Music streaming services provide people with access to vast libraries of music, but also encourage certain patterns of consumption. In this article, I use Spotify as a case and investigate the action potentials for exploring and archiving music. The personal role of music implies we may expect the ‘will to archive’ to be prevalent even if these archives are not based on individual ownership. First, an analysis of Spotify suggests that the machine agency of Spotify pushes people towards exploring music, whereas archiving features are material and depend on human action. Spotify is hence skewed towards prompting users to explore rather than archive music. Next, an analysis of 23 focus-group interviews suggests that users value opportunities to explore music, yet their practices are equally directed towards archiving music. Theoretically, this article delineates how objects with machine agency are different from material objects in terms of affordances. The action potentials of material objects are symmetrically constituted by what the objects provide relative to an active being. The action potentials of objects with machine agency interfere with this symmetry: the machine is designed to act on behalf of the human being, making certain affordances more perceivable than others.
BanduraA (2006) Toward a psychology of human agency. Perspectives on Psychological Science1(2): 164–180.
2.
BeerD (2013) Popular Culture and New Media: The Politics of Circulation. Basingstoke: Palgrave Macmillan.
3.
BucherT (2012) Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media & Society14(7): 1164–1180.
4.
BucherT (2018) If . . . Then: Algorithmic Power and Politics. Oxford: Oxford University Press.
5.
CrawfordK (2016) Can an algorithm be agonistic? Ten scenes from life in calculated publics. Science, Technology & Human Values41(1): 77–92.
6.
DeNoraT (2000) Music in Everyday Life. Cambridge: Cambridge University Press.
7.
EngenVPickeringJBWallandP (2016).Machine agency in human-machine networks; Impacts and trust implications. In: Human-computer interaction. Novel user experiences: 18th international conference, HCI international 2016 proceedings, part III (ed KurosuM), Toronto, ON, Canada, 17–22 July, pp.96–106. Cham: Springer International Publishing.
GibsonJJ (1979) The Ecological Approach to Visual Perception. Boston, MA: Houghton Mifflin.
13.
GilesDCPietrzykowskiSClarkKE (2007) The psychological meaning of personal record collections and the impact of changing technological forms. Journal of Economic Psychology28(4): 429–443.
14.
GillespieT (2013) The relevance of algorithms. In: GillespieTBoczkowskiPFootK (eds) Media Technologies. Cambridge, MA: The MIT Press, pp.167–193.
15.
GillespieT (2017) Algorithmically recognizable: Santorum’s Google problem and Google’s Santorum problem. Information, Communication & Society20(1): 63–80.
16.
HagenAN (2015) The playlist experience: Personal playlists in music streaming services. Popular Music and Society38(5): 625–645.
17.
HagenANLüdersM (2017) Social streaming? Navigating music as personal and social. Convergence23(6): 643–659.
18.
HesmondhalghD (2008) Towards a critical understanding of music, emotion and self-identity. Consumption, Markets and Culture11(4): 329–343.
19.
HutchbyI (2001) Conversation and Technology: From the Telephone to the Internet. Cambridge: Polity Press.
20.
KibbyM (2009) Collect yourself. Information, Communication & Society12(3): 428–443.
21.
KitchinR (2017) Thinking critically about and researching algorithms. Information, Communication & Society20(1): 14–29.
22.
KjusY (2016) Musical exploration via streaming services: The Norwegian experience. Popular Communication14(3): 127–136.
23.
KreitzGNiemeläF (2010) Spotify – Large scale, low latency, P2P music-on-demand streaming. Paper presented at the 10th international conference on peer-to-peer computing. Los Alamitos, CA, 27 August.
24.
LatourB (2005) Reassembling the Social. Oxford: Oxford University Press.
25.
LawJ (1992) Notes on the theory of the actor-network: Ordering, strategy and heterogeneity. Systems Practice5(4): 379–393.
LeonardiPM (2011) When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly35(1): 147–167.
28.
LiuHHuZMianA, et al. (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems56: 156–166.
29.
MarshallL (2014) W(h)ither now? Music collecting in the age of the cloud. In: MarshallLLaingDFrithS (eds) Popular Music Matters: Essays in Honour of Simon Frith. Farnham: Ashgate, pp.61–72.
30.
MorrisJW (2015) Curation by code: Infomediaries and the data mining of taste. European Journal of Cultural Studies18(4–5): 446–463.
31.
NagyPNeffG (2015) Imagined affordance: Reconstructing a keyword for communication theory. Social Media+ Society. Epub ahead of print 30 September. DOI: 10.1177/2056305115603385.
32.
NormanD (2013) The design of everyday things: Revised and expanded edition. New York: Basic Books.
33.
RoseJJonesMTruexD (2005) Socio-theoretic accounts of IS: The problem of agency. Scandinavian Journal of Information Systems17(1): 133–152.
34.
SchrockAR (2015) Communicative affordances of mobile media: Portability, availability, locatability and multimediality. International Journal of Communication9: 1229–1246.
35.
SettyVKreitzGVitenbergR, et al. (2013) The hidden pub/sub of Spotify. Paper presented at the 7th ACM international conference on distributed event-based systems. Arlington, TX, 29June–3July.
36.
TurinoT (1999) Signs of imagination, identity and experience: A peircian semiotic theory for music. Ethnomusicology43(2): 221–255.
37.
Van der HoevenA (2018) Narratives of popular music heritage and cultural identity: The affordances and constraints of popular music memories. European Journal of Cultural Studies21(2): 207–222.
38.
Van DijckJ (2006) Record and hold: Popular music between personal and collective memory. Critical Studies in Media Communication23(5): 357–374.
