How the Structure of Hybrid Networks Shape Content
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THE LONG-TAILS IN CONTENT SERVICES: HOW THE STRUCTURE OF HYBRID NETWORKS SHAPE CONTENT POPULARITY AND RELATED DECISION- MAKING by NIKHIL SRIKRISHNA SRINIVASAN Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Adviser: Dr. Kalle Lyytinen Information Systems Department CASE WESTERN RESERVE UNIVERSITY January, 2013 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Nikhil Srikrishna Srinivasan candidate for the Doctor of Philosophy degree *. (signed) Dr. Kalle Lyytinen (chair of the committee) Dr. Kalle Lyytinen Dr. Fred Collopy Dr. Youngjin Yoo Dr. Samer Faraj Dr. Jagdip Singh (date) 12/15/2012 *We also certify that written approval has been obtained for any proprietary material contained therein. Table of Contents List of Tables 5 List of Figures 7 Acknowledgements 9 Abstract 10 1. Pushing the Envelope of Popularity: The Role of Multi- Modal Networks in IT Mediated Environments 12 1.1 Introduction 12 1.1.1 Research Questions 22 1.1.2 Contribution of Thesis 26 1.2 Organizing Framework and Literature 28 1.2.1 Guiding Literature 31 1.2.2 Network Theory and Methods 34 1.2.3 Networks in Marketing 35 1.2.4 Cognition in Groups and Networks 37 1.2.5 Group Decision-making 38 1.3 Theoretical Development 40 1.3.1 Emergent Popularity 40 1.3.2 Popularity in IT based Environments 42 1.3.3 Networks in Popularity 44 1 1.4 Research Methodology 46 1.4.1 Study Site and Phenomenon Description 47 1.4.2 Methodology and Research Design 54 1.4.3 Validity and Reliability Assurance 58 1.4.4 Summary and Discussion 60 1.5 Conclusion 66 2. Case Study Analysis (Racing to the head: A dynamic analysis of long-tail networks in Social Bookmarking services) 68 2.1 Introduction 68 2.2 Literature Review 70 2.2.1 Network and Diffusion of Information 70 2.2.2 Networks and Viral Marketing of Online Information 72 2.3 Long-tail in Social Bookmarking Services 74 2.4 Research Methodology 78 2.4.1 Analytic Method 82 2.4.2 State Diagrams 85 2.5 Case Descriptions 87 2.5.1 Case 1: Big Brother Protect 87 2.5.2 Case 2: Firefox Memory Leak 95 2.5.3 Case 3: Ludios 103 2.5.4 Case 4: Camcorder Information 108 2 2.6 Findings 111 2.6.1 Structural Analysis 112 2.6.2 Dynamic Analysis 118 2.7 Discussion and Summary 124 3. Quantitative Analysis (Explaining Network Growth and Popularity in Hybrid Content Networks) 131 3.1 Introduction 131 3.2 Popularity, Cognition Sharing and Hybrid Networks 137 3.2.1 Tags as Cognition Artifacts 137 3.2.2 Forming Socio-Technical Networks through Tags 138 3.3 Research Design and Method 144 3.3.1 Research Goals 144 3.3.2 Research Site 146 3.3.3 Construct and Variable Measurement 150 3.3.4 Data Cleaning and Collection 153 3.3.5 Sampling Strategy and Process 155 3.3.6 Descriptive Statistics and Data Distribution 161 3.3.7 Hypothesis Testing 164 3.4 Findings 166 3.5 Discussion and Conclusion 178 3.5.1 Managerial Implications 184 3 3.5.2 Limitations 185 3.5.3 Future Work 185 4. Discussion and Conclusion 187 4.1 Introduction 187 4.2 Summary of Findings 188 4.2.1 Lessons from Case Studies 188 4.2.2 Lessons from Field Study 192 4.2.3 Concluding Remarks 194 4.3 Limitations 196 4.4 Future Research 198 4.5 Managerial Implications 201 5. Appendix 204 6. Bibliography 207 4 List of Tables Table 1 – Summary of Guiding Literature 32 Table 2 – Case Categories and Descriptions 82 Table 3 – Betweenness measures for BBP 92 Table 4 – Network Connectedness measures for BBP 94 Table 5 – Betweenness measures for FML 100 Table 6 – Network Connectedness measures for FML 102 Table 7 – Network Connectedness measures for Ludios 107 Table 8 – Betweenness measures for CamcorderInfo 109 Table 9 – Network Connectedness measures for CamcorderInfo 111 Table 10 – Node Centrality Interpretation across cases 112 Table 11 – Node Class Interpretation across cases 112 Table 12 – Network Connectedness Interpretation across cases 114 Table 13 – Control Variables for the research questions 150 Table 14 – Construct and measures for dependent variables 151 Table 15 – Constructs and measures for the independent variables 152 5 Table 16 – Comparison of Study and Reference samples 158 Table 17 - Descriptive statistics for the independent variables in RQ1 162 Table 18 – Descriptive statistics for the Independent variables for RQ2 162 Table 19 – Shapiro-Wilks Normality test 163 Table 20 – Results of Logit Analysis 168 Table 21 – Analysis of popularity Cox Regression 174 Table 22 – Survival Table 176 6 List of Figures Figure 1 – Long-tail distribution of bookmarks 23 Figure 2 – Peaking Characteristic of bookmark 24 Figure 3 – Social Bookmarking System Operations 53 Figure 4 – Long-tail distribution of bookmarks 80 Figure 5 – Decision tree for case selection 81 Figure 6 – Steady State of Social Bookmarking System 86 Figure 7 – State of various Objects in the Social Bookmarking System 87 Figure 8 – Big Brother Protect 89 Figure 9 – Firefox Memory Leak 96 Figure 10 – Ludios 105 Figure 11 – Camcorder Info 109 Figure 12 – State diagrams for “Rapid” Popular Content 120 Figure 13 – State diagrams for “Slow” Popular Content 121 Figure 14 – State diagrams for “Unpopular” Content 123 7 Figure 15 – Social Bookmarking System Operation 148 Figure 16 – Sampling based on location within the long-tail of content popularity 156 Figure 17 – Generation of network data and measures 160 Figure 18 – Survival function at the mean of covariates 178 8 Acknowledgements This dissertation is the result of the effort of several individuals who have contributed in a variety of ways. I would like to first and foremost thank my dissertation committee whose input has contributed greatly to the thesis and without whose support and encouragement this thesis may not have seen the light of day. I would like to express my gratitude to the committee chair Dr Kalle Lyytinen for his guidance through this process and unwavering commitment in assisting me. I would also like to express my gratitude to my colleagues and friends who have been my travelling partners in this process. Finally, I would like to express my deepest thanks and gratitude to my family whose support and encouragement has been invaluable in making this dissertation possible. 9 The Long-Tails in Content Services: How the Structure of Hybrid Networks Shape Content Popularity and Related Decision-Making Abstract by NIKHIL SRIKRISHNA SRINIVASAN This thesis examines the role that socio-technical networks that drive content popularity. Socio-technical networks are conceptualized as the infrastructure through which distributed individuals make and share cognition. This making and sharing of cognition through socio-technical networks results in an emergent distributed decision-making process. This emergent distributed decision-making process is embedded within the context of the socio-technical networks and consequently the structure of the networks influences it. The emergent distributed decision-making process influences the popularity of content. This thesis examines the question about which factors of socio-technical networks influence the popularity of content. 10 I explore this question through the use of a combination of a case study and quantitative field study. The multi-method approach allows us to use a combination of approaches to explore the static and dynamic factors that influence popularity of content embedded within socio-technical networks. I find that technical artifacts play an important role in the sharing of cognition within content networks and socio-technical networks structures influence both the popularity of content and the duration that content takes to emerge as popular. This thesis has implications for both research and practice. It moves beyond an examination of the consequences and implications of long-tail behaviors to the structural characteristics that underlie such distributions. It also serves the knowledge management community by emphasizing the role of representational or classification systems in managing and disseminating knowledge. It adds to the IT literature by elaborating on the description of long tail characteristics of IT mediated networks. The implications for managers on the basis of this work are clear. Managers should focus on bridging large networks and making sure that participants in these networks have relationships with each other. While we might appreciate the phrase “let a thousand flowers bloom”, managers must not forget the forest for the trees. This work suggests that managers in addition to encouraging the growth of new ideas should also ensure that these ideas are disseminated as widely as possible through cohesive and connected networks. 11 1. Pushing the Envelope of Popularity: The Role of Multi-Modal Networks in IT Mediated Environments 1.1 Introduction The following string of hexadecimal digits: 09 F9 11 02 9D 74 E3 5B D8 41 56 C5 63 56 88 C0 appeared on websites such as Slashdot.com and digg.com in the early weeks of March 2007 and created a storm of controversy for the user communities of these sites. This string comprises the key to decoding HD-DVD titles encoded by the AACS (Advanced Access Content System) content protection system that was developed by the MPAA (Motion Picture Association of America). Publishing this code compromised resources invested in this protection system and the large number of HD-DVD titles that had implemented this protection system. The sequence was posted before the first week in March 2007 on several occasions, but was taken down due to MPAA takedown threats. The string was posted on digg.com, a community where users participate in evaluating or “digg-ing” content.