---
_id: '45299'
abstract:
- lang: eng
  text: Many applications are driven by Machine Learning (ML) today. While complex
    ML models lead to an accurate prediction, their inner decision-making is obfuscated.
    However, especially for high-stakes decisions, interpretability and explainability
    of the model are necessary. Therefore, we develop a holistic interpretability
    and explainability framework (HIEF) to objectively describe and evaluate an intelligent
    system’s explainable AI (XAI) capacities. This guides data scientists to create
    more transparent models. To evaluate our framework, we analyse 50 real estate
    appraisal papers to ensure the robustness of HIEF. Additionally, we identify six
    typical types of intelligent systems, so-called archetypes, which range from explanatory
    to predictive, and demonstrate how researchers can use the framework to identify
    blind-spot topics in their domain. Finally, regarding comprehensiveness, we used
    a random sample of six intelligent systems and conducted an applicability check
    to provide external validity.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. HIEF: a holistic interpretability and explainability framework.
    <i>Journal of Decision Systems</i>. Published online 2023:1-41. doi:<a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>'
  apa: 'Kucklick, J.-P. (2023). HIEF: a holistic interpretability and explainability
    framework. <i>Journal of Decision Systems</i>, 1–41. <a href="https://doi.org/10.1080/12460125.2023.2207268">https://doi.org/10.1080/12460125.2023.2207268</a>'
  bibtex: '@article{Kucklick_2023, title={HIEF: a holistic interpretability and explainability
    framework}, DOI={<a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>},
    journal={Journal of Decision Systems}, publisher={Taylor &#38; Francis}, author={Kucklick,
    Jan-Peter}, year={2023}, pages={1–41} }'
  chicago: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
    Framework.” <i>Journal of Decision Systems</i>, 2023, 1–41. <a href="https://doi.org/10.1080/12460125.2023.2207268">https://doi.org/10.1080/12460125.2023.2207268</a>.'
  ieee: 'J.-P. Kucklick, “HIEF: a holistic interpretability and explainability framework,”
    <i>Journal of Decision Systems</i>, pp. 1–41, 2023, doi: <a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>.'
  mla: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
    Framework.” <i>Journal of Decision Systems</i>, Taylor &#38; Francis, 2023, pp.
    1–41, doi:<a href="https://doi.org/10.1080/12460125.2023.2207268">10.1080/12460125.2023.2207268</a>.'
  short: J.-P. Kucklick, Journal of Decision Systems (2023) 1–41.
date_created: 2023-05-26T05:04:45Z
date_updated: 2023-05-26T05:08:36Z
department:
- _id: '195'
- _id: '196'
doi: 10.1080/12460125.2023.2207268
keyword:
- Explainable AI (XAI)
- machine learning
- interpretability
- real estate appraisal
- framework
- taxonomy
language:
- iso: eng
main_file_link:
- url: https://www.tandfonline.com/doi/full/10.1080/12460125.2023.2207268
page: 1-41
publication: Journal of Decision Systems
publication_identifier:
  issn:
  - 1246-0125
  - 2116-7052
publication_status: published
publisher: Taylor & Francis
status: public
title: 'HIEF: a holistic interpretability and explainability framework'
type: journal_article
user_id: '77066'
year: '2023'
...
---
_id: '27506'
abstract:
- lang: eng
  text: Explainability for machine learning gets more and more important in high-stakes
    decisions like real estate appraisal. While traditional hedonic house pricing
    models are fed with hard information based on housing attributes, recently also
    soft information has been incorporated to increase the predictive performance.
    This soft information can be extracted from image data by complex models like
    Convolutional Neural Networks (CNNs). However, these are intransparent which excludes
    their use for high-stakes financial decisions. To overcome this limitation, we
    examine if a two-stage modeling approach can provide explainability. We combine
    visual interpretability by Regression Activation Maps (RAM) for the CNN and a
    linear regression for the overall prediction. Our experiments are based on 62.000
    family homes in Philadelphia and the results indicate that the CNN learns aspects
    related to vegetation and quality aspects of the house from exterior images, improving
    the predictive accuracy of real estate appraisal by up to 5.4%.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal.
    In: <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>.
    ; 2022.'
  apa: Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate
    Appraisal. <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>.
    Hawaii International Conference on System Science (HICSS), Virtual.
  bibtex: '@inproceedings{Kucklick_2022, title={Visual Interpretability of Image-based
    Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference
    on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }'
  chicago: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate
    Appraisal.” In <i>55th Annual Hawaii International Conference on System Sciences
    (HICSS-55)</i>, 2022.
  ieee: J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,”
    presented at the Hawaii International Conference on System Science (HICSS), Virtual,
    2022.
  mla: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.”
    <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>,
    2022.
  short: 'J.-P. Kucklick, 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: 2021-11-17T07:08:15Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence (XAI)
- Regression Activation Maps
- Real Estate Appraisal
- Convolutional Block Attention Module
- Computer Vision
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Visual Interpretability of Image-based Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_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: <i>55th Annual Hawaii International Conference on
    System Sciences (HICSS-55)</i>. ; 2022.'
  apa: Heuwinkel, T., Kucklick, J.-P., &#38; Müller, O. (2022). Using Geolocated Text
    to Quantify Location in Real Estate Appraisal. <i>55th Annual Hawaii International
    Conference on System Sciences (HICSS-55)</i>. 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 <i>55th Annual Hawaii
    International Conference on System Sciences (HICSS-55)</i>, 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.” <i>55th Annual Hawaii International Conference on System Sciences
    (HICSS-55)</i>, 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: '29539'
abstract:
- lang: eng
  text: Explainable Artificial Intelligence (XAI) is currently an important topic
    for the application of Machine Learning (ML) in high-stakes decision scenarios.
    Related research focuses on evaluating ML algorithms in terms of interpretability.
    However, providing a human understandable explanation of an intelligent system
    does not only relate to the used ML algorithm. The data and features used also
    have a considerable impact on interpretability. In this paper, we develop a taxonomy
    for describing XAI systems based on aspects about the algorithm and data. The
    proposed taxonomy gives researchers and practitioners opportunities to describe
    and evaluate current XAI systems with respect to interpretability and guides the
    future development of this class of systems.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. Towards a model- and data-focused taxonomy of XAI systems. In:
    <i>Wirtschaftsinformatik 2022 Proceedings</i>. ; 2022.'
  apa: Kucklick, J.-P. (2022). Towards a model- and data-focused taxonomy of XAI systems.
    <i>Wirtschaftsinformatik 2022 Proceedings</i>. Wirtschaftsinformatik 2022 (WI22),
    Nürnberg (online).
  bibtex: '@inproceedings{Kucklick_2022, title={Towards a model- and data-focused
    taxonomy of XAI systems}, booktitle={Wirtschaftsinformatik 2022 Proceedings},
    author={Kucklick, Jan-Peter}, year={2022} }'
  chicago: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI
    Systems.” In <i>Wirtschaftsinformatik 2022 Proceedings</i>, 2022.
  ieee: J.-P. Kucklick, “Towards a model- and data-focused taxonomy of XAI systems,”
    presented at the Wirtschaftsinformatik 2022 (WI22), Nürnberg (online), 2022.
  mla: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI Systems.”
    <i>Wirtschaftsinformatik 2022 Proceedings</i>, 2022.
  short: 'J.-P. Kucklick, in: Wirtschaftsinformatik 2022 Proceedings, 2022.'
conference:
  end_date: 2022-02-23
  location: Nürnberg (online)
  name: Wirtschaftsinformatik 2022 (WI22)
  start_date: 2022-02-21
date_created: 2022-01-26T08:22:03Z
date_updated: 2022-01-26T08:24:30Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence
- XAI
- Interpretability
- Decision Support Systems
- Taxonomy
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1056&context=wi2022
oa: '1'
publication: Wirtschaftsinformatik 2022 Proceedings
status: public
title: Towards a model- and data-focused taxonomy of XAI systems
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.
    <i>ACM Transactions on Management Information Systems</i>. Published online 2022.
    doi:<a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>'
  apa: 'Kucklick, J.-P., &#38; Müller, O. (2022). Tackling the Accuracy–Interpretability
    Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
    Appraisal. <i>ACM Transactions on Management Information Systems</i>. <a href="https://doi.org/10.1145/3567430">https://doi.org/10.1145/3567430</a>'
  bibtex: '@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability
    Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
    Appraisal}, DOI={<a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>},
    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.” <i>ACM Transactions on Management Information Systems</i>, 2022. <a
    href="https://doi.org/10.1145/3567430">https://doi.org/10.1145/3567430</a>.'
  ieee: 'J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off:
    Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,”
    <i>ACM Transactions on Management Information Systems</i>, 2022, doi: <a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>.'
  mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability
    Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate
    Appraisal.” <i>ACM Transactions on Management Information Systems</i>, Association
    for Computing Machinery (ACM), 2022, doi:<a href="https://doi.org/10.1145/3567430">10.1145/3567430</a>.'
  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: '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: <i> The AAAI-21 Workshop on Knowledge
    Discovery from Unstructured Data in Financial Services</i>. ; 2021.'
  apa: Kucklick, J.-P., &#38; Müller, O. (2021). A Comparison of Multi-View Learning
    Strategies for Satellite Image-based Real Estate Appraisal. In <i> The AAAI-21
    Workshop on Knowledge Discovery from Unstructured Data in Financial Services</i>.
  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 <i> The AAAI-21
    Workshop on Knowledge Discovery from Unstructured Data in Financial Services</i>,
    2021.
  ieee: J.-P. Kucklick and O. Müller, “A Comparison of Multi-View Learning Strategies
    for Satellite Image-based Real Estate Appraisal,” in <i> The AAAI-21 Workshop
    on Knowledge Discovery from Unstructured Data in Financial Services</i>, 2021.
  mla: Kucklick, Jan-Peter, and Oliver Müller. “A Comparison of Multi-View Learning
    Strategies for Satellite Image-Based Real Estate Appraisal.” <i> The AAAI-21 Workshop
    on Knowledge Discovery from Unstructured Data in Financial Services</i>, 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: <i>European Conference on Information Systems</i>. ; 2021.'
  apa: Kucklick, J.-P., Müller, J., Beverungen, D., &#38; Müller, O. (2021). Quantifying
    the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning
    Approach. In <i>European Conference on Information Systems</i>. 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 <i>European Conference on Information Systems</i>,
    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 <i>European Conference on Information Systems</i>, Virtual, 2021.
  mla: Kucklick, Jan-Peter, et al. “Quantifying the Impact of Location Data for Real
    Estate Appraisal – A GIS-Based Deep Learning Approach.” <i>European Conference
    on Information Systems</i>, 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: '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: <i>Symposium on Statistical Challenges in Electronic
    Commerce Research (SCECR)</i>. ; 2020.'
  apa: 'Kucklick, J.-P., &#38; Müller, O. (2020). Location, location, location: Satellite
    image-based real-estate  appraisal. In <i>Symposium on Statistical Challenges
    in Electronic Commerce Research (SCECR)</i>.'
  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 <i>Symposium on Statistical
    Challenges in Electronic Commerce Research (SCECR)</i>, 2020.'
  ieee: 'J.-P. Kucklick and O. Müller, “Location, location, location: Satellite image-based
    real-estate  appraisal,” in <i>Symposium on Statistical Challenges in Electronic
    Commerce Research (SCECR)</i>, 2020.'
  mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Location, Location, Location: Satellite
    Image-Based Real-Estate  Appraisal.” <i>Symposium on Statistical Challenges in
    Electronic Commerce Research (SCECR)</i>, 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: '35660'
abstract:
- lang: eng
  text: Effective customer loyalty programs are essential for every company. Small
    and medium sized brick-and- mortar stores, such as bakeries, butcher and flower
    shops, often share a common overarching loyalty program, organized by a third-party
    provider. Furthermore, these small shops have limited resources and often cannot
    afford complex BI tools. Out of these reasons we investigated how traditional
    brick-and- mortar stores can benefit from an expansion of service functionalities
    of a loyalty card provider. To answer this question, we cooperated with a cross-industry
    customer loyalty program in a polycentric region. The loyalty program was transformed
    from simple card-based solution to a mobile app for customers and a web- application
    for shop owners. The new solution offers additional BI services for performing
    data analytics and strengthening the position of brick-and-mortar stores. Participating
    shops can work together in order to increase sales and align marketing campaigns.
    Therefore, shopping data from 12 years, 55 shops, and 19,000 customers was analyzed.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
- first_name: Michael Reiner
  full_name: Kamm, Michael Reiner
  last_name: Kamm
- first_name: Johannes
  full_name: Schneider, Johannes
  last_name: Schneider
- first_name: Jan
  full_name: vom Brocke, Jan
  last_name: vom Brocke
citation:
  ama: 'Kucklick J-P, Kamm MR, Schneider J, vom Brocke J. Extending Loyalty Programs
    with BI Functionalities A Case Study for Brick-and-Mortar Stores. In: <i>Proceedings
    of the 53rd Hawaii International Conference on System Sciences</i>. ; 2020.'
  apa: Kucklick, J.-P., Kamm, M. R., Schneider, J., &#38; vom Brocke, J. (2020). Extending
    Loyalty Programs with BI Functionalities A Case Study for Brick-and-Mortar Stores.
    <i>Proceedings of the 53rd Hawaii International Conference on System Sciences</i>.
    53th Annual Hawaii International Conference on System Sciences (HICSS-53).
  bibtex: '@inproceedings{Kucklick_Kamm_Schneider_vom Brocke_2020, title={Extending
    Loyalty Programs with BI Functionalities A Case Study for Brick-and-Mortar Stores},
    booktitle={Proceedings of the 53rd Hawaii International Conference on System Sciences},
    author={Kucklick, Jan-Peter and Kamm, Michael Reiner and Schneider, Johannes and
    vom Brocke, Jan}, year={2020} }'
  chicago: Kucklick, Jan-Peter, Michael Reiner Kamm, Johannes Schneider, and Jan vom
    Brocke. “Extending Loyalty Programs with BI Functionalities A Case Study for Brick-and-Mortar
    Stores.” In <i>Proceedings of the 53rd Hawaii International Conference on System
    Sciences</i>, 2020.
  ieee: J.-P. Kucklick, M. R. Kamm, J. Schneider, and J. vom Brocke, “Extending Loyalty
    Programs with BI Functionalities A Case Study for Brick-and-Mortar Stores,” presented
    at the 53th Annual Hawaii International Conference on System Sciences (HICSS-53),
    2020.
  mla: Kucklick, Jan-Peter, et al. “Extending Loyalty Programs with BI Functionalities
    A Case Study for Brick-and-Mortar Stores.” <i>Proceedings of the 53rd Hawaii International
    Conference on System Sciences</i>, 2020.
  short: 'J.-P. Kucklick, M.R. Kamm, J. Schneider, J. vom Brocke, in: Proceedings
    of the 53rd Hawaii International Conference on System Sciences, 2020.'
conference:
  name: 53th Annual Hawaii International Conference on System Sciences (HICSS-53)
date_created: 2023-01-10T08:39:45Z
date_updated: 2023-01-12T05:32:16Z
department:
- _id: '195'
- _id: '196'
keyword:
- brick-and-mortar stores
- business intelligence
- case study
- loyalty program
language:
- iso: eng
main_file_link:
- url: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/335317f1-8fb6-4561-9faa-27e6a7d29771/content
publication: Proceedings of the 53rd Hawaii International Conference on System Sciences
status: public
title: Extending Loyalty Programs with BI Functionalities A Case Study for Brick-and-Mortar
  Stores
type: conference
user_id: '77066'
year: '2020'
...
---
_id: '35662'
abstract:
- lang: eng
  text: While the analysis and usage of data are increasing in importance, the application
    of sophisticated BI solutions in small stores is limited by available technical
    capabilities and financial resources. This study investigates how brick-and-mortar
    stores can benefit from an expansion of service functionalities of a cross-industry
    loyalty card provider. Digitalizing the loyalty program created new opportunities,
    while the analysis of shopping data of 13 years, 19,000 customers, and 55 shops
    empowered data-based decision support.
author:
- first_name: Michael Reiner
  full_name: Kamm, Michael Reiner
  last_name: Kamm
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
- first_name: Johannes
  full_name: Schneider, Johannes
  last_name: Schneider
- first_name: Jan
  full_name: vom Brocke, Jan
  last_name: vom Brocke
citation:
  ama: 'Kamm MR, Kucklick J-P, Schneider J, vom Brocke J. Data mining for small shops:
    Empowering brick-and-mortar stores through BI functionalities of a loyalty program1.
    <i>Information Systems Management</i>. 2020;38(4):270-286. doi:<a href="https://doi.org/10.1080/10580530.2020.1855486">10.1080/10580530.2020.1855486</a>'
  apa: 'Kamm, M. R., Kucklick, J.-P., Schneider, J., &#38; vom Brocke, J. (2020).
    Data mining for small shops: Empowering brick-and-mortar stores through BI functionalities
    of a loyalty program1. <i>Information Systems Management</i>, <i>38</i>(4), 270–286.
    <a href="https://doi.org/10.1080/10580530.2020.1855486">https://doi.org/10.1080/10580530.2020.1855486</a>'
  bibtex: '@article{Kamm_Kucklick_Schneider_vom Brocke_2020, title={Data mining for
    small shops: Empowering brick-and-mortar stores through BI functionalities of
    a loyalty program1}, volume={38}, DOI={<a href="https://doi.org/10.1080/10580530.2020.1855486">10.1080/10580530.2020.1855486</a>},
    number={4}, journal={Information Systems Management}, publisher={Informa UK Limited},
    author={Kamm, Michael Reiner and Kucklick, Jan-Peter and Schneider, Johannes and
    vom Brocke, Jan}, year={2020}, pages={270–286} }'
  chicago: 'Kamm, Michael Reiner, Jan-Peter Kucklick, Johannes Schneider, and Jan
    vom Brocke. “Data Mining for Small Shops: Empowering Brick-and-Mortar Stores through
    BI Functionalities of a Loyalty Program1.” <i>Information Systems Management</i>
    38, no. 4 (2020): 270–86. <a href="https://doi.org/10.1080/10580530.2020.1855486">https://doi.org/10.1080/10580530.2020.1855486</a>.'
  ieee: 'M. R. Kamm, J.-P. Kucklick, J. Schneider, and J. vom Brocke, “Data mining
    for small shops: Empowering brick-and-mortar stores through BI functionalities
    of a loyalty program1,” <i>Information Systems Management</i>, vol. 38, no. 4,
    pp. 270–286, 2020, doi: <a href="https://doi.org/10.1080/10580530.2020.1855486">10.1080/10580530.2020.1855486</a>.'
  mla: 'Kamm, Michael Reiner, et al. “Data Mining for Small Shops: Empowering Brick-and-Mortar
    Stores through BI Functionalities of a Loyalty Program1.” <i>Information Systems
    Management</i>, vol. 38, no. 4, Informa UK Limited, 2020, pp. 270–86, doi:<a href="https://doi.org/10.1080/10580530.2020.1855486">10.1080/10580530.2020.1855486</a>.'
  short: M.R. Kamm, J.-P. Kucklick, J. Schneider, J. vom Brocke, Information Systems
    Management 38 (2020) 270–286.
date_created: 2023-01-10T08:40:31Z
date_updated: 2023-01-12T06:46:46Z
department:
- _id: '195'
- _id: '196'
doi: 10.1080/10580530.2020.1855486
intvolume: '        38'
issue: '4'
keyword:
- Customer loyalty
- case study
- brick-and-mortar stores
- business intelligence
- loyalty programs
language:
- iso: eng
main_file_link:
- url: https://www.tandfonline.com/doi/full/10.1080/10580530.2020.1855486
page: 270-286
publication: Information Systems Management
publication_identifier:
  issn:
  - 1058-0530
  - 1934-8703
publication_status: published
publisher: Informa UK Limited
status: public
title: 'Data mining for small shops: Empowering brick-and-mortar stores through BI
  functionalities of a loyalty program1'
type: journal_article
user_id: '77066'
volume: 38
year: '2020'
...