Opportunities for and Pitfalls of Using Big Data in Advertising Research

REFERENCES

• Becker, Ingo, Marc Linzmajer, and Florian von Wangenheim (2017), “Channels and Categories: User Browsing Preferences on the Path to Purchase,” Journal of Advertising, 46, XXX–XXX.
   Google Scholar
• Chintagunta, Pradeep, Dominique M. Hanssens, and John R. Hauser (2016), “Marketing Science and Big Data,” Marketing Science, 32 (1), 4–7.
   Web of Science ®Google Scholar
• Ekstrand, Michael D., John T. Riedl, and Joseph A. Konstan (2010), “Collaborative Filtering Recommender Systems,” Foundations and Trends in Human-Computer Interaction, 4 (2), 81–173.
   Google Scholar
• Hennig-Thurau, Thorsten, Edward C. Malthouse, Christian Friege, Sonja Gensler, Lara Lobschat, Arvind Rangaswamy, and Bernd Skiera (2010), “The Impact of New Media on Customer Relationships,” Journal of Service Research, 13 (3), 311–30.
   Web of Science ®Google Scholar
• Hofacker, Charles F., Edward C. Malthouse, and Fareena Sultan (2016), “Big Data and Consumer Behavior: Imminent Opportunities,” Journal of Consumer Marketing, 33 (2), 89–97.
   Web of Science ®Google Scholar
• Hsiao, Cheng (2003), Analysis of Panel Data, 2nd ed., Cambridge, UK: Cambridge University Press.
   Google Scholar
• Huh, Jisu, Atanu Roy, Alexander Pfeuffer, and Jaideep Srivastava (2017), “Development of Trust Scores in Social Media (TSM) Algorithm and Application to Advertising Practice and Research,” Journal of Advertising, 46, XXX–XXX.
   Web of Science ®Google Scholar
• James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2013), An Introduction to Statistical Learning, New York: Springer.
   Google Scholar
• Kumar, V., and Shaphali Gupta (2016), “Conceptualizing the Evolution and Future of Advertising,” Journal of Advertising, 45 (3), 302–17.
   Web of Science ®Google Scholar
• Laney, Doug (2001), “3D Data Management: Controlling Data Volume, Velocity, and Variety,” Application Delivery Strategies from META Group, February 6, https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
   Google Scholar
• Leeflang, Peter, Dick Wittink, Michel Wedel, and Philippe Naert (2000), Building Models for Marketing Decisions, Dordrecht, the Netherlands: Kluwer Academic.
   Google Scholar
• Leskovec, Jure, Anand Rajaraman, and Jeffrey D. Ullman (2014), Mining of Massive Datasets, 2nd ed., Cambridge, UK: Cambridge University Press.
   Google Scholar
• Li, Hairong, and Peking Tan (2014), “Big Data and Small Data: Innovative Paths to Integration,” in Proceedings of the European Advertising Academy Conference, P. Verlegh and C. Segijn, eds., Amsterdam: University of Amstedam, pp. 1–7.
   Google Scholar
• Liu, Xia, Alvin Burns, and Yingjian Hou (2017), “An Investigation of Brand-Related User Generated Content on Twitter,” Journal of Advertising, 46, 236–247, doi:10.1080/00913367.2017.1297273.
   Web of Science ®Google Scholar
• Liu-Thompkins, Yuping, and Edward C. Malthouse (2017), “A Primer on Using Behavioral Data for Testing Theories in Advertising Research,” Journal of Advertising, 46 (1), 1–13.
   Web of Science ®Google Scholar
• Maslowska, Ewa, Edward C. Malthouse, and Tom Collinger (2016), “The Customer Engagement Ecosystem,” Journal of Marketing Management, 32 (5–6), 469–501.
   Web of Science ®Google Scholar
• Mulhern, Frank J. (2016), “Big Data and the Digital Transformation in Advertising,” in The New Advertising: Branding, Content, and Consumer Relationships in the Data-Driven Social Media Era, Ruth E. Brown, Valerie K. Jones, and Ming Wang, eds., Santa Barbara, CA: Praeger, 95–108.
   Google Scholar
• Okazaki, Shintaro, Akihiro Katsukura, and Mamoru Nishiyama (2007), “How Mobile Advertising Works: The Role of Trust in Improving Attitudes and Recall,” Journal of Advertising Research, 47 (2), 165–78.
   Web of Science ®Google Scholar
• Perlich, Claudia, Brian Dalessandro, Rod Hook, Ori Stitelman, Troy Raeder, and Foster Provost (2012), “Bid Optimizing and Inventory Scoring in Targeted Online Advertising,” in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York: ACM, 804–12.
   Google Scholar
• Rust, Roland (2016), “Comment: Is Advertising a Zombie?,” Journal of Advertising, 45 (3), 346–47.
   Web of Science ®Google Scholar
• Schultz, Don (2016), “The Future of Advertising or Whatever We're Going to Call It,” Journal of Advertising, 45 (3), 276–85.
   Web of Science ®Google Scholar
• Sivarajah, Uthayasankar, Muhammad Mustafa Kamal, Zahir Irani, and Vishanth Weerakkody (2017), “Critical Analysis of Big Data Challenges and Analytical Methods,” Journal of Business Research, 70, 263–86.
   Web of Science ®Google Scholar
• Skiera, Bernd (2016), “Data, Data, and Even More Data: Harvesting Insights from the Data Jungle,” GfK Marketing Intelligence Review, 8 (2), 10–17.
   Google Scholar
• Stewart, David W. (2016), “Comment: Speculations of the Future of Advertising Redux,” Journal of Advertising, 45 (3), 348–50.
   Web of Science ®Google Scholar
• Van Doorn, Jenny, Katherine N. Lemon, Vikas Mittal, Stephan Nass, Doreén Pick, Peter Pirner, and Peter C. Verhoef (2010), “Customer Engagement Behavior: Theoretical Foundations and Research Directions,” Journal of Service Research, 13 (3), 253–66.
   Web of Science ®Google Scholar
• Van Noort, Guda, and Lotte Willemsen (2012), “Online Damage Control: The Effects of Proactive versus Reactive Webcare Interventions in Consumer-Generated and Brand-Generated Platforms,” Journal of Interactive Marketing, 26 (3), 131–40.
   Web of Science ®Google Scholar
• Wang, Jun, Weinan Zhang, and Shuai Yuan (2016), “Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting,” preprint arXiv:1610.03013, available https://arxiv.org/abs/1610.03013.
   Google Scholar