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Socio-behavioral elements in data-driven requirements engineering: The case of enterprise cloud software
Hoffmann, Philipp
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Mateja, Deborah
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Spohrer, Kai
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Heinzl, Armin
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URL:
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https://aisel.aisnet.org/ecis2020_rp/172
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2020
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Buchtitel:
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28th European Conference on Information Systems (ECIS2020) : an Online AIS Conference, June 15-17, 2020
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Titel einer Zeitschrift oder einer Reihe:
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European Conference on Information Systems : ECIS
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Band/Volume:
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2020, Paper 172
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Seitenbereich:
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1-16
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Veranstaltungstitel:
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ECIS 2020
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Veranstaltungsort:
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Online
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Veranstaltungsdatum:
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15.-17.06.2020
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Herausgeber:
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Rowe, Frantz
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Ort der Veröffentlichung:
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Atlanta, GA
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Verlag:
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AISeL
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Fakultät für Betriebswirtschaftslehre > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
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Fachgebiet:
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330 Wirtschaft
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Abstract:
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The ongoing transition from on-premise to cloud solutions in the enterprise software market entails important changes in how software vendors interact with their users. Where user involvement has traditionally been a challenge, increasingly large amounts of usage and feedback data now allow for datadriven requirements engineering (RE). Prior research has provided conceptualizations of data-driven RE, introduced initial technical prototypes, and shed light on the general social interactions in RE. However, extant research lacks a comprehensive perspective on the socio-behavioral elements of datadriven RE for enterprise cloud software development and empirical insights. We obtained access to a large enterprise cloud software vendor for a revelatory single-case study and conducted interviews within seven different cloud software products. We demonstrate how data-driven RE affects knowledge transfer, mental models, and trust between stakeholders. We observe a shift from a stakeholder-centric towards a more user-centric RE process by opening new direct requirements elicitation channels between the users of a software and the development organization. Our study reveals that the data-driven approach holds much potential to scale and accelerate RE for enterprise cloud software, but there are still numerous obstacles to overcome in order to achieve high levels of context-awareness, continuity, and automation in RE.
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Zusätzliche Informationen:
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Online-Ressource
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Hoffmann, Philipp
;
Mateja, Deborah
;
Spohrer, Kai
;
Heinzl, Armin
Google Scholar:
Hoffmann, Philipp
;
Mateja, Deborah
;
Spohrer, Kai
;
Heinzl, Armin
ORCID:
Hoffmann, Philipp  ORCID: https://orcid.org/0000-0002-6528-9269, Mateja, Deborah, Spohrer, Kai ORCID: https://orcid.org/0000-0001-8659-7554 and Heinzl, Armin
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