Exploring the Impact of Online Reviews with Brand Equity for Online Software Purchasing Behavior

Exploring the Impact of Online Reviews with Brand Equity for Online Software Purchasing Behavior Completed Research Paper Jason Triche Texas Tech University jason.triche@ttu.edu Mark A. Thompson Texas Tech University mark.thompson@ttu.edu Qing Cao Texas Tech University qing.cao@ttu.edu ABSTRACT Online purchasing is a booming business and is studied extensively in marketing and information systems research. Online product reviews are one factor that influence a consumer s purchasing behavior and attitudes. Brand equity is a popular phenomenon that influences purchasing behaviors and attitudes of consumers. We seek to answer two questions in this research. First, do online consumer reviews contain elements of brand equity? Second, do these elements of brand equity in the online consumer reviews affect software purchasing? We develop an integrative research model which test these constructs on software downloads. We use data collected from CNET s Download.com. We then analyze the effect of these reviews on software downloads using panel data week by week over a 42 week span. All four dimensions of brand equity (brand awareness, brand associations, perceived quality, and brand loyalty) show significant impacts on software downloads with perceived quality being the most influential. Keywords Sentiment Analysis, Software Downloads, E-business, Brand Equity INTRODUCTION Online purchasing is a booming business and is studied extensively in marketing and information systems research (Gefen et al. 2003; Koufaris 2002; McKinney and Yoon 2002; Pavlou and Fygenson 2006; Schlosser et al. 2006). While marketing firms may pursue extensive marketing strategies with physical products, our research looks at factors that influence online purchasing. Online product reviews are one of those factors that influence a consumer s purchasing behavior and attitudes (Bambauer-Sachse and Mangold 2011; Chatterjee 2001; Chevalier and Mayzlin 2003; Duan et al. 2008; Hu et al. 2006; Park et al. 2007). The impact of online product reviews on online purchasing show mixed results in the literature. We are interested in exploring whether brand equity exists in online reviews and whether they impact online purchasing. Brand Equity is a concept that has been studied extensively in marketing literature over the past two decades (Aaker 1991 p. 3; John et al. 2006; Keller 1993; Yoo et al. 2000). Both marketing scholars and practitioners look at brand equity to explain and improve marketing productivity. Keller s (1993) study on conceptualizing, measuring, and managing brand equity is a seminal article in this area of research. Keller (1993 p. 2) defines brand equity as the differential effect of brand knowledge consumer response to the marketing of a brand. Brand equity is studied for two main reasons, financial-based motivation and strategy-based motivation both of which lead to additional research and relevance for practitioners. Software is a flourishing, global business and the largest one hundred software companies in the world together generated software revenues of over $220 billion in 2009 (Verberne 2010). Software is unique in a sense that it can be distributed electronically. In most cases consumers can download the software in a limited version or trial period to test out the features of the product. If the consumer is satisfied with the trial software, then the consumer can purchase the full version. There are many websites like Software.com, Download.com, and Amazon.com where consumers can shop, review, download and purchase a large variety of software. In addition, almost all software vendors allow consumers to purchase their software directly from the company s website. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 1

We seek to answer two questions in this research. First, do online consumer reviews contain elements of brand equity? Second, do these elements of brand equity in the online consumer reviews affect software downloads? We will use existing constructs defined in previous marketing studies and adapt them to the software domain. We develop an integrative research model that test these constructs on software downloads using data collected from a specific software download website, CNET s Download.com. Brand equity in online reviews is measured using sentiment analysis, which categorizes the brand equity dimensions as positive or negative (or non-existent) brand equity. We then analyze the effect of these reviews on software downloads using panel data week by week over a 42 week span. Given our research objective, we will first discuss the existing literature regarding brand equity and sentiment analysis. Next we will discuss our research model and hypotheses. Following our model, we will detail our results and provide a discussion of next steps and limitations. Lastly we will conclude our findings and describe implications of our research both managerially and theoretically. THEORETICAL FOUNDATIONS Brand Equity According to Keller (1993), brand equity occurs when the consumer is familiar with the brand and holds some favorable, strong and unique brand associations in memory. The structure of memory association can be explained with a type of associative model formulation (see Anderson 1983). Consumers may associate a brand with a particular product feature, color, logo, spokesperson, or auditory cues. The importance of brand equity comes into play when marketers try to maximize efficiency from their marketing strategy while trying to decrease their costs. Given higher costs, greater competition, and flattening demand in many markets, firms seek to increase the efficiency of their marketing expenses (Keller 1993 p. 1). The brand equity concept is used to measure the incremental utility or value of a product by its brand name (Yoo and Donthu 2001). Positive brand equity is the degree of marketing advantage a brand would hold over a competitor (Berry 2000); however, brand equity can also be negative. As such, negative brand equity is the degree of marketing disadvantage a brand would hold over a competitor. In current literature, brand equity is measured using different constructs; however, most of the research shares the same few common measures. The four most commonly used constructs are brand awareness, brand loyalty, brand image and perceived quality. Brand Awareness is related to the strength of the brand node or trace in memory. Brand awareness is the likelihood that a brand name will come to mind and the ease with which it does so (Keller 1993 p. 3). Brand awareness is used to measure brand equity in many marketing studies (see Aaker 1991; Berry 2000; Keller 1993; Kim et al. 2002; Yoo and Donthu 2001; Yoo et al. 2000). Aaker (1991) states that brand awareness can be viewed as a continuum ranging from brand recognition at the lowest level, to brand recall at the mid-level, to top-of-mind recall and, finally, the dominant brand. Keller (1993) measures brand awareness using two dimensions, recall and recognition. Keller suggests that measuring brand awareness can be assessed effectively through a variety of aided and unaided memory measures. Aaker (1996) uses statements like I know what this brand stands for to measure the degree someone is aware of a brand. Yoo and Donthu (1997) uses the three items to measure brand awareness, if a person knows what product X can do, if a person can recognize product X among other competing brands and, if a person is aware of product X. Brand associations are the informational nodes linked to the brand node in memory and contain the meaning of the brand for customers (Keller 1993 p. 3). He further defines brand associations by its attributes, benefits and attitudes. Attributes represent the product or service features and in the case of software can also represent capabilities. Benefits, as it relates to brand associations, are the personal values consumers attach to the product or service features (Keller 1993). Brand attitudes are the consumers overall evaluations of a brand (Wilkie 1986) and are important because they often form the basis for consumer behavior (Keller 1993). Perceived quality is another dimension used to measure brand equity (Aaker 1991; Aaker 1996; Yoo and Donthu 1997; Yoo and Donthu 2001; Yoo et al. 2000). Perceived quality is the consumer s judgment about a product s overall excellence or superiority (Zeithaml 1988 p. 3). Quality can be measured by the consumer themselves and not by managers or experts (Yoo and Donthu 2001). Perceived quality also represents overall quality rather than the quality of individual elements of the Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 2

product or service (Aaker and Keller 1990; Boulding and Kirmani 1993; Petroshius and Monroe 1987; Yoo and Donthu 2001). Brand loyalty is also used to measure brand equity. Brand loyalty occurs when favorable beliefs and attitudes for the brand are manifested into repeated buying behavior for that brand (Keller 1993). Aaker (1991) notes that brand loyalty leads to certain marketing advantages such as reduced marketing costs, more new customers, and greater trade leverage. Brand loyalty can be measured by examining consumer s satisfaction with a brand over its competitors or recommendations of the brand to others (Aaker 1996; Yoo and Donthu 1997). Previous literature on brand equity has employed or suggested survey or experimental methods (Aaker 1996; Cobb-Walgren et al. 1995; John et al. 2006; Keller 1993; Kim et al. 2002; Washburn and Plank 2002; Yoo and Donthu 2001) to extract and measure the different constructs of brand equity. However, to our knowledge, no studies have explored brand equity in online reviews. Doing so requires using an alternative approach to examine the content of online reviews. In the next section, we describe the previous work on sentiment analysis. HYPOTHESIS DEVELOPMENT The literature regarding online reviews on product sales is mixed. Some literature states that online reviews have a positive effect on product sales (Chen et al. 2004; Chevalier and Mayzlin 2003; Clemons et al. 2006; Li and Hitt 2007), while others show no real effect (Duan et al. 2008; Godes and Mayzlin 2004; Liu 2006). The brand equity literature suggests that higher brand equity can lead to increase product sales while controlling for marketing expenses. Consumers post online reviews for many different reasons including social benefits, social concerns, economic incentives and entertainment. We present the model first in Figure 1, followed by explanations of the key elements of the model and postulated relationships. In our model, brand equity consists of brand awareness, brand association, perceived quality, and brand loyalty. These dimensions are used to define brand equity and are used in numerous marketing research articles (see Aaker 1991; Aaker 1996; Yoo et al. 2000). Figure 1. Research Model Brand awareness is the strength of a brand in a consumer s memory. The likelihood that a brand will come to mind and ease to which it does is brand awareness (Keller 1993). One way to measure brand awareness is WOM communications since WOM communications is highly accessible from memory (Herr et al. 1991). Online reviews can be seen as a form of WOM. Consumers look to online product reviews more and more when gathering pre-purchase product information (Zhu and Zhang 2010). Therefore, we hypothesize that higher level of positive brand awareness in online reviews lead to higher software downloads. Hypothesis 1: Higher levels of positive brand awareness in online reviews are positively associated with higher software downloads. Brand associations are the perception about a brand and can be defined by its attributes, benefits and attitudes (Keller 1993). Attributes are defined as product features or capabilities. Benefits can be defined as the personal values consumers attached Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 3

to a product. Attitudes are the consumers overall evaluations of a brand. Therefore, we hypothesize that higher level of positive brand associations in online reviews lead to higher software downloads. Hypothesis 2: Higher levels of positive brand associations in online reviews are positively associated with higher software downloads. Perceived quality is the consumer s judgment about a product s overall excellence or superiority (Zeithaml 1988). Software quality can be defined in many ways including functionality, reliability, usability, efficiency, maintainability and portability (International Organization for Standardization 1992). Although the definition of software quality is defined by ISO, perceived quality is defined by the consumer themselves. Therefore we hypothesize that higher level of perceived quality in online reviews lead to higher software downloads. Hypothesis 3: Higher levels of positive perceived quality in online reviews are positively associated with higher software downloads. Brand loyalty occurs when favorable beliefs and attitudes for the brand are manifested into repeated buying behavior for that brand (Keller 1993). Therefore we hypothesize that higher level of brand loyalty in online reviews lead to higher software downloads RESEARCH METHODOLOGY Data Hypothesis 4: Higher levels of positive brand loyalty in online reviews are positively associated with higher software downloads. Data for this research were collected from CNET Download.com (CNETD: http://www.download.com), which is a leading online platform in the software market. CNETD, a part of CNET network, is a library of more than 50,000 free or free-to-try software programs for Windows, Mac, and mobile devices. The most recent week's download number is displayed for each software product. CNETD provides an ideal environment for this study since none of the parties (users, CNETD, and software owners) would benefit directly from the increase of the software download, hence there is no or low incentive for any of them to manipulate the user reviews as a disguised promotional chat (Mayzlin 2006). We collected the entire history of software download data for 10 months starting from May 1, 2009 to February 26, 2010. Each software download record includes software product name, version, average user rating, individual user ratings, title of review, user id of the reviewer, date of the review, summary of the review, pros, cons, and replies to the review (if any). Our sample consists of 216 unique software product versions containing 75,372 reviews. Sentiment Analysis The sentiment analysis in this study is conducted using the Naïve Bayes (NB) process. In the first pretreatment process, after raw material cleanup and sentence tokenization, we stored 594,886 sentences covering 75,372 pieces of reviews. Following the sentiment analysis procedure discussed previously, in the second process, we train the classification system with external knowledge. The external knowledge, in our context, includes the four key words list to represent the four types of brand equity. These keywords are selected manually by reading 1,000 reviews which were randomly chosen from 75,372 reviews. We choose nouns to represent each dimension based on previous research (Aaker 1996; Yoo and Donthu 1997) and experts domain knowledge. In the last sentiment analysis step, we used the Cornell movie-review dataset 1 as the guide to calculate the sentiment (negative or positive) of each review. This step provides the polarity of the sentiment. In this study, the proposed four dimension classification algorithm retrieves a 0.71 F-measure and positive-negative sentiment classification algorithm retrieves a 0.92 F-measure on the test set. On average, F-measure of quaternary (four dimension classification case) and binary (positive-negative sentiment classification case) is around 0.6 and 0.8 respectively (Pang and Lee 2005). Therefore, our classification is fairly accurate. 1 Cornell University is a pioneer in sentiment analysis, and has maintained a vocabulary of sentiment words (e.g., dislike, enjoy) even though it is originally derived from movie reviews. It is now regarded as the de facto sentiment vocabulary list for sentiment analysis. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 4

The result for the four dimension classification is shown in Table 1. There are 537,268 sentences related to brand equity (out of the 594,886 sentences in the dataset) and number of positive sentences is significantly more than that of negative sentences. In addition, we notice the dimension of perceived quality has the largest number of sentences, accounts for almost 49% of the total counts, which suggest users place a lot of attention on the items of this type. Table 1. Distribution of the Four Dimensions of Brand Equity Brand Equity Type # of Positive # of Negative Number of Sentences % Loyalty 83,131 10,440 93571 17 Perceived quality 198,733 63,856 262589 49 Brand Association 93,534 28,874 122408 23 Brand Awareness 41,412 17,288 58,700 11 Total 537,268 100 Panel Data Analysis As we are interested in examining whether the four brand equity type measures derived from the sentiment analysis drive online users reviewing behavior, we construct an equation with the weekly average downloads as the dependent variable and a set of independent variables. Following extant research (e.g., Duan et al. 2008; Gu et al. 2012), we use a log-linear model. In this context, the major independent variable is the measurement of sentiments of the four brand equity types, which are basically a count of positive and negative occurrences in user reviews. The log transformation converts the relationship of those discrete count variables into a linear form for empirical estimation. Log-linear model smoothes the distribution of variables in the linear regression, and the estimated coefficients directly reflect the elasticity of independent and dependent variables. Table 2 describes the variable name and measures. Variable Table 2. Variable Descriptions Description and Measure WeekDownload it Average download for software i at week t. WeekRating it Average user review rating for software i at week t. WeekRatingNum it Average number of user reviews for software i at week t. WeekRank it Average download.com popularity ranking software i at week t. LoyaSenti it Overall sentiment score of Loyalty for software i at week t. QualSenti it Overall sentiment score of Quality for software i at week t. AssoSenti it Overall sentiment score of Associations for software i at week t. AwarSenti it Overall sentiment score of Awareness for software i at week t. To control for any software idiosyncratic factors that could influence user reviews, such as software type, vendor size, and others, we include software fixed effects in the model by adding software-specific dummy variables. The software-specific fixed effects capture the idiosyncratic and time-constant unobserved characteristics associated with each software in our data. The advantage of fixed effects estimation is that it controls for intrinsic review characteristics, which inherently affect user reviews. In addition, fixed effects estimation also allows the error term to be arbitrarily correlated with other explanatory variables, thus making the estimation results more robust. The equation is specified as follows: (1) Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 5

Equation (1) reflects the weekly average software downloads. Let i = 1,..., N index the software. The represents a vector of four independent variables including the log value of the sentiment score for each of the four dimensions of brand equity. reflects the most recent and concurrent valence of online user WOM information for the software. In our equation, we include the previous week s download.com popularity index (ranking) ( ) to control for the most recent software users satisfaction level. We used the previous week s download data since CNET only updates the rankings once a week. Therefore, last week s ranking represents the most up to date information. In addition, is added to control for the number of user reviews for specific software in a given week. The denotes the software-specific fixed effects that capture the idiosyncratic characteristics associated with each software, such as its type, brand name, vendor size, and functionality. The fixed effects capture all non-time varying, unobserved heterogeneity of each review; thus, we are able to control for unobserved differences across software. RESULTS AND DISCUSSION The three-stage least-square (3SLS) procedure was used to estimate equation (1) to consider both the endogeneity of the dependent variables and the correlation of equation (1). The 3SLS estimation results for Eqs. (1) are reported in Table 3. Table 3. Fixed Effects 3SLS Estimation Results Variable Coefficient Std. Err. ln(weekrating it ) 0.11* 0.052 ln(weekratingnum it ) 0.03 0.184 ln(weekrank i,t-1 ) 0.10* 0.048 ln(loyasenti it ) 0.27*** 0.092 ln(qualsenti it ) 0.31*** 0.071 ln(assosenti it ) 0.17** 0.057 ln(awarsenti it ) 0.12* 0.046 Constant -0.06 0.21 Note: ***p <.01; **p <.05; *p <.10 Hypothesis 1 suggests that positive brand awareness is positively associated with higher software downloads. As shown in Table 4, using the model defined in Eq (1), the overall sentiment of brand awareness, ln(awarsenti it ), shows a positive significant impact on software downloads, thus confirming H1. Hypothesis 2 suggests that positive brand associations are positively associated with higher software downloads. As shown in Table 4, using the model defined in Eq (1), the overall sentiment of brand associations, ln(assosenti it ), shows a positive significant impact on software downloads, thus confirming H2. Hypothesis 3 suggests that higher levels of positive perceived quality are positively associated with higher software downloads. Our results show that the sentiment score of perceived quality render the highest impact on software downloads with the highest coefficient (0.31). As such, our hypothesis 3, that higher levels of positive perceived quality are positively associated with higher software downloads, is supported. Hypothesis 4 suggests that positive brand loyalty are positively associated with higher software downloads. As shown in Table 4, using the model defined in Eq (1), the overall sentiment of brand loyalty, ln(loyasenti it ), shows a positive significant impact on software downloads, thus confirming H4. CONCLUSIONS Online consumer reviews play an important role in influencing online purchasing. Research in consumer reviews has examined a myriad of factors that influence online purchasing. Brand equity is a popular phenomenon in both research and practice because of its strength in influencing purchasing behaviors of consumers. In this research we investigated the influence of brand equity in online reviews. Using sentiment analyses, the results of this research show strong support that online consumer reviews contain elements of brand equity. In our data, 90% (537,268 out of 594,886 sentences) contained elements of brand equity. This research also demonstrates that online reviews containing elements of brand equity also Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 6

influence software downloads and ultimately software purchasing. All four positive (negative) dimensions of brand equity (brand awareness, brand associations, perceived quality, and brand loyalty) show positive (negative) impacts on software downloads with perceived quality being the most influential. This research contributes both to practice and to research. First, software companies can use these findings to increase their product downloads. Knowing that consumer reviews that contain elements of positive brand equity positively influence downloads, companies can target their marketing efforts to advertise quality of their software as well as brand qualities like awareness and associations. Second, software brokers (i.e., CNET s download.com) can also benefit by increasing downloads and traffic to their site thus reaping revenue from software companies and other advertisers. By writing and recommending editor reviews that contain elements of brand equity, software brokers can increase software downloads. From a theoretical perspective, this research adds to the growing body of both brand equity and online consumer review literature. By using sentiment analyses we were able to analyze over 75,000 reviews to determine the sentiment down to the sentence level. Previous brand equity literature suggests that brand associations and brand awareness can only be measured using surveys or experiments (Aaker 1996; Washburn and Plank 2002). We demonstrated that these two brand equity elements in addition to brand loyalty and perceived quality can be measured using other methods which allow a larger sample size. LIMITATIONS AND FUTURE STUDY One limitation of this study has to do with the generalization issue as the study is anchored at the software industry. In the future study, we intend to provide a more comprehensive view of measuring and evaluating service quality by mining social media content and performing multiple tests and across various industries. Another methodological limitation associated with the current study is that we used sentence-level analysis and assigned each sentence to one dimension. This approach ignores some cases where content of one sentence may cover more than one dimensions. More sophisticated and advanced sentiment analysis techniques are expected to be developed and applied in our future research endeavors in examining online user-generated content. Other future studies, as we allude to in the aforementioned discussions, includes taking a detailed look at the effect of various information aggregation and display strategy on consumer behavior, examining the effect of different strategies of managing and using user-generated content, and establishing the direct link of different dimensions of user-generated content with product and service sales as well as overall firm performance. REFERENCES Aaker, D.A. 1991. Managing Brand Equity. New York: The Free Press. Aaker, D.A. 1996. "Measuring Brand Equity across Products and Markets," California Management Review (38:3), Spring96, pp. 102-120. Aaker, D.A., and Keller, K.L. 1990. "Consumer Evaluations of Brand Extensions," Journal of Marketing (54:1), pp. 27-41. Anderson, J.R. 1983. The Architecture of Cognition. Cambridge, MA: Harvard University Press. Bambauer-Sachse, S., and Mangold, S. 2011. "Brand Equity Dilution through Negative Online Word-of-Mouth Communication," Journal of Retailing and Consumer Services (18:1), pp. 38-45. Berry, L.L. 2000. "Cultivating Service Brand Equity," Journal of the Academy of Marketing Science (28:1), Winter2000, p. 128. Bickart, B., and Schindler, R.M. 2001. "Internet Forums as Influential Sources of Consumer Information," Journal of interactive marketing (15:3), pp. 31-40. Boulding, W., and Kirmani, A. 1993. "A Consumer-Side Experimental Examination of Signaling Theory: Do Consumers Perceive Warranties as Signals of Quality?," Journal of Consumer Research (20:1), pp. 111-123. Cao, Q., Duan, W., and Gan, Q. 2011. "Exploring Determinants of Voting for the Helpfulness of Online User Reviews: A Text Mining Approach," Decision Support Systems (50:2), pp. 511-521. Chatterjee, P. 2001. "Online Reviews: Do Consumers Use Them?," Advances in consumer research (28), pp. 129-133. Chen, P.Y., Wu, S., and Yoon, J. 2004. "The Impact of Online Recommendations and Consumer Feedback on Sales,"). Chevalier, J.A., and Mayzlin, D. 2003. "The Effect of Word of Mouth on Sales: Online Book Reviews," National Bureau of Economic Research. Clemons, E.K., Gao, G.G., and Hitt, L.M. 2006. "When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry," Journal of Management Information Systems (23:2), pp. 149-171. Cobb-Walgren, C.J., Ruble, C.A., and Donthu, N. 1995. "Brand Equity, Brand Preference, and Purchase Intent," Journal of Advertising (24:3), pp. 25-40. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, Illinois, August 15-17, 2013. 7

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