Socio-behavioral elements in data-driven requirements engineering: The case of enterprise cloud software

Hoffmann, Philipp ; Mateja, Deborah ; Spohrer, Kai ; Heinzl, Armin

Document Type: Conference or workshop publication
Year of publication: 2020
Book title: 28th European Conference on Information Systems (ECIS2020) : an Online AIS Conference, June 15-17, 2020
The title of a journal, publication series: European Conference on Information Systems : ECIS
Volume: 2020, Paper 172
Page range: 1-16
Conference title: ECIS 2020
Location of the conference venue: Online
Date of the conference: 15.-17.06.2020
Publisher: Rowe, Frantz
Place of publication: Atlanta, GA
Publishing house: AISeL
Publication language: English
Institution: Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Business School > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
Subject: 330 Economics
Abstract: 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.
Additional information: Online-Ressource

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