In many firms, the marketing department plays a minor role in new product development (NPD). However, recent research demonstrates that marketing capabilities more strongly influence firm performance than other areas such as research and development. This finding underscores the importance of identifying relevant capabilities that can improve the position of marketing within the NPD process as part of the quest to improve innovation performance. However, thus far, it has remained unclear precisely how the marketing department can increase its influence on NPD to enhance a firm's innovation performance. The results of this study demonstrate that the relationship between marketing capabilities and innovation performance is generally mediated by the decision influence of marketing on NPD. In particular, both marketing research quality and the ability to translate customer needs into product characteristics serve to increase marketing's influence on NPD. This increased influence, in turn, positively contributes to overall firm innovation performance. Hence, these results show that in addition to having the appropriate marketing capabilities, the marketing department must achieve a status in which these capabilities can translate into performance implications.
Online price comparison sites (shopbots) like PriceGrabber.com are the most powerful tools for consumers to easily compare prices and find offers for desired products. Besides providing distributions of actual prices in price comparison tables, shopbots like NexTag.com have recently introduced price charts (line charts) displaying a product's full price history. Price charts should support consumers in forming expectations about future prices. Nevertheless, it is currently unclear how price charts influence consumer price expectations and purchase decisions. The results of this study show that the provision of past prices leads to strong adjustments of price expectations depending on price chart characteristics. In particular, the trend, variance and range of past prices in the chart strongly affect price expectations and purchase timing decisions. Furthermore, in the case of a strong downward trend and high variance in past prices, results show that nearly 50% of the total effect is caused by the visualization of the price history.
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