Measuring the Effects of Event Sponsorship: Theoretical Frameworks and Image Transfer Models

The use of event sponsorship or other types of sponsorship as a means to establish, enhance, or change brand image is a fairly common practice for a variety of organizations. This objective has been identified as one of the two most common goals for organizations to engage in sponsorship programs. The rationale behind the popularity of sponsorship as a marketing tool to achieve image goals is the general assumption that, by linking a brand to an event, the image of the event can be transferred to the image of the sponsor.

Despite the increasing sophistication of event sponsorship and rising academic interest in understanding sponsorship practice, there is still a lack of in-depth understanding of how sponsorship processes work, in particular in what concerns its brand outcomes. The article addresses this issue by documenting, examining, and critically assessing the existing literature. The main explanations of brand image transfer, including theoretical frameworks and models, are presented; empirical research, divided into effectiveness of brand image transfer and the factors affecting such process, is analyzed; and two main issues found in the more recent articles—direction of brand image transfer and multiple sponsorships—are discussed. Finally, existing gaps are identified and research propositions are presented to guide future research.     

Raising the Bar (3)

This editorial summarizes and comments on the papers published in issue 11(3) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper proposes spatial and a-spatial indicators to describe the networks of airline companies around the world. The second paper sets forth a two-regime gravity-type model with an endogenous threshold parameter to assess the effect of labour market conditions on interregional migration flows. The third paper utilizes micro-data to explain student migration flows to higher education institutions. The fourth paper is among the first to make use of simulation-based location quotients in a multiregional input–output model. Finally, the last paper provides a purely economic–theoretical model on cooperative limit pricing in the context of spatial competition.     

Raising the bar (4)

This editorial summarizes and comments on the papers published in issue 11(4) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper deals with common factors and spatial dependence in the error term specification of a production function model. The second paper sets forth a New Economic Geography (NEG) model with production activities that vary in their complexity, so as to analyse the impact on specialization patterns across different regions. The third paper measures the efficiency of local public investments using a relatively unknown econometric technique in which the time span over which the variables in the regression equation are measured is increased by one time period every run. The fourth paper adopts a conditional quantile regression approach to determine the impact of people employed in informal jobs on the wage distribution in Colombia and five of its regions. Finally, the last paper proposes and tests two new Bayesian variable selection approaches for spatial econometric models.     

Diversity and business performance: 50 years of research

This study has carried out a review of the literature appearing on diversity in the last 50 years. Research findings from this period reveal it is impossible to assume there is a pure and simple relationship between diversity and performance without considering a series of variables that affect this relationship. In this study, emphasis has been placed on the analysis of results arrived at through empirical investigation on the relation between the most studied dimensions of diversity and performance. The results presented are part of a more extensive research.     

A Meta‐Analysis of β‐Convergence: the Legendary 2%

Abstract.  The topic of convergence is at the heart of a wide‐ranging debate in the growth literature, and empirical studies of convergence differ widely in their theoretical backgrounds, empirical specifications, and in their treatment of cross‐sectional heterogeneity. Despite these differences, a rate of convergence of about 2% has been found under a variety of different conditions, resulting in the widespread belief that the rate of convergence is a natural constant. We use meta‐analysis to investigate whether there is substance to the 'myth' of the 2% convergence rate and to assess several unresolved issues of interpretation and estimation. Our data set contains approximately 600 estimates taken from a random sample of empirical growth studies published in peer‐reviewed journals. The results indicate that it is misleading to speak of a natural convergence rate since estimates of different growth regressions come from different populations, and we find that correcting for the bias resulting from unobserved heterogeneity in technology levels leads to higher estimates of the rate of convergence. We also find that correcting for endogeneity of the explanatory variables has a substantial effect on the estimates and that measures of financial and fiscal development are important determinants of long‐run differences in per capita income levels. We show that although the odds of a study being published is not uniform for studies with different p ‐values, publication bias has no significant effect on the conclusions of the analysis.