New Empirical Generalizations on the Determinants of Price Elasticity

By Tammo H. A. Bijmolt, H. V. van Heerde, Rik Pieters. 2005

443 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/d5ccb0ef0e5e0089c58be30dfbe22246bf561390

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Citations in the corpus

Explaining behavioural intentions toward co-branded products (2007, background): “Competition in many markets has grown fierce as consumers become more price sensitive (Bijmolt et al. 2005) and increasingly include private-label brands in their consideration sets.”
App Popularity: Where in the World are Consumers Most Sensitive to Price and User Ratings? (2018, background): “Price, ratings valence, and ratings volume are key drivers of demand in general (e.g., Babić Rosario et al. 2016; Bijmolt, Van Heerde, and Pieters 2005) and for apps specifically (Guzman andMaalej 2014; Jung, Baek, and Lee 2012).”
Sales Promotion Models (2008, background): “In a recent meta-analysis, Bijmolt et al. (2005) report that the average short-term sales promotion elasticity is –3.63, which implies that a 20% temporary price cut+ leads to a 73% rise in sales.”
The Role of Big Data and Predictive Analytics in Retailing (2017, background): “…for prices and shelf space at the different levels of aggregation such as category, brand, SKU etc (e.g., Hoch et al. 1995 on store-level price elasticities; Bijmolt et al. 2005 for a review of this literature); assess aggregate effects of prices, promotions and product attributes on sales; etc.”
The Impact of a Product-Harm Crisis on Marketing Effectiveness (2007, background): “Its ownprice elasticities in the short run (−2 52, significant) and long run (−2 01, insignificant) are comparable to the results from, respectively, Bijmolt et al. (2005) and Fok et al. (2006).”
The Kinked Demand Curve and Price Rigidity: Evidence from Scanner Data (2010, methodology): “Nevertheless, we have also used IV methods in the Robustness 9 Positive correlation between the price and the error term would bias our estimates for the (absolute) price elasticity downwards, and vice versa (see also Bijmolt et al., 2005).”
Prices and Promotions in U.S. Retail Markets: Evidence from Big Data (2019, unknown): “to meta-analyses of price elasticities in the marketing literature, in particular Tellis (1988) and Bijmolt et al. (2005). Other studies relate brand or category elasticities and promotion effects to market characteristics (Bolton 1989), demographic and competitor information (Hoch et al.”
The Power and Limits of Predictive Approaches to Observational-Data-Driven Optimization (2016, background): “For example, in econometric studies of marketplace supply-demand- price relationships, the analysis generally has to take endogeneity into consideration (Berry et al. 1995, Bijmolt et al. 2005, Phillips et al. 2012).”
A Meta-Analysis of Electronic Word-of-Mouth Elasticity (2015, background): “Often called observational learning or social learning (Bikhchandani, Hirshleifer, and Welch 1992; Chen, Wang, and Xie 2011), this mimicking behavior could arise because consumers perceive other users’ final choice as more reliable information than their own private information (Chen, Wang, and Xie…”
The dual role of price: decomposing consumers’ reactions to price (2008, background): “For example, metaanalyses of price elasticities quantify the total effect of price on demand ( Bijmolt et al. 2005; Tellis 1988), and scanner panel data (e.g., Erdem et al. 2005; Stiving and Winer 1997) reveal that the informational effect of price influences sales.”
Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling (2018, background): “…Papies, Ebbes, and van Heerde 2017; Sande and Ghosh 2018), endogeneity problems most often arise from the omission of variables that correlate with one or more independent variable(s) and the dependent variable(s) in the regression model (e.g., Bijmolt, van Heerde, and Pieters 2005; Rossi 2014).”