ATHABASCA UNIVERSITY BIG DATA INNOVATION MECHANISMS IN THE CANADIAN TELEVISION INDUSTRY BY DERRICK TREVOR GRAY A DISSERTATION SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF BUSINESS ADMINISTRATION FACULTY OF BUSINESS ATHABASCA, ALBERTA AUGUST, 2019 © DERRICK TREVOR GRAY FACULTY OF GRADUATE STUDIES The future of learning. Approval of Dissertation The undersigned certify that they have read the dissertation entitled: BIG DATA INNOVATION MECHANISMS IN THE CANADIAN TELEVISION INDUSTRY Submitted by: Derrick Trevor Gray In partial fulfillment of the requirements for the degree of Doctor of Business Administration The examination committee certifies that the dissertation and the oral examination is approved Co-Supervisors: Dr. Alain May Athabasca University Dr. Terry Beckman Athabasca University Committee Member: Aleksi Aaltonen Temple University External Examiner: Mike Chiasson University of British Columbia August 20, 2019 1 University Drive, Athabasca, AB, T9S 3A3 Canada P: 780.675-6821 | Toll-free (CAN/U.S.) 1.800.788.9041 (6821) [email protected] | fgs.athabascau.ca | athabascau.ca Dedication This dissertation is dedicated to my family for their ongoing love and support through this journey. I would not have accomplished this without your help. To my son, Hudson, may you always strive to reach your goals, no matter how difficult they may seem. Melissa, thank you for the love, patience, and support you provided me through this challenge. Mom, you always pushed me to work hard and provided me the guidance and encouragement to set my goals high. This dissertation is also in memory of my inspirational grandparents, Lenna and Frank Baker. iii Acknowledgements They often say that it takes a village to raise a child. A similar thing could be said about completing a doctorate. It is no small feat, and only possible through the support, encouragement, assistance, and guidance of many. It is essential to acknowledge all of those who helped me through this journey – each in different ways. The supervisory committee is the backbone of any dissertation. These individuals tirelessly committed time and energy to review my work, providing corrections and suggestions, guiding thought, suggesting references, and most importantly encouraging me. Many hours were spent on phone calls and skype meetings, all of which helped me become a better student and researcher. Thank you to Dr. Alain May, Dr. Aleksi Aaltonen, and Dr. Terry Beckman for filling this role. I am most appreciative of your support and the role you have played. To the cohort of 2013, thank you for the constructive debate we enjoyed through our coursework. I drew a great deal of inspiration from all of you. You all provided support and encouragement that will never be forgotten. To my family and friends, I cannot thank you enough for your ongoing patience and encouragement. None of you ever questioned why I needed time to myself to study, read, write, or why I was taking this journey. Instead, you only encouraged me to continue and provided a sympathetic ear for when I needed it. iv Lastly, I need to acknowledge Dr. Ricardo Gomez-Insausti, for it was his belief in me and his encouragement that led me down this path. Ricardo, I will be eternally grateful for you pushing me along this journey and allowing me to grow as a researcher and academic. v BIG DATA INNOVATION MECHANISMS Abstract As a response to growing fragmentation, niche programming, and increasing competition from other forms of media, the Canadian television industry is undergoing rapid changes through digital innovation. Existing audience information systems are being enhanced with big data in order to allow the television audience marketplace to gain thicker and richer insights about television audiences. This evolution has allowed broadcasters to manufacture new forms of the television audience product with many features that were previously absent in the traditional television audience product. This study investigates the various generative mechanisms allowing Canadian television broadcasters to successfully innovate by enhancing their systems with these new sources of data. Supported by critical realism, affordance theory, and a stepwise analysis method, semi- structured interviews were conducted across three Canadian television broadcasters as a means to understand this digital innovation process. Five generative mechanisms are identified through the digital innovation process allowing for a better understanding of digital innovation with big data, particularly related to the audience product. These findings build on previous work by expanding the current understanding of the digital innovation process as well as the digital infrastructure mechanism by highlighting the central role in which digital infrastructures can play within an industry. Concurrently a new understanding of audiences is further developed by providing evidence that the audience is a manifestation of the underlying data itself – a significant reconceptualization of the audience product. These findings, as well as the analysis process, provide an empirical example of the role of affordances with big data. Despite vi BIG DATA INNOVATION MECHANISMS many calls for research, the role of big data in digital innovation is a significantly underdeveloped area of research. Lastly, by leveraging a quantitative method for pattern identification and grouping, the study further develops the existing critical realist methodology by building upon the analysis process for the identification of generative mechanisms. Keywords: audience information systems, mechanisms, digital innovation, critical realism, big data, affordances vii BIG DATA INNOVATION MECHANISMS Table of Contents Approval Page ……………………………………………………………………………ii Dedication .......................................................................................................................... iii Acknowledgements ............................................................................................................ iv Abstract .............................................................................................................................. vi List of Tables ................................................................................................................... xiii List of Figures and Illustrations ....................................................................................... xvi List of Abbreviations ...................................................................................................... xvii Chapter 1. Introduction ....................................................................................................... 1 Scope of the Study .......................................................................................................... 6 Importance of the Study and Contribution to the Literature ........................................... 9 Research questions ...................................................................................................... 7 Structure of the Dissertation ......................................................................................... 11 Definition of Terms Specific to the Study .................................................................... 12 Chapter 2. The Evolution of Data ..................................................................................... 16 Value of Information ..................................................................................................... 16 Big Data ........................................................................................................................ 23 Big data ..................................................................................................................... 25 Research on big data in academia ............................................................................. 28 The Value of Information, Big Data, and Audience Information Systems .................. 29 Chapter 3. Audience Economics, the Marketplace, and the Systems ............................... 31 Audience Economics .................................................................................................... 32 The typology of the audience product. ..................................................................... 35 Fragmentation and the long tail ................................................................................ 40 Audience Information Systems ..................................................................................... 44 viii BIG DATA INNOVATION MECHANISMS Return path, or set-top box, data. .............................................................................. 45 Audience Economics, the Audience Marketplace, and the Research ........................... 47 Chapter 4. Digital Innovation ........................................................................................... 49 Digital Infrastructures ................................................................................................... 49 Research on digital infrastructures ............................................................................ 51 Digital Innovation ......................................................................................................... 54 Dynamic problem-solution design pairing ................................................................ 56 Socio-cognitive sensemaking ...................................................................................