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Der Beitrag der Informatik zur Musikwirtschaftsforschung

  • Christine Bauer
Chapter
Part of the Musikwirtschafts- und Musikkulturforschung book series (MUSIK)

Zusammenfassung

Dieser Artikel widmet sich der Perspektive der Informatik in der Musikwirtschaftsforschung. Zunächst wird der Erkenntnisgegenstand der Musikwirtschaftsforschung aus dieser Perspektive dargelegt und das zur Verfügung stehende Methodeninstrumentarium aufgezeigt. Dabei untermauert diese Arbeit, dass die Perspektive der Informatik in der Musikwirtschaftsforschung neben einem deskriptiven auch einen normativen Charakter hat; somit beschäftigt sich dieser Bereich auch mit der Konstruktion und Evaluierung von Artefakten in der realen Welt der Musikwirtschaft. Anhand von konkreten Beispielen werden Problemstellungen und Forschungsfragen, die sich der Informatik in der Musikwirtschaftsforschung stellen, erläutert; dies sind im Speziellen die Forschungsbereiche (i) Musikempfehlungssysteme, (ii) Kompetenzaufbau im Einsatz von Technologie sowie (iii) Monitoring und Reporting der digitalen Musiknutzung.

Schlüsselwörter

Musikwirtschaft Angewandte Informatik Musikempfehlungssysteme Digitalisierung Marktmachtverhältnisse Informationsbedarf Strukturen der Musikwirtschaft Markttransparenz Design und Evaluierung von Artefakten 

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Literatur

  1. Adler, M. (2006). Stardom and Talent. In V. A. Ginsburgh & D. Throsby (Hrsg) Handbook of the economics of art and culture (S. 896-906), Amsterdam: North Holland.Google Scholar
  2. Anderson, C. (2006). The Long Tail: Why the Future of Business is Selling Less of More. New York: Hyperion.Google Scholar
  3. Baskerville, R., Lyytinen, K., Sambamurthy, V., & Straub, D. (2011). A response to the design-oriented information systems research memorandum. European Journal of Information Systems 20 (1), 11-15. doi: https://doi.org/10.1057/ejis.2010.56.
  4. Bauer, C. (2012). Bands as Virtual Organisations: Improving the Processes of Band and Event Management with Information and Communication Technologies. Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien: Peter Lang.Google Scholar
  5. Bauer, C., Kholodylo, M., & Strauss, C. (2017). Music Recommender Systems: Challenges and Opportunities for Non-Superstar Artists. 30th Bled eConference, Bled, Slovania, 18.-21. Juni.Google Scholar
  6. Bauer, C., & Kratschmar, A. (2015). Designing a Music-controlled Running Application: a Sports Science and Psychological Perspective. ACM SIGCHI Extended Abstracts of Conference on Human Factors in Computing Systems (CHI 2015), Seoul, South Korea, 18.-23. April.Google Scholar
  7. Bauer, C., & Schedl, M. (2017). Introducing Surprise and Opposition by Design in Recommender Systems. 25th International Conference on User Modeling, Adaptation and Personalization (UMAP 2017): 2nd Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP 2017), Bratislava, Slovakia, 9. Juli.Google Scholar
  8. Bauer, C., & Strauss, C. (2015). Educating artists in management: an analysis of art education programmes in DACH region. Cogent Education 2 (1). doi: https://doi.org/10.1080/2331186x.2015.1045217.
  9. Bauer, C., & Strauss, C. (2017). The dark side of Web 2.0: From self-marketing to self-destruction of music artists. GRES-IT Workshop Proceedings. Working Papers on Information Systems, Information Business and Operations, 02/2016, Vienna, Austria, 22. September 2016.Google Scholar
  10. Bauer, C., Viola, K., & Strauss, C. (2011). Management skills for artists: ‘learning by doing’? International Journal of Cultural Policy 17 (5), 626-644. doi: https://doi.org/10.1080/10286632.2010.531716.
  11. Bauer, C., & Waldner, F. (2013). Reactive Music: When User Behavior affects Sounds in Real- Time. CHI 2013 Extended Abstracts on Human Factors in Computing Systems, Paris, France, 27. April – 2. Mai.Google Scholar
  12. Baym, N. K. (2010). Rethinking the Music Industry. Popular Communication 8 (3),177-180. doi: https://doi.org/10.1080/15405702.2010.493419.
  13. Bernardo, F., & Marins, L. G. (2014). Disintermediation Effects on Independent Approaches to Music Business. International Journal of Music Business Research 3 (2), 7-27.Google Scholar
  14. Boer, D., Fischer, R., Strack, M., Bond, M. H., Lo, E., & Lam, J. (2011). How shared preferences in music create bonds between people: values as the missing link. Personality and Social Psychology Bulletin 37 (9), 1159-1171.Google Scholar
  15. Bonneville-Roussy, A., Rentfrow, P. J., Xu, M. K., & Potter, J. (2013). Music through the ages: trends in musical engagement and preferences from adolescence through middle adulthood. Journal of Personality and Social Psychology 105 (4), 703-717.Google Scholar
  16. Brown, R. A. (2012). Music preferences and personality among Japanese university students. International Journal of Psychology 47 (4), 259-268.Google Scholar
  17. Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011). „You Might Also Like:“ Privacy Risks of Collaborative Filtering. 32nd IEEE Symposium on Security and Privacy (SP 2011), Oakland, CA, 22.-25. Mai.Google Scholar
  18. Caves, R. E. (2000). Creative Industries: Contracts Between Art and Commerce. Cambridge, MaA: Harvard University Press.Google Scholar
  19. Cebrián, T., Planagumà, M., Villegas, P., & Amatriain, X. (2010). Music recommendations with temporal context awareness. 4th ACM Conference on Recommender Systems (RecSys 2010), Barcelona, Spain, 26.-30. September.Google Scholar
  20. Celma, Ò., & Herrera. P. (2008). A New Approach to Evaluating Novel Recommendations. 2nd ACM Conference on Recommender Systems (RecSys 2008), Lausanne, Switzerland, 23.-25. Oktober.Google Scholar
  21. Cheng, Z., & Shen, J. (2014). Just-for-Me: An Adaptive Personalization System for Location- Aware Social Music Recommendation. International Conference on Multimedia Retrieval (ICMR 2014), Glasgow, UK.Google Scholar
  22. Cheng, Z., & Shen, J. (2015). VenueMusic: A Venue-Aware Music Recommender System. 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2015), Santiago, Chile, 9.-13. August.Google Scholar
  23. Coy, W., Nake, F., Pflüger, J.-M., Rolf, A., Seetzen, J., Siefkes, D., & Stransfeld, R. (Hrsg.) (1992). Sichtweisen der Informatik, Theorie der Informatik. Wiesbaden: Vieweg+Teubner Verlag.Google Scholar
  24. Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4. Aufl., Los Angeles, London, New Delhi, Singapore, Washington, DC: Sage.Google Scholar
  25. Daniel, R., & Daniel, L. (2014). Breaking down barriers: the implementation of work integrated learning strategies to transition creative and performing artists to industry. Australian Collaborative Education Network Conference (ACEN 2014), Gold Coast Queensland, Australia, 1.-3. Oktober.Google Scholar
  26. Ferwerda, B. Vall, A., Tkalcic M., & Schedl, M. (2016). Exploring Music Diversity Needs Across Countries. 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016), Halifax, Canada, 13.-17. Juli.Google Scholar
  27. Fleder, D., & Hosanagar, K. (2007). Recommender systems and their impact on sales diversity. 8th ACM Conference on Electronic Commerce (EC 2007), San Diego, CA, 11-15 June.Google Scholar
  28. Hennekam, S., & Bennett, D. (2016). Self-management of work in the creative industries in the Netherlands. International Journal of Arts Management 19 (1), 31-41, 97.Google Scholar
  29. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. Management Information Systems Quarterly 28 (1), 75-105.Google Scholar
  30. Knees, P., Andersen, K., Said, A., & Tkalcic, M. (2016). Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP). UMAP 2016 Extended Proceedings: Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP), Halifax, NS, CA, 16. Juli.Google Scholar
  31. Kulle, J., (1998). Ökonomie der Musikindustrie: Eine Analyse der körperlichen und unkörperlichen Musikverwertung mit Hilfe von Tonträgern und Netzen. Frankfurt am Main: Peter Lang.Google Scholar
  32. Langenheder, W., Müller, G., & Schinzel, B. (Hrsg.) (1992). Informatik cui bono?: GI-FB 8 Fachtagung, Freiburg, 23.-26. September 1992, Informatik aktuell. Berlin: Springer.Google Scholar
  33. Laplante, A.. (2014). Improving music recommender systems: what can we learn from research on music tags? 15th International Society for Music Information Retrieval Conference (ISMIR 2014), Taipei, Taiwan, 27.-31. Oktober.Google Scholar
  34. Limper, J., & Lücke, M. (Hrsg.). (2013). Management in der Musikwirtschaft. Edition Kreativwirtschaft. Stuttgart: Kohlammer.Google Scholar
  35. March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision Support Systems 15 (4), 251-266.Google Scholar
  36. Menger, P.-M. (1999). Artistic Labor Markets and Careers. Annual Review of Sociology 25 (1), 541-574. doi: https://doi.org/10.1146/annurev.soc.25.1.541.
  37. Michel, N. J. (2006). The impact of digital file sharing on the music industry: An empirical analysis. Topics in Economic Analysis & Policy 6 (1), 1-22.Google Scholar
  38. Mietzner, D., & Kamprath, M. (2013). A Competence Portfolio for Professionals in the Creative Industries. Creativity and Innovation Management 22 (3), 280-294. doi: https://doi.org/10.1111/caim.12026.
  39. Montag Stiftung Bildende Kunst Bonn, Akademie der bildenen Künste Wien & Verlag für moderne Kunst. (2008). Job Descriptions: KünstlerInnen in einer veränderten Berufswelt. 3. Symposium der Reihe „Heraus aus dem Elfenbeinturm!“, Nürnberg, 17.-18. Oktober.Google Scholar
  40. Mulligan, M. (2013). The Death of the Long Tail: The Superstar Music Economy. Media Insights & Decisions in Action.Google Scholar
  41. Österle, H. Becker, J., Frank, U., Hess, Th., Karagiannis, D., Krcmar, H., Loos, P., Mertens, P., Oberweis, A., & Sinz, E. J. (2010). Memorandum on design-oriented information systems research. European Journal of Information Systems 20 (1), 7-10.Google Scholar
  42. Peffers, K., Rothenberger, M., Tuunanen, T., & Vaezi, R. (2012). Design Science Research Evaluation. 7th International Conference on Design Science Research in Information Systems (DESRIST 2012), Las Vegas, NV, 14.-15. Mai.Google Scholar
  43. Prat, N., Comyn-Wattiau, I., & Akoka, J. (2014). Artifact evaluation in information systems design-science research: a holistic view. 18th Pacific Asia Conference on Information Systems (PACIS 2014), Chengdu, China, 24.-28. Juni.Google Scholar
  44. Pries-Heje, J., Baskerville, R., & Venable, J. R. (2008). Strategies for Design Science Research Evaluation. European Conference on Information Systems (ECIS 2008), Galway, Ireland, 9.-11. Juni.Google Scholar
  45. Rechenberg, P. (2000). Was ist Informatik? München: Hanser Fachbuch.Google Scholar
  46. Schedl, M. (2016). The LFM-1b Dataset for Music Retrieval and Recommendation. ACM International Conference on Multimedia Retrieval (ICMR 2016), New York, NY, 6.-9. Juni.Google Scholar
  47. Schedl, M,, Gómez, E., & Urbano, J. (2014). Music Information Retrieval: Recent Developments and Applications. Foundations and Trends in Information Retrieval 8 (2-3), 127-261.Google Scholar
  48. Schelepa, S., Wetzel, P., & Wohlfahrt. G. (2008). Zur sozialen Lage der Künstler und Künstlerinnen in Österreich: Endbericht. Wien: L & R Social Research.Google Scholar
  49. Schulze, G. (2003). Superstars. In R. Towse (Hrsg.), A Handbook of Cultural Economics (S. 431-436). Cheltenham, UK: Edward Elgar Publishing.Google Scholar
  50. Seufert, W., Schlegel, R., & Sattelberger, F. (2015). Musikwirtschaft in Deutschland: Studie zur volkswirtschaftlichen Bedeutung von Musikunternehmen unter Berücksichtigung aller Teilsektoren und Ausstrahlungseffekte. Bundesverband Musikindustrie e. V.; Bundesverband der Veranstaltungswirtschaft e. V.; deutscher musikverleger-Verband e. V.; Europäischer Verband der Veranstaltungszentren e. V.; Gesellschaft zur Verwertung von Leistungsschutzrechten mbh LivemusikKommission e. V.; Society of Music Merchants e. V.; Verband der deutschen Konzertdirektionen e. V.; Verband unabhängiger musikunternehmen e. V.Google Scholar
  51. Söndermann, M. (2010). Monitoring zu ausgewählten wirtschaftlichen Eckdaten der Kulturund Kreativwirtschaft 2010: Langfassung. Köln: Büro für Kulturwirtschaftsforschung.Google Scholar
  52. Song, Y., Dixon, S., & Pearce, M. (2012). A Survey of Music Recommendation Systems and Future Perspectives. 9th International Symposium on Computer Music Modelling and Retrieval (CMMR 2012), London, United Kingdom, 19-22 June.Google Scholar
  53. Stigler, George J., & Becker, Gary S. (1977). De Gustibus Non Est Disputandum. The American Economic Review 67 (2), 76-90.Google Scholar
  54. Tschmuck, P. (2012). Creativity and Innovation in the Music Industry. 2. Aufl. Berlin, Heidelberg: Springer.Google Scholar
  55. Venable, J. R., Pries-Heje, J., & Baskerville, R. (2012). A Comprehensive Framework for Evaluation in Design Science Research. 7th International Conference on Design Science Research in Information Systems (DESRIST 2012), Las Vegas, NV, 14.-15. Mai.Google Scholar
  56. Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. Management Information Systems Quarterly 37 (1), 21-54.Google Scholar
  57. Wang, X., Rosenblum, D., & Wang, Y. (2012). Context-aware mobile music recommendation for daily activities. 20th ACM International Conference on Multimedia (MM 2012), Nara, Japan, 29. Oktober – 2. November.Google Scholar
  58. Wilde, T., & Hess, Th. (2007). Forschungsmethoden der Wirtschaftsinformatik: Eine empirische Untersuchung. Wirtschaftsinformatik 49 (4), 280-287.Google Scholar
  59. Zhang, M., & Hurley, N. (2008). Avoiding Monotony: Improving the Diversity of Recommendation Lists. 2nd ACM Conference on Recommender Systems (RecSys 2008), Lausanne, Switzerland, 23.-25. Oktober.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2018

Authors and Affiliations

  1. 1.Institut für Computational Perception Johannes KeplerUniversität Linz AltenbergerLinzÖsterreich

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