Showing papers by "Gerard J. Tellis published in 2011"
TL;DR: Of all the metrics of UGC, volume of chatter has the strongest positive effect on abnormal returns and trading volume and positive UGC has no significant effect on these metrics.
Abstract: User-Generated Content in online platforms or chatter for short provides a valuable source of consumer feedback on market performance of firms. This study examines whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are. The authors aggregate chatter (in the form of product reviews) from multiple websites over a four year period across six markets and fifteen firms. They derive multiple metrics of chatter (volume, positive chatter, negative chatter, and 5-start ratings) and use multivariate time series models to assess the short and long term relationship between chatter and stock market performance. They use three measures of stock market performance: abnormal returns, risk, and trading volume.The findings reveal that two metrics of chatter can predict abnormal returns with a lead of a few days. Of four metrics of chatter, volume shows the strongest relationship with returns and trading volume, followed by negative chatter. Whereas negative chatter has a strong effect on returns and trading volume with a short “wearin” and long “wearout,” positive chatter has no effect on these metrics. Negative chatter also increases volatility (risk) in returns.A portfolio analysis of trading stocks based on their chatter provides a return of 8% over and above normal market returns. In addition to the investing opportunities, the results show managers that chatter is an important metric to follow to gauge the performance of their brands and products. Because chatter is available daily and hourly, it 2 can provide an immediate pulse of performance that is not possible with infrequent sales and earnings reports. The fact that negative chatter is more important than positive, indicates that negatives are more diagnostic than positives. The negatives suggest what aspects of the products managers should focus on.
484 citations
TL;DR: The authors conducted a meta-analysis of 751 short-term and 402 long-term direct-to-consumer brand advertising elasticities estimated in 56 studies published between 1960 and 2008.
Abstract: The authors conduct a meta-analysis of 751 short-term and 402 long-term direct-to-consumer brand advertising elasticities estimated in 56 studies published between 1960 and 2008. The study finds several new empirical generalizations about advertising elasticity. The most important are as follows: The average short-term advertising elasticity is .12, which is substantially lower than the prior meta-analytic mean of .22; there has been a decline in the advertising elasticity over time; and advertising elasticity is higher (1) for durable goods than nondurable goods, (2) in the early stage than the mature stage of the life cycle, (3) for yearly data than quarterly data, and (4) when advertising is measured in gross rating points than monetary terms. The mean long-term advertising elasticity is .24, which is much lower than the implied mean in the prior meta-analysis (.41). Many of the results for short-term elasticity hold for long-term elasticity, with some notable exceptions. The authors discuss t...
377 citations
TL;DR: A new schema is developed to address the problem of circular definitions, inadequate empirical evidence, and lack of a predictive model of disruption, and shows good out-of-sample predictive accuracy.
Abstract: The failure of firms in the face of technological change has been a topic of intense research and debate, spawning the theory (among others) of disruptive technologies. However, the theory suffers from circular definitions, inadequate empirical evidence, and lack of a predictive model. We develop a new schema to address these limitations. The schema generates seven hypotheses and a testable model relating to platform technologies. We test this model and hypotheses with data on 36 technologies from seven markets. Contrary to extant theory, technologies that adopt a lower attack (“potentially disruptive technologies”) (1) are introduced as frequently by incumbents as by entrants, (2) are not cheaper than older technologies, and (3) rarely disrupt firms; and (4) both entrants and lower attacks significantly reduce the hazard of disruption. Moreover, technology disruption is not permanent because of multiple crossings in technology performance and numerous rival technologies coexisting without one disrupting the other. The proposed predictive model of disruption shows good out-of-sample predictive accuracy. We discuss the implications of these findings.
149 citations
TL;DR: The saddle phenomenon is defined as a sudden, sustained, and deep drop in sales of a new product, after a period of rapid growth following takeoff, followed by a gradual recovery to the former peak.
Abstract: The “saddle” is a sudden, sustained, and deep drop in sales of a new product, after a period of rapid growth following takeoff, followed by a gradual recovery to the former peak The authors test for the generalizability of the saddle across products and countries and for three rival explanations: chasms in adopter segments, business cycles, and technological cycles They model both boundary points of the saddle—start of the sales drop and recovery to the initial peak—using split-population models Empirical analysis of historical sales data from ten products across 19 countries shows that the saddle is fairly pervasive The onset of the saddle occurs in 148 product–country combinations On average, the saddle occurs nine years after takeoff, at a mean penetration of 30%, and it lasts for eight years with a 29% drop in sales at its depth The results support explanations of chasms and technological cycles for information/entertainment products and business cycles and technological cycles for kitc
35 citations
Posted Content•
TL;DR: In this article, the authors examined whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are, and found that negative chatter has a strong effect on returns and trading volume, whereas positive chatter has no effect on these metrics.
Abstract: textUser-Generated Content in online platforms or chatter for short provides a valuable source of consumer feedback on market performance of firms. This study examines whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are. The authors aggregate chatter (in the form of product reviews) from multiple websites over a four year period across six markets and fifteen firms. They derive multiple metrics of chatter (volume, positive chatter, negative chatter, and 5-start ratings) and use multivariate time series models to assess the short and long term relationship between chatter and stock market performance. They use three measures of stock market performance: abnormal returns, risk, and trading volume.
The findings reveal that two metrics of chatter can predict abnormal returns with a lead of a few days. Of four metrics of chatter, volume shows the strongest relationship with returns and trading volume, followed by negative chatter. Whereas negative chatter has a strong effect on returns and trading volume with a short “wearin” and long “wearout,” positive chatter has no effect on these metrics. Negative chatter also increases volatility (risk) in returns.
A portfolio analysis of trading stocks based on their chatter provides a return of 8% over and above normal market returns. In addition to the investing opportunities, the results show managers that chatter is an important metric to follow to gauge the performance of their brands and products. Because chatter is available daily and hourly, it 2 can provide an immediate pulse of performance that is not possible with infrequent sales and earnings reports. The fact that negative chatter is more important than positive, indicates that negatives are more diagnostic than positives. The negatives suggest what aspects of the products managers should focus on.
7 citations
TL;DR: To make scanner-panel data more powerful, it is recommended that choice data sets be augmented to correct for their inherent weaknesses.
Abstract: Scanner-panel data have long been important for understanding advertising9s effects. Although the nature of advertising often has been investigated with scanner-panel data, the nature and value of scanner-panel data itself have rarely been considered. Although scanner-panel data are revered—even worshiped—by many, they have measurement issues like any other complex data set. While early estimates of advertising effectiveness from scanner-panel data may have appeared too low, some estimates are not vastly different from other data bases as can be ascertained from recent meta-analyses. Learning about the situations when, where, and how advertising does have large effects, however, is critical, and the future development of scanner-panel data does have a way to go to help answer these key questions. To make scanner-panel data more powerful, we recommend that choice data sets be augmented to correct for their inherent weaknesses.
5 citations
Posted Content•
TL;DR: In this paper, the authors examined whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are, and found that negative chatter has a strong effect on returns and trading volume with a short "wearin" and long "wearout".
Abstract: User-Generated Content in online platforms or chatter for short provides a valuable source of consumer feedback on market performance of firms. This study examines whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are. The authors aggregate chatter (in the form of product reviews) from multiple websites over a four year period across six markets and fifteen firms. They derive multiple metrics of chatter (volume, positive chatter, negative chatter, and 5-start ratings) and use multivariate time series models to assess the short and long term relationship between chatter and stock market performance. They use three measures of stock market performance: abnormal returns, risk, and trading volume. The findings reveal that two metrics of chatter can predict abnormal returns with a lead of a few days. Of four metrics of chatter, volume shows the strongest relationship with returns and trading volume, followed by negative chatter. Whereas negative chatter has a strong effect on returns and trading volume with a short “wearin” and long “wearout,” positive chatter has no effect on these metrics. Negative chatter also increases volatility (risk) in returns. A portfolio analysis of trading stocks based on their chatter provides a return of 8% over and above normal market returns. In addition to the investing opportunities, the results show managers that chatter is an important metric to follow to gauge the performance of their brands and products. Because chatter is available daily and hourly, it 2 can provide an immediate pulse of performance that is not possible with infrequent sales and earnings reports. The fact that negative chatter is more important than positive, indicates that negatives are more diagnostic than positives. The negatives suggest what aspects of the products managers should focus on.
2 citations
01 Jun 2011
TL;DR: Les auteurs conduisent une meta-analyse sur la base de 751 elasticites a court terme and 402 a long terme concernant des publicites de marque visant directement le consommateur issues de 56 etudes p...
Abstract: Les auteurs conduisent une meta-analyse sur la base de 751 elasticites a court terme et 402 a long terme concernant des publicites de marque visant directement le consommateur issues de 56 etudes p...
1 citations
01 Jan 2011
TL;DR: In this paper, the authors investigated the effect of advertising on scanner-panel data and found that advertising does have a large effect on the performance of advertising in the U.S. while early estimates of advertising effectiveness appeared too low.
Abstract: ABSTRACT Scanner-panel data have long been important for understanding advertising's effects. Although the nature of advertising often has been investigated with scanner-panel data, the nature and value of scanner-panel data itself have rarely been considered. Although scanner-panel data are revered—even worshiped—by many, they have measurement issues like any other complex data set. While early estimates of advertising effectiveness from scanner-panel data may have appeared too low, some estimates are not vastly different from other data bases as can be ascertained from recent meta-analyses. Learning about the situations when, where, and how advertising does have large effects, however, is critical, and the future development of scanner-panel data does have a way to go to help answer these key questions. To make scanner-panel data more powerful, we recommend that choice data sets be augmented to correct for their inherent weaknesses.
TL;DR: The authors conducted a meta-analysis of 872 short-term brand-level advertising elasticities estimated in 57 studies published between 1960 and 2008 and found that advertising elasticity has been relatively stable over the last four decades (1964-2004).
Abstract: This study conducts a meta-analysis of 872 short-term brand-level advertising elasticities estimated in 57 studies published between 1960 and 2008. Short-term advertising elasticity is the percent change in a brand’s current period sales for one percent change in the brand’s current period advertising. The study finds 16 new and 7 enduring empirical generalizations on advertising elasticity. The most important ones are the following: The average advertising elasticity is .12, which is significantly lower than the prior meta-analytic mean of .22 (Assmus, Farley and Lehmann 1984). The average advertising elasticity has been relatively stable over the last four decades (1964-2004). Only about half (53%) of the advertising elasticities are significantly greater than zero. Advertising elasticities are higher a) for durable goods than nondurable goods, b) in the early stage of the life cycle than in the mature stage, c) for yearly data than for daily or quarterly data, d) for TV advertising than print advertising, and e) when advertising is measured in relative than in absolute terms. The authors discuss the implications of these findings.
TL;DR: In this paper, the authors provide a framework for informed decision making for firms facing rapid technological change, which consists in identifying the levels of innovation, the competing technologies at each level, the dimensions of performance on which to compare them, and the patterns of evolution on each dimension.
Abstract: For firms facing rapid technological change, knowing which technology to back need not be a guessing game or a purely creative exercise. We provide a framework for informed decisions. The framework consists in identifying the levels of innovation, the competing technologies at each level, the dimensions of performance on which to compare them, and the patterns of evolution on each dimension.