nach oben

Information Systems Frontiers

Erschienen in:

15.07.2017

The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales

verfasst von: Young-Jin Lee, Kellie B. Keeling, Andrew Urbaczewski

Erschienen in: Information Systems Frontiers | Ausgabe 4/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Our objective in this paper is to assess the values of online user reviews for movies compared with the sales impact of post-release ad spending for movies. We use weekly box-office sales and ad spending data for 304 movies released in the U.S. along with online ratings and user characteristics from a social network site for movies. By exploiting the fixed-effects two-stage instrumental variable approach to account for movie heterogeneity and simultaneous relationships among user reviews, ad spending and sales, we found that improving the volume and valence of ratings can have the equivalent effect that ad spending can provide.

Vorheriger Artikel Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling

Nächster Artikel Exploring the Contingent Effect of Community Equity on Users’ Intention to Share Information

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

The expenditure only includes advertising on national broadcast and cable network television.

Flixster.com had over 20 million unique users and about 2 billion user-generated movie reviews in 2008.

We do not consider the first (or second) order lag of weekly ad spending or cumulative ad spending because its effect may still last in the current week.

L is the lag operator and this is the Koyck (1954) transformation.

AVG_NUMRA and AVG_PROFV are dropped due to their high correlations with AVG_NUMRE, 0.70 and 0.96 respectively.

We also observe statistically significant relationship of this in our sample. From the first stage estimation for (2), all the variables of social network characteristics except for AVG_NUMF affect very significantly NUMRAT. We omitted the result table but it is available upon request.

The results, omitted here for brevity, are available upon request from the authors.

Ainslie, A., Drèze, X., & Zufryden, F. (2005). Modeling Movie Life Cycles and Market Share. Marketing Science, 24(3), 508–517. doi:10.1287/mksc.1040.0106.CrossRef

Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet Recommendation Systems. Journal of Marketing Research, 37(3), 363–375. doi:10.1509/jmkr.37.3.363.18779.CrossRef

Auletta, K. (1992). Three Blind Mice: How the TV Networks Lost Their Way. New York: The Easton Press.

Baltagi, B. H. (2006). Estimating an economic model of crime using panel data from north carolina. Journal of Applied Econometrics, 21, 543–547. doi:10.1002/jae.861.CrossRef

Bass, F. M., & Clarke, D. G. (1972). Testing Distributed Lag Models of Advertising Effect. Journal of Marketing, 9(3), 298–308. doi:10.2307/3149541.CrossRef

Breusch, T. S., & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47, 1287–1294. doi:10.2307/1911963.CrossRef

Bruce, N. I., Foutz, N. Z., & Kolsarici, C. (2012). Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products. Journal of Marketing Research. doi:10.1509/jmr.07.0441.

Centeno, R., Hermoso, R., & Fasli, M. (2015). On the inaccuracy of numerical ratings: dealing with biased opinions in social networks. Information Systems Frontiers, 17(4), 809–825. doi:10.1007/s10796-014-9526-1.CrossRef

Charband, Y., & Jafari Navimipour, N. (2016). Online knowledge sharing mechanisms: a systematic review of the state of the art literature and recommendations for future research. Information Systems Frontiers, 18(6), 1131–1151. doi:10.1007/s10796-016-9628-z.CrossRef

Chen, P.-Y., Wu, S., & Yoon, J. (2004). The Impact of Online Recommendations and Consumer Feedback on Sales. In Twenty-Fifth International Conference on Information Systems (pp. 711–723).

Chevalier, J. A., & Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43(3), 345–354. doi:10.1509/jmkr.43.3.345.CrossRef

Chintagunta, P. K., Erdem, T., Rossi, P. E., & Wedel, M. (2006). Structural Modeling in Marketing: Review and Assessment. Marketing Science, 25(6), 604–616. doi:10.1287/mksc.1050.0161.CrossRef

Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets. Marketing Science, 29(5), 944–957. doi:10.1287/mksc.1100.0572.CrossRef

Clark, C. R., Doraszelski, U., & Draganska, M. (2009). The effect of advertising on brand awareness and perceived quality: An empirical investigation using panel data. QME, 7(2), 207–236. doi:10.1007/s11129-009-9066-z.CrossRef

Cornwell, C., & Trumbull, W. N. (1994). Estimating the Economic Model of Crime with Panel Data. The Review of Economics and Statistics, 76, 360–366. doi:10.2307/2109893.CrossRef

Davidson, J. (1995). Estimation and Inference in Econometrics. Economica, 62, 133–134. doi:10.2307/2290953.CrossRef

Dellarocas, C., Gao, G., & Narayan, R. (2010). Are Consumers More Likely to Contribute Online Reviews for Hit or Niche Products? Journal of Management Information Systems, 27(2), 127–158. doi:10.2753/MIS0742-1222270204.CrossRef

Dellarocas, C., Zhang, X., Michael, & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45. doi:10.1002/dir.20087.CrossRef

Duan, W., Gu, B., & Whinston, A. (2009). Informational cascades and software adoption on the Internet: an empirical investigation. MIS Quarterly, 33, 23–48.CrossRef

Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? - An empirical investigation of panel data. Decision Support Systems, 45, 1007–1016. doi:10.1016/j.dss.2008.04.001.CrossRef

Elberse, A., & Anand, B. (2007). The effectiveness of pre-release advertising for motion pictures: An empirical investigation using a simulated market. Information Economics and Policy, 19(3–4), 319–343. doi:10.1016/j.infoecopol.2007.06.003.CrossRef

Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets. Information Systems Research, 19(3), 291–313. doi:10.1287/isre.1080.0193.CrossRef

Godes, D., & Mayzlin, D. (2004). Using Online Conversations to Study Word-of-Mouth Communication. Marketing Science, 23(4), 545–560. doi:10.1287/mksc.1040.0071.CrossRef

Godes, D., & Silva, J. C. (2012). Sequential and Temporal Dynamics of Online Opinion. Marketing Science, 31(3), 448–473. doi:10.1287/mksc.1110.0653.CrossRef

Gopinath, S., Thomas, J. S., & Krishnamurthi, L. (2014). Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance. Marketing Science, 33(2), 241–258. doi:10.1287/mksc.2013.0820.CrossRef

Greene, W. H. (2003). Econometric Analysis. Journal of the American Statistical Association, 97. doi:10.1198/jasa.2002.s458.

Hausman, J. A. (1978). Specification Test in Econometrics. Econometrica, 46, 1251–1271.CrossRef

Hayashi. (2000). Econometrics. Princeton: Princeton University Press.

Hoffman, D. L. (2000). The Revolution Will Not Be Televised: Introduction to the Special Issue on Marketing Science and the Internet. Marketing Science, 19(1), 1–3. doi:10.1287/mksc.19.1.1.15181.CrossRef

Jacobson, R. (1990). Unobservable Effects and Business Performance. Marketing Science, 9(1), 74–85. doi:10.1287/mksc.9.1.74.CrossRef

Kmenta, J. (2010). Mostly Harmless Econometrics: An Empiricist’s Companion. Business Economics, 45(1), 75–76. doi:10.1057/be.2009.37.CrossRef

Koyck, L. M. (1954). Distributed lags and investment analysis. The Economic Journal, 65(259), 523–525.

Lee, Y.-J., Hosanagar, K., & Tan, Y. (2015). Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings. Management Science, 61(9), 2241–2258. doi:10.1287/mnsc.2014.2082.CrossRef

Lee, Y.-J., & Tan, Y. (2013). Effects of Different Types of Free Trials and Ratings in Sampling of Consumer Software: An Empirical Study. Journal of Management Information Systems, 30(3), 213–246. doi:10.2753/MIS0742-1222300308.CrossRef

Li, S. S., & Karahanna, E. (2015). Online Recommendation Systems in a B2C E-Commerce Context : A Review and Future Directions. Journal of the Association for Information Systems, 16(2), 72–107.CrossRef

Li, X., & Hitt, L. M. (2008). Self-Selection and Information Role of Online Product Reviews. Information Systems Research, 19(4), 456–474. doi:10.1287/isre.1070.0154.CrossRef

Li, X., Hitt, L. M., & Zhang, Z. J. (2011). Product Reviews and Competition in Markets for Repeat Purchase Products. Journal of Management Information Systems. doi:10.2753/MIS0742-1222270401.

Liu, Y. (2006). Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of Marketing, 70(3), 74–89. doi:10.1509/jmkg.70.3.74.CrossRef

Luo, X., Raithel, S., & Wiles, M. a. (2013). The Impact of Brand Rating Dispersion on Firm Value. Journal of Marketing Research, 50, 399–415. doi:10.1509/jmr.12.0188.CrossRef

Luo, X., & Zhang, J. (2013). How Do Consumer Buzz and Traffic in Social Media Marketing Predict the Value of the Firm? Journal of Management Information Systems, 30, 213–238. doi:10.2753/MIS0742-1222300208.CrossRef

Ma, X., Khansa, L., Deng, Y., & Kim, S. S. (2014). Impact of Prior Reviews on the Subsequent Review Process in Reputation Systems. Journal of Management Information Systems, 30(3), 279–310.CrossRef

Maeyer, P. D. (2012). Impact of online consumer reviews on sales and price strategies: a review and directions for future research. Journal of Product & Brand Management, 21(2), 132–139. doi:10.1108/10610421211215599.CrossRef

Moe, W. W., & Schweidel, D. a. (2012). Online Product Opinions: Incidence, Evaluation, and Evolution. Marketing Science, 31(3), 372–386. doi:10.1287/mksc.1110.0662.CrossRef

Moe, W. W., & Trusov, M. (2011). The Value of Social Dynamics in Online Product Ratings Forums. Journal of Marketing Research, 48(June), 444–456. doi:10.1509/jmkr.48.3.444.CrossRef

Murthi, B. P. S., & Sarkar, S. (2003). The Role of the Management Sciences in Research on Personalization. Management Science, 49(10), 1344–1362. doi:10.1287/mnsc.49.10.1344.17313.CrossRef

Nelson, C. R., & Startz, R. (1990). The Distribution of the Instrumental Variables Estimator and Its $t$-Ratio When the Instrument is a Poor One. The Journal of Business, 63(S1), S125. doi:10.1086/296497.CrossRef

Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical Analysis of the Impact of Recommender Systems on Sales. Journal of Management Information Systems. doi:10.2753/MIS0742-1222270205.

Rennhoff, A. D., & Wilbur, K. C. (2011). The effectiveness of post-release movie advertising. International Journal of Advertising, 30(2), 305. doi:10.2501/IJA-30-2-305-328.CrossRef

Stephen, A. T., & Galak, J. (2012). The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace. Journal of Marketing Research, 49(5), 624–639. doi:10.1509/jmr.09.0401.CrossRef

Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site. Journal of Marketing, 73(5), 90–102. doi:10.1509/jmkg.73.5.90.CrossRef

Wei, C., Khoury, R., & Fong, S. (2013). Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm. Information Systems Frontiers, 15(4), 533–551. doi:10.1007/s10796-012-9377-6.CrossRef

Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data. booksgooglecom (Vol. 58). doi:10.1515/humr.2003.021.

Wunsch-Vincent, S., & Vickery, G. (2007). Participative Web and User-Created Content. SourceOECD Science & Information Technology (Vol. 2007). OECD Publishing. doi:10.1787/9789264037472-en.

Xiong, G., & Bharadwaj, S. (2014). Prerelease Buzz Evolution Patterns and New Product Performance. Marketing Science, 33(3), 401–421. doi:10.1287/mksc.2013.0828.CrossRef

Ying, Y., Feinberg, F., & Wedel, M. (2006). Leveraging Missing Ratings to Improve Online Recommendation Systems. Journal of Marketing Research, XLIII(August), 355–365. doi:10.1509/jmkr.43.3.355.

Zhang, J., & Piramuthu, S. (2016). Product recommendation with latent review topics. Information Systems Frontiers, 1–9. doi:10.1007/s10796-016-9697-z.

Titel: The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales
verfasst von: Young-Jin Lee
Kellie B. Keeling
Andrew Urbaczewski
Publikationsdatum: 15.07.2017
Verlag: Springer US
Erschienen in: Information Systems Frontiers / Ausgabe 4/2019
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-017-9778-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2019

Erratum to: Online knowledge sharing mechanisms: A systematic review of the state of the art literature and recommendations for future research

Exploring the Contingent Effect of Community Equity on Users’ Intention to Share Information

Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis

Lack of Communication and Collaboration in Enterprise Architecture Development

Differential cloud adoption: A comparative case study of large enterprises and SMEs in Germany

Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users

Premium Partner