Analyzing the relationship between dependent and independent variables in marketing: A comparison of multiple regression with path analysis

Multiple regression models continue to be widely used in marketing. Within the regression framework, researchers have to grapple with and resolve several contentious issues. For example, multicollinearity, nonsimultaneous estimation of parameters, inherent measurement error in independent variables, absence of overall goodness of fit indices, and lack of compelling guidelines for adding and deleting model variables are some common estimation problems associated with this method. In the absence of universally acceptable guidelines, researchers often use judgment calls to deal with these issues. Such ad-hoc approaches, in turn, compromise the potential usefulness of multiple regression models. In this paper, we position path analysis as a competing technique that can address in a relatively unambiguous way, many of the above mentioned limitations of multiple regression. We illustrate the superiority of path analysis by reanalyzing data from selected marketing studies that have used multiple regression models. To enable researchers use path analysis more frequently, we provide a technical appendix depicting use of the EQS software for estimating multiple regression models. We discuss several implications of our results and outline avenues for future research

Marketing strategy for unusual brand differentiation: Trivial attribute effect

This research investigates that brand differentiation creating superior values can be achieved not only by adding meaningful attributes but also meaningless attributes, which is called “trivial attribute effect.” Two studies provided empirical evidences as following; first, trivial attribute effect creates a strong brand differentiation even after subjects realize that trivial attribute has no value. Second, trivial attribute effect is more pronounced in hedonic service category compared to the utilitarian category. Last, the amount of willingness to pay is higher when trivial attribute is presented and evaluated in joint evaluation mode than separate evaluation mode. Finally, we conclude with discussion and provide suggestions for further research

Uncovering the effect of selected moderators on the disconfirmation-satisfaction relationship: A meta-analytic approach

Customer satisfaction occupies a central role in marketing. Not surprisingly, researchers have produced an impressive body of literature that focuses on the causes and consequences of satisfaction. The antecedents of satisfaction have been investigated primarily through the disconfirmation paradigm which holds that satisfaction is the result of conscious mental accounting comparisons undertaken by customers. Furthermore, empirical findings of the disconfirmation-satisfaction link, which are broadly congruent, suggest that when performance conforms to or exceeds initial expectations, a mental state of positive disconfirmation ensues, leading to satisfaction. Despite this insight, a major gap in our understanding concerns lack of generalizability of the disconfirmation model. Specifically, most studies have been conducted in the physical goods setting, thereby raising concerns about the applicability of this model for service exchanges which are more commonplace today. Services differ from goods with respect to intrinsic properties and the manner of delivery. As such, it is possible that the processes underlying customers’ satisfaction judgments will differ between goods and services. To investigate generalizability of the disconfirmation paradigm, this paper reports the results of a meta-analysis that the incorporates effect of four moderating variables, i.e., (a) good or service; (b) measure of expectation; (c) definition of satisfaction; and (d) satisfaction scale, on the focal relationship between disconfirmation and satisfaction. The findings suggest that the effect of disconfirmation on satisfaction is weaker for services than it is for physical goods. By including other moderator variables in the analysis, we find that there is sufficient residual variance (in excess of 50%) to warrant further investigation of the expectationdisconfirmation paradigm. Implications of this research for theory development and the scope for further research are discussed

The Effects of Uncertainties on Network Embeddedness and the Mediating Effect of Information Sharing

Conceptual model that both technology and volume uncertainty promote exchange partners to rely on the network norm of information sharing which is the necessary ingredient of the network embeddedness. Data was collected from the 143 manufacturers in high-tech market in which triadic relationships among the manufacturers (seller), their first vendors (first buyers), and the second vendors (customers of the first buyers) in high-tech markets were particularly focused. Results from the structural equation model and multiple regression analysis reveal that while the technological uncertainty has a positive effect on the network norm of information sharing, the volume uncertainty is not statistically significant. In addition, we find that there existsthe mediator effect of the network norm of information sharing in the relation between the uncertainties and the network embeddedness

An empirical assessment of stimulus presentation mode bias in conjoint analysis

Conjoint analysis, which aims to uncover the optimal combination of attributes influencing customer choice, is widely used by marketers to predict the success of new product and service introductions. In recent years, researchers have incorporated considerable mathematical sophistication into conjoint models and extended its domain to diverse areas such as pricing, market share, profitability, product positioning, distribution channels, and advertising. Despite these advances, the predictive power of conjoint applications is often compromised by response biases and measurement errors. The purpose of this research is to isolate and investigate the impact of one such bias that arises from the manner in which stimuli are presented to respondents. Based upon an appraisal of over four decades of conjoint studies in the major marketing journals, the authors make a case for the possible existence of two types of biases, i.e.: (1) stimulus joint presentation bias, when concept cards are shown simultaneously (side by side) to respondents, and (2) stimulus separate presentation bias, where cards are presented separately (one at a time). Two conjoint experiments were designed to investigate the effects of these biases on respondent choices. Results indicate that bias manifests itself in conjoint designs when there is a mismatch between presentation mode and respondents’ cognitive (evaluable) burden. Left unaddressed, stimulus presentation mode bias may: (1) have a deleterious effect on respondents’ choice behavior; and (2) compromize the predictive accuracy of conjoint models. The authors discuss several approaches that can account for and mitigate the negative impact of presentation mode biases on conjoint outcomes

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation