| Title |
Spotify Tailoring for Promoting Effectiveness in Cross-Functional Autonomous Squads |
| ID_Doc |
71362 |
| Authors |
Salameh, A; Bass, JM |
| Title |
Spotify Tailoring for Promoting Effectiveness in Cross-Functional Autonomous Squads |
| Year |
2019 |
| Published |
|
| DOI |
10.1007/978-3-030-30126-2_3 |
| Abstract |
Organisations tend to tailor agile methods to scale employed practices to have cross-functional autonomous teams while promoting sustainable creative and productive development at a constant pace. Thus, it is important to investigate how organisations tailor agile practices to get the balance right between teams' autonomy and alignment. Spotify model is originally introduced to facilitate the development of music streaming services in a very large-scale project with a Business-to-Consumer (B2C) model. However, developing a large-scale missioncritical project with a Business-to-Business (B2B) model is not essentially supported by the Spotify model. Thus, embracing Spotify model for such projects should be concerned about the question of how Spotify practices are adjusted to promote effectiveness of cross-functional autonomous squads in a mission-critical project with B2B model? In this paper, we conduct a longitudinal embedded case study, which lasted 21 months during which 14 semi-structured interviews were conducted. The Grounded Theory (GT) is adopted to analyse the collected data. As a result, we identify practices and processes that promote effectiveness in cross-functional autonomous squads, which have never been discussed in terms of Spotify model before. We also present "Spotify Tailoring" by highlighting modified and newly introduced practices by the organisation in which the case study was conducted. |
| Author Keywords |
Spotify; Tailoring; Autonomous teams; Cross-functional; Large-scale agile; Offshore; Outsource; Mission-critical; Case study |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000525356100003 |
| WoS Category |
Computer Science, Software Engineering; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://link.springer.com/content/pdf/10.1007%2F978-3-030-30126-2_3.pdf
|