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Top1. Introduction
Understanding and analysing price and sales volume patterns of high-technology products and the dynamics between them is increasingly important as innovation related to high-technology products is taking place at a high pace on several levels, including product categories (like mobile handsets), product models (like Apple iPhone 5s), as well as product features (like WLAN). Prices of new products are important determinants of how fast they spread in the market (Horsky, 1990) and what type of sales patterns the products have during their lifecycle (Golder and Tellis, 1997). Therefore, the timing and extent of price decreases are highly important decisions for product manufacturers.
Many researchers have studied product pricing and price patterns, but most of the research is on the theoretical side. Only few publications explore empirical price patterns (e.g., Hernández-Mireles, 2010; Nair, 2007). These previous studies have shown that product prices often experience a decreasing pattern, which can be either gradual or include sudden transitions. The price pattern is a combination of several forces, including competition, consumer valuations, consumer heterogeneity, as well as the evolution of costs and market saturation over time (Hernández-Mireles, 2010). For instance, increased competition pushes product vendors to decrease their prices more notably and frequently (Álvarez et al., 2006) to increase or sustain their existing market share. The experience curve effect - including learning, technological improvements, and economies of scale (Day and Montgomery, 1983) - decreases costs of production further enabling the decreasing price pattern. Furthermore, differences in consumer valuations (because of heterogeneity) enable the use of price skimming, where the initially high prices decrease when the willingness to pay of the potential market also drops. Because of these reasons, the declining price pattern is also theoretically the preferable one from the producer viewpoint in some conditions (e.g., Bayus, 1994b).
From product lifecycle viewpoint, one area of interest is to identify different turning points in the lifecycle, such as the takeoff point (Golder and Tellis, 1997; Agarwal and Bayus, 2002; Tellis, Stremersch, and Yin, 2003; Peres, Muller, and Mahajan, 2010), which is the point in time when the initial period with low diffusion and sales changes to fast growth. Golder and Tellis (1997) analysed 31 product categories from the US market introduced between 1898 and 1990. For products introduced after the Second World War, they identified that takeoff takes place six years after the start of sales, at 1.7% penetration, and 63% of the introduction price. However, no generally accepted definition for the takeoff point exists. Furthermore, literatures on product variety (Lancaster, 1990) and proliferation (Bayus and Putsis, 1999), dominant design and innovation diffusion theories (Cecere, Corrocher, and Battaglia, 2014; Rogers, 2003), and the diffusion of product features (Kivi, Smura, and Töyli, 2012; Riikonen, Smura, Kivi, and Töyli, 2013) could all benefit from empirical analysis of price and sales patterns of the product models and technology components.