Kenneth L. Bernhardt

A Longitudinal Analysis of Satisfaction and Profitability

Abstract Recently, significant attention has been focused on customer satisfaction. However, few studies link satisfaction with performance, and even fewer studies examine this link through the use of time-series data. In this study, a longitudinal analysis of satisfaction and performance for a national chain of fast-food restaurants is conducted. A total of 342,308 consumer responses, 3,009 employee responses, and 12 months of restaurant performance measures are analyzed. Whereas there is a positive and significant relationship between customer satisfaction and employee satisfaction in any one given time period, no significant relationship between customer satisfaction and performance, and employee satisfaction and performance, is uncovered in this cross-sectional analysis. However, the analysis of time-series data reveals that a positive and significant relationship exists between changes in customer satisfaction and changes in the performance of the firm. Therefore, the impact of an increase in customer satisfaction on profits, although obscured in the short run by many factors, is significantly positive in the long run.

Cases in marketing management

The traditional strengths of this project rest on three main tenets:1. The strong reputations of the authors--they are those rare breed of academic who maintain high profiles from a research standpoint yet have a fierce dedication and talent for what goes on in the classroom.2. The quality and diversity of the cases offered--not everyone can write a good case.3. The first 44 pages of the text that center on orienting the student to the methods and benefits of case oriented education. Specifics include an orientation chapter, a chapter on analyzing cases, a chapter on marketing financial, and a full case example complete with a sample student analysis of that case. The rest of the book is cases only.

Categorical regression in marketing

Market segmentation studies are currently analyzed by many sophisticated analysls techniques, amc ng which are: regresslon, Multiple Classification Analysis (MCA), Aubomatic InteractIon Detector (AID), cluster analysis, factor analy:z:, discriminant analysis, canonical correlation and multidimensional scaling. Frank, Massey and Wmd [4] present a scheme to inchustc when most of these procedures should be utilized in market segmentation analysis. This paper presents a new multivarl& analysis techmque that has great potential for use in m.arket stagmentation analysis, MuItlvariate Nominal Scale Analysis (MNA) is a new data analysis technique developed by Frank M. Andrews I md Robert C. Messenger at the University of Michigan’s Institute for Social Research [I]. Essentially, it is an extension of the Multrpk. Classification Analysis (MCA) program [S] that has been utilized in a ncmher of marketing studies [

Principles of marketing

I, lo], MCA accepts nommaU.y scaled independent variables and assumes an mtervally scaled dependent vanable. MNA accepts both nominal independent and 1 dependent variables, m the context of an additive model.