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Media Business Drivers and Concepts 2020: The Case of David and Goliath Revisited

  • Stina GieseckeEmail author
  • Raphael Giesecke
Chapter
Part of the Media Business and Innovation book series (MEDIA)

Abstract

Whereas diversified global media ‘Goliaths’ enjoy economies of scale and smaller, agile, risk-taking media ‘Davids’ flourish in niche markets, prospects for mid-sized mass media companies are bleak. We argue that such companies suffer from a conservative approach to convergence. They lack radical innovation that offers new meanings to humans, supported by radically new technology. More daring approaches to convergence and especially divergence may unlock vast amounts of underutilised business potential in media businesses. Furthermore we argue that the significant potential of business ecosystem thinking is systematically ignored. Once these challenges and opportunities are tackled, the probability for creating more promising business models will increase significantly. Aiming for a year 2020 forecast, we crowd sourced divergent views on media convergence. Our data consists of 42 individual scenarios authored by 38 media professionals and academics. Using thematic analysis, we synthesized in detail the convergence and divergence of media business drivers in nine dimensions. We suggest that three dimensions, gamification, social values and media companies’ leadership and management, are the key enablers for combining radically new meanings with radically new technology. Innovations based on these enablers may unlock completely new business opportunities. Drawing from and contributing to theories of organisational behaviour, we encourage mass media company leaders to diverge and focus on business models based on a system approach. Concluding, we show why business model development needs thorough understanding of both future divergent convergence and the business system of the company.

Keywords

Business Model Organisational Development Media Company Radical Innovation Media Sector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank the two anonymous reviewers for their constructive comments. We cordially thank all the future scenarios contributors and the Vision 2020 team, especially Man-Sze Li, Esa Sirkkunen and Kai Kuikkaniemi. This work was partially supported by the Finnish Funding Agency for Technology and Innovation TEKES as part of the Tivit Next Media programme. A preliminary version was presented at the Third NEM Summit, 2010.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of ScienceAalto UniversityAaltoFinland

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