The Effect of Online Customer Reviews on Purchasing Decisions: the Case of Video Games

David Bounie, ENST

Marc Bourreau, ENST and CREST

Michel Gensollen, ENST

Patrick Waelbroeck, ECARES and FNRS

Preliminary draft

June 2005

Abstract

Keywords: Customer reviews, Word-of-mouth, Internet, Experience goods

JEL: L82, L86

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1 Introduction The value of complex information goods is hard to assess because it is only possible to

value them after either trying them or understanding its content. In other words, many

information and cultural goods such as books, CD, DVD, games are experience goods

that a consumer needs to "taste" before assessing its quality and its location with respect

to his or her ideal product (horizontal differentiation). Prior to the internet era, consumers

acquired information on experience goods from mainly two channels: from the overall

mass media system (TV, radio, newspapers, etc.) and retail network through

advertisements, critics of experts and free samples in stores on the one hand and from

word-of-mouth (WOM) resulting from discussions with friends and family on the other

hand.

Today, online customer reviews constitute new channels of information acquisition.

Firms such as Amazon, Barnes and Nobles, etc., offer consumers the possibility to read

and/or write positive or negative reviews on goods and to obtain and/or provide

information and advices. Other consumers may then access detailed information on the

quality of an experience good and sometimes may even acquire information on the

reviewer’s preferences through a direct link to his webpage hosted by the provider of the

service. For instance, Amazon.com’s top reviewers have a “web page” where consumers

can consult the archives of the written reviews and assess their favorite readings, movies,

etc. Consumers can then compare their tastes with those of reviewers and thus obtain

information on the horizontal and vertical dimensions of experience goods that they will

most likely enjoy.

However, if online customer reviews make purchase decisions easier for consumers, their

impact remain untested. Do online customers' reviews really influence purchase

decisions? Do online customer reviews constitute a better channel of information than

offline magazine press, online general site of information, etc. for potential buyers?

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Answering these questions is important for business firms and managers. First of all,

major Internet actors have decided to rely heavily on consumers’ online WOM in order to

build brand loyalty. Jeff Bezos, CEO of Amazon.com, considers for instance that it is a

winning marketing tool even better than any TV advertising. Secondly, online WOM can

be exploited by firms for revenue forecasting and planning. Dellarocas et al. (2004)

provide for instance evidence that online movie reviews constitute a measurable proxy

for WOM that can be exploited to forecast studio revenues. However, if online customer

reviews become a strategic investment in business strategies, its efficiency remains

controversial. First, the functioning of such a system relies on voluntary contributions.

Now, the net benefit from these contributions based either on immediate or delayed

rewards are weak for participants compared for instance to “career concern” incentives in

open source communities (Lerner and Tirole, 2002). Some fees or other incentives can

reveal to be necessary to encourage WOM referral (Biyalogorsky and al. 2001, Miller

and al. 2004). Second, online customer reviews are public good and, therefore, benefit for

all consumers and firms in competition (Chevalier and Mayzlin 2003). Third, research in

consumer behaviour indicates that negative WOM can have a harmful effect on the

adoption and diffusion of new products (Arndt 1967, Mizerski 1982, Moldovan and

Goldenberg 2004, Goldenber and al. 2004). Bezos elaborates on this point: “(...) when we

first started letting customers review books, some publishers were startled by this,

because, of course, customers give both positive and negative reviews. I got letters from

publishers in the early days, some quite hostile, saying, "Don't you understand your own

business? You make money when you sell books. Why would you allow negative

reviews?”. Four, consumers might doubt about the sincerity and the origin of reviews

insofar as firms can anonymously post reviews (White 1999, Mayzlin 2003, Harmon

2004) .

Because the efficiency of online customer reviews remains untested and controversial,

the paper tries to assess, first, the impact of online customer reviews on purchasing

decisions and, second, the influence of online customer reviews compared to other

channels of information such as offline press and WOM. We use data at the individual

level and analyze the electronic gaming industry where online customer reviews play a

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major role. This industry is a good sector to focus on for several reasons. First, games are

complex experience goods. Hints and advices provided by experienced gamers in online

customer reviews can be a critical element of the purchase decisions. Secondly, prices of

such experience goods are on average higher than CD or DVD, which can incite

consumers to look for information from different sources (including online and offline

channels) in order to avoid a bad choice. Lastly, the price argument is all the more

significant as the majority of the gamers are young with more limited incomes than the

other categories of population; a bad purchase is thus very penalizing.

We administered an online anonymous survey from June 18 to July 5, 2004 near the

gamer population on one of the leading website of videogames in France, Jeuxvideo.com.

Using 7,024 answers to the questionnaire, we show that online information gathered by

consumers before purchasing video games always positively influence purchasing

decisions. This positive effect is also verified for offline information sources (read

specialized magazine, test trial version). However, we find that face-to-face WOM has

significant and negative impact on video game purchases.

The influence of WOM on consumer behaviour constitutes a broader literature (see

Godes and Mayzlin (2004) for a general survey). Apart from the theoretical works of

Banerjee (1992, 1993), Bikhchandani and al. (1991) and Mayzlin (2001), several

empirical contributions try to assess the influence on WOM on sales or product adoption

and diffusion (Katz and Lazarsfeld (1955), Bass (1969), etc.). Among them, the closest

paper to our research is that of Chevalier and Mayzlin’s (2003). The authors examine the

relationship between a book’s customer reviews and its market share across

BarnesandNoble.com and Amazon.com. Using publicly available data on the two online

booksellers (2,394 observations), the author finds that “the relative market share of a

book across the two sites is related to differences across the sites in the number of

reviews for the book and in differences across the sites in the average star ranking of the

reviews”. The authors then conclude that customer WOM has a causal impact on

consumers' purchasing behavior at the two Internet retail sites. However, studies that use

books as the unit of analysis suffer from several shortcomings. First aggregate ranks are

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poor measures of sales of infrequently purchased books. Secondly, many books do not

receive any comments and this creates truncation issues. Thirdly and most importantly,

there is a fundamental endogeneity issues related to the fact that books with the highest

sales ranks also have the largest number of comments, simply because more people have

read them and can write a comment about them. Finding valid instruments to correct this

issue is an arduous task. Finally, comparing sales differences at two different online

stores only identify the effect of customers review on sales to the extent that consumers'

tastes at both locations are the same, which is a difficult assumption to test.

The reminder of the paper is organized as following. We describe the data in Section 2,.

In Section 3, we comment on our estimation results. We conclude in section 4.

2 Data We administered an anonymous online survey from June 18 to July 5, 2004 near gamers

visiting one of the leading website of video games in France: “jeuxvideo.com”.

Jeuxvideo.com’s experts regularly test new game releases on PC and consoles

(PlayStation, GameCube, Xbox, etc.). Each test is based on five criteria which are

graphics, playability, lifespan, sound and scenario. A global note on a scale from 0 to 20

is then given to the game. This note is the arithmetic mean of the notes given to each

criterion. An editorial review accompanies the final note. Following the editorial review,

gamers are then invited to give their opinion on the game by posting a comment, edited

on the website under the heading “Opinion of the readers”. The reader leaves his pseudo,

his email, his age and the place where he lives. The site gives him the opportunity to give

a note ranging from 0 to 20 and to write a review.

The purpose of the survey is to test the efficiency of such a review system. Overall, 9,503

persons answered to the questionnaire. We focus on students who represent a

homogenous group who represents more than 74% of the sample. 7024 students

answered the most relevant questions and this is the sample that we use in the

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econometric analysis and that we comment on in the following sections. Summary

statistics are provided in the Appendix.

Across the whole of the sample, 43% of the respondents report to buy between 1 and 5

games per year, 36 % between 6 and 10 games per year and 20% more than 11 games per

year. Many respondents purchased alone their games (55%) even if gamers share

“sometimes” (38%) or “very often” (7%) their purchase of videogames.

2.1 Demographics

The respondents are mainly men (93%) and living in urban areas. Respondents are

mainly living in urban cities larger than 10.000 inhabitants (60% against 40% living in

cities with less than 10.000 inhabitants). Students are mostly between 16 and 17. Figure 1

gives a histogram of the age distribution in the population.

Figure 1. Age distribution

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10 15 20 25age

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2.2 Getting video games

Surprisingly, 78% of the respondents usually purchase videogames in traditional stores.

Only 13% of the people use the Internet to legally obtain videogames either from online

websites (9%) or from second-hand stores (4%). Surprisingly too, few respondents report

to obtain illegal copies of videogames either from peer-to-peer networks (4%) or from

friends by the means of pirated versions (3%).

2.3 Playing video games

The respondents play simultaneously on several platforms: the majority (60%) play on 2

or 3 platforms, 25% play on 4 to 6 platforms and finally, 15% of the whole sample play

only on 1 platform.

The PC is by far the most widely used platform (85%), followed by consoles with first

the Play Station 2 (55%), second the GameCube (45%) and last the Xbox (21%). Finally,

48% of the respondents play also on portable consoles.

2.4 Information before purchase

2.4.1 General information sources

Before purchasing a video game, people try to obtain information by consulting

specialized magazine where they can find critics of experts on new releases (61%). The

second source of information privileged by respondents is word-of-mouth from friends

who have already played the games (60%). Also, people like to test themselves the video

games by the way of demo versions (48%). Finally, we note that few persons prefer to

obtain information from general internet sites (8%) or from unauthorized copies (6%).

Table 1 tabulates information sources and the number of video games purchased per year.

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Table 1. Information before purchase

Video purchase

0 1-5 6-10 >10 Total

Specialize magazines 19 1,619 1,666 989 4,293

0.44 37.71 38.81 23.04 100

24.68 53.98 65.38 70.64 61.12

Specialized websites 60 2,762 2,380 1,307 6,509

0.92 42.43 36.56 20.08 100

77.92 92.1 93.41 93.36 92.67

General website 6 251 208 110 575

1.04 43.65 36.17 19.13 100

7.79 8.37 8.16 7.86 8.19

Demo 40 1,433 1,187 680 3,340

1.2 42.9 35.54 20.36 100

51.95 47.78 46.59 48.57 47.55

Word-of-mouth 48 1,875 1,539 774 4,236

1.13 44.26 36.33 18.27 100

62.34 62.52 60.4 55.29 60.31

Physical exchange 8 178 149 105 440

1.82 40.45 33.86 23.86 100

10.39 5.94 5.85 7.5 6.26

2.4.2 Online forums

To test the effect of customers' reviews, we constructed variables that characterizes

consumers who read information from online forums and who read often or very often

customers' reviews. Many students regularly visit online websites or forums. 34% of the

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respondents claim to read “often” customer’s reviews and 50% “always” against 16%

who claim they “sometimes” or “never” consult online forums before purchasing new

video games. Table2 shows that students who often consult forums before going to the

store purchase proportionally more.

Table 2. Consult video games forums before purchase

Video purchase

Consult forums 0 1-5 6-10 >10 Total

Never 16 109 66 30 221

7.24 49.32 29.86 13.57 100

20.78 3.63 2.59 2.14 3.15

Sometimes 15 420 317 173 925

1.62 45.41 34.27 18.7 100

19.48 14 12.44 12.36 13.17

Often 15 999 903 460 2,377

0.63 42.03 37.99 19.35 100

19.48 33.31 35.44 32.86 33.84

Always 31 1,471 1,262 737 3,501

0.89 42.02 36.05 21.05 100

40.26 49.05 49.53 52.64 49.84

Total 77 2,999 2,548 1,400 7,024

1.1 42.7 36.28 19.93 100

The loyalty to the website is important since 67% of the respondents have visited the

jeuxvideo.com website for more than two years and 23% have visited the website for

more than a year (but less than two years). Experienced JV users purchase on average

more than new JV users (Table 3).

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Table 3. JV experience

Video purchase

JV experience 0 1-5 6-10 >10 Total

<6 months 8 132 86 45 271

2.95 48.71 31.73 16.61 100

10.39 4.4 3.38 3.21 3.86

6-12 months 6 206 152 77 441

1.36 46.71 34.47 17.46 100

7.79 6.87 5.97 5.5 6.28

1-2 years 10 685 643 275 1,613

0.62 42.47 39.86 17.05 100

12.99 22.84 25.24 19.64 22.96

> 2 years 53 1,976 1,667 1,003 4,699

1.13 42.05 35.48 21.34 100

68.83 65.89 65.42 71.64 66.9

Total 77 2,999 2,548 1,400 7,024

1.1 42.7 36.28 19.93 100

We also asked questions about information on JV forums. Table 4 reveals that 54% of the

students consult these forums to obtain advices different from the editor' review, while

22% read posts to get information about the game.

Table 4. Read JV forum posts

Video purchase

0 1-5 6-10 >10 Total

Get advice 35 695 463 232 1,425

10

2.46 48.77 32.49 16.28 100

45.45 23.17 18.17 16.57 20.29

Get information 25 1,392 1,139 581 3,137

0.8 44.37 36.31 18.52 100

32.47 46.42 44.7 41.5 44.66

2.5 Community

Asked whether they play to online game network, 23% of the respondents answered to

play them daily, 23% once a week, 16% once a month and 15% once a year. Overall,

77% of the people play online network games. Students who play daily purchase

proportionally more video games (Table 5).

Table 5. Network games

Video purchase

Play network games 0 1-5 6-10 >10 Total

Never 19 744 571 257 1,591

1.19 46.76 35.89 16.15 100

24.68 24.81 22.41 18.36 22.65

Once a year 10 512 363 168 1,053

0.95 48.62 34.47 15.95 100

12.99 17.07 14.25 12 14.99

Once a month 8 500 424 210 1,142

0.7 43.78 37.13 18.39 100

10.39 16.67 16.64 15 16.26

Once a week 16 655 655 317 1,643

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0.97 39.87 39.87 19.29 100

20.78 21.84 25.71 22.64 23.39

Daily 24 588 535 448 1,595

1.5 36.87 33.54 28.09 100

31.17 19.61 21 32 22.71

Total 77 2,999 2,548 1,400 7,024

1.1 42.7 36.28 19.93 100

We can also measure the existence of community participation by answers to the

following questions. 24% of the respondents leave their email address, while 14%

participate to online discussion forums (Table 6).

Table 6. Online community participation

Video purchase 0 1-5 6-10 >10 Total Leave email address 14 597 628 425 1,664 0.84 35.88 37.74 25.54 100

18.18 19.91 24.65 30.36 23.69

Forum participation 7 275 368 347 997 0.7 27.58 36.91 34.8 100

9.09 9.17 14.44 24.79 14.19

3 Estimation method and results

We determine the net contributions of information sources, especially those related to

online communities, on video game purchases. We estimate the following equation:

yi = xi′ β + wi′ α + εi (∗)

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where yi is the number of video games purchased per year for individual i (= 1, ..., n), xi

are individual control variables (sex, age, city size, internet connection type, and income

related variables), wi are information sources (word of mouth, specialized magazines,

internet forums, JV posts) and εi represents unobserved individual characteristics that we

assume uncorrelated with the other variables in the equation. Parameters α and β need to

be estimated.

We estimated equation (∗) by ordinary least squares in section 3.1.1

3.1 Estimation results by ordinary least squares

Estimation results are given in Table 7. The dependent variable is the number of games

purchased per year. We focus on the effect of information sources. Column (1) reports

results on students who are 12 and 15, column (2) corresponds to students between 16

an19 and column (3) corresponds to students who are more than 19.

Table 7. Estimation results

(1) (2) (3) Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Female -0.173 0.44 -0.140 0.32 -0.533 0.30 ∗ City size 10-100 0.124 0.24 0.041 0.18 0.007 0.22 ∗∗ City size >100 0.142 0.27 0.226 0.20 -0.475 0.23 Age1 0.161 0.45 0.146 0.17 0.039 0.24 Age2 0.076 0.43 0.077 0.19 0.737 0.27 ∗∗∗ Age3 -0.262 0.42 0.390 0.31 Age4 -0.234 0.33

1 To apply ordinary least squares, we have constructed a continuous dependent variable that is equal to the mean of the category. If the respondent purchases 6-10 video games per year, the variable is set to 8. It was set to 15 to for the category "≥11" and to 3 for the category "1-5". Estimation by OLS does not require distributional assumption for the unobservable variable. We also estimate the model by applying an ordered probit model. The ordered probit model takes account of the fact that the dependent variable is discrete but assumes normality. Results were qualitatively similar and are available on request.

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Age5 0.901 0.43 ∗∗ Age6 -0.166 0.56 High speed home 0.082 0.30 -0.080 0.23 -0.199 0.28 High wpeed work -0.123 0.22 -0.010 0.20 0.348 0.32 Firewall 0.343 0.23 -0.197 0.15 0.108 0.19 Connect friends 0.256 0.23 -0.175 0.17 0.011 0.19 Connect library 0.212 0.44 0.106 0.37 -0.604 0.39 Connect cybercafe 0.965 0.38 ∗∗∗ 0.835 0.28 ∗∗∗ 0.438 0.36 Download p2p 0.084 0.21 -0.003 0.16 -0.176 0.19 Email address 0.421 0.27 0.378 0.19 ∗∗ 0.201 0.21 Group purchase stimes 0.384 0.22 ∗ 0.141 0.16 0.183 0.19 Group purchase often 1.257 0.36 ∗∗∗ 0.937 0.30 ∗∗∗ 1.041 0.40 ∗∗∗ Get from P2P -1.268 0.70 ∗ -0.709 0.41 ∗ -1.895 0.43 ∗∗∗ Purchase second-hand -0.009 0.50 0.937 0.41 ∗∗ 1.093 0.41 ∗∗∗ Purchase friends 0.251 0.64 -1.463 0.39 ∗ -0.901 0.49 ∗ Purchase other 0.084 0.62 -0.449 0.56 -0.191 0.69 Purchase online -0.530 0.35 -0.068 0.27 -0.206 0.31 Info magazine 0.850 0.22 ∗∗∗ 0.806 0.16 ∗∗∗ 0.649 0.19 ∗∗∗ Info specializ. websites 0.015 0.33 0.530 0.35 0.262 0.39 Info general website -0.010 0.35 -0.285 0.28 -0.001 0.35 Info demo -0.034 0.21 -0.026 0.16 -0.008 0.18 Info friends -0.469 0.22 -0.692 0.16 ∗∗∗ -0.858 0.18 ∗∗∗ Info exhange 0.072 0.41 0.322 0.31 0.243 0.40 Consult forum stimes 0.950 0.61 0.580 0.49 0.870 0.57 Consult forum often 0.746 0.59 0.828 0.46 ∗ 0.921 0.55 ∗ Consult forum always 0.876 0.58 0.580 0.46 1.109 0.54 ∗∗ Play portable console 0.159 0.22 0.794 0.17 ∗∗∗ 1.067 0.20 ∗∗∗ Play pc 0.138 0.28 -0.058 0.23 -0.444 0.27 ∗ Play ps2 1.182 0.21 ∗∗∗ 1.000 0.15 ∗∗∗ 0.861 0.18 ∗∗∗ Play xbox 2.156 0.25 ∗∗∗ 1.682 0.18 ∗∗∗ 1.489 0.22 ∗∗∗ Play gamecube 1.391 0.22 ∗∗∗ 1.447 0.16 ∗∗∗ 1.294 0.19 ∗∗∗ Play other console 0.174 0.22 0.774 0.17 ∗∗∗ 0.819 0.20 ∗∗∗ Play netw. game 1/year -0.629 0.35 ∗ 0.050 0.26 0.215 0.28 Play net. game 1/month 0.044 0.35 0.642 0.26 ∗∗ -0.017 0.29 Play netw. game 1/week 0.239 0.31 0.407 0.24 ∗ 0.428 0.28 Play netw. game daily 0.465 0.31 1.301 0.25 ∗∗∗ 1.346 0.31 ∗ Forum participation 1.126 0.29 ∗∗∗ 1.386 0.23 ∗∗∗ 1.775 0.28 ∗∗∗ JV xp <6 month -0.087 0.43 -0.114 0.46 0.154 0.55 JV xp b/w 6 and 12 -0.167 0.37 -0.304 0.33 -0.357 0.42 JV xp 1-2 year -0.634 0.24 ∗∗∗ -0.186 0.18 0.206 0.23 Read posts opinion 0.261 0.26 0.170 0.19 0.578 0.22 ∗∗∗ Read posts info 0.393 0.28 0.055 0.23 0.329 0.29 Constant 3.563 0.78 ∗∗∗ 2.837 0.62 3.116 0.74

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R2 0.15 0.18 0.22 Number of obs. 1840 3019 2165

As expected, we find that offline information sources (read specialized magazine, test

trial version) have a significant positive effect on video game purchases. Face-to-face

WOM has a significant and negative impact on purchases. We interpret this result as

follows. Consider a negative WOM first. In this case, the negative comment reduces the

probability that the consumer will eventually purchase the game (negative effect). Now

for a positive WOM, the consumer can try the game for himself and this effect would be

capture by the other information variables (test trial version with no effect of WOM) or

ask the friend to lend him the game (negative effect). Overall the effect of face-to-face

communication is negative on purchases. Although these effects also characterize online

WOM such as customers' reviews, internet users cannot physically exchange video

games that are technologically protected.

The effect of information sources related to online community is always positive. People

who often consult internet sites and forums prior to making a purchase have a higher

propensity to purchase video games. Indeed, people who always consult internet forums

do so mainly to read customer reviews and this leads them make more informed

purchases. Students who read forum posts to have an opinion different from the site

reviewer tend to purchase more games, although this effect is only significant for students

between 19 and 25.

Community variables also have a positive effect on video games purchases. Leaving

email address on online forums increases purchases as well as actively participating in

online forums. In addition, students who often play online network games have a higher

propensity to purchase games.

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4 Conclusion

We have analyzed the respective influence of different sources of information on

purchasing decisions. We directly control for the taste for video games in our regressions

and by this way, we limit biases due omitted variable biases that are frequent in other

studies. We find that offline information sources (specialized magazine, trial version) and

online information have a significant positive effect on video game purchases.

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6 Appendix

12-15 16-18 19-25 Female 0.06 0.06 0.09 City size 10-100 0.32 0.35 0.36 City size >100 0.21 0.24 0.33 Age1 0.18 0.36 0.24 Age2 0.30 0.27 0.17 Age3 0.44 0.12 Age4 0.09 Age5 0.05 Age6 0.03 High speed home 0.85 0.86 0.88 High wpeed work 0.29 0.17 0.09 Firewall 0.31 0.51 0.63 Connect friends 0.45 0.46 0.43 Connect library 0.06 0.05 0.06 Connect cybercafe 0.10 0.09 0.07 Download p2p 0.60 0.67 0.67 Email address 0.21 0.24 0.25 Group purchase stimes 0.46 0.38 0.32 Group purchase often 0.10 0.07 0.05 Get from P2P 0.02 0.04 0.05 Purchase second-hand 0.04 0.04 0.05 Purchase friends 0.03 0.04 0.04 Purchase other 0.03 0.02 0.02 Purchase online 0.10 0.09 0.09 Info magazine 0.62 0.62 0.59 Info specializ. websites 0.87 0.95 0.94 Info general website 0.10 0.08 0.07 Info demo 0.48 0.48 0.46 Info friends 0.65 0.62 0.54 Info exhange 0.07 0.06 0.05 Consult forum stimes 0.15 0.12 0.13 Consult forum often 0.34 0.34 0.33 Consult forum always 0.48 0.50 0.51 Play portable console 0.58 0.48 0.41 Play pc 0.83 0.86 0.86 Play ps2 0.53 0.55 0.56 Play xbox 0.20 0.22 0.22 Play gamecube 0.45 0.45 0.46 Play other console 0.34 0.35 0.37 Play netw. game 1/year 0.13 0.14 0.18 Play netw. game 1/month

0.15 0.16 0.18

19

Play netw. game 1/week 0.23 0.25 0.22 Play netw. game daily 0.26 0.24 0.18 Forum participation 0.17 0.14 0.12 JV xp <6 month 0.07 0.03 0.03 JV xp b/w 6 and 12 0.09 0.06 0.05 JV xp 1-2 year 0.27 0.23 0.20 Read posts opinion 0.45 0.56 0.60 Read posts info 0.30 0.21 0.17