IDEAS home Printed from https://ideas.repec.org/a/eee/joinma/v38y2017icp12-28.html
   My bibliography  Save this article

Pre-launch Prediction of Market Performance for Short Lifecycle Products Using Online Community Data

Author

Listed:
  • Divakaran, Pradeep Kumar Ponnamma
  • Palmer, Adrian
  • Søndergaard, Helle Alsted
  • Matkovskyy, Roman

Abstract

Prediction of sales for short life-cycle products can be problematic. Generic predictive models based on past launches may provide only crude historic data which are unsuited for distinctive, innovative products. This paper investigates the role of online communities in providing pre-launch data to predict post-launch sales. We argue that levels of awareness, word-of-mouth, expectations, and adoption intention prevailing within an online community for an upcoming product have an independent direct effect on the product's future sales. Additionally, we test the complementarity effect of these community variables by introducing a higher order construct called Pre-release Community Buzz, to demonstrate the incremental explanatory power of using pre-launch community variables to predict post-launch sales. Data for community variables were collected from a movie-based online community, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). We found strong support for both direct and complementarity effects of community variables in predicting a movie's opening week sales. We also found that community variables mediate the effects of generic predictor variables such as MPAA ratings, star cast, production budget and competition on opening week sales. Tests for robustness demonstrated the value of community variables. Models which included community variables had higher predictive power than those without. Implications for theory and practice are presented.

Suggested Citation

  • Divakaran, Pradeep Kumar Ponnamma & Palmer, Adrian & Søndergaard, Helle Alsted & Matkovskyy, Roman, 2017. "Pre-launch Prediction of Market Performance for Short Lifecycle Products Using Online Community Data," Journal of Interactive Marketing, Elsevier, vol. 38(C), pages 12-28.
    • Handle: RePEc:eee:joinma:v:38:y:2017:i:c:p:12-28
    DOI: 10.1016/j.intmar.2016.10.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1094996816300743
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intmar.2016.10.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Constance Elise Porter & Naveen Donthu, 2008. "Cultivating Trust and Harvesting Value in Virtual Communities," Management Science, INFORMS, vol. 54(1), pages 113-128, January.
    2. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    3. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2007. "Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(2), pages 807-829.
    4. Karniouchina, Ekaterina V., 2011. "Impact of star and movie buzz on motion picture distribution and box office revenue," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 62-74.
    5. Robert E. Krider & Tieshan Li & Yong Liu & Charles B. Weinberg, 2005. "The Lead-Lag Puzzle of Demand and Distribution: A Graphical Method Applied to Movies," Marketing Science, INFORMS, vol. 24(4), pages 635-645, April.
    6. Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, vol. 18(2), pages 115-136.
    7. Monecke, Armin & Leisch, Friedrich, 2012. "semPLS: Structural Equation Modeling Using Partial Least Squares," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i03).
    8. Martin Spann & Bernd Skiera, 2003. "Internet-Based Virtual Stock Markets for Business Forecasting," Management Science, INFORMS, vol. 49(10), pages 1310-1326, October.
    9. Bacile, Todd J. & Ye, Christine & Swilley, Esther, 2014. "From Firm-Controlled to Consumer-Contributed: Consumer Co-Production of Personal Media Marketing Communication," Journal of Interactive Marketing, Elsevier, vol. 28(2), pages 117-133.
    10. Ladhari, Riadh, 2007. "The movie experience: A revised approach to determinants of satisfaction," Journal of Business Research, Elsevier, vol. 60(5), pages 454-462, May.
    11. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    12. Seraj, Mina, 2012. "We Create, We Connect, We Respect, Therefore We Are: Intellectual, Social, and Cultural Value in Online Communities," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 209-222.
    13. Arora, Ashish, 1996. "Testing for complementarities in reduced-form regressions: A note," Economics Letters, Elsevier, vol. 50(1), pages 51-55, January.
    14. Natasha Zhang Foutz & Wolfgang Jank, 2010. "Research Note—Prerelease Demand Forecasting for Motion Pictures Using Functional Shape Analysis of Virtual Stock Markets," Marketing Science, INFORMS, vol. 29(3), pages 568-579, 05-06.
    15. Sanjeev Swami & Jehoshua Eliashberg & Charles B. Weinberg, 1999. "SilverScreener: A Modeling Approach to Movie Screens Management," Marketing Science, INFORMS, vol. 18(3), pages 352-372.
    16. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    17. Barry L. Bayus, 1993. "High-Definition Television: Assessing Demand Forecasts for a Next Generation Consumer Durable," Management Science, INFORMS, vol. 39(11), pages 1319-1333, November.
    18. Marshall, Pablo & Dockendorff, Monika & Ibáñez, Soledad, 2013. "A forecasting system for movie attendance," Journal of Business Research, Elsevier, vol. 66(10), pages 1800-1806.
    19. Anderson, Eugene W & Salisbury, Linda Court, 2003. "The Formation of Market-Level Expectations and Its Covariates," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(1), pages 115-124, June.
    20. Kozinets, Robert V., 1999. "E-tribalized marketing?: the strategic implications of virtual communities of consumption," European Management Journal, Elsevier, vol. 17(3), pages 252-264, June.
    21. Zhang, Jason Q. & Craciun, Georgiana & Shin, Dongwoo, 2010. "When does electronic word-of-mouth matter? A study of consumer product reviews," Journal of Business Research, Elsevier, vol. 63(12), pages 1336-1341, December.
    22. Milgrom, Paul & Roberts, John, 1995. "Complementarities and fit strategy, structure, and organizational change in manufacturing," Journal of Accounting and Economics, Elsevier, vol. 19(2-3), pages 179-208, April.
    23. Vicki McKinney & Kanghyun Yoon & Fatemeh “Mariam” Zahedi, 2002. "The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach," Information Systems Research, INFORMS, vol. 13(3), pages 296-315, September.
    24. Amit M. Joshi & Dominique M. Hanssens, 2009. "Movie Advertising and the Stock Market Valuation of Studios: A Case of “Great Expectations?”," Marketing Science, INFORMS, vol. 28(2), pages 239-250, 03-04.
    25. Ratzmann, Martin & Gudergan, Siegfried P. & Bouncken, Ricarda, 2016. "Capturing heterogeneity and PLS-SEM prediction ability: Alliance governance and innovation," Journal of Business Research, Elsevier, vol. 69(10), pages 4593-4603.
    26. Jiménez, Fernando R. & Mendoza, Norma A., 2013. "Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 226-235.
    27. Eliashberg, Jehoshua & Hegie, Quintus & Ho, Jason & Huisman, Dennis & Miller, Steven J. & Swami, Sanjeev & Weinberg, Charles B. & Wierenga, Berend, 2009. "Demand-driven scheduling of movies in a multiplex," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 75-88.
    28. Jehoshua Eliashberg & Jedid-Jah Jonker & Mohanbir S. Sawhney & Berend Wierenga, 2000. "MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures," Marketing Science, INFORMS, vol. 19(3), pages 226-243, January.
    29. Shmueli, Galit & Ray, Soumya & Velasquez Estrada, Juan Manuel & Chatla, Suneel Babu, 2016. "The elephant in the room: Predictive performance of PLS models," Journal of Business Research, Elsevier, vol. 69(10), pages 4552-4564.
    30. Gerda Gemser & Mark Leenders & Charles Weinberg, 2012. "More effective assessment of market performance in later stages of the product development process: The case of the motion picture industry," Marketing Letters, Springer, vol. 23(4), pages 1019-1031, December.
    31. Chakravarty, Anindita & Liu, Yong & Mazumdar, Tridib, 2010. "The Differential Effects of Online Word-of-Mouth and Critics' Reviews on Pre-release Movie Evaluation," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 185-197.
    32. Shyam Gopinath & Pradeep K. Chintagunta & Sriram Venkataraman, 2013. "Blogs, Advertising, and Local-Market Movie Box Office Performance," Management Science, INFORMS, vol. 59(12), pages 2635-2654, December.
    33. Bruno Cassiman & Reinhilde Veugelers, 2006. "In Search of Complementarity in Innovation Strategy: Internal R& D and External Knowledge Acquisition," Management Science, INFORMS, vol. 52(1), pages 68-82, January.
    34. Arthur De Vany & W. Walls, 1999. "Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office?," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 23(4), pages 285-318, November.
    35. Berg, Joyce E. & Nelson, Forrest D. & Rietz, Thomas A., 2008. "Prediction market accuracy in the long run," International Journal of Forecasting, Elsevier, vol. 24(2), pages 285-300.
    36. Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, vol. 15(2), pages 113-131.
    37. Reinartz, Werner & Haenlein, Michael & Henseler, Jörg, 2009. "An empirical comparison of the efficacy of covariance-based and variance-based SEM," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 332-344.
    38. Vijay Mahajan & Eitan Muller & Subhash Sharma, 1984. "An Empirical Comparison of Awareness Forecasting Models of New Product Introduction," Marketing Science, INFORMS, vol. 3(3), pages 179-197.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yayla, Serdar & Kutlubay, Omer Cem & Cicek, Mesut & Yeniyurt, Sengun, 2023. "Once upon a time in a foreign market: The role of cultural distance in the economic performance of multilateral non-equity partnerships," International Business Review, Elsevier, vol. 32(4).
    2. Mark B. Houston & Ann-Kristin Kupfer & Thorsten Hennig-Thurau & Martin Spann, 2018. "Pre-release consumer buzz," Journal of the Academy of Marketing Science, Springer, vol. 46(2), pages 338-360, March.
    3. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    4. France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
    5. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    6. Divakaran, Pradeep Kumar Ponnamma & Xiong, Jie, 2022. "Eliciting brand association networks: A new method using online community data," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    7. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Devereux, Luke & Pizzi, Gabriele, 2021. "Tweets to escape: Intercultural differences in consumer expectations and risk behavior during the COVID-19 lockdown in three European countries," Journal of Business Research, Elsevier, vol. 130(C), pages 59-69.
    8. Muninger, Marie-Isabelle & Mahr, Dominik & Hammedi, Wafa, 2022. "Social media use: A review of innovation management practices," Journal of Business Research, Elsevier, vol. 143(C), pages 140-156.
    9. Sheng-Yen Chang & Virginia Bodolica & Huei-Hsia Hsu & Hsi-Peng Lu, 2023. "What people talk about online and what they intend to do: related perspectives from text mining and path analysis," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(4), pages 931-956, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    2. Clement, Michel & Wu, Steven & Fischer, Marc, 2014. "Empirical generalizations of demand and supply dynamics for movies," International Journal of Research in Marketing, Elsevier, vol. 31(2), pages 207-223.
    3. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    4. Divakaran, Pradeep Kumar Ponnamma & Xiong, Jie, 2022. "Eliciting brand association networks: A new method using online community data," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    6. Ponnamma Divakaran, Pradeep Kumar, 2018. "Technology-enabled community data for gaining pre-release brand insights," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 217-226.
    7. Lee, Youseok & Kim, Sang-Hoon & Cha, Kyoung Cheon, 2021. "Impact of online information on the diffusion of movies: Focusing on cultural differences," Journal of Business Research, Elsevier, vol. 130(C), pages 603-609.
    8. Jason M. T. Roos & Ron Shachar, 2014. "When Kerry Met Sally: Politics and Perceptions in the Demand for Movies," Management Science, INFORMS, vol. 60(7), pages 1617-1631, July.
    9. France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
    10. Stefan Stremersch & Jorge Gonzalez & Albert Valenti & Julian Villanueva, 2023. "The value of context-specific studies for marketing," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 50-65, January.
    11. Bae, Giwoong & Kim, Hye-jin, 2019. "The impact of movie titles on box office success," Journal of Business Research, Elsevier, vol. 103(C), pages 100-109.
    12. Angela (Xia) Liu & Tridib Mazumdar & Bo Li, 2015. "Counterfactual Decomposition of Movie Star Effects with Star Selection," Management Science, INFORMS, vol. 61(7), pages 1704-1721, July.
    13. Ana Suárez-Vázquez, 2011. "Critic power or star power? The influence of hallmarks of quality of motion pictures: an experimental approach," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 35(2), pages 119-135, May.
    14. Moez Hababou & Nawel Amrouche & Kamel Jedidi, 2016. "Measuring Economic Efficiency in the Motion Picture Industry: a Data Envelopment Analysis Approach," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(3), pages 144-158, December.
    15. Gazley, Aaron & Clark, Gemma & Sinha, Ashish, 2011. "Understanding preferences for motion pictures," Journal of Business Research, Elsevier, vol. 64(8), pages 854-861, August.
    16. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
    17. Anita Elberse & Jehoshua Eliashberg, 2003. "Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures," Marketing Science, INFORMS, vol. 22(3), pages 329-354.
    18. Allègre Hadida, 2010. "Commercial success and artistic recognition of motion picture projects," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 34(1), pages 45-80, February.
    19. Rubin, Dan & Mohr, Iris & Kumar, V., 2022. "Beyond the box office: A conceptual framework for the drivers of audience engagement," Journal of Business Research, Elsevier, vol. 151(C), pages 473-488.
    20. Katherine Goff Inglis & Saeed Zolfaghari, 2017. "A Review of Scheduling Problems and Research Opportunities in Motion Picture Exhibition," Interfaces, INFORMS, vol. 47(2), pages 175-187, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:joinma:v:38:y:2017:i:c:p:12-28. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-interactive-marketing/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.