The Impact of Brand Quality on Shareholder Wealth

This study examines the impact of brand quality on three components of shareholder wealth: stock returns, systematic risk, and idiosyncratic risk. The study finds that brand quality enhances shareholder wealth insofar as unanticipated changes in brand quality are positively associated with stock returns and negatively related to changes in idiosyncratic risk. However, unanticipated changes in brand quality can also erode shareholder wealth because they have a positive association with changes in systematic risk. The study introduces a contingency theory view to the marketing-finance interface by analyzing the moderating role of two factors that are widely followed by investors. The results show an unanticipated increase (decrease) in current-period earnings enhances (depletes) the positive impact of unanticipated changes in brand quality on stock returns and mitigates (enhances) their deleterious effects on changes in systematic risk. Similarly, brand quality is more valuable for firms facing increasing competition (i.e., unanticipated decreases in industry concentration). The results are robust to endogeneity concerns and across alternative models. The authors conclude by discussing the nuanced implications of their findings for shareholder wealth, reporting brand quality to investors, and its use in employee evaluation

Industry structure, competitive strategy, and firm-specific intangibles as determinants of business unit performance towards an integrative model

Vita.Determinants of business performance have been a major focus of research in the disciplines of marketing, strategic management and industrial organization economics during the last two decades. An examination of prior research on this subject reveals that most studies attempt to explain variance in business performance using only industry structure and competitive strategy variables as explanators. However, in recent years, researchers have pointed out that by not controlling for firm-specific intangibles (resources and skills unique to business), prior studies erroneously attribute more of the variance in business performance to industry structure and competitive strategy variables than is really the case. Against this backdrop, this dissertation proposed and tested an integrative model of business performance incorporating the three major determinants of business performance--industry structure, competitive strategy and firm-specific resource and skill variables. The findings reported are based on a mail survey of a national sample of 125 manufacturing businesses. LISREL was used to test the construct validity of the scales developed for the study. Multiple regression and commonality analysis were used to test the hypothesized relationships. The findings indicated that industry structure factors account for only a small percentage of the variance in business performance. In the integrated model, while competitive strategy variables such as product quality and salesforce expenditures were not found to be statistically significant explanators of variance in business performance, market share was found to be a significant explanator of dispersion in business unit performance. Of the firm-specific resource and skill variables, reputation and brand equity of the business unit were found to be key explanators of variance in business unit performance. The results of the commonality analysis indicated that firm-specific resource and skill factors uniquely explained three to five times as much variance as explained by competitive strategy factors, while industry structure factors explained only a negligible percentage of variance in business unit performance

Industry structure, competitive strategy, and firm-specific intangibles as determinants of business unit performance towards an integrative model

Vita.Determinants of business performance have been a major focus of research in the disciplines of marketing, strategic management and industrial organization economics during the last two decades. An examination of prior research on this subject reveals that most studies attempt to explain variance in business performance using only industry structure and competitive strategy variables as explanators. However, in recent years, researchers have pointed out that by not controlling for firm-specific intangibles (resources and skills unique to business), prior studies erroneously attribute more of the variance in business performance to industry structure and competitive strategy variables than is really the case. Against this backdrop, this dissertation proposed and tested an integrative model of business performance incorporating the three major determinants of business performance--industry structure, competitive strategy and firm-specific resource and skill variables. The findings reported are based on a mail survey of a national sample of 125 manufacturing businesses. LISREL was used to test the construct validity of the scales developed for the study. Multiple regression and commonality analysis were used to test the hypothesized relationships. The findings indicated that industry structure factors account for only a small percentage of the variance in business performance. In the integrated model, while competitive strategy variables such as product quality and salesforce expenditures were not found to be statistically significant explanators of variance in business performance, market share was found to be a significant explanator of dispersion in business unit performance. Of the firm-specific resource and skill variables, reputation and brand equity of the business unit were found to be key explanators of variance in business unit performance. The results of the commonality analysis indicated that firm-specific resource and skill factors uniquely explained three to five times as much variance as explained by competitive strategy factors, while industry structure factors explained only a negligible percentage of variance in business unit performance

It’s Not What You Know, It’s Who You Know: A Meta-Analytic Review of Social Networks

This study uses emerging meta-analytic methods to synthesize empirical studies that examine the correlates of social networks. This study draws upon a database of 208 samples from 88 studies spanning the period of 1975 through 2004. Specifically, the study examines the impact of 43 correlates of networks with an overall sample size of 251,580. Additionally, a multivariate generalized least squares (GLS) moderator analysis indicates that measurement factors and research design considerations in model specification significantly biases the observed effects within a given study. The study identifies surpluses and shortages in the empirical literature regarding the impact of social networks