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Abstract
My dissertation focuses on (1) firms’ new product sales forecast and (2) the influence of customer and investor sentiment on firms’ new product advertising adjustment both strategically and tactically during the prerelease period. In the first essay, I explore the use of dynamic online knowledge collaborative activities for prerelease new product sales forecast. Unlike customer buzz from social bonding or creativity thriving online platforms, dynamic online knowledge collaborative activities drive the quality of the co-created content about a forthcoming new product, which in turn offers necessary information for prospective customers to educate themselves before making a purchase decision. The proposed modeling process combining Functional Data Analysis (FDA) and Random Forest (RF) methods precisely captures this dynamic process. The results show that the inclusion of online knowledge collaborative activities significantly enhances the prerelease new product forecast accuracy. In the second essay, I delve into the advertising strategy and tactics of firms’ new product introduction practice. I find support for both customer and investor sentiment playing a critical role affecting new products’ prerelease advertising deployment. In this essay, customer sentiment reflects prospective customers’ emotional attachment to the new product as the end-buyers. Investor sentiment captures the investors’ confidence to further invest in the company, considering the new products’ contributing role in the firm value enhancement. In response to both types of sentiment, firms dynamically adjust their prerelease advertising strategies (i.e., new product promotion and corporate branding) and utilize different advertising tactics (i.e., digital and traditional advertising) in order to effectively and efficiently communicate the benefits of the new product. The outcome model shows that dynamic prerelease advertising adjustments in response to customer sentiment are more effective in driving new product sales and firm value