Is Online Word of Mouth a Complement or Substitute to Traditional Means of Consumer Conversion?

Transcription

1 Is Online Word of Mouth a Complement or Substitute to Traditional Means of Consumer Conversion? Neveen Farag Awad Wayne State University nawad@wayne.edu Chrysanthos Dellarocas University of Maryland Xiaoquan (Michael) Zhang MIT Sloan School of Management Word of mouth is an age old concept. Through the proliferation of online chat rooms, online review sites, and online ratings, information technology (IT) has vastly magnified the potential impact of this ancient social force. A person s opinion no longer impacts just their friends and neighbors; through the use of the Internet, people can now share their opinions and experiences with the world. A recent Forrester study, for example, concluded that approximately 50% of young Net surfers rely on online recommendations to purchase CDs, movies, Videos/DVDs, and games (Forrester Research, 2000). To effectively respond to this new reality, firms need to better understand the emerging relationship between online word of mouth and traditional customer conversion mechanisms, such as offline word of mouth and advertising. A number of important, and currently unanswered, research questions thus emerge: Is participation in online word of mouth a complement or a substitute to participation in traditional word of mouth? To what degree does online word of mouth represent offline word of mouth opinions? Does online word of mouth act as a complement or as a substitute to more traditional sources of information? The answers to these questions can help firms adapt their marketing strategies to respond to the emergence of online forums. They can also be used to guide the design of more effective online forums. To shed some light on these questions we conducted a survey where a nationally representative sample of subjects was asked about the online and offline factors involved in their movie consumption decisions, and was also asked to rate a number of recent movies. We also collected actual online ratings data, and the corresponding demographic information of the online raters. The survey responses were then analyzed, and compared to online ratings for the same movies. These two datasets allowed us to operationalize our research questions into the following, more specific questions: 1. What is the relationship between traditional advertising (such as television advertising) and participation in online as well as offline word of mouth? 2. What is the relationship between participation in offline (online) word of mouth and online (offline) word of mouth? 3. What is the relationship between online word of mouth and infomediary (professional critic) recommendations? How is that relationship evolving over time? Methodology An initial structured questionnaire was developed based on a review of the existing literature. The survey asked respondents to rate 25 recently released movies on the same ten-point scale used by an online 1

2 movie rating site; it also asked a series of questions regarding Internet usage, frequency of use of online forums, and demographic information, including age, gender, and geographic location. The survey was executed during the summer of The survey was developed and pre-tested for content, flow, scope, and purpose on a group of undergraduate business students. The respondents were asked to comment on questions, to demarcate ambiguities, and to introduce any factors that may have been omitted. The questionnaire was refined based on initial feedback. The final questionnaire had fourteen questions related to various constructs, and twenty-five rating questions. The survey was implemented in a web-based survey system, and ed out to over 3000 randomly selected respondents from a nationally representative pool that MarketTools, Inc. owns. We received a total of 2007 respondents, 37 of which had significant missing data, leaving 1970 of useable responses. Partial summary information is shown in Tables 1A&1B. Table 1: Partial summary of survey results Table 1A Yes No After Seeing Movies do you usually 147 (7.0%) 1823 (93.0%) rate them online? Do you usually verbally recommend 1645 (84.0%) 324 (16.0 %) movies to other people? Are you Male or Female Male: 861 (44.0%) Female: 1109 (56.0%) Table 1B How Many Times did you go to the movies in the past month? None > (37%) 344 (17%) 303 (15%) 215 (11%) 152 (8%) 85 (4%) 107 (5%) 34 (2%) Online Word of Mouth Data We collected online word of mouth data from the Internet Movie Database (IMDB). IMDB is a part of the Amazon group of companies. The site offers a rich set of information about current and older movies, including synopses, cast and credits, trailers, news, box office data, theaters and show, links to critics reviews and newsgroup reviews, and user reviews. IMDB solicits numerical user ratings on a scale from 1 ( awful ) to 10 ( excellent ). IMDB stands out among movie sites in terms of the detailed statistical information it provides about user ratings. Online Ratings Data in Relation to Infomediary Recommendations Since much work has been done on using infomediary (professional critic) ratings to predict movie revenue, the natural question becomes: is online word of mouth a complement or substitute to infomediary recommendations? Table 2 shows the correlation scores between infomediary scores, online word of mouth scores from IMDB, and offline word of mouth collected from the survey. Interestingly, only the correlation 2

3 between Infomediaries and online word of mouth surpasses the threshold of 0.70 (Nunally 1967). The low correlation between offline word of mouth and infomediaries emphasizes the importance of these infomediaries in the absence of online word of mouth. However, the higher correlation between online word of mouth and infomediary scores suggests that online word of mouth may be an alternative solution to the information asymmetry problem. The reason behind these different correlations with online versus offline word of mouth is still unclear. Nevertheless, as IS-driven online word of mouth continues to grow, it may produce more accurate information for users than infomediaries, and may eventually supersede infomediaries in the terms of addressing information needs. Future research is needed to examine the evolution of consumer value of infomediaries versus online word of mouth. Table 2: Critics Scores versus Word of Mouth Correlation Infomediaries and Online Word of Mouth Infomediaries and Offline Word of Mouth Complementarities of Online Word of Mouth and Traditional Customer Conversion Since participation in offline or online word of mouth are binary variables, problems would arise if we treated the dependent variable as continuous and estimate a multiple regression model: First, the ordinary least squares estimates would be inefficient. Second, prediction from the model would not be restricted to the interval [0, 1]. Thus, we use Probit models to examine the differences in populations of consumers that participate in offline word of mouth versus those that participate in online word of mouth. It should be noted that there is a much larger sample of users that participate in offline word of mouth (recommending movies to other people) than online word of mouth (online reviews and ratings). Whereas 1645 people responded that they participate in offline word of mouth, only 147 respondents said they participated in online word of mouth, the dataset are therefore structurally different, and are run as different models. The Probit models of factors affecting likelihood to participate in online and offline word of mouth are shown in Tables 2 and 3, respectively. Table 2: Online Word of Mouth Participation Coefficient z P>z Number of Movies Seen in the Last Month Professional Critic Reviews Affect My Movie Selection Television Advertisements Affect My Movie Selection Other People's Recommendations Affect My Movie Selection

4 Online Ratings Affect My Movie Selection I usually recommend Movies to Other People (offline word of mouth) Gender Age Intercept Number of observations (n) 1969 LR χ2(9) Prob > χ Pseudo R Table 3: Offline Word of Mouth Participation Coefficient z P>z Number of Movies Seen in the Last Month Professional Critic Reviews Affect My Movie Selection Television Advertisements Affect My Movie Selection Other People's Recommendations Affect My Movie Selection Online Ratings Affect My Movie Selection I Usually Rate Movies Online (Online word of mouth) Gender Age Intercept Number of observations (n) 1969 LR χ2(9) Prob > χ Pseudo R Initial results suggest the participation in online is a complement to offline word of mouth; people that participate in offline word of mouth are also more likely to participate in online word of mouth, and vice versa. This result suggests that the impact of online word of mouth has great potential for growth; as the Internet becomes increasingly pervasive, an increased number of consumers will participate in online word of mouth. Interestingly, initial results also suggest that online word of mouth is a substitute for another traditional source of information: advertising. The populations of users that participate in online word of mouth as a source of information in their consumption decisions do not identify advertising as playing a significant role (Table 2). This result is very important, as it may be an initial sign that as more and more 4

5 people participate in online word of mouth, the effectiveness of television advertisements will likely diminish. Perhaps the most interesting result is that while the participation in online word of mouth and offline word of mouth appear to be complements, their value as a source of information in making consumption decisions appears to be substitutes; people who make their movie selection based on information garnered from offline word of mouth do not have any significant information value for online word of mouth, and vice versa 1. This result suggests that the population that references and trusts online word of mouth is different than the population that uses offline word of mouth. However, from the previous section we know that offline word of mouth and online word of mouth are highly correlated. Thus, our expectation is that as online word of mouth becomes more pervasive, there will be a convergence of those who use offline word of mouth to also use in online word of mouth. Future research is needed to test the dynamics of these two populations over time. Conclusion This paper is the first to examine the relationship between online word of mouth and traditional source of information, such as infomediaries, offline word of mouth, and advertising. Initial results suggest that while participation in online is a complement to participation in offline word of mouth, the use of online word of mouth is a substitute for both traditional advertising and for offline word of mouth. In addition, it appears that online word of moth may eventually be a substitute for infomediaries in terms of addressing the information asymmetry problems on consumer purchases. However, further exploration is needed to understand the relationship between online word of mouth, infomediaries, and other sources of consumer information. To that end, in the full paper we develop a more elaborate model, whereby additional sources of information are examined as potential items of importance in addressing information asymmetry. In addition, in the full paper we examine the evolution of online word of mouth in relation to traditional sources of information over time. Our long run goal is to extend this analysis to other industries as well, in order to better understand the power and implications of IT-driven word of mouth on product diffusion. References 1. Eliashberg, Jehoshua, and, Shugan, Steven M. (1997). Film critics: Influencers or predictors? Journal of Marketing 61(2): Nunnally, J. (1967). Psychometric Theory. New York, McGraw-Hill. 1 In the interest of space, the tables where the dependent variables are Use of online/offline word of mouth, rather than participation, are omitted from the text. However, these results are available in the full paper. 5