Current Discussion on Implications of High Technologies on Business Models and Investments of Media Corporations in High Technology Start-ups

Impact of High Technologies on Media Business Models

Goal of the Study

So-called high technologies are changing the way media are produced, marketed and consumed (Jacobs Technion-Cornell Institute 2017). The areas of creation, production, and marketing/sales (the core value creation stages of mass media) are particularly affected by these changes. On the one hand, there are opportunities for media companies to adapt their business model (performance model, value creation model and revenue model) on the basis of new technological developments and/or to develop entirely new business models (technology as "enabler"). On the other hand, new technologies require adaptations of the business models (technology as "driving force"). In this context, high technologies are understood as technologies that enable automated data collection, evaluation, interpretation, enrichment and reassembly on the basis of algorithms.

The overarching objective of the research project (only a part of the overall project will be presented at IMMAA 2018) is to investigate the effects of so-called high technologies on the business models and value chains of media companies. The aim is to derive implications and application-oriented recommendations for action for the business model development and business model innovation of media companies.

Against this background, the aim of this submission can be concretized by two main research questions - the answers obtained serve as a basis for the overall research project:

(1)  Which technologies are currently being discussed in scientific as well as in business practice?

(2)  Which technologies are media companies currently investing in (understood as investments or acquisitions in start-ups.)?


As already described in the introduction, high technologies change the business models and value chains of media companies. The processes and stages in the value - known from the theory of media economics and information economy (Picot/Reichwald/Wigand 2001, 62-64) - new-production (transformation and translation) and re-production (transmission and transport) will be successively automated by the use of high technologies. In this context, information production includes the conception, production and marketing of information and entertainment content as well as advertising content (Gläser 2014, 69).

Depending on the type of information (content type) and the area within the media value chain (Gläser 2014, 72), state of development of high technologies are at different stages - from systems in regular operation to applications in early market phase and experiments in early development stages (Napoli 2014; Dörr 2016; Gräfe 2016). 

Even though there is no common definition of the terms and concepts (van Dalen 2012; Clerwall 2014; Napoli 2014; Carlson 2015; Coddington 2015; Lewis/Westlund 2015; Bontchev 2016; Diakopoulus/Koliska 2017; Lindén 2017), the pure listing of the following terms (only a brief excerpt) is impressive and gives an idea of what is already possible today and of what will be possible in (near) future: Algorithmic Journalism, Artificial Intelligence, Automated Journalism, Automatic Content Generation, Big Data, Computational Journalism, Data Journalism, Machine Learning, Programmatic Advertising, Robot Journalism, Software Generated Content.

The relevance of these developments is illustrated by the following four (exemplarily selected) examples:

  • Automated production of news: "Automated Journalism" or "Algorithmic Journalism", is the production of news content with little to almost no human participation (Carlson 2015; Diakopoulus/Koliska 2017). This is already reality and in daily use - especially in the areas of sports, weather, economic and financial news (Dörr 2016).
  • Automated production of program code for video games: Via so-called "Procedural Content Generation", parts of video games are generated automatically by algorithms that generate software code and/or render graphic components on the basis of predefined parameters (Bontchev 2016).
  • Automated suggestions for audiovisual entertainment content: Streaming services, both for music and for series and movies, use algorithms and machine learning, to make recommendations for the user as well as to start automated playlists (Gomez-Uribe/Hunt 2016).
  • Automated placing of personalized advertising: Online advertising is based on big data and artificial intelligence algorithms for some time now (Gensch 2018). Advertising content is not only triggered and placed by automation (programmatic), but is also individualised (in real time) (e.g. by location/environment, time, context, mood, etc.) and adapted in terms of content (visual language, tonality, etc.) (Jacob 2018, 28).

Against the background of the situation described, it can be assumed that business models (Gassmann/Frankenberger/Csik 2013, 6) - defined as a combination of value proposition model (What do we offer to the customer?), value chain model (How do we create and produce our service?) and profit model (How do we achieve value?) - of companies in the media and creative industries will change considerably. 

In the scientific discussion on the topic of business model development and business model innovation, the references and implications to technologies in general and to high technologies in particular are barely addressed, stated by Lambert/Davidson (2012) and Wirtz et al. (2016). This is surprising, since - in the scholar of management-oriented media economy (Zerdick et al. 1999, 139-146; Scholz/Stein/Eisenbeis 2001, 19-21) - technology has always been seen as both an enabler and a driving force. 

Obviously, there is a need for further research efforts. There are preliminary research efforts on the effects of the Internet on the media industry (Seufert 2017) and on the revenue model (Eisenbeis/Härle/Kohlmeyer 2018).

It could be assumed, that access to high technologies and the integration of these technologies into legacy (media) companies will take place either internally through in-house research and development (and then organizationally mostly through spin-off start-ups) or, in particular, externally through investments in or acquisitions of technology start-ups. 



In order to answer the two research questions formulated above, a mix of qualitative and quantitative research methods is applied. 

(1)  On the one hand - in order to obtain an overview of the implications from both, a scientific and a practical business perspective - the content of (1) relevant scientific journals, (2) studies carried out and/or commissioned by companies, and (3) speeches at relevant conferences are analysed (content analysis). A total of 201 documents (journal articles, studies, speeches) has been analysed.

(2)  On the other hand, the investments of media companies and their investment organizations/units has been analysed and evaluated with regard to their target technologies and target industries. For this purpose, the largest German media corporations as well as the large funds with investments of media companies has been considered here. A total of 432 investments were identified and analysed. 

The selection and systematization of technologies is based on the Gartner Hype Cycle of Emerging Technologies (Gartner Inc. 2017).

The results presented here serve as a preliminary report and as a basis for further research in the research project outlined in the introduction.



Altmeppen, K.-D./Karmasin, M., Medienökonomie als transdisziplinäres Lehr- und Forschungsprogramm, in: Altmeppen, K.-D./Karmasin, M.(Hrsg.), Medien und Ökonomie, Band 1/1, Wiesbaden (Westdeutscher Verlag) 2003, 19-51.

Bontchev, B., Modern Trends in the Automatic Generation of Content for Video Games, in: Serdica Journal of Computing 10 (2/2016) 133-166.

Boyd, C., Creative machines: How close are we to AI-Generated Content Marketing?, online: https://www.clickz.com/creative-machines-how-close-are-we-to-ai-generated-content-marketing/111950/, July 12, 2017.

Carlsen, M., The Robotic Reporter - Automated Journalism and the Redefinition of Labor, Compositional Forms, and Journalistic Authority, in: Digital Journalism 3 (3/2015), 416-431.

Clerwall, C., Enter the Robot Journalist, in: Journalism Practice 8 (5/2014), 519-531.

Coddington, M., Clarifying Journalism's Quantitative Turn: A Typology for Evaluating Data Journalism, Compournalism, and Computer-Assisted Reporting. Digital Journalism 3 (3/2015), 331-348.

Diakopoulos, N./Koliska, M., Algorithmic Transparency in the News Media, in: Digital Journalism 5 (7/2017), 809-828.

Dörr, K. N., Mapping the Field of Algorithmic Journalism, in: Digital Journalism 4 (6/2016), 700-722.

Eisenbeis, U./Härle, R. K./Kohlmeyer, S., Media and Internet Revenue Models: Where Western European Investors are Putting Their Money, in: Scholz, C./Wildman, S. M. (Hrsg.), Media Business Models: Connecting Media to their Markets, Porto (Formalpress) 2018, in Druck.

Fritz, W., Marketing-Management und Unternehmenserfolg. Grundlagen und Ergebnisse einer empirischen Untersuchung, Stuttgart (Schäffer-Poeschel) 2. Aufl. 1995.

Gartner Inc., The Gartner Hype Cycle of Emerging Technologies, online:https://www.gartner.com/ smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/, August 15, 2017.

Gassmann, O./Frankenberger, K./Csik, M., Geschäftsmodelle entwickeln. 55 innovative Konzepte mit dem St. Galler Business Model Navigator, München (Carl Hanser) 2013.

Gensch, P., Künstliche Intelligenz für Sales, Marketing und Service. Mit AI und Bots zu einem Algorithmic Business - Konzepte, Technologien und Best Practices, Wiesbaden (Springer) 2018.

Gerpott, T. J./Schlegel, M., Online-Distributionsoptionen für Markenanbieter journalistischer Inhalte, in: Die Betriebswirtschaft 62 (2002), 133-145.

Ghoshal, S./Nohria, N., Horses for Courses: Organizational Forms for Multinational Corporations, in: Sloan Management Review 35 (2/1993), 23-35.

Gläser, M., Medienmanagement, München (Vahlen) 3. Aufl. 2014.

Gomez-Uribe, C. A., The Netflix Recommender System: Algorithms, Business Value, and Innovation, in: ACM Transactions on Management Information Systems 6 (4/2016)

Gräfe, A., Guide to Automated Journalism, New York (Tow Center for Digital Journalism), 2016 

Hannan, M. T./Freeman, J., Structural Inertia and Organizational Change, in: American Sociological Review 49 (1984), 149-164.

Jacob, M., Kundenmanagement in der digitalen Welt, Wiesbaden (Springer) 2018

Jacobs Technion-Cornell Institute, How Technology is Transforming Media Consumption, online: https://tech.cornell.edu/news/how-technology-is-transforming-media-consumption, March 01, 2017.

Kieser, A./Woywode, M., Evolutionstheoretische Ansätze, in: Kieser, A.(Hrsg.), Organisationstheorien, Stuttgart (Kohlhammer) 3. Aufl. 1999, 253-285.

Lambert, S. C./Davidson, R. A., Applications of the Business Model in Studies of Enterprise Success, Innovation and Classification: An Analysis of Empirical Research from 1996 to 2010. European Management Journal, 31 (2012), 668-681.

Lewis, S. C./Westlund, O., Big data and Journalism: Epistemology, Expertise, Economics, and Ethics, in: Digital Journalism, 3 (3/2015), 447-466.

Lindén, C.-G., Algorithms for Journalism: The Future of News Work, in: The Journal of Media Innovations 4 (1/2017), 60-76.

McKelvey, B./Aldrich, H. E., Populations, Natural Selection, and Applied Organizational Science, in: Administrative Science Quarterly 28 (1983), 101-128.

Miller, D., Toward a New Contingency Approach. The Search for Organizational Gestalts, in: Journal of Management Studies 18 (1981), 1-26.

Miller, D./Friesen, P. H., Archetypes of Strategy Formulation, in: Management Science 24 (1978), 921-933.

Napoli, P. M., On Automation in Media Industries: Integrating Algorithmic Media Production into Media Industries Scholarship, in: Media Industries 1 (1/2014), 33-38.

Parasie, S./Dagiral, E., Data-Driven Journalism and the Public Good: "Computer-Assisted Reporters" and "Programmer-Journalists" in Chicago, in: New Media & Society, 15 (6/2012), 853-871.

Patel, S., Will Artificial Intelligence Replace Manual Content Creation?, online: http://contentmarketinginstitute.com/2017/03/artificial-intelligence-manual-creation/, March 23, 2017.

Picot, A./Reichwald, R./Wigand, R. T., Die grenzenlose Unternehmung. Information, Organisation und Management, Wiesbaden (Gabler) 4. Aufl. 2001.

Raffée, H.,Grundprobleme der Betriebswirtschaftslehre, Göttingen (Vandenhoeck und Ruprecht) 1993.

Scholz, C./Stein, V./Eisenbeis, U., Die TIME-Branche: Konzepte - Entwicklungen - Standorte, München/Mehring (Hampp) 2001.

Seufert, Wolfgang (Hrsg.), Media Economics revisited: (wie) verändert das Internet die Ökonomie der Medien?, Baden-Baden (Nomos) 2017.

Simmons, G./Palmer, M./Truong, Y., Inscribing Value on Business Model Innovations: Insights from Industrial Projects Commercializing Disruptive Digital Innovations. Industrial Marketing Management, 42 (5/2013), 744-754. 

Ulrich, H., Die Unternehmung als produktives soziales System. Grundlagen der allgemeinen Unternehmungslehre, Bern (Haupt) 2. Aufl. 1970.

Van Dalen, A., The algorithms behind the headlines: How Machine-Written News Redefines the Core Skills of Human Journalists, in: Journalism Practice, 6 (5-6/2012), 648-658.

Venkatraman, N., The Concept of Fit in Strategic Management. Toward Verbal and Statistical Correspondence, in: Academy of Management Review 14 (1989), 423-444.

Waterman, R. H., The Seven Elements of Strategic Fit, in: Journal of Business Strategy 2 (Winter 1982), 69-73.

Wirtz, B. W. et al., Business Models: Origin, Development and Future Research Perspectives, in: Long Range Planning, 49 (1/2016), 36-54.

Zerdick, A. et al., Die Internet-Ökonomie. Strategien für die digitale Wirtschaft, Berlin etc. (Springer) 1999.



Prof. Dr. Uwe Eisenbeis  Elektronische Visitenkarte
Medienmanagement und Medienökonomie, Geschäfts- und Erlösmodelle, Standortentwicklung und Ökosysteme
Medienökonomie, Medienmanagement, Strategisches Management, Unternehmensführung, Internationales Management, Volkswirtschaftslehre
Medienwirtschaft (Bachelor, 7 Semester)
Fakultät Electronic Media
221, Nobelstraße 10 (Hörsaalbau)
0711 8923-2258
Uwe Eisenbeis

Prof. Dr. Boris Kühnle  Elektronische Visitenkarte
- Performance Management in der Medienbranche - Ökonomische Bedingungen und Bedeutung der Medien- und TIME-Branche (z.B. Branchenanalysen, Standortforschung)
Studiendekan Medienwirtschaft (Bachelor, 7 Semester)
Professor für Medienwirtschaft und Finanzmanagement in TIME-Märkten Schwerpunkte: - Medienwirtschaft - Verlagsmanagement und Konvergenz - Controlling, Management Accounting - Internationale Finanz- und Medienmärkte - New Business und Gaming
Medienwirtschaft (Bachelor, 7 Semester)
Fakultät Electronic Media
221, Nobelstraße 10 (Hörsaalbau)
0711 8923-2246
0711 8923-2206
Boris Kühnle

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Prof. Dr. Uwe Eisenbeis  Elektronische Visitenkarte

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