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Short message service campaign taxonomy for an intelligent marketing system
University of Skövde, School of Informatics. Department of Information Technology, University of Borås, Sweden.
Department of Information Technology, University of Borås, Sweden.
Department of Information Technology, University of Borås, Sweden.
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Informationssystem (IS), Information Systems)ORCID iD: 0000-0002-8900-6139
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. p. 606-613
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868 ; 12
Keywords [en]
fashion industry, artificial intelligence, marketing campaign, taxonomy, campaign builder, intelligent marketing system
National Category
Information Systems
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-18933DOI: 10.1142/9789811223334_0073ISI: 000656123200073ISBN: 978-981-122-332-7 (print)ISBN: 978-981-122-333-4 (electronic)ISBN: 978-981-122-334-1 (electronic)OAI: oai:DiVA.org:his-18933DiVA, id: diva2:1459310
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, 20160035/20170215Available from: 2020-08-19 Created: 2020-08-19 Last updated: 2023-09-21Bibliographically approved
In thesis
1. Designing Advertisement Systems with Human-centered Artificial Intelligence
Open this publication in new window or tab >>Designing Advertisement Systems with Human-centered Artificial Intelligence
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
Digital Advertisement Optimization, Design Knowledge, Information System Design Theory, Artificial Intelligence, Reinforcement Learning, Human-centered AI
National Category
Computer Sciences
Identifiers
urn:nbn:se:his:diva-23234 (URN)978-91-987906-8-9 (ISBN)
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

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