Mehler, Maren F. ; Vetter, Oliver A. (2023)
How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing.
European Conference on Information Systems (ECIS 2023). Kristiansand, Norwegen (11.-16. Juni 2023)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty on pricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers' incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choice-based conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Mehler, Maren F. ; Vetter, Oliver A. |
| Art des Eintrags: | Bibliographie |
| Titel: | How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing |
| Sprache: | Englisch |
| Publikationsjahr: | 14 Juni 2023 |
| Ort: | Atlanta |
| Verlag: | AIS eLibrary |
| Buchtitel: | ECIS 2023 Proceedings |
| Reihe: | ECIS 2023 Research Papers |
| Veranstaltungstitel: | European Conference on Information Systems (ECIS 2023) |
| Veranstaltungsort: | Kristiansand, Norwegen |
| Veranstaltungsdatum: | 11.-16. Juni 2023 |
| URL / URN: | https://aisel.aisnet.org/ecis2023_rp/355 |
| Kurzbeschreibung (Abstract): | Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty on pricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers' incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choice-based conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research. |
| Fachbereich(e)/-gebiet(e): | 01 Fachbereich Rechts- und Wirtschaftswissenschaften 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Wirtschaftsinformatik 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Fachgebiet Software Business & Information Management |
| Hinterlegungsdatum: | 20 Jun 2023 09:46 |
| Letzte Änderung: | 28 Jun 2023 12:58 |
| PPN: | 508947685 |
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