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Designing Advertisement Systems with Human-centered Artificial Intelligence
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, Institutionen för informationsteknologi.ORCID iD: 0000-0002-3553-5983
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Practitioners are urging using Artificial Intelligence (AI) to improve advertisements. Advertisers recognize the importance of incorporating AI into their strategies to remain competitive. In response to this demand, a Design Science Research (DSR) initiative has been started to create a Human-Centered AI (HCAI) tool to enhance advertisement suggestions by analyzing consumer behavior. This dissertation aims to build an advertisement optimization system with HCAI and produce an Information System Design Theory (ISDT) of that class of system. Through architectural models, methods, technological rules, and design principles, nascent Design Theory (DT) is created and serves as an initial stage towards achieving a more abstract design knowledge known as the ISDT. The action design research method is employed to construct and analyze the implemented system instance. The process involves multiple cycles of building, intervening, and evaluating. These cycles are conducted iteratively and incrementally, allowing for the gradual development of the suggested system while simultaneously generating valuable design knowledge. The system is developed and abstracted for design knowledge from both the development process and the actual tool. The dissertation presents nascent design knowledge in the form of models, technological rules, and design principles. Moreover, the dissertation places the nascent DT within the broader context of a more abstract design knowledge called ISDT. The results are then scrutinized based on various components of the DT, including purpose and scope, constructs, principles of form and function, artifact mutability, justificatory knowledge, testable propositions, principles of implementation, and expository instantiation. This dissertation discusses the DSR process, compared to various challenges encountered throughout the research project. Theoretical, empirical, and artefactual research contributions are outlined, and their implications for research and practice are discussed toward the end of the dissertation. The quality of the research is examined, considering the relevance, novelty, usefulness, feasibility, design rigor, evaluation rigor, and transparency of the artifacts produced throughout the dissertation. The dissertation concludes that it delivered ISDT. Moreover, the system serves as a valuable example of how AI can be utilized for optimizing digital advertisements. The dissertation ends with providing recommendations for future research.

Place, publisher, year, edition, pages
Skövde: University of Skövde , 2023. , p. xiv, 361
Series
Dissertation Series ; 54
Keywords [en]
Digital Advertisement Optimization, Design Knowledge, Information System Design Theory, Artificial Intelligence, Reinforcement Learning, Human-centered AI
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:his:diva-23234ISBN: 978-91-987906-8-9 (print)OAI: oai:DiVA.org:his-23234DiVA, id: diva2:1799063
Public defence
2023-11-01, Insikten, Kanikegränd 3B, Skövde, 10:00
Opponent
Supervisors
Funder
Knowledge Foundation, 20160035, 20170215
Note

Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215

Två av sex delarbeten (övriga se rubriken Delarbeten/List of papers):

Sahlin, Johannes, Håkan Sundell, Gideon Mbiydzenyuy, and Jesper Holgersson (2023). “Managing Consumer Concerns of Model-generated Advertisements.” In: Expert Systems with Applications, Submitted.

— (2024). “Nascent Design Theory for Advertisement Optimization Systems with Human-centered Artificial Intelligence.” In: European Conference on Information Systems, Draft.

Available from: 2023-10-02 Created: 2023-09-21 Last updated: 2023-10-03Bibliographically approved
List of papers
1. Short message service campaign taxonomy for an intelligent marketing system
Open this publication in new window or tab >>Short message service campaign taxonomy for an intelligent marketing system
2020 (English)In: Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020) / [ed] Zhong Li, Chunrong Yuan, Jie Lu, Etienne E. Kerre, Singapore: World Scientific, 2020, p. 606-613Conference paper, Published paper (Refereed)
Abstract [en]

This study presents a novel taxonomy of short message service campaigns, for the purpose of building an intelligent marketing system. The main issue of mass marketing is that one size does not fit everybody. In other words, it is challenging to meet different consumer needs. With the help of artificial intelligence, marketers can be supported to overcome some of these challenges. This study uses a mixed methods approach where design science and grounded theory is used to produce a short message service campaign taxonomy for a future intelligent marketing system. Data collection consisted of 386 previously active campaigns used over 33 months to build the taxonomy. An experimental study was conducted to test the effectiveness of the proposed taxonomy. The experiments involved automatic generation of campaign messages. The validity of these campaign messages, and hence the proposed taxonomy, was ascertained by analysing the messages within a business context. The study concludes that the system, intertwined with the taxonomy, performs comparably to a regular campaign. Another proof of concept is that the business context deemed the generated campaign texts to be both semantically and syntactically similar to run them in active campaigns as experiments.

Place, publisher, year, edition, pages
Singapore: World Scientific, 2020
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868 ; 12
Keywords
fashion industry, artificial intelligence, marketing campaign, taxonomy, campaign builder, intelligent marketing system
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-18933 (URN)10.1142/9789811223334_0073 (DOI)000656123200073 ()978-981-122-332-7 (ISBN)978-981-122-333-4 (ISBN)978-981-122-334-1 (ISBN)
Conference
FLINS/ISKE 2020: The 14th International FLINS Conference on Robotics and Artificial Intelligence and the 15th International Conference on Intelligent Systems and Knowledge Engineering, Cologne, Germany, August 18-21, 2020
Funder
Knowledge Foundation, 20160035/20170215
Available from: 2020-08-19 Created: 2020-08-19 Last updated: 2023-09-21Bibliographically approved
2. Evaluating Artificial Short Message Service Campaigns through Rule Based Multi-instance Multi-label Classification
Open this publication in new window or tab >>Evaluating Artificial Short Message Service Campaigns through Rule Based Multi-instance Multi-label Classification
2021 (English)In: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021): Stanford University, Palo Alto, California, USA, March 22-24, 2021 / [ed] A. Martin; K. Hinkelmann; H.-G. Fill; A. Gerber D. Lenat; R. Stolle; F. van Harmelen, CEUR-WS , 2021, Vol. 2846Conference paper, Published paper (Refereed)
Abstract [en]

Marketers need new ways of generating campaigns artificially for their marketing activities. Many marketers assume proprietary systems are individualized enough. This article investigates an order of models used to measure how reliably a system can generate campaigns artificially while producing a campaign classification and generation models that are integrated into an intelligent marketing system. The order is between a Classification Model (CM) and a Generation Model (GM). The order also functions as an iterative model improvement process for developing the models by evaluating the models’ accuracy distributions. The CM received a mean accuracy of 100%. The GM received 98.9% mean accuracy and a reproducibility score of 96.2%, implying the vast potential for increased resource savings, marketing precision, and less consumer annoyance. The conclusion is that the developed system can reliantly construct campaigns

Place, publisher, year, edition, pages
CEUR-WS, 2021
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2846
Keywords
Artificial intelligence, Intelligent marketing system, Iterative model improvement
National Category
Engineering and Technology Computer and Information Sciences Computer Sciences
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-19604 (URN)2-s2.0-85104648031 (Scopus ID)
Conference
AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), Stanford University, Palo Alto, California, USA, March 22-24, 2021
Funder
Knowledge Foundation, 20160035, 20170215
Note

CC BY 4.0

CEUR Workshop Proceedings (CEUR-WS.org) is a free open-access publication service at Sun SITE Central Europe

Available from: 2021-04-12 Created: 2021-04-12 Last updated: 2023-09-21Bibliographically approved
3. Exploring Consumers' Discernment Ability of Autogenerated Advertisements
Open this publication in new window or tab >>Exploring Consumers' Discernment Ability of Autogenerated Advertisements
Show others...
2023 (English)In: Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022) / [ed] Qinglin Sun; Jie Lu; Xianyi Zeng; Etienne E. Kerre; Tianrui Li, World Scientific, 2023, p. 322-329Conference paper, Published paper (Refereed)
Abstract [en]

Autogenerated Advertisements (AGAs) can be a concern for consumers if they suspect that Artificial Intelligence (AI) was involved. Consumers may have an opposing stance against AI, leading companies to miss profit opportunities and reputation loss. Hence, companies need ways of managing consumers’ con-cerns. As a part of designing such advices we explore consumers’ discernment ability (DA) of AGAs. A quantitative survey was used to explore consumers’ DA of AGAs. In order to do this, we administered questionnaires to 233 re-spondents. A statistical analysis including Z-tests, of these responses suggests that consumers can hardly pick out AGAs. This indicates that consumers may be guessing and thus do not possess any significant DA of our AGAs.

Place, publisher, year, edition, pages
World Scientific, 2023
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868, E-ISSN 2972-4465 ; 13
Keywords
Autogenerated ads, Discernment ability, Marketing
National Category
Computer Sciences
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-23230 (URN)10.1142/9789811269264_0038 (DOI)978-981-126-925-7 (ISBN)978-981-126-927-1 (ISBN)
Conference
Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022), Tianjin, China, 26 – 28 August 2022
Funder
Knowledge Foundation, 20160035, 20170215
Note

Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215

Available from: 2023-09-20 Created: 2023-09-20 Last updated: 2023-10-10Bibliographically approved
4. Scoped Literature Review of Artificial Intelligence Marketing Adoptions for Ad Optimization with Reinforcement Learning
Open this publication in new window or tab >>Scoped Literature Review of Artificial Intelligence Marketing Adoptions for Ad Optimization with Reinforcement Learning
2023 (English)In: Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022) / [ed] Qinglin Sun; Jie Lu; Xianyi Zeng; Etienne E. Kerre; Tianrui Li, World Scientific, 2023, p. 416-423Conference paper, Published paper (Refereed)
Abstract [en]

Artificial Intelligence (AI) and Machine Learning (ML) are shaping marketing activities through digital innovations. Competition is a familiar concept for any digital retailer, and the digital transformation provides hopes for gaining a competitive edge over competitors. Those who do not adopt digital innovations risk getting outcompeted by those who do. This study aims to identify AI mar-keting (AIM) adoptions used for ad optimization with Reinforcement Learning (RL). A scoped literature review is used to find ad optimization adoptions re-search trends with RL in AIM. Scoping this is important both to research and practice as it provides spots for novel adaptations and directions of research of digital ad optimization with RL. The results of the review provide several different adoptions of ad optimization with RL in AIM. In short, the major category is Ad Relevance Optimization that takes several different forms depending on the purpose of the adoption. The underlying found themes of adoptions are Ad Attractiveness, Edge Ad, Sequential Ad and Ad Criteria Optimization. In conclusion, AIM adoptions with RL is scarce, and recommendations for future research are suggested based on the findings of the review.

Place, publisher, year, edition, pages
World Scientific, 2023
Series
World Scientific Proceedings Series on Computer Engineering and Information, ISSN 1793-7868, E-ISSN 2972-4465 ; 13
Keywords
Advertisement, Artificial intelligence, Reinforcement learning
National Category
Computer Sciences
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-23233 (URN)10.1142/9789811269264_0049 (DOI)978-981-126-925-7 (ISBN)978-981-126-927-1 (ISBN)
Conference
Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022), Tianjin, China, 26 – 28 August 2022
Funder
Knowledge Foundation
Note

Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215

Available from: 2023-09-21 Created: 2023-09-21 Last updated: 2023-10-10Bibliographically approved

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