http://repub.eur.nl/res/aut/1940/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/25817/ 2011-08-30T00:00:00Z 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. http://repub.eur.nl/res/pub/9406/ 2007-03-28T00:00:00Z Indirect network effects are of prime interest to marketers because they affect the growth and takeoff of software availability for, and hardware sales of, a new product. While prior work on indirect network effects in the economics and marketing literature is valuable, these literatures show two main shortcomings. First, empirical analysis of indirect network effects is rare. Second, in contrast to the importance the prior literature credits to the chicken-and-egg paradox in these markets, the temporal pattern – which leads which? – of indirect network effects remains unstudied. Based on empirical evidence of nine markets, this study shows, among others, that: (1) indirect network effects, as commonly operationalized by prior literature, are weaker than expected from prior literature; (2) in most markets we examined, hardware sales leads software availability, while the reverse almost never happens, contradicting existing beliefs. These findings are supported by multiple methods, such as takeoff and time series analyses, and fit with the histories of the markets we studied. The findings have important implications for academia, public policy and management practice. To academia, it identifies a need for new, and more relevant, conceptualizations of indirect network effects. To public policy, it questions the need for intervention in network markets. To management practice, it downplays the importance of the availability of a large library of software for hardware technology to be successful. http://repub.eur.nl/res/pub/13374/ 2007-01-01T00:00:00Z Indirect network effects are of prime interest to marketers because they affect the growth and takeoff of software availability for and hardware sales of a new product. Although prior work on indirect network effects in the economics and marketing literature is valuable, there are two main shortcomings. First, empirical analysis of indirect network effects is rare. Second, in contrast to the importance prior literature credits to the “chicken-and-egg” paradox in these markets, the temporal pattern (i.e., Which leads to which?) of indirect network effects remains unstudied. Based on empirical evidence of nine markets, this study shows that (1) indirect network effects, as commonly operationalized by prior literature, are weaker than expected from prior literature and (2) in most markets examined, hardware sales “lead” software availability, whereas the reverse almost never happens, contrary to existing beliefs. These findings are supported by multiple methods, such as takeoff and time-series analyses, and fit with the histories of the markets studied herein. For academia, the study identifies a need for new and more relevant conceptualizations of indirect network effects. For public policy, it questions the need for intervention in network markets. For management practice, it downplays the importance of the availability of a large library of software for hardware technology to be successful. http://repub.eur.nl/res/pub/11265/ 2006-07-21T00:00:00Z The abundance of highly disaggregate data (e.g., at five-second intervals) raises the question of the optimal data interval to estimate advertising carryover. The literature assumes that (1) the optimal data interval is the interpurchase time, (2) too disaggregate data causes a disaggregation bias, and (3) recovery of true parameters requires assumption of the underlying advertising process. In contrast, we show that (1) the optimal data interval is what we call unit exposure time, (2) too disaggregate data does not cause any disaggregation bias, and (3) recovery of true parameters does not require assumption of the advertising process but only data at the unit exposure time. These results hold for any linear dynamic model linking sales with current and past advertising. http://repub.eur.nl/res/pub/18553/ 2004-06-24T00:00:00Z