How electronic word of mouth dynamically influences product sales and supplies: an evidence from China film industry

Figures & data

Table 1. Prior Studies on the Effect of eWOM.

Key eWOM metrics	Effects of eWOM	Existing empirical evidence	Our paper
Volume	Volume increases sales.	Godes and Mayzlin (2004); Liu (2006), Duan et al. (2008), Zhou and Duan (2016), Floyd et al. (2014), You et al. (2015), Babić et al. (2016), Hossein and Miller (2018), Lee and Choeh (2018).	√
	Volume increases supplies.	No evidence.	√
Valence	Valence increase sales.	Elberse and Eliashberg (2003), Chevalier and Mayzlin (2006), Chintagunta et al. (2010), Moon et al. (2010), Chen et al. (2011), Minnema et al. (2016), Floyd et al. (2014), You et al. (2015), Babić et al. (2016), Yoon et al. (2019).
	Valence has no significant effect to sales.	Liu (2006), Duan et al. (2008).
	Valence decrease sales.	No evidence.	√
	Valence increase supplies.	Elberse and Eliashberg (2003), Basuroy et al. (2006): Positive communication increase supplies.	User rating increase supplies.

Source: Author's computation.

Figure 1. The effect of eWOM on movie sales and movie screenings in multi-lag terms.

Source: Author's computation.

Figure 2. Difference between the Number of Rating and Reviews.

Source: Maoyan Entertainment (http://piaofang.maoyan.com).

Table 2. Description and Measurement of Key Variables.

Key variables	Description and measurement
(Daily Rating Number)it	Number of user rating for movie i at day t
(Daily Average Rating)it	Average user grade for movie i at day t
(Daily Screens)it	Daily number of screens for movie i at day t
(Daily Revenue)it	Daily revenue for movie i at day t
Wishing to Watch	Number of people who wish to watch a movie before its released
Weekendt	Dummy variable (if the day is Friday, Saturday, Sunday, the code is 1, or 0)

Source: Author's computation.

Table 3. Summary statistics of the data.

	N	Minimum	Maximum	Mean	Standard deviation
Daily rating number	1594	5.000	133741.000	4869.400	9026.591
Daily average rating (range 1–10)	1555	1.060	9.908	7.811	1.358
Daily screens	1596	22.000	123000.000	11329.376	19093.791
Daily revenue	1596	0.200	29402.100	742.517	2159.000
Wishing to Watch	76	871.000	567624.000	76261.276	130045.480

*Daily revenue (Ten thousand yuan; 1 yuan to 0.13 euro).

Source: Author's computation.

Table 4. Key Variables Correlation Matrix of Data.

	Daily Rating Number	Daily Average Rating (range 1–10)	Daily Screens	Daily Revenue	Wishing to Watch
Daily Rating Number	1	0.274**	0.840**	0.913**	0.787**
Daily Average Rating (range 1–10)	0.274**	1	0.242**	0.195**	0.387**
Daily Screens	0.840**	0.242**	1	0.814**	0.831**
Daily Revenue	0.913**	0.195**	0.814**	1	0.900**
Wishing to Watch	0.787**	0.387**	0.831**	0.900**	1

** P<.01.

Source: Author's computation.

Figure 3. Fitting of Daily Revenues. (a) Opening week; (b) Subsequent days.

Source: Author's computation.

Figure 4. Fitting of Daily Screens. (a) Opening week; (b) Subsequent days.

Source: Author's computation.

Table 5. SLS Results (Log (Daily Revenues)_it as the Dependent Variable).

Variable	Equation 1: Opening Week				Equation 2: Subsequent Days
	Coefficient	SE		p Value	Coefficient	SE	p Value
Log (Daily Rating Number${)}_{it}$	0.967	0.028		0.000	0.740	0.031	0.000
Log (Daily Rating Number)it-1	0.074	0.061		0.000	0.141	0.079	0.000
Log (Daily Rating Number)it-2	−0.014	0.027		0.000	0.086	0.036	0.000
Log (Daily Average Rating)it	−0.990	0.075		0.000	−1.036	0.067	0.000
Log (Daily Average Rating)it-1	−0.178	0.199		0.000	−0.495	0.198	0.000
Log (Daily Average Rating)it-2	0.025	0.094		0.001	−0.201	0.096	0.198
Log (Daily Screens)it	0.363	0.034		0.000	0.700	0.017	0.000
Log(Wishing to Watch)	−0.303	0.025		0.000	−0.440	0.017	0.000
Weekendt					0.080	0.019	0.000
Constant	−0.006	0.004		0.081	0.101	0.026	0.000
	R^2 = 0.767 Adjusted R^2 = 0.765				R^2 = 0.713 Adjusted R^2 = 0.711

Source: Author's computation.

Table 6. SLS Results (Log (Daily Screens)_it as the Dependent Variable).

Variable	Equation 1: Opening Week			Equation 2: Subsequent Days
	Coefficient	SE	p Value	Coefficient	SE	p Value
Log(Daily Rating Number${)}_{\mathrm{it}}$	0.263	0.026	0.000	−0.057	0.037	0.129
Log (Daily Rating Number)it-1	−0.043	0.049	0.000	−0.127	0.085	0.193
Log (Daily Rating Number)it-2	0.013	0.020	0.000	−0.114	0.038	0.275
Log (Daily Average Rating)it	1.249	0.047	0.000	1.121	0.069	0.000
Log (Daily Average Rating)it-1	0.170	0.140	0.000	0.460	0.206	0.000
Log (Daily Average Rating)it-2	−0.058	0.067	0.000	0.079	0.100	0.163
Log (Daily Revenue)it	0.188	0.017	0.000	0.757	0.018	0.000
Log(Wishing to Watch)	0.358	0.016	0.000	0.280	0.020	0.000
Weekendt				−0.230	0.019	0.000
Constant	0.002	0.003	0.393	0.247	0.026	0.000
	R^2 = 0.812 Adjusted R^2 = 0.811			R^2 = 0.742 Adjusted R^2 = 0.741

Source: Author's computation.

Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560. https://doi.org/10.1287/mksc.1040.0071

 Web of Science ®Google Scholar

Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89. https://doi.org/10.1509/jmkg.70.3.074

 Web of Science ®Google Scholar

Duan, W., Gu, B., & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242. https://doi.org/10.1016/j.jretai.2008.04.005

 Web of Science ®Google Scholar

Zhou, W., & Duan, W. (2016). Do professional reviews affect online user choices through user reviews? An empirical study. Journal of Management Information Systems, 33(1), 202–228. https://doi.org/10.1080/07421222.2016.1172460

 Web of Science ®Google Scholar

Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232. https://doi.org/10.1016/j.jretai.2014.04.004

 Web of Science ®Google Scholar

You, Y., Vadakkepatt, G. G., & Joshi, A. M. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing, 79(2), 19–39. https://doi.org/10.1509/jm.14.0169

 Web of Science ®Google Scholar

Babić, R. A., Sotgiu, F., & Valck, D. K., & Bijmolt, T. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297–318. https://doi.org/10.1509/jmr.14.0380

 Web of Science ®Google Scholar

Hossein, N., & Miller, D. W. (2018). Predicting motion picture box office performance using temporal tweet patterns. International Journal of Intelligent Computing and Cybernetics, 11(1), 64–80. https://doi.org/10.1108/IJICC-04-2017-0033

 Web of Science ®Google Scholar

Lee, S., & Choeh, J. Y. (2018). The interactive impact of online word-of-mouth and review helpfulness on box office revenue. Management Decision, 56(4), 849–866. https://doi.org/10.1108/MD-06-2017-0561

 Web of Science ®Google Scholar

Elberse, A., & Eliashberg, J. (2003). Demand and supply dynamics for sequentially released products in international markets: The case of motion pictures. Marketing Science, 22(3), 329–354. https://doi.org/10.1287/mksc.22.3.329.17740

 Web of Science ®Google Scholar

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. https://doi.org/10.1509/jmkr.43.3.345

 Web of Science ®Google Scholar

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. https://doi.org/10.1287/mksc.1100.0572

 Web of Science ®Google Scholar

Moon, S., Bergey, P. K., & Iacobucci, D. (2010). Dynamic effects among movie ratings, movie revenues, and viewer satisfaction. Journal of Marketing, 74(1), 108–121. https://doi.org/10.1509/jmkg.74.1.108

 Web of Science ®Google Scholar

Chen, Y., Wang, Q., & Xie, J. (2011). Online social interactions: A natural experiment on word of mouth versus observational learning. Journal of Marketing Research, 48(2), 238–254. https://doi.org/10.1509/jmkr.48.2.238

 Web of Science ®Google Scholar

Minnema, A., Bijmolt, T. H. A., Gensler, S., & Wiesel, T. (2016). To keep or not to keep: Effects of online customer reviews on product returns. Journal of Retailing, 92(3), 253–267. https://doi.org/10.1016/j.jretai.2016.03.001

 Web of Science ®Google Scholar

Yoon, Y., Kim, A. J., & Kim, J., & Choi, J. (2019). The effects of eWOM characteristics on consumer ratings: Evidence from TripAdvisor.com. International Journal of Advertising, 38(5), 1–20. https://doi.org/10.1080/02650487.2018.1541391

 Web of Science ®Google Scholar

Basuroy, S., Desai, K. K., & Talukdar, D. (2006). An empirical investigation of signaling in the motion picture industry. Journal of Marketing Research, 43(2), 287–295. https://doi.org/10.1509/jmkr.43.2.287

 Web of Science ®Google Scholar