_Müller_Beverungen_Müller_2021, title={Quantifying
the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning
Approach}, booktitle={European Conference on Information Systems}, author={Kucklick,
Jan-Peter and Müller, Jennifer and Beverungen, Daniel and Müller, Oliver}, year={2021}
}'
chicago: Kucklick, Jan-Peter, Jennifer Müller, Daniel Beverungen, and Oliver Müller.
“Quantifying the Impact of Location Data for Real Estate Appraisal – A GIS-Based
Deep Learning Approach.” In European Conference on Information Systems,
2021.
ieee: J.-P. Kucklick, J. Müller, D. Beverungen, and O. Müller, “Quantifying the
Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning
Approach,” in European Conference on Information Systems, Virtual, 2021.
mla: Kucklick, Jan-Peter, et al. “Quantifying the Impact of Location Data for Real
Estate Appraisal – A GIS-Based Deep Learning Approach.” European Conference
on Information Systems, 2021.
short: 'J.-P. Kucklick, J. Müller, D. Beverungen, O. Müller, in: European Conference
on Information Systems, 2021.'
conference:
end_date: 2021-06-16
location: Virtual
name: ECIS 2021 - 29th European Conference on Information System
start_date: 2021-06-14
date_created: 2021-06-28T11:30:02Z
date_updated: 2022-01-06T06:55:35Z
department:
- _id: '196'
- _id: '526'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1022&context=ecis2021_rip
oa: '1'
publication: European Conference on Information Systems
status: public
title: Quantifying the Impact of Location Data for Real Estate Appraisal – A GIS-based
Deep Learning Approach
type: conference
user_id: '71922'
year: '2021'
...
---
_id: '26812'
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Leffrang D, Müller O. Should I Follow this Model? The Effect of Uncertainty
Visualization on the Acceptance of Time Series Forecasts. In: IEEE Workshop
on TRust and EXpertise in Visual Analytics. ; 2021. doi:10.1109/TREX53765.2021.00009'
apa: Leffrang, D., & Müller, O. (2021). Should I Follow this Model? The Effect
of Uncertainty Visualization on the Acceptance of Time Series Forecasts. IEEE
Workshop on TRust and EXpertise in Visual Analytics. 2021 IEEE Visualization
conference. https://doi.org/10.1109/TREX53765.2021.00009
bibtex: '@inproceedings{Leffrang_Müller_2021, title={Should I Follow this Model?
The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts},
DOI={10.1109/TREX53765.2021.00009},
booktitle={IEEE Workshop on TRust and EXpertise in Visual Analytics}, author={Leffrang,
Dirk and Müller, Oliver}, year={2021} }'
chicago: Leffrang, Dirk, and Oliver Müller. “Should I Follow This Model? The Effect
of Uncertainty Visualization on the Acceptance of Time Series Forecasts.” In IEEE
Workshop on TRust and EXpertise in Visual Analytics, 2021. https://doi.org/10.1109/TREX53765.2021.00009.
ieee: 'D. Leffrang and O. Müller, “Should I Follow this Model? The Effect of Uncertainty
Visualization on the Acceptance of Time Series Forecasts,” presented at the 2021
IEEE Visualization conference, 2021, doi: 10.1109/TREX53765.2021.00009.'
mla: Leffrang, Dirk, and Oliver Müller. “Should I Follow This Model? The Effect
of Uncertainty Visualization on the Acceptance of Time Series Forecasts.” IEEE
Workshop on TRust and EXpertise in Visual Analytics, 2021, doi:10.1109/TREX53765.2021.00009.
short: 'D. Leffrang, O. Müller, in: IEEE Workshop on TRust and EXpertise in Visual
Analytics, 2021.'
conference:
end_date: 2021-10-19
name: 2021 IEEE Visualization conference
start_date: 2021-10-24
date_created: 2021-10-25T11:11:39Z
date_updated: 2024-01-10T09:55:48Z
department:
- _id: '196'
doi: 10.1109/TREX53765.2021.00009
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://trexvis.github.io/Workshop2021/papers/Leffrang.pdf
oa: '1'
publication: IEEE Workshop on TRust and EXpertise in Visual Analytics
status: public
title: Should I Follow this Model? The Effect of Uncertainty Visualization on the
Acceptance of Time Series Forecasts
type: conference
user_id: '51271'
year: '2021'
...
---
_id: '24547'
abstract:
- lang: eng
text: 'Over the last years, several approaches for the data-driven estimation of
expected possession value (EPV) in basketball and association football (soccer)
have been proposed. In this paper, we develop and evaluate PIVOT: the first such
framework for team handball. Accounting for the fast-paced, dynamic nature and
relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep
learning architecture that relies solely on tracking data. This efficient approach
is capable of predicting the probability that a team will score within the near
future given the fine-grained spatio-temporal distribution of all players and
the ball over the last seconds of the game. Our experiments indicate that PIVOT
is able to produce accurate and calibrated probability estimates, even when trained
on a relatively small dataset. We also showcase two interactive applications of
PIVOT for valuing actual and counterfactual player decisions and actions in real-time.'
author:
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Michael
full_name: Döring, Michael
last_name: Döring
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jochen
full_name: Baumeister, Jochen
id: '46'
last_name: Baumeister
orcid: 0000-0003-2683-5826
citation:
ama: 'Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious
End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking
Data. In: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021).'
apa: 'Müller, O., Caron, M., Döring, M., Heuwinkel, T., & Baumeister, J. (n.d.).
PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data. 8th Workshop on Machine Learning and Data
Mining for Sports Analytics (ECML PKDD 2021). European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021),
Online.'
bibtex: '@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining
for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew
and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }'
chicago: 'Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen
Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player
Actions in Handball Using Tracking Data.” In 8th Workshop on Machine Learning
and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
ieee: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data,” presented at the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.'
mla: 'Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework
for Valuing Player Actions in Handball Using Tracking Data.” 8th Workshop on
Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021).'
short: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop
on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
conference:
end_date: 2021-09-17
location: Online
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery (ECML PKDD 2021)
start_date: 2021-09-13
date_created: 2021-09-16T08:33:04Z
date_updated: 2023-02-28T08:58:24Z
department:
- _id: '196'
- _id: '172'
keyword:
- expected possession value
- handball
- tracking data
- time series classification
- deep learning
language:
- iso: eng
main_file_link:
- url: https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf
publication: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021)
publication_status: inpress
status: public
title: 'PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data'
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '25029'
abstract:
- lang: eng
text: In early 2021, the finance world was taken by storm by the dramatic price
surge of the GameStop Corp. stock. This rise is being, at least in part, attributed
to a group of Redditors belonging to the now-famous r/wallstreetbets (WSB) subreddit
group. In this work, we set out to address if user activity on the WSB subreddit
is associated with the trading volume of the GME stock. Leveraging a unique dataset
containing more than 4.9 million WSB posts and comments, we assert that user activity
is associated with the trading volume of the GameStop stock. We further show that
posts have a significantly higher predictive power than comments and are especially
helpful for predicting unusually high trading volume. Lastly, as recent events
have shown, we believe that these findings have implications for retail and institutional
investors, trading platforms, and policymakers, as these can have disruptive potential.
author:
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Maryna
full_name: Gulenko, Maryna
id: '64226'
last_name: Gulenko
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Caron M, Gulenko M, Müller O. To the Moon! Analyzing the Community of “Degenerates”
Engaged in the Surge of the GME Stock. In: 42nd International Conference on
Information Systems (ICIS 2021). ; 2021.'
apa: Caron, M., Gulenko, M., & Müller, O. (2021). To the Moon! Analyzing the
Community of “Degenerates” Engaged in the Surge of the GME Stock. 42nd International
Conference on Information Systems (ICIS 2021). 42nd International Conference
on Information Systems (ICIS 2021), Austin, Texas.
bibtex: '@inproceedings{Caron_Gulenko_Müller_2021, title={To the Moon! Analyzing
the Community of “Degenerates” Engaged in the Surge of the GME Stock}, booktitle={42nd
International Conference on Information Systems (ICIS 2021)}, author={Caron, Matthew
and Gulenko, Maryna and Müller, Oliver}, year={2021} }'
chicago: Caron, Matthew, Maryna Gulenko, and Oliver Müller. “To the Moon! Analyzing
the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock.” In 42nd
International Conference on Information Systems (ICIS 2021), 2021.
ieee: M. Caron, M. Gulenko, and O. Müller, “To the Moon! Analyzing the Community
of ‘Degenerates’ Engaged in the Surge of the GME Stock,” presented at the 42nd
International Conference on Information Systems (ICIS 2021), Austin, Texas, 2021.
mla: Caron, Matthew, et al. “To the Moon! Analyzing the Community of ‘Degenerates’
Engaged in the Surge of the GME Stock.” 42nd International Conference on Information
Systems (ICIS 2021), 2021.
short: 'M. Caron, M. Gulenko, O. Müller, in: 42nd International Conference on Information
Systems (ICIS 2021), 2021.'
conference:
end_date: 2021-12-15
location: Austin, Texas
name: 42nd International Conference on Information Systems (ICIS 2021)
start_date: 2021-12-12
date_created: 2021-09-24T09:51:35Z
date_updated: 2023-02-28T08:58:16Z
department:
- _id: '196'
keyword:
- Retail investors
- GameStop
- Social Networks
- Reddit
- WallStreetBets
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/icis2021/social_media/social_media/13/
publication: 42nd International Conference on Information Systems (ICIS 2021)
publication_status: published
status: public
title: To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge
of the GME Stock
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '17348'
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Kucklick J-P, Müller O. Location, location, location: Satellite image-based
real-estate appraisal. In: Symposium on Statistical Challenges in Electronic
Commerce Research (SCECR). ; 2020.'
apa: 'Kucklick, J.-P., & Müller, O. (2020). Location, location, location: Satellite
image-based real-estate appraisal. In Symposium on Statistical Challenges
in Electronic Commerce Research (SCECR).'
bibtex: '@inproceedings{Kucklick_Müller_2020, title={Location, location, location:
Satellite image-based real-estate appraisal}, booktitle={Symposium on Statistical
Challenges in Electronic Commerce Research (SCECR)}, author={Kucklick, Jan-Peter
and Müller, Oliver}, year={2020} }'
chicago: 'Kucklick, Jan-Peter, and Oliver Müller. “Location, Location, Location:
Satellite Image-Based Real-Estate Appraisal.” In Symposium on Statistical
Challenges in Electronic Commerce Research (SCECR), 2020.'
ieee: 'J.-P. Kucklick and O. Müller, “Location, location, location: Satellite image-based
real-estate appraisal,” in Symposium on Statistical Challenges in Electronic
Commerce Research (SCECR), 2020.'
mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Location, Location, Location: Satellite
Image-Based Real-Estate Appraisal.” Symposium on Statistical Challenges in
Electronic Commerce Research (SCECR), 2020.'
short: 'J.-P. Kucklick, O. Müller, in: Symposium on Statistical Challenges in Electronic
Commerce Research (SCECR), 2020.'
conference:
name: Symposium on Statistical Challenges in Electronic Commerce Research (SCECR)
date_created: 2020-06-27T12:41:10Z
date_updated: 2022-01-06T06:53:08Z
department:
- _id: '196'
external_id:
arxiv:
- '2006.11406'
language:
- iso: eng
publication: Symposium on Statistical Challenges in Electronic Commerce Research (SCECR)
status: public
title: 'Location, location, location: Satellite image-based real-estate appraisal'
type: conference
user_id: '71922'
year: '2020'
...
---
_id: '17140'
author:
- first_name: Tiemo
full_name: Thiess, Tiemo
last_name: Thiess
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Lorenzo
full_name: Tonelli, Lorenzo
last_name: Tonelli
citation:
ama: 'Thiess T, Müller O, Tonelli L. Design Principles for Explainable Sales Win-Propensity
Prediction Systems. In: International Conference on Wirtschaftsinformatik.
; 2020. doi:https://doi.org/10.30844/wi_2020_c8-thiess'
apa: Thiess, T., Müller, O., & Tonelli, L. (2020). Design Principles for Explainable
Sales Win-Propensity Prediction Systems. In International Conference on Wirtschaftsinformatik.
https://doi.org/10.30844/wi_2020_c8-thiess
bibtex: '@inproceedings{Thiess_Müller_Tonelli_2020, title={Design Principles for
Explainable Sales Win-Propensity Prediction Systems}, DOI={https://doi.org/10.30844/wi_2020_c8-thiess},
booktitle={International Conference on Wirtschaftsinformatik}, author={Thiess,
Tiemo and Müller, Oliver and Tonelli, Lorenzo}, year={2020} }'
chicago: Thiess, Tiemo, Oliver Müller, and Lorenzo Tonelli. “Design Principles for
Explainable Sales Win-Propensity Prediction Systems.” In International Conference
on Wirtschaftsinformatik, 2020. https://doi.org/10.30844/wi_2020_c8-thiess.
ieee: T. Thiess, O. Müller, and L. Tonelli, “Design Principles for Explainable Sales
Win-Propensity Prediction Systems,” in International Conference on Wirtschaftsinformatik,
2020.
mla: Thiess, Tiemo, et al. “Design Principles for Explainable Sales Win-Propensity
Prediction Systems.” International Conference on Wirtschaftsinformatik,
2020, doi:https://doi.org/10.30844/wi_2020_c8-thiess.
short: 'T. Thiess, O. Müller, L. Tonelli, in: International Conference on Wirtschaftsinformatik,
2020.'
date_created: 2020-06-23T09:55:49Z
date_updated: 2022-01-06T06:53:05Z
department:
- _id: '196'
doi: https://doi.org/10.30844/wi_2020_c8-thiess
language:
- iso: eng
publication: International Conference on Wirtschaftsinformatik
status: public
title: Design Principles for Explainable Sales Win-Propensity Prediction Systems
type: conference
user_id: '72849'
year: '2020'
...
---
_id: '17095'
abstract:
- lang: eng
text: In order to sustain their competitive advantage, data driven organizations
must continue investing in business intelligence and analytics (BI&A) while mitigating
inherent cost increases. Research shows that examining outlays by individual BI&A
artifact (e.g. reports, analytics) is necessary, but introduction in practice
is cumbersome and adoption is slow. BI&A service-oriented cost allocation (BIASOCA)
represents an improvement to this situation. This approach enables to render the
BI&A cost pool accountable and improves cost transparency, which leads to a higher
BI&A penetration of economically viable applications in organizations. Against
this background, this paper aims at designing and implementing BIASOCA in a medium-sized
company. To record organizational impact and increase customer acceptance, this
study is carried out as action design research (ADR). Our findings indicate improvements
in BI&A management from working with consumers to locate cost savings and drivers.
After invoicing, consumers’ BI&A awareness increased, releasing resources while
also making a better understanding of BIASOCA necessary. We detail how to implement
BIASOCA in a real-life setting and the challenges attendant in so doing. Our research
contributes to theory and practice with a set of design principles highlighting,
besides the accuracy of cost accounting, the importance of collaboration, model
comprehensibility and strategic alignment.
author:
- first_name: Raphael
full_name: Grytz, Raphael
id: '4481'
last_name: Grytz
- first_name: Artus
full_name: Krohn-Grimberghe, Artus
last_name: Krohn-Grimberghe
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Grytz R, Krohn-Grimberghe A, Müller O. Business Intelligence & Analytics
Cost Accounting: An Action Design Research Approach. In: European Conference
on Information Systems. ; 2020.'
apa: 'Grytz, R., Krohn-Grimberghe, A., & Müller, O. (2020). Business Intelligence
& Analytics Cost Accounting: An Action Design Research Approach. European
Conference on Information Systems.'
bibtex: '@inproceedings{Grytz_Krohn-Grimberghe_Müller_2020, title={Business Intelligence
& Analytics Cost Accounting: An Action Design Research Approach}, booktitle={European
Conference on Information Systems}, author={Grytz, Raphael and Krohn-Grimberghe,
Artus and Müller, Oliver}, year={2020} }'
chicago: 'Grytz, Raphael, Artus Krohn-Grimberghe, and Oliver Müller. “Business Intelligence
& Analytics Cost Accounting: An Action Design Research Approach.” In European
Conference on Information Systems, 2020.'
ieee: 'R. Grytz, A. Krohn-Grimberghe, and O. Müller, “Business Intelligence &
Analytics Cost Accounting: An Action Design Research Approach,” 2020.'
mla: 'Grytz, Raphael, et al. “Business Intelligence & Analytics Cost Accounting:
An Action Design Research Approach.” European Conference on Information Systems,
2020.'
short: 'R. Grytz, A. Krohn-Grimberghe, O. Müller, in: European Conference on Information
Systems, 2020.'
date_created: 2020-06-17T09:49:08Z
date_updated: 2022-08-17T07:20:01Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/ecis2020_rp/121/
publication: European Conference on Information Systems
status: public
title: 'Business Intelligence & Analytics Cost Accounting: An Action Design Research
Approach'
type: conference
user_id: '72849'
year: '2020'
...
---
_id: '21563'
abstract:
- lang: eng
text: Historically, the field of financial forecasting almost exclusively relied
on so-called hard information – i.e., numerical data with well-defined and unambiguous
meaning. Over the last few decades, however, researchers and practitioners alike
have, following the advances in natural language understanding, started recognizing
the benefits of integrating soft information into financial modelling. In line
with the above, this paper examines whether contemporary attention-based sequence-to-sequence
models, known as Transformers, can help improve stock return volatility prediction
when applied to corporate annual reports. Using a publicly available benchmark
dataset, we show, in an empirical analysis, that out-of-the-box Transformer models
have the ability to outmatch current state-of-the-art results and, more importantly,
that our proposed feature-based Transformer approach can outperform a robust numerical
baseline. To the best of our knowledge, this is the first empirical study focusing
on stock return volatility prediction (1) to ever experiment with state-of-the-art
Transformer architectures and (2) to demonstrate that a model based solely on
soft information can surpass its numerical counterpart. Furthermore, we show that
by including an additional numerical feature into our best text-only model, we
can push the performance of our model even further, suggesting that soft and hard
information contain different predictive signals.
author:
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Caron M, Müller O. Hardening Soft Information: A Transformer-Based Approach
to Forecasting Stock Return Volatility. In: 2020 IEEE International Conference
on Big Data (Big Data). ; 2020:4383-4391. doi:10.1109/BigData50022.2020.9378134'
apa: 'Caron, M., & Müller, O. (2020). Hardening Soft Information: A Transformer-Based
Approach to Forecasting Stock Return Volatility. 2020 IEEE International Conference
on Big Data (Big Data), 4383–4391. https://doi.org/10.1109/BigData50022.2020.9378134'
bibtex: '@inproceedings{Caron_Müller_2020, title={Hardening Soft Information: A
Transformer-Based Approach to Forecasting Stock Return Volatility}, DOI={10.1109/BigData50022.2020.9378134},
booktitle={2020 IEEE International Conference on Big Data (Big Data)}, author={Caron,
Matthew and Müller, Oliver}, year={2020}, pages={4383–4391} }'
chicago: 'Caron, Matthew, and Oliver Müller. “Hardening Soft Information: A Transformer-Based
Approach to Forecasting Stock Return Volatility.” In 2020 IEEE International
Conference on Big Data (Big Data), 4383–91, 2020. https://doi.org/10.1109/BigData50022.2020.9378134.'
ieee: 'M. Caron and O. Müller, “Hardening Soft Information: A Transformer-Based
Approach to Forecasting Stock Return Volatility,” in 2020 IEEE International
Conference on Big Data (Big Data), Online, 2020, pp. 4383–4391, doi: 10.1109/BigData50022.2020.9378134.'
mla: 'Caron, Matthew, and Oliver Müller. “Hardening Soft Information: A Transformer-Based
Approach to Forecasting Stock Return Volatility.” 2020 IEEE International Conference
on Big Data (Big Data), 2020, pp. 4383–91, doi:10.1109/BigData50022.2020.9378134.'
short: 'M. Caron, O. Müller, in: 2020 IEEE International Conference on Big Data
(Big Data), 2020, pp. 4383–4391.'
conference:
end_date: 2020-12-13
location: Online
name: 2020 IEEE International Conference on Big Data (Big Data)
start_date: 2020-12-10
date_created: 2021-03-24T13:09:55Z
date_updated: 2024-01-15T12:32:37Z
department:
- _id: '196'
doi: 10.1109/BigData50022.2020.9378134
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9378134
page: 4383-4391
publication: 2020 IEEE International Conference on Big Data (Big Data)
publication_identifier:
eisbn:
- 978-1-7281-6251-5
publication_status: published
status: public
title: 'Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock
Return Volatility'
type: conference
user_id: '60721'
year: '2020'
...
---
_id: '4682'
author:
- first_name: T.
full_name: Schmiedel, T.
last_name: Schmiedel
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: J.
full_name: vom Brocke, J.
last_name: vom Brocke
citation:
ama: 'Schmiedel T, Müller O, vom Brocke J. Topic Modeling as a Strategy of Inquiry
in Organizational Research: A Tutorial With an Application Example on Organizational
Culture. Organizational Research Methods. 2019:941--968. doi:https://doi.org/10.1177/1094428118773858'
apa: 'Schmiedel, T., Müller, O., & vom Brocke, J. (2019). Topic Modeling as
a Strategy of Inquiry in Organizational Research: A Tutorial With an Application
Example on Organizational Culture. Organizational Research Methods, 941--968.
https://doi.org/10.1177/1094428118773858'
bibtex: '@article{Schmiedel_Müller_vom Brocke_2019, title={Topic Modeling as a Strategy
of Inquiry in Organizational Research: A Tutorial With an Application Example
on Organizational Culture}, DOI={https://doi.org/10.1177/1094428118773858},
journal={Organizational Research Methods}, author={Schmiedel, T. and Müller, Oliver
and vom Brocke, J.}, year={2019}, pages={941--968} }'
chicago: 'Schmiedel, T., Oliver Müller, and J. vom Brocke. “Topic Modeling as a
Strategy of Inquiry in Organizational Research: A Tutorial With an Application
Example on Organizational Culture.” Organizational Research Methods, 2019,
941--968. https://doi.org/10.1177/1094428118773858.'
ieee: 'T. Schmiedel, O. Müller, and J. vom Brocke, “Topic Modeling as a Strategy
of Inquiry in Organizational Research: A Tutorial With an Application Example
on Organizational Culture,” Organizational Research Methods, pp. 941--968,
2019.'
mla: 'Schmiedel, T., et al. “Topic Modeling as a Strategy of Inquiry in Organizational
Research: A Tutorial With an Application Example on Organizational Culture.” Organizational
Research Methods, 2019, pp. 941--968, doi:https://doi.org/10.1177/1094428118773858.'
short: T. Schmiedel, O. Müller, J. vom Brocke, Organizational Research Methods (2019)
941--968.
date_created: 2018-10-12T08:24:26Z
date_updated: 2022-01-06T07:01:18Z
doi: https://doi.org/10.1177/1094428118773858
extern: '1'
keyword:
- online reviews
- organizational culture
- structural topic model
- topic modeling
- tutorial
language:
- iso: eng
page: '941--968 '
publication: Organizational Research Methods
status: public
title: 'Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial
With an Application Example on Organizational Culture'
type: journal_article
user_id: '72849'
year: '2019'
...
---
_id: '16251'
author:
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Müller O. Structuring Unstructured Data—Or: How Machine Learning Can Make
You a Wine Sommelier. In: The Art of Structuring. ; 2019. doi:10.1007/978-3-030-06234-7_29'
apa: 'Müller, O. (2019). Structuring Unstructured Data—Or: How Machine Learning
Can Make You a Wine Sommelier. In The Art of Structuring. https://doi.org/10.1007/978-3-030-06234-7_29'
bibtex: '@inbook{Müller_2019, title={Structuring Unstructured Data—Or: How Machine
Learning Can Make You a Wine Sommelier}, DOI={10.1007/978-3-030-06234-7_29},
booktitle={The Art of Structuring}, author={Müller, Oliver}, year={2019} }'
chicago: 'Müller, Oliver. “Structuring Unstructured Data—Or: How Machine Learning
Can Make You a Wine Sommelier.” In The Art of Structuring, 2019. https://doi.org/10.1007/978-3-030-06234-7_29.'
ieee: 'O. Müller, “Structuring Unstructured Data—Or: How Machine Learning Can Make
You a Wine Sommelier,” in The Art of Structuring, 2019.'
mla: 'Müller, Oliver. “Structuring Unstructured Data—Or: How Machine Learning Can
Make You a Wine Sommelier.” The Art of Structuring, 2019, doi:10.1007/978-3-030-06234-7_29.'
short: 'O. Müller, in: The Art of Structuring, 2019.'
date_created: 2020-03-06T07:17:56Z
date_updated: 2022-01-06T06:52:47Z
doi: 10.1007/978-3-030-06234-7_29
language:
- iso: eng
publication: The Art of Structuring
publication_identifier:
isbn:
- '9783030062330'
- '9783030062347'
publication_status: published
status: public
title: 'Structuring Unstructured Data—Or: How Machine Learning Can Make You a Wine
Sommelier'
type: book_chapter
user_id: '72849'
year: '2019'
...
---
_id: '17096'
abstract:
- lang: eng
text: Augmented Reality (AR) technologies have evolved rapidly over the last years,
particularly with regard to user interfaces, input devices, and cameras used in
mobile devices for object and gesture recognition. While early AR systems relied
on pre-defined trigger images or QR code markers, modern AR applications leverage
machine learning techniques to identify objects in their physical environments.
So far, only few empirical studies have investigated AR's potential for supporting
learning and task assistance using such marker-less AR. In order to address this
research gap, we implemented an AR application (app)with the aim to analyze the
effectiveness of marker-less AR applied in a mundane setting which can be used
for on-the-job training and more formal educational settings. The results of our
laboratory experiment show that while participants working with AR needed significantly
more time to fulfill the given task, the participants who were supported by AR
learned significantly more.
author:
- first_name: Peter
full_name: Sommerauer, Peter
last_name: Sommerauer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Leonard
full_name: Maxim, Leonard
last_name: Maxim
- first_name: Nils
full_name: Østman, Nils
last_name: Østman
citation:
ama: 'Sommerauer P, Müller O, Maxim L, Østman N. The Effect of Marker-less Augmented
Reality on Task and Learning Performance. In: International Conference on Wirtschaftsinformatik.
; 2019.'
apa: Sommerauer, P., Müller, O., Maxim, L., & Østman, N. (2019). The Effect
of Marker-less Augmented Reality on Task and Learning Performance. International
Conference on Wirtschaftsinformatik.
bibtex: '@inproceedings{Sommerauer_Müller_Maxim_Østman_2019, title={The Effect of
Marker-less Augmented Reality on Task and Learning Performance}, booktitle={International
Conference on Wirtschaftsinformatik}, author={Sommerauer, Peter and Müller, Oliver
and Maxim, Leonard and Østman, Nils}, year={2019} }'
chicago: Sommerauer, Peter, Oliver Müller, Leonard Maxim, and Nils Østman. “The
Effect of Marker-Less Augmented Reality on Task and Learning Performance.” In
International Conference on Wirtschaftsinformatik, 2019.
ieee: P. Sommerauer, O. Müller, L. Maxim, and N. Østman, “The Effect of Marker-less
Augmented Reality on Task and Learning Performance,” 2019.
mla: Sommerauer, Peter, et al. “The Effect of Marker-Less Augmented Reality on Task
and Learning Performance.” International Conference on Wirtschaftsinformatik,
2019.
short: 'P. Sommerauer, O. Müller, L. Maxim, N. Østman, in: International Conference
on Wirtschaftsinformatik, 2019.'
date_created: 2020-06-17T10:01:00Z
date_updated: 2022-08-17T07:20:42Z
department:
- _id: '196'
extern: '1'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/wi2019/track13/papers/9/
publication: International Conference on Wirtschaftsinformatik
status: public
title: The Effect of Marker-less Augmented Reality on Task and Learning Performance
type: conference
user_id: '72849'
year: '2019'
...
---
_id: '4684'
abstract:
- lang: eng
text: Recent years have seen the emergence of physical products that are digitally
networked with other products and with information systems to enable complex business
scenarios in manufacturing, mobility, or healthcare. These “smart products”, which
enable the co-creation of “smart service” that is based on monitoring, optimization,
remote control, and autonomous adaptation of products, profoundly transform service
systems into what we call “smart service systems”. In a multi-method study that
includes conceptual research and qualitative data from in-depth interviews, we
conceptualize “smart service” and “smart service systems” based on using smart
products as boundary objects that integrate service consumers’ and service providers’
resources and activities. Smart products allow both actors to retrieve and to
analyze aggregated field evidence and to adapt service systems based on contextual
data. We discuss the implications that the introduction of smart service systems
have for foundational concepts of service science and conclude that smart service
systems are characterized by technology-mediated, continuous, and routinized interactions.
article_type: original
author:
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Martin
full_name: Matzner, Martin
last_name: Matzner
- first_name: Jan
full_name: Mendling, Jan
last_name: Mendling
- first_name: Jan
full_name: vom Brocke, Jan
last_name: vom Brocke
citation:
ama: Beverungen D, Müller O, Matzner M, Mendling J, vom Brocke J. Conceptualizing
smart service systems. Electronic Markets. 2019;29:7-18. doi:10.1007/s12525-017-0270-5
apa: Beverungen, D., Müller, O., Matzner, M., Mendling, J., & vom Brocke, J.
(2019). Conceptualizing smart service systems. Electronic Markets, 29,
7–18. https://doi.org/10.1007/s12525-017-0270-5
bibtex: '@article{Beverungen_Müller_Matzner_Mendling_vom Brocke_2019, title={Conceptualizing
smart service systems}, volume={29}, DOI={10.1007/s12525-017-0270-5},
journal={Electronic Markets}, publisher={SpringerNature}, author={Beverungen,
Daniel and Müller, Oliver and Matzner, Martin and Mendling, Jan and vom Brocke,
Jan}, year={2019}, pages={7–18} }'
chicago: 'Beverungen, Daniel, Oliver Müller, Martin Matzner, Jan Mendling, and Jan
vom Brocke. “Conceptualizing Smart Service Systems.” Electronic Markets
29 (2019): 7–18. https://doi.org/10.1007/s12525-017-0270-5.'
ieee: 'D. Beverungen, O. Müller, M. Matzner, J. Mendling, and J. vom Brocke, “Conceptualizing
smart service systems,” Electronic Markets, vol. 29, pp. 7–18, 2019, doi:
10.1007/s12525-017-0270-5.'
mla: Beverungen, Daniel, et al. “Conceptualizing Smart Service Systems.” Electronic
Markets, vol. 29, SpringerNature, 2019, pp. 7–18, doi:10.1007/s12525-017-0270-5.
short: D. Beverungen, O. Müller, M. Matzner, J. Mendling, J. vom Brocke, Electronic
Markets 29 (2019) 7–18.
date_created: 2018-10-12T08:24:45Z
date_updated: 2024-04-18T12:55:05Z
ddc:
- '380'
department:
- _id: '195'
doi: 10.1007/s12525-017-0270-5
file:
- access_level: closed
content_type: application/pdf
creator: dabe
date_created: 2024-04-18T12:53:07Z
date_updated: 2024-04-18T12:53:07Z
file_id: '53575'
file_name: Beverungen_et_al-Conceptualizing_Smart_Service_Systems.pdf
file_size: 599681
relation: main_file
success: 1
file_date_updated: 2024-04-18T12:53:07Z
has_accepted_license: '1'
intvolume: ' 29'
jel:
- L8
keyword:
- Boundary object
- Internet of things
- Service science
- Smart products
- Smart service
language:
- iso: eng
page: 7-18
project:
- _id: '1070'
call_identifier: MSCA-RISE-2014
grant_number: '645751'
name: 'RISE_BPM: Propelling Business Process Management by Research and Innovation
Staff Exchange'
publication: Electronic Markets
publication_identifier:
issn:
- '14228890'
publication_status: published
publisher: SpringerNature
quality_controlled: '1'
status: public
title: Conceptualizing smart service systems
type: journal_article
user_id: '59677'
volume: 29
year: '2019'
...
---
_id: '4676'
author:
- first_name: Peter
full_name: Sommerauer, Peter
last_name: Sommerauer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Sommerauer P, Müller O. Augmented Reality in Informal Learning Environments:
Investigating Short-term and Long-term Effects. In: Hawaii International Conference
on System Sciences. ; 2018. doi:10.24251/HICSS.2018.176'
apa: 'Sommerauer, P., & Müller, O. (2018). Augmented Reality in Informal Learning
Environments: Investigating Short-term and Long-term Effects. In Hawaii International
Conference on System Sciences. https://doi.org/10.24251/HICSS.2018.176'
bibtex: '@inproceedings{Sommerauer_Müller_2018, title={Augmented Reality in Informal
Learning Environments: Investigating Short-term and Long-term Effects}, DOI={10.24251/HICSS.2018.176}, booktitle={Hawaii
International Conference on System Sciences}, author={Sommerauer, Peter and Müller,
Oliver}, year={2018} }'
chicago: 'Sommerauer, Peter, and Oliver Müller. “Augmented Reality in Informal Learning
Environments: Investigating Short-Term and Long-Term Effects.” In Hawaii International
Conference on System Sciences, 2018. https://doi.org/10.24251/HICSS.2018.176.'
ieee: 'P. Sommerauer and O. Müller, “Augmented Reality in Informal Learning Environments:
Investigating Short-term and Long-term Effects,” in Hawaii International Conference
on System Sciences, 2018.'
mla: 'Sommerauer, Peter, and Oliver Müller. “Augmented Reality in Informal Learning
Environments: Investigating Short-Term and Long-Term Effects.” Hawaii International
Conference on System Sciences, 2018, doi:10.24251/HICSS.2018.176.'
short: 'P. Sommerauer, O. Müller, in: Hawaii International Conference on System
Sciences, 2018.'
date_created: 2018-10-12T08:14:40Z
date_updated: 2022-01-06T07:01:18Z
doi: 10.24251/HICSS.2018.176
extern: '1'
language:
- iso: eng
publication: Hawaii International Conference on System Sciences
status: public
title: 'Augmented Reality in Informal Learning Environments: Investigating Short-term
and Long-term Effects'
type: conference
user_id: '72849'
year: '2018'
...
---
_id: '4679'
author:
- first_name: Roope
full_name: Jaakonmäki, Roope
last_name: Jaakonmäki
- first_name: Alexander
full_name: Simons, Alexander
last_name: Simons
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Jan
full_name: vom Brocke, Jan
last_name: vom Brocke
citation:
ama: 'Jaakonmäki R, Simons A, Müller O, vom Brocke J. ECM implementations in practice:
objectives, processes, and technologies. Journal of Enterprise Information
Management. 2018;(5):704--723. doi:10.1108/JEIM-11-2016-0187'
apa: 'Jaakonmäki, R., Simons, A., Müller, O., & vom Brocke, J. (2018). ECM implementations
in practice: objectives, processes, and technologies. Journal of Enterprise
Information Management, (5), 704--723. https://doi.org/10.1108/JEIM-11-2016-0187'
bibtex: '@article{Jaakonmäki_Simons_Müller_vom Brocke_2018, title={ECM implementations
in practice: objectives, processes, and technologies}, DOI={10.1108/JEIM-11-2016-0187},
number={5}, journal={Journal of Enterprise Information Management}, author={Jaakonmäki,
Roope and Simons, Alexander and Müller, Oliver and vom Brocke, Jan}, year={2018},
pages={704--723} }'
chicago: 'Jaakonmäki, Roope, Alexander Simons, Oliver Müller, and Jan vom Brocke.
“ECM Implementations in Practice: Objectives, Processes, and Technologies.” Journal
of Enterprise Information Management, no. 5 (2018): 704--723. https://doi.org/10.1108/JEIM-11-2016-0187.'
ieee: 'R. Jaakonmäki, A. Simons, O. Müller, and J. vom Brocke, “ECM implementations
in practice: objectives, processes, and technologies,” Journal of Enterprise
Information Management, no. 5, pp. 704--723, 2018.'
mla: 'Jaakonmäki, Roope, et al. “ECM Implementations in Practice: Objectives, Processes,
and Technologies.” Journal of Enterprise Information Management, no. 5,
2018, pp. 704--723, doi:10.1108/JEIM-11-2016-0187.'
short: R. Jaakonmäki, A. Simons, O. Müller, J. vom Brocke, Journal of Enterprise
Information Management (2018) 704--723.
date_created: 2018-10-12T08:22:37Z
date_updated: 2022-01-06T07:01:18Z
doi: 10.1108/JEIM-11-2016-0187
extern: '1'
issue: '5'
page: 704--723
publication: Journal of Enterprise Information Management
publication_identifier:
issn:
- 1741-0398
status: public
title: 'ECM implementations in practice: objectives, processes, and technologies'
type: journal_article
user_id: '72849'
year: '2018'
...