计量经济分析 = Econometric analysis

Both practitioners and academics understand that consumer loyalty and satisfaction are linked inextricably. They also understand that this relation is asymmetric. Although loyal consumers are most ...

https://www.jstor.org/stable/pdf/1252099.pdf

Whence Consumer Loyalty

Customer loyalty is viewed as the strength of the relationship between an individual’s relative attitude and repeat patronage. The relationship is seen as mediated by social norms and situational factors. Cognitive, affective, and conative antecedents of relative attitude are identified as contributing to loyalty, along with motivational, perceptual, and behavioral consequences. Implications for research and for the management of loyalty are derived.

Customer Loyalty: Toward an Integrated Conceptual Framework:

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Consumer perceived value: The development of a multiple item scale

Marketing Applications of Functional Data Analysis

Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content.Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user-generated content in Twitter. The authors discuss the managerial implications.

Volatility Spillovers across User-Generated Content and Stock Market Performance

For firms facing rapid technological change, knowing which technology to back need not be a guessing game or a purely creative exercise. We provide a framework for informed decisions. The framework consists in identifying the levels of innovation, the competing technologies at each level, the dimensions of performance on which to compare them, and the patterns of evolution on each dimension.

Challenges of Technological Evolution in Contemporary Markets

Current complex dynamic markets are characterized by numerous brands, each with multiple products and price points, and differentiated on a variety of product attributes plus a large number of new product introductions. This study seeks to analyze dynamic pricing paths in a highly complex branded market, consisting of 663 products under 79 brand names of digital cameras. The authors develop a method to classify dynamic pricing strategies and analyze the choice and correlates of observed pricing paths in the introduction and early growth phase of this market. The authors find that, despite numerous recommendations in the literature for skimming or penetration pricing, market pricing dominates in practice. In particular, the authors find five patterns: skimming (20% frequency), penetration (20% frequency), and three variants of market-pricing patterns (60% frequency), where new products are launched at market-prices. Skimming pricing launches the new product 16% above market price and subsequently increases the price relative to the market price. Penetration pricing launches the new product 18% below the market price and subsequently lowers the price relative to the market price. Firms exhibit a mix of these pricing paths across their portfolio. The specific pricing paths correlate with market, firm, and brand characteristics such as competitive intensity, market pioneering, brand reputation, and experience effects. The authors discuss managerial implications.

/pdf/skimming-or-penetration-strategic-dynamic-pricing-for-new-8u1rccsm75.pdf

Skimming or Penetration? Strategic Dynamic Pricing for New Products

Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content. 
Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user-generated content in Twitter. The authors discuss the managerial implications.

/pdf/big-data-analysis-of-volatility-spillovers-of-brands-across-xae225d00s.pdf

Gerard J. Tellis

Papers

Marketing Applications of Functional Data Analysis

Volatility Spillovers across User-Generated Content and Stock Market Performance

Challenges of Technological Evolution in Contemporary Markets

Skimming or Penetration? Strategic Dynamic Pricing for New Products

Big Data Analysis of Volatility Spillovers of Brands across Social Media and Stock Market Performance